tinygrad Examples
Output from running the Python scripts in test/.
Note, while these examples were running, builds were also running, hitting 128 processors, so these examples aren’t benchmarks.
In all runs so far, just one GPU was used in the tinygrad examples.
beautiful_cartpole.py
0%| | 0/40 [00:00<?, ?it/s]
sz: 16 steps/s: 4.53 action_loss: -8.405 entropy_loss: -0.677 critic_loss: 83.913 reward: 16.00: 0%| | 0/40 [00:03<?, ?it/s]
sz: 16 steps/s: 4.53 action_loss: -8.405 entropy_loss: -0.677 critic_loss: 83.913 reward: 16.00: 2%|▎ | 1/40 [00:03<02:18, 3.54s/it]
sz: 42 steps/s: 9.89 action_loss: -9.755 entropy_loss: -0.656 critic_loss: 130.257 reward: 26.00: 2%|▎ | 1/40 [00:04<02:18, 3.54s/it]
sz: 42 steps/s: 9.89 action_loss: -9.755 entropy_loss: -0.656 critic_loss: 130.257 reward: 26.00: 5%|▌ | 2/40 [00:04<01:11, 1.88s/it]
sz: 62 steps/s: 12.84 action_loss: -9.323 entropy_loss: -0.632 critic_loss: 120.510 reward: 20.00: 5%|▌ | 2/40 [00:04<01:11, 1.88s/it]
sz: 62 steps/s: 12.84 action_loss: -9.323 entropy_loss: -0.632 critic_loss: 120.510 reward: 20.00: 8%|▊ | 3/40 [00:04<00:47, 1.28s/it]
sz: 125 steps/s: 22.83 action_loss: -16.834 entropy_loss: -0.612 critic_loss: 461.137 reward: 63.00: 8%|▊ | 3/40 [00:05<00:47, 1.28s/it]
sz: 125 steps/s: 22.83 action_loss: -16.834 entropy_loss: -0.612 critic_loss: 461.137 reward: 63.00: 10%|█ | 4/40 [00:05<00:37, 1.03s/it]
sz: 140 steps/s: 22.87 action_loss: -14.485 entropy_loss: -0.604 critic_loss: 378.145 reward: 15.00: 10%|█ | 4/40 [00:06<00:37, 1.03s/it]
sz: 140 steps/s: 22.87 action_loss: -14.485 entropy_loss: -0.604 critic_loss: 378.145 reward: 15.00: 12%|█▎ | 5/40 [00:06<00:31, 1.12it/s]
sz: 158 steps/s: 23.51 action_loss: -12.541 entropy_loss: -0.609 critic_loss: 317.384 reward: 18.00: 12%|█▎ | 5/40 [00:06<00:31, 1.12it/s]
sz: 158 steps/s: 23.51 action_loss: -12.541 entropy_loss: -0.609 critic_loss: 317.384 reward: 18.00: 15%|█▌ | 6/40 [00:06<00:26, 1.26it/s]
sz: 173 steps/s: 23.68 action_loss: -10.413 entropy_loss: -0.618 critic_loss: 270.751 reward: 15.00: 15%|█▌ | 6/40 [00:07<00:26, 1.26it/s]
sz: 173 steps/s: 23.68 action_loss: -10.413 entropy_loss: -0.618 critic_loss: 270.751 reward: 15.00: 18%|█▊ | 7/40 [00:07<00:23, 1.38it/s]
sz: 197 steps/s: 24.93 action_loss: -9.082 entropy_loss: -0.623 critic_loss: 249.494 reward: 24.00: 18%|█▊ | 7/40 [00:07<00:23, 1.38it/s]
sz: 197 steps/s: 24.93 action_loss: -9.082 entropy_loss: -0.623 critic_loss: 249.494 reward: 24.00: 20%|██ | 8/40 [00:07<00:21, 1.46it/s]
sz: 234 steps/s: 27.33 action_loss: -8.351 entropy_loss: -0.608 critic_loss: 237.388 reward: 37.00: 20%|██ | 8/40 [00:08<00:21, 1.46it/s]
sz: 234 steps/s: 27.33 action_loss: -8.351 entropy_loss: -0.608 critic_loss: 237.388 reward: 37.00: 22%|██▎ | 9/40 [00:08<00:20, 1.48it/s]
sz: 284 steps/s: 30.63 action_loss: -8.378 entropy_loss: -0.566 critic_loss: 238.783 reward: 50.00: 22%|██▎ | 9/40 [00:09<00:20, 1.48it/s]
sz: 284 steps/s: 30.63 action_loss: -8.378 entropy_loss: -0.566 critic_loss: 238.783 reward: 50.00: 25%|██▌ | 10/40 [00:09<00:20, 1.46it/s]
sz: 327 steps/s: 32.95 action_loss: -7.069 entropy_loss: -0.520 critic_loss: 217.788 reward: 43.00: 25%|██▌ | 10/40 [00:09<00:20, 1.46it/s]
sz: 327 steps/s: 32.95 action_loss: -7.069 entropy_loss: -0.520 critic_loss: 217.788 reward: 43.00: 28%|██▊ | 11/40 [00:09<00:19, 1.48it/s]
sz: 356 steps/s: 33.55 action_loss: -3.947 entropy_loss: -0.461 critic_loss: 144.527 reward: 29.00: 28%|██▊ | 11/40 [00:10<00:19, 1.48it/s]
sz: 356 steps/s: 33.55 action_loss: -3.947 entropy_loss: -0.461 critic_loss: 144.527 reward: 29.00: 30%|███ | 12/40 [00:10<00:19, 1.47it/s]
sz: 479 steps/s: 42.10 action_loss: -8.053 entropy_loss: -0.448 critic_loss: 421.731 reward: 123.00: 30%|███ | 12/40 [00:11<00:19, 1.47it/s]
sz: 479 steps/s: 42.10 action_loss: -8.053 entropy_loss: -0.448 critic_loss: 421.731 reward: 123.00: 32%|███▎ | 13/40 [00:11<00:19, 1.42it/s]
sz: 645 steps/s: 52.32 action_loss: -15.570 entropy_loss: -0.464 critic_loss: 810.073 reward: 166.00: 32%|███▎ | 13/40 [00:12<00:19, 1.42it/s]
sz: 645 steps/s: 52.32 action_loss: -15.570 entropy_loss: -0.464 critic_loss: 810.073 reward: 166.00: 35%|███▌ | 14/40 [00:12<00:20, 1.28it/s]
sz: 792 steps/s: 60.34 action_loss: -15.626 entropy_loss: -0.414 critic_loss: 871.600 reward: 147.00: 35%|███▌ | 14/40 [00:13<00:20, 1.28it/s]
sz: 792 steps/s: 60.34 action_loss: -15.626 entropy_loss: -0.414 critic_loss: 871.600 reward: 147.00: 38%|███▊ | 15/40 [00:13<00:19, 1.27it/s]
sz: 1203 steps/s: 83.40 action_loss: -28.025 entropy_loss: -0.473 critic_loss: 1730.603 reward: 411.00: 38%|███▊ | 15/40 [00:14<00:19, 1.27it/s]
sz: 1203 steps/s: 83.40 action_loss: -28.025 entropy_loss: -0.473 critic_loss: 1730.603 reward: 411.00: 40%|████ | 16/40 [00:14<00:22, 1.06it/s]
sz: 1435 steps/s: 93.00 action_loss: -25.648 entropy_loss: -0.458 critic_loss: 1608.802 reward: 232.00: 40%|████ | 16/40 [00:15<00:22, 1.06it/s]
sz: 1435 steps/s: 93.00 action_loss: -25.648 entropy_loss: -0.458 critic_loss: 1608.802 reward: 232.00: 42%|████▎ | 17/40 [00:15<00:22, 1.04it/s]
sz: 1592 steps/s: 96.96 action_loss: -16.278 entropy_loss: -0.413 critic_loss: 1147.564 reward: 157.00: 42%|████▎ | 17/40 [00:16<00:22, 1.04it/s]
sz: 1592 steps/s: 96.96 action_loss: -16.278 entropy_loss: -0.413 critic_loss: 1147.564 reward: 157.00: 45%|████▌ | 18/40 [00:16<00:21, 1.03it/s]
sz: 1789 steps/s: 103.18 action_loss: -19.750 entropy_loss: -0.398 critic_loss: 1422.291 reward: 197.00: 45%|████▌ | 18/40 [00:17<00:21, 1.03it/s]
sz: 1789 steps/s: 103.18 action_loss: -19.750 entropy_loss: -0.398 critic_loss: 1422.291 reward: 197.00: 48%|████▊ | 19/40 [00:17<00:20, 1.05it/s]
sz: 1949 steps/s: 106.81 action_loss: -10.565 entropy_loss: -0.397 critic_loss: 895.935 reward: 160.00: 48%|████▊ | 19/40 [00:18<00:20, 1.05it/s]
sz: 1949 steps/s: 106.81 action_loss: -10.565 entropy_loss: -0.397 critic_loss: 895.935 reward: 160.00: 50%|█████ | 20/40 [00:18<00:18, 1.06it/s]
sz: 2000 steps/s: 110.75 action_loss: -9.772 entropy_loss: -0.385 critic_loss: 871.258 reward: 182.00: 50%|█████ | 20/40 [00:19<00:18, 1.06it/s]
sz: 2000 steps/s: 110.75 action_loss: -9.772 entropy_loss: -0.385 critic_loss: 871.258 reward: 182.00: 52%|█████▎ | 21/40 [00:19<00:18, 1.05it/s]
sz: 2000 steps/s: 121.57 action_loss: -11.975 entropy_loss: -0.382 critic_loss: 808.180 reward: 261.00: 52%|█████▎ | 21/40 [00:19<00:18, 1.05it/s]
sz: 2000 steps/s: 121.57 action_loss: -11.975 entropy_loss: -0.382 critic_loss: 808.180 reward: 261.00: 55%|█████▌ | 22/40 [00:19<00:14, 1.25it/s]
sz: 2000 steps/s: 133.60 action_loss: -10.460 entropy_loss: -0.392 critic_loss: 741.236 reward: 296.00: 55%|█████▌ | 22/40 [00:20<00:14, 1.25it/s]
sz: 2000 steps/s: 133.60 action_loss: -10.460 entropy_loss: -0.392 critic_loss: 741.236 reward: 296.00: 57%|█████▊ | 23/40 [00:20<00:11, 1.44it/s]
sz: 2000 steps/s: 153.39 action_loss: -9.818 entropy_loss: -0.397 critic_loss: 624.722 reward: 500.00: 57%|█████▊ | 23/40 [00:20<00:11, 1.44it/s]
sz: 2000 steps/s: 153.39 action_loss: -9.818 entropy_loss: -0.397 critic_loss: 624.722 reward: 500.00: 60%|██████ | 24/40 [00:20<00:10, 1.46it/s]
sz: 2000 steps/s: 171.97 action_loss: -8.829 entropy_loss: -0.434 critic_loss: 686.303 reward: 500.00: 60%|██████ | 24/40 [00:21<00:10, 1.46it/s]
sz: 2000 steps/s: 171.97 action_loss: -8.829 entropy_loss: -0.434 critic_loss: 686.303 reward: 500.00: 62%|██████▎ | 25/40 [00:21<00:10, 1.48it/s]
sz: 2000 steps/s: 189.42 action_loss: -12.795 entropy_loss: -0.473 critic_loss: 664.423 reward: 500.00: 62%|██████▎ | 25/40 [00:22<00:10, 1.48it/s]
sz: 2000 steps/s: 189.42 action_loss: -12.795 entropy_loss: -0.473 critic_loss: 664.423 reward: 500.00: 65%|██████▌ | 26/40 [00:22<00:09, 1.49it/s]
sz: 2000 steps/s: 205.86 action_loss: -7.701 entropy_loss: -0.469 critic_loss: 597.681 reward: 500.00: 65%|██████▌ | 26/40 [00:22<00:09, 1.49it/s]
sz: 2000 steps/s: 205.86 action_loss: -7.701 entropy_loss: -0.469 critic_loss: 597.681 reward: 500.00: 68%|██████▊ | 27/40 [00:22<00:08, 1.49it/s]
sz: 2000 steps/s: 221.37 action_loss: -2.992 entropy_loss: -0.482 critic_loss: 482.757 reward: 500.00: 68%|██████▊ | 27/40 [00:23<00:08, 1.49it/s]
sz: 2000 steps/s: 221.37 action_loss: -2.992 entropy_loss: -0.482 critic_loss: 482.757 reward: 500.00: 70%|███████ | 28/40 [00:23<00:08, 1.50it/s]
sz: 2000 steps/s: 236.03 action_loss: 1.380 entropy_loss: -0.453 critic_loss: 538.772 reward: 500.00: 70%|███████ | 28/40 [00:24<00:08, 1.50it/s]
sz: 2000 steps/s: 236.03 action_loss: 1.380 entropy_loss: -0.453 critic_loss: 538.772 reward: 500.00: 72%|███████▎ | 29/40 [00:24<00:07, 1.50it/s]
sz: 2000 steps/s: 249.51 action_loss: 3.675 entropy_loss: -0.430 critic_loss: 589.797 reward: 500.00: 72%|███████▎ | 29/40 [00:24<00:07, 1.50it/s]
sz: 2000 steps/s: 249.51 action_loss: 3.675 entropy_loss: -0.430 critic_loss: 589.797 reward: 500.00: 75%|███████▌ | 30/40 [00:24<00:06, 1.48it/s]
sz: 2000 steps/s: 262.02 action_loss: 1.766 entropy_loss: -0.456 critic_loss: 630.521 reward: 500.00: 75%|███████▌ | 30/40 [00:25<00:06, 1.48it/s]
sz: 2000 steps/s: 262.02 action_loss: 1.766 entropy_loss: -0.456 critic_loss: 630.521 reward: 500.00: 78%|███████▊ | 31/40 [00:25<00:06, 1.45it/s]
sz: 2000 steps/s: 274.49 action_loss: 2.491 entropy_loss: -0.429 critic_loss: 615.586 reward: 500.00: 78%|███████▊ | 31/40 [00:26<00:06, 1.45it/s]
sz: 2000 steps/s: 274.49 action_loss: 2.491 entropy_loss: -0.429 critic_loss: 615.586 reward: 500.00: 80%|████████ | 32/40 [00:26<00:05, 1.47it/s]
sz: 2000 steps/s: 286.35 action_loss: -0.218 entropy_loss: -0.476 critic_loss: 532.887 reward: 500.00: 80%|████████ | 32/40 [00:26<00:05, 1.47it/s]
sz: 2000 steps/s: 286.35 action_loss: -0.218 entropy_loss: -0.476 critic_loss: 532.887 reward: 500.00: 82%|████████▎ | 33/40 [00:26<00:04, 1.48it/s]
sz: 2000 steps/s: 297.63 action_loss: -0.294 entropy_loss: -0.486 critic_loss: 535.985 reward: 500.00: 82%|████████▎ | 33/40 [00:27<00:04, 1.48it/s]
sz: 2000 steps/s: 297.63 action_loss: -0.294 entropy_loss: -0.486 critic_loss: 535.985 reward: 500.00: 85%|████████▌ | 34/40 [00:27<00:04, 1.49it/s]
sz: 2000 steps/s: 308.38 action_loss: -0.592 entropy_loss: -0.502 critic_loss: 491.457 reward: 500.00: 85%|████████▌ | 34/40 [00:28<00:04, 1.49it/s]
sz: 2000 steps/s: 308.38 action_loss: -0.592 entropy_loss: -0.502 critic_loss: 491.457 reward: 500.00: 88%|████████▊ | 35/40 [00:28<00:03, 1.49it/s]
sz: 2000 steps/s: 318.63 action_loss: 2.020 entropy_loss: -0.532 critic_loss: 754.644 reward: 500.00: 88%|████████▊ | 35/40 [00:28<00:03, 1.49it/s]
sz: 2000 steps/s: 318.63 action_loss: 2.020 entropy_loss: -0.532 critic_loss: 754.644 reward: 500.00: 90%|█████████ | 36/40 [00:28<00:02, 1.50it/s]
sz: 2000 steps/s: 328.42 action_loss: -0.480 entropy_loss: -0.542 critic_loss: 522.170 reward: 500.00: 90%|█████████ | 36/40 [00:29<00:02, 1.50it/s]
sz: 2000 steps/s: 328.42 action_loss: -0.480 entropy_loss: -0.542 critic_loss: 522.170 reward: 500.00: 92%|█████████▎| 37/40 [00:29<00:01, 1.50it/s]
sz: 2000 steps/s: 337.78 action_loss: -1.788 entropy_loss: -0.560 critic_loss: 586.351 reward: 500.00: 92%|█████████▎| 37/40 [00:30<00:01, 1.50it/s]
sz: 2000 steps/s: 337.78 action_loss: -1.788 entropy_loss: -0.560 critic_loss: 586.351 reward: 500.00: 95%|█████████▌| 38/40 [00:30<00:01, 1.49it/s]
sz: 2000 steps/s: 346.58 action_loss: 1.829 entropy_loss: -0.512 critic_loss: 649.962 reward: 500.00: 95%|█████████▌| 38/40 [00:30<00:01, 1.49it/s]
sz: 2000 steps/s: 346.58 action_loss: 1.829 entropy_loss: -0.512 critic_loss: 649.962 reward: 500.00: 98%|█████████▊| 39/40 [00:30<00:00, 1.50it/s]
sz: 2000 steps/s: 355.17 action_loss: -1.843 entropy_loss: -0.548 critic_loss: 540.821 reward: 500.00: 98%|█████████▊| 39/40 [00:31<00:00, 1.50it/s]
sz: 2000 steps/s: 355.17 action_loss: -1.843 entropy_loss: -0.548 critic_loss: 540.821 reward: 500.00: 100%|██████████| 40/40 [00:31<00:00, 1.50it/s]
sz: 2000 steps/s: 355.17 action_loss: -1.843 entropy_loss: -0.548 critic_loss: 540.821 reward: 500.00: 100%|██████████| 40/40 [00:31<00:00, 1.27it/s]
test reward: 500.0
beautiful_mnist.py
0%| | 0/70 [00:00<?, ?it/s]
loss: 2.85 test_accuracy: nan%: 0%| | 0/70 [00:05<?, ?it/s]
loss: 2.85 test_accuracy: nan%: 1%|▏ | 1/70 [00:05<06:52, 5.98s/it]
loss: 1.75 test_accuracy: nan%: 1%|▏ | 1/70 [00:07<06:52, 5.98s/it]
loss: 1.75 test_accuracy: nan%: 3%|▎ | 2/70 [00:07<03:51, 3.41s/it]
loss: 1.33 test_accuracy: nan%: 3%|▎ | 2/70 [00:07<03:51, 3.41s/it]
loss: 1.00 test_accuracy: nan%: 3%|▎ | 2/70 [00:07<03:51, 3.41s/it]
loss: 0.83 test_accuracy: nan%: 3%|▎ | 2/70 [00:07<03:51, 3.41s/it]
loss: 0.68 test_accuracy: nan%: 3%|▎ | 2/70 [00:07<03:51, 3.41s/it]
loss: 0.68 test_accuracy: nan%: 9%|▊ | 6/70 [00:07<00:51, 1.24it/s]
loss: 0.55 test_accuracy: nan%: 9%|▊ | 6/70 [00:07<00:51, 1.24it/s]
loss: 0.50 test_accuracy: nan%: 9%|▊ | 6/70 [00:07<00:51, 1.24it/s]
loss: 0.44 test_accuracy: nan%: 9%|▊ | 6/70 [00:07<00:51, 1.24it/s]
loss: 0.41 test_accuracy: 85.98%: 9%|▊ | 6/70 [00:08<00:51, 1.24it/s]
loss: 0.41 test_accuracy: 85.98%: 14%|█▍ | 10/70 [00:08<00:28, 2.09it/s]
loss: 0.38 test_accuracy: 85.98%: 14%|█▍ | 10/70 [00:08<00:28, 2.09it/s]
loss: 0.33 test_accuracy: 85.98%: 14%|█▍ | 10/70 [00:08<00:28, 2.09it/s]
loss: 0.30 test_accuracy: 85.98%: 14%|█▍ | 10/70 [00:08<00:28, 2.09it/s]
loss: 0.27 test_accuracy: 85.98%: 14%|█▍ | 10/70 [00:08<00:28, 2.09it/s]
loss: 0.27 test_accuracy: 85.98%: 20%|██ | 14/70 [00:08<00:16, 3.49it/s]
loss: 0.25 test_accuracy: 85.98%: 20%|██ | 14/70 [00:08<00:16, 3.49it/s]
loss: 0.26 test_accuracy: 85.98%: 20%|██ | 14/70 [00:08<00:16, 3.49it/s]
loss: 0.25 test_accuracy: 85.98%: 20%|██ | 14/70 [00:08<00:16, 3.49it/s]
loss: 0.23 test_accuracy: 85.98%: 20%|██ | 14/70 [00:08<00:16, 3.49it/s]
loss: 0.23 test_accuracy: 85.98%: 26%|██▌ | 18/70 [00:08<00:09, 5.30it/s]
loss: 0.19 test_accuracy: 85.98%: 26%|██▌ | 18/70 [00:08<00:09, 5.30it/s]
loss: 0.19 test_accuracy: 94.39%: 26%|██▌ | 18/70 [00:08<00:09, 5.30it/s]
loss: 0.20 test_accuracy: 94.39%: 26%|██▌ | 18/70 [00:08<00:09, 5.30it/s]
loss: 0.20 test_accuracy: 94.39%: 30%|███ | 21/70 [00:08<00:07, 6.93it/s]
loss: 0.19 test_accuracy: 94.39%: 30%|███ | 21/70 [00:08<00:07, 6.93it/s]
loss: 0.14 test_accuracy: 94.39%: 30%|███ | 21/70 [00:08<00:07, 6.93it/s]
loss: 0.15 test_accuracy: 94.39%: 30%|███ | 21/70 [00:08<00:07, 6.93it/s]
loss: 0.14 test_accuracy: 94.39%: 30%|███ | 21/70 [00:08<00:07, 6.93it/s]
loss: 0.14 test_accuracy: 94.39%: 36%|███▌ | 25/70 [00:08<00:04, 9.69it/s]
loss: 0.14 test_accuracy: 94.39%: 36%|███▌ | 25/70 [00:08<00:04, 9.69it/s]
loss: 0.15 test_accuracy: 94.39%: 36%|███▌ | 25/70 [00:08<00:04, 9.69it/s]
loss: 0.14 test_accuracy: 94.39%: 36%|███▌ | 25/70 [00:08<00:04, 9.69it/s]
loss: 0.11 test_accuracy: 94.39%: 36%|███▌ | 25/70 [00:09<00:04, 9.69it/s]
loss: 0.11 test_accuracy: 94.39%: 41%|████▏ | 29/70 [00:09<00:03, 12.82it/s]
loss: 0.12 test_accuracy: 96.40%: 41%|████▏ | 29/70 [00:09<00:03, 12.82it/s]
loss: 0.15 test_accuracy: 96.40%: 41%|████▏ | 29/70 [00:09<00:03, 12.82it/s]
loss: 0.11 test_accuracy: 96.40%: 41%|████▏ | 29/70 [00:09<00:03, 12.82it/s]
loss: 0.10 test_accuracy: 96.40%: 41%|████▏ | 29/70 [00:09<00:03, 12.82it/s]
loss: 0.10 test_accuracy: 96.40%: 47%|████▋ | 33/70 [00:09<00:02, 15.72it/s]
loss: 0.08 test_accuracy: 96.40%: 47%|████▋ | 33/70 [00:09<00:02, 15.72it/s]
loss: 0.08 test_accuracy: 96.40%: 47%|████▋ | 33/70 [00:09<00:02, 15.72it/s]
loss: 0.11 test_accuracy: 96.40%: 47%|████▋ | 33/70 [00:09<00:02, 15.72it/s]
loss: 0.11 test_accuracy: 96.40%: 47%|████▋ | 33/70 [00:09<00:02, 15.72it/s]
loss: 0.11 test_accuracy: 96.40%: 53%|█████▎ | 37/70 [00:09<00:01, 19.05it/s]
loss: 0.09 test_accuracy: 96.40%: 53%|█████▎ | 37/70 [00:09<00:01, 19.05it/s]
loss: 0.10 test_accuracy: 96.40%: 53%|█████▎ | 37/70 [00:09<00:01, 19.05it/s]
loss: 0.09 test_accuracy: 97.22%: 53%|█████▎ | 37/70 [00:09<00:01, 19.05it/s]
loss: 0.07 test_accuracy: 97.22%: 53%|█████▎ | 37/70 [00:09<00:01, 19.05it/s]
loss: 0.07 test_accuracy: 97.22%: 59%|█████▊ | 41/70 [00:09<00:01, 21.47it/s]
loss: 0.10 test_accuracy: 97.22%: 59%|█████▊ | 41/70 [00:09<00:01, 21.47it/s]
loss: 0.09 test_accuracy: 97.22%: 59%|█████▊ | 41/70 [00:09<00:01, 21.47it/s]
loss: 0.08 test_accuracy: 97.22%: 59%|█████▊ | 41/70 [00:09<00:01, 21.47it/s]
loss: 0.09 test_accuracy: 97.22%: 59%|█████▊ | 41/70 [00:09<00:01, 21.47it/s]
loss: 0.09 test_accuracy: 97.22%: 64%|██████▍ | 45/70 [00:09<00:01, 24.40it/s]
loss: 0.10 test_accuracy: 97.22%: 64%|██████▍ | 45/70 [00:09<00:01, 24.40it/s]
loss: 0.09 test_accuracy: 97.22%: 64%|██████▍ | 45/70 [00:09<00:01, 24.40it/s]
loss: 0.07 test_accuracy: 97.22%: 64%|██████▍ | 45/70 [00:09<00:01, 24.40it/s]
loss: 0.09 test_accuracy: 97.22%: 64%|██████▍ | 45/70 [00:09<00:01, 24.40it/s]
loss: 0.09 test_accuracy: 97.22%: 70%|███████ | 49/70 [00:09<00:00, 26.90it/s]
loss: 0.10 test_accuracy: 97.70%: 70%|███████ | 49/70 [00:09<00:00, 26.90it/s]
loss: 0.06 test_accuracy: 97.70%: 70%|███████ | 49/70 [00:09<00:00, 26.90it/s]
loss: 0.06 test_accuracy: 97.70%: 70%|███████ | 49/70 [00:09<00:00, 26.90it/s]
loss: 0.10 test_accuracy: 97.70%: 70%|███████ | 49/70 [00:09<00:00, 26.90it/s]
loss: 0.10 test_accuracy: 97.70%: 76%|███████▌ | 53/70 [00:09<00:00, 27.73it/s]
loss: 0.08 test_accuracy: 97.70%: 76%|███████▌ | 53/70 [00:09<00:00, 27.73it/s]
loss: 0.08 test_accuracy: 97.70%: 76%|███████▌ | 53/70 [00:09<00:00, 27.73it/s]
loss: 0.06 test_accuracy: 97.70%: 76%|███████▌ | 53/70 [00:09<00:00, 27.73it/s]
loss: 0.06 test_accuracy: 97.70%: 76%|███████▌ | 53/70 [00:09<00:00, 27.73it/s]
loss: 0.06 test_accuracy: 97.70%: 81%|████████▏ | 57/70 [00:09<00:00, 29.62it/s]
loss: 0.08 test_accuracy: 97.70%: 81%|████████▏ | 57/70 [00:09<00:00, 29.62it/s]
loss: 0.08 test_accuracy: 97.70%: 81%|████████▏ | 57/70 [00:09<00:00, 29.62it/s]
loss: 0.07 test_accuracy: 98.14%: 81%|████████▏ | 57/70 [00:09<00:00, 29.62it/s]
loss: 0.05 test_accuracy: 98.14%: 81%|████████▏ | 57/70 [00:09<00:00, 29.62it/s]
loss: 0.05 test_accuracy: 98.14%: 87%|████████▋ | 61/70 [00:09<00:00, 29.67it/s]
loss: 0.07 test_accuracy: 98.14%: 87%|████████▋ | 61/70 [00:10<00:00, 29.67it/s]
loss: 0.06 test_accuracy: 98.14%: 87%|████████▋ | 61/70 [00:10<00:00, 29.67it/s]
loss: 0.09 test_accuracy: 98.14%: 87%|████████▋ | 61/70 [00:10<00:00, 29.67it/s]
loss: 0.07 test_accuracy: 98.14%: 87%|████████▋ | 61/70 [00:10<00:00, 29.67it/s]
loss: 0.07 test_accuracy: 98.14%: 93%|█████████▎| 65/70 [00:10<00:00, 31.13it/s]
loss: 0.06 test_accuracy: 98.14%: 93%|█████████▎| 65/70 [00:10<00:00, 31.13it/s]
loss: 0.06 test_accuracy: 98.14%: 93%|█████████▎| 65/70 [00:10<00:00, 31.13it/s]
loss: 0.06 test_accuracy: 98.14%: 93%|█████████▎| 65/70 [00:10<00:00, 31.13it/s]
loss: 0.08 test_accuracy: 98.14%: 93%|█████████▎| 65/70 [00:10<00:00, 31.13it/s]
loss: 0.08 test_accuracy: 98.14%: 99%|█████████▊| 69/70 [00:10<00:00, 32.21it/s]
loss: 0.06 test_accuracy: 98.42%: 99%|█████████▊| 69/70 [00:10<00:00, 32.21it/s]
loss: 0.06 test_accuracy: 98.42%: 100%|██████████| 70/70 [00:10<00:00, 6.81it/s]
benchmark_train_efficientnet.py
NUM:2 BS:8 CNT:10
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80%|████████ | 8/10 [00:10<00:00, 2.54it/s]
90%|█████████ | 9/10 [00:10<00:00, 3.20it/s]
100%|██████████| 10/10 [00:10<00:00, 3.89it/s]
100%|██████████| 10/10 [00:10<00:00, 1.09s/it]
175.27 ms cpy, 9470.44 ms run, 62.31 ms build, 9347.02 ms realize, 61.11 ms CL, 0.06 loss, 421 tensors, 0.04 GB used, 1.22 GFLOPS
12.54 ms cpy, 103.18 ms run, 55.31 ms build, 45.06 ms realize, 2.82 ms CL, -0.02 loss, 421 tensors, 0.04 GB used, 111.65 GFLOPS
11.01 ms cpy, 142.94 ms run, 53.91 ms build, 86.17 ms realize, 2.85 ms CL, 0.07 loss, 421 tensors, 0.04 GB used, 80.60 GFLOPS
11.05 ms cpy, 102.45 ms run, 53.68 ms build, 45.98 ms realize, 2.79 ms CL, 0.03 loss, 421 tensors, 0.04 GB used, 112.45 GFLOPS
11.07 ms cpy, 102.35 ms run, 53.75 ms build, 45.86 ms realize, 2.74 ms CL, 0.07 loss, 421 tensors, 0.04 GB used, 112.56 GFLOPS
11.14 ms cpy, 101.95 ms run, 53.89 ms build, 45.27 ms realize, 2.78 ms CL, -0.00 loss, 421 tensors, 0.04 GB used, 113.01 GFLOPS
11.14 ms cpy, 143.39 ms run, 54.09 ms build, 86.44 ms realize, 2.86 ms CL, 0.03 loss, 421 tensors, 0.04 GB used, 80.34 GFLOPS
11.97 ms cpy, 103.14 ms run, 54.24 ms build, 46.12 ms realize, 2.78 ms CL, -0.04 loss, 421 tensors, 0.04 GB used, 111.70 GFLOPS
11.29 ms cpy, 102.81 ms run, 54.46 ms build, 45.58 ms realize, 2.77 ms CL, 0.04 loss, 421 tensors, 0.04 GB used, 112.06 GFLOPS
11.15 ms cpy, 103.26 ms run, 54.59 ms build, 45.89 ms realize, 2.77 ms CL, -0.05 loss, 421 tensors, 0.04 GB used, 111.57 GFLOPS
coder.py
create model: 155.25 ms
download weights: 24.86 ms
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loaded weights in 14431.85 ms, 4.54 GB loaded at 0.31 GB/s
weights -> model: 18909.50 ms
[32m<|im_start|> system
You are Quentin. Quentin is a useful assistant who writes Python code to answer questions. He keeps the code as short as possible and doesn't read from user input<|im_end|>
[0mQ:
compile_efficientnet.py
Traceback (most recent call last):
File "/home/jebba/devel/tinygrad/tinygrad/examples/compile_efficientnet.py", line 13, in <module>
prg, inp_sizes, out_sizes, state = export_model(model, mode, Tensor.randn(1,3,224,224))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/extra/export_model.py", line 313, in export_model
assert Device.DEFAULT in EXPORT_SUPPORTED_DEVICE, "only WEBGPU, WEBGL, CLANG, CUDA, GPU, METAL are supported"
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AssertionError: only WEBGPU, WEBGL, CLANG, CUDA, GPU, METAL are supported
compile_tensorflow.py.txt
2024-02-06 13:09:00.382257: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
2024-02-06 13:09:00.420457: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-02-06 13:09:00.420503: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-02-06 13:09:00.421410: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-02-06 13:09:00.426991: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
2024-02-06 13:09:00.427163: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-02-06 13:09:01.185053: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-02-06 13:09:02.593911: I tensorflow/core/grappler/devices.cc:66] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0
2024-02-06 13:09:02.594059: I tensorflow/core/grappler/clusters/single_machine.cc:361] Starting new session
2024-02-06 13:09:02.691872: I tensorflow/core/grappler/devices.cc:66] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0
2024-02-06 13:09:02.692025: I tensorflow/core/grappler/clusters/single_machine.cc:361] Starting new session
tinygrad: [0.29635584354400635, 0.5070338845252991, 0.6352834105491638, 0.15874029695987701]
compiled: [0.296356, 0.507034, 0.635283, 0.15874]
keras: [0.29635587 0.5070339 0.6352834 0.15874033]
#include <string.h>
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define max(x,y) ((x>y)?x:y)
#define int64 long
#define half __fp16
#define uchar unsigned char
#include <stdbool.h>
float buf_0[64];
float input0[128];
float buf_1[2048];
float buf_2[64];
float buf_3[128];
float buf_4[2048];
float output0[16];
float buf_5[512];
void r_16_32(float* restrict data0, const float* restrict data1, const float* restrict data2, const float* restrict data3) {
float val0 = data1[0];
float val1 = data1[1];
float val2 = data1[2];
float val3 = data1[3];
float val4 = data1[4];
float val5 = data1[5];
float val6 = data1[6];
float val7 = data1[7];
float val8 = data1[8];
float val9 = data1[9];
float val10 = data1[10];
float val11 = data1[11];
float val12 = data1[12];
float val13 = data1[13];
float val14 = data1[14];
float val15 = data1[15];
float val16 = data1[16];
float val17 = data1[17];
float val18 = data1[18];
float val19 = data1[19];
float val20 = data1[20];
float val21 = data1[21];
float val22 = data1[22];
float val23 = data1[23];
float val24 = data1[24];
float val25 = data1[25];
float val26 = data1[26];
float val27 = data1[27];
float val28 = data1[28];
float val29 = data1[29];
float val30 = data1[30];
float val31 = data1[31];
for (int ridx0 = 0; ridx0 < 16; ridx0++) {
float acc0 = 0.0f;
float val32 = data2[ridx0];
float val33 = data2[ridx0+16];
float val34 = data2[ridx0+32];
float val35 = data2[ridx0+48];
float val36 = data2[ridx0+64];
float val37 = data2[ridx0+80];
float val38 = data2[ridx0+96];
float val39 = data2[ridx0+112];
float val40 = data2[ridx0+128];
float val41 = data2[ridx0+144];
float val42 = data2[ridx0+160];
float val43 = data2[ridx0+176];
float val44 = data2[ridx0+192];
float val45 = data2[ridx0+208];
float val46 = data2[ridx0+224];
float val47 = data2[ridx0+240];
float val48 = data2[ridx0+256];
float val49 = data2[ridx0+272];
float val50 = data2[ridx0+288];
float val51 = data2[ridx0+304];
float val52 = data2[ridx0+320];
float val53 = data2[ridx0+336];
float val54 = data2[ridx0+352];
float val55 = data2[ridx0+368];
float val56 = data2[ridx0+384];
float val57 = data2[ridx0+400];
float val58 = data2[ridx0+416];
float val59 = data2[ridx0+432];
float val60 = data2[ridx0+448];
float val61 = data2[ridx0+464];
float val62 = data2[ridx0+480];
float val63 = data2[ridx0+496];
float val64 = data3[ridx0];
float alu0 = max(((val31*val63)+((val30*val62)+((val29*val61)+((val28*val60)+((val27*val59)+((val26*val58)+((val25*val57)+((val24*val56)+((val23*val55)+((val22*val54)+((val21*val53)+((val20*val52)+((val19*val51)+((val18*val50)+((val17*val49)+((val16*val48)+((val15*val47)+((val14*val46)+((val13*val45)+((val12*val44)+((val11*val43)+((val10*val42)+((val9*val41)+((val8*val40)+((val7*val39)+((val6*val38)+((val5*val37)+((val4*val36)+((val3*val35)+((val2*val34)+((val1*val33)+((val0*val32)+acc0)))))))))))))))))))))))))))))))),0.0f);
data0[ridx0] = (alu0*val64);
}
}
void r_32_16(float* restrict data0, const float* restrict data1, const float* restrict data2) {
float val0 = data1[0];
float val1 = data1[1];
float val2 = data1[2];
float val3 = data1[3];
float val4 = data1[4];
float val5 = data1[5];
float val6 = data1[6];
float val7 = data1[7];
float val8 = data1[8];
float val9 = data1[9];
float val10 = data1[10];
float val11 = data1[11];
float val12 = data1[12];
float val13 = data1[13];
float val14 = data1[14];
float val15 = data1[15];
for (int ridx0 = 0; ridx0 < 32; ridx0++) {
float acc0 = 0.0f;
float val16 = data2[ridx0];
float val17 = data2[ridx0+32];
float val18 = data2[ridx0+64];
float val19 = data2[ridx0+96];
float val20 = data2[ridx0+128];
float val21 = data2[ridx0+160];
float val22 = data2[ridx0+192];
float val23 = data2[ridx0+224];
float val24 = data2[ridx0+256];
float val25 = data2[ridx0+288];
float val26 = data2[ridx0+320];
float val27 = data2[ridx0+352];
float val28 = data2[ridx0+384];
float val29 = data2[ridx0+416];
float val30 = data2[ridx0+448];
float val31 = data2[ridx0+480];
float alu0 = max(((val15*val31)+((val14*val30)+((val13*val29)+((val12*val28)+((val11*val27)+((val10*val26)+((val9*val25)+((val8*val24)+((val7*val23)+((val6*val22)+((val5*val21)+((val4*val20)+((val3*val19)+((val2*val18)+((val1*val17)+((val0*val16)+acc0)))))))))))))))),0.0f);
data0[ridx0] = alu0;
}
}
void r_4_32(float* restrict data0, const float* restrict data1, const float* restrict data2) {
float val0 = data1[0];
float val1 = data1[1];
float val2 = data1[2];
float val3 = data1[3];
float val4 = data1[4];
float val5 = data1[5];
float val6 = data1[6];
float val7 = data1[7];
float val8 = data1[8];
float val9 = data1[9];
float val10 = data1[10];
float val11 = data1[11];
float val12 = data1[12];
float val13 = data1[13];
float val14 = data1[14];
float val15 = data1[15];
float val16 = data1[16];
float val17 = data1[17];
float val18 = data1[18];
float val19 = data1[19];
float val20 = data1[20];
float val21 = data1[21];
float val22 = data1[22];
float val23 = data1[23];
float val24 = data1[24];
float val25 = data1[25];
float val26 = data1[26];
float val27 = data1[27];
float val28 = data1[28];
float val29 = data1[29];
float val30 = data1[30];
float val31 = data1[31];
for (int ridx0 = 0; ridx0 < 4; ridx0++) {
float acc0 = 0.0f;
float val32 = data2[ridx0];
float val33 = data2[ridx0+4];
float val34 = data2[ridx0+8];
float val35 = data2[ridx0+12];
float val36 = data2[ridx0+16];
float val37 = data2[ridx0+20];
float val38 = data2[ridx0+24];
float val39 = data2[ridx0+28];
float val40 = data2[ridx0+32];
float val41 = data2[ridx0+36];
float val42 = data2[ridx0+40];
float val43 = data2[ridx0+44];
float val44 = data2[ridx0+48];
float val45 = data2[ridx0+52];
float val46 = data2[ridx0+56];
float val47 = data2[ridx0+60];
float val48 = data2[ridx0+64];
float val49 = data2[ridx0+68];
float val50 = data2[ridx0+72];
float val51 = data2[ridx0+76];
float val52 = data2[ridx0+80];
float val53 = data2[ridx0+84];
float val54 = data2[ridx0+88];
float val55 = data2[ridx0+92];
float val56 = data2[ridx0+96];
float val57 = data2[ridx0+100];
float val58 = data2[ridx0+104];
float val59 = data2[ridx0+108];
float val60 = data2[ridx0+112];
float val61 = data2[ridx0+116];
float val62 = data2[ridx0+120];
float val63 = data2[ridx0+124];
data0[ridx0] = (1.0f/(1.0f+exp2((((val31*val63)+((val30*val62)+((val29*val61)+((val28*val60)+((val27*val59)+((val26*val58)+((val25*val57)+((val24*val56)+((val23*val55)+((val22*val54)+((val21*val53)+((val20*val52)+((val19*val51)+((val18*val50)+((val17*val49)+((val16*val48)+((val15*val47)+((val14*val46)+((val13*val45)+((val12*val44)+((val11*val43)+((val10*val42)+((val9*val41)+((val8*val40)+((val7*val39)+((val6*val38)+((val5*val37)+((val4*val36)+((val3*val35)+((val2*val34)+((val1*val33)+((val0*val32)+acc0))))))))))))))))))))))))))))))))*(-1.4426950408889634f)))));
}
}
void net(float* input0, float* output0) {
r_16_32(buf_0, input0, buf_1, buf_2);
r_32_16(buf_3, buf_0, buf_4);
r_4_32(output0, buf_3, buf_5);
}
void initialize(float *weights) {
memcpy(buf_1, weights + 0, 8192);
memcpy(buf_2, weights + 512, 256);
memcpy(buf_4, weights + 528, 8192);
memcpy(buf_5, weights + 1040, 2048);
}
int main(int argc, char *argv[]) {
// read in the weights from disk
FILE *f = fopen("/tmp/tf_weights", "rb");
float *weights = (float *)malloc(4672);
fread(weights, 1, 4672, f);
fclose(f);
// init the net
initialize(weights);
// test run
float input[32];
float outputs[4];
for (int i = 0; i < 32; i++) scanf("%f", &input[i]);
net(input, outputs);
printf("%f %f %f %f\n", outputs[0], outputs[1], outputs[2], outputs[3]);
}
conversation.py
[nltk_data] Downloading package punkt to /home/jebba/nltk_data...
[nltk_data] Package punkt is already up-to-date!
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loaded weights in 173.76 ms, 0.11 GB loaded at 0.62 GB/s
Traceback (most recent call last):
File "/home/jebba/devel/tinygrad/tinygrad/examples/conversation.py", line 261, in <module>
synth, emotion_embedding, text_mapper, hps, model_has_multiple_speakers = init_vits(args.vits_model_to_use, args.vits_emotion_path, args.vits_speaker_id, args.vits_seed)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/examples/conversation.py", line 166, in init_vits
net_g = load_model(text_mapper.symbols, hps, model_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/examples/vits.py", line 535, in load_model
_ = load_checkpoint(fetch(model[1]), net_g, None)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/examples/vits.py", line 540, in load_checkpoint
checkpoint_dict = torch_load(checkpoint_path)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/tinygrad/nn/state.py", line 145, in torch_load
_, _, _, rwd, _, ids, base_offset = pkl.load(), pkl.load(), pkl.load(), f.tell(), pkl.load(), pkl.load(), f.tell()
^^^^^^^^^^
_pickle.UnpicklingError: invalid load key, '<'.
efficientnet.py
281 8.961816 tabby, tabby cat
did inference in 5905.02 ms
f16_w_uint32.py
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.]
gpt2.py
using HIP backend
using gpt2-medium
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loaded weights in 1034.68 ms, 1.63 GB loaded at 1.57 GB/s
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Generating text...
What is the answer to life, the universe, and everything? You can't. If you can solve it, you'll find a way. But don't try to do it alone. See what I have done, and what you can do to solve it. The universe has no solution."
Note that Domino is not referring to the existence of God: He is referencing the existence of nine Upside-Down Order-Holes.
P.S. I just re-read the RIT book, pointed out this in the comments,
handcode_resnet50_opt.py
optimizing for HIP
*** 2.25 ms : kernel 0 r_[34m64[0m[90m_[0m[34m8[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m3[0m[90m_[0m[31m7[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m7[0m[90m[0m [49, 8, 64] [4, 16, 2] takes 2.25 ms, 6881 GFLOPS
*** 2.58 ms : kernel 1 r_[34m2048[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m2[0m[90m_[0m[36m8[0m[90m_[0m[36m8[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90m[0m [7, 7, 2048] [8, 8, 2] takes 0.33 ms, 351 GFLOPS
*** 2.77 ms : kernel 2 r_[34m64[0m[90m_[0m[34m2[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m16[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 2, 64] [16, 8] takes 0.19 ms, 9245 GFLOPS
*** 3.72 ms : kernel 3 r_[34m64[0m[90m_[0m[34m2[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m8[0m[90m_[0m[36m8[0m[90m_[0m[36m2[0m[90m_[0m[31m64[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90m[0m [49, 2, 64] [2, 8, 8] takes 0.95 ms, 15765 GFLOPS
*** 4.38 ms : kernel 4 r_[34m64[0m[90m_[0m[34m8[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m16[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 8, 64] [16, 8] takes 0.66 ms, 9984 GFLOPS
*** 5.09 ms : kernel 5 r_[34m64[0m[90m_[0m[34m8[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m16[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn1[0m [49, 8, 64] [16, 8] takes 0.71 ms, 10228 GFLOPS
*** 5.66 ms : kernel 6 r_[34m64[0m[90m_[0m[34m2[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m64[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 2, 64] [16, 8] takes 0.57 ms, 11626 GFLOPS
*** 6.60 ms : kernel 7 r_[34m64[0m[90m_[0m[34m2[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m8[0m[90m_[0m[36m8[0m[90m_[0m[36m2[0m[90m_[0m[31m64[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90mn1[0m [49, 2, 64] [2, 8, 8] takes 0.95 ms, 15765 GFLOPS
*** 7.32 ms : kernel 8 r_[34m64[0m[90m_[0m[34m8[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m16[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn2[0m [49, 8, 64] [16, 8] takes 0.71 ms, 9875 GFLOPS
*** 7.89 ms : kernel 9 r_[34m64[0m[90m_[0m[34m2[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m64[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn1[0m [49, 2, 64] [16, 8] takes 0.57 ms, 11626 GFLOPS
*** 8.84 ms : kernel 10 r_[34m64[0m[90m_[0m[34m2[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m8[0m[90m_[0m[36m8[0m[90m_[0m[36m2[0m[90m_[0m[31m64[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90mn2[0m [49, 2, 64] [2, 8, 8] takes 0.95 ms, 15765 GFLOPS
*** 9.55 ms : kernel 11 r_[34m64[0m[90m_[0m[34m8[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m16[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn3[0m [49, 8, 64] [16, 8] takes 0.71 ms, 9875 GFLOPS
*** 10.38 ms : kernel 12 r_[34m64[0m[90m_[0m[34m4[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m64[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 4, 64] [16, 8] takes 0.83 ms, 16069 GFLOPS
*** 11.95 ms : kernel 13 r_[34m32[0m[90m_[0m[34m2[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m128[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90m[0m [49, 2, 32] [4, 16, 2] takes 1.57 ms, 9461 GFLOPS
*** 13.60 ms : kernel 14 r_[34m32[0m[90m_[0m[34m8[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m64[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 8, 32] [4, 16, 2] takes 1.65 ms, 7984 GFLOPS
*** 14.37 ms : kernel 15 r_[34m32[0m[90m_[0m[34m8[0m[90m_[0m[34m49[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m32[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 8, 32] [4, 16, 2] takes 0.77 ms, 8986 GFLOPS
*** 15.19 ms : kernel 16 r_[34m32[0m[90m_[0m[34m2[0m[90m_[0m[34m49[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m128[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 2, 32] [4, 16, 2] takes 0.82 ms, 8049 GFLOPS
*** 16.47 ms : kernel 17 r_[34m32[0m[90m_[0m[34m2[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m128[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90mn1[0m [49, 2, 32] [4, 16, 2] takes 1.28 ms, 11623 GFLOPS
*** 17.25 ms : kernel 18 r_[34m32[0m[90m_[0m[34m8[0m[90m_[0m[34m49[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m32[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn1[0m [49, 8, 32] [4, 16, 2] takes 0.78 ms, 8731 GFLOPS
*** 18.07 ms : kernel 19 r_[34m32[0m[90m_[0m[34m2[0m[90m_[0m[34m49[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m128[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn1[0m [49, 2, 32] [4, 16, 2] takes 0.82 ms, 8049 GFLOPS
*** 19.35 ms : kernel 20 r_[34m32[0m[90m_[0m[34m2[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m128[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90mn2[0m [49, 2, 32] [4, 16, 2] takes 1.28 ms, 11623 GFLOPS
*** 20.13 ms : kernel 21 r_[34m32[0m[90m_[0m[34m8[0m[90m_[0m[34m49[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m32[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn2[0m [49, 8, 32] [4, 16, 2] takes 0.78 ms, 8731 GFLOPS
*** 20.95 ms : kernel 22 r_[34m32[0m[90m_[0m[34m2[0m[90m_[0m[34m49[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m128[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn2[0m [49, 2, 32] [4, 16, 2] takes 0.82 ms, 8049 GFLOPS
*** 22.23 ms : kernel 23 r_[34m32[0m[90m_[0m[34m2[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m128[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90mn3[0m [49, 2, 32] [4, 16, 2] takes 1.28 ms, 11623 GFLOPS
*** 23.01 ms : kernel 24 r_[34m32[0m[90m_[0m[34m8[0m[90m_[0m[34m49[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m32[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn3[0m [49, 8, 32] [4, 16, 2] takes 0.78 ms, 8731 GFLOPS
*** 24.26 ms : kernel 25 r_[34m32[0m[90m_[0m[34m4[0m[90m_[0m[34m49[0m[90m_[0m[36m2[0m[90m_[0m[36m16[0m[90m_[0m[36m4[0m[90m_[0m[31m128[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 4, 32] [4, 16, 2] takes 1.25 ms, 10619 GFLOPS
*** 26.39 ms : kernel 26 r_[34m16[0m[90m_[0m[34m4[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m16[0m[90m_[0m[36m2[0m[90m_[0m[36m2[0m[90m_[0m[31m256[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90m[0m [49, 4, 16] [2, 2, 16] takes 2.13 ms, 6957 GFLOPS
*** 29.24 ms : kernel 27 r_[34m16[0m[90m_[0m[34m16[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m16[0m[90m_[0m[36m2[0m[90m_[0m[36m2[0m[90m_[0m[31m128[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 16, 16] [2, 2, 16] takes 2.85 ms, 4618 GFLOPS
*** 30.42 ms : kernel 28 r_[34m8[0m[90m_[0m[34m16[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m64[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 16, 8] [16, 8] takes 1.18 ms, 5702 GFLOPS
*** 31.83 ms : kernel 29 r_[34m8[0m[90m_[0m[34m4[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m256[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 4, 8] [16, 8] takes 1.41 ms, 4669 GFLOPS
*** 33.35 ms : kernel 30 r_[34m16[0m[90m_[0m[34m4[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m16[0m[90m_[0m[36m2[0m[90m_[0m[36m2[0m[90m_[0m[31m256[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90mn1[0m [49, 4, 16] [2, 2, 16] takes 1.52 ms, 9776 GFLOPS
*** 34.55 ms : kernel 31 r_[34m8[0m[90m_[0m[34m16[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m64[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn1[0m [49, 16, 8] [16, 8] takes 1.20 ms, 5569 GFLOPS
*** 35.97 ms : kernel 32 r_[34m8[0m[90m_[0m[34m4[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m256[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn1[0m [49, 4, 8] [16, 8] takes 1.41 ms, 4669 GFLOPS
*** 37.48 ms : kernel 33 r_[34m16[0m[90m_[0m[34m4[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m16[0m[90m_[0m[36m2[0m[90m_[0m[36m2[0m[90m_[0m[31m256[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90mn2[0m [49, 4, 16] [2, 2, 16] takes 1.52 ms, 9776 GFLOPS
*** 38.68 ms : kernel 34 r_[34m8[0m[90m_[0m[34m16[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m64[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn2[0m [49, 16, 8] [16, 8] takes 1.20 ms, 5569 GFLOPS
*** 40.10 ms : kernel 35 r_[34m8[0m[90m_[0m[34m4[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m256[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn2[0m [49, 4, 8] [16, 8] takes 1.41 ms, 4669 GFLOPS
*** 41.61 ms : kernel 36 r_[34m16[0m[90m_[0m[34m4[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m16[0m[90m_[0m[36m2[0m[90m_[0m[36m2[0m[90m_[0m[31m256[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90mn3[0m [49, 4, 16] [2, 2, 16] takes 1.52 ms, 9776 GFLOPS
*** 42.82 ms : kernel 37 r_[34m8[0m[90m_[0m[34m16[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m64[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn3[0m [49, 16, 8] [16, 8] takes 1.20 ms, 5569 GFLOPS
*** 44.23 ms : kernel 38 r_[34m8[0m[90m_[0m[34m4[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m256[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn3[0m [49, 4, 8] [16, 8] takes 1.41 ms, 4669 GFLOPS
*** 45.75 ms : kernel 39 r_[34m16[0m[90m_[0m[34m4[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m16[0m[90m_[0m[36m2[0m[90m_[0m[36m2[0m[90m_[0m[31m256[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90mn4[0m [49, 4, 16] [2, 2, 16] takes 1.52 ms, 9776 GFLOPS
*** 46.95 ms : kernel 40 r_[34m8[0m[90m_[0m[34m16[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m64[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn4[0m [49, 16, 8] [16, 8] takes 1.20 ms, 5569 GFLOPS
*** 48.36 ms : kernel 41 r_[34m8[0m[90m_[0m[34m4[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m256[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn4[0m [49, 4, 8] [16, 8] takes 1.41 ms, 4669 GFLOPS
*** 49.88 ms : kernel 42 r_[34m16[0m[90m_[0m[34m4[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m16[0m[90m_[0m[36m2[0m[90m_[0m[36m2[0m[90m_[0m[31m256[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m3[0m[90m_[0m[35m3[0m[90mn5[0m [49, 4, 16] [2, 2, 16] takes 1.52 ms, 9776 GFLOPS
*** 51.08 ms : kernel 43 r_[34m8[0m[90m_[0m[34m16[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m64[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn5[0m [49, 16, 8] [16, 8] takes 1.20 ms, 5569 GFLOPS
*** 53.78 ms : kernel 44 r_[34m8[0m[90m_[0m[34m8[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m256[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 8, 8] [16, 8] takes 2.70 ms, 4896 GFLOPS
*** 100.26 ms : kernel 45 r_[34m8[0m[90m_[0m[34m8[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m512[0m[90m_[0m[31m3[0m[90m_[0m[31m3[0m[90m_[0m[33m7[0m[90m_[0m[33m7[0m[90m_[0m[33m4[0m[90m[0m [8, 8] [16, 8] takes 46.48 ms, 319 GFLOPS
*** 104.56 ms : kernel 46 r_[34m2[0m[90m_[0m[34m32[0m[90m_[0m[34m7[0m[90m_[0m[34m7[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m256[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 32, 2] [16, 8] takes 4.31 ms, 3055 GFLOPS
*** 106.54 ms : kernel 47 r_[34m2[0m[90m_[0m[34m32[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m128[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 32, 2] [16, 8] takes 1.98 ms, 3367 GFLOPS
*** 108.96 ms : kernel 48 r_[34m2[0m[90m_[0m[34m8[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m512[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [49, 8, 2] [16, 8] takes 2.41 ms, 2731 GFLOPS
*** 126.40 ms : kernel 49 r_[34m8[0m[90m_[0m[34m8[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m512[0m[90m_[0m[31m3[0m[90m_[0m[31m3[0m[90m_[0m[33m7[0m[90m_[0m[33m7[0m[90m_[0m[33m4[0m[90mn1[0m [8, 8] [16, 8] takes 17.44 ms, 849 GFLOPS
*** 128.35 ms : kernel 50 r_[34m2[0m[90m_[0m[34m32[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m128[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn1[0m [49, 32, 2] [16, 8] takes 1.95 ms, 3401 GFLOPS
*** 130.76 ms : kernel 51 r_[34m2[0m[90m_[0m[34m8[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m512[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn1[0m [49, 8, 2] [16, 8] takes 2.41 ms, 2731 GFLOPS
*** 148.20 ms : kernel 52 r_[34m8[0m[90m_[0m[34m8[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m512[0m[90m_[0m[31m3[0m[90m_[0m[31m3[0m[90m_[0m[33m7[0m[90m_[0m[33m7[0m[90m_[0m[33m4[0m[90mn2[0m [8, 8] [16, 8] takes 17.44 ms, 849 GFLOPS
*** 150.15 ms : kernel 53 r_[34m2[0m[90m_[0m[34m32[0m[90m_[0m[34m49[0m[90m_[0m[36m8[0m[90m_[0m[36m16[0m[90m_[0m[31m128[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90mn2[0m [49, 32, 2] [16, 8] takes 1.95 ms, 3401 GFLOPS
*** 150.24 ms : kernel 54 r_[34m1024[0m[90m_[0m[36m32[0m[90m_[0m[31m49[0m[90m_[0m[33m4[0m[90m[0m [1024] [32] takes 0.08 ms, 79 GFLOPS
*** 150.43 ms : kernel 55 r_[34m125[0m[90m_[0m[36m16[0m[90m_[0m[36m2[0m[90m_[0m[31m512[0m[90m_[0m[33m4[0m[90m_[0m[33m4[0m[90m_[0m[35m4[0m[90m[0m [125] [2, 16] takes 0.19 ms, 1382 GFLOPS
*** 150.45 ms : kernel 56 r_[34m2[0m[90m_[0m[36m32[0m[90m_[0m[31m250[0m[90m_[0m[35m4[0m[90m[0m [2] [32] takes 0.03 ms, 2 GFLOPS
*** 150.53 ms : kernel 57 r_[34m2[0m[90m_[0m[36m32[0m[90m_[0m[31m250[0m[90m_[0m[35m4[0m[90mn1[0m [2] [32] takes 0.07 ms, 3 GFLOPS
*** 150.54 ms : kernel 58 E_[34m2[0m[90m_[0m[34m125[0m[90m_[0m[36m32[0m[90m_[0m[36m2[0m[90m_[0m[33m4[0m[90m[0m [125, 2] [2, 32] takes 0.01 ms, 9 GFLOPS
******* total 150.54 ms, 3515 GFLOPS
hlb_cifar10.py
shuffling training dataset in 1337.90 ms (epoch=0)
0 15108.61 ms run, 15098.47 ms python, 10.13 ms HIP, 1198.26 loss, 0.000015 LR, 0.84 GB used, 44.80 GFLOPS, 676.91 GOPS
1 5837.29 ms run, 5834.97 ms python, 2.32 ms HIP, 1197.49 loss, 0.000030 LR, 4.54 GB used, 115.64 GFLOPS, 675.05 GOPS
2 74.37 ms run, 4.43 ms python, 69.93 ms HIP, 1188.08 loss, 0.000045 LR, 4.54 GB used, 9077.39 GFLOPS, 675.05 GOPS
3 73.70 ms run, 2.85 ms python, 70.85 ms HIP, 1171.24 loss, 0.000060 LR, 4.54 GB used, 9159.11 GFLOPS, 675.05 GOPS
4 71.96 ms run, 2.86 ms python, 69.10 ms HIP, 1160.91 loss, 0.000075 LR, 4.54 GB used, 9380.72 GFLOPS, 675.05 GOPS
5 70.66 ms run, 2.79 ms python, 67.87 ms HIP, 1158.75 loss, 0.000090 LR, 4.54 GB used, 9553.50 GFLOPS, 675.05 GOPS
6 70.78 ms run, 2.77 ms python, 68.02 ms HIP, 1149.44 loss, 0.000105 LR, 4.54 GB used, 9537.02 GFLOPS, 675.05 GOPS
7 69.50 ms run, 2.79 ms python, 66.71 ms HIP, 1172.52 loss, 0.000120 LR, 4.54 GB used, 9713.08 GFLOPS, 675.05 GOPS
8 69.50 ms run, 2.76 ms python, 66.73 ms HIP, 1143.33 loss, 0.000135 LR, 4.54 GB used, 9713.20 GFLOPS, 675.05 GOPS
9 69.41 ms run, 2.74 ms python, 66.67 ms HIP, 1129.94 loss, 0.000149 LR, 4.54 GB used, 9725.38 GFLOPS, 675.05 GOPS
10 69.42 ms run, 2.96 ms python, 66.46 ms HIP, 1114.84 loss, 0.000164 LR, 4.54 GB used, 9724.70 GFLOPS, 675.05 GOPS
11 69.34 ms run, 2.75 ms python, 66.59 ms HIP, 1099.61 loss, 0.000179 LR, 4.54 GB used, 9735.30 GFLOPS, 675.05 GOPS
12 68.99 ms run, 2.71 ms python, 66.28 ms HIP, 1092.18 loss, 0.000194 LR, 4.54 GB used, 9784.85 GFLOPS, 675.05 GOPS
13 68.94 ms run, 2.72 ms python, 66.22 ms HIP, 1068.63 loss, 0.000209 LR, 4.54 GB used, 9791.70 GFLOPS, 675.05 GOPS
14 68.94 ms run, 2.74 ms python, 66.19 ms HIP, 1066.79 loss, 0.000224 LR, 4.54 GB used, 9792.07 GFLOPS, 675.05 GOPS
15 69.61 ms run, 2.74 ms python, 66.86 ms HIP, 1065.05 loss, 0.000239 LR, 4.54 GB used, 9698.12 GFLOPS, 675.05 GOPS
16 68.99 ms run, 2.76 ms python, 66.23 ms HIP, 1030.24 loss, 0.000254 LR, 4.54 GB used, 9785.08 GFLOPS, 675.05 GOPS
17 69.05 ms run, 2.74 ms python, 66.31 ms HIP, 1034.32 loss, 0.000269 LR, 4.54 GB used, 9776.24 GFLOPS, 675.05 GOPS
18 69.09 ms run, 2.79 ms python, 66.30 ms HIP, 1012.50 loss, 0.000284 LR, 4.54 GB used, 9770.48 GFLOPS, 675.05 GOPS
19 68.84 ms run, 2.75 ms python, 66.10 ms HIP, 995.76 loss, 0.000299 LR, 4.54 GB used, 9805.71 GFLOPS, 675.05 GOPS
20 69.74 ms run, 2.72 ms python, 67.02 ms HIP, 985.93 loss, 0.000314 LR, 4.54 GB used, 9679.54 GFLOPS, 675.05 GOPS
21 69.37 ms run, 2.67 ms python, 66.69 ms HIP, 970.91 loss, 0.000329 LR, 4.54 GB used, 9731.73 GFLOPS, 675.05 GOPS
22 69.33 ms run, 2.76 ms python, 66.58 ms HIP, 978.42 loss, 0.000344 LR, 4.54 GB used, 9736.07 GFLOPS, 675.05 GOPS
23 69.28 ms run, 2.69 ms python, 66.58 ms HIP, 993.64 loss, 0.000359 LR, 4.54 GB used, 9744.05 GFLOPS, 675.05 GOPS
24 69.03 ms run, 2.72 ms python, 66.31 ms HIP, 950.65 loss, 0.000374 LR, 4.54 GB used, 9779.28 GFLOPS, 675.05 GOPS
25 69.20 ms run, 2.72 ms python, 66.48 ms HIP, 928.26 loss, 0.000389 LR, 4.54 GB used, 9754.64 GFLOPS, 675.05 GOPS
26 69.84 ms run, 2.67 ms python, 67.16 ms HIP, 941.32 loss, 0.000404 LR, 4.54 GB used, 9665.76 GFLOPS, 675.05 GOPS
27 69.34 ms run, 2.70 ms python, 66.64 ms HIP, 932.31 loss, 0.000418 LR, 4.54 GB used, 9734.64 GFLOPS, 675.05 GOPS
28 69.07 ms run, 2.75 ms python, 66.32 ms HIP, 928.35 loss, 0.000433 LR, 4.54 GB used, 9773.78 GFLOPS, 675.05 GOPS
29 68.97 ms run, 2.76 ms python, 66.22 ms HIP, 896.15 loss, 0.000448 LR, 4.54 GB used, 9787.18 GFLOPS, 675.05 GOPS
30 68.71 ms run, 2.74 ms python, 65.97 ms HIP, 928.76 loss, 0.000463 LR, 4.54 GB used, 9824.42 GFLOPS, 675.05 GOPS
31 69.22 ms run, 2.78 ms python, 66.44 ms HIP, 907.65 loss, 0.000478 LR, 4.54 GB used, 9751.77 GFLOPS, 675.05 GOPS
32 69.47 ms run, 2.75 ms python, 66.73 ms HIP, 892.33 loss, 0.000493 LR, 4.54 GB used, 9716.56 GFLOPS, 675.05 GOPS
33 68.84 ms run, 2.69 ms python, 66.15 ms HIP, 873.42 loss, 0.000508 LR, 4.54 GB used, 9805.90 GFLOPS, 675.05 GOPS
34 69.59 ms run, 2.72 ms python, 66.86 ms HIP, 873.31 loss, 0.000523 LR, 4.54 GB used, 9700.65 GFLOPS, 675.05 GOPS
35 69.19 ms run, 2.70 ms python, 66.49 ms HIP, 880.48 loss, 0.000538 LR, 4.54 GB used, 9757.04 GFLOPS, 675.05 GOPS
36 69.74 ms run, 2.74 ms python, 67.01 ms HIP, 889.98 loss, 0.000553 LR, 4.54 GB used, 9678.76 GFLOPS, 675.05 GOPS
37 68.99 ms run, 2.72 ms python, 66.27 ms HIP, 944.16 loss, 0.000568 LR, 4.54 GB used, 9784.54 GFLOPS, 675.05 GOPS
38 69.25 ms run, 2.69 ms python, 66.56 ms HIP, 906.17 loss, 0.000583 LR, 4.54 GB used, 9747.65 GFLOPS, 675.05 GOPS
39 69.12 ms run, 2.73 ms python, 66.40 ms HIP, 912.06 loss, 0.000598 LR, 4.54 GB used, 9765.62 GFLOPS, 675.05 GOPS
40 69.11 ms run, 2.68 ms python, 66.42 ms HIP, 872.09 loss, 0.000613 LR, 4.54 GB used, 9768.14 GFLOPS, 675.05 GOPS
41 68.29 ms run, 2.70 ms python, 65.59 ms HIP, 847.76 loss, 0.000628 LR, 4.54 GB used, 9885.47 GFLOPS, 675.05 GOPS
42 69.38 ms run, 2.74 ms python, 66.64 ms HIP, 847.61 loss, 0.000643 LR, 4.54 GB used, 9729.01 GFLOPS, 675.05 GOPS
43 69.86 ms run, 2.73 ms python, 67.12 ms HIP, 852.14 loss, 0.000658 LR, 4.54 GB used, 9663.11 GFLOPS, 675.05 GOPS
44 69.20 ms run, 2.74 ms python, 66.47 ms HIP, 858.28 loss, 0.000673 LR, 4.54 GB used, 9754.62 GFLOPS, 675.05 GOPS
45 69.28 ms run, 2.71 ms python, 66.57 ms HIP, 883.35 loss, 0.000688 LR, 4.54 GB used, 9743.96 GFLOPS, 675.05 GOPS
46 69.97 ms run, 2.66 ms python, 67.31 ms HIP, 854.11 loss, 0.000702 LR, 4.54 GB used, 9647.80 GFLOPS, 675.05 GOPS
47 69.51 ms run, 2.72 ms python, 66.78 ms HIP, 807.23 loss, 0.000717 LR, 4.54 GB used, 9712.03 GFLOPS, 675.05 GOPS
48 69.31 ms run, 2.71 ms python, 66.60 ms HIP, 809.48 loss, 0.000732 LR, 4.54 GB used, 9739.65 GFLOPS, 675.05 GOPS
49 69.46 ms run, 2.70 ms python, 66.76 ms HIP, 838.32 loss, 0.000747 LR, 4.54 GB used, 9718.94 GFLOPS, 675.05 GOPS
50 69.30 ms run, 2.72 ms python, 66.58 ms HIP, 825.54 loss, 0.000762 LR, 4.54 GB used, 9741.24 GFLOPS, 675.05 GOPS
51 69.55 ms run, 2.67 ms python, 66.88 ms HIP, 781.49 loss, 0.000777 LR, 4.54 GB used, 9706.30 GFLOPS, 675.05 GOPS
52 69.25 ms run, 2.71 ms python, 66.54 ms HIP, 841.42 loss, 0.000792 LR, 4.54 GB used, 9747.96 GFLOPS, 675.05 GOPS
53 69.24 ms run, 2.66 ms python, 66.58 ms HIP, 798.52 loss, 0.000807 LR, 4.54 GB used, 9748.76 GFLOPS, 675.05 GOPS
54 69.38 ms run, 2.68 ms python, 66.70 ms HIP, 809.88 loss, 0.000822 LR, 4.54 GB used, 9729.31 GFLOPS, 675.05 GOPS
55 69.59 ms run, 2.67 ms python, 66.92 ms HIP, 823.67 loss, 0.000837 LR, 4.54 GB used, 9700.17 GFLOPS, 675.05 GOPS
56 68.85 ms run, 2.74 ms python, 66.11 ms HIP, 815.30 loss, 0.000852 LR, 4.54 GB used, 9804.06 GFLOPS, 675.05 GOPS
57 69.44 ms run, 2.70 ms python, 66.74 ms HIP, 800.62 loss, 0.000867 LR, 4.54 GB used, 9721.16 GFLOPS, 675.05 GOPS
58 69.37 ms run, 2.74 ms python, 66.63 ms HIP, 782.18 loss, 0.000882 LR, 4.54 GB used, 9731.23 GFLOPS, 675.05 GOPS
59 69.41 ms run, 2.72 ms python, 66.69 ms HIP, 811.72 loss, 0.000897 LR, 4.54 GB used, 9725.58 GFLOPS, 675.05 GOPS
60 68.69 ms run, 2.70 ms python, 65.99 ms HIP, 834.58 loss, 0.000912 LR, 4.54 GB used, 9826.86 GFLOPS, 675.05 GOPS
61 70.02 ms run, 2.67 ms python, 67.35 ms HIP, 817.35 loss, 0.000927 LR, 4.54 GB used, 9640.55 GFLOPS, 675.05 GOPS
62 69.54 ms run, 2.67 ms python, 66.87 ms HIP, 840.39 loss, 0.000942 LR, 4.54 GB used, 9707.34 GFLOPS, 675.05 GOPS
63 69.11 ms run, 2.89 ms python, 66.22 ms HIP, 789.49 loss, 0.000957 LR, 4.54 GB used, 9767.09 GFLOPS, 675.05 GOPS
64 69.37 ms run, 2.79 ms python, 66.58 ms HIP, 767.33 loss, 0.000971 LR, 4.54 GB used, 9730.40 GFLOPS, 675.05 GOPS
65 68.84 ms run, 2.74 ms python, 66.10 ms HIP, 735.83 loss, 0.000986 LR, 4.54 GB used, 9806.38 GFLOPS, 675.05 GOPS
66 69.71 ms run, 2.81 ms python, 66.90 ms HIP, 767.32 loss, 0.001001 LR, 4.54 GB used, 9683.70 GFLOPS, 675.05 GOPS
67 69.49 ms run, 2.73 ms python, 66.76 ms HIP, 740.48 loss, 0.001016 LR, 4.54 GB used, 9714.26 GFLOPS, 675.05 GOPS
68 69.04 ms run, 2.74 ms python, 66.31 ms HIP, 754.44 loss, 0.001031 LR, 4.54 GB used, 9777.48 GFLOPS, 675.05 GOPS
69 68.58 ms run, 2.78 ms python, 65.80 ms HIP, 751.04 loss, 0.001046 LR, 4.54 GB used, 9843.00 GFLOPS, 675.05 GOPS
70 69.71 ms run, 2.75 ms python, 66.95 ms HIP, 758.91 loss, 0.001061 LR, 4.54 GB used, 9684.11 GFLOPS, 675.05 GOPS
71 69.41 ms run, 2.76 ms python, 66.64 ms HIP, 753.18 loss, 0.001076 LR, 4.54 GB used, 9725.77 GFLOPS, 675.05 GOPS
72 69.59 ms run, 2.72 ms python, 66.87 ms HIP, 770.21 loss, 0.001091 LR, 4.54 GB used, 9699.87 GFLOPS, 675.05 GOPS
73 69.39 ms run, 2.72 ms python, 66.67 ms HIP, 758.43 loss, 0.001106 LR, 4.54 GB used, 9727.67 GFLOPS, 675.05 GOPS
74 69.18 ms run, 2.72 ms python, 66.46 ms HIP, 734.02 loss, 0.001121 LR, 4.54 GB used, 9757.70 GFLOPS, 675.05 GOPS
75 68.85 ms run, 2.75 ms python, 66.09 ms HIP, 737.91 loss, 0.001136 LR, 4.54 GB used, 9805.03 GFLOPS, 675.05 GOPS
76 69.30 ms run, 2.71 ms python, 66.59 ms HIP, 727.93 loss, 0.001151 LR, 4.54 GB used, 9741.20 GFLOPS, 675.05 GOPS
77 69.46 ms run, 2.74 ms python, 66.71 ms HIP, 746.44 loss, 0.001166 LR, 4.54 GB used, 9719.09 GFLOPS, 675.05 GOPS
78 69.37 ms run, 2.76 ms python, 66.62 ms HIP, 729.42 loss, 0.001181 LR, 4.54 GB used, 9731.01 GFLOPS, 675.05 GOPS
79 69.36 ms run, 2.86 ms python, 66.50 ms HIP, 763.18 loss, 0.001196 LR, 4.54 GB used, 9731.99 GFLOPS, 675.05 GOPS
80 69.00 ms run, 2.73 ms python, 66.27 ms HIP, 728.07 loss, 0.001211 LR, 4.54 GB used, 9783.30 GFLOPS, 675.05 GOPS
81 69.05 ms run, 2.75 ms python, 66.30 ms HIP, 732.20 loss, 0.001226 LR, 4.54 GB used, 9776.00 GFLOPS, 675.05 GOPS
82 69.80 ms run, 2.73 ms python, 67.08 ms HIP, 731.84 loss, 0.001240 LR, 4.54 GB used, 9670.57 GFLOPS, 675.05 GOPS
83 69.34 ms run, 2.72 ms python, 66.62 ms HIP, 723.89 loss, 0.001255 LR, 4.54 GB used, 9735.15 GFLOPS, 675.05 GOPS
84 69.09 ms run, 2.75 ms python, 66.34 ms HIP, 716.49 loss, 0.001270 LR, 4.54 GB used, 9770.21 GFLOPS, 675.05 GOPS
85 68.92 ms run, 2.73 ms python, 66.19 ms HIP, 721.01 loss, 0.001285 LR, 4.54 GB used, 9795.17 GFLOPS, 675.05 GOPS
86 69.31 ms run, 2.73 ms python, 66.58 ms HIP, 726.47 loss, 0.001300 LR, 4.54 GB used, 9739.78 GFLOPS, 675.05 GOPS
87 69.16 ms run, 2.83 ms python, 66.33 ms HIP, 743.07 loss, 0.001315 LR, 4.54 GB used, 9761.19 GFLOPS, 675.05 GOPS
88 69.55 ms run, 2.77 ms python, 66.78 ms HIP, 751.18 loss, 0.001330 LR, 4.54 GB used, 9706.38 GFLOPS, 675.05 GOPS
89 69.36 ms run, 2.74 ms python, 66.61 ms HIP, 720.70 loss, 0.001345 LR, 4.54 GB used, 9732.68 GFLOPS, 675.05 GOPS
90 69.07 ms run, 2.72 ms python, 66.35 ms HIP, 715.80 loss, 0.001360 LR, 4.54 GB used, 9773.50 GFLOPS, 675.05 GOPS
91 68.65 ms run, 2.73 ms python, 65.91 ms HIP, 716.98 loss, 0.001375 LR, 4.54 GB used, 9833.84 GFLOPS, 675.05 GOPS
92 69.23 ms run, 2.79 ms python, 66.45 ms HIP, 715.26 loss, 0.001390 LR, 4.54 GB used, 9750.10 GFLOPS, 675.05 GOPS
93 69.16 ms run, 2.74 ms python, 66.42 ms HIP, 692.31 loss, 0.001405 LR, 4.54 GB used, 9760.43 GFLOPS, 675.05 GOPS
94 69.43 ms run, 2.71 ms python, 66.72 ms HIP, 678.61 loss, 0.001420 LR, 4.54 GB used, 9723.11 GFLOPS, 675.05 GOPS
95 69.07 ms run, 2.76 ms python, 66.31 ms HIP, 702.36 loss, 0.001435 LR, 4.54 GB used, 9772.82 GFLOPS, 675.05 GOPS
96 68.79 ms run, 2.72 ms python, 66.07 ms HIP, 657.52 loss, 0.001450 LR, 4.54 GB used, 9813.28 GFLOPS, 675.05 GOPS
97 68.94 ms run, 2.75 ms python, 66.19 ms HIP, 665.23 loss, 0.001465 LR, 4.54 GB used, 9792.20 GFLOPS, 675.05 GOPS
shuffling training dataset in 755.98 ms (epoch=1)
98 831.82 ms run, 759.18 ms python, 72.64 ms HIP, 665.56 loss, 0.001480 LR, 4.54 GB used, 811.88 GFLOPS, 675.34 GOPS
99 73.72 ms run, 2.85 ms python, 70.87 ms HIP, 695.91 loss, 0.001495 LR, 4.54 GB used, 9157.12 GFLOPS, 675.05 GOPS
100 72.84 ms run, 2.77 ms python, 70.07 ms HIP, 715.10 loss, 0.001510 LR, 4.54 GB used, 9267.20 GFLOPS, 675.05 GOPS
101 71.20 ms run, 2.87 ms python, 68.33 ms HIP, 703.98 loss, 0.001524 LR, 4.54 GB used, 9480.52 GFLOPS, 675.05 GOPS
102 71.12 ms run, 2.77 ms python, 68.34 ms HIP, 691.39 loss, 0.001539 LR, 4.54 GB used, 9492.06 GFLOPS, 675.05 GOPS
103 69.92 ms run, 2.75 ms python, 67.17 ms HIP, 678.40 loss, 0.001554 LR, 4.54 GB used, 9655.06 GFLOPS, 675.05 GOPS
104 69.41 ms run, 2.71 ms python, 66.70 ms HIP, 679.19 loss, 0.001569 LR, 4.54 GB used, 9725.23 GFLOPS, 675.05 GOPS
105 69.77 ms run, 2.70 ms python, 67.07 ms HIP, 684.38 loss, 0.001584 LR, 4.54 GB used, 9675.54 GFLOPS, 675.05 GOPS
106 69.57 ms run, 2.72 ms python, 66.85 ms HIP, 679.57 loss, 0.001599 LR, 4.54 GB used, 9702.74 GFLOPS, 675.05 GOPS
107 69.07 ms run, 2.76 ms python, 66.31 ms HIP, 675.04 loss, 0.001614 LR, 4.54 GB used, 9773.89 GFLOPS, 675.05 GOPS
108 69.64 ms run, 2.76 ms python, 66.88 ms HIP, 663.53 loss, 0.001629 LR, 4.54 GB used, 9693.30 GFLOPS, 675.05 GOPS
109 70.40 ms run, 2.83 ms python, 67.57 ms HIP, 669.80 loss, 0.001644 LR, 4.54 GB used, 9589.22 GFLOPS, 675.05 GOPS
110 69.53 ms run, 2.72 ms python, 66.82 ms HIP, 675.51 loss, 0.001659 LR, 4.54 GB used, 9708.04 GFLOPS, 675.05 GOPS
111 68.61 ms run, 2.80 ms python, 65.81 ms HIP, 675.21 loss, 0.001674 LR, 4.54 GB used, 9838.29 GFLOPS, 675.05 GOPS
112 68.84 ms run, 2.69 ms python, 66.15 ms HIP, 697.70 loss, 0.001689 LR, 4.54 GB used, 9806.06 GFLOPS, 675.05 GOPS
113 68.99 ms run, 2.69 ms python, 66.30 ms HIP, 699.45 loss, 0.001704 LR, 4.54 GB used, 9785.10 GFLOPS, 675.05 GOPS
114 69.07 ms run, 2.74 ms python, 66.34 ms HIP, 666.35 loss, 0.001719 LR, 4.54 GB used, 9772.85 GFLOPS, 675.05 GOPS
115 69.38 ms run, 2.73 ms python, 66.65 ms HIP, 685.84 loss, 0.001734 LR, 4.54 GB used, 9729.75 GFLOPS, 675.05 GOPS
116 69.55 ms run, 2.82 ms python, 66.74 ms HIP, 675.04 loss, 0.001749 LR, 4.54 GB used, 9705.29 GFLOPS, 675.05 GOPS
117 69.10 ms run, 2.72 ms python, 66.37 ms HIP, 659.46 loss, 0.001764 LR, 4.54 GB used, 9769.32 GFLOPS, 675.05 GOPS
118 69.31 ms run, 2.72 ms python, 66.59 ms HIP, 664.42 loss, 0.001779 LR, 4.54 GB used, 9739.88 GFLOPS, 675.05 GOPS
119 69.29 ms run, 2.69 ms python, 66.60 ms HIP, 687.12 loss, 0.001793 LR, 4.54 GB used, 9742.33 GFLOPS, 675.05 GOPS
120 69.46 ms run, 2.71 ms python, 66.76 ms HIP, 702.90 loss, 0.001808 LR, 4.54 GB used, 9717.85 GFLOPS, 675.05 GOPS
121 69.46 ms run, 2.74 ms python, 66.72 ms HIP, 705.35 loss, 0.001823 LR, 4.54 GB used, 9718.82 GFLOPS, 675.05 GOPS
122 69.70 ms run, 2.76 ms python, 66.94 ms HIP, 664.24 loss, 0.001838 LR, 4.54 GB used, 9685.62 GFLOPS, 675.05 GOPS
123 69.16 ms run, 2.75 ms python, 66.41 ms HIP, 670.65 loss, 0.001853 LR, 4.54 GB used, 9760.12 GFLOPS, 675.05 GOPS
124 69.02 ms run, 2.68 ms python, 66.34 ms HIP, 659.85 loss, 0.001868 LR, 4.54 GB used, 9780.20 GFLOPS, 675.05 GOPS
125 69.18 ms run, 2.79 ms python, 66.39 ms HIP, 668.93 loss, 0.001883 LR, 4.54 GB used, 9757.52 GFLOPS, 675.05 GOPS
126 69.38 ms run, 2.70 ms python, 66.68 ms HIP, 661.00 loss, 0.001898 LR, 4.54 GB used, 9729.95 GFLOPS, 675.05 GOPS
127 69.16 ms run, 2.74 ms python, 66.42 ms HIP, 654.85 loss, 0.001913 LR, 4.54 GB used, 9760.06 GFLOPS, 675.05 GOPS
128 69.01 ms run, 2.74 ms python, 66.27 ms HIP, 676.15 loss, 0.001928 LR, 4.54 GB used, 9781.70 GFLOPS, 675.05 GOPS
129 68.86 ms run, 2.70 ms python, 66.16 ms HIP, 676.86 loss, 0.001943 LR, 4.54 GB used, 9802.63 GFLOPS, 675.05 GOPS
130 68.56 ms run, 2.70 ms python, 65.86 ms HIP, 663.13 loss, 0.001958 LR, 4.54 GB used, 9846.06 GFLOPS, 675.05 GOPS
131 69.10 ms run, 2.68 ms python, 66.41 ms HIP, 645.75 loss, 0.001973 LR, 4.54 GB used, 9769.36 GFLOPS, 675.05 GOPS
132 70.37 ms run, 2.70 ms python, 67.67 ms HIP, 670.99 loss, 0.001988 LR, 4.54 GB used, 9593.29 GFLOPS, 675.05 GOPS
133 69.58 ms run, 2.70 ms python, 66.88 ms HIP, 661.26 loss, 0.002003 LR, 4.54 GB used, 9701.80 GFLOPS, 675.05 GOPS
134 69.39 ms run, 2.69 ms python, 66.71 ms HIP, 669.69 loss, 0.002018 LR, 4.54 GB used, 9727.68 GFLOPS, 675.05 GOPS
135 69.94 ms run, 2.70 ms python, 67.24 ms HIP, 673.50 loss, 0.002033 LR, 4.54 GB used, 9651.33 GFLOPS, 675.05 GOPS
136 70.10 ms run, 2.76 ms python, 67.34 ms HIP, 657.75 loss, 0.002048 LR, 4.54 GB used, 9630.03 GFLOPS, 675.05 GOPS
137 70.11 ms run, 2.72 ms python, 67.38 ms HIP, 660.81 loss, 0.002063 LR, 4.54 GB used, 9628.57 GFLOPS, 675.05 GOPS
138 69.64 ms run, 2.73 ms python, 66.91 ms HIP, 671.17 loss, 0.002077 LR, 4.54 GB used, 9693.96 GFLOPS, 675.05 GOPS
139 69.33 ms run, 2.72 ms python, 66.61 ms HIP, 688.35 loss, 0.002092 LR, 4.54 GB used, 9736.29 GFLOPS, 675.05 GOPS
140 69.38 ms run, 2.71 ms python, 66.67 ms HIP, 648.27 loss, 0.002107 LR, 4.54 GB used, 9729.40 GFLOPS, 675.05 GOPS
141 70.56 ms run, 2.72 ms python, 67.84 ms HIP, 645.85 loss, 0.002122 LR, 4.54 GB used, 9567.45 GFLOPS, 675.05 GOPS
142 69.73 ms run, 2.69 ms python, 67.04 ms HIP, 665.99 loss, 0.002137 LR, 4.54 GB used, 9680.96 GFLOPS, 675.05 GOPS
143 69.40 ms run, 2.80 ms python, 66.60 ms HIP, 692.06 loss, 0.002152 LR, 4.54 GB used, 9726.19 GFLOPS, 675.05 GOPS
144 68.76 ms run, 2.68 ms python, 66.08 ms HIP, 675.49 loss, 0.002167 LR, 4.54 GB used, 9817.55 GFLOPS, 675.05 GOPS
145 68.66 ms run, 2.68 ms python, 65.99 ms HIP, 715.16 loss, 0.002182 LR, 4.54 GB used, 9831.15 GFLOPS, 675.05 GOPS
146 69.04 ms run, 2.71 ms python, 66.33 ms HIP, 681.11 loss, 0.002197 LR, 4.54 GB used, 9777.15 GFLOPS, 675.05 GOPS
147 69.47 ms run, 2.67 ms python, 66.79 ms HIP, 713.74 loss, 0.002212 LR, 4.54 GB used, 9717.48 GFLOPS, 675.05 GOPS
148 69.13 ms run, 2.67 ms python, 66.47 ms HIP, 696.30 loss, 0.002227 LR, 4.54 GB used, 9764.18 GFLOPS, 675.05 GOPS
149 69.17 ms run, 2.67 ms python, 66.50 ms HIP, 651.40 loss, 0.002242 LR, 4.54 GB used, 9759.92 GFLOPS, 675.05 GOPS
150 69.74 ms run, 2.74 ms python, 67.00 ms HIP, 656.05 loss, 0.002257 LR, 4.54 GB used, 9680.08 GFLOPS, 675.05 GOPS
151 69.64 ms run, 2.68 ms python, 66.96 ms HIP, 659.93 loss, 0.002272 LR, 4.54 GB used, 9692.70 GFLOPS, 675.05 GOPS
152 70.08 ms run, 2.73 ms python, 67.35 ms HIP, 655.41 loss, 0.002287 LR, 4.54 GB used, 9633.18 GFLOPS, 675.05 GOPS
153 69.50 ms run, 2.71 ms python, 66.79 ms HIP, 642.93 loss, 0.002302 LR, 4.54 GB used, 9713.06 GFLOPS, 675.05 GOPS
154 69.17 ms run, 2.66 ms python, 66.51 ms HIP, 661.86 loss, 0.002317 LR, 4.54 GB used, 9758.88 GFLOPS, 675.05 GOPS
155 69.83 ms run, 2.70 ms python, 67.13 ms HIP, 656.04 loss, 0.002332 LR, 4.54 GB used, 9667.62 GFLOPS, 675.05 GOPS
156 69.37 ms run, 2.71 ms python, 66.66 ms HIP, 671.41 loss, 0.002346 LR, 4.54 GB used, 9731.45 GFLOPS, 675.05 GOPS
157 69.66 ms run, 2.75 ms python, 66.90 ms HIP, 670.28 loss, 0.002361 LR, 4.54 GB used, 9691.02 GFLOPS, 675.05 GOPS
158 70.13 ms run, 2.73 ms python, 67.39 ms HIP, 653.53 loss, 0.002376 LR, 4.54 GB used, 9625.92 GFLOPS, 675.05 GOPS
159 69.86 ms run, 2.68 ms python, 67.18 ms HIP, 645.35 loss, 0.002391 LR, 4.54 GB used, 9662.92 GFLOPS, 675.05 GOPS
160 69.78 ms run, 2.70 ms python, 67.08 ms HIP, 667.87 loss, 0.002406 LR, 4.54 GB used, 9674.32 GFLOPS, 675.05 GOPS
161 68.98 ms run, 2.72 ms python, 66.26 ms HIP, 646.49 loss, 0.002421 LR, 4.54 GB used, 9786.22 GFLOPS, 675.05 GOPS
162 69.74 ms run, 2.67 ms python, 67.07 ms HIP, 649.51 loss, 0.002436 LR, 4.54 GB used, 9679.95 GFLOPS, 675.05 GOPS
163 69.49 ms run, 2.71 ms python, 66.79 ms HIP, 643.96 loss, 0.002451 LR, 4.54 GB used, 9714.14 GFLOPS, 675.05 GOPS
164 69.13 ms run, 2.68 ms python, 66.45 ms HIP, 656.23 loss, 0.002466 LR, 4.54 GB used, 9764.87 GFLOPS, 675.05 GOPS
165 69.57 ms run, 2.66 ms python, 66.90 ms HIP, 670.91 loss, 0.002481 LR, 4.54 GB used, 9703.63 GFLOPS, 675.05 GOPS
166 69.08 ms run, 2.66 ms python, 66.42 ms HIP, 653.54 loss, 0.002496 LR, 4.54 GB used, 9771.72 GFLOPS, 675.05 GOPS
167 69.15 ms run, 2.69 ms python, 66.46 ms HIP, 664.16 loss, 0.002511 LR, 4.54 GB used, 9762.65 GFLOPS, 675.05 GOPS
168 69.67 ms run, 2.68 ms python, 66.98 ms HIP, 649.77 loss, 0.002526 LR, 4.54 GB used, 9689.66 GFLOPS, 675.05 GOPS
169 69.39 ms run, 2.67 ms python, 66.72 ms HIP, 644.44 loss, 0.002541 LR, 4.54 GB used, 9728.23 GFLOPS, 675.05 GOPS
170 69.52 ms run, 2.71 ms python, 66.81 ms HIP, 629.33 loss, 0.002556 LR, 4.54 GB used, 9710.44 GFLOPS, 675.05 GOPS
171 69.34 ms run, 2.73 ms python, 66.61 ms HIP, 655.48 loss, 0.002571 LR, 4.54 GB used, 9735.60 GFLOPS, 675.05 GOPS
172 69.39 ms run, 2.70 ms python, 66.70 ms HIP, 669.01 loss, 0.002586 LR, 4.54 GB used, 9727.61 GFLOPS, 675.05 GOPS
173 68.89 ms run, 2.68 ms python, 66.21 ms HIP, 678.96 loss, 0.002601 LR, 4.54 GB used, 9798.66 GFLOPS, 675.05 GOPS
174 69.34 ms run, 2.71 ms python, 66.63 ms HIP, 695.76 loss, 0.002615 LR, 4.54 GB used, 9735.53 GFLOPS, 675.05 GOPS
175 68.74 ms run, 2.71 ms python, 66.04 ms HIP, 657.40 loss, 0.002630 LR, 4.54 GB used, 9819.56 GFLOPS, 675.05 GOPS
176 69.06 ms run, 2.68 ms python, 66.37 ms HIP, 649.10 loss, 0.002645 LR, 4.54 GB used, 9775.22 GFLOPS, 675.05 GOPS
177 69.97 ms run, 2.66 ms python, 67.30 ms HIP, 640.65 loss, 0.002660 LR, 4.54 GB used, 9648.05 GFLOPS, 675.05 GOPS
178 69.51 ms run, 2.69 ms python, 66.82 ms HIP, 627.96 loss, 0.002675 LR, 4.54 GB used, 9711.71 GFLOPS, 675.05 GOPS
179 68.84 ms run, 2.64 ms python, 66.20 ms HIP, 677.60 loss, 0.002690 LR, 4.54 GB used, 9805.53 GFLOPS, 675.05 GOPS
180 68.97 ms run, 2.68 ms python, 66.29 ms HIP, 646.24 loss, 0.002705 LR, 4.54 GB used, 9787.94 GFLOPS, 675.05 GOPS
181 69.45 ms run, 2.67 ms python, 66.78 ms HIP, 667.96 loss, 0.002720 LR, 4.54 GB used, 9720.16 GFLOPS, 675.05 GOPS
182 69.08 ms run, 2.66 ms python, 66.42 ms HIP, 629.36 loss, 0.002735 LR, 4.54 GB used, 9771.26 GFLOPS, 675.05 GOPS
183 69.24 ms run, 2.66 ms python, 66.58 ms HIP, 662.15 loss, 0.002750 LR, 4.54 GB used, 9749.65 GFLOPS, 675.05 GOPS
184 69.35 ms run, 2.67 ms python, 66.68 ms HIP, 655.82 loss, 0.002765 LR, 4.54 GB used, 9733.97 GFLOPS, 675.05 GOPS
185 69.36 ms run, 2.67 ms python, 66.68 ms HIP, 660.38 loss, 0.002780 LR, 4.54 GB used, 9733.08 GFLOPS, 675.05 GOPS
186 69.16 ms run, 2.68 ms python, 66.48 ms HIP, 653.15 loss, 0.002795 LR, 4.54 GB used, 9760.66 GFLOPS, 675.05 GOPS
187 69.70 ms run, 2.73 ms python, 66.97 ms HIP, 660.77 loss, 0.002810 LR, 4.54 GB used, 9685.19 GFLOPS, 675.05 GOPS
188 69.29 ms run, 2.68 ms python, 66.62 ms HIP, 639.66 loss, 0.002825 LR, 4.54 GB used, 9741.91 GFLOPS, 675.05 GOPS
189 69.36 ms run, 2.68 ms python, 66.68 ms HIP, 677.11 loss, 0.002840 LR, 4.54 GB used, 9732.29 GFLOPS, 675.05 GOPS
190 68.75 ms run, 2.77 ms python, 65.97 ms HIP, 657.45 loss, 0.002855 LR, 4.54 GB used, 9819.44 GFLOPS, 675.05 GOPS
191 69.18 ms run, 2.70 ms python, 66.48 ms HIP, 657.44 loss, 0.002870 LR, 4.54 GB used, 9757.84 GFLOPS, 675.05 GOPS
192 69.28 ms run, 2.70 ms python, 66.58 ms HIP, 644.28 loss, 0.002885 LR, 4.54 GB used, 9743.48 GFLOPS, 675.05 GOPS
193 69.36 ms run, 2.64 ms python, 66.72 ms HIP, 664.24 loss, 0.002899 LR, 4.54 GB used, 9732.33 GFLOPS, 675.05 GOPS
194 69.23 ms run, 2.66 ms python, 66.57 ms HIP, 658.53 loss, 0.002914 LR, 4.54 GB used, 9751.01 GFLOPS, 675.05 GOPS
195 69.44 ms run, 2.65 ms python, 66.79 ms HIP, 625.03 loss, 0.002929 LR, 4.54 GB used, 9721.33 GFLOPS, 675.05 GOPS
shuffling training dataset in 755.85 ms (epoch=2)
196 832.06 ms run, 759.15 ms python, 72.91 ms HIP, 659.99 loss, 0.002944 LR, 4.54 GB used, 811.65 GFLOPS, 675.34 GOPS
197 73.49 ms run, 2.80 ms python, 70.69 ms HIP, 634.10 loss, 0.002959 LR, 4.54 GB used, 9185.29 GFLOPS, 675.05 GOPS
198 72.67 ms run, 2.79 ms python, 69.89 ms HIP, 629.92 loss, 0.002974 LR, 4.54 GB used, 9288.55 GFLOPS, 675.05 GOPS
199 71.07 ms run, 2.72 ms python, 68.35 ms HIP, 619.03 loss, 0.002989 LR, 4.54 GB used, 9498.03 GFLOPS, 675.05 GOPS
200 70.13 ms run, 2.70 ms python, 67.43 ms HIP, 622.81 loss, 0.003004 LR, 4.54 GB used, 9625.49 GFLOPS, 675.05 GOPS
201 69.80 ms run, 2.69 ms python, 67.11 ms HIP, 653.30 loss, 0.003019 LR, 4.54 GB used, 9671.01 GFLOPS, 675.05 GOPS
202 70.01 ms run, 2.66 ms python, 67.35 ms HIP, 669.36 loss, 0.003034 LR, 4.54 GB used, 9642.53 GFLOPS, 675.05 GOPS
203 69.19 ms run, 2.68 ms python, 66.51 ms HIP, 636.66 loss, 0.003049 LR, 4.54 GB used, 9756.09 GFLOPS, 675.05 GOPS
204 69.73 ms run, 2.70 ms python, 67.03 ms HIP, 638.22 loss, 0.003064 LR, 4.54 GB used, 9681.18 GFLOPS, 675.05 GOPS
205 70.08 ms run, 2.67 ms python, 67.41 ms HIP, 637.03 loss, 0.003079 LR, 4.54 GB used, 9631.95 GFLOPS, 675.05 GOPS
206 69.47 ms run, 2.73 ms python, 66.74 ms HIP, 650.79 loss, 0.003094 LR, 4.54 GB used, 9716.82 GFLOPS, 675.05 GOPS
207 69.78 ms run, 2.73 ms python, 67.05 ms HIP, 626.88 loss, 0.003109 LR, 4.54 GB used, 9674.44 GFLOPS, 675.05 GOPS
208 69.31 ms run, 2.66 ms python, 66.65 ms HIP, 640.41 loss, 0.003124 LR, 4.54 GB used, 9739.37 GFLOPS, 675.05 GOPS
209 70.36 ms run, 2.71 ms python, 67.65 ms HIP, 655.87 loss, 0.003139 LR, 4.54 GB used, 9593.98 GFLOPS, 675.05 GOPS
210 69.85 ms run, 2.67 ms python, 67.18 ms HIP, 641.10 loss, 0.003154 LR, 4.54 GB used, 9663.86 GFLOPS, 675.05 GOPS
211 69.48 ms run, 2.67 ms python, 66.80 ms HIP, 612.88 loss, 0.003168 LR, 4.54 GB used, 9716.12 GFLOPS, 675.05 GOPS
212 69.22 ms run, 2.71 ms python, 66.51 ms HIP, 620.64 loss, 0.003183 LR, 4.54 GB used, 9751.83 GFLOPS, 675.05 GOPS
213 69.32 ms run, 2.73 ms python, 66.59 ms HIP, 632.55 loss, 0.003198 LR, 4.54 GB used, 9737.44 GFLOPS, 675.05 GOPS
214 69.71 ms run, 2.69 ms python, 67.02 ms HIP, 647.31 loss, 0.003213 LR, 4.54 GB used, 9683.01 GFLOPS, 675.05 GOPS
215 69.61 ms run, 2.72 ms python, 66.89 ms HIP, 656.43 loss, 0.003228 LR, 4.54 GB used, 9697.12 GFLOPS, 675.05 GOPS
216 69.15 ms run, 2.66 ms python, 66.49 ms HIP, 625.25 loss, 0.003243 LR, 4.54 GB used, 9761.35 GFLOPS, 675.05 GOPS
217 69.35 ms run, 2.67 ms python, 66.67 ms HIP, 657.98 loss, 0.003258 LR, 4.54 GB used, 9733.98 GFLOPS, 675.05 GOPS
218 69.91 ms run, 2.73 ms python, 67.19 ms HIP, 629.03 loss, 0.003273 LR, 4.54 GB used, 9655.39 GFLOPS, 675.05 GOPS
219 69.45 ms run, 2.76 ms python, 66.69 ms HIP, 627.14 loss, 0.003288 LR, 4.54 GB used, 9719.18 GFLOPS, 675.05 GOPS
220 69.20 ms run, 2.68 ms python, 66.52 ms HIP, 627.50 loss, 0.003303 LR, 4.54 GB used, 9754.88 GFLOPS, 675.05 GOPS
221 69.24 ms run, 2.70 ms python, 66.54 ms HIP, 640.78 loss, 0.003318 LR, 4.54 GB used, 9749.73 GFLOPS, 675.05 GOPS
222 69.41 ms run, 2.77 ms python, 66.64 ms HIP, 645.41 loss, 0.003333 LR, 4.54 GB used, 9724.79 GFLOPS, 675.05 GOPS
223 69.49 ms run, 2.70 ms python, 66.79 ms HIP, 653.10 loss, 0.003348 LR, 4.54 GB used, 9714.20 GFLOPS, 675.05 GOPS
224 69.62 ms run, 2.69 ms python, 66.93 ms HIP, 638.37 loss, 0.003363 LR, 4.54 GB used, 9695.58 GFLOPS, 675.05 GOPS
225 69.07 ms run, 2.66 ms python, 66.41 ms HIP, 638.95 loss, 0.003378 LR, 4.54 GB used, 9773.32 GFLOPS, 675.05 GOPS
226 68.82 ms run, 2.67 ms python, 66.15 ms HIP, 620.94 loss, 0.003393 LR, 4.54 GB used, 9808.29 GFLOPS, 675.05 GOPS
227 68.87 ms run, 2.68 ms python, 66.19 ms HIP, 627.10 loss, 0.003408 LR, 4.54 GB used, 9801.68 GFLOPS, 675.05 GOPS
228 69.23 ms run, 2.68 ms python, 66.54 ms HIP, 638.22 loss, 0.003423 LR, 4.54 GB used, 9751.06 GFLOPS, 675.05 GOPS
229 69.44 ms run, 2.65 ms python, 66.78 ms HIP, 630.57 loss, 0.003437 LR, 4.54 GB used, 9721.73 GFLOPS, 675.05 GOPS
230 68.87 ms run, 2.66 ms python, 66.21 ms HIP, 641.26 loss, 0.003433 LR, 4.54 GB used, 9802.19 GFLOPS, 675.05 GOPS
231 69.57 ms run, 2.75 ms python, 66.82 ms HIP, 643.91 loss, 0.003429 LR, 4.54 GB used, 9703.59 GFLOPS, 675.05 GOPS
232 69.32 ms run, 2.68 ms python, 66.64 ms HIP, 635.29 loss, 0.003424 LR, 4.54 GB used, 9737.75 GFLOPS, 675.05 GOPS
233 69.05 ms run, 2.71 ms python, 66.34 ms HIP, 662.56 loss, 0.003420 LR, 4.54 GB used, 9776.68 GFLOPS, 675.05 GOPS
234 69.36 ms run, 2.66 ms python, 66.69 ms HIP, 676.33 loss, 0.003416 LR, 4.54 GB used, 9732.88 GFLOPS, 675.05 GOPS
235 69.24 ms run, 2.71 ms python, 66.52 ms HIP, 685.30 loss, 0.003411 LR, 4.54 GB used, 9749.93 GFLOPS, 675.05 GOPS
236 69.62 ms run, 2.68 ms python, 66.94 ms HIP, 650.38 loss, 0.003407 LR, 4.54 GB used, 9696.14 GFLOPS, 675.05 GOPS
237 69.17 ms run, 2.66 ms python, 66.51 ms HIP, 663.55 loss, 0.003403 LR, 4.54 GB used, 9759.73 GFLOPS, 675.05 GOPS
238 69.29 ms run, 2.66 ms python, 66.63 ms HIP, 647.40 loss, 0.003398 LR, 4.54 GB used, 9742.70 GFLOPS, 675.05 GOPS
239 68.74 ms run, 2.65 ms python, 66.08 ms HIP, 619.72 loss, 0.003394 LR, 4.54 GB used, 9820.60 GFLOPS, 675.05 GOPS
240 69.47 ms run, 2.69 ms python, 66.78 ms HIP, 636.31 loss, 0.003390 LR, 4.54 GB used, 9717.35 GFLOPS, 675.05 GOPS
241 69.68 ms run, 2.69 ms python, 66.99 ms HIP, 645.92 loss, 0.003385 LR, 4.54 GB used, 9687.70 GFLOPS, 675.05 GOPS
242 69.31 ms run, 2.68 ms python, 66.63 ms HIP, 632.35 loss, 0.003381 LR, 4.54 GB used, 9738.96 GFLOPS, 675.05 GOPS
243 69.45 ms run, 2.68 ms python, 66.77 ms HIP, 633.76 loss, 0.003377 LR, 4.54 GB used, 9719.79 GFLOPS, 675.05 GOPS
244 69.14 ms run, 2.72 ms python, 66.42 ms HIP, 637.14 loss, 0.003372 LR, 4.54 GB used, 9763.85 GFLOPS, 675.05 GOPS
245 68.80 ms run, 2.66 ms python, 66.13 ms HIP, 642.28 loss, 0.003368 LR, 4.54 GB used, 9811.88 GFLOPS, 675.05 GOPS
246 69.97 ms run, 2.69 ms python, 67.29 ms HIP, 647.99 loss, 0.003364 LR, 4.54 GB used, 9647.21 GFLOPS, 675.05 GOPS
247 69.52 ms run, 2.67 ms python, 66.85 ms HIP, 619.52 loss, 0.003359 LR, 4.54 GB used, 9709.50 GFLOPS, 675.05 GOPS
248 69.24 ms run, 2.65 ms python, 66.59 ms HIP, 628.87 loss, 0.003355 LR, 4.54 GB used, 9749.61 GFLOPS, 675.05 GOPS
249 69.69 ms run, 2.68 ms python, 67.01 ms HIP, 613.62 loss, 0.003350 LR, 4.54 GB used, 9686.41 GFLOPS, 675.05 GOPS
250 68.42 ms run, 2.64 ms python, 65.78 ms HIP, 639.52 loss, 0.003346 LR, 4.54 GB used, 9866.10 GFLOPS, 675.05 GOPS
251 70.09 ms run, 2.63 ms python, 67.46 ms HIP, 633.70 loss, 0.003342 LR, 4.54 GB used, 9631.68 GFLOPS, 675.05 GOPS
252 68.83 ms run, 2.72 ms python, 66.11 ms HIP, 604.80 loss, 0.003337 LR, 4.54 GB used, 9807.18 GFLOPS, 675.05 GOPS
253 69.05 ms run, 2.72 ms python, 66.33 ms HIP, 621.34 loss, 0.003333 LR, 4.54 GB used, 9775.99 GFLOPS, 675.05 GOPS
254 69.15 ms run, 2.74 ms python, 66.41 ms HIP, 629.10 loss, 0.003329 LR, 4.54 GB used, 9762.59 GFLOPS, 675.05 GOPS
255 69.57 ms run, 2.69 ms python, 66.88 ms HIP, 639.65 loss, 0.003324 LR, 4.54 GB used, 9703.32 GFLOPS, 675.05 GOPS
256 70.20 ms run, 2.76 ms python, 67.45 ms HIP, 615.65 loss, 0.003320 LR, 4.54 GB used, 9615.88 GFLOPS, 675.05 GOPS
257 69.17 ms run, 2.69 ms python, 66.48 ms HIP, 633.85 loss, 0.003316 LR, 4.54 GB used, 9759.26 GFLOPS, 675.05 GOPS
258 69.09 ms run, 2.70 ms python, 66.38 ms HIP, 614.25 loss, 0.003311 LR, 4.54 GB used, 9770.97 GFLOPS, 675.05 GOPS
259 68.95 ms run, 2.68 ms python, 66.27 ms HIP, 616.02 loss, 0.003307 LR, 4.54 GB used, 9790.04 GFLOPS, 675.05 GOPS
260 69.49 ms run, 2.67 ms python, 66.82 ms HIP, 629.08 loss, 0.003303 LR, 4.54 GB used, 9714.19 GFLOPS, 675.05 GOPS
261 69.19 ms run, 2.66 ms python, 66.52 ms HIP, 618.21 loss, 0.003298 LR, 4.54 GB used, 9756.94 GFLOPS, 675.05 GOPS
262 69.17 ms run, 2.63 ms python, 66.54 ms HIP, 647.45 loss, 0.003294 LR, 4.54 GB used, 9758.55 GFLOPS, 675.05 GOPS
263 68.86 ms run, 2.67 ms python, 66.19 ms HIP, 623.23 loss, 0.003290 LR, 4.54 GB used, 9803.43 GFLOPS, 675.05 GOPS
264 69.24 ms run, 2.66 ms python, 66.58 ms HIP, 661.51 loss, 0.003285 LR, 4.54 GB used, 9748.80 GFLOPS, 675.05 GOPS
265 69.57 ms run, 2.66 ms python, 66.92 ms HIP, 644.22 loss, 0.003281 LR, 4.54 GB used, 9702.66 GFLOPS, 675.05 GOPS
266 68.90 ms run, 2.66 ms python, 66.24 ms HIP, 636.75 loss, 0.003276 LR, 4.54 GB used, 9797.61 GFLOPS, 675.05 GOPS
267 68.77 ms run, 2.64 ms python, 66.13 ms HIP, 642.13 loss, 0.003272 LR, 4.54 GB used, 9815.75 GFLOPS, 675.05 GOPS
268 69.19 ms run, 2.69 ms python, 66.50 ms HIP, 629.14 loss, 0.003268 LR, 4.54 GB used, 9756.40 GFLOPS, 675.05 GOPS
269 69.51 ms run, 2.67 ms python, 66.85 ms HIP, 616.75 loss, 0.003263 LR, 4.54 GB used, 9710.94 GFLOPS, 675.05 GOPS
270 69.58 ms run, 2.68 ms python, 66.90 ms HIP, 644.76 loss, 0.003259 LR, 4.54 GB used, 9701.83 GFLOPS, 675.05 GOPS
271 69.62 ms run, 2.65 ms python, 66.97 ms HIP, 644.48 loss, 0.003255 LR, 4.54 GB used, 9696.53 GFLOPS, 675.05 GOPS
272 69.69 ms run, 2.66 ms python, 67.03 ms HIP, 632.84 loss, 0.003250 LR, 4.54 GB used, 9686.25 GFLOPS, 675.05 GOPS
273 69.17 ms run, 2.78 ms python, 66.39 ms HIP, 616.68 loss, 0.003246 LR, 4.54 GB used, 9758.53 GFLOPS, 675.05 GOPS
274 69.09 ms run, 2.67 ms python, 66.42 ms HIP, 622.35 loss, 0.003242 LR, 4.54 GB used, 9770.47 GFLOPS, 675.05 GOPS
275 69.79 ms run, 2.74 ms python, 67.05 ms HIP, 645.60 loss, 0.003237 LR, 4.54 GB used, 9672.81 GFLOPS, 675.05 GOPS
276 69.70 ms run, 2.66 ms python, 67.03 ms HIP, 612.62 loss, 0.003233 LR, 4.54 GB used, 9685.45 GFLOPS, 675.05 GOPS
277 68.45 ms run, 2.70 ms python, 65.75 ms HIP, 602.66 loss, 0.003229 LR, 4.54 GB used, 9861.52 GFLOPS, 675.05 GOPS
278 69.37 ms run, 2.65 ms python, 66.72 ms HIP, 616.91 loss, 0.003224 LR, 4.54 GB used, 9730.95 GFLOPS, 675.05 GOPS
279 70.57 ms run, 2.74 ms python, 67.83 ms HIP, 630.11 loss, 0.003220 LR, 4.54 GB used, 9566.13 GFLOPS, 675.05 GOPS
280 69.70 ms run, 2.67 ms python, 67.03 ms HIP, 650.35 loss, 0.003216 LR, 4.54 GB used, 9685.41 GFLOPS, 675.05 GOPS
281 69.23 ms run, 2.67 ms python, 66.57 ms HIP, 612.47 loss, 0.003211 LR, 4.54 GB used, 9750.26 GFLOPS, 675.05 GOPS
282 69.25 ms run, 2.68 ms python, 66.57 ms HIP, 632.91 loss, 0.003207 LR, 4.54 GB used, 9748.08 GFLOPS, 675.05 GOPS
283 68.70 ms run, 2.66 ms python, 66.05 ms HIP, 602.64 loss, 0.003202 LR, 4.54 GB used, 9825.40 GFLOPS, 675.05 GOPS
284 69.28 ms run, 2.66 ms python, 66.62 ms HIP, 616.49 loss, 0.003198 LR, 4.54 GB used, 9744.36 GFLOPS, 675.05 GOPS
285 69.14 ms run, 2.68 ms python, 66.47 ms HIP, 645.61 loss, 0.003194 LR, 4.54 GB used, 9762.77 GFLOPS, 675.05 GOPS
286 69.64 ms run, 2.78 ms python, 66.86 ms HIP, 615.17 loss, 0.003189 LR, 4.54 GB used, 9693.46 GFLOPS, 675.05 GOPS
287 69.62 ms run, 2.71 ms python, 66.91 ms HIP, 642.57 loss, 0.003185 LR, 4.54 GB used, 9695.77 GFLOPS, 675.05 GOPS
288 69.24 ms run, 2.65 ms python, 66.59 ms HIP, 628.23 loss, 0.003181 LR, 4.54 GB used, 9749.11 GFLOPS, 675.05 GOPS
289 69.29 ms run, 2.63 ms python, 66.66 ms HIP, 652.79 loss, 0.003176 LR, 4.54 GB used, 9742.13 GFLOPS, 675.05 GOPS
290 69.26 ms run, 2.66 ms python, 66.60 ms HIP, 646.73 loss, 0.003172 LR, 4.54 GB used, 9746.35 GFLOPS, 675.05 GOPS
291 69.11 ms run, 2.68 ms python, 66.43 ms HIP, 608.35 loss, 0.003168 LR, 4.54 GB used, 9767.87 GFLOPS, 675.05 GOPS
292 69.11 ms run, 2.64 ms python, 66.48 ms HIP, 607.70 loss, 0.003163 LR, 4.54 GB used, 9767.05 GFLOPS, 675.05 GOPS
293 68.87 ms run, 2.66 ms python, 66.21 ms HIP, 592.41 loss, 0.003159 LR, 4.54 GB used, 9801.96 GFLOPS, 675.05 GOPS
shuffling training dataset in 756.48 ms (epoch=3)
294 832.10 ms run, 759.70 ms python, 72.40 ms HIP, 608.68 loss, 0.003155 LR, 4.54 GB used, 811.61 GFLOPS, 675.34 GOPS
295 74.06 ms run, 2.77 ms python, 71.30 ms HIP, 610.76 loss, 0.003150 LR, 4.54 GB used, 9114.37 GFLOPS, 675.05 GOPS
296 72.09 ms run, 2.76 ms python, 69.33 ms HIP, 602.13 loss, 0.003146 LR, 4.54 GB used, 9364.36 GFLOPS, 675.05 GOPS
297 71.02 ms run, 2.70 ms python, 68.32 ms HIP, 605.34 loss, 0.003142 LR, 4.54 GB used, 9505.38 GFLOPS, 675.05 GOPS
298 70.45 ms run, 2.65 ms python, 67.79 ms HIP, 596.22 loss, 0.003137 LR, 4.54 GB used, 9582.37 GFLOPS, 675.05 GOPS
299 70.28 ms run, 2.71 ms python, 67.57 ms HIP, 588.64 loss, 0.003133 LR, 4.54 GB used, 9604.62 GFLOPS, 675.05 GOPS
300 69.83 ms run, 2.73 ms python, 67.10 ms HIP, 621.56 loss, 0.003128 LR, 4.54 GB used, 9667.02 GFLOPS, 675.05 GOPS
301 69.90 ms run, 2.75 ms python, 67.15 ms HIP, 629.12 loss, 0.003124 LR, 4.54 GB used, 9657.46 GFLOPS, 675.05 GOPS
302 69.82 ms run, 2.69 ms python, 67.13 ms HIP, 612.17 loss, 0.003120 LR, 4.54 GB used, 9668.33 GFLOPS, 675.05 GOPS
303 69.37 ms run, 2.68 ms python, 66.69 ms HIP, 590.49 loss, 0.003115 LR, 4.54 GB used, 9730.96 GFLOPS, 675.05 GOPS
304 69.43 ms run, 2.69 ms python, 66.74 ms HIP, 604.18 loss, 0.003111 LR, 4.54 GB used, 9721.99 GFLOPS, 675.05 GOPS
305 69.31 ms run, 2.68 ms python, 66.62 ms HIP, 618.26 loss, 0.003107 LR, 4.54 GB used, 9740.19 GFLOPS, 675.05 GOPS
306 69.64 ms run, 2.65 ms python, 66.99 ms HIP, 601.56 loss, 0.003102 LR, 4.54 GB used, 9693.74 GFLOPS, 675.05 GOPS
307 69.08 ms run, 2.70 ms python, 66.38 ms HIP, 602.58 loss, 0.003098 LR, 4.54 GB used, 9772.04 GFLOPS, 675.05 GOPS
308 69.51 ms run, 2.66 ms python, 66.86 ms HIP, 592.20 loss, 0.003094 LR, 4.54 GB used, 9710.86 GFLOPS, 675.05 GOPS
309 68.93 ms run, 2.76 ms python, 66.18 ms HIP, 581.20 loss, 0.003089 LR, 4.54 GB used, 9793.17 GFLOPS, 675.05 GOPS
310 69.35 ms run, 2.67 ms python, 66.68 ms HIP, 595.50 loss, 0.003085 LR, 4.54 GB used, 9733.98 GFLOPS, 675.05 GOPS
311 69.28 ms run, 2.64 ms python, 66.64 ms HIP, 602.04 loss, 0.003081 LR, 4.54 GB used, 9743.81 GFLOPS, 675.05 GOPS
312 69.76 ms run, 2.67 ms python, 67.10 ms HIP, 613.41 loss, 0.003076 LR, 4.54 GB used, 9676.11 GFLOPS, 675.05 GOPS
313 69.49 ms run, 2.74 ms python, 66.75 ms HIP, 612.33 loss, 0.003072 LR, 4.54 GB used, 9714.14 GFLOPS, 675.05 GOPS
314 69.55 ms run, 2.67 ms python, 66.88 ms HIP, 601.82 loss, 0.003068 LR, 4.54 GB used, 9706.48 GFLOPS, 675.05 GOPS
315 69.46 ms run, 2.70 ms python, 66.76 ms HIP, 599.91 loss, 0.003063 LR, 4.54 GB used, 9718.26 GFLOPS, 675.05 GOPS
316 69.61 ms run, 2.73 ms python, 66.88 ms HIP, 603.31 loss, 0.003059 LR, 4.54 GB used, 9697.68 GFLOPS, 675.05 GOPS
317 69.45 ms run, 2.67 ms python, 66.78 ms HIP, 606.40 loss, 0.003054 LR, 4.54 GB used, 9720.29 GFLOPS, 675.05 GOPS
318 69.77 ms run, 2.67 ms python, 67.10 ms HIP, 599.24 loss, 0.003050 LR, 4.54 GB used, 9675.79 GFLOPS, 675.05 GOPS
319 69.22 ms run, 2.66 ms python, 66.56 ms HIP, 580.43 loss, 0.003046 LR, 4.54 GB used, 9752.39 GFLOPS, 675.05 GOPS
320 69.93 ms run, 2.67 ms python, 67.26 ms HIP, 605.67 loss, 0.003041 LR, 4.54 GB used, 9653.45 GFLOPS, 675.05 GOPS
321 69.24 ms run, 2.63 ms python, 66.61 ms HIP, 618.09 loss, 0.003037 LR, 4.54 GB used, 9748.92 GFLOPS, 675.05 GOPS
322 69.77 ms run, 2.69 ms python, 67.09 ms HIP, 624.88 loss, 0.003033 LR, 4.54 GB used, 9674.90 GFLOPS, 675.05 GOPS
323 69.21 ms run, 2.78 ms python, 66.43 ms HIP, 617.77 loss, 0.003028 LR, 4.54 GB used, 9754.27 GFLOPS, 675.05 GOPS
324 68.91 ms run, 2.65 ms python, 66.26 ms HIP, 599.90 loss, 0.003024 LR, 4.54 GB used, 9796.42 GFLOPS, 675.05 GOPS
325 69.33 ms run, 2.69 ms python, 66.64 ms HIP, 604.47 loss, 0.003020 LR, 4.54 GB used, 9736.84 GFLOPS, 675.05 GOPS
326 68.80 ms run, 2.65 ms python, 66.16 ms HIP, 616.35 loss, 0.003015 LR, 4.54 GB used, 9811.55 GFLOPS, 675.05 GOPS
327 69.53 ms run, 2.66 ms python, 66.87 ms HIP, 599.59 loss, 0.003011 LR, 4.54 GB used, 9708.45 GFLOPS, 675.05 GOPS
328 68.94 ms run, 2.67 ms python, 66.27 ms HIP, 615.25 loss, 0.003007 LR, 4.54 GB used, 9792.02 GFLOPS, 675.05 GOPS
329 69.34 ms run, 2.65 ms python, 66.69 ms HIP, 599.05 loss, 0.003002 LR, 4.54 GB used, 9735.76 GFLOPS, 675.05 GOPS
330 69.63 ms run, 2.63 ms python, 67.00 ms HIP, 623.61 loss, 0.002998 LR, 4.54 GB used, 9694.07 GFLOPS, 675.05 GOPS
331 69.41 ms run, 2.64 ms python, 66.77 ms HIP, 646.88 loss, 0.002994 LR, 4.54 GB used, 9725.43 GFLOPS, 675.05 GOPS
332 69.64 ms run, 2.74 ms python, 66.90 ms HIP, 604.86 loss, 0.002989 LR, 4.54 GB used, 9693.58 GFLOPS, 675.05 GOPS
333 69.29 ms run, 2.66 ms python, 66.63 ms HIP, 590.82 loss, 0.002985 LR, 4.54 GB used, 9742.17 GFLOPS, 675.05 GOPS
334 69.44 ms run, 2.68 ms python, 66.76 ms HIP, 600.96 loss, 0.002980 LR, 4.54 GB used, 9720.99 GFLOPS, 675.05 GOPS
335 69.36 ms run, 2.68 ms python, 66.68 ms HIP, 599.57 loss, 0.002976 LR, 4.54 GB used, 9732.55 GFLOPS, 675.05 GOPS
336 69.23 ms run, 2.66 ms python, 66.57 ms HIP, 600.89 loss, 0.002972 LR, 4.54 GB used, 9750.55 GFLOPS, 675.05 GOPS
337 69.57 ms run, 2.65 ms python, 66.92 ms HIP, 606.04 loss, 0.002967 LR, 4.54 GB used, 9702.82 GFLOPS, 675.05 GOPS
338 68.98 ms run, 2.71 ms python, 66.26 ms HIP, 600.90 loss, 0.002963 LR, 4.54 GB used, 9786.77 GFLOPS, 675.05 GOPS
339 68.97 ms run, 2.65 ms python, 66.32 ms HIP, 627.56 loss, 0.002959 LR, 4.54 GB used, 9787.82 GFLOPS, 675.05 GOPS
340 68.49 ms run, 2.68 ms python, 65.81 ms HIP, 616.45 loss, 0.002954 LR, 4.54 GB used, 9856.42 GFLOPS, 675.05 GOPS
341 68.23 ms run, 2.73 ms python, 65.50 ms HIP, 584.24 loss, 0.002950 LR, 4.54 GB used, 9893.86 GFLOPS, 675.05 GOPS
342 68.86 ms run, 2.66 ms python, 66.20 ms HIP, 603.59 loss, 0.002946 LR, 4.54 GB used, 9802.71 GFLOPS, 675.05 GOPS
343 69.03 ms run, 2.67 ms python, 66.36 ms HIP, 605.93 loss, 0.002941 LR, 4.54 GB used, 9778.69 GFLOPS, 675.05 GOPS
344 68.69 ms run, 2.68 ms python, 66.00 ms HIP, 593.60 loss, 0.002937 LR, 4.54 GB used, 9827.99 GFLOPS, 675.05 GOPS
345 69.16 ms run, 2.68 ms python, 66.49 ms HIP, 594.98 loss, 0.002933 LR, 4.54 GB used, 9760.08 GFLOPS, 675.05 GOPS
346 69.51 ms run, 2.71 ms python, 66.80 ms HIP, 576.23 loss, 0.002928 LR, 4.54 GB used, 9712.07 GFLOPS, 675.05 GOPS
347 69.89 ms run, 2.65 ms python, 67.24 ms HIP, 583.97 loss, 0.002924 LR, 4.54 GB used, 9658.74 GFLOPS, 675.05 GOPS
348 69.54 ms run, 2.66 ms python, 66.88 ms HIP, 606.75 loss, 0.002920 LR, 4.54 GB used, 9707.00 GFLOPS, 675.05 GOPS
349 69.49 ms run, 2.60 ms python, 66.88 ms HIP, 586.33 loss, 0.002915 LR, 4.54 GB used, 9714.95 GFLOPS, 675.05 GOPS
350 70.11 ms run, 2.65 ms python, 67.46 ms HIP, 623.88 loss, 0.002911 LR, 4.54 GB used, 9627.77 GFLOPS, 675.05 GOPS
351 69.72 ms run, 2.65 ms python, 67.07 ms HIP, 633.02 loss, 0.002906 LR, 4.54 GB used, 9681.56 GFLOPS, 675.05 GOPS
352 69.37 ms run, 2.68 ms python, 66.70 ms HIP, 594.73 loss, 0.002902 LR, 4.54 GB used, 9730.85 GFLOPS, 675.05 GOPS
353 69.62 ms run, 2.67 ms python, 66.95 ms HIP, 577.12 loss, 0.002898 LR, 4.54 GB used, 9695.89 GFLOPS, 675.05 GOPS
354 69.93 ms run, 2.66 ms python, 67.27 ms HIP, 617.79 loss, 0.002893 LR, 4.54 GB used, 9653.10 GFLOPS, 675.05 GOPS
355 69.53 ms run, 2.70 ms python, 66.82 ms HIP, 619.25 loss, 0.002889 LR, 4.54 GB used, 9709.34 GFLOPS, 675.05 GOPS
356 69.69 ms run, 2.68 ms python, 67.01 ms HIP, 604.83 loss, 0.002885 LR, 4.54 GB used, 9687.09 GFLOPS, 675.05 GOPS
357 69.60 ms run, 2.67 ms python, 66.93 ms HIP, 586.27 loss, 0.002880 LR, 4.54 GB used, 9699.40 GFLOPS, 675.05 GOPS
358 69.78 ms run, 2.69 ms python, 67.09 ms HIP, 608.11 loss, 0.002876 LR, 4.54 GB used, 9674.21 GFLOPS, 675.05 GOPS
359 69.36 ms run, 2.69 ms python, 66.67 ms HIP, 595.93 loss, 0.002872 LR, 4.54 GB used, 9732.25 GFLOPS, 675.05 GOPS
360 69.24 ms run, 2.70 ms python, 66.54 ms HIP, 618.43 loss, 0.002867 LR, 4.54 GB used, 9750.03 GFLOPS, 675.05 GOPS
361 68.86 ms run, 2.62 ms python, 66.23 ms HIP, 611.55 loss, 0.002863 LR, 4.54 GB used, 9803.30 GFLOPS, 675.05 GOPS
362 69.63 ms run, 2.66 ms python, 66.97 ms HIP, 605.26 loss, 0.002859 LR, 4.54 GB used, 9695.27 GFLOPS, 675.05 GOPS
363 69.32 ms run, 2.75 ms python, 66.56 ms HIP, 611.81 loss, 0.002854 LR, 4.54 GB used, 9738.57 GFLOPS, 675.05 GOPS
364 68.96 ms run, 2.68 ms python, 66.29 ms HIP, 607.24 loss, 0.002850 LR, 4.54 GB used, 9788.85 GFLOPS, 675.05 GOPS
365 69.30 ms run, 2.70 ms python, 66.60 ms HIP, 613.96 loss, 0.002846 LR, 4.54 GB used, 9740.94 GFLOPS, 675.05 GOPS
366 70.14 ms run, 2.64 ms python, 67.50 ms HIP, 616.48 loss, 0.002841 LR, 4.54 GB used, 9624.49 GFLOPS, 675.05 GOPS
367 69.24 ms run, 2.68 ms python, 66.56 ms HIP, 617.21 loss, 0.002837 LR, 4.54 GB used, 9749.79 GFLOPS, 675.05 GOPS
368 69.28 ms run, 2.68 ms python, 66.60 ms HIP, 601.58 loss, 0.002832 LR, 4.54 GB used, 9743.34 GFLOPS, 675.05 GOPS
369 69.19 ms run, 2.76 ms python, 66.43 ms HIP, 601.81 loss, 0.002828 LR, 4.54 GB used, 9756.90 GFLOPS, 675.05 GOPS
370 69.64 ms run, 2.72 ms python, 66.92 ms HIP, 608.82 loss, 0.002824 LR, 4.54 GB used, 9693.09 GFLOPS, 675.05 GOPS
371 69.56 ms run, 2.71 ms python, 66.84 ms HIP, 611.55 loss, 0.002819 LR, 4.54 GB used, 9705.04 GFLOPS, 675.05 GOPS
372 69.52 ms run, 2.69 ms python, 66.82 ms HIP, 589.56 loss, 0.002815 LR, 4.54 GB used, 9710.32 GFLOPS, 675.05 GOPS
373 69.17 ms run, 2.69 ms python, 66.48 ms HIP, 607.22 loss, 0.002811 LR, 4.54 GB used, 9759.81 GFLOPS, 675.05 GOPS
374 69.31 ms run, 2.72 ms python, 66.59 ms HIP, 605.37 loss, 0.002806 LR, 4.54 GB used, 9739.58 GFLOPS, 675.05 GOPS
375 69.51 ms run, 2.67 ms python, 66.83 ms HIP, 591.62 loss, 0.002802 LR, 4.54 GB used, 9711.56 GFLOPS, 675.05 GOPS
376 69.93 ms run, 2.65 ms python, 67.27 ms HIP, 599.20 loss, 0.002798 LR, 4.54 GB used, 9653.40 GFLOPS, 675.05 GOPS
377 69.33 ms run, 2.64 ms python, 66.68 ms HIP, 626.14 loss, 0.002793 LR, 4.54 GB used, 9737.21 GFLOPS, 675.05 GOPS
378 69.49 ms run, 2.65 ms python, 66.84 ms HIP, 600.17 loss, 0.002789 LR, 4.54 GB used, 9714.43 GFLOPS, 675.05 GOPS
379 70.17 ms run, 2.71 ms python, 67.46 ms HIP, 593.42 loss, 0.002785 LR, 4.54 GB used, 9620.51 GFLOPS, 675.05 GOPS
380 68.98 ms run, 2.64 ms python, 66.34 ms HIP, 606.59 loss, 0.002780 LR, 4.54 GB used, 9786.28 GFLOPS, 675.05 GOPS
381 68.92 ms run, 2.68 ms python, 66.24 ms HIP, 600.88 loss, 0.002776 LR, 4.54 GB used, 9795.10 GFLOPS, 675.05 GOPS
382 69.30 ms run, 2.65 ms python, 66.65 ms HIP, 581.13 loss, 0.002772 LR, 4.54 GB used, 9740.97 GFLOPS, 675.05 GOPS
383 69.86 ms run, 2.80 ms python, 67.06 ms HIP, 600.93 loss, 0.002767 LR, 4.54 GB used, 9663.49 GFLOPS, 675.05 GOPS
384 69.91 ms run, 2.66 ms python, 67.25 ms HIP, 644.06 loss, 0.002763 LR, 4.54 GB used, 9655.47 GFLOPS, 675.05 GOPS
385 71.23 ms run, 2.68 ms python, 68.55 ms HIP, 579.32 loss, 0.002758 LR, 4.54 GB used, 9476.84 GFLOPS, 675.05 GOPS
386 69.61 ms run, 2.57 ms python, 67.04 ms HIP, 606.70 loss, 0.002754 LR, 4.54 GB used, 9697.09 GFLOPS, 675.05 GOPS
387 69.46 ms run, 2.74 ms python, 66.72 ms HIP, 598.76 loss, 0.002750 LR, 4.54 GB used, 9718.86 GFLOPS, 675.05 GOPS
388 69.43 ms run, 2.63 ms python, 66.80 ms HIP, 593.29 loss, 0.002745 LR, 4.54 GB used, 9722.94 GFLOPS, 675.05 GOPS
389 69.34 ms run, 2.68 ms python, 66.65 ms HIP, 615.35 loss, 0.002741 LR, 4.54 GB used, 9735.92 GFLOPS, 675.05 GOPS
390 69.72 ms run, 2.62 ms python, 67.09 ms HIP, 584.42 loss, 0.002737 LR, 4.54 GB used, 9682.56 GFLOPS, 675.05 GOPS
391 69.64 ms run, 2.69 ms python, 66.94 ms HIP, 567.33 loss, 0.002732 LR, 4.54 GB used, 9693.97 GFLOPS, 675.05 GOPS
shuffling training dataset in 756.63 ms (epoch=4)
392 832.02 ms run, 759.77 ms python, 72.25 ms HIP, 582.36 loss, 0.002728 LR, 4.54 GB used, 811.69 GFLOPS, 675.34 GOPS
393 74.18 ms run, 2.79 ms python, 71.39 ms HIP, 568.34 loss, 0.002724 LR, 4.54 GB used, 9100.01 GFLOPS, 675.05 GOPS
394 72.54 ms run, 2.70 ms python, 69.84 ms HIP, 577.83 loss, 0.002719 LR, 4.54 GB used, 9306.23 GFLOPS, 675.05 GOPS
395 71.62 ms run, 2.70 ms python, 68.92 ms HIP, 585.91 loss, 0.002715 LR, 4.54 GB used, 9425.68 GFLOPS, 675.05 GOPS
396 70.43 ms run, 2.68 ms python, 67.76 ms HIP, 577.13 loss, 0.002711 LR, 4.54 GB used, 9584.01 GFLOPS, 675.05 GOPS
397 70.22 ms run, 2.66 ms python, 67.56 ms HIP, 569.58 loss, 0.002706 LR, 4.54 GB used, 9612.95 GFLOPS, 675.05 GOPS
398 69.91 ms run, 2.65 ms python, 67.27 ms HIP, 577.52 loss, 0.002702 LR, 4.54 GB used, 9655.27 GFLOPS, 675.05 GOPS
399 69.58 ms run, 2.76 ms python, 66.82 ms HIP, 612.04 loss, 0.002698 LR, 4.54 GB used, 9701.20 GFLOPS, 675.05 GOPS
400 69.40 ms run, 2.69 ms python, 66.72 ms HIP, 604.85 loss, 0.002693 LR, 4.54 GB used, 9726.28 GFLOPS, 675.05 GOPS
401 69.48 ms run, 2.75 ms python, 66.73 ms HIP, 596.98 loss, 0.002689 LR, 4.54 GB used, 9715.27 GFLOPS, 675.05 GOPS
402 69.22 ms run, 2.64 ms python, 66.58 ms HIP, 592.40 loss, 0.002684 LR, 4.54 GB used, 9752.47 GFLOPS, 675.05 GOPS
403 69.29 ms run, 2.69 ms python, 66.60 ms HIP, 606.83 loss, 0.002680 LR, 4.54 GB used, 9742.85 GFLOPS, 675.05 GOPS
404 68.71 ms run, 2.67 ms python, 66.05 ms HIP, 591.62 loss, 0.002676 LR, 4.54 GB used, 9824.14 GFLOPS, 675.05 GOPS
405 68.61 ms run, 2.69 ms python, 65.92 ms HIP, 583.69 loss, 0.002671 LR, 4.54 GB used, 9838.98 GFLOPS, 675.05 GOPS
406 69.00 ms run, 2.65 ms python, 66.36 ms HIP, 571.33 loss, 0.002667 LR, 4.54 GB used, 9782.59 GFLOPS, 675.05 GOPS
407 68.76 ms run, 2.65 ms python, 66.11 ms HIP, 588.53 loss, 0.002663 LR, 4.54 GB used, 9817.98 GFLOPS, 675.05 GOPS
408 69.14 ms run, 2.67 ms python, 66.46 ms HIP, 615.86 loss, 0.002658 LR, 4.54 GB used, 9764.08 GFLOPS, 675.05 GOPS
409 68.86 ms run, 2.69 ms python, 66.16 ms HIP, 592.83 loss, 0.002654 LR, 4.54 GB used, 9803.67 GFLOPS, 675.05 GOPS
410 68.96 ms run, 2.68 ms python, 66.28 ms HIP, 593.65 loss, 0.002650 LR, 4.54 GB used, 9788.92 GFLOPS, 675.05 GOPS
411 69.54 ms run, 2.68 ms python, 66.85 ms HIP, 581.44 loss, 0.002645 LR, 4.54 GB used, 9707.84 GFLOPS, 675.05 GOPS
412 69.68 ms run, 2.64 ms python, 67.04 ms HIP, 579.73 loss, 0.002641 LR, 4.54 GB used, 9688.36 GFLOPS, 675.05 GOPS
413 69.88 ms run, 2.64 ms python, 67.24 ms HIP, 581.51 loss, 0.002637 LR, 4.54 GB used, 9659.46 GFLOPS, 675.05 GOPS
414 69.52 ms run, 2.70 ms python, 66.82 ms HIP, 585.01 loss, 0.002632 LR, 4.54 GB used, 9710.34 GFLOPS, 675.05 GOPS
415 69.01 ms run, 2.76 ms python, 66.25 ms HIP, 589.84 loss, 0.002628 LR, 4.54 GB used, 9782.43 GFLOPS, 675.05 GOPS
416 68.36 ms run, 2.71 ms python, 65.65 ms HIP, 577.49 loss, 0.002624 LR, 4.54 GB used, 9875.12 GFLOPS, 675.05 GOPS
417 69.35 ms run, 2.67 ms python, 66.68 ms HIP, 576.82 loss, 0.002619 LR, 4.54 GB used, 9734.03 GFLOPS, 675.05 GOPS
418 69.35 ms run, 2.65 ms python, 66.70 ms HIP, 598.78 loss, 0.002615 LR, 4.54 GB used, 9734.31 GFLOPS, 675.05 GOPS
419 69.68 ms run, 2.66 ms python, 67.02 ms HIP, 582.48 loss, 0.002610 LR, 4.54 GB used, 9687.70 GFLOPS, 675.05 GOPS
420 70.64 ms run, 2.65 ms python, 67.99 ms HIP, 594.60 loss, 0.002606 LR, 4.54 GB used, 9555.55 GFLOPS, 675.05 GOPS
421 70.61 ms run, 2.72 ms python, 67.89 ms HIP, 592.59 loss, 0.002602 LR, 4.54 GB used, 9560.37 GFLOPS, 675.05 GOPS
422 70.40 ms run, 2.62 ms python, 67.77 ms HIP, 577.48 loss, 0.002597 LR, 4.54 GB used, 9589.23 GFLOPS, 675.05 GOPS
423 70.03 ms run, 2.72 ms python, 67.31 ms HIP, 617.56 loss, 0.002593 LR, 4.54 GB used, 9640.01 GFLOPS, 675.05 GOPS
424 70.42 ms run, 2.65 ms python, 67.78 ms HIP, 615.94 loss, 0.002589 LR, 4.54 GB used, 9585.44 GFLOPS, 675.05 GOPS
425 70.32 ms run, 2.64 ms python, 67.69 ms HIP, 603.24 loss, 0.002584 LR, 4.54 GB used, 9599.25 GFLOPS, 675.05 GOPS
426 69.76 ms run, 2.78 ms python, 66.98 ms HIP, 584.85 loss, 0.002580 LR, 4.54 GB used, 9676.22 GFLOPS, 675.05 GOPS
427 69.61 ms run, 2.65 ms python, 66.95 ms HIP, 592.30 loss, 0.002576 LR, 4.54 GB used, 9698.03 GFLOPS, 675.05 GOPS
428 69.93 ms run, 2.73 ms python, 67.19 ms HIP, 579.46 loss, 0.002571 LR, 4.54 GB used, 9653.71 GFLOPS, 675.05 GOPS
429 69.83 ms run, 2.66 ms python, 67.18 ms HIP, 595.40 loss, 0.002567 LR, 4.54 GB used, 9666.58 GFLOPS, 675.05 GOPS
430 69.72 ms run, 2.70 ms python, 67.01 ms HIP, 589.41 loss, 0.002563 LR, 4.54 GB used, 9682.79 GFLOPS, 675.05 GOPS
431 69.85 ms run, 2.63 ms python, 67.22 ms HIP, 589.12 loss, 0.002558 LR, 4.54 GB used, 9664.38 GFLOPS, 675.05 GOPS
432 69.36 ms run, 2.71 ms python, 66.65 ms HIP, 583.52 loss, 0.002554 LR, 4.54 GB used, 9732.81 GFLOPS, 675.05 GOPS
433 69.06 ms run, 2.63 ms python, 66.43 ms HIP, 601.16 loss, 0.002550 LR, 4.54 GB used, 9774.94 GFLOPS, 675.05 GOPS
434 69.68 ms run, 2.63 ms python, 67.05 ms HIP, 595.74 loss, 0.002545 LR, 4.54 GB used, 9688.07 GFLOPS, 675.05 GOPS
435 69.27 ms run, 2.65 ms python, 66.62 ms HIP, 578.42 loss, 0.002541 LR, 4.54 GB used, 9744.83 GFLOPS, 675.05 GOPS
436 69.72 ms run, 2.64 ms python, 67.08 ms HIP, 579.35 loss, 0.002536 LR, 4.54 GB used, 9681.54 GFLOPS, 675.05 GOPS
437 69.53 ms run, 2.69 ms python, 66.84 ms HIP, 575.27 loss, 0.002532 LR, 4.54 GB used, 9708.32 GFLOPS, 675.05 GOPS
438 69.31 ms run, 2.65 ms python, 66.66 ms HIP, 589.69 loss, 0.002528 LR, 4.54 GB used, 9738.86 GFLOPS, 675.05 GOPS
439 68.81 ms run, 2.70 ms python, 66.11 ms HIP, 577.37 loss, 0.002523 LR, 4.54 GB used, 9810.67 GFLOPS, 675.05 GOPS
440 69.26 ms run, 2.64 ms python, 66.62 ms HIP, 594.12 loss, 0.002519 LR, 4.54 GB used, 9746.05 GFLOPS, 675.05 GOPS
441 69.77 ms run, 2.69 ms python, 67.08 ms HIP, 601.86 loss, 0.002515 LR, 4.54 GB used, 9675.42 GFLOPS, 675.05 GOPS
442 69.41 ms run, 2.68 ms python, 66.73 ms HIP, 584.64 loss, 0.002510 LR, 4.54 GB used, 9725.30 GFLOPS, 675.05 GOPS
443 69.14 ms run, 2.74 ms python, 66.40 ms HIP, 593.23 loss, 0.002506 LR, 4.54 GB used, 9764.04 GFLOPS, 675.05 GOPS
444 68.74 ms run, 2.64 ms python, 66.11 ms HIP, 579.12 loss, 0.002502 LR, 4.54 GB used, 9820.21 GFLOPS, 675.05 GOPS
445 69.27 ms run, 2.63 ms python, 66.65 ms HIP, 575.39 loss, 0.002497 LR, 4.54 GB used, 9744.64 GFLOPS, 675.05 GOPS
446 69.20 ms run, 2.62 ms python, 66.58 ms HIP, 581.47 loss, 0.002493 LR, 4.54 GB used, 9755.36 GFLOPS, 675.05 GOPS
447 69.39 ms run, 2.66 ms python, 66.73 ms HIP, 580.66 loss, 0.002489 LR, 4.54 GB used, 9727.94 GFLOPS, 675.05 GOPS
448 69.30 ms run, 2.61 ms python, 66.69 ms HIP, 577.62 loss, 0.002484 LR, 4.54 GB used, 9740.59 GFLOPS, 675.05 GOPS
449 70.02 ms run, 2.73 ms python, 67.29 ms HIP, 586.47 loss, 0.002480 LR, 4.54 GB used, 9640.13 GFLOPS, 675.05 GOPS
450 69.28 ms run, 2.70 ms python, 66.58 ms HIP, 591.01 loss, 0.002476 LR, 4.54 GB used, 9743.76 GFLOPS, 675.05 GOPS
451 69.43 ms run, 2.75 ms python, 66.68 ms HIP, 603.39 loss, 0.002471 LR, 4.54 GB used, 9723.26 GFLOPS, 675.05 GOPS
452 68.91 ms run, 2.66 ms python, 66.25 ms HIP, 608.29 loss, 0.002467 LR, 4.54 GB used, 9796.44 GFLOPS, 675.05 GOPS
453 69.21 ms run, 2.68 ms python, 66.53 ms HIP, 589.10 loss, 0.002463 LR, 4.54 GB used, 9753.33 GFLOPS, 675.05 GOPS
454 68.97 ms run, 2.73 ms python, 66.25 ms HIP, 604.86 loss, 0.002458 LR, 4.54 GB used, 9786.83 GFLOPS, 675.05 GOPS
455 69.34 ms run, 2.64 ms python, 66.70 ms HIP, 586.66 loss, 0.002454 LR, 4.54 GB used, 9734.98 GFLOPS, 675.05 GOPS
456 69.24 ms run, 2.67 ms python, 66.56 ms HIP, 576.25 loss, 0.002449 LR, 4.54 GB used, 9749.74 GFLOPS, 675.05 GOPS
457 69.19 ms run, 2.67 ms python, 66.51 ms HIP, 589.74 loss, 0.002445 LR, 4.54 GB used, 9756.83 GFLOPS, 675.05 GOPS
458 68.60 ms run, 2.65 ms python, 65.96 ms HIP, 593.15 loss, 0.002441 LR, 4.54 GB used, 9839.60 GFLOPS, 675.05 GOPS
459 69.54 ms run, 2.71 ms python, 66.83 ms HIP, 582.82 loss, 0.002436 LR, 4.54 GB used, 9707.40 GFLOPS, 675.05 GOPS
460 69.08 ms run, 2.63 ms python, 66.45 ms HIP, 590.13 loss, 0.002432 LR, 4.54 GB used, 9771.37 GFLOPS, 675.05 GOPS
461 69.15 ms run, 2.74 ms python, 66.41 ms HIP, 586.29 loss, 0.002428 LR, 4.54 GB used, 9762.12 GFLOPS, 675.05 GOPS
462 69.12 ms run, 2.62 ms python, 66.50 ms HIP, 582.98 loss, 0.002423 LR, 4.54 GB used, 9766.12 GFLOPS, 675.05 GOPS
463 69.67 ms run, 2.64 ms python, 67.03 ms HIP, 565.42 loss, 0.002419 LR, 4.54 GB used, 9689.48 GFLOPS, 675.05 GOPS
464 69.61 ms run, 2.65 ms python, 66.96 ms HIP, 573.48 loss, 0.002415 LR, 4.54 GB used, 9697.12 GFLOPS, 675.05 GOPS
465 69.30 ms run, 2.72 ms python, 66.59 ms HIP, 593.76 loss, 0.002410 LR, 4.54 GB used, 9740.30 GFLOPS, 675.05 GOPS
466 69.44 ms run, 2.71 ms python, 66.74 ms HIP, 566.20 loss, 0.002406 LR, 4.54 GB used, 9720.68 GFLOPS, 675.05 GOPS
467 69.39 ms run, 2.69 ms python, 66.70 ms HIP, 581.88 loss, 0.002402 LR, 4.54 GB used, 9728.38 GFLOPS, 675.05 GOPS
468 69.24 ms run, 2.64 ms python, 66.60 ms HIP, 578.37 loss, 0.002397 LR, 4.54 GB used, 9749.12 GFLOPS, 675.05 GOPS
469 69.55 ms run, 2.65 ms python, 66.90 ms HIP, 585.02 loss, 0.002393 LR, 4.54 GB used, 9705.48 GFLOPS, 675.05 GOPS
470 69.91 ms run, 2.74 ms python, 67.17 ms HIP, 582.28 loss, 0.002389 LR, 4.54 GB used, 9656.55 GFLOPS, 675.05 GOPS
471 69.71 ms run, 2.64 ms python, 67.07 ms HIP, 585.25 loss, 0.002384 LR, 4.54 GB used, 9684.00 GFLOPS, 675.05 GOPS
472 69.15 ms run, 2.74 ms python, 66.41 ms HIP, 581.99 loss, 0.002380 LR, 4.54 GB used, 9761.66 GFLOPS, 675.05 GOPS
473 69.56 ms run, 2.66 ms python, 66.90 ms HIP, 577.38 loss, 0.002375 LR, 4.54 GB used, 9704.28 GFLOPS, 675.05 GOPS
474 70.51 ms run, 2.73 ms python, 67.78 ms HIP, 580.28 loss, 0.002371 LR, 4.54 GB used, 9574.16 GFLOPS, 675.05 GOPS
475 69.59 ms run, 2.70 ms python, 66.88 ms HIP, 586.50 loss, 0.002367 LR, 4.54 GB used, 9700.53 GFLOPS, 675.05 GOPS
476 69.72 ms run, 2.72 ms python, 67.00 ms HIP, 597.28 loss, 0.002362 LR, 4.54 GB used, 9682.55 GFLOPS, 675.05 GOPS
477 69.12 ms run, 2.66 ms python, 66.46 ms HIP, 589.34 loss, 0.002358 LR, 4.54 GB used, 9766.60 GFLOPS, 675.05 GOPS
478 69.86 ms run, 2.63 ms python, 67.23 ms HIP, 573.59 loss, 0.002354 LR, 4.54 GB used, 9662.94 GFLOPS, 675.05 GOPS
479 69.15 ms run, 2.71 ms python, 66.44 ms HIP, 562.75 loss, 0.002349 LR, 4.54 GB used, 9761.58 GFLOPS, 675.05 GOPS
480 69.32 ms run, 2.71 ms python, 66.61 ms HIP, 593.10 loss, 0.002345 LR, 4.54 GB used, 9737.73 GFLOPS, 675.05 GOPS
481 69.49 ms run, 2.72 ms python, 66.77 ms HIP, 579.93 loss, 0.002341 LR, 4.54 GB used, 9714.20 GFLOPS, 675.05 GOPS
482 70.00 ms run, 2.64 ms python, 67.36 ms HIP, 590.04 loss, 0.002336 LR, 4.54 GB used, 9643.66 GFLOPS, 675.05 GOPS
483 68.91 ms run, 2.71 ms python, 66.20 ms HIP, 584.13 loss, 0.002332 LR, 4.54 GB used, 9795.87 GFLOPS, 675.05 GOPS
484 69.08 ms run, 2.64 ms python, 66.45 ms HIP, 594.45 loss, 0.002328 LR, 4.54 GB used, 9771.60 GFLOPS, 675.05 GOPS
485 69.07 ms run, 2.65 ms python, 66.42 ms HIP, 598.35 loss, 0.002323 LR, 4.54 GB used, 9772.80 GFLOPS, 675.05 GOPS
486 68.98 ms run, 2.66 ms python, 66.31 ms HIP, 566.22 loss, 0.002319 LR, 4.54 GB used, 9786.77 GFLOPS, 675.05 GOPS
487 69.25 ms run, 2.67 ms python, 66.58 ms HIP, 571.19 loss, 0.002315 LR, 4.54 GB used, 9747.93 GFLOPS, 675.05 GOPS
488 70.21 ms run, 2.62 ms python, 67.59 ms HIP, 581.04 loss, 0.002310 LR, 4.54 GB used, 9614.92 GFLOPS, 675.05 GOPS
489 69.87 ms run, 2.67 ms python, 67.20 ms HIP, 553.44 loss, 0.002306 LR, 4.54 GB used, 9660.97 GFLOPS, 675.05 GOPS
shuffling training dataset in 753.43 ms (epoch=5)
490 828.96 ms run, 756.58 ms python, 72.39 ms HIP, 560.60 loss, 0.002301 LR, 4.54 GB used, 814.68 GFLOPS, 675.34 GOPS
491 73.86 ms run, 2.78 ms python, 71.08 ms HIP, 548.40 loss, 0.002297 LR, 4.54 GB used, 9139.33 GFLOPS, 675.05 GOPS
492 72.50 ms run, 2.68 ms python, 69.82 ms HIP, 559.04 loss, 0.002293 LR, 4.54 GB used, 9310.86 GFLOPS, 675.05 GOPS
493 71.02 ms run, 2.68 ms python, 68.34 ms HIP, 559.92 loss, 0.002288 LR, 4.54 GB used, 9504.53 GFLOPS, 675.05 GOPS
494 71.47 ms run, 2.72 ms python, 68.75 ms HIP, 566.71 loss, 0.002284 LR, 4.54 GB used, 9445.23 GFLOPS, 675.05 GOPS
495 70.28 ms run, 2.71 ms python, 67.57 ms HIP, 566.45 loss, 0.002280 LR, 4.54 GB used, 9605.55 GFLOPS, 675.05 GOPS
496 70.03 ms run, 2.65 ms python, 67.38 ms HIP, 573.89 loss, 0.002275 LR, 4.54 GB used, 9639.65 GFLOPS, 675.05 GOPS
497 70.01 ms run, 2.64 ms python, 67.37 ms HIP, 571.42 loss, 0.002271 LR, 4.54 GB used, 9641.88 GFLOPS, 675.05 GOPS
498 69.38 ms run, 2.66 ms python, 66.72 ms HIP, 562.14 loss, 0.002267 LR, 4.54 GB used, 9729.92 GFLOPS, 675.05 GOPS
499 69.61 ms run, 2.75 ms python, 66.86 ms HIP, 551.55 loss, 0.002262 LR, 4.54 GB used, 9697.79 GFLOPS, 675.05 GOPS
500 69.50 ms run, 2.65 ms python, 66.84 ms HIP, 557.15 loss, 0.002258 LR, 4.54 GB used, 9713.18 GFLOPS, 675.05 GOPS
501 69.49 ms run, 2.65 ms python, 66.83 ms HIP, 591.58 loss, 0.002254 LR, 4.54 GB used, 9714.97 GFLOPS, 675.05 GOPS
502 69.68 ms run, 2.66 ms python, 67.02 ms HIP, 603.83 loss, 0.002249 LR, 4.54 GB used, 9688.20 GFLOPS, 675.05 GOPS
503 69.32 ms run, 2.71 ms python, 66.61 ms HIP, 562.22 loss, 0.002245 LR, 4.54 GB used, 9737.52 GFLOPS, 675.05 GOPS
504 69.64 ms run, 2.64 ms python, 67.01 ms HIP, 580.17 loss, 0.002241 LR, 4.54 GB used, 9692.68 GFLOPS, 675.05 GOPS
505 69.34 ms run, 2.67 ms python, 66.67 ms HIP, 566.71 loss, 0.002236 LR, 4.54 GB used, 9735.38 GFLOPS, 675.05 GOPS
506 69.94 ms run, 2.63 ms python, 67.31 ms HIP, 591.87 loss, 0.002232 LR, 4.54 GB used, 9651.92 GFLOPS, 675.05 GOPS
507 69.58 ms run, 2.64 ms python, 66.94 ms HIP, 566.65 loss, 0.002227 LR, 4.54 GB used, 9701.37 GFLOPS, 675.05 GOPS
508 68.80 ms run, 2.64 ms python, 66.15 ms HIP, 569.30 loss, 0.002223 LR, 4.54 GB used, 9811.99 GFLOPS, 675.05 GOPS
509 69.45 ms run, 2.64 ms python, 66.82 ms HIP, 554.92 loss, 0.002219 LR, 4.54 GB used, 9719.59 GFLOPS, 675.05 GOPS
510 68.93 ms run, 2.62 ms python, 66.30 ms HIP, 593.63 loss, 0.002214 LR, 4.54 GB used, 9793.74 GFLOPS, 675.05 GOPS
511 69.09 ms run, 2.73 ms python, 66.36 ms HIP, 574.09 loss, 0.002210 LR, 4.54 GB used, 9770.91 GFLOPS, 675.05 GOPS
512 69.08 ms run, 2.71 ms python, 66.37 ms HIP, 558.48 loss, 0.002206 LR, 4.54 GB used, 9771.45 GFLOPS, 675.05 GOPS
513 69.20 ms run, 2.66 ms python, 66.54 ms HIP, 571.87 loss, 0.002201 LR, 4.54 GB used, 9754.31 GFLOPS, 675.05 GOPS
514 69.76 ms run, 2.68 ms python, 67.08 ms HIP, 566.25 loss, 0.002197 LR, 4.54 GB used, 9676.61 GFLOPS, 675.05 GOPS
515 69.53 ms run, 2.67 ms python, 66.86 ms HIP, 575.40 loss, 0.002193 LR, 4.54 GB used, 9708.89 GFLOPS, 675.05 GOPS
516 69.52 ms run, 2.64 ms python, 66.87 ms HIP, 585.70 loss, 0.002188 LR, 4.54 GB used, 9710.32 GFLOPS, 675.05 GOPS
517 69.81 ms run, 2.66 ms python, 67.16 ms HIP, 575.09 loss, 0.002184 LR, 4.54 GB used, 9669.11 GFLOPS, 675.05 GOPS
518 69.25 ms run, 2.65 ms python, 66.60 ms HIP, 577.65 loss, 0.002180 LR, 4.54 GB used, 9748.00 GFLOPS, 675.05 GOPS
519 69.38 ms run, 2.64 ms python, 66.74 ms HIP, 562.22 loss, 0.002175 LR, 4.54 GB used, 9729.67 GFLOPS, 675.05 GOPS
520 69.59 ms run, 2.66 ms python, 66.92 ms HIP, 588.31 loss, 0.002171 LR, 4.54 GB used, 9700.68 GFLOPS, 675.05 GOPS
521 69.61 ms run, 2.68 ms python, 66.92 ms HIP, 563.62 loss, 0.002167 LR, 4.54 GB used, 9697.95 GFLOPS, 675.05 GOPS
522 69.46 ms run, 2.64 ms python, 66.81 ms HIP, 575.65 loss, 0.002162 LR, 4.54 GB used, 9718.78 GFLOPS, 675.05 GOPS
523 69.85 ms run, 2.63 ms python, 67.22 ms HIP, 582.18 loss, 0.002158 LR, 4.54 GB used, 9664.21 GFLOPS, 675.05 GOPS
524 69.46 ms run, 2.60 ms python, 66.86 ms HIP, 574.66 loss, 0.002153 LR, 4.54 GB used, 9718.70 GFLOPS, 675.05 GOPS
525 69.56 ms run, 2.65 ms python, 66.91 ms HIP, 586.75 loss, 0.002149 LR, 4.54 GB used, 9704.57 GFLOPS, 675.05 GOPS
526 69.75 ms run, 2.71 ms python, 67.05 ms HIP, 586.70 loss, 0.002145 LR, 4.54 GB used, 9677.48 GFLOPS, 675.05 GOPS
527 69.51 ms run, 2.71 ms python, 66.80 ms HIP, 571.59 loss, 0.002140 LR, 4.54 GB used, 9711.94 GFLOPS, 675.05 GOPS
528 69.46 ms run, 2.62 ms python, 66.84 ms HIP, 564.29 loss, 0.002136 LR, 4.54 GB used, 9718.11 GFLOPS, 675.05 GOPS
529 69.58 ms run, 2.65 ms python, 66.93 ms HIP, 572.51 loss, 0.002132 LR, 4.54 GB used, 9701.15 GFLOPS, 675.05 GOPS
530 69.35 ms run, 2.66 ms python, 66.69 ms HIP, 570.33 loss, 0.002127 LR, 4.54 GB used, 9733.48 GFLOPS, 675.05 GOPS
531 69.59 ms run, 2.66 ms python, 66.93 ms HIP, 566.56 loss, 0.002123 LR, 4.54 GB used, 9699.89 GFLOPS, 675.05 GOPS
532 69.29 ms run, 2.65 ms python, 66.64 ms HIP, 581.82 loss, 0.002119 LR, 4.54 GB used, 9742.58 GFLOPS, 675.05 GOPS
533 69.48 ms run, 2.66 ms python, 66.82 ms HIP, 561.05 loss, 0.002114 LR, 4.54 GB used, 9715.61 GFLOPS, 675.05 GOPS
534 69.53 ms run, 2.65 ms python, 66.89 ms HIP, 594.77 loss, 0.002110 LR, 4.54 GB used, 9708.01 GFLOPS, 675.05 GOPS
535 69.76 ms run, 2.72 ms python, 67.04 ms HIP, 558.92 loss, 0.002106 LR, 4.54 GB used, 9676.33 GFLOPS, 675.05 GOPS
536 69.24 ms run, 2.67 ms python, 66.57 ms HIP, 586.19 loss, 0.002101 LR, 4.54 GB used, 9748.85 GFLOPS, 675.05 GOPS
537 69.22 ms run, 2.71 ms python, 66.51 ms HIP, 578.50 loss, 0.002097 LR, 4.54 GB used, 9752.34 GFLOPS, 675.05 GOPS
538 69.18 ms run, 2.65 ms python, 66.54 ms HIP, 572.07 loss, 0.002093 LR, 4.54 GB used, 9757.39 GFLOPS, 675.05 GOPS
539 69.19 ms run, 2.60 ms python, 66.60 ms HIP, 565.12 loss, 0.002088 LR, 4.54 GB used, 9756.18 GFLOPS, 675.05 GOPS
540 68.98 ms run, 2.59 ms python, 66.39 ms HIP, 562.70 loss, 0.002084 LR, 4.54 GB used, 9785.63 GFLOPS, 675.05 GOPS
541 69.21 ms run, 2.62 ms python, 66.59 ms HIP, 579.45 loss, 0.002079 LR, 4.54 GB used, 9752.89 GFLOPS, 675.05 GOPS
542 68.45 ms run, 2.61 ms python, 65.84 ms HIP, 572.89 loss, 0.002075 LR, 4.54 GB used, 9861.98 GFLOPS, 675.05 GOPS
543 70.01 ms run, 2.66 ms python, 67.35 ms HIP, 569.13 loss, 0.002071 LR, 4.54 GB used, 9641.54 GFLOPS, 675.05 GOPS
544 69.05 ms run, 2.64 ms python, 66.42 ms HIP, 569.87 loss, 0.002066 LR, 4.54 GB used, 9775.58 GFLOPS, 675.05 GOPS
545 69.32 ms run, 2.65 ms python, 66.67 ms HIP, 555.79 loss, 0.002062 LR, 4.54 GB used, 9738.47 GFLOPS, 675.05 GOPS
546 69.17 ms run, 2.61 ms python, 66.56 ms HIP, 558.11 loss, 0.002058 LR, 4.54 GB used, 9759.22 GFLOPS, 675.05 GOPS
547 69.30 ms run, 2.74 ms python, 66.56 ms HIP, 557.49 loss, 0.002053 LR, 4.54 GB used, 9740.97 GFLOPS, 675.05 GOPS
548 69.23 ms run, 2.66 ms python, 66.57 ms HIP, 569.55 loss, 0.002049 LR, 4.54 GB used, 9750.78 GFLOPS, 675.05 GOPS
549 69.23 ms run, 2.66 ms python, 66.57 ms HIP, 564.51 loss, 0.002045 LR, 4.54 GB used, 9750.81 GFLOPS, 675.05 GOPS
550 69.27 ms run, 2.67 ms python, 66.60 ms HIP, 564.71 loss, 0.002040 LR, 4.54 GB used, 9744.46 GFLOPS, 675.05 GOPS
551 68.79 ms run, 2.65 ms python, 66.15 ms HIP, 571.34 loss, 0.002036 LR, 4.54 GB used, 9812.75 GFLOPS, 675.05 GOPS
552 69.87 ms run, 2.65 ms python, 67.22 ms HIP, 560.34 loss, 0.002032 LR, 4.54 GB used, 9661.72 GFLOPS, 675.05 GOPS
553 69.18 ms run, 2.64 ms python, 66.54 ms HIP, 581.30 loss, 0.002027 LR, 4.54 GB used, 9757.62 GFLOPS, 675.05 GOPS
554 69.23 ms run, 2.63 ms python, 66.60 ms HIP, 567.74 loss, 0.002023 LR, 4.54 GB used, 9751.29 GFLOPS, 675.05 GOPS
555 69.11 ms run, 2.69 ms python, 66.42 ms HIP, 565.75 loss, 0.002019 LR, 4.54 GB used, 9767.84 GFLOPS, 675.05 GOPS
556 69.28 ms run, 2.61 ms python, 66.67 ms HIP, 560.14 loss, 0.002014 LR, 4.54 GB used, 9743.20 GFLOPS, 675.05 GOPS
557 69.16 ms run, 2.64 ms python, 66.52 ms HIP, 566.14 loss, 0.002010 LR, 4.54 GB used, 9761.05 GFLOPS, 675.05 GOPS
558 69.59 ms run, 2.67 ms python, 66.93 ms HIP, 576.05 loss, 0.002005 LR, 4.54 GB used, 9699.90 GFLOPS, 675.05 GOPS
559 69.34 ms run, 2.65 ms python, 66.70 ms HIP, 568.57 loss, 0.002001 LR, 4.54 GB used, 9734.75 GFLOPS, 675.05 GOPS
560 68.96 ms run, 2.62 ms python, 66.34 ms HIP, 547.41 loss, 0.001997 LR, 4.54 GB used, 9788.71 GFLOPS, 675.05 GOPS
561 69.55 ms run, 2.62 ms python, 66.93 ms HIP, 575.74 loss, 0.001992 LR, 4.54 GB used, 9705.91 GFLOPS, 675.05 GOPS
562 69.27 ms run, 2.67 ms python, 66.60 ms HIP, 568.30 loss, 0.001988 LR, 4.54 GB used, 9745.32 GFLOPS, 675.05 GOPS
563 70.53 ms run, 2.65 ms python, 67.88 ms HIP, 573.20 loss, 0.001984 LR, 4.54 GB used, 9570.72 GFLOPS, 675.05 GOPS
564 69.40 ms run, 2.70 ms python, 66.70 ms HIP, 569.90 loss, 0.001979 LR, 4.54 GB used, 9727.09 GFLOPS, 675.05 GOPS
565 68.97 ms run, 2.67 ms python, 66.30 ms HIP, 584.92 loss, 0.001975 LR, 4.54 GB used, 9786.93 GFLOPS, 675.05 GOPS
566 69.16 ms run, 2.63 ms python, 66.53 ms HIP, 574.28 loss, 0.001971 LR, 4.54 GB used, 9760.44 GFLOPS, 675.05 GOPS
567 69.56 ms run, 2.67 ms python, 66.89 ms HIP, 574.55 loss, 0.001966 LR, 4.54 GB used, 9704.27 GFLOPS, 675.05 GOPS
568 69.87 ms run, 2.70 ms python, 67.17 ms HIP, 576.63 loss, 0.001962 LR, 4.54 GB used, 9661.82 GFLOPS, 675.05 GOPS
569 69.29 ms run, 2.72 ms python, 66.57 ms HIP, 583.46 loss, 0.001958 LR, 4.54 GB used, 9742.41 GFLOPS, 675.05 GOPS
570 69.35 ms run, 2.66 ms python, 66.69 ms HIP, 576.70 loss, 0.001953 LR, 4.54 GB used, 9733.60 GFLOPS, 675.05 GOPS
571 69.36 ms run, 2.64 ms python, 66.72 ms HIP, 572.55 loss, 0.001949 LR, 4.54 GB used, 9732.30 GFLOPS, 675.05 GOPS
572 69.22 ms run, 2.64 ms python, 66.57 ms HIP, 561.28 loss, 0.001945 LR, 4.54 GB used, 9752.43 GFLOPS, 675.05 GOPS
573 69.22 ms run, 2.67 ms python, 66.55 ms HIP, 556.27 loss, 0.001940 LR, 4.54 GB used, 9752.26 GFLOPS, 675.05 GOPS
574 69.27 ms run, 2.64 ms python, 66.63 ms HIP, 564.08 loss, 0.001936 LR, 4.54 GB used, 9744.94 GFLOPS, 675.05 GOPS
575 70.44 ms run, 2.62 ms python, 67.82 ms HIP, 553.18 loss, 0.001931 LR, 4.54 GB used, 9582.95 GFLOPS, 675.05 GOPS
576 70.21 ms run, 2.73 ms python, 67.48 ms HIP, 559.14 loss, 0.001927 LR, 4.54 GB used, 9614.34 GFLOPS, 675.05 GOPS
577 69.58 ms run, 2.64 ms python, 66.94 ms HIP, 573.60 loss, 0.001923 LR, 4.54 GB used, 9701.77 GFLOPS, 675.05 GOPS
578 69.91 ms run, 2.61 ms python, 67.29 ms HIP, 575.48 loss, 0.001918 LR, 4.54 GB used, 9656.14 GFLOPS, 675.05 GOPS
579 69.53 ms run, 2.63 ms python, 66.91 ms HIP, 573.98 loss, 0.001914 LR, 4.54 GB used, 9708.07 GFLOPS, 675.05 GOPS
580 69.42 ms run, 2.76 ms python, 66.65 ms HIP, 574.23 loss, 0.001910 LR, 4.54 GB used, 9724.32 GFLOPS, 675.05 GOPS
581 69.01 ms run, 2.64 ms python, 66.37 ms HIP, 569.52 loss, 0.001905 LR, 4.54 GB used, 9781.98 GFLOPS, 675.05 GOPS
582 69.88 ms run, 2.66 ms python, 67.21 ms HIP, 562.67 loss, 0.001901 LR, 4.54 GB used, 9660.34 GFLOPS, 675.05 GOPS
583 69.47 ms run, 2.64 ms python, 66.82 ms HIP, 548.51 loss, 0.001897 LR, 4.54 GB used, 9717.55 GFLOPS, 675.05 GOPS
584 69.13 ms run, 2.64 ms python, 66.50 ms HIP, 568.97 loss, 0.001892 LR, 4.54 GB used, 9764.27 GFLOPS, 675.05 GOPS
585 69.10 ms run, 2.63 ms python, 66.48 ms HIP, 565.35 loss, 0.001888 LR, 4.54 GB used, 9768.65 GFLOPS, 675.05 GOPS
586 68.92 ms run, 2.62 ms python, 66.29 ms HIP, 578.32 loss, 0.001884 LR, 4.54 GB used, 9795.32 GFLOPS, 675.05 GOPS
587 69.53 ms run, 2.64 ms python, 66.89 ms HIP, 538.04 loss, 0.001879 LR, 4.54 GB used, 9708.32 GFLOPS, 675.05 GOPS
shuffling training dataset in 1532.30 ms (epoch=6)
588 1607.52 ms run, 1535.42 ms python, 72.11 ms HIP, 571.49 loss, 0.001875 LR, 4.54 GB used, 420.28 GFLOPS, 675.61 GOPS
589 73.93 ms run, 2.84 ms python, 71.09 ms HIP, 564.70 loss, 0.001871 LR, 4.54 GB used, 9130.80 GFLOPS, 675.05 GOPS
590 72.13 ms run, 2.73 ms python, 69.41 ms HIP, 570.04 loss, 0.001866 LR, 4.54 GB used, 9358.34 GFLOPS, 675.05 GOPS
591 71.02 ms run, 2.75 ms python, 68.28 ms HIP, 569.78 loss, 0.001862 LR, 4.54 GB used, 9504.48 GFLOPS, 675.05 GOPS
592 70.83 ms run, 3.07 ms python, 67.76 ms HIP, 578.29 loss, 0.001857 LR, 4.54 GB used, 9531.00 GFLOPS, 675.05 GOPS
593 69.67 ms run, 2.67 ms python, 67.00 ms HIP, 573.34 loss, 0.001853 LR, 4.54 GB used, 9689.20 GFLOPS, 675.05 GOPS
594 69.52 ms run, 2.66 ms python, 66.86 ms HIP, 578.10 loss, 0.001849 LR, 4.54 GB used, 9710.61 GFLOPS, 675.05 GOPS
595 69.88 ms run, 2.70 ms python, 67.19 ms HIP, 591.64 loss, 0.001844 LR, 4.54 GB used, 9659.64 GFLOPS, 675.05 GOPS
596 69.11 ms run, 2.66 ms python, 66.45 ms HIP, 603.30 loss, 0.001840 LR, 4.54 GB used, 9767.51 GFLOPS, 675.05 GOPS
597 68.57 ms run, 2.68 ms python, 65.89 ms HIP, 567.99 loss, 0.001836 LR, 4.54 GB used, 9844.15 GFLOPS, 675.05 GOPS
598 69.68 ms run, 2.68 ms python, 67.00 ms HIP, 575.65 loss, 0.001831 LR, 4.54 GB used, 9687.80 GFLOPS, 675.05 GOPS
599 68.71 ms run, 2.64 ms python, 66.07 ms HIP, 570.96 loss, 0.001827 LR, 4.54 GB used, 9825.15 GFLOPS, 675.05 GOPS
600 68.25 ms run, 2.65 ms python, 65.60 ms HIP, 557.41 loss, 0.001823 LR, 4.54 GB used, 9890.67 GFLOPS, 675.05 GOPS
601 69.30 ms run, 2.66 ms python, 66.64 ms HIP, 571.70 loss, 0.001818 LR, 4.54 GB used, 9740.51 GFLOPS, 675.05 GOPS
602 69.25 ms run, 2.63 ms python, 66.61 ms HIP, 569.83 loss, 0.001814 LR, 4.54 GB used, 9748.63 GFLOPS, 675.05 GOPS
603 69.96 ms run, 2.64 ms python, 67.32 ms HIP, 571.17 loss, 0.001810 LR, 4.54 GB used, 9648.41 GFLOPS, 675.05 GOPS
604 69.57 ms run, 2.68 ms python, 66.89 ms HIP, 575.71 loss, 0.001805 LR, 4.54 GB used, 9702.87 GFLOPS, 675.05 GOPS
605 68.99 ms run, 2.62 ms python, 66.37 ms HIP, 577.77 loss, 0.001801 LR, 4.54 GB used, 9784.19 GFLOPS, 675.05 GOPS
606 68.93 ms run, 2.64 ms python, 66.29 ms HIP, 569.05 loss, 0.001797 LR, 4.54 GB used, 9793.24 GFLOPS, 675.05 GOPS
607 69.57 ms run, 2.66 ms python, 66.91 ms HIP, 573.34 loss, 0.001792 LR, 4.54 GB used, 9703.31 GFLOPS, 675.05 GOPS
608 69.33 ms run, 2.66 ms python, 66.67 ms HIP, 570.30 loss, 0.001788 LR, 4.54 GB used, 9737.12 GFLOPS, 675.05 GOPS
609 69.04 ms run, 2.66 ms python, 66.38 ms HIP, 570.39 loss, 0.001783 LR, 4.54 GB used, 9777.89 GFLOPS, 675.05 GOPS
610 69.05 ms run, 2.68 ms python, 66.37 ms HIP, 566.33 loss, 0.001779 LR, 4.54 GB used, 9776.76 GFLOPS, 675.05 GOPS
611 69.30 ms run, 2.63 ms python, 66.66 ms HIP, 566.64 loss, 0.001775 LR, 4.54 GB used, 9741.48 GFLOPS, 675.05 GOPS
612 68.96 ms run, 2.61 ms python, 66.34 ms HIP, 569.52 loss, 0.001770 LR, 4.54 GB used, 9789.49 GFLOPS, 675.05 GOPS
613 68.96 ms run, 2.62 ms python, 66.34 ms HIP, 581.94 loss, 0.001766 LR, 4.54 GB used, 9788.75 GFLOPS, 675.05 GOPS
614 69.12 ms run, 2.64 ms python, 66.48 ms HIP, 578.21 loss, 0.001762 LR, 4.54 GB used, 9766.66 GFLOPS, 675.05 GOPS
615 69.22 ms run, 2.68 ms python, 66.54 ms HIP, 577.52 loss, 0.001757 LR, 4.54 GB used, 9751.63 GFLOPS, 675.05 GOPS
616 69.22 ms run, 2.65 ms python, 66.57 ms HIP, 573.82 loss, 0.001753 LR, 4.54 GB used, 9752.63 GFLOPS, 675.05 GOPS
617 68.98 ms run, 2.65 ms python, 66.33 ms HIP, 570.04 loss, 0.001749 LR, 4.54 GB used, 9785.64 GFLOPS, 675.05 GOPS
618 68.79 ms run, 2.63 ms python, 66.16 ms HIP, 569.29 loss, 0.001744 LR, 4.54 GB used, 9813.12 GFLOPS, 675.05 GOPS
619 69.47 ms run, 2.62 ms python, 66.86 ms HIP, 587.24 loss, 0.001740 LR, 4.54 GB used, 9716.59 GFLOPS, 675.05 GOPS
620 69.73 ms run, 2.65 ms python, 67.07 ms HIP, 568.84 loss, 0.001736 LR, 4.54 GB used, 9681.47 GFLOPS, 675.05 GOPS
621 69.34 ms run, 2.65 ms python, 66.70 ms HIP, 566.43 loss, 0.001731 LR, 4.54 GB used, 9734.93 GFLOPS, 675.05 GOPS
622 69.13 ms run, 2.63 ms python, 66.50 ms HIP, 569.45 loss, 0.001727 LR, 4.54 GB used, 9764.84 GFLOPS, 675.05 GOPS
623 68.65 ms run, 2.63 ms python, 66.02 ms HIP, 581.13 loss, 0.001723 LR, 4.54 GB used, 9832.96 GFLOPS, 675.05 GOPS
624 69.20 ms run, 2.67 ms python, 66.54 ms HIP, 564.13 loss, 0.001718 LR, 4.54 GB used, 9754.55 GFLOPS, 675.05 GOPS
625 68.98 ms run, 2.64 ms python, 66.34 ms HIP, 573.42 loss, 0.001714 LR, 4.54 GB used, 9786.71 GFLOPS, 675.05 GOPS
626 69.54 ms run, 2.72 ms python, 66.82 ms HIP, 563.98 loss, 0.001709 LR, 4.54 GB used, 9707.19 GFLOPS, 675.05 GOPS
627 69.17 ms run, 2.61 ms python, 66.56 ms HIP, 569.14 loss, 0.001705 LR, 4.54 GB used, 9758.61 GFLOPS, 675.05 GOPS
628 69.14 ms run, 2.64 ms python, 66.50 ms HIP, 567.16 loss, 0.001701 LR, 4.54 GB used, 9763.47 GFLOPS, 675.05 GOPS
629 69.67 ms run, 2.62 ms python, 67.04 ms HIP, 573.60 loss, 0.001696 LR, 4.54 GB used, 9689.26 GFLOPS, 675.05 GOPS
630 70.03 ms run, 2.68 ms python, 67.34 ms HIP, 562.19 loss, 0.001692 LR, 4.54 GB used, 9639.76 GFLOPS, 675.05 GOPS
631 70.27 ms run, 2.70 ms python, 67.58 ms HIP, 575.71 loss, 0.001688 LR, 4.54 GB used, 9605.96 GFLOPS, 675.05 GOPS
632 69.10 ms run, 2.68 ms python, 66.41 ms HIP, 570.74 loss, 0.001683 LR, 4.54 GB used, 9769.67 GFLOPS, 675.05 GOPS
633 69.40 ms run, 2.66 ms python, 66.74 ms HIP, 567.73 loss, 0.001679 LR, 4.54 GB used, 9727.11 GFLOPS, 675.05 GOPS
634 69.35 ms run, 2.60 ms python, 66.75 ms HIP, 568.36 loss, 0.001675 LR, 4.54 GB used, 9734.21 GFLOPS, 675.05 GOPS
635 69.23 ms run, 2.63 ms python, 66.60 ms HIP, 586.04 loss, 0.001670 LR, 4.54 GB used, 9751.31 GFLOPS, 675.05 GOPS
636 69.23 ms run, 2.66 ms python, 66.57 ms HIP, 574.17 loss, 0.001666 LR, 4.54 GB used, 9751.14 GFLOPS, 675.05 GOPS
637 69.20 ms run, 2.67 ms python, 66.54 ms HIP, 579.93 loss, 0.001662 LR, 4.54 GB used, 9754.30 GFLOPS, 675.05 GOPS
638 69.40 ms run, 2.72 ms python, 66.68 ms HIP, 560.20 loss, 0.001657 LR, 4.54 GB used, 9727.38 GFLOPS, 675.05 GOPS
639 69.54 ms run, 2.66 ms python, 66.88 ms HIP, 576.22 loss, 0.001653 LR, 4.54 GB used, 9707.03 GFLOPS, 675.05 GOPS
640 70.02 ms run, 2.63 ms python, 67.39 ms HIP, 583.38 loss, 0.001649 LR, 4.54 GB used, 9640.45 GFLOPS, 675.05 GOPS
641 69.67 ms run, 2.70 ms python, 66.96 ms HIP, 563.03 loss, 0.001644 LR, 4.54 GB used, 9689.69 GFLOPS, 675.05 GOPS
642 69.39 ms run, 2.65 ms python, 66.74 ms HIP, 577.68 loss, 0.001640 LR, 4.54 GB used, 9727.86 GFLOPS, 675.05 GOPS
643 69.51 ms run, 2.60 ms python, 66.91 ms HIP, 561.45 loss, 0.001635 LR, 4.54 GB used, 9710.91 GFLOPS, 675.05 GOPS
644 68.99 ms run, 2.65 ms python, 66.34 ms HIP, 580.14 loss, 0.001631 LR, 4.54 GB used, 9784.95 GFLOPS, 675.05 GOPS
645 69.26 ms run, 2.67 ms python, 66.59 ms HIP, 579.88 loss, 0.001627 LR, 4.54 GB used, 9747.02 GFLOPS, 675.05 GOPS
646 69.48 ms run, 2.61 ms python, 66.88 ms HIP, 575.16 loss, 0.001622 LR, 4.54 GB used, 9715.32 GFLOPS, 675.05 GOPS
647 69.02 ms run, 2.64 ms python, 66.38 ms HIP, 596.25 loss, 0.001618 LR, 4.54 GB used, 9781.13 GFLOPS, 675.05 GOPS
648 70.13 ms run, 2.62 ms python, 67.51 ms HIP, 564.59 loss, 0.001614 LR, 4.54 GB used, 9626.22 GFLOPS, 675.05 GOPS
649 69.09 ms run, 2.76 ms python, 66.33 ms HIP, 569.52 loss, 0.001609 LR, 4.54 GB used, 9770.15 GFLOPS, 675.05 GOPS
650 69.10 ms run, 2.65 ms python, 66.45 ms HIP, 586.70 loss, 0.001605 LR, 4.54 GB used, 9769.54 GFLOPS, 675.05 GOPS
651 69.49 ms run, 2.65 ms python, 66.83 ms HIP, 577.35 loss, 0.001601 LR, 4.54 GB used, 9714.46 GFLOPS, 675.05 GOPS
652 70.05 ms run, 2.66 ms python, 67.40 ms HIP, 575.93 loss, 0.001596 LR, 4.54 GB used, 9636.33 GFLOPS, 675.05 GOPS
653 69.37 ms run, 2.62 ms python, 66.75 ms HIP, 576.70 loss, 0.001592 LR, 4.54 GB used, 9730.73 GFLOPS, 675.05 GOPS
654 69.51 ms run, 2.67 ms python, 66.84 ms HIP, 566.77 loss, 0.001588 LR, 4.54 GB used, 9711.19 GFLOPS, 675.05 GOPS
655 69.64 ms run, 2.63 ms python, 67.01 ms HIP, 576.23 loss, 0.001583 LR, 4.54 GB used, 9693.35 GFLOPS, 675.05 GOPS
656 69.26 ms run, 2.63 ms python, 66.63 ms HIP, 562.23 loss, 0.001579 LR, 4.54 GB used, 9746.43 GFLOPS, 675.05 GOPS
657 69.32 ms run, 2.64 ms python, 66.68 ms HIP, 564.83 loss, 0.001575 LR, 4.54 GB used, 9738.11 GFLOPS, 675.05 GOPS
658 69.05 ms run, 2.63 ms python, 66.42 ms HIP, 577.87 loss, 0.001570 LR, 4.54 GB used, 9775.95 GFLOPS, 675.05 GOPS
659 69.75 ms run, 2.66 ms python, 67.09 ms HIP, 574.14 loss, 0.001566 LR, 4.54 GB used, 9677.60 GFLOPS, 675.05 GOPS
660 69.21 ms run, 2.74 ms python, 66.47 ms HIP, 582.64 loss, 0.001561 LR, 4.54 GB used, 9753.05 GFLOPS, 675.05 GOPS
661 69.58 ms run, 2.62 ms python, 66.96 ms HIP, 586.00 loss, 0.001557 LR, 4.54 GB used, 9701.75 GFLOPS, 675.05 GOPS
662 70.11 ms run, 2.63 ms python, 67.48 ms HIP, 552.82 loss, 0.001553 LR, 4.54 GB used, 9628.76 GFLOPS, 675.05 GOPS
663 69.24 ms run, 2.61 ms python, 66.63 ms HIP, 569.97 loss, 0.001548 LR, 4.54 GB used, 9749.22 GFLOPS, 675.05 GOPS
664 69.93 ms run, 2.62 ms python, 67.31 ms HIP, 576.78 loss, 0.001544 LR, 4.54 GB used, 9653.37 GFLOPS, 675.05 GOPS
665 69.92 ms run, 2.79 ms python, 67.13 ms HIP, 565.56 loss, 0.001540 LR, 4.54 GB used, 9654.51 GFLOPS, 675.05 GOPS
666 69.48 ms run, 2.70 ms python, 66.79 ms HIP, 579.94 loss, 0.001535 LR, 4.54 GB used, 9715.04 GFLOPS, 675.05 GOPS
667 68.98 ms run, 2.66 ms python, 66.32 ms HIP, 564.24 loss, 0.001531 LR, 4.54 GB used, 9785.96 GFLOPS, 675.05 GOPS
668 69.42 ms run, 2.62 ms python, 66.80 ms HIP, 573.23 loss, 0.001527 LR, 4.54 GB used, 9724.37 GFLOPS, 675.05 GOPS
669 69.36 ms run, 2.65 ms python, 66.71 ms HIP, 571.86 loss, 0.001522 LR, 4.54 GB used, 9732.52 GFLOPS, 675.05 GOPS
670 69.28 ms run, 2.63 ms python, 66.66 ms HIP, 596.87 loss, 0.001518 LR, 4.54 GB used, 9743.53 GFLOPS, 675.05 GOPS
671 69.96 ms run, 2.71 ms python, 67.25 ms HIP, 570.27 loss, 0.001514 LR, 4.54 GB used, 9648.68 GFLOPS, 675.05 GOPS
672 69.56 ms run, 2.63 ms python, 66.93 ms HIP, 575.17 loss, 0.001509 LR, 4.54 GB used, 9704.28 GFLOPS, 675.05 GOPS
673 69.26 ms run, 2.64 ms python, 66.62 ms HIP, 563.80 loss, 0.001505 LR, 4.54 GB used, 9746.84 GFLOPS, 675.05 GOPS
674 69.59 ms run, 2.65 ms python, 66.94 ms HIP, 575.78 loss, 0.001501 LR, 4.54 GB used, 9700.17 GFLOPS, 675.05 GOPS
675 69.84 ms run, 2.62 ms python, 67.22 ms HIP, 563.96 loss, 0.001496 LR, 4.54 GB used, 9665.74 GFLOPS, 675.05 GOPS
676 69.58 ms run, 2.64 ms python, 66.94 ms HIP, 575.17 loss, 0.001492 LR, 4.54 GB used, 9701.21 GFLOPS, 675.05 GOPS
677 69.35 ms run, 2.63 ms python, 66.72 ms HIP, 576.46 loss, 0.001488 LR, 4.54 GB used, 9733.49 GFLOPS, 675.05 GOPS
678 69.84 ms run, 2.66 ms python, 67.18 ms HIP, 563.43 loss, 0.001483 LR, 4.54 GB used, 9665.56 GFLOPS, 675.05 GOPS
679 69.77 ms run, 2.63 ms python, 67.14 ms HIP, 582.41 loss, 0.001479 LR, 4.54 GB used, 9675.46 GFLOPS, 675.05 GOPS
680 69.82 ms run, 2.62 ms python, 67.20 ms HIP, 565.88 loss, 0.001474 LR, 4.54 GB used, 9667.93 GFLOPS, 675.05 GOPS
681 69.72 ms run, 2.68 ms python, 67.04 ms HIP, 571.82 loss, 0.001470 LR, 4.54 GB used, 9681.60 GFLOPS, 675.05 GOPS
682 69.43 ms run, 2.60 ms python, 66.84 ms HIP, 566.03 loss, 0.001466 LR, 4.54 GB used, 9722.06 GFLOPS, 675.05 GOPS
683 69.22 ms run, 2.76 ms python, 66.46 ms HIP, 568.31 loss, 0.001461 LR, 4.54 GB used, 9751.85 GFLOPS, 675.05 GOPS
684 69.83 ms run, 2.65 ms python, 67.18 ms HIP, 573.44 loss, 0.001457 LR, 4.54 GB used, 9667.25 GFLOPS, 675.05 GOPS
685 69.66 ms run, 2.66 ms python, 66.99 ms HIP, 533.51 loss, 0.001453 LR, 4.54 GB used, 9690.88 GFLOPS, 675.05 GOPS
shuffling training dataset in 1159.49 ms (epoch=7)
686 1235.82 ms run, 1162.57 ms python, 73.25 ms HIP, 554.17 loss, 0.001448 LR, 4.54 GB used, 546.69 GFLOPS, 675.61 GOPS
687 74.20 ms run, 2.77 ms python, 71.43 ms HIP, 546.76 loss, 0.001444 LR, 4.54 GB used, 9097.61 GFLOPS, 675.05 GOPS
688 72.21 ms run, 2.71 ms python, 69.50 ms HIP, 554.29 loss, 0.001440 LR, 4.54 GB used, 9348.90 GFLOPS, 675.05 GOPS
689 71.74 ms run, 2.66 ms python, 69.08 ms HIP, 560.47 loss, 0.001435 LR, 4.54 GB used, 9409.93 GFLOPS, 675.05 GOPS
690 70.23 ms run, 2.69 ms python, 67.54 ms HIP, 552.45 loss, 0.001431 LR, 4.54 GB used, 9612.55 GFLOPS, 675.05 GOPS
691 69.90 ms run, 2.70 ms python, 67.20 ms HIP, 558.22 loss, 0.001427 LR, 4.54 GB used, 9656.94 GFLOPS, 675.05 GOPS
692 69.89 ms run, 2.65 ms python, 67.24 ms HIP, 548.10 loss, 0.001422 LR, 4.54 GB used, 9658.98 GFLOPS, 675.05 GOPS
693 69.28 ms run, 2.64 ms python, 66.64 ms HIP, 550.90 loss, 0.001418 LR, 4.54 GB used, 9743.44 GFLOPS, 675.05 GOPS
694 69.40 ms run, 2.71 ms python, 66.69 ms HIP, 545.27 loss, 0.001414 LR, 4.54 GB used, 9727.47 GFLOPS, 675.05 GOPS
695 69.11 ms run, 2.70 ms python, 66.40 ms HIP, 555.22 loss, 0.001409 LR, 4.54 GB used, 9768.15 GFLOPS, 675.05 GOPS
696 69.04 ms run, 2.64 ms python, 66.41 ms HIP, 553.44 loss, 0.001405 LR, 4.54 GB used, 9777.12 GFLOPS, 675.05 GOPS
697 69.41 ms run, 2.62 ms python, 66.79 ms HIP, 542.86 loss, 0.001400 LR, 4.54 GB used, 9726.06 GFLOPS, 675.05 GOPS
698 70.41 ms run, 2.65 ms python, 67.76 ms HIP, 538.98 loss, 0.001396 LR, 4.54 GB used, 9588.01 GFLOPS, 675.05 GOPS
699 69.45 ms run, 2.72 ms python, 66.73 ms HIP, 547.87 loss, 0.001392 LR, 4.54 GB used, 9719.51 GFLOPS, 675.05 GOPS
700 69.36 ms run, 2.65 ms python, 66.72 ms HIP, 550.62 loss, 0.001387 LR, 4.54 GB used, 9732.17 GFLOPS, 675.05 GOPS
701 69.73 ms run, 2.61 ms python, 67.12 ms HIP, 548.99 loss, 0.001383 LR, 4.54 GB used, 9680.67 GFLOPS, 675.05 GOPS
702 69.49 ms run, 2.66 ms python, 66.83 ms HIP, 562.56 loss, 0.001379 LR, 4.54 GB used, 9714.51 GFLOPS, 675.05 GOPS
703 69.42 ms run, 2.72 ms python, 66.70 ms HIP, 545.80 loss, 0.001374 LR, 4.54 GB used, 9724.47 GFLOPS, 675.05 GOPS
704 69.53 ms run, 2.65 ms python, 66.88 ms HIP, 543.95 loss, 0.001370 LR, 4.54 GB used, 9708.93 GFLOPS, 675.05 GOPS
705 69.95 ms run, 2.65 ms python, 67.31 ms HIP, 550.51 loss, 0.001366 LR, 4.54 GB used, 9650.16 GFLOPS, 675.05 GOPS
706 69.06 ms run, 2.64 ms python, 66.42 ms HIP, 563.62 loss, 0.001361 LR, 4.54 GB used, 9774.28 GFLOPS, 675.05 GOPS
707 69.70 ms run, 2.66 ms python, 67.04 ms HIP, 553.62 loss, 0.001357 LR, 4.54 GB used, 9684.64 GFLOPS, 675.05 GOPS
708 69.86 ms run, 2.63 ms python, 67.24 ms HIP, 548.91 loss, 0.001353 LR, 4.54 GB used, 9662.51 GFLOPS, 675.05 GOPS
709 69.76 ms run, 2.64 ms python, 67.12 ms HIP, 553.52 loss, 0.001348 LR, 4.54 GB used, 9676.87 GFLOPS, 675.05 GOPS
710 69.71 ms run, 2.63 ms python, 67.09 ms HIP, 552.99 loss, 0.001344 LR, 4.54 GB used, 9683.37 GFLOPS, 675.05 GOPS
711 69.57 ms run, 2.65 ms python, 66.91 ms HIP, 554.13 loss, 0.001340 LR, 4.54 GB used, 9703.72 GFLOPS, 675.05 GOPS
712 70.20 ms run, 2.64 ms python, 67.56 ms HIP, 559.88 loss, 0.001335 LR, 4.54 GB used, 9616.10 GFLOPS, 675.05 GOPS
713 69.14 ms run, 2.70 ms python, 66.44 ms HIP, 561.09 loss, 0.001331 LR, 4.54 GB used, 9762.93 GFLOPS, 675.05 GOPS
714 69.68 ms run, 2.67 ms python, 67.01 ms HIP, 570.73 loss, 0.001326 LR, 4.54 GB used, 9688.16 GFLOPS, 675.05 GOPS
715 69.28 ms run, 2.64 ms python, 66.64 ms HIP, 549.33 loss, 0.001322 LR, 4.54 GB used, 9744.16 GFLOPS, 675.05 GOPS
716 69.36 ms run, 2.65 ms python, 66.72 ms HIP, 560.05 loss, 0.001318 LR, 4.54 GB used, 9732.05 GFLOPS, 675.05 GOPS
717 68.89 ms run, 2.65 ms python, 66.24 ms HIP, 558.79 loss, 0.001313 LR, 4.54 GB used, 9799.27 GFLOPS, 675.05 GOPS
718 69.34 ms run, 2.64 ms python, 66.70 ms HIP, 567.84 loss, 0.001309 LR, 4.54 GB used, 9735.95 GFLOPS, 675.05 GOPS
719 69.56 ms run, 2.61 ms python, 66.95 ms HIP, 556.73 loss, 0.001305 LR, 4.54 GB used, 9705.03 GFLOPS, 675.05 GOPS
720 68.84 ms run, 2.63 ms python, 66.21 ms HIP, 552.36 loss, 0.001300 LR, 4.54 GB used, 9805.99 GFLOPS, 675.05 GOPS
721 69.28 ms run, 2.62 ms python, 66.67 ms HIP, 548.49 loss, 0.001296 LR, 4.54 GB used, 9743.15 GFLOPS, 675.05 GOPS
722 69.55 ms run, 2.66 ms python, 66.89 ms HIP, 545.67 loss, 0.001292 LR, 4.54 GB used, 9705.72 GFLOPS, 675.05 GOPS
723 69.17 ms run, 2.62 ms python, 66.54 ms HIP, 562.48 loss, 0.001287 LR, 4.54 GB used, 9759.35 GFLOPS, 675.05 GOPS
724 69.49 ms run, 2.65 ms python, 66.84 ms HIP, 557.03 loss, 0.001283 LR, 4.54 GB used, 9714.21 GFLOPS, 675.05 GOPS
725 69.28 ms run, 2.68 ms python, 66.60 ms HIP, 552.82 loss, 0.001279 LR, 4.54 GB used, 9743.30 GFLOPS, 675.05 GOPS
726 70.04 ms run, 2.65 ms python, 67.39 ms HIP, 554.57 loss, 0.001274 LR, 4.54 GB used, 9638.21 GFLOPS, 675.05 GOPS
727 69.25 ms run, 2.62 ms python, 66.63 ms HIP, 546.55 loss, 0.001270 LR, 4.54 GB used, 9748.51 GFLOPS, 675.05 GOPS
728 69.16 ms run, 2.64 ms python, 66.52 ms HIP, 559.60 loss, 0.001266 LR, 4.54 GB used, 9760.15 GFLOPS, 675.05 GOPS
729 69.81 ms run, 2.63 ms python, 67.18 ms HIP, 552.28 loss, 0.001261 LR, 4.54 GB used, 9670.00 GFLOPS, 675.05 GOPS
730 69.50 ms run, 2.64 ms python, 66.86 ms HIP, 562.51 loss, 0.001257 LR, 4.54 GB used, 9712.98 GFLOPS, 675.05 GOPS
731 69.31 ms run, 2.62 ms python, 66.70 ms HIP, 557.94 loss, 0.001252 LR, 4.54 GB used, 9739.10 GFLOPS, 675.05 GOPS
732 69.84 ms run, 2.63 ms python, 67.21 ms HIP, 557.27 loss, 0.001248 LR, 4.54 GB used, 9665.30 GFLOPS, 675.05 GOPS
733 69.26 ms run, 2.74 ms python, 66.52 ms HIP, 545.38 loss, 0.001244 LR, 4.54 GB used, 9746.24 GFLOPS, 675.05 GOPS
734 69.50 ms run, 2.68 ms python, 66.82 ms HIP, 557.56 loss, 0.001239 LR, 4.54 GB used, 9713.20 GFLOPS, 675.05 GOPS
735 69.29 ms run, 2.65 ms python, 66.65 ms HIP, 539.31 loss, 0.001235 LR, 4.54 GB used, 9741.67 GFLOPS, 675.05 GOPS
736 69.40 ms run, 2.67 ms python, 66.73 ms HIP, 550.31 loss, 0.001231 LR, 4.54 GB used, 9726.19 GFLOPS, 675.05 GOPS
737 69.08 ms run, 2.64 ms python, 66.44 ms HIP, 550.49 loss, 0.001226 LR, 4.54 GB used, 9771.43 GFLOPS, 675.05 GOPS
738 69.15 ms run, 2.61 ms python, 66.54 ms HIP, 569.39 loss, 0.001222 LR, 4.54 GB used, 9762.12 GFLOPS, 675.05 GOPS
739 68.99 ms run, 2.69 ms python, 66.30 ms HIP, 557.04 loss, 0.001218 LR, 4.54 GB used, 9784.27 GFLOPS, 675.05 GOPS
740 69.56 ms run, 2.62 ms python, 66.94 ms HIP, 559.56 loss, 0.001213 LR, 4.54 GB used, 9704.54 GFLOPS, 675.05 GOPS
741 69.13 ms run, 2.62 ms python, 66.52 ms HIP, 551.71 loss, 0.001209 LR, 4.54 GB used, 9764.18 GFLOPS, 675.05 GOPS
742 69.37 ms run, 2.71 ms python, 66.65 ms HIP, 547.54 loss, 0.001205 LR, 4.54 GB used, 9731.62 GFLOPS, 675.05 GOPS
743 68.66 ms run, 2.63 ms python, 66.04 ms HIP, 557.30 loss, 0.001200 LR, 4.54 GB used, 9831.25 GFLOPS, 675.05 GOPS
744 69.95 ms run, 2.65 ms python, 67.31 ms HIP, 558.61 loss, 0.001196 LR, 4.54 GB used, 9649.86 GFLOPS, 675.05 GOPS
745 69.91 ms run, 2.65 ms python, 67.26 ms HIP, 550.68 loss, 0.001192 LR, 4.54 GB used, 9656.39 GFLOPS, 675.05 GOPS
746 70.53 ms run, 2.62 ms python, 67.91 ms HIP, 555.99 loss, 0.001187 LR, 4.54 GB used, 9570.85 GFLOPS, 675.05 GOPS
747 70.22 ms run, 2.75 ms python, 67.47 ms HIP, 555.76 loss, 0.001183 LR, 4.54 GB used, 9612.83 GFLOPS, 675.05 GOPS
748 69.29 ms run, 2.68 ms python, 66.61 ms HIP, 557.85 loss, 0.001178 LR, 4.54 GB used, 9742.13 GFLOPS, 675.05 GOPS
749 69.46 ms run, 2.64 ms python, 66.82 ms HIP, 562.48 loss, 0.001174 LR, 4.54 GB used, 9717.84 GFLOPS, 675.05 GOPS
750 69.44 ms run, 2.65 ms python, 66.80 ms HIP, 539.62 loss, 0.001170 LR, 4.54 GB used, 9721.07 GFLOPS, 675.05 GOPS
751 69.20 ms run, 2.60 ms python, 66.60 ms HIP, 550.59 loss, 0.001165 LR, 4.54 GB used, 9754.40 GFLOPS, 675.05 GOPS
752 68.78 ms run, 2.64 ms python, 66.14 ms HIP, 545.35 loss, 0.001161 LR, 4.54 GB used, 9814.52 GFLOPS, 675.05 GOPS
753 69.49 ms run, 2.62 ms python, 66.87 ms HIP, 562.82 loss, 0.001157 LR, 4.54 GB used, 9714.61 GFLOPS, 675.05 GOPS
754 69.30 ms run, 2.71 ms python, 66.59 ms HIP, 553.77 loss, 0.001152 LR, 4.54 GB used, 9740.49 GFLOPS, 675.05 GOPS
755 69.49 ms run, 2.70 ms python, 66.79 ms HIP, 558.77 loss, 0.001148 LR, 4.54 GB used, 9714.21 GFLOPS, 675.05 GOPS
756 69.36 ms run, 2.67 ms python, 66.69 ms HIP, 553.38 loss, 0.001144 LR, 4.54 GB used, 9732.63 GFLOPS, 675.05 GOPS
757 69.12 ms run, 2.67 ms python, 66.45 ms HIP, 558.24 loss, 0.001139 LR, 4.54 GB used, 9766.93 GFLOPS, 675.05 GOPS
758 68.50 ms run, 2.62 ms python, 65.88 ms HIP, 542.75 loss, 0.001135 LR, 4.54 GB used, 9855.19 GFLOPS, 675.05 GOPS
759 69.96 ms run, 2.65 ms python, 67.31 ms HIP, 567.17 loss, 0.001131 LR, 4.54 GB used, 9648.36 GFLOPS, 675.05 GOPS
760 68.99 ms run, 2.63 ms python, 66.36 ms HIP, 565.41 loss, 0.001126 LR, 4.54 GB used, 9784.37 GFLOPS, 675.05 GOPS
761 69.63 ms run, 2.64 ms python, 66.99 ms HIP, 561.65 loss, 0.001122 LR, 4.54 GB used, 9694.19 GFLOPS, 675.05 GOPS
762 69.51 ms run, 2.63 ms python, 66.89 ms HIP, 551.63 loss, 0.001118 LR, 4.54 GB used, 9710.90 GFLOPS, 675.05 GOPS
763 69.10 ms run, 2.74 ms python, 66.36 ms HIP, 552.97 loss, 0.001113 LR, 4.54 GB used, 9769.21 GFLOPS, 675.05 GOPS
764 69.26 ms run, 2.65 ms python, 66.62 ms HIP, 544.57 loss, 0.001109 LR, 4.54 GB used, 9745.87 GFLOPS, 675.05 GOPS
765 69.40 ms run, 2.64 ms python, 66.76 ms HIP, 545.29 loss, 0.001104 LR, 4.54 GB used, 9726.92 GFLOPS, 675.05 GOPS
766 69.41 ms run, 2.62 ms python, 66.79 ms HIP, 545.26 loss, 0.001100 LR, 4.54 GB used, 9725.45 GFLOPS, 675.05 GOPS
767 69.17 ms run, 2.62 ms python, 66.55 ms HIP, 543.88 loss, 0.001096 LR, 4.54 GB used, 9759.39 GFLOPS, 675.05 GOPS
768 69.20 ms run, 2.70 ms python, 66.51 ms HIP, 550.40 loss, 0.001091 LR, 4.54 GB used, 9754.92 GFLOPS, 675.05 GOPS
769 69.48 ms run, 2.63 ms python, 66.85 ms HIP, 551.71 loss, 0.001087 LR, 4.54 GB used, 9715.69 GFLOPS, 675.05 GOPS
770 69.94 ms run, 2.73 ms python, 67.21 ms HIP, 559.03 loss, 0.001083 LR, 4.54 GB used, 9651.86 GFLOPS, 675.05 GOPS
771 70.06 ms run, 2.63 ms python, 67.43 ms HIP, 568.44 loss, 0.001078 LR, 4.54 GB used, 9635.35 GFLOPS, 675.05 GOPS
772 69.31 ms run, 2.77 ms python, 66.54 ms HIP, 541.96 loss, 0.001074 LR, 4.54 GB used, 9740.15 GFLOPS, 675.05 GOPS
773 69.65 ms run, 2.65 ms python, 67.01 ms HIP, 550.99 loss, 0.001070 LR, 4.54 GB used, 9691.46 GFLOPS, 675.05 GOPS
774 69.71 ms run, 2.68 ms python, 67.03 ms HIP, 564.27 loss, 0.001065 LR, 4.54 GB used, 9683.71 GFLOPS, 675.05 GOPS
775 70.05 ms run, 2.71 ms python, 67.34 ms HIP, 542.48 loss, 0.001061 LR, 4.54 GB used, 9636.43 GFLOPS, 675.05 GOPS
776 69.61 ms run, 2.63 ms python, 66.98 ms HIP, 550.75 loss, 0.001057 LR, 4.54 GB used, 9697.27 GFLOPS, 675.05 GOPS
777 69.24 ms run, 2.74 ms python, 66.50 ms HIP, 538.54 loss, 0.001052 LR, 4.54 GB used, 9749.74 GFLOPS, 675.05 GOPS
778 69.04 ms run, 2.63 ms python, 66.42 ms HIP, 545.96 loss, 0.001048 LR, 4.54 GB used, 9776.91 GFLOPS, 675.05 GOPS
779 68.63 ms run, 2.69 ms python, 65.94 ms HIP, 542.59 loss, 0.001044 LR, 4.54 GB used, 9836.05 GFLOPS, 675.05 GOPS
780 69.08 ms run, 2.69 ms python, 66.39 ms HIP, 571.61 loss, 0.001039 LR, 4.54 GB used, 9771.52 GFLOPS, 675.05 GOPS
781 69.83 ms run, 2.64 ms python, 67.19 ms HIP, 547.73 loss, 0.001035 LR, 4.54 GB used, 9667.32 GFLOPS, 675.05 GOPS
782 69.63 ms run, 2.65 ms python, 66.98 ms HIP, 550.16 loss, 0.001030 LR, 4.54 GB used, 9695.20 GFLOPS, 675.05 GOPS
783 69.15 ms run, 2.69 ms python, 66.46 ms HIP, 526.68 loss, 0.001026 LR, 4.54 GB used, 9762.53 GFLOPS, 675.05 GOPS
shuffling training dataset in 1248.18 ms (epoch=8)
784 1323.85 ms run, 1251.22 ms python, 72.63 ms HIP, 546.24 loss, 0.001022 LR, 4.54 GB used, 510.34 GFLOPS, 675.61 GOPS
785 74.02 ms run, 2.81 ms python, 71.21 ms HIP, 541.10 loss, 0.001017 LR, 4.54 GB used, 9119.92 GFLOPS, 675.05 GOPS
786 72.56 ms run, 2.70 ms python, 69.86 ms HIP, 551.43 loss, 0.001013 LR, 4.54 GB used, 9303.12 GFLOPS, 675.05 GOPS
787 71.45 ms run, 2.70 ms python, 68.74 ms HIP, 547.53 loss, 0.001009 LR, 4.54 GB used, 9448.06 GFLOPS, 675.05 GOPS
788 71.17 ms run, 2.69 ms python, 68.48 ms HIP, 532.55 loss, 0.001004 LR, 4.54 GB used, 9484.84 GFLOPS, 675.05 GOPS
789 70.74 ms run, 2.63 ms python, 68.11 ms HIP, 535.94 loss, 0.001000 LR, 4.54 GB used, 9542.38 GFLOPS, 675.05 GOPS
790 69.83 ms run, 2.72 ms python, 67.11 ms HIP, 536.94 loss, 0.000996 LR, 4.54 GB used, 9666.81 GFLOPS, 675.05 GOPS
791 70.25 ms run, 2.66 ms python, 67.60 ms HIP, 529.04 loss, 0.000991 LR, 4.54 GB used, 9608.96 GFLOPS, 675.05 GOPS
792 69.84 ms run, 2.64 ms python, 67.20 ms HIP, 529.08 loss, 0.000987 LR, 4.54 GB used, 9665.85 GFLOPS, 675.05 GOPS
793 69.01 ms run, 2.63 ms python, 66.38 ms HIP, 548.76 loss, 0.000983 LR, 4.54 GB used, 9782.01 GFLOPS, 675.05 GOPS
794 69.28 ms run, 2.65 ms python, 66.62 ms HIP, 543.41 loss, 0.000978 LR, 4.54 GB used, 9743.95 GFLOPS, 675.05 GOPS
795 68.82 ms run, 2.66 ms python, 66.16 ms HIP, 543.51 loss, 0.000974 LR, 4.54 GB used, 9809.33 GFLOPS, 675.05 GOPS
796 69.00 ms run, 2.65 ms python, 66.35 ms HIP, 544.11 loss, 0.000970 LR, 4.54 GB used, 9782.78 GFLOPS, 675.05 GOPS
797 68.95 ms run, 2.67 ms python, 66.28 ms HIP, 528.20 loss, 0.000965 LR, 4.54 GB used, 9790.26 GFLOPS, 675.05 GOPS
798 69.07 ms run, 2.65 ms python, 66.42 ms HIP, 539.38 loss, 0.000961 LR, 4.54 GB used, 9773.15 GFLOPS, 675.05 GOPS
799 69.50 ms run, 2.62 ms python, 66.88 ms HIP, 544.07 loss, 0.000956 LR, 4.54 GB used, 9713.17 GFLOPS, 675.05 GOPS
800 69.56 ms run, 2.61 ms python, 66.95 ms HIP, 531.35 loss, 0.000952 LR, 4.54 GB used, 9704.85 GFLOPS, 675.05 GOPS
801 69.45 ms run, 2.59 ms python, 66.86 ms HIP, 532.82 loss, 0.000948 LR, 4.54 GB used, 9719.67 GFLOPS, 675.05 GOPS
802 68.74 ms run, 2.67 ms python, 66.07 ms HIP, 541.33 loss, 0.000943 LR, 4.54 GB used, 9820.35 GFLOPS, 675.05 GOPS
803 69.12 ms run, 2.64 ms python, 66.48 ms HIP, 528.60 loss, 0.000939 LR, 4.54 GB used, 9766.26 GFLOPS, 675.05 GOPS
804 69.52 ms run, 2.75 ms python, 66.77 ms HIP, 525.63 loss, 0.000935 LR, 4.54 GB used, 9710.05 GFLOPS, 675.05 GOPS
805 69.72 ms run, 2.62 ms python, 67.10 ms HIP, 536.51 loss, 0.000930 LR, 4.54 GB used, 9681.63 GFLOPS, 675.05 GOPS
806 69.32 ms run, 2.64 ms python, 66.68 ms HIP, 532.97 loss, 0.000926 LR, 4.54 GB used, 9737.54 GFLOPS, 675.05 GOPS
807 68.84 ms run, 2.66 ms python, 66.19 ms HIP, 532.18 loss, 0.000922 LR, 4.54 GB used, 9805.46 GFLOPS, 675.05 GOPS
808 69.55 ms run, 2.65 ms python, 66.90 ms HIP, 533.36 loss, 0.000917 LR, 4.54 GB used, 9706.15 GFLOPS, 675.05 GOPS
809 69.58 ms run, 2.70 ms python, 66.88 ms HIP, 535.91 loss, 0.000913 LR, 4.54 GB used, 9701.92 GFLOPS, 675.05 GOPS
810 69.74 ms run, 2.65 ms python, 67.09 ms HIP, 543.84 loss, 0.000909 LR, 4.54 GB used, 9678.97 GFLOPS, 675.05 GOPS
811 69.24 ms run, 2.61 ms python, 66.63 ms HIP, 547.66 loss, 0.000904 LR, 4.54 GB used, 9749.23 GFLOPS, 675.05 GOPS
812 69.11 ms run, 2.72 ms python, 66.39 ms HIP, 536.60 loss, 0.000900 LR, 4.54 GB used, 9768.01 GFLOPS, 675.05 GOPS
813 68.71 ms run, 2.64 ms python, 66.07 ms HIP, 542.15 loss, 0.000896 LR, 4.54 GB used, 9824.97 GFLOPS, 675.05 GOPS
814 69.23 ms run, 2.66 ms python, 66.57 ms HIP, 534.80 loss, 0.000891 LR, 4.54 GB used, 9750.34 GFLOPS, 675.05 GOPS
815 69.53 ms run, 2.68 ms python, 66.85 ms HIP, 531.42 loss, 0.000887 LR, 4.54 GB used, 9709.17 GFLOPS, 675.05 GOPS
816 70.02 ms run, 2.60 ms python, 67.41 ms HIP, 528.62 loss, 0.000882 LR, 4.54 GB used, 9641.35 GFLOPS, 675.05 GOPS
817 69.68 ms run, 2.64 ms python, 67.04 ms HIP, 538.65 loss, 0.000878 LR, 4.54 GB used, 9688.05 GFLOPS, 675.05 GOPS
818 69.74 ms run, 2.63 ms python, 67.10 ms HIP, 534.32 loss, 0.000874 LR, 4.54 GB used, 9680.00 GFLOPS, 675.05 GOPS
819 69.87 ms run, 2.64 ms python, 67.23 ms HIP, 528.08 loss, 0.000869 LR, 4.54 GB used, 9661.57 GFLOPS, 675.05 GOPS
820 69.37 ms run, 2.70 ms python, 66.67 ms HIP, 541.12 loss, 0.000865 LR, 4.54 GB used, 9730.47 GFLOPS, 675.05 GOPS
821 69.86 ms run, 2.64 ms python, 67.21 ms HIP, 527.85 loss, 0.000861 LR, 4.54 GB used, 9663.30 GFLOPS, 675.05 GOPS
822 69.52 ms run, 2.68 ms python, 66.84 ms HIP, 531.59 loss, 0.000856 LR, 4.54 GB used, 9710.65 GFLOPS, 675.05 GOPS
823 69.42 ms run, 2.62 ms python, 66.80 ms HIP, 538.39 loss, 0.000852 LR, 4.54 GB used, 9724.43 GFLOPS, 675.05 GOPS
824 70.23 ms run, 2.62 ms python, 67.61 ms HIP, 541.76 loss, 0.000848 LR, 4.54 GB used, 9611.38 GFLOPS, 675.05 GOPS
825 69.53 ms run, 2.68 ms python, 66.85 ms HIP, 528.67 loss, 0.000843 LR, 4.54 GB used, 9708.25 GFLOPS, 675.05 GOPS
826 68.89 ms run, 2.61 ms python, 66.28 ms HIP, 535.09 loss, 0.000839 LR, 4.54 GB used, 9798.49 GFLOPS, 675.05 GOPS
827 69.24 ms run, 2.68 ms python, 66.56 ms HIP, 533.99 loss, 0.000835 LR, 4.54 GB used, 9749.74 GFLOPS, 675.05 GOPS
828 69.66 ms run, 2.64 ms python, 67.02 ms HIP, 530.62 loss, 0.000830 LR, 4.54 GB used, 9690.68 GFLOPS, 675.05 GOPS
829 69.72 ms run, 2.66 ms python, 67.06 ms HIP, 537.17 loss, 0.000826 LR, 4.54 GB used, 9682.39 GFLOPS, 675.05 GOPS
830 69.32 ms run, 2.66 ms python, 66.66 ms HIP, 538.48 loss, 0.000822 LR, 4.54 GB used, 9737.89 GFLOPS, 675.05 GOPS
831 69.51 ms run, 2.63 ms python, 66.88 ms HIP, 535.70 loss, 0.000817 LR, 4.54 GB used, 9710.83 GFLOPS, 675.05 GOPS
832 69.41 ms run, 2.62 ms python, 66.79 ms HIP, 525.67 loss, 0.000813 LR, 4.54 GB used, 9725.40 GFLOPS, 675.05 GOPS
833 69.50 ms run, 2.67 ms python, 66.83 ms HIP, 532.23 loss, 0.000808 LR, 4.54 GB used, 9712.42 GFLOPS, 675.05 GOPS
834 69.33 ms run, 2.63 ms python, 66.69 ms HIP, 528.42 loss, 0.000804 LR, 4.54 GB used, 9737.16 GFLOPS, 675.05 GOPS
835 69.31 ms run, 2.68 ms python, 66.63 ms HIP, 535.32 loss, 0.000800 LR, 4.54 GB used, 9739.56 GFLOPS, 675.05 GOPS
836 69.73 ms run, 2.67 ms python, 67.07 ms HIP, 536.20 loss, 0.000795 LR, 4.54 GB used, 9680.20 GFLOPS, 675.05 GOPS
837 69.43 ms run, 2.64 ms python, 66.78 ms HIP, 538.89 loss, 0.000791 LR, 4.54 GB used, 9723.36 GFLOPS, 675.05 GOPS
838 69.13 ms run, 2.61 ms python, 66.52 ms HIP, 535.20 loss, 0.000787 LR, 4.54 GB used, 9764.50 GFLOPS, 675.05 GOPS
839 69.10 ms run, 2.60 ms python, 66.50 ms HIP, 533.34 loss, 0.000782 LR, 4.54 GB used, 9768.46 GFLOPS, 675.05 GOPS
840 68.19 ms run, 2.62 ms python, 65.57 ms HIP, 539.02 loss, 0.000778 LR, 4.54 GB used, 9899.76 GFLOPS, 675.05 GOPS
841 69.23 ms run, 2.65 ms python, 66.57 ms HIP, 536.14 loss, 0.000774 LR, 4.54 GB used, 9751.34 GFLOPS, 675.05 GOPS
842 69.47 ms run, 2.65 ms python, 66.83 ms HIP, 534.74 loss, 0.000769 LR, 4.54 GB used, 9716.57 GFLOPS, 675.05 GOPS
843 69.61 ms run, 2.62 ms python, 67.00 ms HIP, 539.64 loss, 0.000765 LR, 4.54 GB used, 9697.05 GFLOPS, 675.05 GOPS
844 69.04 ms run, 2.64 ms python, 66.40 ms HIP, 530.05 loss, 0.000761 LR, 4.54 GB used, 9777.32 GFLOPS, 675.05 GOPS
845 69.49 ms run, 2.65 ms python, 66.83 ms HIP, 536.00 loss, 0.000756 LR, 4.54 GB used, 9714.84 GFLOPS, 675.05 GOPS
846 69.04 ms run, 2.70 ms python, 66.34 ms HIP, 534.40 loss, 0.000752 LR, 4.54 GB used, 9777.87 GFLOPS, 675.05 GOPS
847 69.78 ms run, 2.63 ms python, 67.15 ms HIP, 536.08 loss, 0.000748 LR, 4.54 GB used, 9673.98 GFLOPS, 675.05 GOPS
848 68.62 ms run, 2.61 ms python, 66.02 ms HIP, 541.98 loss, 0.000743 LR, 4.54 GB used, 9836.97 GFLOPS, 675.05 GOPS
849 69.78 ms run, 2.60 ms python, 67.19 ms HIP, 526.80 loss, 0.000739 LR, 4.54 GB used, 9673.56 GFLOPS, 675.05 GOPS
850 69.34 ms run, 2.64 ms python, 66.70 ms HIP, 531.86 loss, 0.000734 LR, 4.54 GB used, 9735.31 GFLOPS, 675.05 GOPS
851 69.14 ms run, 2.65 ms python, 66.49 ms HIP, 534.87 loss, 0.000730 LR, 4.54 GB used, 9764.06 GFLOPS, 675.05 GOPS
852 69.53 ms run, 2.63 ms python, 66.90 ms HIP, 523.91 loss, 0.000726 LR, 4.54 GB used, 9709.10 GFLOPS, 675.05 GOPS
853 69.87 ms run, 3.20 ms python, 66.67 ms HIP, 535.79 loss, 0.000721 LR, 4.54 GB used, 9660.99 GFLOPS, 675.05 GOPS
854 69.08 ms run, 2.67 ms python, 66.42 ms HIP, 529.02 loss, 0.000717 LR, 4.54 GB used, 9771.59 GFLOPS, 675.05 GOPS
855 68.56 ms run, 2.65 ms python, 65.90 ms HIP, 525.92 loss, 0.000713 LR, 4.54 GB used, 9846.09 GFLOPS, 675.05 GOPS
856 69.18 ms run, 2.64 ms python, 66.54 ms HIP, 536.56 loss, 0.000708 LR, 4.54 GB used, 9757.63 GFLOPS, 675.05 GOPS
857 69.45 ms run, 2.70 ms python, 66.75 ms HIP, 520.37 loss, 0.000704 LR, 4.54 GB used, 9719.67 GFLOPS, 675.05 GOPS
858 69.41 ms run, 2.64 ms python, 66.77 ms HIP, 531.96 loss, 0.000700 LR, 4.54 GB used, 9725.64 GFLOPS, 675.05 GOPS
859 70.08 ms run, 2.66 ms python, 67.41 ms HIP, 540.40 loss, 0.000695 LR, 4.54 GB used, 9632.94 GFLOPS, 675.05 GOPS
860 69.25 ms run, 2.65 ms python, 66.60 ms HIP, 535.21 loss, 0.000691 LR, 4.54 GB used, 9747.91 GFLOPS, 675.05 GOPS
861 69.53 ms run, 2.64 ms python, 66.90 ms HIP, 538.28 loss, 0.000687 LR, 4.54 GB used, 9708.03 GFLOPS, 675.05 GOPS
862 69.53 ms run, 2.63 ms python, 66.90 ms HIP, 523.75 loss, 0.000682 LR, 4.54 GB used, 9708.76 GFLOPS, 675.05 GOPS
863 69.10 ms run, 2.63 ms python, 66.47 ms HIP, 540.04 loss, 0.000678 LR, 4.54 GB used, 9768.97 GFLOPS, 675.05 GOPS
864 69.50 ms run, 2.65 ms python, 66.86 ms HIP, 532.45 loss, 0.000674 LR, 4.54 GB used, 9712.18 GFLOPS, 675.05 GOPS
865 69.51 ms run, 2.71 ms python, 66.80 ms HIP, 535.42 loss, 0.000669 LR, 4.54 GB used, 9710.89 GFLOPS, 675.05 GOPS
866 68.93 ms run, 2.71 ms python, 66.22 ms HIP, 530.29 loss, 0.000665 LR, 4.54 GB used, 9793.27 GFLOPS, 675.05 GOPS
867 69.27 ms run, 2.68 ms python, 66.59 ms HIP, 513.62 loss, 0.000660 LR, 4.54 GB used, 9744.63 GFLOPS, 675.05 GOPS
868 69.18 ms run, 2.59 ms python, 66.58 ms HIP, 533.72 loss, 0.000656 LR, 4.54 GB used, 9758.46 GFLOPS, 675.05 GOPS
869 69.22 ms run, 2.64 ms python, 66.58 ms HIP, 520.23 loss, 0.000652 LR, 4.54 GB used, 9752.79 GFLOPS, 675.05 GOPS
870 69.57 ms run, 2.63 ms python, 66.94 ms HIP, 531.70 loss, 0.000647 LR, 4.54 GB used, 9702.61 GFLOPS, 675.05 GOPS
871 69.55 ms run, 2.68 ms python, 66.87 ms HIP, 540.77 loss, 0.000643 LR, 4.54 GB used, 9705.38 GFLOPS, 675.05 GOPS
872 69.33 ms run, 2.63 ms python, 66.70 ms HIP, 533.11 loss, 0.000639 LR, 4.54 GB used, 9736.47 GFLOPS, 675.05 GOPS
873 69.83 ms run, 2.65 ms python, 67.19 ms HIP, 527.30 loss, 0.000634 LR, 4.54 GB used, 9666.60 GFLOPS, 675.05 GOPS
874 69.72 ms run, 2.68 ms python, 67.03 ms HIP, 537.70 loss, 0.000630 LR, 4.54 GB used, 9682.89 GFLOPS, 675.05 GOPS
875 69.59 ms run, 2.64 ms python, 66.95 ms HIP, 547.15 loss, 0.000626 LR, 4.54 GB used, 9700.12 GFLOPS, 675.05 GOPS
876 69.41 ms run, 2.65 ms python, 66.77 ms HIP, 532.26 loss, 0.000621 LR, 4.54 GB used, 9725.11 GFLOPS, 675.05 GOPS
877 69.81 ms run, 2.61 ms python, 67.20 ms HIP, 546.89 loss, 0.000617 LR, 4.54 GB used, 9669.47 GFLOPS, 675.05 GOPS
878 69.05 ms run, 2.61 ms python, 66.43 ms HIP, 526.37 loss, 0.000613 LR, 4.54 GB used, 9776.26 GFLOPS, 675.05 GOPS
879 69.45 ms run, 2.61 ms python, 66.84 ms HIP, 537.78 loss, 0.000608 LR, 4.54 GB used, 9720.34 GFLOPS, 675.05 GOPS
880 69.33 ms run, 2.68 ms python, 66.64 ms HIP, 532.12 loss, 0.000604 LR, 4.54 GB used, 9737.14 GFLOPS, 675.05 GOPS
881 69.40 ms run, 2.69 ms python, 66.71 ms HIP, 512.16 loss, 0.000600 LR, 4.54 GB used, 9727.35 GFLOPS, 675.05 GOPS
shuffling training dataset in 1159.44 ms (epoch=9)
882 1235.03 ms run, 1162.57 ms python, 72.46 ms HIP, 520.87 loss, 0.000595 LR, 4.54 GB used, 547.04 GFLOPS, 675.61 GOPS
883 74.16 ms run, 2.88 ms python, 71.29 ms HIP, 516.05 loss, 0.000591 LR, 4.54 GB used, 9102.24 GFLOPS, 675.05 GOPS
884 72.66 ms run, 2.72 ms python, 69.94 ms HIP, 525.34 loss, 0.000586 LR, 4.54 GB used, 9291.01 GFLOPS, 675.05 GOPS
885 71.26 ms run, 2.67 ms python, 68.59 ms HIP, 506.30 loss, 0.000582 LR, 4.54 GB used, 9472.61 GFLOPS, 675.05 GOPS
886 70.69 ms run, 2.71 ms python, 67.98 ms HIP, 511.95 loss, 0.000578 LR, 4.54 GB used, 9550.00 GFLOPS, 675.05 GOPS
887 70.02 ms run, 2.62 ms python, 67.40 ms HIP, 516.78 loss, 0.000573 LR, 4.54 GB used, 9640.87 GFLOPS, 675.05 GOPS
888 69.19 ms run, 2.66 ms python, 66.53 ms HIP, 515.21 loss, 0.000569 LR, 4.54 GB used, 9757.04 GFLOPS, 675.05 GOPS
889 69.68 ms run, 2.62 ms python, 67.06 ms HIP, 517.03 loss, 0.000565 LR, 4.54 GB used, 9687.99 GFLOPS, 675.05 GOPS
890 69.62 ms run, 2.63 ms python, 66.99 ms HIP, 507.96 loss, 0.000560 LR, 4.54 GB used, 9696.45 GFLOPS, 675.05 GOPS
891 69.55 ms run, 2.64 ms python, 66.91 ms HIP, 515.07 loss, 0.000556 LR, 4.54 GB used, 9705.92 GFLOPS, 675.05 GOPS
892 69.32 ms run, 2.63 ms python, 66.69 ms HIP, 521.80 loss, 0.000552 LR, 4.54 GB used, 9738.24 GFLOPS, 675.05 GOPS
893 69.32 ms run, 2.63 ms python, 66.69 ms HIP, 524.90 loss, 0.000547 LR, 4.54 GB used, 9737.80 GFLOPS, 675.05 GOPS
894 69.05 ms run, 2.63 ms python, 66.42 ms HIP, 518.19 loss, 0.000543 LR, 4.54 GB used, 9776.12 GFLOPS, 675.05 GOPS
895 68.94 ms run, 2.61 ms python, 66.34 ms HIP, 514.16 loss, 0.000539 LR, 4.54 GB used, 9791.23 GFLOPS, 675.05 GOPS
896 69.71 ms run, 2.61 ms python, 67.09 ms HIP, 514.89 loss, 0.000534 LR, 4.54 GB used, 9683.90 GFLOPS, 675.05 GOPS
897 69.34 ms run, 2.65 ms python, 66.69 ms HIP, 511.81 loss, 0.000530 LR, 4.54 GB used, 9734.89 GFLOPS, 675.05 GOPS
898 69.24 ms run, 2.64 ms python, 66.61 ms HIP, 512.98 loss, 0.000526 LR, 4.54 GB used, 9749.11 GFLOPS, 675.05 GOPS
899 69.35 ms run, 2.63 ms python, 66.72 ms HIP, 508.40 loss, 0.000521 LR, 4.54 GB used, 9734.27 GFLOPS, 675.05 GOPS
900 68.77 ms run, 2.63 ms python, 66.14 ms HIP, 511.02 loss, 0.000517 LR, 4.54 GB used, 9815.58 GFLOPS, 675.05 GOPS
901 69.17 ms run, 2.64 ms python, 66.53 ms HIP, 515.47 loss, 0.000513 LR, 4.54 GB used, 9759.06 GFLOPS, 675.05 GOPS
902 69.13 ms run, 2.64 ms python, 66.49 ms HIP, 514.25 loss, 0.000508 LR, 4.54 GB used, 9764.42 GFLOPS, 675.05 GOPS
903 69.23 ms run, 2.74 ms python, 66.49 ms HIP, 514.87 loss, 0.000504 LR, 4.54 GB used, 9751.23 GFLOPS, 675.05 GOPS
904 69.54 ms run, 2.67 ms python, 66.87 ms HIP, 510.42 loss, 0.000499 LR, 4.54 GB used, 9707.15 GFLOPS, 675.05 GOPS
905 68.88 ms run, 2.63 ms python, 66.25 ms HIP, 511.87 loss, 0.000495 LR, 4.54 GB used, 9800.72 GFLOPS, 675.05 GOPS
906 68.98 ms run, 2.66 ms python, 66.32 ms HIP, 508.94 loss, 0.000491 LR, 4.54 GB used, 9785.88 GFLOPS, 675.05 GOPS
907 68.82 ms run, 2.65 ms python, 66.18 ms HIP, 519.92 loss, 0.000486 LR, 4.54 GB used, 9808.29 GFLOPS, 675.05 GOPS
908 68.83 ms run, 2.69 ms python, 66.14 ms HIP, 507.73 loss, 0.000482 LR, 4.54 GB used, 9806.73 GFLOPS, 675.05 GOPS
909 69.73 ms run, 2.63 ms python, 67.10 ms HIP, 518.91 loss, 0.000478 LR, 4.54 GB used, 9680.49 GFLOPS, 675.05 GOPS
910 69.38 ms run, 2.64 ms python, 66.74 ms HIP, 520.09 loss, 0.000473 LR, 4.54 GB used, 9729.58 GFLOPS, 675.05 GOPS
911 69.32 ms run, 2.66 ms python, 66.66 ms HIP, 520.63 loss, 0.000469 LR, 4.54 GB used, 9738.13 GFLOPS, 675.05 GOPS
912 69.55 ms run, 2.62 ms python, 66.93 ms HIP, 501.50 loss, 0.000465 LR, 4.54 GB used, 9705.62 GFLOPS, 675.05 GOPS
913 68.85 ms run, 2.63 ms python, 66.22 ms HIP, 515.14 loss, 0.000460 LR, 4.54 GB used, 9804.22 GFLOPS, 675.05 GOPS
914 69.07 ms run, 2.64 ms python, 66.43 ms HIP, 515.34 loss, 0.000456 LR, 4.54 GB used, 9773.31 GFLOPS, 675.05 GOPS
915 69.08 ms run, 2.66 ms python, 66.42 ms HIP, 515.66 loss, 0.000452 LR, 4.54 GB used, 9771.55 GFLOPS, 675.05 GOPS
916 69.16 ms run, 2.69 ms python, 66.47 ms HIP, 522.36 loss, 0.000447 LR, 4.54 GB used, 9760.84 GFLOPS, 675.05 GOPS
917 69.71 ms run, 2.60 ms python, 67.11 ms HIP, 514.66 loss, 0.000443 LR, 4.54 GB used, 9683.72 GFLOPS, 675.05 GOPS
918 69.54 ms run, 2.64 ms python, 66.90 ms HIP, 515.18 loss, 0.000439 LR, 4.54 GB used, 9707.37 GFLOPS, 675.05 GOPS
919 69.04 ms run, 2.63 ms python, 66.41 ms HIP, 515.85 loss, 0.000434 LR, 4.54 GB used, 9777.36 GFLOPS, 675.05 GOPS
920 69.66 ms run, 2.64 ms python, 67.02 ms HIP, 509.09 loss, 0.000430 LR, 4.54 GB used, 9689.92 GFLOPS, 675.05 GOPS
921 69.06 ms run, 2.60 ms python, 66.46 ms HIP, 507.59 loss, 0.000425 LR, 4.54 GB used, 9775.43 GFLOPS, 675.05 GOPS
922 69.37 ms run, 2.62 ms python, 66.75 ms HIP, 513.96 loss, 0.000421 LR, 4.54 GB used, 9730.39 GFLOPS, 675.05 GOPS
923 69.24 ms run, 2.59 ms python, 66.65 ms HIP, 509.86 loss, 0.000417 LR, 4.54 GB used, 9749.35 GFLOPS, 675.05 GOPS
924 69.20 ms run, 2.63 ms python, 66.57 ms HIP, 516.51 loss, 0.000412 LR, 4.54 GB used, 9755.15 GFLOPS, 675.05 GOPS
925 69.61 ms run, 2.61 ms python, 67.00 ms HIP, 504.50 loss, 0.000408 LR, 4.54 GB used, 9698.17 GFLOPS, 675.05 GOPS
926 68.96 ms run, 2.66 ms python, 66.30 ms HIP, 512.93 loss, 0.000404 LR, 4.54 GB used, 9789.02 GFLOPS, 675.05 GOPS
927 69.25 ms run, 2.63 ms python, 66.61 ms HIP, 510.84 loss, 0.000399 LR, 4.54 GB used, 9748.62 GFLOPS, 675.05 GOPS
928 69.52 ms run, 2.63 ms python, 66.90 ms HIP, 522.02 loss, 0.000395 LR, 4.54 GB used, 9709.53 GFLOPS, 675.05 GOPS
929 69.28 ms run, 2.62 ms python, 66.66 ms HIP, 512.55 loss, 0.000391 LR, 4.54 GB used, 9743.84 GFLOPS, 675.05 GOPS
930 69.60 ms run, 2.64 ms python, 66.96 ms HIP, 503.71 loss, 0.000386 LR, 4.54 GB used, 9699.44 GFLOPS, 675.05 GOPS
931 69.42 ms run, 2.67 ms python, 66.75 ms HIP, 509.71 loss, 0.000382 LR, 4.54 GB used, 9723.59 GFLOPS, 675.05 GOPS
932 69.20 ms run, 2.63 ms python, 66.57 ms HIP, 506.57 loss, 0.000378 LR, 4.54 GB used, 9754.58 GFLOPS, 675.05 GOPS
933 69.03 ms run, 2.68 ms python, 66.35 ms HIP, 509.72 loss, 0.000373 LR, 4.54 GB used, 9779.67 GFLOPS, 675.05 GOPS
934 68.65 ms run, 2.66 ms python, 65.99 ms HIP, 519.52 loss, 0.000369 LR, 4.54 GB used, 9832.97 GFLOPS, 675.05 GOPS
935 69.10 ms run, 2.63 ms python, 66.47 ms HIP, 523.69 loss, 0.000365 LR, 4.54 GB used, 9769.73 GFLOPS, 675.05 GOPS
936 68.79 ms run, 2.62 ms python, 66.16 ms HIP, 515.27 loss, 0.000360 LR, 4.54 GB used, 9813.41 GFLOPS, 675.05 GOPS
937 70.71 ms run, 2.65 ms python, 68.06 ms HIP, 518.81 loss, 0.000356 LR, 4.54 GB used, 9546.80 GFLOPS, 675.05 GOPS
938 69.36 ms run, 2.64 ms python, 66.71 ms HIP, 504.38 loss, 0.000351 LR, 4.54 GB used, 9733.15 GFLOPS, 675.05 GOPS
939 69.48 ms run, 2.64 ms python, 66.84 ms HIP, 507.79 loss, 0.000347 LR, 4.54 GB used, 9715.61 GFLOPS, 675.05 GOPS
940 68.71 ms run, 2.69 ms python, 66.02 ms HIP, 514.01 loss, 0.000343 LR, 4.54 GB used, 9824.71 GFLOPS, 675.05 GOPS
941 69.19 ms run, 2.64 ms python, 66.55 ms HIP, 507.03 loss, 0.000338 LR, 4.54 GB used, 9756.46 GFLOPS, 675.05 GOPS
942 69.27 ms run, 2.67 ms python, 66.60 ms HIP, 506.73 loss, 0.000334 LR, 4.54 GB used, 9744.75 GFLOPS, 675.05 GOPS
943 69.16 ms run, 2.65 ms python, 66.52 ms HIP, 524.35 loss, 0.000330 LR, 4.54 GB used, 9760.49 GFLOPS, 675.05 GOPS
944 68.93 ms run, 2.62 ms python, 66.31 ms HIP, 501.72 loss, 0.000325 LR, 4.54 GB used, 9793.02 GFLOPS, 675.05 GOPS
945 69.79 ms run, 2.62 ms python, 67.17 ms HIP, 522.89 loss, 0.000321 LR, 4.54 GB used, 9672.81 GFLOPS, 675.05 GOPS
946 69.44 ms run, 2.69 ms python, 66.75 ms HIP, 500.60 loss, 0.000317 LR, 4.54 GB used, 9720.65 GFLOPS, 675.05 GOPS
947 70.38 ms run, 2.59 ms python, 67.79 ms HIP, 526.98 loss, 0.000312 LR, 4.54 GB used, 9591.57 GFLOPS, 675.05 GOPS
948 69.53 ms run, 2.66 ms python, 66.87 ms HIP, 509.99 loss, 0.000308 LR, 4.54 GB used, 9708.92 GFLOPS, 675.05 GOPS
949 69.39 ms run, 2.60 ms python, 66.78 ms HIP, 507.01 loss, 0.000304 LR, 4.54 GB used, 9728.96 GFLOPS, 675.05 GOPS
950 69.55 ms run, 2.61 ms python, 66.94 ms HIP, 510.29 loss, 0.000299 LR, 4.54 GB used, 9705.98 GFLOPS, 675.05 GOPS
951 69.38 ms run, 2.69 ms python, 66.69 ms HIP, 508.08 loss, 0.000295 LR, 4.54 GB used, 9729.52 GFLOPS, 675.05 GOPS
952 69.67 ms run, 2.60 ms python, 67.07 ms HIP, 511.04 loss, 0.000291 LR, 4.54 GB used, 9688.63 GFLOPS, 675.05 GOPS
953 68.88 ms run, 2.66 ms python, 66.22 ms HIP, 506.13 loss, 0.000286 LR, 4.54 GB used, 9800.60 GFLOPS, 675.05 GOPS
954 69.86 ms run, 2.64 ms python, 67.22 ms HIP, 503.12 loss, 0.000282 LR, 4.54 GB used, 9662.69 GFLOPS, 675.05 GOPS
955 70.15 ms run, 2.67 ms python, 67.48 ms HIP, 509.37 loss, 0.000277 LR, 4.54 GB used, 9622.85 GFLOPS, 675.05 GOPS
956 68.98 ms run, 2.67 ms python, 66.31 ms HIP, 514.90 loss, 0.000273 LR, 4.54 GB used, 9786.24 GFLOPS, 675.05 GOPS
957 69.87 ms run, 2.61 ms python, 67.26 ms HIP, 508.61 loss, 0.000269 LR, 4.54 GB used, 9661.85 GFLOPS, 675.05 GOPS
958 69.52 ms run, 2.64 ms python, 66.88 ms HIP, 506.84 loss, 0.000264 LR, 4.54 GB used, 9710.24 GFLOPS, 675.05 GOPS
959 69.13 ms run, 2.62 ms python, 66.50 ms HIP, 513.77 loss, 0.000260 LR, 4.54 GB used, 9765.39 GFLOPS, 675.05 GOPS
960 69.53 ms run, 2.62 ms python, 66.91 ms HIP, 512.25 loss, 0.000256 LR, 4.54 GB used, 9709.08 GFLOPS, 675.05 GOPS
961 69.65 ms run, 2.64 ms python, 67.01 ms HIP, 516.46 loss, 0.000251 LR, 4.54 GB used, 9692.63 GFLOPS, 675.05 GOPS
962 69.41 ms run, 2.64 ms python, 66.77 ms HIP, 513.07 loss, 0.000247 LR, 4.54 GB used, 9724.91 GFLOPS, 675.05 GOPS
963 69.36 ms run, 2.62 ms python, 66.74 ms HIP, 499.24 loss, 0.000243 LR, 4.54 GB used, 9732.38 GFLOPS, 675.05 GOPS
964 69.86 ms run, 2.68 ms python, 67.18 ms HIP, 508.07 loss, 0.000238 LR, 4.54 GB used, 9663.18 GFLOPS, 675.05 GOPS
965 69.71 ms run, 2.64 ms python, 67.07 ms HIP, 503.10 loss, 0.000234 LR, 4.54 GB used, 9683.32 GFLOPS, 675.05 GOPS
966 69.43 ms run, 2.64 ms python, 66.79 ms HIP, 503.75 loss, 0.000230 LR, 4.54 GB used, 9722.44 GFLOPS, 675.05 GOPS
967 69.82 ms run, 2.65 ms python, 67.17 ms HIP, 507.46 loss, 0.000225 LR, 4.54 GB used, 9668.10 GFLOPS, 675.05 GOPS
968 69.49 ms run, 2.62 ms python, 66.87 ms HIP, 501.20 loss, 0.000221 LR, 4.54 GB used, 9713.78 GFLOPS, 675.05 GOPS
969 69.42 ms run, 2.63 ms python, 66.79 ms HIP, 518.10 loss, 0.000217 LR, 4.54 GB used, 9723.82 GFLOPS, 675.05 GOPS
970 69.72 ms run, 2.63 ms python, 67.08 ms HIP, 504.68 loss, 0.000212 LR, 4.54 GB used, 9682.81 GFLOPS, 675.05 GOPS
971 69.56 ms run, 2.69 ms python, 66.87 ms HIP, 518.01 loss, 0.000208 LR, 4.54 GB used, 9704.35 GFLOPS, 675.05 GOPS
972 69.55 ms run, 2.61 ms python, 66.94 ms HIP, 508.69 loss, 0.000203 LR, 4.54 GB used, 9706.43 GFLOPS, 675.05 GOPS
973 69.74 ms run, 2.67 ms python, 67.07 ms HIP, 503.42 loss, 0.000199 LR, 4.54 GB used, 9680.06 GFLOPS, 675.05 GOPS
974 69.33 ms run, 2.68 ms python, 66.65 ms HIP, 507.29 loss, 0.000195 LR, 4.54 GB used, 9736.17 GFLOPS, 675.05 GOPS
975 69.80 ms run, 2.61 ms python, 67.18 ms HIP, 498.26 loss, 0.000190 LR, 4.54 GB used, 9671.72 GFLOPS, 675.05 GOPS
976 69.22 ms run, 2.63 ms python, 66.59 ms HIP, 512.58 loss, 0.000186 LR, 4.54 GB used, 9752.85 GFLOPS, 675.05 GOPS
977 69.06 ms run, 2.63 ms python, 66.44 ms HIP, 513.81 loss, 0.000182 LR, 4.54 GB used, 9774.40 GFLOPS, 675.05 GOPS
978 68.90 ms run, 2.62 ms python, 66.28 ms HIP, 501.11 loss, 0.000177 LR, 4.54 GB used, 9797.71 GFLOPS, 675.05 GOPS
979 69.44 ms run, 2.63 ms python, 66.81 ms HIP, 508.49 loss, 0.000173 LR, 4.54 GB used, 9720.73 GFLOPS, 675.05 GOPS
shuffling training dataset in 1154.76 ms (epoch=10)
980 1230.07 ms run, 1157.80 ms python, 72.27 ms HIP, 496.13 loss, 0.000169 LR, 4.54 GB used, 549.25 GFLOPS, 675.61 GOPS
981 73.90 ms run, 2.78 ms python, 71.13 ms HIP, 495.23 loss, 0.000164 LR, 4.54 GB used, 9134.13 GFLOPS, 675.05 GOPS
982 72.39 ms run, 2.67 ms python, 69.72 ms HIP, 496.88 loss, 0.000160 LR, 4.54 GB used, 9325.09 GFLOPS, 675.05 GOPS
983 71.53 ms run, 2.73 ms python, 68.80 ms HIP, 490.28 loss, 0.000156 LR, 4.54 GB used, 9436.97 GFLOPS, 675.05 GOPS
984 70.56 ms run, 2.68 ms python, 67.88 ms HIP, 489.54 loss, 0.000151 LR, 4.54 GB used, 9567.28 GFLOPS, 675.05 GOPS
985 69.59 ms run, 2.66 ms python, 66.92 ms HIP, 495.99 loss, 0.000147 LR, 4.54 GB used, 9700.61 GFLOPS, 675.05 GOPS
986 70.18 ms run, 2.67 ms python, 67.51 ms HIP, 503.37 loss, 0.000143 LR, 4.54 GB used, 9619.28 GFLOPS, 675.05 GOPS
987 69.76 ms run, 2.73 ms python, 67.02 ms HIP, 495.15 loss, 0.000138 LR, 4.54 GB used, 9677.25 GFLOPS, 675.05 GOPS
988 69.16 ms run, 2.78 ms python, 66.38 ms HIP, 501.15 loss, 0.000134 LR, 4.54 GB used, 9760.82 GFLOPS, 675.05 GOPS
989 70.33 ms run, 2.66 ms python, 67.67 ms HIP, 500.14 loss, 0.000129 LR, 4.54 GB used, 9598.59 GFLOPS, 675.05 GOPS
990 69.20 ms run, 2.63 ms python, 66.56 ms HIP, 496.23 loss, 0.000125 LR, 4.54 GB used, 9755.47 GFLOPS, 675.05 GOPS
991 68.96 ms run, 2.66 ms python, 66.29 ms HIP, 500.80 loss, 0.000121 LR, 4.54 GB used, 9789.60 GFLOPS, 675.05 GOPS
992 69.12 ms run, 2.67 ms python, 66.45 ms HIP, 497.86 loss, 0.000116 LR, 4.54 GB used, 9766.96 GFLOPS, 675.05 GOPS
993 69.31 ms run, 2.69 ms python, 66.62 ms HIP, 498.34 loss, 0.000112 LR, 4.54 GB used, 9739.42 GFLOPS, 675.05 GOPS
994 70.40 ms run, 2.62 ms python, 67.78 ms HIP, 495.24 loss, 0.000108 LR, 4.54 GB used, 9588.55 GFLOPS, 675.05 GOPS
995 70.55 ms run, 2.61 ms python, 67.93 ms HIP, 494.52 loss, 0.000103 LR, 4.54 GB used, 9568.90 GFLOPS, 675.05 GOPS
996 69.80 ms run, 2.63 ms python, 67.18 ms HIP, 494.78 loss, 0.000099 LR, 4.54 GB used, 9670.67 GFLOPS, 675.05 GOPS
997 69.70 ms run, 2.69 ms python, 67.01 ms HIP, 494.93 loss, 0.000095 LR, 4.54 GB used, 9684.90 GFLOPS, 675.05 GOPS
998 69.11 ms run, 2.65 ms python, 66.46 ms HIP, 497.52 loss, 0.000090 LR, 4.54 GB used, 9768.35 GFLOPS, 675.05 GOPS
999 69.46 ms run, 2.65 ms python, 66.81 ms HIP, 491.65 loss, 0.000086 LR, 4.54 GB used, 9718.04 GFLOPS, 675.05 GOPS
shuffling test dataset in 185.55 ms (epoch=0)
eval 9616/10240 93.91%, 0.40 val_loss STEP=1000 (in 1416.17 ms)
llama.py
using HIP backend
using LLaMA-7B model
Traceback (most recent call last):
File "/home/jebba/devel/tinygrad/tinygrad/examples/llama.py", line 386, in <module>
llama = LLaMa.build(MODEL_PATH, TOKENIZER_PATH, model_gen=args.gen, model_size=args.size, quantize=args.quantize, device=device)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/examples/llama.py", line 155, in build
sp_model = SentencePieceProcessor(model_file=str(tokenizer_path))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/venv/lib/python3.11/site-packages/sentencepiece/__init__.py", line 447, in Init
self.Load(model_file=model_file, model_proto=model_proto)
File "/home/jebba/devel/tinygrad/tinygrad/venv/lib/python3.11/site-packages/sentencepiece/__init__.py", line 905, in Load
return self.LoadFromFile(model_file)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/venv/lib/python3.11/site-packages/sentencepiece/__init__.py", line 310, in LoadFromFile
return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
OSError: Not found: "/home/jebba/devel/tinygrad/tinygrad/weights/LLaMA/tokenizer.model": No such file or directory Error #2
mask_rcnn.py
Traceback (most recent call last):
File "/home/jebba/devel/tinygrad/tinygrad/venv/lib/python3.11/site-packages/PIL/Image.py", line 3135, in open
fp.seek(0)
^^^^^^^
AttributeError: 'NoneType' object has no attribute 'seek'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/jebba/devel/tinygrad/tinygrad/examples/mask_rcnn.py", line 290, in <module>
img = Image.open(args.image)
^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/venv/lib/python3.11/site-packages/PIL/Image.py", line 3137, in open
fp = io.BytesIO(fp.read())
^^^^^^^
AttributeError: 'NoneType' object has no attribute 'read'
mixtral.py
Traceback (most recent call last):
File "/home/jebba/devel/tinygrad/tinygrad/examples/mixtral.py", line 33, in <module>
state = torch_load(args.weights + "/consolidated.00.pth.b")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/tinygrad/nn/state.py", line 77, in torch_load
t = Tensor.empty(os.stat(fn).st_size, dtype=dtypes.uint8, device=f"disk:{fn}")
^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: '/home/jebba/devel/tinygrad/tinygrad/weights/mixtral-8x7b-32kseqlen/consolidated.00.pth.b'
mnist_gan.py
0%| | 0/300 [00:00<?, ?it/s]
Generator loss: 1.392429107471424, Discriminator loss: 1.2234591077876222: 0%| | 0/300 [00:52<?, ?it/s]
Generator loss: 1.392429107471424, Discriminator loss: 1.2234591077876222: 0%| | 1/300 [00:52<4:23:15, 52.83s/it]
Generator loss: 0.742053180713864, Discriminator loss: 1.3610961077844395: 0%| | 1/300 [01:10<4:23:15, 52.83s/it]
Generator loss: 0.742053180713864, Discriminator loss: 1.3610961077844395: 1%| | 2/300 [01:10<2:39:21, 32.09s/it]
Generator loss: 0.7467478732852375, Discriminator loss: 1.3732893361764795: 1%| | 2/300 [01:28<2:39:21, 32.09s/it]
Generator loss: 0.7467478732852375, Discriminator loss: 1.3732893361764795: 1%| | 3/300 [01:28<2:06:44, 25.61s/it]
Generator loss: 0.7906162344357547, Discriminator loss: 1.280748259057017: 1%| | 3/300 [01:45<2:06:44, 25.61s/it]
Generator loss: 0.7906162344357547, Discriminator loss: 1.280748259057017: 1%|▏ | 4/300 [01:45<1:49:47, 22.25s/it]
Generator loss: 1.3312474861741066, Discriminator loss: 1.0106008916216738: 1%|▏ | 4/300 [01:56<1:49:47, 22.25s/it]
Generator loss: 1.3312474861741066, Discriminator loss: 1.0106008916216738: 2%|▏ | 5/300 [01:56<1:28:50, 18.07s/it]
Generator loss: 1.783903177608462, Discriminator loss: 0.7816843103398295: 2%|▏ | 5/300 [02:05<1:28:50, 18.07s/it]
Generator loss: 1.783903177608462, Discriminator loss: 0.7816843103398295: 2%|▏ | 6/300 [02:05<1:13:42, 15.04s/it]
Generator loss: 2.0209721011274002, Discriminator loss: 0.7753454947515446: 2%|▏ | 6/300 [02:21<1:13:42, 15.04s/it]
Generator loss: 2.0209721011274002, Discriminator loss: 0.7753454947515446: 2%|▏ | 7/300 [02:21<1:15:22, 15.43s/it]
Generator loss: 1.7905255674439318, Discriminator loss: 0.8429553416721961: 2%|▏ | 7/300 [02:36<1:15:22, 15.43s/it]
Generator loss: 1.7905255674439318, Discriminator loss: 0.8429553416721961: 3%|▎ | 8/300 [02:36<1:13:46, 15.16s/it]
Generator loss: 1.8539055073085953, Discriminator loss: 0.8918271034079439: 3%|▎ | 8/300 [02:50<1:13:46, 15.16s/it]
Generator loss: 1.8539055073085953, Discriminator loss: 0.8918271034079439: 3%|▎ | 9/300 [02:50<1:12:13, 14.89s/it]
Generator loss: 1.3095603074659319, Discriminator loss: 1.036813088637941: 3%|▎ | 9/300 [03:05<1:12:13, 14.89s/it]
Generator loss: 1.3095603074659319, Discriminator loss: 1.036813088637941: 3%|▎ | 10/300 [03:05<1:13:03, 15.12s/it]
Generator loss: 1.7761961335645002, Discriminator loss: 0.8414131459944388: 3%|▎ | 10/300 [03:18<1:13:03, 15.12s/it]
Generator loss: 1.7761961335645002, Discriminator loss: 0.8414131459944388: 4%|▎ | 11/300 [03:18<1:08:35, 14.24s/it]
Generator loss: 2.118274845621165, Discriminator loss: 0.6414801717242774: 4%|▎ | 11/300 [03:27<1:08:35, 14.24s/it]
Generator loss: 2.118274845621165, Discriminator loss: 0.6414801717242774: 4%|▍ | 12/300 [03:27<1:01:02, 12.72s/it]
Generator loss: 2.3443840575568817, Discriminator loss: 0.6470773925676065: 4%|▍ | 12/300 [03:36<1:01:02, 12.72s/it]
Generator loss: 2.3443840575568817, Discriminator loss: 0.6470773925676065: 4%|▍ | 13/300 [03:36<55:28, 11.60s/it]
Generator loss: 2.4566313190495266, Discriminator loss: 0.5863546194399104: 4%|▍ | 13/300 [03:45<55:28, 11.60s/it]
Generator loss: 2.4566313190495266, Discriminator loss: 0.5863546194399104: 5%|▍ | 14/300 [03:45<51:37, 10.83s/it]
Generator loss: 2.6201670660692105, Discriminator loss: 0.6070238498642164: 5%|▍ | 14/300 [03:54<51:37, 10.83s/it]
Generator loss: 2.6201670660692105, Discriminator loss: 0.6070238498642164: 5%|▌ | 15/300 [03:54<48:49, 10.28s/it]
Generator loss: 2.6223588322891906, Discriminator loss: 0.5109086793792599: 5%|▌ | 15/300 [04:02<48:49, 10.28s/it]
Generator loss: 2.6223588322891906, Discriminator loss: 0.5109086793792599: 5%|▌ | 16/300 [04:02<46:03, 9.73s/it]
Generator loss: 2.5351900554755153, Discriminator loss: 0.5703090098412598: 5%|▌ | 16/300 [04:10<46:03, 9.73s/it]
Generator loss: 2.5351900554755153, Discriminator loss: 0.5703090098412598: 6%|▌ | 17/300 [04:10<42:44, 9.06s/it]
Generator loss: 2.224751223536099, Discriminator loss: 0.6933160715681665: 6%|▌ | 17/300 [04:17<42:44, 9.06s/it]
Generator loss: 2.224751223536099, Discriminator loss: 0.6933160715681665: 6%|▌ | 18/300 [04:17<39:40, 8.44s/it]
Generator loss: 2.2140680621652042, Discriminator loss: 0.6735449100241941: 6%|▌ | 18/300 [04:24<39:40, 8.44s/it]
Generator loss: 2.2140680621652042, Discriminator loss: 0.6735449100241941: 6%|▋ | 19/300 [04:24<37:23, 7.98s/it]
Generator loss: 1.9777411288198303, Discriminator loss: 0.692805947845473: 6%|▋ | 19/300 [04:31<37:23, 7.98s/it]
Generator loss: 1.9777411288198303, Discriminator loss: 0.692805947845473: 7%|▋ | 20/300 [04:31<35:43, 7.66s/it]
Generator loss: 1.8741068846600897, Discriminator loss: 0.7449631901348338: 7%|▋ | 20/300 [04:38<35:43, 7.66s/it]
Generator loss: 1.8741068846600897, Discriminator loss: 0.7449631901348338: 7%|▋ | 21/300 [04:38<34:32, 7.43s/it]
Generator loss: 1.9462997685460484, Discriminator loss: 0.61833321062081: 7%|▋ | 21/300 [04:45<34:32, 7.43s/it]
Generator loss: 1.9462997685460484, Discriminator loss: 0.61833321062081: 7%|▋ | 22/300 [04:45<33:38, 7.26s/it]
Generator loss: 2.117940893944572, Discriminator loss: 0.6480395973605269: 7%|▋ | 22/300 [04:51<33:38, 7.26s/it]
Generator loss: 2.117940893944572, Discriminator loss: 0.6480395973605269: 8%|▊ | 23/300 [04:51<33:01, 7.15s/it]
Generator loss: 2.0316804112756954, Discriminator loss: 0.5923001387101763: 8%|▊ | 23/300 [04:58<33:01, 7.15s/it]
Generator loss: 2.0316804112756954, Discriminator loss: 0.5923001387101763: 8%|▊ | 24/300 [04:58<32:34, 7.08s/it]
Generator loss: 2.110353120109614, Discriminator loss: 0.5987094748107826: 8%|▊ | 24/300 [05:05<32:34, 7.08s/it]
Generator loss: 2.110353120109614, Discriminator loss: 0.5987094748107826: 8%|▊ | 25/300 [05:05<32:13, 7.03s/it]
Generator loss: 2.12758731053156, Discriminator loss: 0.6420223644989378: 8%|▊ | 25/300 [05:12<32:13, 7.03s/it]
Generator loss: 2.12758731053156, Discriminator loss: 0.6420223644989378: 9%|▊ | 26/300 [05:12<31:53, 6.99s/it]
Generator loss: 1.9290453379645067, Discriminator loss: 0.7173247856690603: 9%|▊ | 26/300 [05:19<31:53, 6.99s/it]
Generator loss: 1.9290453379645067, Discriminator loss: 0.7173247856690603: 9%|▉ | 27/300 [05:19<31:42, 6.97s/it]
Generator loss: 1.9236001933322233, Discriminator loss: 0.7348716399248909: 9%|▉ | 27/300 [05:26<31:42, 6.97s/it]
Generator loss: 1.9236001933322233, Discriminator loss: 0.7348716399248909: 9%|▉ | 28/300 [05:26<31:32, 6.96s/it]
Generator loss: 1.9243448284618996, Discriminator loss: 0.6838795056237894: 9%|▉ | 28/300 [05:33<31:32, 6.96s/it]
Generator loss: 1.9243448284618996, Discriminator loss: 0.6838795056237894: 10%|▉ | 29/300 [05:33<31:26, 6.96s/it]
Generator loss: 1.9530906686011482, Discriminator loss: 0.6574842614286086: 10%|▉ | 29/300 [05:40<31:26, 6.96s/it]
Generator loss: 1.9530906686011482, Discriminator loss: 0.6574842614286086: 10%|█ | 30/300 [05:40<31:42, 7.05s/it]
Generator loss: 1.987439405392198, Discriminator loss: 0.6398052040706662: 10%|█ | 30/300 [05:47<31:42, 7.05s/it]
Generator loss: 1.987439405392198, Discriminator loss: 0.6398052040706662: 10%|█ | 31/300 [05:47<30:59, 6.91s/it]
Generator loss: 2.044340126654681, Discriminator loss: 0.6637535426108276: 10%|█ | 31/300 [05:53<30:59, 6.91s/it]
Generator loss: 2.044340126654681, Discriminator loss: 0.6637535426108276: 11%|█ | 32/300 [05:53<30:30, 6.83s/it]
Generator loss: 1.987223917070557, Discriminator loss: 0.6655241660773754: 11%|█ | 32/300 [06:00<30:30, 6.83s/it]
Generator loss: 1.987223917070557, Discriminator loss: 0.6655241660773754: 11%|█ | 33/300 [06:00<30:05, 6.76s/it]
Generator loss: 1.9940989157732796, Discriminator loss: 0.6812811452237999: 11%|█ | 33/300 [06:07<30:05, 6.76s/it]
Generator loss: 1.9940989157732796, Discriminator loss: 0.6812811452237999: 11%|█▏ | 34/300 [06:07<29:52, 6.74s/it]
Generator loss: 1.9257937928333002, Discriminator loss: 0.6992776880369467: 11%|█▏ | 34/300 [06:13<29:52, 6.74s/it]
Generator loss: 1.9257937928333002, Discriminator loss: 0.6992776880369467: 12%|█▏ | 35/300 [06:13<29:33, 6.69s/it]
Generator loss: 1.9709613064632696, Discriminator loss: 0.6311690412900027: 12%|█▏ | 35/300 [06:20<29:33, 6.69s/it]
Generator loss: 1.9709613064632696, Discriminator loss: 0.6311690412900027: 12%|█▏ | 36/300 [06:20<29:22, 6.68s/it]
Generator loss: 1.860101638471379, Discriminator loss: 0.7256270524333505: 12%|█▏ | 36/300 [06:27<29:22, 6.68s/it]
Generator loss: 1.860101638471379, Discriminator loss: 0.7256270524333505: 12%|█▏ | 37/300 [06:27<29:12, 6.66s/it]
Generator loss: 1.7695811439086409, Discriminator loss: 0.7627294414183673: 12%|█▏ | 37/300 [06:33<29:12, 6.66s/it]
Generator loss: 1.7695811439086409, Discriminator loss: 0.7627294414183673: 13%|█▎ | 38/300 [06:33<29:06, 6.67s/it]
Generator loss: 1.710711133392418, Discriminator loss: 0.7783913958598586: 13%|█▎ | 38/300 [06:40<29:06, 6.67s/it]
Generator loss: 1.710711133392418, Discriminator loss: 0.7783913958598586: 13%|█▎ | 39/300 [06:40<28:51, 6.63s/it]
Generator loss: 1.6298308666138088, Discriminator loss: 0.8163589966647765: 13%|█▎ | 39/300 [06:46<28:51, 6.63s/it]
Generator loss: 1.6298308666138088, Discriminator loss: 0.8163589966647765: 13%|█▎ | 40/300 [06:46<28:42, 6.62s/it]
Generator loss: 1.6522921586737913, Discriminator loss: 0.7979075299466357: 13%|█▎ | 40/300 [06:53<28:42, 6.62s/it]
Generator loss: 1.6522921586737913, Discriminator loss: 0.7979075299466357: 14%|█▎ | 41/300 [06:53<28:35, 6.62s/it]
Generator loss: 1.672510720351163, Discriminator loss: 0.7909478799385183: 14%|█▎ | 41/300 [07:00<28:35, 6.62s/it]
Generator loss: 1.672510720351163, Discriminator loss: 0.7909478799385183: 14%|█▍ | 42/300 [07:00<28:29, 6.63s/it]
Generator loss: 1.6667017340660095, Discriminator loss: 0.7982207159785664: 14%|█▍ | 42/300 [07:06<28:29, 6.63s/it]
Generator loss: 1.6667017340660095, Discriminator loss: 0.7982207159785664: 14%|█▍ | 43/300 [07:06<28:17, 6.61s/it]
Generator loss: 1.65825504020733, Discriminator loss: 0.7997007856474203: 14%|█▍ | 43/300 [07:13<28:17, 6.61s/it]
Generator loss: 1.65825504020733, Discriminator loss: 0.7997007856474203: 15%|█▍ | 44/300 [07:13<28:10, 6.61s/it]
Generator loss: 1.6209210477331106, Discriminator loss: 0.8447715745252722: 15%|█▍ | 44/300 [07:20<28:10, 6.61s/it]
Generator loss: 1.6209210477331106, Discriminator loss: 0.8447715745252722: 15%|█▌ | 45/300 [07:20<28:08, 6.62s/it]
Generator loss: 1.5814028741682278, Discriminator loss: 0.8335039265015546: 15%|█▌ | 45/300 [07:26<28:08, 6.62s/it]
Generator loss: 1.5814028741682278, Discriminator loss: 0.8335039265015546: 15%|█▌ | 46/300 [07:26<28:02, 6.63s/it]
Generator loss: 1.584620900452137, Discriminator loss: 0.8714165779597619: 15%|█▌ | 46/300 [07:33<28:02, 6.63s/it]
Generator loss: 1.584620900452137, Discriminator loss: 0.8714165779597619: 16%|█▌ | 47/300 [07:33<28:01, 6.65s/it]
Generator loss: 1.5452900244032635, Discriminator loss: 0.841843509060495: 16%|█▌ | 47/300 [07:39<28:01, 6.65s/it]
Generator loss: 1.5452900244032635, Discriminator loss: 0.841843509060495: 16%|█▌ | 48/300 [07:39<27:47, 6.62s/it]
Generator loss: 1.542084024671246, Discriminator loss: 0.8812410480835858: 16%|█▌ | 48/300 [07:46<27:47, 6.62s/it]
Generator loss: 1.542084024671246, Discriminator loss: 0.8812410480835858: 16%|█▋ | 49/300 [07:46<27:36, 6.60s/it]
Generator loss: 1.486083539969781, Discriminator loss: 0.8791569059385973: 16%|█▋ | 49/300 [07:53<27:36, 6.60s/it]
Generator loss: 1.486083539969781, Discriminator loss: 0.8791569059385973: 17%|█▋ | 50/300 [07:53<27:27, 6.59s/it]
Generator loss: 1.5112405147622614, Discriminator loss: 0.8912068890298114: 17%|█▋ | 50/300 [07:59<27:27, 6.59s/it]
Generator loss: 1.5112405147622614, Discriminator loss: 0.8912068890298114: 17%|█▋ | 51/300 [07:59<27:29, 6.62s/it]
Generator loss: 1.4893103610066807, Discriminator loss: 0.8984880587633919: 17%|█▋ | 51/300 [08:06<27:29, 6.62s/it]
Generator loss: 1.4893103610066807, Discriminator loss: 0.8984880587633919: 17%|█▋ | 52/300 [08:06<27:22, 6.62s/it]
Generator loss: 1.5350522933637394, Discriminator loss: 0.8891902101390502: 17%|█▋ | 52/300 [08:13<27:22, 6.62s/it]
Generator loss: 1.5350522933637394, Discriminator loss: 0.8891902101390502: 18%|█▊ | 53/300 [08:13<27:16, 6.63s/it]
Generator loss: 1.5181330429280506, Discriminator loss: 0.899337364031988: 18%|█▊ | 53/300 [08:19<27:16, 6.63s/it]
Generator loss: 1.5181330429280506, Discriminator loss: 0.899337364031988: 18%|█▊ | 54/300 [08:19<27:10, 6.63s/it]
Generator loss: 1.5080074895830715, Discriminator loss: 0.8935804108486456: 18%|█▊ | 54/300 [08:26<27:10, 6.63s/it]
Generator loss: 1.5080074895830715, Discriminator loss: 0.8935804108486456: 18%|█▊ | 55/300 [08:26<27:11, 6.66s/it]
Generator loss: 1.4986476161900688, Discriminator loss: 0.9006087337346638: 18%|█▊ | 55/300 [08:33<27:11, 6.66s/it]
Generator loss: 1.4986476161900688, Discriminator loss: 0.9006087337346638: 19%|█▊ | 56/300 [08:33<27:02, 6.65s/it]
Generator loss: 1.478811984114787, Discriminator loss: 0.9054143661085297: 19%|█▊ | 56/300 [08:39<27:02, 6.65s/it]
Generator loss: 1.478811984114787, Discriminator loss: 0.9054143661085297: 19%|█▉ | 57/300 [08:39<26:52, 6.63s/it]
Generator loss: 1.5209060469094444, Discriminator loss: 0.9018364202450303: 19%|█▉ | 57/300 [08:46<26:52, 6.63s/it]
Generator loss: 1.5209060469094444, Discriminator loss: 0.9018364202450303: 19%|█▉ | 58/300 [08:46<26:39, 6.61s/it]
Generator loss: 1.4833326471202515, Discriminator loss: 0.8973440760198761: 19%|█▉ | 58/300 [08:52<26:39, 6.61s/it]
Generator loss: 1.4833326471202515, Discriminator loss: 0.8973440760198761: 20%|█▉ | 59/300 [08:52<26:29, 6.59s/it]
Generator loss: 1.5183102063396399, Discriminator loss: 0.895128549898372: 20%|█▉ | 59/300 [08:59<26:29, 6.59s/it]
Generator loss: 1.5183102063396399, Discriminator loss: 0.895128549898372: 20%|██ | 60/300 [08:59<26:25, 6.61s/it]
Generator loss: 1.5287320368430193, Discriminator loss: 0.8949343741816633: 20%|██ | 60/300 [09:05<26:25, 6.61s/it]
Generator loss: 1.5287320368430193, Discriminator loss: 0.8949343741816633: 20%|██ | 61/300 [09:05<26:15, 6.59s/it]
Generator loss: 1.5220893643358175, Discriminator loss: 0.9018049928195336: 20%|██ | 61/300 [09:12<26:15, 6.59s/it]
Generator loss: 1.5220893643358175, Discriminator loss: 0.9018049928195336: 21%|██ | 62/300 [09:12<26:06, 6.58s/it]
Generator loss: 1.5172377960646855, Discriminator loss: 0.8894347741323358: 21%|██ | 62/300 [09:19<26:06, 6.58s/it]
Generator loss: 1.5172377960646855, Discriminator loss: 0.8894347741323358: 21%|██ | 63/300 [09:19<26:04, 6.60s/it]
Generator loss: 1.5071446154924, Discriminator loss: 0.896211767459617: 21%|██ | 63/300 [09:25<26:04, 6.60s/it]
Generator loss: 1.5071446154924, Discriminator loss: 0.896211767459617: 21%|██▏ | 64/300 [09:25<26:07, 6.64s/it]
Generator loss: 1.521011765827151, Discriminator loss: 0.9010778538444463: 21%|██▏ | 64/300 [09:32<26:07, 6.64s/it]
Generator loss: 1.521011765827151, Discriminator loss: 0.9010778538444463: 22%|██▏ | 65/300 [09:32<26:00, 6.64s/it]
Generator loss: 1.5088214804144466, Discriminator loss: 0.8929120166336789: 22%|██▏ | 65/300 [09:39<26:00, 6.64s/it]
Generator loss: 1.5088214804144466, Discriminator loss: 0.8929120166336789: 22%|██▏ | 66/300 [09:39<25:53, 6.64s/it]
Generator loss: 1.5387992946540607, Discriminator loss: 0.8957542154718848: 22%|██▏ | 66/300 [09:45<25:53, 6.64s/it]
Generator loss: 1.5387992946540607, Discriminator loss: 0.8957542154718848: 22%|██▏ | 67/300 [09:45<25:45, 6.63s/it]
Generator loss: 1.5478724832920467, Discriminator loss: 0.8870287239551544: 22%|██▏ | 67/300 [09:52<25:45, 6.63s/it]
Generator loss: 1.5478724832920467, Discriminator loss: 0.8870287239551544: 23%|██▎ | 68/300 [09:52<25:44, 6.66s/it]
Generator loss: 1.5466448664665222, Discriminator loss: 0.8750020063975278: 23%|██▎ | 68/300 [09:59<25:44, 6.66s/it]
Generator loss: 1.5466448664665222, Discriminator loss: 0.8750020063975278: 23%|██▎ | 69/300 [09:59<25:35, 6.65s/it]
Generator loss: 1.5378692430608414, Discriminator loss: 0.8932287136421484: 23%|██▎ | 69/300 [10:05<25:35, 6.65s/it]
Generator loss: 1.5378692430608414, Discriminator loss: 0.8932287136421484: 23%|██▎ | 70/300 [10:05<25:25, 6.63s/it]
Generator loss: 1.5668704426463913, Discriminator loss: 0.866356801460771: 23%|██▎ | 70/300 [10:12<25:25, 6.63s/it]
Generator loss: 1.5668704426463913, Discriminator loss: 0.866356801460771: 24%|██▎ | 71/300 [10:12<25:18, 6.63s/it]
Generator loss: 1.5750630353303516, Discriminator loss: 0.8744459419566042: 24%|██▎ | 71/300 [10:18<25:18, 6.63s/it]
Generator loss: 1.5750630353303516, Discriminator loss: 0.8744459419566042: 24%|██▍ | 72/300 [10:18<25:12, 6.63s/it]
Generator loss: 1.5859676148085033, Discriminator loss: 0.8690760284662247: 24%|██▍ | 72/300 [10:25<25:12, 6.63s/it]
Generator loss: 1.5859676148085033, Discriminator loss: 0.8690760284662247: 24%|██▍ | 73/300 [10:25<25:11, 6.66s/it]
Generator loss: 1.5942121735390495, Discriminator loss: 0.8656245947760695: 24%|██▍ | 73/300 [10:32<25:11, 6.66s/it]
Generator loss: 1.5942121735390495, Discriminator loss: 0.8656245947760695: 25%|██▍ | 74/300 [10:32<25:03, 6.65s/it]
Generator loss: 1.5940522244747948, Discriminator loss: 0.8590863057795692: 25%|██▍ | 74/300 [10:38<25:03, 6.65s/it]
Generator loss: 1.5940522244747948, Discriminator loss: 0.8590863057795692: 25%|██▌ | 75/300 [10:38<24:55, 6.65s/it]
Generator loss: 1.625133322442279, Discriminator loss: 0.8461951021762455: 25%|██▌ | 75/300 [10:45<24:55, 6.65s/it]
Generator loss: 1.625133322442279, Discriminator loss: 0.8461951021762455: 25%|██▌ | 76/300 [10:45<24:47, 6.64s/it]
Generator loss: 1.6150044386877733, Discriminator loss: 0.8720687991556: 25%|██▌ | 76/300 [10:52<24:47, 6.64s/it]
Generator loss: 1.6150044386877733, Discriminator loss: 0.8720687991556: 26%|██▌ | 77/300 [10:52<24:45, 6.66s/it]
Generator loss: 1.5708426285315962, Discriminator loss: 0.8573583491584834: 26%|██▌ | 77/300 [10:58<24:45, 6.66s/it]
Generator loss: 1.5708426285315962, Discriminator loss: 0.8573583491584834: 26%|██▌ | 78/300 [10:58<24:34, 6.64s/it]
Generator loss: 1.5813802248414826, Discriminator loss: 0.8782505826915011: 26%|██▌ | 78/300 [11:05<24:34, 6.64s/it]
Generator loss: 1.5813802248414826, Discriminator loss: 0.8782505826915011: 26%|██▋ | 79/300 [11:05<24:24, 6.63s/it]
Generator loss: 1.567047947908149, Discriminator loss: 0.8690432971891235: 26%|██▋ | 79/300 [11:12<24:24, 6.63s/it]
Generator loss: 1.567047947908149, Discriminator loss: 0.8690432971891235: 27%|██▋ | 80/300 [11:12<24:13, 6.61s/it]
Generator loss: 1.583584943676696, Discriminator loss: 0.8649900998262798: 27%|██▋ | 80/300 [11:18<24:13, 6.61s/it]
Generator loss: 1.583584943676696, Discriminator loss: 0.8649900998262798: 27%|██▋ | 81/300 [11:18<24:13, 6.64s/it]
Generator loss: 1.5720649168771856, Discriminator loss: 0.8745317069046638: 27%|██▋ | 81/300 [11:25<24:13, 6.64s/it]
Generator loss: 1.5720649168771856, Discriminator loss: 0.8745317069046638: 27%|██▋ | 82/300 [11:25<24:05, 6.63s/it]
Generator loss: 1.5929746877621203, Discriminator loss: 0.884861863711301: 27%|██▋ | 82/300 [11:31<24:05, 6.63s/it]
Generator loss: 1.5929746877621203, Discriminator loss: 0.884861863711301: 28%|██▊ | 83/300 [11:31<23:56, 6.62s/it]
Generator loss: 1.5993040927192743, Discriminator loss: 0.8676384678658318: 28%|██▊ | 83/300 [11:38<23:56, 6.62s/it]
Generator loss: 1.5993040927192743, Discriminator loss: 0.8676384678658318: 28%|██▊ | 84/300 [11:38<23:48, 6.61s/it]
Generator loss: 1.6024239904740278, Discriminator loss: 0.8599830475800178: 28%|██▊ | 84/300 [11:45<23:48, 6.61s/it]
Generator loss: 1.6024239904740278, Discriminator loss: 0.8599830475800178: 28%|██▊ | 85/300 [11:45<23:43, 6.62s/it]
Generator loss: 1.5926220316220732, Discriminator loss: 0.8531831862295375: 28%|██▊ | 85/300 [11:51<23:43, 6.62s/it]
Generator loss: 1.5926220316220732, Discriminator loss: 0.8531831862295375: 29%|██▊ | 86/300 [11:51<23:42, 6.65s/it]
Generator loss: 1.6410702589680166, Discriminator loss: 0.8368448203100878: 29%|██▊ | 86/300 [11:58<23:42, 6.65s/it]
Generator loss: 1.6410702589680166, Discriminator loss: 0.8368448203100878: 29%|██▉ | 87/300 [11:58<23:34, 6.64s/it]
Generator loss: 1.612115778028965, Discriminator loss: 0.866695671397097: 29%|██▉ | 87/300 [12:05<23:34, 6.64s/it]
Generator loss: 1.612115778028965, Discriminator loss: 0.866695671397097: 29%|██▉ | 88/300 [12:05<23:26, 6.63s/it]
Generator loss: 1.6191413227249594, Discriminator loss: 0.8370693299700233: 29%|██▉ | 88/300 [12:11<23:26, 6.63s/it]
Generator loss: 1.6191413227249594, Discriminator loss: 0.8370693299700233: 30%|██▉ | 89/300 [12:11<23:19, 6.63s/it]
Generator loss: 1.6128742370535345, Discriminator loss: 0.8590594262761229: 30%|██▉ | 89/300 [12:18<23:19, 6.63s/it]
Generator loss: 1.6128742370535345, Discriminator loss: 0.8590594262761229: 30%|███ | 90/300 [12:18<23:18, 6.66s/it]
Generator loss: 1.6292471666546429, Discriminator loss: 0.8545262178077417: 30%|███ | 90/300 [12:25<23:18, 6.66s/it]
Generator loss: 1.6292471666546429, Discriminator loss: 0.8545262178077417: 30%|███ | 91/300 [12:25<23:07, 6.64s/it]
Generator loss: 1.645205161150764, Discriminator loss: 0.8198951066416853: 30%|███ | 91/300 [12:31<23:07, 6.64s/it]
Generator loss: 1.645205161150764, Discriminator loss: 0.8198951066416853: 31%|███ | 92/300 [12:31<23:00, 6.64s/it]
Generator loss: 1.625604101401918, Discriminator loss: 0.8479677473797518: 31%|███ | 92/300 [12:38<23:00, 6.64s/it]
Generator loss: 1.625604101401918, Discriminator loss: 0.8479677473797518: 31%|███ | 93/300 [12:38<22:51, 6.63s/it]
Generator loss: 1.6520345750100471, Discriminator loss: 0.8335142885060871: 31%|███ | 93/300 [12:44<22:51, 6.63s/it]
Generator loss: 1.6520345750100471, Discriminator loss: 0.8335142885060871: 31%|███▏ | 94/300 [12:44<22:49, 6.65s/it]
Generator loss: 1.6731279856141876, Discriminator loss: 0.8364165591842988: 31%|███▏ | 94/300 [12:51<22:49, 6.65s/it]
Generator loss: 1.6731279856141876, Discriminator loss: 0.8364165591842988: 32%|███▏ | 95/300 [12:51<22:40, 6.64s/it]
Generator loss: 1.6628490156110596, Discriminator loss: 0.8328475991592688: 32%|███▏ | 95/300 [12:58<22:40, 6.64s/it]
Generator loss: 1.6628490156110596, Discriminator loss: 0.8328475991592688: 32%|███▏ | 96/300 [12:58<22:33, 6.63s/it]
Generator loss: 1.6615130590165363, Discriminator loss: 0.8281888374510933: 32%|███▏ | 96/300 [13:04<22:33, 6.63s/it]
Generator loss: 1.6615130590165363, Discriminator loss: 0.8281888374510933: 32%|███▏ | 97/300 [13:04<22:25, 6.63s/it]
Generator loss: 1.6148593184702538, Discriminator loss: 0.853957395781489: 32%|███▏ | 97/300 [13:11<22:25, 6.63s/it]
Generator loss: 1.6148593184702538, Discriminator loss: 0.853957395781489: 33%|███▎ | 98/300 [13:11<22:17, 6.62s/it]
Generator loss: 1.6571329468313385, Discriminator loss: 0.8237307260141653: 33%|███▎ | 98/300 [13:18<22:17, 6.62s/it]
Generator loss: 1.6571329468313385, Discriminator loss: 0.8237307260141653: 33%|███▎ | 99/300 [13:18<22:15, 6.65s/it]
Generator loss: 1.6786996196298039, Discriminator loss: 0.8207834148231674: 33%|███▎ | 99/300 [13:24<22:15, 6.65s/it]
Generator loss: 1.6786996196298039, Discriminator loss: 0.8207834148231674: 33%|███▎ | 100/300 [13:24<22:07, 6.64s/it]
Generator loss: 1.6578268540256165, Discriminator loss: 0.8502317338305361: 33%|███▎ | 100/300 [13:31<22:07, 6.64s/it]
Generator loss: 1.6578268540256165, Discriminator loss: 0.8502317338305361: 34%|███▎ | 101/300 [13:31<22:00, 6.63s/it]
Generator loss: 1.6500777036828154, Discriminator loss: 0.8360809832811356: 34%|███▎ | 101/300 [13:37<22:00, 6.63s/it]
Generator loss: 1.6500777036828154, Discriminator loss: 0.8360809832811356: 34%|███▍ | 102/300 [13:37<21:49, 6.61s/it]
Generator loss: 1.667227153392399, Discriminator loss: 0.8129227284122916: 34%|███▍ | 102/300 [13:44<21:49, 6.61s/it]
Generator loss: 1.667227153392399, Discriminator loss: 0.8129227284122916: 34%|███▍ | 103/300 [13:44<21:47, 6.64s/it]
Generator loss: 1.6800949520924513, Discriminator loss: 0.8065380576778861: 34%|███▍ | 103/300 [13:51<21:47, 6.64s/it]
Generator loss: 1.6800949520924513, Discriminator loss: 0.8065380576778861: 35%|███▍ | 104/300 [13:51<21:41, 6.64s/it]
Generator loss: 1.7048907402683706, Discriminator loss: 0.8073845773058779: 35%|███▍ | 104/300 [13:57<21:41, 6.64s/it]
Generator loss: 1.7048907402683706, Discriminator loss: 0.8073845773058779: 35%|███▌ | 105/300 [13:57<21:33, 6.63s/it]
Generator loss: 1.678999839898418, Discriminator loss: 0.8190580729176017: 35%|███▌ | 105/300 [14:04<21:33, 6.63s/it]
Generator loss: 1.678999839898418, Discriminator loss: 0.8190580729176017: 35%|███▌ | 106/300 [14:04<21:22, 6.61s/it]
Generator loss: 1.6873094570987366, Discriminator loss: 0.8126953007543788: 35%|███▌ | 106/300 [14:11<21:22, 6.61s/it]
Generator loss: 1.6873094570987366, Discriminator loss: 0.8126953007543788: 36%|███▌ | 107/300 [14:11<21:22, 6.65s/it]
Generator loss: 1.678116421927424, Discriminator loss: 0.8270426212864763: 36%|███▌ | 107/300 [14:17<21:22, 6.65s/it]
Generator loss: 1.678116421927424, Discriminator loss: 0.8270426212864763: 36%|███▌ | 108/300 [14:17<21:15, 6.64s/it]
Generator loss: 1.6950164301430477, Discriminator loss: 0.8026039232225979: 36%|███▌ | 108/300 [14:24<21:15, 6.64s/it]
Generator loss: 1.6950164301430477, Discriminator loss: 0.8026039232225979: 36%|███▋ | 109/300 [14:24<21:04, 6.62s/it]
Generator loss: 1.7014941169935114, Discriminator loss: 0.7969357147812843: 36%|███▋ | 109/300 [14:30<21:04, 6.62s/it]
Generator loss: 1.7014941169935114, Discriminator loss: 0.7969357147812843: 37%|███▋ | 110/300 [14:30<20:53, 6.60s/it]
Generator loss: 1.719758610953303, Discriminator loss: 0.7976097617955769: 37%|███▋ | 110/300 [14:37<20:53, 6.60s/it]
Generator loss: 1.719758610953303, Discriminator loss: 0.7976097617955769: 37%|███▋ | 111/300 [14:37<20:44, 6.59s/it]
Generator loss: 1.7388707302949007, Discriminator loss: 0.7936992846867618: 37%|███▋ | 111/300 [14:44<20:44, 6.59s/it]
Generator loss: 1.7388707302949007, Discriminator loss: 0.7936992846867618: 37%|███▋ | 112/300 [14:44<20:40, 6.60s/it]
Generator loss: 1.7232475412242554, Discriminator loss: 0.7827038243412971: 37%|███▋ | 112/300 [14:50<20:40, 6.60s/it]
Generator loss: 1.7232475412242554, Discriminator loss: 0.7827038243412971: 38%|███▊ | 113/300 [14:50<20:31, 6.59s/it]
Generator loss: 1.7635971491827684, Discriminator loss: 0.7883220731335527: 38%|███▊ | 113/300 [14:57<20:31, 6.59s/it]
Generator loss: 1.7635971491827684, Discriminator loss: 0.7883220731335527: 38%|███▊ | 114/300 [14:57<20:22, 6.58s/it]
Generator loss: 1.7574936063850628, Discriminator loss: 0.7908676126424004: 38%|███▊ | 114/300 [15:03<20:22, 6.58s/it]
Generator loss: 1.7574936063850628, Discriminator loss: 0.7908676126424004: 38%|███▊ | 115/300 [15:03<20:15, 6.57s/it]
Generator loss: 1.7410221165593933, Discriminator loss: 0.796536782208611: 38%|███▊ | 115/300 [15:10<20:15, 6.57s/it]
Generator loss: 1.7410221165593933, Discriminator loss: 0.796536782208611: 39%|███▊ | 116/300 [15:10<20:12, 6.59s/it]
Generator loss: 1.7575040148461567, Discriminator loss: 0.7798431997790056: 39%|███▊ | 116/300 [15:16<20:12, 6.59s/it]
Generator loss: 1.7575040148461567, Discriminator loss: 0.7798431997790056: 39%|███▉ | 117/300 [15:16<20:03, 6.58s/it]
Generator loss: 1.7489790890146704, Discriminator loss: 0.7853775230400702: 39%|███▉ | 117/300 [15:23<20:03, 6.58s/it]
Generator loss: 1.7489790890146704, Discriminator loss: 0.7853775230400702: 39%|███▉ | 118/300 [15:23<19:55, 6.57s/it]
Generator loss: 1.7569141054854673, Discriminator loss: 0.7929856251267826: 39%|███▉ | 118/300 [15:30<19:55, 6.57s/it]
Generator loss: 1.7569141054854673, Discriminator loss: 0.7929856251267826: 40%|███▉ | 119/300 [15:30<19:48, 6.56s/it]
Generator loss: 1.7644893553327112, Discriminator loss: 0.7732302866437856: 40%|███▉ | 119/300 [15:36<19:48, 6.56s/it]
Generator loss: 1.7644893553327112, Discriminator loss: 0.7732302866437856: 40%|████ | 120/300 [15:36<19:45, 6.59s/it]
Generator loss: 1.7821472046129845, Discriminator loss: 0.7615856252172414: 40%|████ | 120/300 [15:43<19:45, 6.59s/it]
Generator loss: 1.7821472046129845, Discriminator loss: 0.7615856252172414: 40%|████ | 121/300 [15:43<19:27, 6.52s/it]
Generator loss: 1.7836939162191223, Discriminator loss: 0.7784580820623566: 40%|████ | 121/300 [15:49<19:27, 6.52s/it]
Generator loss: 1.7836939162191223, Discriminator loss: 0.7784580820623566: 41%|████ | 122/300 [15:49<19:13, 6.48s/it]
Generator loss: 1.7743018295835047, Discriminator loss: 0.7797000899034388: 41%|████ | 122/300 [15:55<19:13, 6.48s/it]
Generator loss: 1.7743018295835047, Discriminator loss: 0.7797000899034388: 41%|████ | 123/300 [15:55<19:01, 6.45s/it]
Generator loss: 1.7860442395595943, Discriminator loss: 0.779537913553855: 41%|████ | 123/300 [16:02<19:01, 6.45s/it]
Generator loss: 1.7860442395595943, Discriminator loss: 0.779537913553855: 41%|████▏ | 124/300 [16:02<18:52, 6.43s/it]
Generator loss: 1.7961063314886654, Discriminator loss: 0.7632119296228185: 41%|████▏ | 124/300 [16:08<18:52, 6.43s/it]
Generator loss: 1.7961063314886654, Discriminator loss: 0.7632119296228185: 42%|████▏ | 125/300 [16:08<18:46, 6.44s/it]
Generator loss: 1.7912442175781025, Discriminator loss: 0.7627700041322147: 42%|████▏ | 125/300 [16:15<18:46, 6.44s/it]
Generator loss: 1.7912442175781025, Discriminator loss: 0.7627700041322147: 42%|████▏ | 126/300 [16:15<18:37, 6.42s/it]
Generator loss: 1.8034317142823164, Discriminator loss: 0.7996259073124212: 42%|████▏ | 126/300 [16:21<18:37, 6.42s/it]
Generator loss: 1.8034317142823164, Discriminator loss: 0.7996259073124212: 42%|████▏ | 127/300 [16:21<18:31, 6.42s/it]
Generator loss: 1.7972839588628096, Discriminator loss: 0.7540580669746679: 42%|████▏ | 127/300 [16:27<18:31, 6.42s/it]
Generator loss: 1.7972839588628096, Discriminator loss: 0.7540580669746679: 43%|████▎ | 128/300 [16:27<18:22, 6.41s/it]
Generator loss: 1.819234922528267, Discriminator loss: 0.751131749328445: 43%|████▎ | 128/300 [16:34<18:22, 6.41s/it]
Generator loss: 1.819234922528267, Discriminator loss: 0.751131749328445: 43%|████▎ | 129/300 [16:34<18:14, 6.40s/it]
Generator loss: 1.8124022290987127, Discriminator loss: 0.757165546364644: 43%|████▎ | 129/300 [16:40<18:14, 6.40s/it]
Generator loss: 1.8124022290987127, Discriminator loss: 0.757165546364644: 43%|████▎ | 130/300 [16:40<18:06, 6.39s/it]
Generator loss: 1.8343695323256886, Discriminator loss: 0.7554672592703033: 43%|████▎ | 130/300 [16:46<18:06, 6.39s/it]
Generator loss: 1.8343695323256886, Discriminator loss: 0.7554672592703033: 44%|████▎ | 131/300 [16:46<17:57, 6.38s/it]
Generator loss: 1.8135301514583475, Discriminator loss: 0.7552496725145508: 44%|████▎ | 131/300 [16:53<17:57, 6.38s/it]
Generator loss: 1.8135301514583475, Discriminator loss: 0.7552496725145508: 44%|████▍ | 132/300 [16:53<17:53, 6.39s/it]
Generator loss: 1.7663639468305252, Discriminator loss: 0.843885213136673: 44%|████▍ | 132/300 [16:59<17:53, 6.39s/it]
Generator loss: 1.7663639468305252, Discriminator loss: 0.843885213136673: 44%|████▍ | 133/300 [16:59<17:53, 6.43s/it]
Generator loss: 1.7719000402618856, Discriminator loss: 0.7477432011681444: 44%|████▍ | 133/300 [17:06<17:53, 6.43s/it]
Generator loss: 1.7719000402618856, Discriminator loss: 0.7477432011681444: 45%|████▍ | 134/300 [17:06<17:46, 6.43s/it]
Generator loss: 1.8142524659633636, Discriminator loss: 0.7370717661345706: 45%|████▍ | 134/300 [17:12<17:46, 6.43s/it]
Generator loss: 1.8142524659633636, Discriminator loss: 0.7370717661345706: 45%|████▌ | 135/300 [17:12<17:38, 6.41s/it]
Generator loss: 1.8233654722571373, Discriminator loss: 0.7425804826266625: 45%|████▌ | 135/300 [17:19<17:38, 6.41s/it]
Generator loss: 1.8233654722571373, Discriminator loss: 0.7425804826266625: 45%|████▌ | 136/300 [17:19<17:32, 6.42s/it]
Generator loss: 1.8471009555984945, Discriminator loss: 0.7435824047116673: 45%|████▌ | 136/300 [17:25<17:32, 6.42s/it]
Generator loss: 1.8471009555984945, Discriminator loss: 0.7435824047116673: 46%|████▌ | 137/300 [17:25<17:30, 6.44s/it]
Generator loss: 1.8517840381930857, Discriminator loss: 0.7490521963028347: 46%|████▌ | 137/300 [17:32<17:30, 6.44s/it]
Generator loss: 1.8517840381930857, Discriminator loss: 0.7490521963028347: 46%|████▌ | 138/300 [17:32<17:23, 6.44s/it]
Generator loss: 1.8829423031386208, Discriminator loss: 0.7372819693649516: 46%|████▌ | 138/300 [17:38<17:23, 6.44s/it]
Generator loss: 1.8829423031386208, Discriminator loss: 0.7372819693649516: 46%|████▋ | 139/300 [17:38<17:24, 6.49s/it]
Generator loss: 1.8680496364831924, Discriminator loss: 0.7670731268384877: 46%|████▋ | 139/300 [17:45<17:24, 6.49s/it]
Generator loss: 1.8680496364831924, Discriminator loss: 0.7670731268384877: 47%|████▋ | 140/300 [17:45<17:16, 6.48s/it]
Generator loss: 1.857833515633555, Discriminator loss: 0.7407474833376267: 47%|████▋ | 140/300 [17:51<17:16, 6.48s/it]
Generator loss: 1.857833515633555, Discriminator loss: 0.7407474833376267: 47%|████▋ | 141/300 [17:51<17:08, 6.47s/it]
Generator loss: 1.8678517113713657, Discriminator loss: 0.7261389905915541: 47%|████▋ | 141/300 [17:58<17:08, 6.47s/it]
Generator loss: 1.8678517113713657, Discriminator loss: 0.7261389905915541: 47%|████▋ | 142/300 [17:58<17:00, 6.46s/it]
Generator loss: 1.8683480313595604, Discriminator loss: 0.7343018848229858: 47%|████▋ | 142/300 [18:04<17:00, 6.46s/it]
Generator loss: 1.8683480313595604, Discriminator loss: 0.7343018848229858: 48%|████▊ | 143/300 [18:04<16:56, 6.47s/it]
Generator loss: 1.8793934167307966, Discriminator loss: 0.735251775559257: 48%|████▊ | 143/300 [18:11<16:56, 6.47s/it]
Generator loss: 1.8793934167307966, Discriminator loss: 0.735251775559257: 48%|████▊ | 144/300 [18:11<16:49, 6.47s/it]
Generator loss: 1.8663447096067316, Discriminator loss: 0.7562430614934248: 48%|████▊ | 144/300 [18:17<16:49, 6.47s/it]
Generator loss: 1.8663447096067316, Discriminator loss: 0.7562430614934248: 48%|████▊ | 145/300 [18:17<16:46, 6.49s/it]
Generator loss: 1.853991342818036, Discriminator loss: 0.7314641164506183: 48%|████▊ | 145/300 [18:23<16:46, 6.49s/it]
Generator loss: 1.853991342818036, Discriminator loss: 0.7314641164506183: 49%|████▊ | 146/300 [18:23<16:36, 6.47s/it]
Generator loss: 1.8588006592848723, Discriminator loss: 0.7245916391120237: 49%|████▊ | 146/300 [18:30<16:36, 6.47s/it]
Generator loss: 1.8588006592848723, Discriminator loss: 0.7245916391120237: 49%|████▉ | 147/300 [18:30<16:31, 6.48s/it]
Generator loss: 1.887835018336773, Discriminator loss: 0.7272283965173889: 49%|████▉ | 147/300 [18:36<16:31, 6.48s/it]
Generator loss: 1.887835018336773, Discriminator loss: 0.7272283965173889: 49%|████▉ | 148/300 [18:36<16:21, 6.45s/it]
Generator loss: 1.870099083027419, Discriminator loss: 0.7440555796903723: 49%|████▉ | 148/300 [18:43<16:21, 6.45s/it]
Generator loss: 1.870099083027419, Discriminator loss: 0.7440555796903723: 50%|████▉ | 149/300 [18:43<16:14, 6.45s/it]
Generator loss: 1.8805445914759356, Discriminator loss: 0.7166378572583199: 50%|████▉ | 149/300 [18:49<16:14, 6.45s/it]
Generator loss: 1.8805445914759356, Discriminator loss: 0.7166378572583199: 50%|█████ | 150/300 [18:49<16:06, 6.44s/it]
Generator loss: 1.9056233597152374, Discriminator loss: 0.730847950805636: 50%|█████ | 150/300 [18:56<16:06, 6.44s/it]
Generator loss: 1.9056233597152374, Discriminator loss: 0.730847950805636: 50%|█████ | 151/300 [18:56<16:03, 6.47s/it]
Generator loss: 1.9140165527077282, Discriminator loss: 0.7123787074404604: 50%|█████ | 151/300 [19:02<16:03, 6.47s/it]
Generator loss: 1.9140165527077282, Discriminator loss: 0.7123787074404604: 51%|█████ | 152/300 [19:02<15:56, 6.46s/it]
Generator loss: 1.9222180150887545, Discriminator loss: 0.7199763120973811: 51%|█████ | 152/300 [19:09<15:56, 6.46s/it]
Generator loss: 1.9222180150887545, Discriminator loss: 0.7199763120973811: 51%|█████ | 153/300 [19:09<15:50, 6.46s/it]
Generator loss: 1.8877925149658148, Discriminator loss: 0.7476223606397124: 51%|█████ | 153/300 [19:15<15:50, 6.46s/it]
Generator loss: 1.8877925149658148, Discriminator loss: 0.7476223606397124: 51%|█████▏ | 154/300 [19:15<15:40, 6.44s/it]
Generator loss: 1.883141139850897, Discriminator loss: 0.7233283677521873: 51%|█████▏ | 154/300 [19:22<15:40, 6.44s/it]
Generator loss: 1.883141139850897, Discriminator loss: 0.7233283677521873: 52%|█████▏ | 155/300 [19:22<15:34, 6.45s/it]
Generator loss: 1.8941808974041658, Discriminator loss: 0.7128842459882007: 52%|█████▏ | 155/300 [19:28<15:34, 6.45s/it]
Generator loss: 1.8941808974041658, Discriminator loss: 0.7128842459882007: 52%|█████▏ | 156/300 [19:28<15:31, 6.47s/it]
Generator loss: 1.9113889801151611, Discriminator loss: 0.7131333828848951: 52%|█████▏ | 156/300 [19:35<15:31, 6.47s/it]
Generator loss: 1.9113889801151611, Discriminator loss: 0.7131333828848951: 52%|█████▏ | 157/300 [19:35<15:30, 6.51s/it]
Generator loss: 1.907938465476036, Discriminator loss: 0.7078887916663114: 52%|█████▏ | 157/300 [19:41<15:30, 6.51s/it]
Generator loss: 1.907938465476036, Discriminator loss: 0.7078887916663114: 53%|█████▎ | 158/300 [19:41<15:24, 6.51s/it]
Generator loss: 1.911291787729544, Discriminator loss: 0.7252739961532986: 53%|█████▎ | 158/300 [19:48<15:24, 6.51s/it]
Generator loss: 1.911291787729544, Discriminator loss: 0.7252739961532986: 53%|█████▎ | 159/300 [19:48<15:16, 6.50s/it]
Generator loss: 1.9184895096456303, Discriminator loss: 0.7103787857820006: 53%|█████▎ | 159/300 [19:54<15:16, 6.50s/it]
Generator loss: 1.9184895096456303, Discriminator loss: 0.7103787857820006: 53%|█████▎ | 160/300 [19:54<15:07, 6.48s/it]
Generator loss: 1.6518787658076903, Discriminator loss: 1.6146739887840607: 53%|█████▎ | 160/300 [20:01<15:07, 6.48s/it]
Generator loss: 1.6518787658076903, Discriminator loss: 1.6146739887840607: 54%|█████▎ | 161/300 [20:01<14:59, 6.47s/it]
Generator loss: 1.1205224596402223, Discriminator loss: 1.0990365059936749: 54%|█████▎ | 161/300 [20:07<14:59, 6.47s/it]
Generator loss: 1.1205224596402223, Discriminator loss: 1.0990365059936749: 54%|█████▍ | 162/300 [20:07<14:54, 6.48s/it]
Generator loss: 1.3062065813471289, Discriminator loss: 0.9539765860227978: 54%|█████▍ | 162/300 [20:13<14:54, 6.48s/it]
Generator loss: 1.3062065813471289, Discriminator loss: 0.9539765860227978: 54%|█████▍ | 163/300 [20:13<14:47, 6.48s/it]
Generator loss: 1.5725498107426308, Discriminator loss: 0.8217732069246909: 54%|█████▍ | 163/300 [20:20<14:47, 6.48s/it]
Generator loss: 1.5725498107426308, Discriminator loss: 0.8217732069246909: 55%|█████▍ | 164/300 [20:20<14:43, 6.50s/it]
Generator loss: 1.6757422453340363, Discriminator loss: 0.7526570115895832: 55%|█████▍ | 164/300 [20:26<14:43, 6.50s/it]
Generator loss: 1.6757422453340363, Discriminator loss: 0.7526570115895832: 55%|█████▌ | 165/300 [20:26<14:35, 6.49s/it]
Generator loss: 1.7489459041286917, Discriminator loss: 0.7313809710390428: 55%|█████▌ | 165/300 [20:33<14:35, 6.49s/it]
Generator loss: 1.7489459041286917, Discriminator loss: 0.7313809710390428: 55%|█████▌ | 166/300 [20:33<14:26, 6.46s/it]
Generator loss: 1.793169997194234, Discriminator loss: 0.7243372022229082: 55%|█████▌ | 166/300 [20:39<14:26, 6.46s/it]
Generator loss: 1.793169997194234, Discriminator loss: 0.7243372022229082: 56%|█████▌ | 167/300 [20:39<14:17, 6.45s/it]
Generator loss: 1.8074285098735023, Discriminator loss: 0.7162313873276991: 56%|█████▌ | 167/300 [20:46<14:17, 6.45s/it]
Generator loss: 1.8074285098735023, Discriminator loss: 0.7162313873276991: 56%|█████▌ | 168/300 [20:46<14:11, 6.45s/it]
Generator loss: 1.8447622586699093, Discriminator loss: 0.7173904425957623: 56%|█████▌ | 168/300 [20:52<14:11, 6.45s/it]
Generator loss: 1.8447622586699093, Discriminator loss: 0.7173904425957623: 56%|█████▋ | 169/300 [20:52<14:06, 6.46s/it]
Generator loss: 1.8570084054680431, Discriminator loss: 0.7179789012845825: 56%|█████▋ | 169/300 [20:59<14:06, 6.46s/it]
Generator loss: 1.8570084054680431, Discriminator loss: 0.7179789012845825: 57%|█████▋ | 170/300 [20:59<14:03, 6.49s/it]
Generator loss: 1.8499646011520834, Discriminator loss: 0.7092674168593743: 57%|█████▋ | 170/300 [21:05<14:03, 6.49s/it]
Generator loss: 1.8499646011520834, Discriminator loss: 0.7092674168593743: 57%|█████▋ | 171/300 [21:05<13:55, 6.48s/it]
Generator loss: 1.8719150879803825, Discriminator loss: 0.7033579261863933: 57%|█████▋ | 171/300 [21:12<13:55, 6.48s/it]
Generator loss: 1.8719150879803825, Discriminator loss: 0.7033579261863933: 57%|█████▋ | 172/300 [21:12<13:47, 6.47s/it]
Generator loss: 1.8675539554918514, Discriminator loss: 0.7109484506003997: 57%|█████▋ | 172/300 [21:18<13:47, 6.47s/it]
Generator loss: 1.8675539554918514, Discriminator loss: 0.7109484506003997: 58%|█████▊ | 173/300 [21:18<13:39, 6.46s/it]
Generator loss: 1.886877525378676, Discriminator loss: 0.715422235429287: 58%|█████▊ | 173/300 [21:25<13:39, 6.46s/it]
Generator loss: 1.886877525378676, Discriminator loss: 0.715422235429287: 58%|█████▊ | 174/300 [21:25<13:30, 6.43s/it]
Generator loss: 1.8639318084015566, Discriminator loss: 0.7092845672193695: 58%|█████▊ | 174/300 [21:31<13:30, 6.43s/it]
Generator loss: 1.8639318084015566, Discriminator loss: 0.7092845672193695: 58%|█████▊ | 175/300 [21:31<13:23, 6.43s/it]
Generator loss: 1.8762365180779905, Discriminator loss: 0.7277653979904511: 58%|█████▊ | 175/300 [21:37<13:23, 6.43s/it]
Generator loss: 1.8762365180779905, Discriminator loss: 0.7277653979904511: 59%|█████▊ | 176/300 [21:37<13:20, 6.46s/it]
Generator loss: 1.8840555084102295, Discriminator loss: 0.7117495133596308: 59%|█████▊ | 176/300 [21:44<13:20, 6.46s/it]
Generator loss: 1.8840555084102295, Discriminator loss: 0.7117495133596308: 59%|█████▉ | 177/300 [21:44<13:09, 6.42s/it]
Generator loss: 1.8722961474867428, Discriminator loss: 0.7381857567850281: 59%|█████▉ | 177/300 [21:50<13:09, 6.42s/it]
Generator loss: 1.8722961474867428, Discriminator loss: 0.7381857567850281: 59%|█████▉ | 178/300 [21:50<12:58, 6.38s/it]
Generator loss: 1.879811488530215, Discriminator loss: 0.7050962079973782: 59%|█████▉ | 178/300 [21:57<12:58, 6.38s/it]
Generator loss: 1.879811488530215, Discriminator loss: 0.7050962079973782: 60%|█████▉ | 179/300 [21:57<12:54, 6.40s/it]
Generator loss: 1.8758899578276802, Discriminator loss: 0.7048510142108974: 60%|█████▉ | 179/300 [22:03<12:54, 6.40s/it]
Generator loss: 1.8758899578276802, Discriminator loss: 0.7048510142108974: 60%|██████ | 180/300 [22:03<12:50, 6.42s/it]
Generator loss: 1.8873987224172144, Discriminator loss: 0.7038660505238701: 60%|██████ | 180/300 [22:09<12:50, 6.42s/it]
Generator loss: 1.8873987224172144, Discriminator loss: 0.7038660505238701: 60%|██████ | 181/300 [22:09<12:44, 6.42s/it]
Generator loss: 1.8888916346956701, Discriminator loss: 0.7137206582462087: 60%|██████ | 181/300 [22:16<12:44, 6.42s/it]
Generator loss: 1.8888916346956701, Discriminator loss: 0.7137206582462087: 61%|██████ | 182/300 [22:16<12:37, 6.42s/it]
Generator loss: 1.9036011213765425, Discriminator loss: 0.7122918327941614: 61%|██████ | 182/300 [22:22<12:37, 6.42s/it]
Generator loss: 1.9036011213765425, Discriminator loss: 0.7122918327941614: 61%|██████ | 183/300 [22:22<12:27, 6.39s/it]
Generator loss: 1.8918571752660416, Discriminator loss: 0.7075192012331065: 61%|██████ | 183/300 [22:28<12:27, 6.39s/it]
Generator loss: 1.8918571752660416, Discriminator loss: 0.7075192012331065: 61%|██████▏ | 184/300 [22:28<12:18, 6.37s/it]
Generator loss: 1.9028138803208576, Discriminator loss: 0.7169827043133623: 61%|██████▏ | 184/300 [22:35<12:18, 6.37s/it]
Generator loss: 1.9028138803208576, Discriminator loss: 0.7169827043133623: 62%|██████▏ | 185/300 [22:35<12:13, 6.38s/it]
Generator loss: 1.8884707106386913, Discriminator loss: 0.7273798655061161: 62%|██████▏ | 185/300 [22:41<12:13, 6.38s/it]
Generator loss: 1.8884707106386913, Discriminator loss: 0.7273798655061161: 62%|██████▏ | 186/300 [22:41<12:10, 6.41s/it]
Generator loss: 1.89948268848307, Discriminator loss: 0.6963861961575115: 62%|██████▏ | 186/300 [22:48<12:10, 6.41s/it]
Generator loss: 1.89948268848307, Discriminator loss: 0.6963861961575115: 62%|██████▏ | 187/300 [22:48<12:03, 6.41s/it]
Generator loss: 1.8734046777381617, Discriminator loss: 0.71642957058023: 62%|██████▏ | 187/300 [22:54<12:03, 6.41s/it]
Generator loss: 1.8734046777381617, Discriminator loss: 0.71642957058023: 63%|██████▎ | 188/300 [22:54<11:57, 6.41s/it]
Generator loss: 1.897829573820619, Discriminator loss: 0.7021876989918596: 63%|██████▎ | 188/300 [23:00<11:57, 6.41s/it]
Generator loss: 1.897829573820619, Discriminator loss: 0.7021876989918596: 63%|██████▎ | 189/300 [23:00<11:47, 6.38s/it]
Generator loss: 1.9079211196478676, Discriminator loss: 0.7051354537115377: 63%|██████▎ | 189/300 [23:07<11:47, 6.38s/it]
Generator loss: 1.9079211196478676, Discriminator loss: 0.7051354537115377: 63%|██████▎ | 190/300 [23:07<11:38, 6.35s/it]
Generator loss: 1.8897704271709217, Discriminator loss: 0.7202173291760332: 63%|██████▎ | 190/300 [23:13<11:38, 6.35s/it]
Generator loss: 1.8897704271709217, Discriminator loss: 0.7202173291760332: 64%|██████▎ | 191/300 [23:13<11:29, 6.32s/it]
Generator loss: 1.896269340725506, Discriminator loss: 0.6983693526948199: 64%|██████▎ | 191/300 [23:19<11:29, 6.32s/it]
Generator loss: 1.896269340725506, Discriminator loss: 0.6983693526948199: 64%|██████▍ | 192/300 [23:19<11:22, 6.32s/it]
Generator loss: 1.9090866350075777, Discriminator loss: 0.7042679291437653: 64%|██████▍ | 192/300 [23:26<11:22, 6.32s/it]
Generator loss: 1.9090866350075777, Discriminator loss: 0.7042679291437653: 64%|██████▍ | 193/300 [23:26<11:15, 6.32s/it]
Generator loss: 1.9113440697684008, Discriminator loss: 0.6876289436922354: 64%|██████▍ | 193/300 [23:32<11:15, 6.32s/it]
Generator loss: 1.9113440697684008, Discriminator loss: 0.6876289436922354: 65%|██████▍ | 194/300 [23:32<11:13, 6.35s/it]
Generator loss: 1.903952716904528, Discriminator loss: 0.7130992004976553: 65%|██████▍ | 194/300 [23:38<11:13, 6.35s/it]
Generator loss: 1.903952716904528, Discriminator loss: 0.7130992004976553: 65%|██████▌ | 195/300 [23:38<11:06, 6.35s/it]
Generator loss: 1.9276543259620667, Discriminator loss: 0.6819565396975068: 65%|██████▌ | 195/300 [23:45<11:06, 6.35s/it]
Generator loss: 1.9276543259620667, Discriminator loss: 0.6819565396975068: 65%|██████▌ | 196/300 [23:45<11:00, 6.35s/it]
Generator loss: 1.9072776890414602, Discriminator loss: 0.7404027844176573: 65%|██████▌ | 196/300 [23:51<11:00, 6.35s/it]
Generator loss: 1.9072776890414602, Discriminator loss: 0.7404027844176573: 66%|██████▌ | 197/300 [23:51<10:53, 6.35s/it]
Generator loss: 1.9140455117997002, Discriminator loss: 0.695412377224249: 66%|██████▌ | 197/300 [23:57<10:53, 6.35s/it]
Generator loss: 1.9140455117997002, Discriminator loss: 0.695412377224249: 66%|██████▌ | 198/300 [23:57<10:47, 6.35s/it]
Generator loss: 1.939264622681281, Discriminator loss: 0.6866086449693231: 66%|██████▌ | 198/300 [24:04<10:47, 6.35s/it]
Generator loss: 1.939264622681281, Discriminator loss: 0.6866086449693231: 66%|██████▋ | 199/300 [24:04<10:40, 6.34s/it]
Generator loss: 1.8983901568195398, Discriminator loss: 0.7000106559956775: 66%|██████▋ | 199/300 [24:10<10:40, 6.34s/it]
Generator loss: 1.8983901568195398, Discriminator loss: 0.7000106559956775: 67%|██████▋ | 200/300 [24:10<10:37, 6.37s/it]
Generator loss: 1.9391444851370419, Discriminator loss: 0.6873982890563852: 67%|██████▋ | 200/300 [24:17<10:37, 6.37s/it]
Generator loss: 1.9391444851370419, Discriminator loss: 0.6873982890563852: 67%|██████▋ | 201/300 [24:17<10:29, 6.36s/it]
Generator loss: 1.937527555753203, Discriminator loss: 0.6812970143030671: 67%|██████▋ | 201/300 [24:23<10:29, 6.36s/it]
Generator loss: 1.937527555753203, Discriminator loss: 0.6812970143030671: 67%|██████▋ | 202/300 [24:23<10:22, 6.36s/it]
Generator loss: 1.9439553898923538, Discriminator loss: 0.6840969950837248: 67%|██████▋ | 202/300 [24:29<10:22, 6.36s/it]
Generator loss: 1.9439553898923538, Discriminator loss: 0.6840969950837248: 68%|██████▊ | 203/300 [24:29<10:15, 6.35s/it]
Generator loss: 1.9516027254216812, Discriminator loss: 0.6815296915524146: 68%|██████▊ | 203/300 [24:36<10:15, 6.35s/it]
Generator loss: 1.9516027254216812, Discriminator loss: 0.6815296915524146: 68%|██████▊ | 204/300 [24:36<10:09, 6.35s/it]
Generator loss: 1.947387943373007, Discriminator loss: 0.7077010602635496: 68%|██████▊ | 204/300 [24:42<10:09, 6.35s/it]
Generator loss: 1.947387943373007, Discriminator loss: 0.7077010602635496: 68%|██████▊ | 205/300 [24:42<10:02, 6.34s/it]
Generator loss: 1.943607038434814, Discriminator loss: 0.6858728102901402: 68%|██████▊ | 205/300 [24:48<10:02, 6.34s/it]
Generator loss: 1.943607038434814, Discriminator loss: 0.6858728102901402: 69%|██████▊ | 206/300 [24:48<09:56, 6.34s/it]
Generator loss: 1.955567304702366, Discriminator loss: 0.6870937211548581: 69%|██████▊ | 206/300 [24:55<09:56, 6.34s/it]
Generator loss: 1.955567304702366, Discriminator loss: 0.6870937211548581: 69%|██████▉ | 207/300 [24:55<09:52, 6.37s/it]
Generator loss: 1.939436419045224, Discriminator loss: 0.684797972878989: 69%|██████▉ | 207/300 [25:01<09:52, 6.37s/it]
Generator loss: 1.939436419045224, Discriminator loss: 0.684797972878989: 69%|██████▉ | 208/300 [25:01<09:45, 6.36s/it]
Generator loss: 1.951779685476247, Discriminator loss: 0.6865587295854793: 69%|██████▉ | 208/300 [25:07<09:45, 6.36s/it]
Generator loss: 1.951779685476247, Discriminator loss: 0.6865587295854793: 70%|██████▉ | 209/300 [25:07<09:38, 6.36s/it]
Generator loss: 1.9524799935957964, Discriminator loss: 0.6790948058752453: 70%|██████▉ | 209/300 [25:14<09:38, 6.36s/it]
Generator loss: 1.9524799935957964, Discriminator loss: 0.6790948058752453: 70%|███████ | 210/300 [25:14<09:31, 6.35s/it]
Generator loss: 1.9670421332120895, Discriminator loss: 0.6861023486537092: 70%|███████ | 210/300 [25:20<09:31, 6.35s/it]
Generator loss: 1.9670421332120895, Discriminator loss: 0.6861023486537092: 70%|███████ | 211/300 [25:20<09:24, 6.35s/it]
Generator loss: 1.9545674709712757, Discriminator loss: 0.6863196763922187: 70%|███████ | 211/300 [25:26<09:24, 6.35s/it]
Generator loss: 1.9545674709712757, Discriminator loss: 0.6863196763922187: 71%|███████ | 212/300 [25:26<09:18, 6.34s/it]
Generator loss: 1.958948059117093, Discriminator loss: 0.6826620995998383: 71%|███████ | 212/300 [25:33<09:18, 6.34s/it]
Generator loss: 1.958948059117093, Discriminator loss: 0.6826620995998383: 71%|███████ | 213/300 [25:33<09:14, 6.37s/it]
Generator loss: 1.9341519448687048, Discriminator loss: 0.6950189532602534: 71%|███████ | 213/300 [25:39<09:14, 6.37s/it]
Generator loss: 1.9341519448687048, Discriminator loss: 0.6950189532602534: 71%|███████▏ | 214/300 [25:39<09:06, 6.36s/it]
Generator loss: 1.9501662600566358, Discriminator loss: 0.6898260752067846: 71%|███████▏ | 214/300 [25:46<09:06, 6.36s/it]
Generator loss: 1.9501662600566358, Discriminator loss: 0.6898260752067846: 72%|███████▏ | 215/300 [25:46<08:59, 6.35s/it]
Generator loss: 1.9576767919694675, Discriminator loss: 0.6672668312402332: 72%|███████▏ | 215/300 [25:52<08:59, 6.35s/it]
Generator loss: 1.9576767919694675, Discriminator loss: 0.6672668312402332: 72%|███████▏ | 216/300 [25:52<08:53, 6.35s/it]
Generator loss: 1.9782413156593548, Discriminator loss: 0.6659881433143335: 72%|███████▏ | 216/300 [25:58<08:53, 6.35s/it]
Generator loss: 1.9782413156593548, Discriminator loss: 0.6659881433143335: 72%|███████▏ | 217/300 [25:58<08:46, 6.35s/it]
Generator loss: 1.97869812039768, Discriminator loss: 0.6723256720339551: 72%|███████▏ | 217/300 [26:05<08:46, 6.35s/it]
Generator loss: 1.97869812039768, Discriminator loss: 0.6723256720339551: 73%|███████▎ | 218/300 [26:05<08:40, 6.34s/it]
Generator loss: 1.9745400394586956, Discriminator loss: 0.6871117230723885: 73%|███████▎ | 218/300 [26:11<08:40, 6.34s/it]
Generator loss: 1.9745400394586956, Discriminator loss: 0.6871117230723885: 73%|███████▎ | 219/300 [26:11<08:35, 6.37s/it]
Generator loss: 1.9665792154915192, Discriminator loss: 0.6812433035058134: 73%|███████▎ | 219/300 [26:17<08:35, 6.37s/it]
Generator loss: 1.9665792154915192, Discriminator loss: 0.6812433035058134: 73%|███████▎ | 220/300 [26:17<08:28, 6.36s/it]
Generator loss: 1.9785992275266087, Discriminator loss: 0.67591892182827: 73%|███████▎ | 220/300 [26:24<08:28, 6.36s/it]
Generator loss: 1.9785992275266087, Discriminator loss: 0.67591892182827: 74%|███████▎ | 221/300 [26:24<08:21, 6.35s/it]
Generator loss: 1.973692320725497, Discriminator loss: 0.676657390945098: 74%|███████▎ | 221/300 [26:30<08:21, 6.35s/it]
Generator loss: 1.973692320725497, Discriminator loss: 0.676657390945098: 74%|███████▍ | 222/300 [26:30<08:15, 6.35s/it]
Generator loss: 1.9833708035157007, Discriminator loss: 0.6975493062944973: 74%|███████▍ | 222/300 [26:36<08:15, 6.35s/it]
Generator loss: 1.9833708035157007, Discriminator loss: 0.6975493062944973: 74%|███████▍ | 223/300 [26:36<08:08, 6.34s/it]
Generator loss: 1.9668212234973907, Discriminator loss: 0.6715009891811539: 74%|███████▍ | 223/300 [26:43<08:08, 6.34s/it]
Generator loss: 1.9668212234973907, Discriminator loss: 0.6715009891811539: 75%|███████▍ | 224/300 [26:43<08:02, 6.34s/it]
Generator loss: 1.9748070257551529, Discriminator loss: 0.6686904412858626: 75%|███████▍ | 224/300 [26:49<08:02, 6.34s/it]
Generator loss: 1.9748070257551529, Discriminator loss: 0.6686904412858626: 75%|███████▌ | 225/300 [26:49<07:57, 6.37s/it]
Generator loss: 2.0004925999571297, Discriminator loss: 0.6750858426094055: 75%|███████▌ | 225/300 [26:55<07:57, 6.37s/it]
Generator loss: 2.0004925999571297, Discriminator loss: 0.6750858426094055: 75%|███████▌ | 226/300 [26:55<07:52, 6.38s/it]
Generator loss: 1.9894653199350132, Discriminator loss: 0.6713695771553937: 75%|███████▌ | 226/300 [27:02<07:52, 6.38s/it]
Generator loss: 1.9894653199350132, Discriminator loss: 0.6713695771553937: 76%|███████▌ | 227/300 [27:02<07:47, 6.40s/it]
Generator loss: 1.9966776563840754, Discriminator loss: 0.6589639384080382: 76%|███████▌ | 227/300 [27:08<07:47, 6.40s/it]
Generator loss: 1.9966776563840754, Discriminator loss: 0.6589639384080382: 76%|███████▌ | 228/300 [27:08<07:40, 6.39s/it]
Generator loss: 1.994829827810035, Discriminator loss: 0.6754506212823531: 76%|███████▌ | 228/300 [27:15<07:40, 6.39s/it]
Generator loss: 1.994829827810035, Discriminator loss: 0.6754506212823531: 76%|███████▋ | 229/300 [27:15<07:33, 6.39s/it]
Generator loss: 2.001828783575226, Discriminator loss: 0.650514531223213: 76%|███████▋ | 229/300 [27:21<07:33, 6.39s/it]
Generator loss: 2.001828783575226, Discriminator loss: 0.650514531223213: 77%|███████▋ | 230/300 [27:21<07:28, 6.40s/it]
Generator loss: 2.0208084548220917, Discriminator loss: 0.6529218304683181: 77%|███████▋ | 230/300 [27:28<07:28, 6.40s/it]
Generator loss: 2.0208084548220917, Discriminator loss: 0.6529218304683181: 77%|███████▋ | 231/300 [27:28<07:23, 6.43s/it]
Generator loss: 2.000203351764118, Discriminator loss: 0.665237603818669: 77%|███████▋ | 231/300 [27:34<07:23, 6.43s/it]
Generator loss: 2.000203351764118, Discriminator loss: 0.665237603818669: 77%|███████▋ | 232/300 [27:34<07:15, 6.40s/it]
Generator loss: 2.0168945166994545, Discriminator loss: 0.6575310440624461: 77%|███████▋ | 232/300 [27:40<07:15, 6.40s/it]
Generator loss: 2.0168945166994545, Discriminator loss: 0.6575310440624461: 78%|███████▊ | 233/300 [27:40<07:08, 6.40s/it]
Generator loss: 2.0304839768830467, Discriminator loss: 0.6567836140885073: 78%|███████▊ | 233/300 [27:47<07:08, 6.40s/it]
Generator loss: 2.0304839768830467, Discriminator loss: 0.6567836140885073: 78%|███████▊ | 234/300 [27:47<07:02, 6.40s/it]
Generator loss: 1.983285310513833, Discriminator loss: 0.692187463535982: 78%|███████▊ | 234/300 [27:53<07:02, 6.40s/it]
Generator loss: 1.983285310513833, Discriminator loss: 0.692187463535982: 78%|███████▊ | 235/300 [27:53<06:55, 6.39s/it]
Generator loss: 1.9890784682596432, Discriminator loss: 0.6605642341515597: 78%|███████▊ | 235/300 [28:00<06:55, 6.39s/it]
Generator loss: 1.9890784682596432, Discriminator loss: 0.6605642341515597: 79%|███████▊ | 236/300 [28:00<06:49, 6.39s/it]
Generator loss: 2.0142978377201977, Discriminator loss: 0.653194293818053: 79%|███████▊ | 236/300 [28:06<06:49, 6.39s/it]
Generator loss: 2.0142978377201977, Discriminator loss: 0.653194293818053: 79%|███████▉ | 237/300 [28:06<06:44, 6.42s/it]
Generator loss: 2.049765216953614, Discriminator loss: 0.6443047887262177: 79%|███████▉ | 237/300 [28:12<06:44, 6.42s/it]
Generator loss: 2.049765216953614, Discriminator loss: 0.6443047887262177: 79%|███████▉ | 238/300 [28:12<06:38, 6.43s/it]
Generator loss: 2.0278301063705895, Discriminator loss: 0.64878829963067: 79%|███████▉ | 238/300 [28:19<06:38, 6.43s/it]
Generator loss: 2.0278301063705895, Discriminator loss: 0.64878829963067: 80%|███████▉ | 239/300 [28:19<06:31, 6.42s/it]
Generator loss: 2.043862792498925, Discriminator loss: 0.658869181485737: 80%|███████▉ | 239/300 [28:25<06:31, 6.42s/it]
Generator loss: 2.043862792498925, Discriminator loss: 0.658869181485737: 80%|████████ | 240/300 [28:25<06:24, 6.41s/it]
Generator loss: 2.0490779552389595, Discriminator loss: 0.6539010339800049: 80%|████████ | 240/300 [28:32<06:24, 6.41s/it]
Generator loss: 2.0490779552389595, Discriminator loss: 0.6539010339800049: 80%|████████ | 241/300 [28:32<06:18, 6.41s/it]
Generator loss: 2.0562286771395626, Discriminator loss: 0.6415835838107502: 80%|████████ | 241/300 [28:38<06:18, 6.41s/it]
Generator loss: 2.0562286771395626, Discriminator loss: 0.6415835838107502: 81%|████████ | 242/300 [28:38<06:12, 6.42s/it]
Generator loss: 2.0574584980221355, Discriminator loss: 0.633175873581101: 81%|████████ | 242/300 [28:45<06:12, 6.42s/it]
Generator loss: 2.0574584980221355, Discriminator loss: 0.633175873581101: 81%|████████ | 243/300 [28:45<06:07, 6.44s/it]
Generator loss: 2.0515074326711544, Discriminator loss: 0.6391502916812897: 81%|████████ | 243/300 [28:51<06:07, 6.44s/it]
Generator loss: 2.0515074326711544, Discriminator loss: 0.6391502916812897: 81%|████████▏ | 244/300 [28:51<05:59, 6.43s/it]
Generator loss: 2.0736149461830364, Discriminator loss: 0.6443934633451349: 81%|████████▏ | 244/300 [28:57<05:59, 6.43s/it]
Generator loss: 2.0736149461830364, Discriminator loss: 0.6443934633451349: 82%|████████▏ | 245/300 [28:57<05:53, 6.43s/it]
Generator loss: 2.0439358862007366, Discriminator loss: 0.675808913567487: 82%|████████▏ | 245/300 [29:04<05:53, 6.43s/it]
Generator loss: 2.0439358862007366, Discriminator loss: 0.675808913567487: 82%|████████▏ | 246/300 [29:04<05:46, 6.42s/it]
Generator loss: 2.0692701777991127, Discriminator loss: 0.6446802813340636: 82%|████████▏ | 246/300 [29:10<05:46, 6.42s/it]
Generator loss: 2.0692701777991127, Discriminator loss: 0.6446802813340636: 82%|████████▏ | 247/300 [29:10<05:40, 6.42s/it]
Generator loss: 2.049621323452276, Discriminator loss: 0.6281508106519195: 82%|████████▏ | 247/300 [29:17<05:40, 6.42s/it]
Generator loss: 2.049621323452276, Discriminator loss: 0.6281508106519195: 83%|████████▎ | 248/300 [29:17<05:33, 6.41s/it]
Generator loss: 2.068838319357704, Discriminator loss: 0.6554880567333278: 83%|████████▎ | 248/300 [29:23<05:33, 6.41s/it]
Generator loss: 2.068838319357704, Discriminator loss: 0.6554880567333278: 83%|████████▎ | 249/300 [29:23<05:27, 6.42s/it]
Generator loss: 2.0447138083331726, Discriminator loss: 0.6430171342457042: 83%|████████▎ | 249/300 [29:30<05:27, 6.42s/it]
Generator loss: 2.0447138083331726, Discriminator loss: 0.6430171342457042: 83%|████████▎ | 250/300 [29:30<05:22, 6.44s/it]
Generator loss: 2.0798104010960636, Discriminator loss: 0.6381222447928261: 83%|████████▎ | 250/300 [29:36<05:22, 6.44s/it]
Generator loss: 2.0798104010960636, Discriminator loss: 0.6381222447928261: 84%|████████▎ | 251/300 [29:36<05:15, 6.43s/it]
Generator loss: 2.071589014985982, Discriminator loss: 0.6498981937766075: 84%|████████▎ | 251/300 [29:42<05:15, 6.43s/it]
Generator loss: 2.071589014985982, Discriminator loss: 0.6498981937766075: 84%|████████▍ | 252/300 [29:42<05:08, 6.43s/it]
Generator loss: 2.0728830090340447, Discriminator loss: 0.6422935833825785: 84%|████████▍ | 252/300 [29:49<05:08, 6.43s/it]
Generator loss: 2.0728830090340447, Discriminator loss: 0.6422935833825785: 84%|████████▍ | 253/300 [29:49<05:01, 6.42s/it]
Generator loss: 2.059836439350072, Discriminator loss: 0.6349203437566757: 84%|████████▍ | 253/300 [29:55<05:01, 6.42s/it]
Generator loss: 2.059836439350072, Discriminator loss: 0.6349203437566757: 85%|████████▍ | 254/300 [29:55<04:55, 6.41s/it]
Generator loss: 2.091802353368086, Discriminator loss: 0.6277351261061781: 85%|████████▍ | 254/300 [30:02<04:55, 6.41s/it]
Generator loss: 2.091802353368086, Discriminator loss: 0.6277351261061781: 85%|████████▌ | 255/300 [30:02<04:48, 6.42s/it]
Generator loss: 2.0656274004894146, Discriminator loss: 0.6457043259459383: 85%|████████▌ | 255/300 [30:08<04:48, 6.42s/it]
Generator loss: 2.0656274004894146, Discriminator loss: 0.6457043259459383: 85%|████████▌ | 256/300 [30:08<04:43, 6.45s/it]
Generator loss: 2.1072967964060165, Discriminator loss: 0.6247626034652486: 85%|████████▌ | 256/300 [30:15<04:43, 6.45s/it]
Generator loss: 2.1072967964060165, Discriminator loss: 0.6247626034652486: 86%|████████▌ | 257/300 [30:15<04:36, 6.44s/it]
Generator loss: 2.0962683190317715, Discriminator loss: 0.631443692919086: 86%|████████▌ | 257/300 [30:21<04:36, 6.44s/it]
Generator loss: 2.0962683190317715, Discriminator loss: 0.631443692919086: 86%|████████▌ | 258/300 [30:21<04:30, 6.43s/it]
Generator loss: 2.1010903006090835, Discriminator loss: 0.6387709094321027: 86%|████████▌ | 258/300 [30:27<04:30, 6.43s/it]
Generator loss: 2.1010903006090835, Discriminator loss: 0.6387709094321027: 86%|████████▋ | 259/300 [30:27<04:23, 6.43s/it]
Generator loss: 2.108405414749594, Discriminator loss: 0.6291532516479492: 86%|████████▋ | 259/300 [30:34<04:23, 6.43s/it]
Generator loss: 2.108405414749594, Discriminator loss: 0.6291532516479492: 87%|████████▋ | 260/300 [30:34<04:17, 6.43s/it]
Generator loss: 2.1114456373102524, Discriminator loss: 0.6167570541010183: 87%|████████▋ | 260/300 [30:40<04:17, 6.43s/it]
Generator loss: 2.1114456373102524, Discriminator loss: 0.6167570541010183: 87%|████████▋ | 261/300 [30:40<04:10, 6.43s/it]
Generator loss: 2.1329608182696735, Discriminator loss: 0.6212209826883148: 87%|████████▋ | 261/300 [30:47<04:10, 6.43s/it]
Generator loss: 2.1329608182696735, Discriminator loss: 0.6212209826883148: 87%|████████▋ | 262/300 [30:47<04:05, 6.45s/it]
Generator loss: 2.120411331162733, Discriminator loss: 0.6373535583124441: 87%|████████▋ | 262/300 [30:53<04:05, 6.45s/it]
Generator loss: 2.120411331162733, Discriminator loss: 0.6373535583124441: 88%|████████▊ | 263/300 [30:53<03:59, 6.48s/it]
Generator loss: 2.099952261237537, Discriminator loss: 0.6335013679721776: 88%|████████▊ | 263/300 [31:00<03:59, 6.48s/it]
Generator loss: 2.099952261237537, Discriminator loss: 0.6335013679721776: 88%|████████▊ | 264/300 [31:00<03:54, 6.52s/it]
Generator loss: 2.1003993810976254, Discriminator loss: 0.6331959783154375: 88%|████████▊ | 264/300 [31:07<03:54, 6.52s/it]
Generator loss: 2.1003993810976254, Discriminator loss: 0.6331959783154375: 88%|████████▊ | 265/300 [31:07<03:49, 6.55s/it]
Generator loss: 2.088849120280322, Discriminator loss: 0.6298180586274933: 88%|████████▊ | 265/300 [31:13<03:49, 6.55s/it]
Generator loss: 2.088849120280322, Discriminator loss: 0.6298180586274933: 89%|████████▊ | 266/300 [31:13<03:43, 6.57s/it]
Generator loss: 2.1161189938292786, Discriminator loss: 0.6166971799205331: 89%|████████▊ | 266/300 [31:20<03:43, 6.57s/it]
Generator loss: 2.1161189938292786, Discriminator loss: 0.6166971799205331: 89%|████████▉ | 267/300 [31:20<03:37, 6.59s/it]
Generator loss: 2.142376761226093, Discriminator loss: 0.6130306541043169: 89%|████████▉ | 267/300 [31:27<03:37, 6.59s/it]
Generator loss: 2.142376761226093, Discriminator loss: 0.6130306541043169: 89%|████████▉ | 268/300 [31:27<03:32, 6.65s/it]
Generator loss: 2.1195085732375873, Discriminator loss: 0.6244634438086959: 89%|████████▉ | 268/300 [31:33<03:32, 6.65s/it]
Generator loss: 2.1195085732375873, Discriminator loss: 0.6244634438086959: 90%|████████▉ | 269/300 [31:33<03:26, 6.67s/it]
Generator loss: 2.136966422200203, Discriminator loss: 0.6256932173581684: 90%|████████▉ | 269/300 [31:40<03:26, 6.67s/it]
Generator loss: 2.136966422200203, Discriminator loss: 0.6256932173581684: 90%|█████████ | 270/300 [31:40<03:20, 6.69s/it]
Generator loss: 2.1253008071114037, Discriminator loss: 0.6211840646231875: 90%|█████████ | 270/300 [31:47<03:20, 6.69s/it]
Generator loss: 2.1253008071114037, Discriminator loss: 0.6211840646231875: 90%|█████████ | 271/300 [31:47<03:14, 6.70s/it]
Generator loss: 2.1245981340899185, Discriminator loss: 0.6270312956150841: 90%|█████████ | 271/300 [31:53<03:14, 6.70s/it]
Generator loss: 2.1245981340899185, Discriminator loss: 0.6270312956150841: 91%|█████████ | 272/300 [31:53<03:07, 6.71s/it]
Generator loss: 2.151730140342432, Discriminator loss: 0.6084378054913353: 91%|█████████ | 272/300 [32:00<03:07, 6.71s/it]
Generator loss: 2.151730140342432, Discriminator loss: 0.6084378054913353: 91%|█████████ | 273/300 [32:00<03:01, 6.72s/it]
Generator loss: 2.1285239063641606, Discriminator loss: 0.6252047546646174: 91%|█████████ | 273/300 [32:07<03:01, 6.72s/it]
Generator loss: 2.1285239063641606, Discriminator loss: 0.6252047546646174: 91%|█████████▏| 274/300 [32:07<02:54, 6.73s/it]
Generator loss: 2.1367137300617554, Discriminator loss: 0.6136608995935496: 91%|█████████▏| 274/300 [32:14<02:54, 6.73s/it]
Generator loss: 2.1367137300617554, Discriminator loss: 0.6136608995935496: 92%|█████████▏| 275/300 [32:14<02:47, 6.71s/it]
Generator loss: 2.147204104153549, Discriminator loss: 0.6176723807173616: 92%|█████████▏| 275/300 [32:20<02:47, 6.71s/it]
Generator loss: 2.147204104153549, Discriminator loss: 0.6176723807173616: 92%|█████████▏| 276/300 [32:20<02:41, 6.71s/it]
Generator loss: 2.1437266688136494, Discriminator loss: 0.6095429987591856: 92%|█████████▏| 276/300 [32:27<02:41, 6.71s/it]
Generator loss: 2.1437266688136494, Discriminator loss: 0.6095429987591856: 92%|█████████▏| 277/300 [32:27<02:33, 6.68s/it]
Generator loss: 2.152301244875964, Discriminator loss: 0.618432903991026: 92%|█████████▏| 277/300 [32:34<02:33, 6.68s/it]
Generator loss: 2.152301244875964, Discriminator loss: 0.618432903991026: 93%|█████████▎| 278/300 [32:34<02:27, 6.69s/it]
Generator loss: 2.1435503083116867, Discriminator loss: 0.6124763900742811: 93%|█████████▎| 278/300 [32:40<02:27, 6.69s/it]
Generator loss: 2.1435503083116867, Discriminator loss: 0.6124763900742811: 93%|█████████▎| 279/300 [32:40<02:20, 6.69s/it]
Generator loss: 2.1760846148518955, Discriminator loss: 0.6109043350991081: 93%|█████████▎| 279/300 [32:47<02:20, 6.69s/it]
Generator loss: 2.1760846148518955, Discriminator loss: 0.6109043350991081: 93%|█████████▎| 280/300 [32:47<02:14, 6.70s/it]
Generator loss: 2.1473377986865887, Discriminator loss: 0.6142729858265203: 93%|█████████▎| 280/300 [32:54<02:14, 6.70s/it]
Generator loss: 2.1473377986865887, Discriminator loss: 0.6142729858265203: 94%|█████████▎| 281/300 [32:54<02:07, 6.70s/it]
Generator loss: 2.147419778739705, Discriminator loss: 0.6075115444905618: 94%|█████████▎| 281/300 [33:00<02:07, 6.70s/it]
Generator loss: 2.147419778739705, Discriminator loss: 0.6075115444905618: 94%|█████████▍| 282/300 [33:00<02:00, 6.68s/it]
Generator loss: 2.1586636971024906, Discriminator loss: 0.6037897603476748: 94%|█████████▍| 282/300 [33:07<02:00, 6.68s/it]
Generator loss: 2.1586636971024906, Discriminator loss: 0.6037897603476748: 94%|█████████▍| 283/300 [33:07<01:53, 6.67s/it]
Generator loss: 2.1525688136325165, Discriminator loss: 0.611905101029312: 94%|█████████▍| 283/300 [33:14<01:53, 6.67s/it]
Generator loss: 2.1525688136325165, Discriminator loss: 0.611905101029312: 95%|█████████▍| 284/300 [33:14<01:46, 6.66s/it]
Generator loss: 2.1883829025661243, Discriminator loss: 0.6092779662679223: 95%|█████████▍| 284/300 [33:20<01:46, 6.66s/it]
Generator loss: 2.1883829025661243, Discriminator loss: 0.6092779662679223: 95%|█████████▌| 285/300 [33:20<01:39, 6.66s/it]
Generator loss: 2.175755638410063, Discriminator loss: 0.5928393164101768: 95%|█████████▌| 285/300 [33:27<01:39, 6.66s/it]
Generator loss: 2.175755638410063, Discriminator loss: 0.5928393164101768: 95%|█████████▌| 286/300 [33:27<01:33, 6.67s/it]
Generator loss: 2.1687005433966133, Discriminator loss: 0.6157251898856724: 95%|█████████▌| 286/300 [33:34<01:33, 6.67s/it]
Generator loss: 2.1687005433966133, Discriminator loss: 0.6157251898856724: 96%|█████████▌| 287/300 [33:34<01:26, 6.65s/it]
Generator loss: 2.16642308585784, Discriminator loss: 0.6245952195980969: 96%|█████████▌| 287/300 [33:40<01:26, 6.65s/it]
Generator loss: 2.16642308585784, Discriminator loss: 0.6245952195980969: 96%|█████████▌| 288/300 [33:40<01:19, 6.63s/it]
Generator loss: 2.178683908546672, Discriminator loss: 0.593368058476378: 96%|█████████▌| 288/300 [33:47<01:19, 6.63s/it]
Generator loss: 2.178683908546672, Discriminator loss: 0.593368058476378: 96%|█████████▋| 289/300 [33:47<01:12, 6.62s/it]
Generator loss: 2.181570036446347, Discriminator loss: 0.5935841163291651: 96%|█████████▋| 289/300 [33:53<01:12, 6.62s/it]
Generator loss: 2.181570036446347, Discriminator loss: 0.5935841163291651: 97%|█████████▋| 290/300 [33:53<01:06, 6.62s/it]
Generator loss: 2.180004572167116, Discriminator loss: 0.6197993030004642: 97%|█████████▋| 290/300 [34:00<01:06, 6.62s/it]
Generator loss: 2.180004572167116, Discriminator loss: 0.6197993030004642: 97%|█████████▋| 291/300 [34:00<00:59, 6.61s/it]
Generator loss: 2.157050187096876, Discriminator loss: 0.5961371349061236: 97%|█████████▋| 291/300 [34:07<00:59, 6.61s/it]
Generator loss: 2.157050187096876, Discriminator loss: 0.5961371349061236: 97%|█████████▋| 292/300 [34:07<00:52, 6.62s/it]
Generator loss: 2.181761022876291, Discriminator loss: 0.5929972853730706: 97%|█████████▋| 292/300 [34:13<00:52, 6.62s/it]
Generator loss: 2.181761022876291, Discriminator loss: 0.5929972853730706: 98%|█████████▊| 293/300 [34:13<00:46, 6.65s/it]
Generator loss: 2.198557503959712, Discriminator loss: 0.6052266035009833: 98%|█████████▊| 293/300 [34:20<00:46, 6.65s/it]
Generator loss: 2.198557503959712, Discriminator loss: 0.6052266035009833: 98%|█████████▊| 294/300 [34:20<00:39, 6.64s/it]
Generator loss: 2.1911894468700184, Discriminator loss: 0.5976759361870149: 98%|█████████▊| 294/300 [34:27<00:39, 6.64s/it]
Generator loss: 2.1911894468700184, Discriminator loss: 0.5976759361870149: 98%|█████████▊| 295/300 [34:27<00:33, 6.62s/it]
Generator loss: 2.207013448371607, Discriminator loss: 0.5941765610786045: 98%|█████████▊| 295/300 [34:33<00:33, 6.62s/it]
Generator loss: 2.207013448371607, Discriminator loss: 0.5941765610786045: 99%|█████████▊| 296/300 [34:33<00:26, 6.60s/it]
Generator loss: 2.2090228690820584, Discriminator loss: 0.5952651058049763: 99%|█████████▊| 296/300 [34:40<00:26, 6.60s/it]
Generator loss: 2.2090228690820584, Discriminator loss: 0.5952651058049763: 99%|█████████▉| 297/300 [34:40<00:19, 6.61s/it]
Generator loss: 2.207116046372582, Discriminator loss: 0.5886701748651617: 99%|█████████▉| 297/300 [34:46<00:19, 6.61s/it]
Generator loss: 2.207116046372582, Discriminator loss: 0.5886701748651617: 99%|█████████▉| 298/300 [34:46<00:13, 6.60s/it]
Generator loss: 2.222158696721582, Discriminator loss: 0.5882657041006228: 99%|█████████▉| 298/300 [34:53<00:13, 6.60s/it]
Generator loss: 2.222158696721582, Discriminator loss: 0.5882657041006228: 100%|█████████▉| 299/300 [34:53<00:06, 6.64s/it]
Generator loss: 2.2106246729107464, Discriminator loss: 0.5984119886861128: 100%|█████████▉| 299/300 [35:00<00:06, 6.64s/it]
Generator loss: 2.2106246729107464, Discriminator loss: 0.5984119886861128: 100%|██████████| 300/300 [35:00<00:00, 6.60s/it]
Generator loss: 2.2106246729107464, Discriminator loss: 0.5984119886861128: 100%|██████████| 300/300 [35:00<00:00, 7.00s/it]
Training Completed!
serious_mnist.py
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loss 2.32 accuracy 0.09: 0%| | 0/1875 [00:07<?, ?it/s]
loss 2.32 accuracy 0.09: 0%| | 1/1875 [00:07<3:53:47, 7.49s/it]
loss 2.32 accuracy 0.06: 0%| | 1/1875 [00:10<3:53:47, 7.49s/it]
loss 2.32 accuracy 0.06: 0%| | 2/1875 [00:10<2:23:39, 4.60s/it]
loss 2.34 accuracy 0.12: 0%| | 2/1875 [00:10<2:23:39, 4.60s/it]
loss 2.29 accuracy 0.25: 0%| | 2/1875 [00:10<2:23:39, 4.60s/it]
loss 2.29 accuracy 0.22: 0%| | 2/1875 [00:10<2:23:39, 4.60s/it]
loss 2.24 accuracy 0.22: 0%| | 2/1875 [00:10<2:23:39, 4.60s/it]
loss 2.26 accuracy 0.06: 0%| | 2/1875 [00:10<2:23:39, 4.60s/it]
loss 2.26 accuracy 0.06: 0%| | 7/1875 [00:10<28:06, 1.11it/s]
loss 2.35 accuracy 0.19: 0%| | 7/1875 [00:10<28:06, 1.11it/s]
loss 2.37 accuracy 0.19: 0%| | 7/1875 [00:10<28:06, 1.11it/s]
loss 2.25 accuracy 0.12: 0%| | 7/1875 [00:10<28:06, 1.11it/s]
loss 2.16 accuracy 0.31: 0%| | 7/1875 [00:10<28:06, 1.11it/s]
loss 2.24 accuracy 0.12: 0%| | 7/1875 [00:10<28:06, 1.11it/s]
loss 2.24 accuracy 0.12: 1%| | 12/1875 [00:10<13:23, 2.32it/s]
loss 2.16 accuracy 0.16: 1%| | 12/1875 [00:10<13:23, 2.32it/s]
loss 2.14 accuracy 0.09: 1%| | 12/1875 [00:10<13:23, 2.32it/s]
loss 2.18 accuracy 0.22: 1%| | 12/1875 [00:10<13:23, 2.32it/s]
loss 2.18 accuracy 0.25: 1%| | 12/1875 [00:10<13:23, 2.32it/s]
loss 2.03 accuracy 0.41: 1%| | 12/1875 [00:10<13:23, 2.32it/s]
loss 2.03 accuracy 0.41: 1%| | 17/1875 [00:10<07:51, 3.94it/s]
loss 2.00 accuracy 0.25: 1%| | 17/1875 [00:10<07:51, 3.94it/s]
loss 1.91 accuracy 0.47: 1%| | 17/1875 [00:10<07:51, 3.94it/s]
loss 2.09 accuracy 0.22: 1%| | 17/1875 [00:10<07:51, 3.94it/s]
loss 1.93 accuracy 0.34: 1%| | 17/1875 [00:10<07:51, 3.94it/s]
loss 1.78 accuracy 0.38: 1%| | 17/1875 [00:10<07:51, 3.94it/s]
loss 1.78 accuracy 0.38: 1%| | 22/1875 [00:10<05:05, 6.06it/s]
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loss 1.94 accuracy 0.38: 1%| | 22/1875 [00:10<05:05, 6.06it/s]
loss 1.88 accuracy 0.47: 1%| | 22/1875 [00:10<05:05, 6.06it/s]
loss 1.90 accuracy 0.38: 1%| | 22/1875 [00:10<05:05, 6.06it/s]
loss 1.89 accuracy 0.41: 1%| | 22/1875 [00:10<05:05, 6.06it/s]
loss 1.89 accuracy 0.41: 1%|▏ | 27/1875 [00:10<03:31, 8.74it/s]
loss 1.78 accuracy 0.31: 1%|▏ | 27/1875 [00:10<03:31, 8.74it/s]
loss 1.76 accuracy 0.41: 1%|▏ | 27/1875 [00:10<03:31, 8.74it/s]
loss 1.96 accuracy 0.19: 1%|▏ | 27/1875 [00:10<03:31, 8.74it/s]
loss 1.74 accuracy 0.31: 1%|▏ | 27/1875 [00:10<03:31, 8.74it/s]
loss 1.62 accuracy 0.44: 1%|▏ | 27/1875 [00:10<03:31, 8.74it/s]
loss 1.62 accuracy 0.44: 2%|▏ | 32/1875 [00:10<02:33, 12.00it/s]
loss 1.69 accuracy 0.44: 2%|▏ | 32/1875 [00:10<02:33, 12.00it/s]
loss 1.74 accuracy 0.41: 2%|▏ | 32/1875 [00:10<02:33, 12.00it/s]
loss 1.77 accuracy 0.38: 2%|▏ | 32/1875 [00:10<02:33, 12.00it/s]
loss 1.67 accuracy 0.44: 2%|▏ | 32/1875 [00:10<02:33, 12.00it/s]
loss 1.58 accuracy 0.47: 2%|▏ | 32/1875 [00:10<02:33, 12.00it/s]
loss 1.58 accuracy 0.47: 2%|▏ | 37/1875 [00:10<01:56, 15.75it/s]
loss 1.72 accuracy 0.34: 2%|▏ | 37/1875 [00:10<01:56, 15.75it/s]
loss 1.58 accuracy 0.41: 2%|▏ | 37/1875 [00:10<01:56, 15.75it/s]
loss 1.63 accuracy 0.50: 2%|▏ | 37/1875 [00:10<01:56, 15.75it/s]
loss 1.66 accuracy 0.44: 2%|▏ | 37/1875 [00:10<01:56, 15.75it/s]
loss 1.78 accuracy 0.38: 2%|▏ | 37/1875 [00:10<01:56, 15.75it/s]
loss 1.78 accuracy 0.38: 2%|▏ | 42/1875 [00:10<01:32, 19.90it/s]
loss 1.42 accuracy 0.47: 2%|▏ | 42/1875 [00:10<01:32, 19.90it/s]
loss 1.38 accuracy 0.53: 2%|▏ | 42/1875 [00:10<01:32, 19.90it/s]
loss 1.71 accuracy 0.34: 2%|▏ | 42/1875 [00:11<01:32, 19.90it/s]
loss 1.47 accuracy 0.41: 2%|▏ | 42/1875 [00:11<01:32, 19.90it/s]
loss 1.51 accuracy 0.47: 2%|▏ | 42/1875 [00:11<01:32, 19.90it/s]
loss 1.51 accuracy 0.47: 3%|▎ | 47/1875 [00:11<01:15, 24.18it/s]
loss 1.61 accuracy 0.38: 3%|▎ | 47/1875 [00:11<01:15, 24.18it/s]
loss 1.33 accuracy 0.53: 3%|▎ | 47/1875 [00:11<01:15, 24.18it/s]
loss 1.51 accuracy 0.56: 3%|▎ | 47/1875 [00:11<01:15, 24.18it/s]
loss 1.26 accuracy 0.69: 3%|▎ | 47/1875 [00:11<01:15, 24.18it/s]
loss 1.26 accuracy 0.56: 3%|▎ | 47/1875 [00:11<01:15, 24.18it/s]
loss 1.26 accuracy 0.56: 3%|▎ | 52/1875 [00:11<01:04, 28.33it/s]
loss 1.31 accuracy 0.56: 3%|▎ | 52/1875 [00:11<01:04, 28.33it/s]
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loss 1.29 accuracy 0.62: 3%|▎ | 52/1875 [00:11<01:04, 28.33it/s]
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loss 0.31 accuracy 0.91: 59%|█████▉ | 1102/1875 [00:34<00:16, 46.08it/s]
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loss 0.24 accuracy 0.97: 59%|█████▉ | 1112/1875 [00:34<00:16, 46.07it/s]
loss 0.18 accuracy 0.94: 59%|█████▉ | 1112/1875 [00:34<00:16, 46.07it/s]
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loss 0.44 accuracy 0.84: 60%|█████▉ | 1117/1875 [00:34<00:16, 46.07it/s]
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loss 0.31 accuracy 0.91: 60%|██████ | 1127/1875 [00:34<00:16, 45.87it/s]
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loss 0.27 accuracy 0.88: 60%|██████ | 1132/1875 [00:34<00:16, 45.88it/s]
loss 0.04 accuracy 1.00: 60%|██████ | 1132/1875 [00:34<00:16, 45.88it/s]
loss 0.09 accuracy 0.97: 60%|██████ | 1132/1875 [00:34<00:16, 45.88it/s]
loss 0.09 accuracy 0.97: 61%|██████ | 1137/1875 [00:34<00:16, 45.89it/s]
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loss 0.34 accuracy 0.97: 61%|██████ | 1137/1875 [00:34<00:16, 45.89it/s]
loss 0.07 accuracy 0.97: 61%|██████ | 1137/1875 [00:34<00:16, 45.89it/s]
loss 0.39 accuracy 0.84: 61%|██████ | 1137/1875 [00:34<00:16, 45.89it/s]
loss 0.39 accuracy 0.84: 61%|██████ | 1142/1875 [00:34<00:16, 45.77it/s]
loss 0.14 accuracy 0.97: 61%|██████ | 1142/1875 [00:34<00:16, 45.77it/s]
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loss 0.12 accuracy 0.97: 61%|██████ | 1142/1875 [00:34<00:16, 45.77it/s]
loss 0.17 accuracy 0.94: 61%|██████ | 1142/1875 [00:34<00:16, 45.77it/s]
loss 0.22 accuracy 0.91: 61%|██████ | 1142/1875 [00:34<00:16, 45.77it/s]
loss 0.22 accuracy 0.91: 61%|██████ | 1147/1875 [00:34<00:15, 45.76it/s]
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loss 0.03 accuracy 1.00: 61%|██████ | 1147/1875 [00:35<00:15, 45.76it/s]
loss 0.10 accuracy 0.94: 61%|██████ | 1147/1875 [00:35<00:15, 45.76it/s]
loss 0.05 accuracy 1.00: 61%|██████ | 1147/1875 [00:35<00:15, 45.76it/s]
loss 0.05 accuracy 1.00: 61%|██████▏ | 1152/1875 [00:35<00:15, 45.73it/s]
loss 0.08 accuracy 0.97: 61%|██████▏ | 1152/1875 [00:35<00:15, 45.73it/s]
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loss 0.03 accuracy 1.00: 61%|██████▏ | 1152/1875 [00:35<00:15, 45.73it/s]
loss 0.30 accuracy 0.94: 61%|██████▏ | 1152/1875 [00:35<00:15, 45.73it/s]
loss 0.13 accuracy 0.94: 61%|██████▏ | 1152/1875 [00:35<00:15, 45.73it/s]
loss 0.13 accuracy 0.94: 62%|██████▏ | 1157/1875 [00:35<00:15, 45.73it/s]
loss 0.06 accuracy 1.00: 62%|██████▏ | 1157/1875 [00:35<00:15, 45.73it/s]
loss 0.17 accuracy 0.97: 62%|██████▏ | 1157/1875 [00:35<00:15, 45.73it/s]
loss 0.23 accuracy 0.91: 62%|██████▏ | 1157/1875 [00:35<00:15, 45.73it/s]
loss 0.12 accuracy 0.94: 62%|██████▏ | 1157/1875 [00:35<00:15, 45.73it/s]
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loss 0.06 accuracy 1.00: 62%|██████▏ | 1162/1875 [00:35<00:15, 45.81it/s]
loss 0.19 accuracy 0.94: 62%|██████▏ | 1162/1875 [00:35<00:15, 45.81it/s]
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loss 0.21 accuracy 0.94: 62%|██████▏ | 1162/1875 [00:35<00:15, 45.81it/s]
loss 0.19 accuracy 0.91: 62%|██████▏ | 1162/1875 [00:35<00:15, 45.81it/s]
loss 0.19 accuracy 0.91: 62%|██████▏ | 1167/1875 [00:35<00:15, 45.85it/s]
loss 0.11 accuracy 0.97: 62%|██████▏ | 1167/1875 [00:35<00:15, 45.85it/s]
loss 0.23 accuracy 0.94: 62%|██████▏ | 1167/1875 [00:35<00:15, 45.85it/s]
loss 0.13 accuracy 0.97: 62%|██████▏ | 1167/1875 [00:35<00:15, 45.85it/s]
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loss 0.09 accuracy 0.97: 63%|██████▎ | 1172/1875 [00:35<00:15, 45.96it/s]
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loss 0.16 accuracy 0.97: 63%|██████▎ | 1172/1875 [00:35<00:15, 45.96it/s]
loss 0.06 accuracy 0.97: 63%|██████▎ | 1172/1875 [00:35<00:15, 45.96it/s]
loss 0.06 accuracy 1.00: 63%|██████▎ | 1172/1875 [00:35<00:15, 45.96it/s]
loss 0.13 accuracy 0.97: 63%|██████▎ | 1172/1875 [00:35<00:15, 45.96it/s]
loss 0.13 accuracy 0.97: 63%|██████▎ | 1177/1875 [00:35<00:15, 45.99it/s]
loss 0.26 accuracy 0.91: 63%|██████▎ | 1177/1875 [00:35<00:15, 45.99it/s]
loss 0.28 accuracy 0.94: 63%|██████▎ | 1177/1875 [00:35<00:15, 45.99it/s]
loss 0.06 accuracy 1.00: 63%|██████▎ | 1177/1875 [00:35<00:15, 45.99it/s]
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loss 0.10 accuracy 0.97: 63%|██████▎ | 1182/1875 [00:35<00:15, 46.04it/s]
loss 0.35 accuracy 0.88: 63%|██████▎ | 1182/1875 [00:35<00:15, 46.04it/s]
loss 0.09 accuracy 0.97: 63%|██████▎ | 1182/1875 [00:35<00:15, 46.04it/s]
loss 0.34 accuracy 0.91: 63%|██████▎ | 1182/1875 [00:35<00:15, 46.04it/s]
loss 0.08 accuracy 0.97: 63%|██████▎ | 1182/1875 [00:35<00:15, 46.04it/s]
loss 0.18 accuracy 0.94: 63%|██████▎ | 1182/1875 [00:35<00:15, 46.04it/s]
loss 0.18 accuracy 0.94: 63%|██████▎ | 1187/1875 [00:35<00:14, 46.06it/s]
loss 0.28 accuracy 0.91: 63%|██████▎ | 1187/1875 [00:35<00:14, 46.06it/s]
loss 0.14 accuracy 0.97: 63%|██████▎ | 1187/1875 [00:35<00:14, 46.06it/s]
loss 0.04 accuracy 1.00: 63%|██████▎ | 1187/1875 [00:35<00:14, 46.06it/s]
loss 0.08 accuracy 1.00: 63%|██████▎ | 1187/1875 [00:35<00:14, 46.06it/s]
loss 0.18 accuracy 0.94: 63%|██████▎ | 1187/1875 [00:35<00:14, 46.06it/s]
loss 0.18 accuracy 0.94: 64%|██████▎ | 1192/1875 [00:35<00:14, 46.07it/s]
loss 0.21 accuracy 0.94: 64%|██████▎ | 1192/1875 [00:35<00:14, 46.07it/s]
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loss 0.26 accuracy 0.94: 64%|██████▎ | 1192/1875 [00:36<00:14, 46.07it/s]
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loss 0.19 accuracy 0.91: 64%|██████▍ | 1197/1875 [00:36<00:14, 46.14it/s]
loss 0.11 accuracy 1.00: 64%|██████▍ | 1197/1875 [00:36<00:14, 46.14it/s]
loss 0.20 accuracy 0.91: 64%|██████▍ | 1197/1875 [00:36<00:14, 46.14it/s]
loss 0.13 accuracy 0.94: 64%|██████▍ | 1197/1875 [00:36<00:14, 46.14it/s]
loss 0.13 accuracy 0.94: 64%|██████▍ | 1202/1875 [00:36<00:14, 46.16it/s]
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loss 0.13 accuracy 0.94: 64%|██████▍ | 1202/1875 [00:36<00:14, 46.16it/s]
loss 0.13 accuracy 0.94: 64%|██████▍ | 1207/1875 [00:36<00:14, 46.15it/s]
loss 0.11 accuracy 0.97: 64%|██████▍ | 1207/1875 [00:36<00:14, 46.15it/s]
loss 0.09 accuracy 0.97: 64%|██████▍ | 1207/1875 [00:36<00:14, 46.15it/s]
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loss 0.06 accuracy 1.00: 64%|██████▍ | 1207/1875 [00:36<00:14, 46.15it/s]
loss 0.43 accuracy 0.94: 64%|██████▍ | 1207/1875 [00:36<00:14, 46.15it/s]
loss 0.43 accuracy 0.94: 65%|██████▍ | 1212/1875 [00:36<00:14, 46.19it/s]
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loss 0.22 accuracy 0.94: 65%|██████▍ | 1212/1875 [00:36<00:14, 46.19it/s]
loss 0.22 accuracy 0.94: 65%|██████▍ | 1217/1875 [00:36<00:14, 46.18it/s]
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loss 0.04 accuracy 1.00: 65%|██████▌ | 1222/1875 [00:36<00:14, 46.17it/s]
loss 0.09 accuracy 0.97: 65%|██████▌ | 1222/1875 [00:36<00:14, 46.17it/s]
loss 0.18 accuracy 0.94: 65%|██████▌ | 1222/1875 [00:36<00:14, 46.17it/s]
loss 0.03 accuracy 1.00: 65%|██████▌ | 1222/1875 [00:36<00:14, 46.17it/s]
loss 0.24 accuracy 0.97: 65%|██████▌ | 1222/1875 [00:36<00:14, 46.17it/s]
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loss 0.04 accuracy 1.00: 65%|██████▌ | 1227/1875 [00:36<00:14, 46.15it/s]
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loss 0.04 accuracy 1.00: 66%|██████▌ | 1232/1875 [00:36<00:13, 46.15it/s]
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loss 0.07 accuracy 0.97: 67%|██████▋ | 1262/1875 [00:37<00:13, 46.01it/s]
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loss 0.24 accuracy 0.91: 68%|██████▊ | 1267/1875 [00:37<00:13, 45.86it/s]
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loss 0.03 accuracy 1.00: 68%|██████▊ | 1272/1875 [00:37<00:13, 45.84it/s]
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loss 0.12 accuracy 0.97: 68%|██████▊ | 1272/1875 [00:37<00:13, 45.84it/s]
loss 0.26 accuracy 0.91: 68%|██████▊ | 1272/1875 [00:37<00:13, 45.84it/s]
loss 0.26 accuracy 0.94: 68%|██████▊ | 1272/1875 [00:37<00:13, 45.84it/s]
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loss 0.07 accuracy 1.00: 68%|██████▊ | 1277/1875 [00:37<00:13, 45.79it/s]
loss 0.24 accuracy 0.94: 68%|██████▊ | 1277/1875 [00:37<00:13, 45.79it/s]
loss 0.04 accuracy 1.00: 68%|██████▊ | 1277/1875 [00:37<00:13, 45.79it/s]
loss 0.17 accuracy 0.97: 68%|██████▊ | 1277/1875 [00:37<00:13, 45.79it/s]
loss 0.12 accuracy 0.94: 68%|██████▊ | 1277/1875 [00:37<00:13, 45.79it/s]
loss 0.22 accuracy 0.97: 68%|██████▊ | 1277/1875 [00:37<00:13, 45.79it/s]
loss 0.22 accuracy 0.97: 68%|██████▊ | 1282/1875 [00:37<00:12, 45.72it/s]
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loss 0.18 accuracy 0.94: 68%|██████▊ | 1282/1875 [00:37<00:12, 45.72it/s]
loss 0.16 accuracy 0.94: 68%|██████▊ | 1282/1875 [00:37<00:12, 45.72it/s]
loss 0.13 accuracy 0.97: 68%|██████▊ | 1282/1875 [00:38<00:12, 45.72it/s]
loss 0.14 accuracy 0.94: 68%|██████▊ | 1282/1875 [00:38<00:12, 45.72it/s]
loss 0.14 accuracy 0.94: 69%|██████▊ | 1287/1875 [00:38<00:12, 45.77it/s]
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loss 0.15 accuracy 0.94: 69%|██████▊ | 1287/1875 [00:38<00:12, 45.77it/s]
loss 0.23 accuracy 0.94: 69%|██████▊ | 1287/1875 [00:38<00:12, 45.77it/s]
loss 0.04 accuracy 1.00: 69%|██████▊ | 1287/1875 [00:38<00:12, 45.77it/s]
loss 0.10 accuracy 0.97: 69%|██████▊ | 1287/1875 [00:38<00:12, 45.77it/s]
loss 0.10 accuracy 0.97: 69%|██████▉ | 1292/1875 [00:38<00:12, 45.69it/s]
loss 0.14 accuracy 0.94: 69%|██████▉ | 1292/1875 [00:38<00:12, 45.69it/s]
loss 0.32 accuracy 0.94: 69%|██████▉ | 1292/1875 [00:38<00:12, 45.69it/s]
loss 0.07 accuracy 0.97: 69%|██████▉ | 1292/1875 [00:38<00:12, 45.69it/s]
loss 0.22 accuracy 0.91: 69%|██████▉ | 1292/1875 [00:38<00:12, 45.69it/s]
loss 0.24 accuracy 0.94: 69%|██████▉ | 1292/1875 [00:38<00:12, 45.69it/s]
loss 0.24 accuracy 0.94: 69%|██████▉ | 1297/1875 [00:38<00:12, 45.74it/s]
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loss 0.13 accuracy 0.97: 69%|██████▉ | 1297/1875 [00:38<00:12, 45.74it/s]
loss 0.26 accuracy 0.94: 69%|██████▉ | 1297/1875 [00:38<00:12, 45.74it/s]
loss 0.20 accuracy 0.97: 69%|██████▉ | 1297/1875 [00:38<00:12, 45.74it/s]
loss 0.20 accuracy 0.97: 69%|██████▉ | 1297/1875 [00:38<00:12, 45.74it/s]
loss 0.20 accuracy 0.97: 69%|██████▉ | 1302/1875 [00:38<00:12, 45.82it/s]
loss 0.09 accuracy 0.97: 69%|██████▉ | 1302/1875 [00:38<00:12, 45.82it/s]
loss 0.09 accuracy 0.97: 69%|██████▉ | 1302/1875 [00:38<00:12, 45.82it/s]
loss 0.06 accuracy 0.97: 69%|██████▉ | 1302/1875 [00:38<00:12, 45.82it/s]
loss 0.18 accuracy 0.97: 69%|██████▉ | 1302/1875 [00:38<00:12, 45.82it/s]
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loss 0.07 accuracy 1.00: 70%|██████▉ | 1307/1875 [00:38<00:12, 45.90it/s]
loss 0.03 accuracy 1.00: 70%|██████▉ | 1307/1875 [00:38<00:12, 45.90it/s]
loss 0.22 accuracy 0.91: 70%|██████▉ | 1307/1875 [00:38<00:12, 45.90it/s]
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loss 0.19 accuracy 0.94: 70%|██████▉ | 1307/1875 [00:38<00:12, 45.90it/s]
loss 0.05 accuracy 1.00: 70%|██████▉ | 1307/1875 [00:38<00:12, 45.90it/s]
loss 0.05 accuracy 1.00: 70%|██████▉ | 1312/1875 [00:38<00:12, 45.98it/s]
loss 0.16 accuracy 0.97: 70%|██████▉ | 1312/1875 [00:38<00:12, 45.98it/s]
loss 0.30 accuracy 0.91: 70%|██████▉ | 1312/1875 [00:38<00:12, 45.98it/s]
loss 0.12 accuracy 0.97: 70%|██████▉ | 1312/1875 [00:38<00:12, 45.98it/s]
loss 0.21 accuracy 0.97: 70%|██████▉ | 1312/1875 [00:38<00:12, 45.98it/s]
loss 0.30 accuracy 0.94: 70%|██████▉ | 1312/1875 [00:38<00:12, 45.98it/s]
loss 0.30 accuracy 0.94: 70%|███████ | 1317/1875 [00:38<00:12, 46.01it/s]
loss 0.15 accuracy 0.94: 70%|███████ | 1317/1875 [00:38<00:12, 46.01it/s]
loss 0.06 accuracy 1.00: 70%|███████ | 1317/1875 [00:38<00:12, 46.01it/s]
loss 0.16 accuracy 0.97: 70%|███████ | 1317/1875 [00:38<00:12, 46.01it/s]
loss 0.02 accuracy 1.00: 70%|███████ | 1317/1875 [00:38<00:12, 46.01it/s]
loss 0.34 accuracy 0.88: 70%|███████ | 1317/1875 [00:38<00:12, 46.01it/s]
loss 0.34 accuracy 0.88: 71%|███████ | 1322/1875 [00:38<00:12, 46.06it/s]
loss 0.04 accuracy 1.00: 71%|███████ | 1322/1875 [00:38<00:12, 46.06it/s]
loss 0.10 accuracy 0.97: 71%|███████ | 1322/1875 [00:38<00:12, 46.06it/s]
loss 0.06 accuracy 1.00: 71%|███████ | 1322/1875 [00:38<00:12, 46.06it/s]
loss 0.04 accuracy 1.00: 71%|███████ | 1322/1875 [00:38<00:12, 46.06it/s]
loss 0.37 accuracy 0.94: 71%|███████ | 1322/1875 [00:38<00:12, 46.06it/s]
loss 0.37 accuracy 0.94: 71%|███████ | 1327/1875 [00:38<00:11, 46.07it/s]
loss 0.06 accuracy 1.00: 71%|███████ | 1327/1875 [00:38<00:11, 46.07it/s]
loss 0.05 accuracy 1.00: 71%|███████ | 1327/1875 [00:38<00:11, 46.07it/s]
loss 0.04 accuracy 1.00: 71%|███████ | 1327/1875 [00:38<00:11, 46.07it/s]
loss 0.07 accuracy 0.97: 71%|███████ | 1327/1875 [00:38<00:11, 46.07it/s]
loss 0.03 accuracy 1.00: 71%|███████ | 1327/1875 [00:39<00:11, 46.07it/s]
loss 0.03 accuracy 1.00: 71%|███████ | 1332/1875 [00:39<00:11, 46.07it/s]
loss 0.14 accuracy 0.94: 71%|███████ | 1332/1875 [00:39<00:11, 46.07it/s]
loss 0.13 accuracy 0.97: 71%|███████ | 1332/1875 [00:39<00:11, 46.07it/s]
loss 0.11 accuracy 0.97: 71%|███████ | 1332/1875 [00:39<00:11, 46.07it/s]
loss 0.16 accuracy 0.97: 71%|███████ | 1332/1875 [00:39<00:11, 46.07it/s]
loss 0.02 accuracy 1.00: 71%|███████ | 1332/1875 [00:39<00:11, 46.07it/s]
loss 0.02 accuracy 1.00: 71%|███████▏ | 1337/1875 [00:39<00:11, 46.09it/s]
loss 0.09 accuracy 0.97: 71%|███████▏ | 1337/1875 [00:39<00:11, 46.09it/s]
loss 0.10 accuracy 1.00: 71%|███████▏ | 1337/1875 [00:39<00:11, 46.09it/s]
loss 0.05 accuracy 1.00: 71%|███████▏ | 1337/1875 [00:39<00:11, 46.09it/s]
loss 0.18 accuracy 0.97: 71%|███████▏ | 1337/1875 [00:39<00:11, 46.09it/s]
loss 0.06 accuracy 1.00: 71%|███████▏ | 1337/1875 [00:39<00:11, 46.09it/s]
loss 0.06 accuracy 1.00: 72%|███████▏ | 1342/1875 [00:39<00:11, 46.05it/s]
loss 0.18 accuracy 0.94: 72%|███████▏ | 1342/1875 [00:39<00:11, 46.05it/s]
loss 0.17 accuracy 0.94: 72%|███████▏ | 1342/1875 [00:39<00:11, 46.05it/s]
loss 0.04 accuracy 1.00: 72%|███████▏ | 1342/1875 [00:39<00:11, 46.05it/s]
loss 0.10 accuracy 0.97: 72%|███████▏ | 1342/1875 [00:39<00:11, 46.05it/s]
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loss 0.07 accuracy 0.97: 72%|███████▏ | 1347/1875 [00:39<00:11, 46.00it/s]
loss 0.22 accuracy 0.91: 72%|███████▏ | 1347/1875 [00:39<00:11, 46.00it/s]
loss 0.02 accuracy 1.00: 72%|███████▏ | 1347/1875 [00:39<00:11, 46.00it/s]
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loss 0.07 accuracy 1.00: 72%|███████▏ | 1352/1875 [00:39<00:11, 45.86it/s]
loss 0.13 accuracy 0.94: 72%|███████▏ | 1352/1875 [00:39<00:11, 45.86it/s]
loss 0.24 accuracy 0.94: 72%|███████▏ | 1352/1875 [00:39<00:11, 45.86it/s]
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loss 0.38 accuracy 0.91: 73%|███████▎ | 1362/1875 [00:39<00:11, 45.84it/s]
loss 0.01 accuracy 1.00: 73%|███████▎ | 1362/1875 [00:39<00:11, 45.84it/s]
loss 0.01 accuracy 1.00: 73%|███████▎ | 1367/1875 [00:39<00:11, 45.70it/s]
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loss 0.07 accuracy 0.97: 73%|███████▎ | 1367/1875 [00:39<00:11, 45.70it/s]
loss 0.23 accuracy 0.94: 73%|███████▎ | 1367/1875 [00:39<00:11, 45.70it/s]
loss 0.04 accuracy 1.00: 73%|███████▎ | 1367/1875 [00:39<00:11, 45.70it/s]
loss 0.04 accuracy 1.00: 73%|███████▎ | 1372/1875 [00:39<00:11, 45.72it/s]
loss 0.08 accuracy 0.97: 73%|███████▎ | 1372/1875 [00:39<00:11, 45.72it/s]
loss 0.35 accuracy 0.91: 73%|███████▎ | 1372/1875 [00:39<00:11, 45.72it/s]
loss 0.03 accuracy 1.00: 73%|███████▎ | 1372/1875 [00:39<00:11, 45.72it/s]
loss 0.18 accuracy 0.94: 73%|███████▎ | 1372/1875 [00:39<00:11, 45.72it/s]
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loss 0.08 accuracy 1.00: 76%|███████▌ | 1427/1875 [00:41<00:09, 46.06it/s]
loss 0.18 accuracy 0.94: 76%|███████▌ | 1427/1875 [00:41<00:09, 46.06it/s]
loss 0.05 accuracy 1.00: 76%|███████▌ | 1427/1875 [00:41<00:09, 46.06it/s]
loss 0.19 accuracy 0.94: 76%|███████▌ | 1427/1875 [00:41<00:09, 46.06it/s]
loss 0.08 accuracy 0.97: 76%|███████▌ | 1427/1875 [00:41<00:09, 46.06it/s]
loss 0.08 accuracy 0.97: 76%|███████▋ | 1432/1875 [00:41<00:09, 46.03it/s]
loss 0.19 accuracy 0.97: 76%|███████▋ | 1432/1875 [00:41<00:09, 46.03it/s]
loss 0.07 accuracy 0.97: 76%|███████▋ | 1432/1875 [00:41<00:09, 46.03it/s]
loss 0.26 accuracy 0.94: 76%|███████▋ | 1432/1875 [00:41<00:09, 46.03it/s]
loss 0.36 accuracy 0.84: 76%|███████▋ | 1432/1875 [00:41<00:09, 46.03it/s]
loss 0.02 accuracy 1.00: 76%|███████▋ | 1432/1875 [00:41<00:09, 46.03it/s]
loss 0.02 accuracy 1.00: 77%|███████▋ | 1437/1875 [00:41<00:09, 45.95it/s]
loss 0.16 accuracy 0.97: 77%|███████▋ | 1437/1875 [00:41<00:09, 45.95it/s]
loss 0.04 accuracy 1.00: 77%|███████▋ | 1437/1875 [00:41<00:09, 45.95it/s]
loss 0.19 accuracy 0.94: 77%|███████▋ | 1437/1875 [00:41<00:09, 45.95it/s]
loss 0.05 accuracy 1.00: 77%|███████▋ | 1437/1875 [00:41<00:09, 45.95it/s]
loss 0.05 accuracy 1.00: 77%|███████▋ | 1437/1875 [00:41<00:09, 45.95it/s]
loss 0.05 accuracy 1.00: 77%|███████▋ | 1442/1875 [00:41<00:09, 45.83it/s]
loss 0.06 accuracy 1.00: 77%|███████▋ | 1442/1875 [00:41<00:09, 45.83it/s]
loss 0.11 accuracy 0.94: 77%|███████▋ | 1442/1875 [00:41<00:09, 45.83it/s]
loss 0.06 accuracy 0.97: 77%|███████▋ | 1442/1875 [00:41<00:09, 45.83it/s]
loss 0.26 accuracy 0.94: 77%|███████▋ | 1442/1875 [00:41<00:09, 45.83it/s]
loss 0.16 accuracy 0.97: 77%|███████▋ | 1442/1875 [00:41<00:09, 45.83it/s]
loss 0.16 accuracy 0.97: 77%|███████▋ | 1447/1875 [00:41<00:09, 45.82it/s]
loss 0.11 accuracy 0.97: 77%|███████▋ | 1447/1875 [00:41<00:09, 45.82it/s]
loss 0.02 accuracy 1.00: 77%|███████▋ | 1447/1875 [00:41<00:09, 45.82it/s]
loss 0.03 accuracy 1.00: 77%|███████▋ | 1447/1875 [00:41<00:09, 45.82it/s]
loss 0.05 accuracy 1.00: 77%|███████▋ | 1447/1875 [00:41<00:09, 45.82it/s]
loss 0.28 accuracy 0.94: 77%|███████▋ | 1447/1875 [00:41<00:09, 45.82it/s]
loss 0.28 accuracy 0.94: 77%|███████▋ | 1452/1875 [00:41<00:09, 45.67it/s]
loss 0.03 accuracy 1.00: 77%|███████▋ | 1452/1875 [00:41<00:09, 45.67it/s]
loss 0.11 accuracy 0.97: 77%|███████▋ | 1452/1875 [00:41<00:09, 45.67it/s]
loss 0.20 accuracy 0.94: 77%|███████▋ | 1452/1875 [00:41<00:09, 45.67it/s]
loss 0.07 accuracy 0.97: 77%|███████▋ | 1452/1875 [00:41<00:09, 45.67it/s]
loss 0.22 accuracy 0.94: 77%|███████▋ | 1452/1875 [00:41<00:09, 45.67it/s]
loss 0.22 accuracy 0.94: 78%|███████▊ | 1457/1875 [00:41<00:09, 45.71it/s]
loss 0.19 accuracy 0.97: 78%|███████▊ | 1457/1875 [00:41<00:09, 45.71it/s]
loss 0.06 accuracy 1.00: 78%|███████▊ | 1457/1875 [00:41<00:09, 45.71it/s]
loss 0.17 accuracy 0.97: 78%|███████▊ | 1457/1875 [00:41<00:09, 45.71it/s]
loss 0.10 accuracy 0.97: 78%|███████▊ | 1457/1875 [00:41<00:09, 45.71it/s]
loss 0.04 accuracy 1.00: 78%|███████▊ | 1457/1875 [00:41<00:09, 45.71it/s]
loss 0.04 accuracy 1.00: 78%|███████▊ | 1462/1875 [00:41<00:09, 45.68it/s]
loss 0.15 accuracy 0.94: 78%|███████▊ | 1462/1875 [00:41<00:09, 45.68it/s]
loss 0.03 accuracy 1.00: 78%|███████▊ | 1462/1875 [00:41<00:09, 45.68it/s]
loss 0.17 accuracy 0.94: 78%|███████▊ | 1462/1875 [00:41<00:09, 45.68it/s]
loss 0.10 accuracy 0.97: 78%|███████▊ | 1462/1875 [00:41<00:09, 45.68it/s]
loss 0.19 accuracy 0.91: 78%|███████▊ | 1462/1875 [00:41<00:09, 45.68it/s]
loss 0.19 accuracy 0.91: 78%|███████▊ | 1467/1875 [00:41<00:08, 45.70it/s]
loss 0.03 accuracy 1.00: 78%|███████▊ | 1467/1875 [00:41<00:08, 45.70it/s]
loss 0.15 accuracy 0.94: 78%|███████▊ | 1467/1875 [00:41<00:08, 45.70it/s]
loss 0.02 accuracy 1.00: 78%|███████▊ | 1467/1875 [00:42<00:08, 45.70it/s]
loss 0.20 accuracy 0.94: 78%|███████▊ | 1467/1875 [00:42<00:08, 45.70it/s]
loss 0.10 accuracy 0.97: 78%|███████▊ | 1467/1875 [00:42<00:08, 45.70it/s]
loss 0.10 accuracy 0.97: 79%|███████▊ | 1472/1875 [00:42<00:08, 45.77it/s]
loss 0.16 accuracy 0.94: 79%|███████▊ | 1472/1875 [00:42<00:08, 45.77it/s]
loss 0.20 accuracy 0.97: 79%|███████▊ | 1472/1875 [00:42<00:08, 45.77it/s]
loss 0.08 accuracy 0.97: 79%|███████▊ | 1472/1875 [00:42<00:08, 45.77it/s]
loss 0.14 accuracy 0.97: 79%|███████▊ | 1472/1875 [00:42<00:08, 45.77it/s]
loss 0.27 accuracy 0.97: 79%|███████▊ | 1472/1875 [00:42<00:08, 45.77it/s]
loss 0.27 accuracy 0.97: 79%|███████▉ | 1477/1875 [00:42<00:08, 45.87it/s]
loss 0.32 accuracy 0.94: 79%|███████▉ | 1477/1875 [00:42<00:08, 45.87it/s]
loss 0.19 accuracy 0.94: 79%|███████▉ | 1477/1875 [00:42<00:08, 45.87it/s]
loss 0.13 accuracy 0.94: 79%|███████▉ | 1477/1875 [00:42<00:08, 45.87it/s]
loss 0.19 accuracy 0.94: 79%|███████▉ | 1477/1875 [00:42<00:08, 45.87it/s]
loss 0.26 accuracy 0.91: 79%|███████▉ | 1477/1875 [00:42<00:08, 45.87it/s]
loss 0.26 accuracy 0.91: 79%|███████▉ | 1482/1875 [00:42<00:08, 45.91it/s]
loss 0.05 accuracy 0.97: 79%|███████▉ | 1482/1875 [00:42<00:08, 45.91it/s]
loss 0.10 accuracy 0.97: 79%|███████▉ | 1482/1875 [00:42<00:08, 45.91it/s]
loss 0.10 accuracy 0.97: 79%|███████▉ | 1482/1875 [00:42<00:08, 45.91it/s]
loss 0.07 accuracy 1.00: 79%|███████▉ | 1482/1875 [00:42<00:08, 45.91it/s]
loss 0.24 accuracy 0.94: 79%|███████▉ | 1482/1875 [00:42<00:08, 45.91it/s]
loss 0.24 accuracy 0.94: 79%|███████▉ | 1487/1875 [00:42<00:08, 45.92it/s]
loss 0.08 accuracy 0.97: 79%|███████▉ | 1487/1875 [00:42<00:08, 45.92it/s]
loss 0.09 accuracy 0.97: 79%|███████▉ | 1487/1875 [00:42<00:08, 45.92it/s]
loss 0.13 accuracy 0.94: 79%|███████▉ | 1487/1875 [00:42<00:08, 45.92it/s]
loss 0.03 accuracy 1.00: 79%|███████▉ | 1487/1875 [00:42<00:08, 45.92it/s]
loss 0.05 accuracy 1.00: 79%|███████▉ | 1487/1875 [00:42<00:08, 45.92it/s]
loss 0.05 accuracy 1.00: 80%|███████▉ | 1492/1875 [00:42<00:08, 45.95it/s]
loss 0.11 accuracy 0.97: 80%|███████▉ | 1492/1875 [00:42<00:08, 45.95it/s]
loss 0.09 accuracy 0.97: 80%|███████▉ | 1492/1875 [00:42<00:08, 45.95it/s]
loss 0.20 accuracy 0.94: 80%|███████▉ | 1492/1875 [00:42<00:08, 45.95it/s]
loss 0.13 accuracy 0.91: 80%|███████▉ | 1492/1875 [00:42<00:08, 45.95it/s]
loss 0.17 accuracy 0.97: 80%|███████▉ | 1492/1875 [00:42<00:08, 45.95it/s]
loss 0.17 accuracy 0.97: 80%|███████▉ | 1497/1875 [00:42<00:08, 45.98it/s]
loss 0.03 accuracy 1.00: 80%|███████▉ | 1497/1875 [00:42<00:08, 45.98it/s]
loss 0.26 accuracy 0.91: 80%|███████▉ | 1497/1875 [00:42<00:08, 45.98it/s]
loss 0.05 accuracy 0.97: 80%|███████▉ | 1497/1875 [00:42<00:08, 45.98it/s]
loss 0.03 accuracy 1.00: 80%|███████▉ | 1497/1875 [00:42<00:08, 45.98it/s]
loss 0.11 accuracy 0.97: 80%|███████▉ | 1497/1875 [00:42<00:08, 45.98it/s]
loss 0.11 accuracy 0.97: 80%|████████ | 1502/1875 [00:42<00:08, 45.93it/s]
loss 0.06 accuracy 0.97: 80%|████████ | 1502/1875 [00:42<00:08, 45.93it/s]
loss 0.07 accuracy 0.97: 80%|████████ | 1502/1875 [00:42<00:08, 45.93it/s]
loss 0.19 accuracy 0.97: 80%|████████ | 1502/1875 [00:42<00:08, 45.93it/s]
loss 0.19 accuracy 0.94: 80%|████████ | 1502/1875 [00:42<00:08, 45.93it/s]
loss 0.04 accuracy 1.00: 80%|████████ | 1502/1875 [00:42<00:08, 45.93it/s]
loss 0.04 accuracy 1.00: 80%|████████ | 1507/1875 [00:42<00:08, 45.87it/s]
loss 0.19 accuracy 0.94: 80%|████████ | 1507/1875 [00:42<00:08, 45.87it/s]
loss 0.05 accuracy 0.97: 80%|████████ | 1507/1875 [00:42<00:08, 45.87it/s]
loss 0.09 accuracy 0.97: 80%|████████ | 1507/1875 [00:42<00:08, 45.87it/s]
loss 0.20 accuracy 0.94: 80%|████████ | 1507/1875 [00:42<00:08, 45.87it/s]
loss 0.07 accuracy 0.97: 80%|████████ | 1507/1875 [00:42<00:08, 45.87it/s]
loss 0.07 accuracy 0.97: 81%|████████ | 1512/1875 [00:42<00:07, 45.84it/s]
loss 0.05 accuracy 1.00: 81%|████████ | 1512/1875 [00:42<00:07, 45.84it/s]
loss 0.04 accuracy 1.00: 81%|████████ | 1512/1875 [00:42<00:07, 45.84it/s]
loss 0.08 accuracy 0.97: 81%|████████ | 1512/1875 [00:42<00:07, 45.84it/s]
loss 0.06 accuracy 0.97: 81%|████████ | 1512/1875 [00:43<00:07, 45.84it/s]
loss 0.09 accuracy 0.94: 81%|████████ | 1512/1875 [00:43<00:07, 45.84it/s]
loss 0.09 accuracy 0.94: 81%|████████ | 1517/1875 [00:43<00:07, 45.87it/s]
loss 0.15 accuracy 0.97: 81%|████████ | 1517/1875 [00:43<00:07, 45.87it/s]
loss 0.13 accuracy 0.94: 81%|████████ | 1517/1875 [00:43<00:07, 45.87it/s]
loss 0.02 accuracy 1.00: 81%|████████ | 1517/1875 [00:43<00:07, 45.87it/s]
loss 0.13 accuracy 0.94: 81%|████████ | 1517/1875 [00:43<00:07, 45.87it/s]
loss 0.25 accuracy 0.97: 81%|████████ | 1517/1875 [00:43<00:07, 45.87it/s]
loss 0.25 accuracy 0.97: 81%|████████ | 1522/1875 [00:43<00:07, 45.84it/s]
loss 0.21 accuracy 0.94: 81%|████████ | 1522/1875 [00:43<00:07, 45.84it/s]
loss 0.05 accuracy 1.00: 81%|████████ | 1522/1875 [00:43<00:07, 45.84it/s]
loss 0.03 accuracy 1.00: 81%|████████ | 1522/1875 [00:43<00:07, 45.84it/s]
loss 0.02 accuracy 1.00: 81%|████████ | 1522/1875 [00:43<00:07, 45.84it/s]
loss 0.29 accuracy 0.91: 81%|████████ | 1522/1875 [00:43<00:07, 45.84it/s]
loss 0.29 accuracy 0.91: 81%|████████▏ | 1527/1875 [00:43<00:07, 45.75it/s]
loss 0.14 accuracy 0.91: 81%|████████▏ | 1527/1875 [00:43<00:07, 45.75it/s]
loss 0.05 accuracy 1.00: 81%|████████▏ | 1527/1875 [00:43<00:07, 45.75it/s]
loss 0.08 accuracy 0.97: 81%|████████▏ | 1527/1875 [00:43<00:07, 45.75it/s]
loss 0.08 accuracy 0.97: 81%|████████▏ | 1527/1875 [00:43<00:07, 45.75it/s]
loss 0.08 accuracy 1.00: 81%|████████▏ | 1527/1875 [00:43<00:07, 45.75it/s]
loss 0.08 accuracy 1.00: 82%|████████▏ | 1532/1875 [00:43<00:07, 45.75it/s]
loss 0.15 accuracy 1.00: 82%|████████▏ | 1532/1875 [00:43<00:07, 45.75it/s]
loss 0.16 accuracy 0.97: 82%|████████▏ | 1532/1875 [00:43<00:07, 45.75it/s]
loss 0.22 accuracy 0.97: 82%|████████▏ | 1532/1875 [00:43<00:07, 45.75it/s]
loss 0.13 accuracy 0.94: 82%|████████▏ | 1532/1875 [00:43<00:07, 45.75it/s]
loss 0.05 accuracy 0.97: 82%|████████▏ | 1532/1875 [00:43<00:07, 45.75it/s]
loss 0.05 accuracy 0.97: 82%|████████▏ | 1537/1875 [00:43<00:07, 45.73it/s]
loss 0.03 accuracy 1.00: 82%|████████▏ | 1537/1875 [00:43<00:07, 45.73it/s]
loss 0.11 accuracy 0.94: 82%|████████▏ | 1537/1875 [00:43<00:07, 45.73it/s]
loss 0.04 accuracy 1.00: 82%|████████▏ | 1537/1875 [00:43<00:07, 45.73it/s]
loss 0.07 accuracy 0.97: 82%|████████▏ | 1537/1875 [00:43<00:07, 45.73it/s]
loss 0.08 accuracy 0.97: 82%|████████▏ | 1537/1875 [00:43<00:07, 45.73it/s]
loss 0.08 accuracy 0.97: 82%|████████▏ | 1542/1875 [00:43<00:07, 45.76it/s]
loss 0.21 accuracy 0.94: 82%|████████▏ | 1542/1875 [00:43<00:07, 45.76it/s]
loss 0.10 accuracy 0.97: 82%|████████▏ | 1542/1875 [00:43<00:07, 45.76it/s]
loss 0.03 accuracy 1.00: 82%|████████▏ | 1542/1875 [00:43<00:07, 45.76it/s]
loss 0.03 accuracy 1.00: 82%|████████▏ | 1542/1875 [00:43<00:07, 45.76it/s]
loss 0.20 accuracy 0.91: 82%|████████▏ | 1542/1875 [00:43<00:07, 45.76it/s]
loss 0.20 accuracy 0.91: 83%|████████▎ | 1547/1875 [00:43<00:07, 45.84it/s]
loss 0.11 accuracy 0.94: 83%|████████▎ | 1547/1875 [00:43<00:07, 45.84it/s]
loss 0.32 accuracy 0.94: 83%|████████▎ | 1547/1875 [00:43<00:07, 45.84it/s]
loss 0.04 accuracy 1.00: 83%|████████▎ | 1547/1875 [00:43<00:07, 45.84it/s]
loss 0.06 accuracy 0.97: 83%|████████▎ | 1547/1875 [00:43<00:07, 45.84it/s]
loss 0.02 accuracy 1.00: 83%|████████▎ | 1547/1875 [00:43<00:07, 45.84it/s]
loss 0.02 accuracy 1.00: 83%|████████▎ | 1552/1875 [00:43<00:07, 45.91it/s]
loss 0.02 accuracy 1.00: 83%|████████▎ | 1552/1875 [00:43<00:07, 45.91it/s]
loss 0.10 accuracy 0.97: 83%|████████▎ | 1552/1875 [00:43<00:07, 45.91it/s]
loss 0.21 accuracy 0.97: 83%|████████▎ | 1552/1875 [00:43<00:07, 45.91it/s]
loss 0.14 accuracy 0.97: 83%|████████▎ | 1552/1875 [00:43<00:07, 45.91it/s]
loss 0.03 accuracy 1.00: 83%|████████▎ | 1552/1875 [00:43<00:07, 45.91it/s]
loss 0.03 accuracy 1.00: 83%|████████▎ | 1557/1875 [00:43<00:06, 45.96it/s]
loss 0.07 accuracy 1.00: 83%|████████▎ | 1557/1875 [00:43<00:06, 45.96it/s]
loss 0.04 accuracy 1.00: 83%|████████▎ | 1557/1875 [00:43<00:06, 45.96it/s]
loss 0.02 accuracy 1.00: 83%|████████▎ | 1557/1875 [00:43<00:06, 45.96it/s]
loss 0.27 accuracy 0.94: 83%|████████▎ | 1557/1875 [00:44<00:06, 45.96it/s]
loss 0.24 accuracy 0.94: 83%|████████▎ | 1557/1875 [00:44<00:06, 45.96it/s]
loss 0.24 accuracy 0.94: 83%|████████▎ | 1562/1875 [00:44<00:06, 46.02it/s]
loss 0.08 accuracy 0.97: 83%|████████▎ | 1562/1875 [00:44<00:06, 46.02it/s]
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loss 0.08 accuracy 0.97: 83%|████████▎ | 1562/1875 [00:44<00:06, 46.02it/s]
loss 0.07 accuracy 1.00: 83%|████████▎ | 1562/1875 [00:44<00:06, 46.02it/s]
loss 0.03 accuracy 1.00: 83%|████████▎ | 1562/1875 [00:44<00:06, 46.02it/s]
loss 0.03 accuracy 1.00: 84%|████████▎ | 1567/1875 [00:44<00:06, 46.05it/s]
loss 0.14 accuracy 0.94: 84%|████████▎ | 1567/1875 [00:44<00:06, 46.05it/s]
loss 0.08 accuracy 0.97: 84%|████████▎ | 1567/1875 [00:44<00:06, 46.05it/s]
loss 0.04 accuracy 1.00: 84%|████████▎ | 1567/1875 [00:44<00:06, 46.05it/s]
loss 0.04 accuracy 1.00: 84%|████████▎ | 1567/1875 [00:44<00:06, 46.05it/s]
loss 0.09 accuracy 0.97: 84%|████████▎ | 1567/1875 [00:44<00:06, 46.05it/s]
loss 0.09 accuracy 0.97: 84%|████████▍ | 1572/1875 [00:44<00:06, 46.07it/s]
loss 0.11 accuracy 0.97: 84%|████████▍ | 1572/1875 [00:44<00:06, 46.07it/s]
loss 0.10 accuracy 0.97: 84%|████████▍ | 1572/1875 [00:44<00:06, 46.07it/s]
loss 0.04 accuracy 1.00: 84%|████████▍ | 1572/1875 [00:44<00:06, 46.07it/s]
loss 0.02 accuracy 1.00: 84%|████████▍ | 1572/1875 [00:44<00:06, 46.07it/s]
loss 0.19 accuracy 0.94: 84%|████████▍ | 1572/1875 [00:44<00:06, 46.07it/s]
loss 0.19 accuracy 0.94: 84%|████████▍ | 1577/1875 [00:44<00:06, 46.10it/s]
loss 0.06 accuracy 1.00: 84%|████████▍ | 1577/1875 [00:44<00:06, 46.10it/s]
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loss 0.07 accuracy 0.97: 84%|████████▍ | 1582/1875 [00:44<00:06, 46.06it/s]
loss 0.04 accuracy 1.00: 84%|████████▍ | 1582/1875 [00:44<00:06, 46.06it/s]
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loss 0.11 accuracy 0.97: 84%|████████▍ | 1582/1875 [00:44<00:06, 46.06it/s]
loss 0.12 accuracy 0.97: 84%|████████▍ | 1582/1875 [00:44<00:06, 46.06it/s]
loss 0.12 accuracy 0.97: 85%|████████▍ | 1587/1875 [00:44<00:06, 46.05it/s]
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loss 0.02 accuracy 1.00: 85%|████████▍ | 1587/1875 [00:44<00:06, 46.05it/s]
loss 0.06 accuracy 0.97: 85%|████████▍ | 1587/1875 [00:44<00:06, 46.05it/s]
loss 0.10 accuracy 0.97: 85%|████████▍ | 1587/1875 [00:44<00:06, 46.05it/s]
loss 0.02 accuracy 1.00: 85%|████████▍ | 1587/1875 [00:44<00:06, 46.05it/s]
loss 0.02 accuracy 1.00: 85%|████████▍ | 1592/1875 [00:44<00:06, 46.02it/s]
loss 0.13 accuracy 0.97: 85%|████████▍ | 1592/1875 [00:44<00:06, 46.02it/s]
loss 0.46 accuracy 0.88: 85%|████████▍ | 1592/1875 [00:44<00:06, 46.02it/s]
loss 0.04 accuracy 1.00: 85%|████████▍ | 1592/1875 [00:44<00:06, 46.02it/s]
loss 0.03 accuracy 1.00: 85%|████████▍ | 1592/1875 [00:44<00:06, 46.02it/s]
loss 0.05 accuracy 1.00: 85%|████████▍ | 1592/1875 [00:44<00:06, 46.02it/s]
loss 0.05 accuracy 1.00: 85%|████████▌ | 1597/1875 [00:44<00:06, 45.93it/s]
loss 0.26 accuracy 0.94: 85%|████████▌ | 1597/1875 [00:44<00:06, 45.93it/s]
loss 0.07 accuracy 0.97: 85%|████████▌ | 1597/1875 [00:44<00:06, 45.93it/s]
loss 0.05 accuracy 1.00: 85%|████████▌ | 1597/1875 [00:44<00:06, 45.93it/s]
loss 0.12 accuracy 0.97: 85%|████████▌ | 1597/1875 [00:44<00:06, 45.93it/s]
loss 0.09 accuracy 0.97: 85%|████████▌ | 1597/1875 [00:44<00:06, 45.93it/s]
loss 0.09 accuracy 0.97: 85%|████████▌ | 1602/1875 [00:44<00:05, 45.81it/s]
loss 0.09 accuracy 1.00: 85%|████████▌ | 1602/1875 [00:44<00:05, 45.81it/s]
loss 0.05 accuracy 1.00: 85%|████████▌ | 1602/1875 [00:44<00:05, 45.81it/s]
loss 0.09 accuracy 0.97: 85%|████████▌ | 1602/1875 [00:44<00:05, 45.81it/s]
loss 0.10 accuracy 0.94: 85%|████████▌ | 1602/1875 [00:44<00:05, 45.81it/s]
loss 0.02 accuracy 1.00: 85%|████████▌ | 1602/1875 [00:45<00:05, 45.81it/s]
loss 0.02 accuracy 1.00: 86%|████████▌ | 1607/1875 [00:45<00:05, 45.82it/s]
loss 0.13 accuracy 0.97: 86%|████████▌ | 1607/1875 [00:45<00:05, 45.82it/s]
loss 0.07 accuracy 0.97: 86%|████████▌ | 1607/1875 [00:45<00:05, 45.82it/s]
loss 0.10 accuracy 0.97: 86%|████████▌ | 1607/1875 [00:45<00:05, 45.82it/s]
loss 0.21 accuracy 0.97: 86%|████████▌ | 1607/1875 [00:45<00:05, 45.82it/s]
loss 0.09 accuracy 0.97: 86%|████████▌ | 1607/1875 [00:45<00:05, 45.82it/s]
loss 0.09 accuracy 0.97: 86%|████████▌ | 1612/1875 [00:45<00:05, 45.66it/s]
loss 0.17 accuracy 0.97: 86%|████████▌ | 1612/1875 [00:45<00:05, 45.66it/s]
loss 0.46 accuracy 0.94: 86%|████████▌ | 1612/1875 [00:45<00:05, 45.66it/s]
loss 0.11 accuracy 0.97: 86%|████████▌ | 1612/1875 [00:45<00:05, 45.66it/s]
loss 0.03 accuracy 1.00: 86%|████████▌ | 1612/1875 [00:45<00:05, 45.66it/s]
loss 0.06 accuracy 1.00: 86%|████████▌ | 1612/1875 [00:45<00:05, 45.66it/s]
loss 0.06 accuracy 1.00: 86%|████████▌ | 1617/1875 [00:45<00:05, 45.70it/s]
loss 0.19 accuracy 0.97: 86%|████████▌ | 1617/1875 [00:45<00:05, 45.70it/s]
loss 0.07 accuracy 1.00: 86%|████████▌ | 1617/1875 [00:45<00:05, 45.70it/s]
loss 0.09 accuracy 0.97: 86%|████████▌ | 1617/1875 [00:45<00:05, 45.70it/s]
loss 0.08 accuracy 0.97: 86%|████████▌ | 1617/1875 [00:45<00:05, 45.70it/s]
loss 0.14 accuracy 0.97: 86%|████████▌ | 1617/1875 [00:45<00:05, 45.70it/s]
loss 0.14 accuracy 0.97: 87%|████████▋ | 1622/1875 [00:45<00:05, 45.65it/s]
loss 0.04 accuracy 1.00: 87%|████████▋ | 1622/1875 [00:45<00:05, 45.65it/s]
loss 0.09 accuracy 0.97: 87%|████████▋ | 1622/1875 [00:45<00:05, 45.65it/s]
loss 0.03 accuracy 1.00: 87%|████████▋ | 1622/1875 [00:45<00:05, 45.65it/s]
loss 0.06 accuracy 0.97: 87%|████████▋ | 1622/1875 [00:45<00:05, 45.65it/s]
loss 0.02 accuracy 1.00: 87%|████████▋ | 1622/1875 [00:45<00:05, 45.65it/s]
loss 0.02 accuracy 1.00: 87%|████████▋ | 1627/1875 [00:45<00:05, 45.72it/s]
loss 0.09 accuracy 1.00: 87%|████████▋ | 1627/1875 [00:45<00:05, 45.72it/s]
loss 0.04 accuracy 1.00: 87%|████████▋ | 1627/1875 [00:45<00:05, 45.72it/s]
loss 0.15 accuracy 0.97: 87%|████████▋ | 1627/1875 [00:45<00:05, 45.72it/s]
loss 0.03 accuracy 1.00: 87%|████████▋ | 1627/1875 [00:45<00:05, 45.72it/s]
loss 0.06 accuracy 1.00: 87%|████████▋ | 1627/1875 [00:45<00:05, 45.72it/s]
loss 0.06 accuracy 1.00: 87%|████████▋ | 1632/1875 [00:45<00:05, 45.82it/s]
loss 0.41 accuracy 0.97: 87%|████████▋ | 1632/1875 [00:45<00:05, 45.82it/s]
loss 0.10 accuracy 0.97: 87%|████████▋ | 1632/1875 [00:45<00:05, 45.82it/s]
loss 0.08 accuracy 0.97: 87%|████████▋ | 1632/1875 [00:45<00:05, 45.82it/s]
loss 0.04 accuracy 1.00: 87%|████████▋ | 1632/1875 [00:45<00:05, 45.82it/s]
loss 0.02 accuracy 1.00: 87%|████████▋ | 1632/1875 [00:45<00:05, 45.82it/s]
loss 0.02 accuracy 1.00: 87%|████████▋ | 1637/1875 [00:45<00:05, 45.90it/s]
loss 0.22 accuracy 0.97: 87%|████████▋ | 1637/1875 [00:45<00:05, 45.90it/s]
loss 0.23 accuracy 0.94: 87%|████████▋ | 1637/1875 [00:45<00:05, 45.90it/s]
loss 0.10 accuracy 0.97: 87%|████████▋ | 1637/1875 [00:45<00:05, 45.90it/s]
loss 0.19 accuracy 0.97: 87%|████████▋ | 1637/1875 [00:45<00:05, 45.90it/s]
loss 0.13 accuracy 0.97: 87%|████████▋ | 1637/1875 [00:45<00:05, 45.90it/s]
loss 0.13 accuracy 0.97: 88%|████████▊ | 1642/1875 [00:45<00:05, 45.96it/s]
loss 0.07 accuracy 0.97: 88%|████████▊ | 1642/1875 [00:45<00:05, 45.96it/s]
loss 0.03 accuracy 1.00: 88%|████████▊ | 1642/1875 [00:45<00:05, 45.96it/s]
loss 0.27 accuracy 0.94: 88%|████████▊ | 1642/1875 [00:45<00:05, 45.96it/s]
loss 0.11 accuracy 0.97: 88%|████████▊ | 1642/1875 [00:45<00:05, 45.96it/s]
loss 0.04 accuracy 1.00: 88%|████████▊ | 1642/1875 [00:45<00:05, 45.96it/s]
loss 0.04 accuracy 1.00: 88%|████████▊ | 1647/1875 [00:45<00:04, 46.02it/s]
loss 0.08 accuracy 0.97: 88%|████████▊ | 1647/1875 [00:45<00:04, 46.02it/s]
loss 0.04 accuracy 1.00: 88%|████████▊ | 1647/1875 [00:45<00:04, 46.02it/s]
loss 0.07 accuracy 0.97: 88%|████████▊ | 1647/1875 [00:45<00:04, 46.02it/s]
loss 0.06 accuracy 1.00: 88%|████████▊ | 1647/1875 [00:45<00:04, 46.02it/s]
loss 0.14 accuracy 0.94: 88%|████████▊ | 1647/1875 [00:45<00:04, 46.02it/s]
loss 0.14 accuracy 0.94: 88%|████████▊ | 1652/1875 [00:45<00:04, 46.10it/s]
loss 0.02 accuracy 1.00: 88%|████████▊ | 1652/1875 [00:46<00:04, 46.10it/s]
loss 0.02 accuracy 1.00: 88%|████████▊ | 1652/1875 [00:46<00:04, 46.10it/s]
loss 0.10 accuracy 0.94: 88%|████████▊ | 1652/1875 [00:46<00:04, 46.10it/s]
loss 0.10 accuracy 0.97: 88%|████████▊ | 1652/1875 [00:46<00:04, 46.10it/s]
loss 0.04 accuracy 1.00: 88%|████████▊ | 1652/1875 [00:46<00:04, 46.10it/s]
loss 0.04 accuracy 1.00: 88%|████████▊ | 1657/1875 [00:46<00:04, 46.10it/s]
loss 0.13 accuracy 0.97: 88%|████████▊ | 1657/1875 [00:46<00:04, 46.10it/s]
loss 0.10 accuracy 0.97: 88%|████████▊ | 1657/1875 [00:46<00:04, 46.10it/s]
loss 0.06 accuracy 1.00: 88%|████████▊ | 1657/1875 [00:46<00:04, 46.10it/s]
loss 0.18 accuracy 0.91: 88%|████████▊ | 1657/1875 [00:46<00:04, 46.10it/s]
loss 0.13 accuracy 0.97: 88%|████████▊ | 1657/1875 [00:46<00:04, 46.10it/s]
loss 0.13 accuracy 0.97: 89%|████████▊ | 1662/1875 [00:46<00:04, 46.10it/s]
loss 0.04 accuracy 1.00: 89%|████████▊ | 1662/1875 [00:46<00:04, 46.10it/s]
loss 0.06 accuracy 0.97: 89%|████████▊ | 1662/1875 [00:46<00:04, 46.10it/s]
loss 0.13 accuracy 0.97: 89%|████████▊ | 1662/1875 [00:46<00:04, 46.10it/s]
loss 0.03 accuracy 1.00: 89%|████████▊ | 1662/1875 [00:46<00:04, 46.10it/s]
loss 0.05 accuracy 0.97: 89%|████████▊ | 1662/1875 [00:46<00:04, 46.10it/s]
loss 0.05 accuracy 0.97: 89%|████████▉ | 1667/1875 [00:46<00:04, 46.08it/s]
loss 0.09 accuracy 0.97: 89%|████████▉ | 1667/1875 [00:46<00:04, 46.08it/s]
loss 0.12 accuracy 0.97: 89%|████████▉ | 1667/1875 [00:46<00:04, 46.08it/s]
loss 0.17 accuracy 0.97: 89%|████████▉ | 1667/1875 [00:46<00:04, 46.08it/s]
loss 0.13 accuracy 0.97: 89%|████████▉ | 1667/1875 [00:46<00:04, 46.08it/s]
loss 0.14 accuracy 0.97: 89%|████████▉ | 1667/1875 [00:46<00:04, 46.08it/s]
loss 0.14 accuracy 0.97: 89%|████████▉ | 1672/1875 [00:46<00:04, 46.04it/s]
loss 0.08 accuracy 0.97: 89%|████████▉ | 1672/1875 [00:46<00:04, 46.04it/s]
loss 0.06 accuracy 1.00: 89%|████████▉ | 1672/1875 [00:46<00:04, 46.04it/s]
loss 0.16 accuracy 0.97: 89%|████████▉ | 1672/1875 [00:46<00:04, 46.04it/s]
loss 0.13 accuracy 0.91: 89%|████████▉ | 1672/1875 [00:46<00:04, 46.04it/s]
loss 0.63 accuracy 0.91: 89%|████████▉ | 1672/1875 [00:46<00:04, 46.04it/s]
loss 0.63 accuracy 0.91: 89%|████████▉ | 1677/1875 [00:46<00:04, 46.02it/s]
loss 0.05 accuracy 1.00: 89%|████████▉ | 1677/1875 [00:46<00:04, 46.02it/s]
loss 0.07 accuracy 0.97: 89%|████████▉ | 1677/1875 [00:46<00:04, 46.02it/s]
loss 0.02 accuracy 1.00: 89%|████████▉ | 1677/1875 [00:46<00:04, 46.02it/s]
loss 0.03 accuracy 1.00: 89%|████████▉ | 1677/1875 [00:46<00:04, 46.02it/s]
loss 0.09 accuracy 0.97: 89%|████████▉ | 1677/1875 [00:46<00:04, 46.02it/s]
loss 0.09 accuracy 0.97: 90%|████████▉ | 1682/1875 [00:46<00:04, 45.98it/s]
loss 0.21 accuracy 0.97: 90%|████████▉ | 1682/1875 [00:46<00:04, 45.98it/s]
loss 0.09 accuracy 0.97: 90%|████████▉ | 1682/1875 [00:46<00:04, 45.98it/s]
loss 0.12 accuracy 0.97: 90%|████████▉ | 1682/1875 [00:46<00:04, 45.98it/s]
loss 0.04 accuracy 1.00: 90%|████████▉ | 1682/1875 [00:46<00:04, 45.98it/s]
loss 0.04 accuracy 1.00: 90%|████████▉ | 1682/1875 [00:46<00:04, 45.98it/s]
loss 0.04 accuracy 1.00: 90%|████████▉ | 1687/1875 [00:46<00:04, 45.85it/s]
loss 0.13 accuracy 0.97: 90%|████████▉ | 1687/1875 [00:46<00:04, 45.85it/s]
loss 0.04 accuracy 1.00: 90%|████████▉ | 1687/1875 [00:46<00:04, 45.85it/s]
loss 0.06 accuracy 1.00: 90%|████████▉ | 1687/1875 [00:46<00:04, 45.85it/s]
loss 0.05 accuracy 1.00: 90%|████████▉ | 1687/1875 [00:46<00:04, 45.85it/s]
loss 0.18 accuracy 0.94: 90%|████████▉ | 1687/1875 [00:46<00:04, 45.85it/s]
loss 0.18 accuracy 0.94: 90%|█████████ | 1692/1875 [00:46<00:03, 45.80it/s]
loss 0.14 accuracy 0.97: 90%|█████████ | 1692/1875 [00:46<00:03, 45.80it/s]
loss 0.15 accuracy 0.91: 90%|█████████ | 1692/1875 [00:46<00:03, 45.80it/s]
loss 0.08 accuracy 0.97: 90%|█████████ | 1692/1875 [00:46<00:03, 45.80it/s]
loss 0.04 accuracy 1.00: 90%|█████████ | 1692/1875 [00:46<00:03, 45.80it/s]
loss 0.04 accuracy 1.00: 90%|█████████ | 1692/1875 [00:46<00:03, 45.80it/s]
loss 0.04 accuracy 1.00: 91%|█████████ | 1697/1875 [00:46<00:03, 45.71it/s]
loss 0.14 accuracy 0.97: 91%|█████████ | 1697/1875 [00:46<00:03, 45.71it/s]
loss 0.23 accuracy 0.97: 91%|█████████ | 1697/1875 [00:47<00:03, 45.71it/s]
loss 0.40 accuracy 0.94: 91%|█████████ | 1697/1875 [00:47<00:03, 45.71it/s]
loss 0.03 accuracy 1.00: 91%|█████████ | 1697/1875 [00:47<00:03, 45.71it/s]
loss 0.10 accuracy 0.97: 91%|█████████ | 1697/1875 [00:47<00:03, 45.71it/s]
loss 0.10 accuracy 0.97: 91%|█████████ | 1702/1875 [00:47<00:03, 45.71it/s]
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loss 0.23 accuracy 0.94: 91%|█████████ | 1707/1875 [00:47<00:03, 45.72it/s]
loss 0.15 accuracy 0.97: 91%|█████████ | 1707/1875 [00:47<00:03, 45.72it/s]
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loss 0.10 accuracy 0.97: 93%|█████████▎| 1737/1875 [00:47<00:02, 46.05it/s]
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test set accuracy is 0.973000
Traceback (most recent call last):
File "/home/jebba/devel/tinygrad/tinygrad/examples/serious_mnist.py", line 136, in <module>
model.save(f'examples/checkpoint{accuracy * 1e6:.0f}')
File "/home/jebba/devel/tinygrad/tinygrad/examples/serious_mnist.py", line 72, in save
with open(filename+'.npy', 'wb') as f:
^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'examples/checkpoint973000.npy'
simple_conv_bn.py
running network
so_vits_svc.py
Traceback (most recent call last):
File "/home/jebba/devel/tinygrad/tinygrad/examples/so_vits_svc.py", line 10, in <module>
from examples.vits import ResidualCouplingBlock, PosteriorEncoder, Encoder, ResBlock1, ResBlock2, LRELU_SLOPE, sequence_mask, split, download_if_not_present, get_hparams_from_file, load_checkpoint, weight_norm, HParams
ImportError: cannot import name 'download_if_not_present' from 'examples.vits' (/home/jebba/devel/tinygrad/tinygrad/examples/vits.py)
stable_diffusion.py
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ram used: 4.25 GB, cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.out_proj.bias: 97%|█████████▋| 1096/1131 [00:02<00:00, 742.75it/s]
ram used: 4.25 GB, cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm1.weight: 97%|█████████▋| 1096/1131 [00:02<00:00, 742.75it/s]
ram used: 4.25 GB, cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm1.bias: 97%|█████████▋| 1096/1131 [00:02<00:00, 742.75it/s]
ram used: 4.25 GB, cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc1.weight: 97%|█████████▋| 1096/1131 [00:02<00:00, 742.75it/s]
ram used: 4.26 GB, cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc1.bias: 97%|█████████▋| 1096/1131 [00:02<00:00, 742.75it/s]
ram used: 4.26 GB, cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc2.weight: 97%|█████████▋| 1096/1131 [00:02<00:00, 742.75it/s]
ram used: 4.26 GB, cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc2.bias: 97%|█████████▋| 1096/1131 [00:02<00:00, 742.75it/s]
ram used: 4.26 GB, cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm2.weight: 97%|█████████▋| 1096/1131 [00:02<00:00, 742.75it/s]
ram used: 4.26 GB, cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm2.bias: 97%|█████████▋| 1096/1131 [00:02<00:00, 742.75it/s]
ram used: 4.26 GB, cond_stage_model.transformer.text_model.final_layer_norm.weight: 97%|█████████▋| 1096/1131 [00:02<00:00, 742.75it/s]
ram used: 4.26 GB, cond_stage_model.transformer.text_model.final_layer_norm.bias: 97%|█████████▋| 1096/1131 [00:02<00:00, 742.75it/s]
ram used: 4.26 GB, cond_stage_model.transformer.text_model.final_layer_norm.bias: 100%|██████████| 1131/1131 [00:02<00:00, 440.76it/s]
loaded weights in 2571.99 ms, 4.26 GB loaded at 1.66 GB/s
got CLIP context (1, 77, 768)
got unconditional CLIP context (1, 77, 768)
running for [1, 201, 401, 601, 801] timesteps
0%| | 0/5 [00:00<?, ?it/s]
4 801: 0%| | 0/5 [00:00<?, ?it/s]
4 801: 20%|██ | 1/5 [00:13<00:55, 13.99s/it]
3 601: 20%|██ | 1/5 [00:13<00:55, 13.99s/it]
3 601: 40%|████ | 2/5 [00:14<00:18, 6.01s/it]
2 401: 40%|████ | 2/5 [00:14<00:18, 6.01s/it]
1 201: 40%|████ | 2/5 [00:14<00:18, 6.01s/it]
0 1: 40%|████ | 2/5 [00:14<00:18, 6.01s/it]
0 1: 100%|██████████| 5/5 [00:14<00:00, 2.88s/it]
decode (1, 512, 64, 64)
decode (1, 512, 128, 128)
decode (1, 512, 256, 256)
decode (1, 256, 512, 512)
(512, 512, 3)
saving /tmp/rendered.png
Error: no "view" mailcap rules found for type "image/png"
/usr/bin/xdg-open: 882: www-browser: not found
/usr/bin/xdg-open: 882: links2: not found
/usr/bin/xdg-open: 882: elinks: not found
/usr/bin/xdg-open: 882: links: not found
/usr/bin/xdg-open: 882: lynx: not found
/usr/bin/xdg-open: 882: w3m: not found
xdg-open: no method available for opening '/tmp/tmphj455rbh.PNG'
train_efficientnet.py
parameter count 296
training with batch size 16 for 2048 steps
0%| | 0/2048 [00:00<?, ?it/s]
loss 2.68 accuracy 0.00 -- 60.07 + 59.54 + 31476.56 + 219.46 = 31815.64: 0%| | 0/2048 [00:31<?, ?it/s]
loss 2.68 accuracy 0.00 -- 60.07 + 59.54 + 31476.56 + 219.46 = 31815.64: 0%| | 1/2048 [00:31<18:06:37, 31.85s/it]
loss 2.68 accuracy 0.06 -- 54.64 + 153.94 + 14521.00 + 5.87 = 14735.45: 0%| | 1/2048 [00:46<18:06:37, 31.85s/it]
loss 2.68 accuracy 0.06 -- 54.64 + 153.94 + 14521.00 + 5.87 = 14735.45: 0%| | 2/2048 [00:46<12:23:21, 21.80s/it]
loss 2.92 accuracy 0.12 -- 55.55 + 56.57 + 620.43 + 4.97 = 737.52: 0%| | 2/2048 [00:47<12:23:21, 21.80s/it]
loss 2.92 accuracy 0.12 -- 55.55 + 56.57 + 620.43 + 4.97 = 737.52: 0%| | 3/2048 [00:47<6:55:39, 12.20s/it]
loss 3.24 accuracy 0.12 -- 56.70 + 56.73 + 501.23 + 5.02 = 619.68: 0%| | 3/2048 [00:48<6:55:39, 12.20s/it]
loss 3.24 accuracy 0.12 -- 56.70 + 56.73 + 501.23 + 5.02 = 619.68: 0%| | 4/2048 [00:48<4:20:08, 7.64s/it]
loss 2.99 accuracy 0.19 -- 166.41 + 57.14 + 505.88 + 4.99 = 734.43: 0%| | 4/2048 [00:48<4:20:08, 7.64s/it]
loss 2.99 accuracy 0.19 -- 166.41 + 57.14 + 505.88 + 4.99 = 734.43: 0%| | 5/2048 [00:48<2:55:36, 5.16s/it]
loss 3.15 accuracy 0.12 -- 57.06 + 57.74 + 633.05 + 5.03 = 752.89: 0%| | 5/2048 [00:49<2:55:36, 5.16s/it]
loss 3.15 accuracy 0.12 -- 57.06 + 57.74 + 633.05 + 5.03 = 752.89: 0%| | 6/2048 [00:49<2:04:52, 3.67s/it]
loss 3.58 accuracy 0.19 -- 57.63 + 57.02 + 513.18 + 5.00 = 632.83: 0%| | 6/2048 [00:50<2:04:52, 3.67s/it]
loss 3.58 accuracy 0.19 -- 57.63 + 57.02 + 513.18 + 5.00 = 632.83: 0%| | 7/2048 [00:50<1:31:21, 2.69s/it]
loss 3.65 accuracy 0.12 -- 56.84 + 57.49 + 629.53 + 5.02 = 748.88: 0%| | 7/2048 [00:51<1:31:21, 2.69s/it]
loss 3.65 accuracy 0.12 -- 56.84 + 57.49 + 629.53 + 5.02 = 748.88: 0%| | 8/2048 [00:51<1:10:38, 2.08s/it]
loss 3.52 accuracy 0.12 -- 57.34 + 57.04 + 511.09 + 4.98 = 630.45: 0%| | 8/2048 [00:51<1:10:38, 2.08s/it]
loss 3.52 accuracy 0.12 -- 57.34 + 57.04 + 511.09 + 4.98 = 630.45: 0%| | 9/2048 [00:51<55:31, 1.63s/it]
loss 3.62 accuracy 0.12 -- 56.32 + 170.81 + 509.88 + 4.98 = 741.99: 0%| | 9/2048 [00:52<55:31, 1.63s/it]
loss 3.62 accuracy 0.12 -- 56.32 + 170.81 + 509.88 + 4.98 = 741.99: 0%| | 10/2048 [00:52<46:48, 1.38s/it]
loss 3.23 accuracy 0.06 -- 56.67 + 56.50 + 509.40 + 4.98 = 627.56: 0%| | 10/2048 [00:53<46:48, 1.38s/it]
loss 3.23 accuracy 0.06 -- 56.67 + 56.50 + 509.40 + 4.98 = 627.56: 1%| | 11/2048 [00:53<39:16, 1.16s/it]
loss 3.66 accuracy 0.00 -- 57.39 + 57.07 + 506.83 + 5.00 = 626.28: 1%| | 11/2048 [00:53<39:16, 1.16s/it]
loss 3.66 accuracy 0.00 -- 57.39 + 57.07 + 506.83 + 5.00 = 626.28: 1%| | 12/2048 [00:53<35:12, 1.04s/it]
loss 3.66 accuracy 0.06 -- 56.38 + 57.46 + 501.93 + 4.92 = 620.70: 1%| | 12/2048 [00:54<35:12, 1.04s/it]
loss 3.66 accuracy 0.06 -- 56.38 + 57.46 + 501.93 + 4.92 = 620.70: 1%| | 13/2048 [00:54<31:11, 1.09it/s]
loss 2.83 accuracy 0.19 -- 161.58 + 56.90 + 497.35 + 4.97 = 720.81: 1%| | 13/2048 [00:55<31:11, 1.09it/s]
loss 2.83 accuracy 0.19 -- 161.58 + 56.90 + 497.35 + 4.97 = 720.81: 1%| | 14/2048 [00:55<29:25, 1.15it/s]
loss 2.59 accuracy 0.25 -- 56.46 + 171.02 + 510.33 + 5.01 = 742.81: 1%| | 14/2048 [00:56<29:25, 1.15it/s]
loss 2.59 accuracy 0.25 -- 56.46 + 171.02 + 510.33 + 5.01 = 742.81: 1%| | 15/2048 [00:56<28:24, 1.19it/s]
loss 4.20 accuracy 0.00 -- 57.55 + 57.12 + 506.81 + 4.95 = 626.43: 1%| | 15/2048 [00:56<28:24, 1.19it/s]
loss 4.20 accuracy 0.00 -- 57.55 + 57.12 + 506.81 + 4.95 = 626.43: 1%| | 16/2048 [00:56<26:31, 1.28it/s]
loss 3.20 accuracy 0.25 -- 166.51 + 57.89 + 506.22 + 4.98 = 735.60: 1%| | 16/2048 [00:57<26:31, 1.28it/s]
loss 3.20 accuracy 0.25 -- 166.51 + 57.89 + 506.22 + 4.98 = 735.60: 1%| | 17/2048 [00:57<26:40, 1.27it/s]
loss 2.69 accuracy 0.19 -- 56.37 + 57.45 + 632.64 + 4.96 = 751.41: 1%| | 17/2048 [00:58<26:40, 1.27it/s]
loss 2.69 accuracy 0.19 -- 56.37 + 57.45 + 632.64 + 4.96 = 751.41: 1%| | 18/2048 [00:58<26:33, 1.27it/s]
loss 3.83 accuracy 0.12 -- 57.34 + 56.92 + 513.08 + 4.96 = 632.30: 1%| | 18/2048 [00:58<26:33, 1.27it/s]
loss 3.83 accuracy 0.12 -- 57.34 + 56.92 + 513.08 + 4.96 = 632.30: 1%| | 19/2048 [00:58<25:16, 1.34it/s]
loss 3.53 accuracy 0.00 -- 56.67 + 57.76 + 627.03 + 4.96 = 746.42: 1%| | 19/2048 [00:59<25:16, 1.34it/s]
loss 3.53 accuracy 0.00 -- 56.67 + 57.76 + 627.03 + 4.96 = 746.42: 1%| | 20/2048 [00:59<25:32, 1.32it/s]
loss 2.85 accuracy 0.12 -- 57.14 + 56.67 + 511.39 + 5.01 = 630.21: 1%| | 20/2048 [01:00<25:32, 1.32it/s]
loss 2.85 accuracy 0.12 -- 57.14 + 56.67 + 511.39 + 5.01 = 630.21: 1%| | 21/2048 [01:00<24:32, 1.38it/s]
loss 3.75 accuracy 0.00 -- 56.73 + 170.30 + 510.66 + 4.94 = 742.63: 1%| | 21/2048 [01:01<24:32, 1.38it/s]
loss 3.75 accuracy 0.00 -- 56.73 + 170.30 + 510.66 + 4.94 = 742.63: 1%| | 22/2048 [01:01<24:58, 1.35it/s]
loss 3.16 accuracy 0.00 -- 56.37 + 56.61 + 508.78 + 4.96 = 626.71: 1%| | 22/2048 [01:01<24:58, 1.35it/s]
loss 3.16 accuracy 0.00 -- 56.37 + 56.61 + 508.78 + 4.96 = 626.71: 1%| | 23/2048 [01:01<24:06, 1.40it/s]
loss 2.89 accuracy 0.25 -- 57.06 + 56.84 + 506.63 + 4.96 = 625.48: 1%| | 23/2048 [01:02<24:06, 1.40it/s]
loss 2.89 accuracy 0.25 -- 57.06 + 56.84 + 506.63 + 4.96 = 625.48: 1%| | 24/2048 [01:02<24:35, 1.37it/s]
loss 3.16 accuracy 0.19 -- 56.42 + 57.74 + 510.59 + 4.93 = 629.68: 1%| | 24/2048 [01:03<24:35, 1.37it/s]
loss 3.16 accuracy 0.19 -- 56.42 + 57.74 + 510.59 + 4.93 = 629.68: 1%| | 25/2048 [01:03<23:51, 1.41it/s]
loss 3.20 accuracy 0.06 -- 161.59 + 57.25 + 497.86 + 4.92 = 721.62: 1%| | 25/2048 [01:03<23:51, 1.41it/s]
loss 3.20 accuracy 0.06 -- 161.59 + 57.25 + 497.86 + 4.92 = 721.62: 1%|▏ | 26/2048 [01:03<24:16, 1.39it/s]
loss 3.72 accuracy 0.19 -- 56.32 + 171.12 + 509.18 + 4.96 = 741.58: 1%|▏ | 26/2048 [01:04<24:16, 1.39it/s]
loss 3.72 accuracy 0.19 -- 56.32 + 171.12 + 509.18 + 4.96 = 741.58: 1%|▏ | 27/2048 [01:04<24:45, 1.36it/s]
loss 2.21 accuracy 0.25 -- 57.25 + 56.99 + 505.71 + 4.99 = 624.95: 1%|▏ | 27/2048 [01:05<24:45, 1.36it/s]
loss 2.21 accuracy 0.25 -- 57.25 + 56.99 + 505.71 + 4.99 = 624.95: 1%|▏ | 28/2048 [01:05<23:54, 1.41it/s]
loss 2.45 accuracy 0.12 -- 166.51 + 57.68 + 504.50 + 4.97 = 733.66: 1%|▏ | 28/2048 [01:06<23:54, 1.41it/s]
loss 2.45 accuracy 0.12 -- 166.51 + 57.68 + 504.50 + 4.97 = 733.66: 1%|▏ | 29/2048 [01:06<24:24, 1.38it/s]
loss 2.34 accuracy 0.31 -- 56.83 + 57.68 + 631.80 + 4.96 = 751.27: 1%|▏ | 29/2048 [01:06<24:24, 1.38it/s]
loss 2.34 accuracy 0.31 -- 56.83 + 57.68 + 631.80 + 4.96 = 751.27: 1%|▏ | 30/2048 [01:06<24:56, 1.35it/s]
loss 3.25 accuracy 0.12 -- 57.14 + 56.79 + 512.39 + 5.04 = 631.36: 1%|▏ | 30/2048 [01:07<24:56, 1.35it/s]
loss 3.25 accuracy 0.12 -- 57.14 + 56.79 + 512.39 + 5.04 = 631.36: 2%|▏ | 31/2048 [01:07<24:06, 1.39it/s]
loss 2.57 accuracy 0.25 -- 56.36 + 57.58 + 627.26 + 5.01 = 746.21: 2%|▏ | 31/2048 [01:08<24:06, 1.39it/s]
loss 2.57 accuracy 0.25 -- 56.36 + 57.58 + 627.26 + 5.01 = 746.21: 2%|▏ | 32/2048 [01:08<24:39, 1.36it/s]
loss 3.08 accuracy 0.06 -- 57.02 + 56.94 + 512.38 + 4.95 = 631.28: 2%|▏ | 32/2048 [01:09<24:39, 1.36it/s]
loss 3.08 accuracy 0.06 -- 57.02 + 56.94 + 512.38 + 4.95 = 631.28: 2%|▏ | 33/2048 [01:09<23:53, 1.41it/s]
loss 2.50 accuracy 0.19 -- 56.46 + 170.47 + 510.74 + 4.92 = 742.59: 2%|▏ | 33/2048 [01:09<23:53, 1.41it/s]
loss 2.50 accuracy 0.19 -- 56.46 + 170.47 + 510.74 + 4.92 = 742.59: 2%|▏ | 34/2048 [01:09<24:28, 1.37it/s]
loss 2.49 accuracy 0.25 -- 56.65 + 56.58 + 509.35 + 4.95 = 627.53: 2%|▏ | 34/2048 [01:10<24:28, 1.37it/s]
loss 2.49 accuracy 0.25 -- 56.65 + 56.58 + 509.35 + 4.95 = 627.53: 2%|▏ | 35/2048 [01:10<23:42, 1.41it/s]
loss 2.66 accuracy 0.19 -- 57.15 + 56.66 + 506.31 + 4.96 = 625.09: 2%|▏ | 35/2048 [01:11<23:42, 1.41it/s]
loss 2.66 accuracy 0.19 -- 57.15 + 56.66 + 506.31 + 4.96 = 625.09: 2%|▏ | 36/2048 [01:11<24:16, 1.38it/s]
loss 2.45 accuracy 0.06 -- 56.72 + 57.53 + 503.47 + 4.93 = 622.65: 2%|▏ | 36/2048 [01:11<24:16, 1.38it/s]
loss 2.45 accuracy 0.06 -- 56.72 + 57.53 + 503.47 + 4.93 = 622.65: 2%|▏ | 37/2048 [01:11<23:31, 1.42it/s]
loss 2.52 accuracy 0.12 -- 161.43 + 57.13 + 498.34 + 4.95 = 721.86: 2%|▏ | 37/2048 [01:12<23:31, 1.42it/s]
loss 2.52 accuracy 0.12 -- 161.43 + 57.13 + 498.34 + 4.95 = 721.86: 2%|▏ | 38/2048 [01:12<23:59, 1.40it/s]
loss 2.90 accuracy 0.19 -- 56.60 + 172.00 + 510.17 + 4.94 = 743.70: 2%|▏ | 38/2048 [01:13<23:59, 1.40it/s]
loss 2.90 accuracy 0.19 -- 56.60 + 172.00 + 510.17 + 4.94 = 743.70: 2%|▏ | 39/2048 [01:13<24:32, 1.36it/s]
loss 3.13 accuracy 0.12 -- 57.61 + 56.68 + 507.47 + 4.94 = 626.71: 2%|▏ | 39/2048 [01:14<24:32, 1.36it/s]
loss 3.13 accuracy 0.12 -- 57.61 + 56.68 + 507.47 + 4.94 = 626.71: 2%|▏ | 40/2048 [01:14<23:44, 1.41it/s]
loss 2.63 accuracy 0.12 -- 166.51 + 57.57 + 504.92 + 4.96 = 733.95: 2%|▏ | 40/2048 [01:14<23:44, 1.41it/s]
loss 2.63 accuracy 0.12 -- 166.51 + 57.57 + 504.92 + 4.96 = 733.95: 2%|▏ | 41/2048 [01:14<24:14, 1.38it/s]
loss 3.17 accuracy 0.06 -- 56.51 + 57.76 + 632.84 + 4.97 = 752.08: 2%|▏ | 41/2048 [01:15<24:14, 1.38it/s]
loss 3.17 accuracy 0.06 -- 56.51 + 57.76 + 632.84 + 4.97 = 752.08: 2%|▏ | 42/2048 [01:15<24:46, 1.35it/s]
loss 2.67 accuracy 0.19 -- 57.36 + 57.28 + 515.07 + 4.93 = 634.63: 2%|▏ | 42/2048 [01:16<24:46, 1.35it/s]
loss 2.67 accuracy 0.19 -- 57.36 + 57.28 + 515.07 + 4.93 = 634.63: 2%|▏ | 43/2048 [01:16<23:58, 1.39it/s]
loss 2.22 accuracy 0.25 -- 57.00 + 57.66 + 626.79 + 4.93 = 746.37: 2%|▏ | 43/2048 [01:17<23:58, 1.39it/s]
loss 2.22 accuracy 0.25 -- 57.00 + 57.66 + 626.79 + 4.93 = 746.37: 2%|▏ | 44/2048 [01:17<24:31, 1.36it/s]
loss 2.57 accuracy 0.19 -- 57.19 + 56.80 + 509.33 + 4.97 = 628.30: 2%|▏ | 44/2048 [01:17<24:31, 1.36it/s]
loss 2.57 accuracy 0.19 -- 57.19 + 56.80 + 509.33 + 4.97 = 628.30: 2%|▏ | 45/2048 [01:17<23:43, 1.41it/s]
loss 2.96 accuracy 0.12 -- 56.87 + 171.29 + 512.56 + 4.99 = 745.72: 2%|▏ | 45/2048 [01:18<23:43, 1.41it/s]
loss 2.96 accuracy 0.12 -- 56.87 + 171.29 + 512.56 + 4.99 = 745.72: 2%|▏ | 46/2048 [01:18<24:20, 1.37it/s]
loss 2.29 accuracy 0.19 -- 56.80 + 56.98 + 508.70 + 4.93 = 627.41: 2%|▏ | 46/2048 [01:19<24:20, 1.37it/s]
loss 2.29 accuracy 0.19 -- 56.80 + 56.98 + 508.70 + 4.93 = 627.41: 2%|▏ | 47/2048 [01:19<23:35, 1.41it/s]
loss 2.38 accuracy 0.31 -- 56.89 + 56.38 + 505.75 + 4.96 = 623.98: 2%|▏ | 47/2048 [01:19<23:35, 1.41it/s]
loss 2.38 accuracy 0.31 -- 56.89 + 56.38 + 505.75 + 4.96 = 623.98: 2%|▏ | 48/2048 [01:19<24:07, 1.38it/s]
loss 2.82 accuracy 0.12 -- 56.45 + 57.53 + 503.36 + 4.99 = 622.34: 2%|▏ | 48/2048 [01:20<24:07, 1.38it/s]
loss 2.82 accuracy 0.12 -- 56.45 + 57.53 + 503.36 + 4.99 = 622.34: 2%|▏ | 49/2048 [01:20<23:22, 1.43it/s]
loss 2.84 accuracy 0.06 -- 161.31 + 57.22 + 498.24 + 4.95 = 721.72: 2%|▏ | 49/2048 [01:21<23:22, 1.43it/s]
loss 2.84 accuracy 0.06 -- 161.31 + 57.22 + 498.24 + 4.95 = 721.72: 2%|▏ | 50/2048 [01:21<23:50, 1.40it/s]
loss 2.82 accuracy 0.25 -- 56.49 + 170.20 + 509.57 + 4.94 = 741.20: 2%|▏ | 50/2048 [01:22<23:50, 1.40it/s]
loss 2.82 accuracy 0.25 -- 56.49 + 170.20 + 509.57 + 4.94 = 741.20: 2%|▏ | 51/2048 [01:22<24:21, 1.37it/s]
loss 2.55 accuracy 0.06 -- 57.40 + 56.92 + 505.32 + 4.97 = 624.61: 2%|▏ | 51/2048 [01:22<24:21, 1.37it/s]
loss 2.55 accuracy 0.06 -- 57.40 + 56.92 + 505.32 + 4.97 = 624.61: 3%|▎ | 52/2048 [01:22<23:33, 1.41it/s]
loss 2.79 accuracy 0.06 -- 166.14 + 57.77 + 505.58 + 4.95 = 734.43: 3%|▎ | 52/2048 [01:23<23:33, 1.41it/s]
loss 2.79 accuracy 0.06 -- 166.14 + 57.77 + 505.58 + 4.95 = 734.43: 3%|▎ | 53/2048 [01:23<24:04, 1.38it/s]
loss 3.23 accuracy 0.00 -- 56.58 + 57.94 + 635.67 + 4.95 = 755.14: 3%|▎ | 53/2048 [01:24<24:04, 1.38it/s]
loss 3.23 accuracy 0.00 -- 56.58 + 57.94 + 635.67 + 4.95 = 755.14: 3%|▎ | 54/2048 [01:24<24:39, 1.35it/s]
loss 2.25 accuracy 0.25 -- 57.36 + 56.66 + 514.99 + 4.96 = 633.97: 3%|▎ | 54/2048 [01:24<24:39, 1.35it/s]
loss 2.25 accuracy 0.25 -- 57.36 + 56.66 + 514.99 + 4.96 = 633.97: 3%|▎ | 55/2048 [01:24<23:50, 1.39it/s]
loss 2.72 accuracy 0.00 -- 56.95 + 57.45 + 626.68 + 4.96 = 746.04: 3%|▎ | 55/2048 [01:25<23:50, 1.39it/s]
loss 2.72 accuracy 0.00 -- 56.95 + 57.45 + 626.68 + 4.96 = 746.04: 3%|▎ | 56/2048 [01:25<24:23, 1.36it/s]
loss 2.89 accuracy 0.19 -- 57.37 + 56.63 + 510.67 + 4.92 = 629.59: 3%|▎ | 56/2048 [01:26<24:23, 1.36it/s]
loss 2.89 accuracy 0.19 -- 57.37 + 56.63 + 510.67 + 4.92 = 629.59: 3%|▎ | 57/2048 [01:26<23:36, 1.41it/s]
loss 2.52 accuracy 0.19 -- 56.62 + 169.89 + 510.45 + 4.96 = 741.92: 3%|▎ | 57/2048 [01:27<23:36, 1.41it/s]
loss 2.52 accuracy 0.19 -- 56.62 + 169.89 + 510.45 + 4.96 = 741.92: 3%|▎ | 58/2048 [01:27<24:10, 1.37it/s]
loss 2.18 accuracy 0.19 -- 56.57 + 56.86 + 508.15 + 4.95 = 626.53: 3%|▎ | 58/2048 [01:27<24:10, 1.37it/s]
loss 2.18 accuracy 0.19 -- 56.57 + 56.86 + 508.15 + 4.95 = 626.53: 3%|▎ | 59/2048 [01:27<23:25, 1.42it/s]
loss 2.77 accuracy 0.19 -- 57.27 + 56.81 + 505.29 + 4.92 = 624.28: 3%|▎ | 59/2048 [01:28<23:25, 1.42it/s]
loss 2.77 accuracy 0.19 -- 57.27 + 56.81 + 505.29 + 4.92 = 624.28: 3%|▎ | 60/2048 [01:28<23:57, 1.38it/s]
loss 2.40 accuracy 0.06 -- 56.41 + 57.76 + 504.98 + 4.95 = 624.09: 3%|▎ | 60/2048 [01:29<23:57, 1.38it/s]
loss 2.40 accuracy 0.06 -- 56.41 + 57.76 + 504.98 + 4.95 = 624.09: 3%|▎ | 61/2048 [01:29<23:14, 1.42it/s]
loss 2.54 accuracy 0.25 -- 161.78 + 57.10 + 497.81 + 4.95 = 721.64: 3%|▎ | 61/2048 [01:29<23:14, 1.42it/s]
loss 2.54 accuracy 0.25 -- 161.78 + 57.10 + 497.81 + 4.95 = 721.64: 3%|▎ | 62/2048 [01:29<23:42, 1.40it/s]
loss 1.99 accuracy 0.31 -- 56.19 + 169.55 + 508.93 + 4.96 = 739.63: 3%|▎ | 62/2048 [01:30<23:42, 1.40it/s]
loss 1.99 accuracy 0.31 -- 56.19 + 169.55 + 508.93 + 4.96 = 739.63: 3%|▎ | 63/2048 [01:30<24:12, 1.37it/s]
loss 2.31 accuracy 0.19 -- 57.19 + 56.49 + 504.98 + 4.96 = 623.62: 3%|▎ | 63/2048 [01:31<24:12, 1.37it/s]
loss 2.31 accuracy 0.19 -- 57.19 + 56.49 + 504.98 + 4.96 = 623.62: 3%|▎ | 64/2048 [01:31<23:24, 1.41it/s]
loss 2.24 accuracy 0.25 -- 165.95 + 57.86 + 503.87 + 4.96 = 732.64: 3%|▎ | 64/2048 [01:32<23:24, 1.41it/s]
loss 2.24 accuracy 0.25 -- 165.95 + 57.86 + 503.87 + 4.96 = 732.64: 3%|▎ | 65/2048 [01:32<23:54, 1.38it/s]
loss 2.30 accuracy 0.06 -- 56.39 + 57.30 + 634.27 + 4.99 = 752.95: 3%|▎ | 65/2048 [01:32<23:54, 1.38it/s]
loss 2.30 accuracy 0.06 -- 56.39 + 57.30 + 634.27 + 4.99 = 752.95: 3%|▎ | 66/2048 [01:32<24:28, 1.35it/s]
loss 2.42 accuracy 0.12 -- 57.70 + 57.06 + 512.80 + 4.92 = 632.48: 3%|▎ | 66/2048 [01:33<24:28, 1.35it/s]
loss 2.42 accuracy 0.12 -- 57.70 + 57.06 + 512.80 + 4.92 = 632.48: 3%|▎ | 67/2048 [01:33<23:39, 1.40it/s]
loss 2.42 accuracy 0.19 -- 56.63 + 57.77 + 627.63 + 4.95 = 746.98: 3%|▎ | 67/2048 [01:34<23:39, 1.40it/s]
loss 2.42 accuracy 0.19 -- 56.63 + 57.77 + 627.63 + 4.95 = 746.98: 3%|▎ | 68/2048 [01:34<24:13, 1.36it/s]
loss 2.10 accuracy 0.19 -- 57.41 + 57.06 + 510.26 + 4.95 = 629.68: 3%|▎ | 68/2048 [01:34<24:13, 1.36it/s]
loss 2.10 accuracy 0.19 -- 57.41 + 57.06 + 510.26 + 4.95 = 629.68: 3%|▎ | 69/2048 [01:34<23:27, 1.41it/s]
loss 2.33 accuracy 0.25 -- 56.52 + 169.98 + 509.45 + 4.97 = 740.92: 3%|▎ | 69/2048 [01:35<23:27, 1.41it/s]
loss 2.33 accuracy 0.25 -- 56.52 + 169.98 + 509.45 + 4.97 = 740.92: 3%|▎ | 70/2048 [01:35<24:21, 1.35it/s]
loss 2.46 accuracy 0.25 -- 56.48 + 56.69 + 507.07 + 4.96 = 625.19: 3%|▎ | 70/2048 [01:36<24:21, 1.35it/s]
loss 2.46 accuracy 0.25 -- 56.48 + 56.69 + 507.07 + 4.96 = 625.19: 3%|▎ | 71/2048 [01:36<23:30, 1.40it/s]
loss 2.55 accuracy 0.25 -- 57.14 + 57.07 + 506.64 + 4.92 = 625.77: 3%|▎ | 71/2048 [01:37<23:30, 1.40it/s]
loss 2.55 accuracy 0.25 -- 57.14 + 57.07 + 506.64 + 4.92 = 625.77: 4%|▎ | 72/2048 [01:37<23:59, 1.37it/s]
loss 2.54 accuracy 0.19 -- 56.46 + 57.77 + 502.40 + 4.94 = 621.57: 4%|▎ | 72/2048 [01:37<23:59, 1.37it/s]
loss 2.54 accuracy 0.19 -- 56.46 + 57.77 + 502.40 + 4.94 = 621.57: 4%|▎ | 73/2048 [01:37<23:11, 1.42it/s]
loss 2.63 accuracy 0.06 -- 161.08 + 57.27 + 498.39 + 4.91 = 721.65: 4%|▎ | 73/2048 [01:38<23:11, 1.42it/s]
loss 2.63 accuracy 0.06 -- 161.08 + 57.27 + 498.39 + 4.91 = 721.65: 4%|▎ | 74/2048 [01:38<23:37, 1.39it/s]
loss 2.35 accuracy 0.06 -- 56.17 + 170.50 + 508.45 + 4.92 = 740.04: 4%|▎ | 74/2048 [01:39<23:37, 1.39it/s]
loss 2.35 accuracy 0.06 -- 56.17 + 170.50 + 508.45 + 4.92 = 740.04: 4%|▎ | 75/2048 [01:39<24:06, 1.36it/s]
loss 2.07 accuracy 0.19 -- 56.85 + 56.61 + 504.88 + 4.88 = 623.21: 4%|▎ | 75/2048 [01:40<24:06, 1.36it/s]
loss 2.07 accuracy 0.19 -- 56.85 + 56.61 + 504.88 + 4.88 = 623.21: 4%|▎ | 76/2048 [01:40<23:17, 1.41it/s]
loss 2.45 accuracy 0.12 -- 299.43 + 102.71 + 563.90 + 5.17 = 971.21: 4%|▎ | 76/2048 [01:41<23:17, 1.41it/s]
loss 2.45 accuracy 0.12 -- 299.43 + 102.71 + 563.90 + 5.17 = 971.21: 4%|▍ | 77/2048 [01:41<26:09, 1.26it/s]
loss 2.24 accuracy 0.12 -- 60.17 + 60.60 + 666.19 + 5.00 = 791.96: 4%|▍ | 77/2048 [01:41<26:09, 1.26it/s]
loss 2.24 accuracy 0.12 -- 60.17 + 60.60 + 666.19 + 5.00 = 791.96: 4%|▍ | 78/2048 [01:41<26:24, 1.24it/s]
loss 2.33 accuracy 0.12 -- 58.22 + 58.99 + 533.25 + 5.00 = 655.47: 4%|▍ | 78/2048 [01:42<26:24, 1.24it/s]
loss 2.33 accuracy 0.12 -- 58.22 + 58.99 + 533.25 + 5.00 = 655.47: 4%|▍ | 79/2048 [01:42<25:12, 1.30it/s]
loss 2.39 accuracy 0.19 -- 58.16 + 59.63 + 657.85 + 5.02 = 780.67: 4%|▍ | 79/2048 [01:43<25:12, 1.30it/s]
loss 2.39 accuracy 0.19 -- 58.16 + 59.63 + 657.85 + 5.02 = 780.67: 4%|▍ | 80/2048 [01:43<25:36, 1.28it/s]
loss 2.35 accuracy 0.19 -- 57.73 + 56.80 + 510.00 + 4.88 = 629.41: 4%|▍ | 80/2048 [01:43<25:36, 1.28it/s]
loss 2.35 accuracy 0.19 -- 57.73 + 56.80 + 510.00 + 4.88 = 629.41: 4%|▍ | 81/2048 [01:43<24:22, 1.34it/s]
loss 2.18 accuracy 0.25 -- 56.52 + 171.05 + 508.90 + 4.89 = 741.36: 4%|▍ | 81/2048 [01:44<24:22, 1.34it/s]
loss 2.18 accuracy 0.25 -- 56.52 + 171.05 + 508.90 + 4.89 = 741.36: 4%|▍ | 82/2048 [01:44<24:37, 1.33it/s]
loss 2.37 accuracy 0.25 -- 57.18 + 56.73 + 507.95 + 4.88 = 626.74: 4%|▍ | 82/2048 [01:45<24:37, 1.33it/s]
loss 2.37 accuracy 0.25 -- 57.18 + 56.73 + 507.95 + 4.88 = 626.74: 4%|▍ | 83/2048 [01:45<23:39, 1.38it/s]
loss 2.51 accuracy 0.19 -- 56.79 + 56.61 + 506.11 + 4.88 = 624.39: 4%|▍ | 83/2048 [01:46<23:39, 1.38it/s]
loss 2.51 accuracy 0.19 -- 56.79 + 56.61 + 506.11 + 4.88 = 624.39: 4%|▍ | 84/2048 [01:46<24:03, 1.36it/s]
loss 2.49 accuracy 0.19 -- 56.30 + 57.36 + 503.51 + 4.90 = 622.07: 4%|▍ | 84/2048 [01:46<24:03, 1.36it/s]
loss 2.49 accuracy 0.19 -- 56.30 + 57.36 + 503.51 + 4.90 = 622.07: 4%|▍ | 85/2048 [01:46<23:12, 1.41it/s]
loss 2.18 accuracy 0.19 -- 163.99 + 56.96 + 498.16 + 4.87 = 723.98: 4%|▍ | 85/2048 [01:47<23:12, 1.41it/s]
loss 2.18 accuracy 0.19 -- 163.99 + 56.96 + 498.16 + 4.87 = 723.98: 4%|▍ | 86/2048 [01:47<23:36, 1.38it/s]
loss 2.32 accuracy 0.06 -- 56.21 + 171.62 + 510.57 + 4.94 = 743.33: 4%|▍ | 86/2048 [01:48<23:36, 1.38it/s]
loss 2.32 accuracy 0.06 -- 56.21 + 171.62 + 510.57 + 4.94 = 743.33: 4%|▍ | 87/2048 [01:48<24:05, 1.36it/s]
loss 2.29 accuracy 0.12 -- 56.90 + 56.52 + 505.62 + 4.92 = 623.96: 4%|▍ | 87/2048 [01:48<24:05, 1.36it/s]
loss 2.29 accuracy 0.12 -- 56.90 + 56.52 + 505.62 + 4.92 = 623.96: 4%|▍ | 88/2048 [01:48<23:14, 1.41it/s]
loss 2.05 accuracy 0.38 -- 168.24 + 57.38 + 504.51 + 4.89 = 735.02: 4%|▍ | 88/2048 [01:49<23:14, 1.41it/s]
loss 2.05 accuracy 0.38 -- 168.24 + 57.38 + 504.51 + 4.89 = 735.02: 4%|▍ | 89/2048 [01:49<23:43, 1.38it/s]
loss 2.09 accuracy 0.06 -- 56.53 + 57.52 + 634.54 + 4.89 = 753.47: 4%|▍ | 89/2048 [01:50<23:43, 1.38it/s]
loss 2.09 accuracy 0.06 -- 56.53 + 57.52 + 634.54 + 4.89 = 753.47: 4%|▍ | 90/2048 [01:50<24:14, 1.35it/s]
loss 2.06 accuracy 0.19 -- 57.51 + 57.05 + 514.55 + 4.89 = 634.00: 4%|▍ | 90/2048 [01:51<24:14, 1.35it/s]
loss 2.06 accuracy 0.19 -- 57.51 + 57.05 + 514.55 + 4.89 = 634.00: 4%|▍ | 91/2048 [01:51<23:26, 1.39it/s]
loss 2.11 accuracy 0.25 -- 56.34 + 57.30 + 630.04 + 4.89 = 748.57: 4%|▍ | 91/2048 [01:51<23:26, 1.39it/s]
loss 2.11 accuracy 0.25 -- 56.34 + 57.30 + 630.04 + 4.89 = 748.57: 4%|▍ | 92/2048 [01:51<23:59, 1.36it/s]
loss 2.39 accuracy 0.06 -- 57.09 + 56.58 + 512.42 + 4.89 = 630.98: 4%|▍ | 92/2048 [01:52<23:59, 1.36it/s]
loss 2.39 accuracy 0.06 -- 57.09 + 56.58 + 512.42 + 4.89 = 630.98: 5%|▍ | 93/2048 [01:52<23:13, 1.40it/s]
loss 2.29 accuracy 0.00 -- 56.31 + 173.65 + 511.67 + 4.89 = 746.52: 5%|▍ | 93/2048 [01:53<23:13, 1.40it/s]
loss 2.29 accuracy 0.00 -- 56.31 + 173.65 + 511.67 + 4.89 = 746.52: 5%|▍ | 94/2048 [01:53<23:48, 1.37it/s]
loss 2.99 accuracy 0.06 -- 56.38 + 56.44 + 509.84 + 4.89 = 627.55: 5%|▍ | 94/2048 [01:54<23:48, 1.37it/s]
loss 2.99 accuracy 0.06 -- 56.38 + 56.44 + 509.84 + 4.89 = 627.55: 5%|▍ | 95/2048 [01:54<23:03, 1.41it/s]
loss 2.44 accuracy 0.00 -- 56.81 + 56.63 + 506.91 + 4.90 = 625.25: 5%|▍ | 95/2048 [01:54<23:03, 1.41it/s]
loss 2.44 accuracy 0.00 -- 56.81 + 56.63 + 506.91 + 4.90 = 625.25: 5%|▍ | 96/2048 [01:54<23:36, 1.38it/s]
loss 2.11 accuracy 0.25 -- 56.16 + 57.61 + 503.34 + 4.91 = 622.02: 5%|▍ | 96/2048 [01:55<23:36, 1.38it/s]
loss 2.11 accuracy 0.25 -- 56.16 + 57.61 + 503.34 + 4.91 = 622.02: 5%|▍ | 97/2048 [01:55<22:50, 1.42it/s]
loss 2.00 accuracy 0.19 -- 164.18 + 57.11 + 497.57 + 4.91 = 723.77: 5%|▍ | 97/2048 [01:56<22:50, 1.42it/s]
loss 2.00 accuracy 0.19 -- 164.18 + 57.11 + 497.57 + 4.91 = 723.77: 5%|▍ | 98/2048 [01:56<23:18, 1.39it/s]
loss 2.82 accuracy 0.25 -- 56.15 + 171.95 + 509.83 + 4.89 = 742.82: 5%|▍ | 98/2048 [01:57<23:18, 1.39it/s]
loss 2.82 accuracy 0.25 -- 56.15 + 171.95 + 509.83 + 4.89 = 742.82: 5%|▍ | 99/2048 [01:57<23:49, 1.36it/s]
loss 2.33 accuracy 0.19 -- 57.16 + 56.40 + 505.48 + 4.91 = 623.96: 5%|▍ | 99/2048 [01:57<23:49, 1.36it/s]
loss 2.33 accuracy 0.19 -- 57.16 + 56.40 + 505.48 + 4.91 = 623.96: 5%|▍ | 100/2048 [01:57<23:01, 1.41it/s]
loss 2.19 accuracy 0.25 -- 168.88 + 57.41 + 505.31 + 4.92 = 736.52: 5%|▍ | 100/2048 [01:58<23:01, 1.41it/s]
loss 2.19 accuracy 0.25 -- 168.88 + 57.41 + 505.31 + 4.92 = 736.52: 5%|▍ | 101/2048 [01:58<23:32, 1.38it/s]
loss 2.25 accuracy 0.12 -- 56.40 + 58.54 + 636.19 + 4.92 = 756.04: 5%|▍ | 101/2048 [01:59<23:32, 1.38it/s]
loss 2.25 accuracy 0.12 -- 56.40 + 58.54 + 636.19 + 4.92 = 756.04: 5%|▍ | 102/2048 [01:59<24:06, 1.35it/s]
loss 2.18 accuracy 0.06 -- 57.06 + 56.66 + 513.96 + 4.90 = 632.57: 5%|▍ | 102/2048 [01:59<24:06, 1.35it/s]
loss 2.18 accuracy 0.06 -- 57.06 + 56.66 + 513.96 + 4.90 = 632.57: 5%|▌ | 103/2048 [01:59<23:17, 1.39it/s]
loss 2.83 accuracy 0.00 -- 56.38 + 57.24 + 630.24 + 4.92 = 748.78: 5%|▌ | 103/2048 [02:00<23:17, 1.39it/s]
loss 2.83 accuracy 0.00 -- 56.38 + 57.24 + 630.24 + 4.92 = 748.78: 5%|▌ | 104/2048 [02:00<23:50, 1.36it/s]
loss 2.13 accuracy 0.19 -- 57.32 + 56.92 + 510.92 + 4.91 = 630.07: 5%|▌ | 104/2048 [02:01<23:50, 1.36it/s]
loss 2.13 accuracy 0.19 -- 57.32 + 56.92 + 510.92 + 4.91 = 630.07: 5%|▌ | 105/2048 [02:01<23:04, 1.40it/s]
loss 2.14 accuracy 0.06 -- 56.20 + 173.14 + 510.19 + 4.90 = 744.43: 5%|▌ | 105/2048 [02:02<23:04, 1.40it/s]
loss 2.14 accuracy 0.06 -- 56.20 + 173.14 + 510.19 + 4.90 = 744.43: 5%|▌ | 106/2048 [02:02<23:38, 1.37it/s]
loss 2.32 accuracy 0.19 -- 56.71 + 56.60 + 508.37 + 4.94 = 626.62: 5%|▌ | 106/2048 [02:02<23:38, 1.37it/s]
loss 2.32 accuracy 0.19 -- 56.71 + 56.60 + 508.37 + 4.94 = 626.62: 5%|▌ | 107/2048 [02:02<22:53, 1.41it/s]
loss 2.05 accuracy 0.25 -- 66.62 + 67.52 + 513.98 + 4.89 = 653.01: 5%|▌ | 107/2048 [02:03<22:53, 1.41it/s]
loss 2.05 accuracy 0.25 -- 66.62 + 67.52 + 513.98 + 4.89 = 653.01: 5%|▌ | 108/2048 [02:03<24:42, 1.31it/s]
loss 1.98 accuracy 0.25 -- 56.12 + 57.40 + 503.46 + 4.89 = 621.88: 5%|▌ | 108/2048 [02:04<24:42, 1.31it/s]
loss 1.98 accuracy 0.25 -- 56.12 + 57.40 + 503.46 + 4.89 = 621.88: 5%|▌ | 109/2048 [02:04<23:36, 1.37it/s]
loss 2.48 accuracy 0.12 -- 164.12 + 56.85 + 497.11 + 4.88 = 722.96: 5%|▌ | 109/2048 [02:05<23:36, 1.37it/s]
loss 2.48 accuracy 0.12 -- 164.12 + 56.85 + 497.11 + 4.88 = 722.96: 5%|▌ | 110/2048 [02:05<23:47, 1.36it/s]
loss 2.08 accuracy 0.25 -- 56.28 + 171.83 + 512.36 + 4.91 = 745.37: 5%|▌ | 110/2048 [02:05<23:47, 1.36it/s]
loss 2.08 accuracy 0.25 -- 56.28 + 171.83 + 512.36 + 4.91 = 745.37: 5%|▌ | 111/2048 [02:05<24:07, 1.34it/s]
loss 2.20 accuracy 0.19 -- 57.54 + 57.25 + 507.58 + 4.94 = 627.30: 5%|▌ | 111/2048 [02:06<24:07, 1.34it/s]
loss 2.20 accuracy 0.19 -- 57.54 + 57.25 + 507.58 + 4.94 = 627.30: 5%|▌ | 112/2048 [02:06<23:13, 1.39it/s]
loss 2.21 accuracy 0.25 -- 168.50 + 57.37 + 506.37 + 4.89 = 737.13: 5%|▌ | 112/2048 [02:07<23:13, 1.39it/s]
loss 2.21 accuracy 0.25 -- 168.50 + 57.37 + 506.37 + 4.89 = 737.13: 6%|▌ | 113/2048 [02:07<23:38, 1.36it/s]
loss 2.33 accuracy 0.19 -- 56.80 + 57.45 + 637.17 + 4.97 = 756.39: 6%|▌ | 113/2048 [02:08<23:38, 1.36it/s]
loss 2.33 accuracy 0.19 -- 56.80 + 57.45 + 637.17 + 4.97 = 756.39: 6%|▌ | 114/2048 [02:08<24:07, 1.34it/s]
loss 2.30 accuracy 0.06 -- 57.42 + 56.76 + 516.45 + 4.91 = 635.54: 6%|▌ | 114/2048 [02:08<24:07, 1.34it/s]
loss 2.30 accuracy 0.06 -- 57.42 + 56.76 + 516.45 + 4.91 = 635.54: 6%|▌ | 115/2048 [02:08<23:16, 1.38it/s]
loss 2.09 accuracy 0.25 -- 56.93 + 59.77 + 632.29 + 4.93 = 753.91: 6%|▌ | 115/2048 [02:09<23:16, 1.38it/s]
loss 2.09 accuracy 0.25 -- 56.93 + 59.77 + 632.29 + 4.93 = 753.91: 6%|▌ | 116/2048 [02:09<23:50, 1.35it/s]
loss 2.21 accuracy 0.31 -- 57.53 + 56.92 + 512.99 + 4.91 = 632.36: 6%|▌ | 116/2048 [02:10<23:50, 1.35it/s]
loss 2.21 accuracy 0.31 -- 57.53 + 56.92 + 512.99 + 4.91 = 632.36: 6%|▌ | 117/2048 [02:10<23:02, 1.40it/s]
loss 2.14 accuracy 0.25 -- 56.98 + 172.06 + 513.29 + 4.95 = 747.27: 6%|▌ | 117/2048 [02:10<23:02, 1.40it/s]
loss 2.14 accuracy 0.25 -- 56.98 + 172.06 + 513.29 + 4.95 = 747.27: 6%|▌ | 118/2048 [02:10<23:36, 1.36it/s]
loss 2.01 accuracy 0.31 -- 57.46 + 56.96 + 510.27 + 4.93 = 629.62: 6%|▌ | 118/2048 [02:11<23:36, 1.36it/s]
loss 2.01 accuracy 0.31 -- 57.46 + 56.96 + 510.27 + 4.93 = 629.62: 6%|▌ | 119/2048 [02:11<23:11, 1.39it/s]
loss 2.31 accuracy 0.12 -- 57.30 + 56.82 + 508.06 + 4.92 = 627.10: 6%|▌ | 119/2048 [02:12<23:11, 1.39it/s]
loss 2.31 accuracy 0.12 -- 57.30 + 56.82 + 508.06 + 4.92 = 627.10: 6%|▌ | 120/2048 [02:12<23:37, 1.36it/s]
loss 2.35 accuracy 0.12 -- 56.79 + 57.69 + 507.46 + 4.92 = 626.86: 6%|▌ | 120/2048 [02:12<23:37, 1.36it/s]
loss 2.35 accuracy 0.12 -- 56.79 + 57.69 + 507.46 + 4.92 = 626.86: 6%|▌ | 121/2048 [02:12<22:50, 1.41it/s]
loss 2.48 accuracy 0.12 -- 164.00 + 57.10 + 499.85 + 4.94 = 725.90: 6%|▌ | 121/2048 [02:13<22:50, 1.41it/s]
loss 2.48 accuracy 0.12 -- 164.00 + 57.10 + 499.85 + 4.94 = 725.90: 6%|▌ | 122/2048 [02:13<23:13, 1.38it/s]
loss 2.10 accuracy 0.25 -- 56.52 + 172.57 + 512.89 + 4.95 = 746.94: 6%|▌ | 122/2048 [02:14<23:13, 1.38it/s]
loss 2.10 accuracy 0.25 -- 56.52 + 172.57 + 512.89 + 4.95 = 746.94: 6%|▌ | 123/2048 [02:14<23:42, 1.35it/s]
loss 2.35 accuracy 0.19 -- 57.54 + 56.75 + 508.21 + 4.91 = 627.42: 6%|▌ | 123/2048 [02:15<23:42, 1.35it/s]
loss 2.35 accuracy 0.19 -- 57.54 + 56.75 + 508.21 + 4.91 = 627.42: 6%|▌ | 124/2048 [02:15<22:53, 1.40it/s]
loss 2.11 accuracy 0.19 -- 168.71 + 57.16 + 506.84 + 4.94 = 737.66: 6%|▌ | 124/2048 [02:15<22:53, 1.40it/s]
loss 2.11 accuracy 0.19 -- 168.71 + 57.16 + 506.84 + 4.94 = 737.66: 6%|▌ | 125/2048 [02:15<23:22, 1.37it/s]
loss 2.33 accuracy 0.25 -- 57.03 + 57.84 + 637.25 + 4.93 = 757.06: 6%|▌ | 125/2048 [02:16<23:22, 1.37it/s]
loss 2.33 accuracy 0.25 -- 57.03 + 57.84 + 637.25 + 4.93 = 757.06: 6%|▌ | 126/2048 [02:16<24:13, 1.32it/s]
loss 1.86 accuracy 0.38 -- 57.79 + 56.64 + 516.31 + 4.95 = 635.70: 6%|▌ | 126/2048 [02:17<24:13, 1.32it/s]
loss 1.86 accuracy 0.38 -- 57.79 + 56.64 + 516.31 + 4.95 = 635.70: 6%|▌ | 127/2048 [02:17<23:19, 1.37it/s]
loss 2.07 accuracy 0.25 -- 56.49 + 57.88 + 631.75 + 4.90 = 751.02: 6%|▌ | 127/2048 [02:18<23:19, 1.37it/s]
loss 2.07 accuracy 0.25 -- 56.49 + 57.88 + 631.75 + 4.90 = 751.02: 6%|▋ | 128/2048 [02:18<23:47, 1.35it/s]
loss 2.44 accuracy 0.19 -- 57.35 + 56.65 + 512.75 + 4.96 = 631.70: 6%|▋ | 128/2048 [02:18<23:47, 1.35it/s]
loss 2.44 accuracy 0.19 -- 57.35 + 56.65 + 512.75 + 4.96 = 631.70: 6%|▋ | 129/2048 [02:18<22:57, 1.39it/s]
loss 2.72 accuracy 0.06 -- 57.21 + 172.68 + 512.15 + 4.94 = 746.98: 6%|▋ | 129/2048 [02:19<22:57, 1.39it/s]
loss 2.72 accuracy 0.06 -- 57.21 + 172.68 + 512.15 + 4.94 = 746.98: 6%|▋ | 130/2048 [02:19<23:29, 1.36it/s]
loss 1.97 accuracy 0.31 -- 56.74 + 56.52 + 509.16 + 4.92 = 627.34: 6%|▋ | 130/2048 [02:20<23:29, 1.36it/s]
loss 1.97 accuracy 0.31 -- 56.74 + 56.52 + 509.16 + 4.92 = 627.34: 6%|▋ | 131/2048 [02:20<22:42, 1.41it/s]
loss 1.96 accuracy 0.25 -- 57.28 + 56.70 + 510.21 + 4.91 = 629.10: 6%|▋ | 131/2048 [02:21<22:42, 1.41it/s]
loss 1.96 accuracy 0.25 -- 57.28 + 56.70 + 510.21 + 4.91 = 629.10: 6%|▋ | 132/2048 [02:21<23:14, 1.37it/s]
loss 1.83 accuracy 0.38 -- 56.41 + 57.49 + 504.92 + 4.93 = 623.75: 6%|▋ | 132/2048 [02:21<23:14, 1.37it/s]
loss 1.83 accuracy 0.38 -- 56.41 + 57.49 + 504.92 + 4.93 = 623.75: 6%|▋ | 133/2048 [02:21<22:30, 1.42it/s]
loss 2.27 accuracy 0.12 -- 164.79 + 57.08 + 499.86 + 4.91 = 726.65: 6%|▋ | 133/2048 [02:22<22:30, 1.42it/s]
loss 2.27 accuracy 0.12 -- 164.79 + 57.08 + 499.86 + 4.91 = 726.65: 7%|▋ | 134/2048 [02:22<22:57, 1.39it/s]
loss 2.22 accuracy 0.19 -- 56.63 + 172.73 + 511.81 + 4.95 = 746.13: 7%|▋ | 134/2048 [02:23<22:57, 1.39it/s]
loss 2.22 accuracy 0.19 -- 56.63 + 172.73 + 511.81 + 4.95 = 746.13: 7%|▋ | 135/2048 [02:23<23:27, 1.36it/s]
loss 2.32 accuracy 0.19 -- 57.06 + 56.75 + 507.38 + 4.89 = 626.08: 7%|▋ | 135/2048 [02:23<23:27, 1.36it/s]
loss 2.32 accuracy 0.19 -- 57.06 + 56.75 + 507.38 + 4.89 = 626.08: 7%|▋ | 136/2048 [02:23<22:40, 1.41it/s]
loss 2.10 accuracy 0.12 -- 169.74 + 57.23 + 506.27 + 4.91 = 738.14: 7%|▋ | 136/2048 [02:24<22:40, 1.41it/s]
loss 2.10 accuracy 0.12 -- 169.74 + 57.23 + 506.27 + 4.91 = 738.14: 7%|▋ | 137/2048 [02:24<23:10, 1.37it/s]
loss 2.07 accuracy 0.25 -- 56.96 + 58.00 + 635.54 + 4.96 = 755.47: 7%|▋ | 137/2048 [02:25<23:10, 1.37it/s]
loss 2.07 accuracy 0.25 -- 56.96 + 58.00 + 635.54 + 4.96 = 755.47: 7%|▋ | 138/2048 [02:25<23:41, 1.34it/s]
loss 2.37 accuracy 0.06 -- 57.04 + 56.86 + 515.98 + 4.90 = 634.79: 7%|▋ | 138/2048 [02:26<23:41, 1.34it/s]
loss 2.37 accuracy 0.06 -- 57.04 + 56.86 + 515.98 + 4.90 = 634.79: 7%|▋ | 139/2048 [02:26<22:53, 1.39it/s]
loss 2.22 accuracy 0.25 -- 57.00 + 57.28 + 631.30 + 4.94 = 750.53: 7%|▋ | 139/2048 [02:26<22:53, 1.39it/s]
loss 2.22 accuracy 0.25 -- 57.00 + 57.28 + 631.30 + 4.94 = 750.53: 7%|▋ | 140/2048 [02:26<23:46, 1.34it/s]
loss 2.31 accuracy 0.12 -- 57.24 + 56.69 + 511.23 + 4.93 = 630.09: 7%|▋ | 140/2048 [02:27<23:46, 1.34it/s]
loss 2.31 accuracy 0.12 -- 57.24 + 56.69 + 511.23 + 4.93 = 630.09: 7%|▋ | 141/2048 [02:27<22:54, 1.39it/s]
loss 2.09 accuracy 0.44 -- 56.88 + 171.64 + 512.40 + 4.94 = 745.86: 7%|▋ | 141/2048 [02:28<22:54, 1.39it/s]
loss 2.09 accuracy 0.44 -- 56.88 + 171.64 + 512.40 + 4.94 = 745.86: 7%|▋ | 142/2048 [02:28<23:23, 1.36it/s]
loss 2.37 accuracy 0.12 -- 56.85 + 56.98 + 511.36 + 4.88 = 630.07: 7%|▋ | 142/2048 [02:29<23:23, 1.36it/s]
loss 2.37 accuracy 0.12 -- 56.85 + 56.98 + 511.36 + 4.88 = 630.07: 7%|▋ | 143/2048 [02:29<22:37, 1.40it/s]
loss 2.15 accuracy 0.25 -- 57.32 + 56.56 + 620.44 + 5.27 = 739.59: 7%|▋ | 143/2048 [02:29<22:37, 1.40it/s]
loss 2.15 accuracy 0.25 -- 57.32 + 56.56 + 620.44 + 5.27 = 739.59: 7%|▋ | 144/2048 [02:29<24:11, 1.31it/s]
loss 2.00 accuracy 0.38 -- 64.58 + 62.25 + 506.10 + 4.92 = 637.85: 7%|▋ | 144/2048 [02:30<24:11, 1.31it/s]
loss 2.00 accuracy 0.38 -- 64.58 + 62.25 + 506.10 + 4.92 = 637.85: 7%|▋ | 145/2048 [02:30<23:20, 1.36it/s]
loss 1.91 accuracy 0.25 -- 163.47 + 56.87 + 499.36 + 4.89 = 724.59: 7%|▋ | 145/2048 [02:31<23:20, 1.36it/s]
loss 1.91 accuracy 0.25 -- 163.47 + 56.87 + 499.36 + 4.89 = 724.59: 7%|▋ | 146/2048 [02:31<23:29, 1.35it/s]
loss 2.23 accuracy 0.19 -- 56.77 + 172.28 + 513.99 + 4.92 = 747.96: 7%|▋ | 146/2048 [02:32<23:29, 1.35it/s]
loss 2.23 accuracy 0.19 -- 56.77 + 172.28 + 513.99 + 4.92 = 747.96: 7%|▋ | 147/2048 [02:32<23:48, 1.33it/s]
loss 2.77 accuracy 0.12 -- 57.02 + 56.90 + 509.43 + 4.88 = 628.23: 7%|▋ | 147/2048 [02:32<23:48, 1.33it/s]
loss 2.77 accuracy 0.12 -- 57.02 + 56.90 + 509.43 + 4.88 = 628.23: 7%|▋ | 148/2048 [02:32<22:53, 1.38it/s]
loss 2.19 accuracy 0.12 -- 168.88 + 57.69 + 507.05 + 4.90 = 738.53: 7%|▋ | 148/2048 [02:33<22:53, 1.38it/s]
loss 2.19 accuracy 0.12 -- 168.88 + 57.69 + 507.05 + 4.90 = 738.53: 7%|▋ | 149/2048 [02:33<23:17, 1.36it/s]
loss 2.25 accuracy 0.12 -- 56.84 + 57.61 + 636.93 + 4.89 = 756.27: 7%|▋ | 149/2048 [02:34<23:17, 1.36it/s]
loss 2.25 accuracy 0.12 -- 56.84 + 57.61 + 636.93 + 4.89 = 756.27: 7%|▋ | 150/2048 [02:34<23:43, 1.33it/s]
loss 1.90 accuracy 0.25 -- 57.47 + 56.70 + 514.85 + 4.90 = 633.92: 7%|▋ | 150/2048 [02:34<23:43, 1.33it/s]
loss 1.90 accuracy 0.25 -- 57.47 + 56.70 + 514.85 + 4.90 = 633.92: 7%|▋ | 151/2048 [02:34<22:53, 1.38it/s]
loss 2.24 accuracy 0.25 -- 56.65 + 57.49 + 629.80 + 4.91 = 748.84: 7%|▋ | 151/2048 [02:35<22:53, 1.38it/s]
loss 2.24 accuracy 0.25 -- 56.65 + 57.49 + 629.80 + 4.91 = 748.84: 7%|▋ | 152/2048 [02:35<23:22, 1.35it/s]
loss 2.01 accuracy 0.31 -- 57.27 + 56.62 + 512.15 + 4.93 = 630.97: 7%|▋ | 152/2048 [02:36<23:22, 1.35it/s]
loss 2.01 accuracy 0.31 -- 57.27 + 56.62 + 512.15 + 4.93 = 630.97: 7%|▋ | 153/2048 [02:36<22:35, 1.40it/s]
loss 2.15 accuracy 0.12 -- 57.08 + 171.91 + 513.96 + 4.95 = 747.91: 7%|▋ | 153/2048 [02:37<22:35, 1.40it/s]
loss 2.15 accuracy 0.12 -- 57.08 + 171.91 + 513.96 + 4.95 = 747.91: 8%|▊ | 154/2048 [02:37<23:09, 1.36it/s]
loss 2.17 accuracy 0.12 -- 56.86 + 56.93 + 510.64 + 4.93 = 629.36: 8%|▊ | 154/2048 [02:37<23:09, 1.36it/s]
loss 2.17 accuracy 0.12 -- 56.86 + 56.93 + 510.64 + 4.93 = 629.36: 8%|▊ | 155/2048 [02:37<22:25, 1.41it/s]
loss 2.52 accuracy 0.12 -- 57.06 + 56.65 + 507.95 + 4.93 = 626.58: 8%|▊ | 155/2048 [02:38<22:25, 1.41it/s]
loss 2.52 accuracy 0.12 -- 57.06 + 56.65 + 507.95 + 4.93 = 626.58: 8%|▊ | 156/2048 [02:38<22:55, 1.38it/s]
loss 2.81 accuracy 0.12 -- 56.52 + 57.54 + 505.01 + 4.93 = 624.01: 8%|▊ | 156/2048 [02:39<22:55, 1.38it/s]
loss 2.81 accuracy 0.12 -- 56.52 + 57.54 + 505.01 + 4.93 = 624.01: 8%|▊ | 157/2048 [02:39<22:12, 1.42it/s]
loss 2.05 accuracy 0.06 -- 163.52 + 57.22 + 499.79 + 4.88 = 725.41: 8%|▊ | 157/2048 [02:39<22:12, 1.42it/s]
loss 2.05 accuracy 0.06 -- 163.52 + 57.22 + 499.79 + 4.88 = 725.41: 8%|▊ | 158/2048 [02:39<22:38, 1.39it/s]
loss 1.97 accuracy 0.19 -- 56.30 + 171.44 + 510.95 + 4.88 = 743.58: 8%|▊ | 158/2048 [02:40<22:38, 1.39it/s]
loss 1.97 accuracy 0.19 -- 56.30 + 171.44 + 510.95 + 4.88 = 743.58: 8%|▊ | 159/2048 [02:40<23:07, 1.36it/s]
loss 2.12 accuracy 0.25 -- 56.95 + 56.33 + 505.41 + 4.95 = 623.65: 8%|▊ | 159/2048 [02:41<23:07, 1.36it/s]
loss 2.12 accuracy 0.25 -- 56.95 + 56.33 + 505.41 + 4.95 = 623.65: 8%|▊ | 160/2048 [02:41<22:19, 1.41it/s]
loss 2.07 accuracy 0.19 -- 169.23 + 57.17 + 506.95 + 4.93 = 738.29: 8%|▊ | 160/2048 [02:42<22:19, 1.41it/s]
loss 2.07 accuracy 0.19 -- 169.23 + 57.17 + 506.95 + 4.93 = 738.29: 8%|▊ | 161/2048 [02:42<22:51, 1.38it/s]
loss 2.32 accuracy 0.00 -- 56.79 + 57.18 + 636.49 + 4.94 = 755.40: 8%|▊ | 161/2048 [02:42<22:51, 1.38it/s]
loss 2.32 accuracy 0.00 -- 56.79 + 57.18 + 636.49 + 4.94 = 755.40: 8%|▊ | 162/2048 [02:42<23:22, 1.34it/s]
loss 1.97 accuracy 0.31 -- 57.67 + 56.96 + 514.96 + 4.92 = 634.51: 8%|▊ | 162/2048 [02:43<23:22, 1.34it/s]
loss 1.97 accuracy 0.31 -- 57.67 + 56.96 + 514.96 + 4.92 = 634.51: 8%|▊ | 163/2048 [02:43<22:35, 1.39it/s]
loss 2.74 accuracy 0.12 -- 56.47 + 57.61 + 630.52 + 4.93 = 749.53: 8%|▊ | 163/2048 [02:44<22:35, 1.39it/s]
loss 2.74 accuracy 0.12 -- 56.47 + 57.61 + 630.52 + 4.93 = 749.53: 8%|▊ | 164/2048 [02:44<23:07, 1.36it/s]
loss 2.02 accuracy 0.25 -- 57.34 + 56.89 + 513.60 + 4.95 = 632.78: 8%|▊ | 164/2048 [02:45<23:07, 1.36it/s]
loss 2.02 accuracy 0.25 -- 57.34 + 56.89 + 513.60 + 4.95 = 632.78: 8%|▊ | 165/2048 [02:45<22:23, 1.40it/s]
loss 2.11 accuracy 0.12 -- 56.52 + 171.89 + 510.56 + 4.92 = 743.89: 8%|▊ | 165/2048 [02:45<22:23, 1.40it/s]
loss 2.11 accuracy 0.12 -- 56.52 + 171.89 + 510.56 + 4.92 = 743.89: 8%|▊ | 166/2048 [02:45<22:55, 1.37it/s]
loss 1.97 accuracy 0.31 -- 57.26 + 56.55 + 509.10 + 4.89 = 627.81: 8%|▊ | 166/2048 [02:46<22:55, 1.37it/s]
loss 1.97 accuracy 0.31 -- 57.26 + 56.55 + 509.10 + 4.89 = 627.81: 8%|▊ | 167/2048 [02:46<22:12, 1.41it/s]
loss 2.23 accuracy 0.12 -- 57.19 + 56.49 + 508.97 + 4.92 = 627.58: 8%|▊ | 167/2048 [02:47<22:12, 1.41it/s]
loss 2.23 accuracy 0.12 -- 57.19 + 56.49 + 508.97 + 4.92 = 627.58: 8%|▊ | 168/2048 [02:47<22:45, 1.38it/s]
loss 1.89 accuracy 0.31 -- 56.58 + 57.20 + 504.29 + 4.90 = 622.97: 8%|▊ | 168/2048 [02:47<22:45, 1.38it/s]
loss 1.89 accuracy 0.31 -- 56.58 + 57.20 + 504.29 + 4.90 = 622.97: 8%|▊ | 169/2048 [02:47<22:01, 1.42it/s]
loss 2.49 accuracy 0.19 -- 163.59 + 57.06 + 498.10 + 4.97 = 723.73: 8%|▊ | 169/2048 [02:48<22:01, 1.42it/s]
loss 2.49 accuracy 0.19 -- 163.59 + 57.06 + 498.10 + 4.97 = 723.73: 8%|▊ | 170/2048 [02:48<22:27, 1.39it/s]
loss 2.04 accuracy 0.19 -- 57.12 + 172.37 + 512.25 + 4.91 = 746.65: 8%|▊ | 170/2048 [02:49<22:27, 1.39it/s]
loss 2.04 accuracy 0.19 -- 57.12 + 172.37 + 512.25 + 4.91 = 746.65: 8%|▊ | 171/2048 [02:49<22:59, 1.36it/s]
loss 2.29 accuracy 0.19 -- 57.48 + 56.50 + 510.72 + 4.92 = 629.61: 8%|▊ | 171/2048 [02:50<22:59, 1.36it/s]
loss 2.29 accuracy 0.19 -- 57.48 + 56.50 + 510.72 + 4.92 = 629.61: 8%|▊ | 172/2048 [02:50<22:14, 1.41it/s]
loss 1.98 accuracy 0.31 -- 169.24 + 57.41 + 505.79 + 4.90 = 737.35: 8%|▊ | 172/2048 [02:50<22:14, 1.41it/s]
loss 1.98 accuracy 0.31 -- 169.24 + 57.41 + 505.79 + 4.90 = 737.35: 8%|▊ | 173/2048 [02:50<22:43, 1.37it/s]
loss 2.11 accuracy 0.06 -- 56.80 + 57.32 + 636.36 + 4.90 = 755.38: 8%|▊ | 173/2048 [02:51<22:43, 1.37it/s]
loss 2.11 accuracy 0.06 -- 56.80 + 57.32 + 636.36 + 4.90 = 755.38: 8%|▊ | 174/2048 [02:51<23:14, 1.34it/s]
loss 2.19 accuracy 0.12 -- 57.36 + 56.94 + 516.25 + 4.93 = 635.49: 8%|▊ | 174/2048 [02:52<23:14, 1.34it/s]
loss 2.19 accuracy 0.12 -- 57.36 + 56.94 + 516.25 + 4.93 = 635.49: 9%|▊ | 175/2048 [02:52<22:28, 1.39it/s]
loss 2.35 accuracy 0.06 -- 56.71 + 57.53 + 634.73 + 4.90 = 753.86: 9%|▊ | 175/2048 [02:53<22:28, 1.39it/s]
loss 2.35 accuracy 0.06 -- 56.71 + 57.53 + 634.73 + 4.90 = 753.86: 9%|▊ | 176/2048 [02:53<23:02, 1.35it/s]
loss 2.22 accuracy 0.25 -- 57.22 + 56.85 + 512.31 + 4.91 = 631.29: 9%|▊ | 176/2048 [02:53<23:02, 1.35it/s]
loss 2.22 accuracy 0.25 -- 57.22 + 56.85 + 512.31 + 4.91 = 631.29: 9%|▊ | 177/2048 [02:53<22:16, 1.40it/s]
loss 2.32 accuracy 0.06 -- 56.72 + 172.46 + 512.29 + 4.94 = 746.41: 9%|▊ | 177/2048 [02:54<22:16, 1.40it/s]
loss 2.32 accuracy 0.06 -- 56.72 + 172.46 + 512.29 + 4.94 = 746.41: 9%|▊ | 178/2048 [02:54<22:49, 1.37it/s]
loss 1.94 accuracy 0.38 -- 56.87 + 56.51 + 509.52 + 4.94 = 627.83: 9%|▊ | 178/2048 [02:55<22:49, 1.37it/s]
loss 1.94 accuracy 0.38 -- 56.87 + 56.51 + 509.52 + 4.94 = 627.83: 9%|▊ | 179/2048 [02:55<22:05, 1.41it/s]
loss 2.28 accuracy 0.06 -- 57.41 + 56.51 + 507.89 + 4.93 = 626.74: 9%|▊ | 179/2048 [02:55<22:05, 1.41it/s]
loss 2.28 accuracy 0.06 -- 57.41 + 56.51 + 507.89 + 4.93 = 626.74: 9%|▉ | 180/2048 [02:55<22:37, 1.38it/s]
loss 1.94 accuracy 0.25 -- 56.52 + 57.80 + 504.99 + 4.92 = 624.24: 9%|▉ | 180/2048 [02:56<22:37, 1.38it/s]
loss 1.94 accuracy 0.25 -- 56.52 + 57.80 + 504.99 + 4.92 = 624.24: 9%|▉ | 181/2048 [02:56<21:54, 1.42it/s]
loss 2.05 accuracy 0.38 -- 164.59 + 57.28 + 500.50 + 4.94 = 727.30: 9%|▉ | 181/2048 [02:57<21:54, 1.42it/s]
loss 2.05 accuracy 0.38 -- 164.59 + 57.28 + 500.50 + 4.94 = 727.30: 9%|▉ | 182/2048 [02:57<22:42, 1.37it/s]
loss 2.28 accuracy 0.19 -- 56.40 + 172.88 + 511.83 + 4.95 = 746.06: 9%|▉ | 182/2048 [02:58<22:42, 1.37it/s]
loss 2.28 accuracy 0.19 -- 56.40 + 172.88 + 511.83 + 4.95 = 746.06: 9%|▉ | 183/2048 [02:58<23:05, 1.35it/s]
loss 2.40 accuracy 0.06 -- 57.77 + 56.96 + 509.54 + 4.91 = 629.18: 9%|▉ | 183/2048 [02:58<23:05, 1.35it/s]
loss 2.40 accuracy 0.06 -- 57.77 + 56.96 + 509.54 + 4.91 = 629.18: 9%|▉ | 184/2048 [02:58<22:16, 1.39it/s]
loss 1.92 accuracy 0.19 -- 169.13 + 57.23 + 507.24 + 4.93 = 738.54: 9%|▉ | 184/2048 [02:59<22:16, 1.39it/s]
loss 1.92 accuracy 0.19 -- 169.13 + 57.23 + 507.24 + 4.93 = 738.54: 9%|▉ | 185/2048 [02:59<22:43, 1.37it/s]
loss 2.04 accuracy 0.25 -- 56.77 + 57.83 + 637.08 + 4.91 = 756.58: 9%|▉ | 185/2048 [03:00<22:43, 1.37it/s]
loss 2.04 accuracy 0.25 -- 56.77 + 57.83 + 637.08 + 4.91 = 756.58: 9%|▉ | 186/2048 [03:00<23:11, 1.34it/s]
loss 2.33 accuracy 0.19 -- 57.50 + 56.78 + 517.36 + 4.92 = 636.56: 9%|▉ | 186/2048 [03:01<23:11, 1.34it/s]
loss 2.33 accuracy 0.19 -- 57.50 + 56.78 + 517.36 + 4.92 = 636.56: 9%|▉ | 187/2048 [03:01<22:24, 1.38it/s]
loss 2.19 accuracy 0.25 -- 56.87 + 57.57 + 632.57 + 4.94 = 751.95: 9%|▉ | 187/2048 [03:01<22:24, 1.38it/s]
loss 2.19 accuracy 0.25 -- 56.87 + 57.57 + 632.57 + 4.94 = 751.95: 9%|▉ | 188/2048 [03:01<22:55, 1.35it/s]
loss 2.34 accuracy 0.12 -- 57.35 + 57.10 + 511.51 + 4.91 = 630.87: 9%|▉ | 188/2048 [03:02<22:55, 1.35it/s]
loss 2.34 accuracy 0.12 -- 57.35 + 57.10 + 511.51 + 4.91 = 630.87: 9%|▉ | 189/2048 [03:02<22:09, 1.40it/s]
loss 2.06 accuracy 0.31 -- 56.61 + 171.98 + 512.44 + 4.89 = 745.92: 9%|▉ | 189/2048 [03:03<22:09, 1.40it/s]
loss 2.06 accuracy 0.31 -- 56.61 + 171.98 + 512.44 + 4.89 = 745.92: 9%|▉ | 190/2048 [03:03<22:41, 1.36it/s]
loss 1.74 accuracy 0.44 -- 56.83 + 56.69 + 509.75 + 4.92 = 628.20: 9%|▉ | 190/2048 [03:03<22:41, 1.36it/s]
loss 1.74 accuracy 0.44 -- 56.83 + 56.69 + 509.75 + 4.92 = 628.20: 9%|▉ | 191/2048 [03:03<21:57, 1.41it/s]
loss 2.21 accuracy 0.19 -- 57.00 + 56.49 + 507.99 + 4.95 = 626.42: 9%|▉ | 191/2048 [03:04<21:57, 1.41it/s]
loss 2.21 accuracy 0.19 -- 57.00 + 56.49 + 507.99 + 4.95 = 626.42: 9%|▉ | 192/2048 [03:04<22:28, 1.38it/s]
loss 2.49 accuracy 0.19 -- 57.09 + 58.09 + 508.30 + 4.93 = 628.41: 9%|▉ | 192/2048 [03:05<22:28, 1.38it/s]
loss 2.49 accuracy 0.19 -- 57.09 + 58.09 + 508.30 + 4.93 = 628.41: 9%|▉ | 193/2048 [03:05<21:48, 1.42it/s]
loss 1.81 accuracy 0.38 -- 163.69 + 57.27 + 500.14 + 4.94 = 726.03: 9%|▉ | 193/2048 [03:06<21:48, 1.42it/s]
loss 1.81 accuracy 0.38 -- 163.69 + 57.27 + 500.14 + 4.94 = 726.03: 9%|▉ | 194/2048 [03:06<22:14, 1.39it/s]
loss 2.65 accuracy 0.25 -- 56.18 + 172.23 + 511.22 + 4.93 = 744.56: 9%|▉ | 194/2048 [03:06<22:14, 1.39it/s]
loss 2.65 accuracy 0.25 -- 56.18 + 172.23 + 511.22 + 4.93 = 744.56: 10%|▉ | 195/2048 [03:06<22:42, 1.36it/s]
loss 1.69 accuracy 0.31 -- 57.36 + 56.79 + 508.60 + 4.95 = 627.69: 10%|▉ | 195/2048 [03:07<22:42, 1.36it/s]
loss 1.69 accuracy 0.31 -- 57.36 + 56.79 + 508.60 + 4.95 = 627.69: 10%|▉ | 196/2048 [03:07<22:17, 1.38it/s]
loss 1.94 accuracy 0.38 -- 168.32 + 57.32 + 507.41 + 4.93 = 737.98: 10%|▉ | 196/2048 [03:08<22:17, 1.38it/s]
loss 1.94 accuracy 0.38 -- 168.32 + 57.32 + 507.41 + 4.93 = 737.98: 10%|▉ | 197/2048 [03:08<22:40, 1.36it/s]
loss 2.44 accuracy 0.12 -- 57.02 + 57.82 + 638.13 + 5.01 = 757.98: 10%|▉ | 197/2048 [03:09<22:40, 1.36it/s]
loss 2.44 accuracy 0.12 -- 57.02 + 57.82 + 638.13 + 5.01 = 757.98: 10%|▉ | 198/2048 [03:09<23:08, 1.33it/s]
loss 2.11 accuracy 0.19 -- 57.63 + 57.05 + 516.01 + 4.94 = 635.62: 10%|▉ | 198/2048 [03:09<23:08, 1.33it/s]
loss 2.11 accuracy 0.19 -- 57.63 + 57.05 + 516.01 + 4.94 = 635.62: 10%|▉ | 199/2048 [03:09<22:18, 1.38it/s]
loss 1.91 accuracy 0.31 -- 56.81 + 57.88 + 632.58 + 4.94 = 752.22: 10%|▉ | 199/2048 [03:10<22:18, 1.38it/s]
loss 1.91 accuracy 0.31 -- 56.81 + 57.88 + 632.58 + 4.94 = 752.22: 10%|▉ | 200/2048 [03:10<22:48, 1.35it/s]
loss 2.18 accuracy 0.25 -- 57.42 + 57.02 + 511.97 + 4.95 = 631.35: 10%|▉ | 200/2048 [03:11<22:48, 1.35it/s]
loss 2.18 accuracy 0.25 -- 57.42 + 57.02 + 511.97 + 4.95 = 631.35: 10%|▉ | 201/2048 [03:11<22:02, 1.40it/s]
loss 2.10 accuracy 0.12 -- 56.63 + 171.83 + 511.85 + 4.92 = 745.22: 10%|▉ | 201/2048 [03:11<22:02, 1.40it/s]
loss 2.10 accuracy 0.12 -- 56.63 + 171.83 + 511.85 + 4.92 = 745.22: 10%|▉ | 202/2048 [03:11<22:33, 1.36it/s]
loss 2.07 accuracy 0.12 -- 56.89 + 56.81 + 509.83 + 4.92 = 628.44: 10%|▉ | 202/2048 [03:12<22:33, 1.36it/s]
loss 2.07 accuracy 0.12 -- 56.89 + 56.81 + 509.83 + 4.92 = 628.44: 10%|▉ | 203/2048 [03:12<22:09, 1.39it/s]
loss 1.77 accuracy 0.38 -- 57.74 + 56.96 + 507.62 + 4.92 = 627.25: 10%|▉ | 203/2048 [03:13<22:09, 1.39it/s]
loss 1.77 accuracy 0.38 -- 57.74 + 56.96 + 507.62 + 4.92 = 627.25: 10%|▉ | 204/2048 [03:13<22:34, 1.36it/s]
loss 2.33 accuracy 0.31 -- 56.72 + 57.69 + 505.65 + 4.91 = 624.97: 10%|▉ | 204/2048 [03:14<22:34, 1.36it/s]
loss 2.33 accuracy 0.31 -- 56.72 + 57.69 + 505.65 + 4.91 = 624.97: 10%|█ | 205/2048 [03:14<21:48, 1.41it/s]
loss 2.38 accuracy 0.38 -- 164.29 + 57.38 + 499.15 + 4.93 = 725.75: 10%|█ | 205/2048 [03:14<21:48, 1.41it/s]
loss 2.38 accuracy 0.38 -- 164.29 + 57.38 + 499.15 + 4.93 = 725.75: 10%|█ | 206/2048 [03:14<22:11, 1.38it/s]
loss 2.31 accuracy 0.44 -- 56.76 + 172.77 + 511.93 + 4.93 = 746.37: 10%|█ | 206/2048 [03:15<22:11, 1.38it/s]
loss 2.31 accuracy 0.44 -- 56.76 + 172.77 + 511.93 + 4.93 = 746.37: 10%|█ | 207/2048 [03:15<22:38, 1.35it/s]
loss 2.28 accuracy 0.25 -- 57.23 + 56.98 + 508.28 + 4.96 = 627.46: 10%|█ | 207/2048 [03:16<22:38, 1.35it/s]
loss 2.28 accuracy 0.25 -- 57.23 + 56.98 + 508.28 + 4.96 = 627.46: 10%|█ | 208/2048 [03:16<21:52, 1.40it/s]
loss 2.10 accuracy 0.19 -- 170.13 + 57.33 + 508.59 + 4.92 = 740.97: 10%|█ | 208/2048 [03:17<21:52, 1.40it/s]
loss 2.10 accuracy 0.19 -- 170.13 + 57.33 + 508.59 + 4.92 = 740.97: 10%|█ | 209/2048 [03:17<22:21, 1.37it/s]
loss 2.04 accuracy 0.25 -- 56.47 + 57.74 + 636.53 + 4.94 = 755.68: 10%|█ | 209/2048 [03:17<22:21, 1.37it/s]
loss 2.04 accuracy 0.25 -- 56.47 + 57.74 + 636.53 + 4.94 = 755.68: 10%|█ | 210/2048 [03:17<23:10, 1.32it/s]
loss 2.06 accuracy 0.12 -- 57.73 + 56.99 + 515.56 + 4.95 = 635.24: 10%|█ | 210/2048 [03:18<23:10, 1.32it/s]
loss 2.06 accuracy 0.12 -- 57.73 + 56.99 + 515.56 + 4.95 = 635.24: 10%|█ | 211/2048 [03:18<22:17, 1.37it/s]
loss 1.95 accuracy 0.19 -- 56.64 + 57.67 + 630.68 + 4.92 = 749.92: 10%|█ | 211/2048 [03:19<22:17, 1.37it/s]
loss 1.95 accuracy 0.19 -- 56.64 + 57.67 + 630.68 + 4.92 = 749.92: 10%|█ | 212/2048 [03:19<22:43, 1.35it/s]
loss 2.15 accuracy 0.31 -- 57.47 + 56.67 + 512.03 + 4.92 = 631.10: 10%|█ | 212/2048 [03:19<22:43, 1.35it/s]
loss 2.15 accuracy 0.31 -- 57.47 + 56.67 + 512.03 + 4.92 = 631.10: 10%|█ | 213/2048 [03:19<21:56, 1.39it/s]
loss 2.36 accuracy 0.38 -- 56.99 + 172.26 + 512.35 + 4.91 = 746.52: 10%|█ | 213/2048 [03:20<21:56, 1.39it/s]
loss 2.36 accuracy 0.38 -- 56.99 + 172.26 + 512.35 + 4.91 = 746.52: 10%|█ | 214/2048 [03:20<22:26, 1.36it/s]
loss 2.56 accuracy 0.06 -- 57.11 + 56.92 + 509.65 + 4.88 = 628.56: 10%|█ | 214/2048 [03:21<22:26, 1.36it/s]
loss 2.56 accuracy 0.06 -- 57.11 + 56.92 + 509.65 + 4.88 = 628.56: 10%|█ | 215/2048 [03:21<21:43, 1.41it/s]
loss 1.86 accuracy 0.38 -- 57.22 + 56.76 + 509.52 + 4.93 = 628.44: 10%|█ | 215/2048 [03:22<21:43, 1.41it/s]
loss 1.86 accuracy 0.38 -- 57.22 + 56.76 + 509.52 + 4.93 = 628.44: 11%|█ | 216/2048 [03:22<22:13, 1.37it/s]
loss 1.81 accuracy 0.50 -- 56.43 + 57.95 + 504.49 + 4.93 = 623.80: 11%|█ | 216/2048 [03:22<22:13, 1.37it/s]
loss 1.81 accuracy 0.50 -- 56.43 + 57.95 + 504.49 + 4.93 = 623.80: 11%|█ | 217/2048 [03:22<21:30, 1.42it/s]
loss 2.34 accuracy 0.44 -- 164.31 + 57.23 + 500.07 + 4.92 = 726.53: 11%|█ | 217/2048 [03:23<21:30, 1.42it/s]
loss 2.34 accuracy 0.44 -- 164.31 + 57.23 + 500.07 + 4.92 = 726.53: 11%|█ | 218/2048 [03:23<21:56, 1.39it/s]
loss 2.13 accuracy 0.12 -- 56.16 + 172.72 + 510.93 + 4.87 = 744.68: 11%|█ | 218/2048 [03:24<21:56, 1.39it/s]
loss 2.13 accuracy 0.12 -- 56.16 + 172.72 + 510.93 + 4.87 = 744.68: 11%|█ | 219/2048 [03:24<22:25, 1.36it/s]
loss 2.30 accuracy 0.12 -- 57.26 + 56.70 + 508.37 + 4.89 = 627.22: 11%|█ | 219/2048 [03:24<22:25, 1.36it/s]
loss 2.30 accuracy 0.12 -- 57.26 + 56.70 + 508.37 + 4.89 = 627.22: 11%|█ | 220/2048 [03:24<21:40, 1.41it/s]
loss 2.17 accuracy 0.31 -- 168.09 + 57.29 + 508.04 + 4.93 = 738.35: 11%|█ | 220/2048 [03:25<21:40, 1.41it/s]
loss 2.17 accuracy 0.31 -- 168.09 + 57.29 + 508.04 + 4.93 = 738.35: 11%|█ | 221/2048 [03:25<22:10, 1.37it/s]
loss 2.07 accuracy 0.38 -- 56.72 + 57.71 + 637.06 + 4.98 = 756.47: 11%|█ | 221/2048 [03:26<22:10, 1.37it/s]
loss 2.07 accuracy 0.38 -- 56.72 + 57.71 + 637.06 + 4.98 = 756.47: 11%|█ | 222/2048 [03:26<22:39, 1.34it/s]
loss 1.87 accuracy 0.19 -- 57.10 + 56.67 + 514.90 + 4.91 = 633.58: 11%|█ | 222/2048 [03:27<22:39, 1.34it/s]
loss 1.87 accuracy 0.19 -- 57.10 + 56.67 + 514.90 + 4.91 = 633.58: 11%|█ | 223/2048 [03:27<21:53, 1.39it/s]
loss 1.91 accuracy 0.25 -- 56.77 + 57.59 + 630.70 + 4.95 = 750.02: 11%|█ | 223/2048 [03:27<21:53, 1.39it/s]
loss 1.91 accuracy 0.25 -- 56.77 + 57.59 + 630.70 + 4.95 = 750.02: 11%|█ | 224/2048 [03:27<22:43, 1.34it/s]
loss 2.20 accuracy 0.12 -- 57.39 + 56.57 + 511.18 + 4.93 = 630.07: 11%|█ | 224/2048 [03:28<22:43, 1.34it/s]
loss 2.20 accuracy 0.12 -- 57.39 + 56.57 + 511.18 + 4.93 = 630.07: 11%|█ | 225/2048 [03:28<21:53, 1.39it/s]
loss 1.94 accuracy 0.19 -- 56.93 + 172.48 + 511.92 + 4.92 = 746.25: 11%|█ | 225/2048 [03:29<21:53, 1.39it/s]
loss 1.94 accuracy 0.19 -- 56.93 + 172.48 + 511.92 + 4.92 = 746.25: 11%|█ | 226/2048 [03:29<22:21, 1.36it/s]
loss 2.24 accuracy 0.25 -- 56.71 + 56.69 + 509.32 + 4.92 = 627.64: 11%|█ | 226/2048 [03:30<22:21, 1.36it/s]
loss 2.24 accuracy 0.25 -- 56.71 + 56.69 + 509.32 + 4.92 = 627.64: 11%|█ | 227/2048 [03:30<21:36, 1.40it/s]
loss 2.17 accuracy 0.12 -- 57.19 + 56.76 + 510.94 + 4.94 = 629.83: 11%|█ | 227/2048 [03:30<21:36, 1.40it/s]
loss 2.17 accuracy 0.12 -- 57.19 + 56.76 + 510.94 + 4.94 = 629.83: 11%|█ | 228/2048 [03:30<22:07, 1.37it/s]
loss 1.93 accuracy 0.25 -- 56.53 + 57.88 + 505.77 + 4.94 = 625.11: 11%|█ | 228/2048 [03:31<22:07, 1.37it/s]
loss 1.93 accuracy 0.25 -- 56.53 + 57.88 + 505.77 + 4.94 = 625.11: 11%|█ | 229/2048 [03:31<21:24, 1.42it/s]
loss 2.05 accuracy 0.31 -- 163.16 + 57.54 + 499.64 + 4.92 = 725.25: 11%|█ | 229/2048 [03:32<21:24, 1.42it/s]
loss 2.05 accuracy 0.31 -- 163.16 + 57.54 + 499.64 + 4.92 = 725.25: 11%|█ | 230/2048 [03:32<21:49, 1.39it/s]
loss 1.81 accuracy 0.25 -- 56.38 + 172.27 + 512.92 + 4.91 = 746.47: 11%|█ | 230/2048 [03:33<21:49, 1.39it/s]
loss 1.81 accuracy 0.25 -- 56.38 + 172.27 + 512.92 + 4.91 = 746.47: 11%|█▏ | 231/2048 [03:33<22:18, 1.36it/s]
loss 2.12 accuracy 0.19 -- 57.66 + 57.07 + 505.25 + 4.91 = 624.90: 11%|█▏ | 231/2048 [03:33<22:18, 1.36it/s]
loss 2.12 accuracy 0.19 -- 57.66 + 57.07 + 505.25 + 4.91 = 624.90: 11%|█▏ | 232/2048 [03:33<21:31, 1.41it/s]
loss 1.81 accuracy 0.25 -- 168.09 + 57.09 + 505.84 + 4.94 = 735.97: 11%|█▏ | 232/2048 [03:34<21:31, 1.41it/s]
loss 1.81 accuracy 0.25 -- 168.09 + 57.09 + 505.84 + 4.94 = 735.97: 11%|█▏ | 233/2048 [03:34<21:59, 1.38it/s]
loss 2.00 accuracy 0.31 -- 56.89 + 57.72 + 636.23 + 4.92 = 755.76: 11%|█▏ | 233/2048 [03:35<21:59, 1.38it/s]
loss 2.00 accuracy 0.31 -- 56.89 + 57.72 + 636.23 + 4.92 = 755.76: 11%|█▏ | 234/2048 [03:35<22:29, 1.34it/s]
loss 1.80 accuracy 0.38 -- 57.39 + 56.74 + 515.63 + 4.93 = 634.70: 11%|█▏ | 234/2048 [03:35<22:29, 1.34it/s]
loss 1.80 accuracy 0.38 -- 57.39 + 56.74 + 515.63 + 4.93 = 634.70: 11%|█▏ | 235/2048 [03:35<21:43, 1.39it/s]
loss 1.81 accuracy 0.38 -- 56.70 + 57.68 + 631.36 + 4.94 = 750.68: 11%|█▏ | 235/2048 [03:36<21:43, 1.39it/s]
loss 1.81 accuracy 0.38 -- 56.70 + 57.68 + 631.36 + 4.94 = 750.68: 12%|█▏ | 236/2048 [03:36<22:14, 1.36it/s]
loss 1.61 accuracy 0.62 -- 57.75 + 57.18 + 513.12 + 4.92 = 632.98: 12%|█▏ | 236/2048 [03:37<22:14, 1.36it/s]
loss 1.61 accuracy 0.62 -- 57.75 + 57.18 + 513.12 + 4.92 = 632.98: 12%|█▏ | 237/2048 [03:37<21:42, 1.39it/s]
loss 2.14 accuracy 0.25 -- 56.78 + 172.18 + 512.48 + 4.92 = 746.36: 12%|█▏ | 237/2048 [03:38<21:42, 1.39it/s]
loss 2.14 accuracy 0.25 -- 56.78 + 172.18 + 512.48 + 4.92 = 746.36: 12%|█▏ | 238/2048 [03:38<22:11, 1.36it/s]
loss 1.85 accuracy 0.25 -- 56.79 + 56.68 + 508.50 + 4.90 = 626.86: 12%|█▏ | 238/2048 [03:38<22:11, 1.36it/s]
loss 1.85 accuracy 0.25 -- 56.79 + 56.68 + 508.50 + 4.90 = 626.86: 12%|█▏ | 239/2048 [03:38<21:26, 1.41it/s]
loss 1.77 accuracy 0.50 -- 57.48 + 56.99 + 509.82 + 4.94 = 629.22: 12%|█▏ | 239/2048 [03:39<21:26, 1.41it/s]
loss 1.77 accuracy 0.50 -- 57.48 + 56.99 + 509.82 + 4.94 = 629.22: 12%|█▏ | 240/2048 [03:39<21:57, 1.37it/s]
loss 2.57 accuracy 0.12 -- 56.87 + 57.56 + 505.73 + 4.92 = 625.09: 12%|█▏ | 240/2048 [03:40<21:57, 1.37it/s]
loss 2.57 accuracy 0.12 -- 56.87 + 57.56 + 505.73 + 4.92 = 625.09: 12%|█▏ | 241/2048 [03:40<21:15, 1.42it/s]
loss 2.70 accuracy 0.06 -- 163.39 + 57.46 + 500.06 + 4.93 = 725.83: 12%|█▏ | 241/2048 [03:40<21:15, 1.42it/s]
loss 2.70 accuracy 0.06 -- 163.39 + 57.46 + 500.06 + 4.93 = 725.83: 12%|█▏ | 242/2048 [03:40<21:40, 1.39it/s]
loss 2.01 accuracy 0.38 -- 56.35 + 172.59 + 511.52 + 4.93 = 745.39: 12%|█▏ | 242/2048 [03:41<21:40, 1.39it/s]
loss 2.01 accuracy 0.38 -- 56.35 + 172.59 + 511.52 + 4.93 = 745.39: 12%|█▏ | 243/2048 [03:41<22:08, 1.36it/s]
loss 1.81 accuracy 0.25 -- 56.94 + 56.70 + 507.62 + 4.95 = 626.22: 12%|█▏ | 243/2048 [03:42<22:08, 1.36it/s]
loss 1.81 accuracy 0.25 -- 56.94 + 56.70 + 507.62 + 4.95 = 626.22: 12%|█▏ | 244/2048 [03:42<21:23, 1.41it/s]
loss 1.62 accuracy 0.38 -- 169.45 + 57.36 + 507.84 + 4.93 = 739.57: 12%|█▏ | 244/2048 [03:43<21:23, 1.41it/s]
loss 1.62 accuracy 0.38 -- 169.45 + 57.36 + 507.84 + 4.93 = 739.57: 12%|█▏ | 245/2048 [03:43<22:12, 1.35it/s]
loss 2.19 accuracy 0.19 -- 56.75 + 58.01 + 636.82 + 4.93 = 756.51: 12%|█▏ | 245/2048 [03:43<22:12, 1.35it/s]
loss 2.19 accuracy 0.19 -- 56.75 + 58.01 + 636.82 + 4.93 = 756.51: 12%|█▏ | 246/2048 [03:43<22:35, 1.33it/s]
loss 2.37 accuracy 0.12 -- 57.45 + 57.20 + 518.44 + 4.89 = 637.99: 12%|█▏ | 246/2048 [03:44<22:35, 1.33it/s]
loss 2.37 accuracy 0.12 -- 57.45 + 57.20 + 518.44 + 4.89 = 637.99: 12%|█▏ | 247/2048 [03:44<21:48, 1.38it/s]
loss 2.56 accuracy 0.19 -- 56.94 + 57.70 + 630.43 + 4.92 = 749.99: 12%|█▏ | 247/2048 [03:45<21:48, 1.38it/s]
loss 2.56 accuracy 0.19 -- 56.94 + 57.70 + 630.43 + 4.92 = 749.99: 12%|█▏ | 248/2048 [03:45<22:14, 1.35it/s]
loss 1.57 accuracy 0.44 -- 57.23 + 56.74 + 511.75 + 4.90 = 630.62: 12%|█▏ | 248/2048 [03:46<22:14, 1.35it/s]
loss 1.57 accuracy 0.44 -- 57.23 + 56.74 + 511.75 + 4.90 = 630.62: 12%|█▏ | 249/2048 [03:46<21:29, 1.40it/s]
loss 2.67 accuracy 0.12 -- 56.55 + 171.81 + 512.12 + 4.96 = 745.44: 12%|█▏ | 249/2048 [03:46<21:29, 1.40it/s]
loss 2.67 accuracy 0.12 -- 56.55 + 171.81 + 512.12 + 4.96 = 745.44: 12%|█▏ | 250/2048 [03:46<21:58, 1.36it/s]
loss 2.05 accuracy 0.25 -- 57.08 + 57.04 + 512.03 + 4.94 = 631.08: 12%|█▏ | 250/2048 [03:47<21:58, 1.36it/s]
loss 2.05 accuracy 0.25 -- 57.08 + 57.04 + 512.03 + 4.94 = 631.08: 12%|█▏ | 251/2048 [03:47<21:17, 1.41it/s]
loss 1.82 accuracy 0.25 -- 57.53 + 56.89 + 510.12 + 4.91 = 629.44: 12%|█▏ | 251/2048 [03:48<21:17, 1.41it/s]
loss 1.82 accuracy 0.25 -- 57.53 + 56.89 + 510.12 + 4.91 = 629.44: 12%|█▏ | 252/2048 [03:48<21:48, 1.37it/s]
loss 1.90 accuracy 0.12 -- 56.76 + 57.68 + 506.84 + 4.92 = 626.20: 12%|█▏ | 252/2048 [03:48<21:48, 1.37it/s]
loss 1.90 accuracy 0.12 -- 56.76 + 57.68 + 506.84 + 4.92 = 626.20: 12%|█▏ | 253/2048 [03:48<21:07, 1.42it/s]
loss 1.65 accuracy 0.19 -- 163.22 + 57.32 + 499.41 + 4.93 = 724.89: 12%|█▏ | 253/2048 [03:49<21:07, 1.42it/s]
loss 1.65 accuracy 0.19 -- 163.22 + 57.32 + 499.41 + 4.93 = 724.89: 12%|█▏ | 254/2048 [03:49<21:31, 1.39it/s]
loss 2.02 accuracy 0.06 -- 56.28 + 172.01 + 511.96 + 4.94 = 745.19: 12%|█▏ | 254/2048 [03:50<21:31, 1.39it/s]
loss 2.02 accuracy 0.06 -- 56.28 + 172.01 + 511.96 + 4.94 = 745.19: 12%|█▏ | 255/2048 [03:50<21:59, 1.36it/s]
loss 1.94 accuracy 0.44 -- 57.52 + 56.94 + 507.49 + 4.94 = 626.89: 12%|█▏ | 255/2048 [03:51<21:59, 1.36it/s]
loss 1.94 accuracy 0.44 -- 57.52 + 56.94 + 507.49 + 4.94 = 626.89: 12%|█▎ | 256/2048 [03:51<21:14, 1.41it/s]
loss 2.03 accuracy 0.38 -- 170.15 + 57.36 + 506.24 + 4.92 = 738.66: 12%|█▎ | 256/2048 [03:51<21:14, 1.41it/s]
loss 2.03 accuracy 0.38 -- 170.15 + 57.36 + 506.24 + 4.92 = 738.66: 13%|█▎ | 257/2048 [03:51<21:43, 1.37it/s]
loss 2.14 accuracy 0.12 -- 56.54 + 57.65 + 635.40 + 4.93 = 754.52: 13%|█▎ | 257/2048 [03:52<21:43, 1.37it/s]
loss 2.14 accuracy 0.12 -- 56.54 + 57.65 + 635.40 + 4.93 = 754.52: 13%|█▎ | 258/2048 [03:52<22:11, 1.34it/s]
loss 2.35 accuracy 0.25 -- 57.75 + 56.90 + 517.26 + 4.92 = 636.84: 13%|█▎ | 258/2048 [03:53<22:11, 1.34it/s]
loss 2.35 accuracy 0.25 -- 57.75 + 56.90 + 517.26 + 4.92 = 636.84: 13%|█▎ | 259/2048 [03:53<21:28, 1.39it/s]
loss 2.11 accuracy 0.19 -- 56.73 + 57.38 + 630.77 + 4.95 = 749.83: 13%|█▎ | 259/2048 [03:54<21:28, 1.39it/s]
loss 2.11 accuracy 0.19 -- 56.73 + 57.38 + 630.77 + 4.95 = 749.83: 13%|█▎ | 260/2048 [03:54<21:58, 1.36it/s]
loss 2.46 accuracy 0.19 -- 57.45 + 56.76 + 512.58 + 4.92 = 631.71: 13%|█▎ | 260/2048 [03:54<21:58, 1.36it/s]
loss 2.46 accuracy 0.19 -- 57.45 + 56.76 + 512.58 + 4.92 = 631.71: 13%|█▎ | 261/2048 [03:54<21:15, 1.40it/s]
loss 2.42 accuracy 0.19 -- 56.66 + 171.69 + 511.43 + 4.93 = 744.72: 13%|█▎ | 261/2048 [03:55<21:15, 1.40it/s]
loss 2.42 accuracy 0.19 -- 56.66 + 171.69 + 511.43 + 4.93 = 744.72: 13%|█▎ | 262/2048 [03:55<21:46, 1.37it/s]
loss 1.97 accuracy 0.25 -- 56.65 + 56.64 + 509.10 + 4.94 = 627.33: 13%|█▎ | 262/2048 [03:56<21:46, 1.37it/s]
loss 1.97 accuracy 0.25 -- 56.65 + 56.64 + 509.10 + 4.94 = 627.33: 13%|█▎ | 263/2048 [03:56<21:04, 1.41it/s]
loss 2.83 accuracy 0.06 -- 57.32 + 56.68 + 508.42 + 4.90 = 627.32: 13%|█▎ | 263/2048 [03:56<21:04, 1.41it/s]
loss 2.83 accuracy 0.06 -- 57.32 + 56.68 + 508.42 + 4.90 = 627.32: 13%|█▎ | 264/2048 [03:56<21:53, 1.36it/s]
loss 1.86 accuracy 0.44 -- 56.33 + 57.64 + 504.85 + 4.96 = 623.78: 13%|█▎ | 264/2048 [03:57<21:53, 1.36it/s]
loss 1.86 accuracy 0.44 -- 56.33 + 57.64 + 504.85 + 4.96 = 623.78: 13%|█▎ | 265/2048 [03:57<21:07, 1.41it/s]
loss 2.68 accuracy 0.44 -- 163.25 + 57.08 + 498.53 + 4.90 = 723.76: 13%|█▎ | 265/2048 [03:58<21:07, 1.41it/s]
loss 2.68 accuracy 0.44 -- 163.25 + 57.08 + 498.53 + 4.90 = 723.76: 13%|█▎ | 266/2048 [03:58<21:28, 1.38it/s]
loss 1.99 accuracy 0.31 -- 56.39 + 172.53 + 510.43 + 4.93 = 744.28: 13%|█▎ | 266/2048 [03:59<21:28, 1.38it/s]
loss 1.99 accuracy 0.31 -- 56.39 + 172.53 + 510.43 + 4.93 = 744.28: 13%|█▎ | 267/2048 [03:59<21:53, 1.36it/s]
loss 2.00 accuracy 0.31 -- 57.21 + 56.83 + 507.28 + 4.90 = 626.23: 13%|█▎ | 267/2048 [03:59<21:53, 1.36it/s]
loss 2.00 accuracy 0.31 -- 57.21 + 56.83 + 507.28 + 4.90 = 626.23: 13%|█▎ | 268/2048 [03:59<21:08, 1.40it/s]
loss 1.75 accuracy 0.31 -- 168.90 + 57.69 + 506.47 + 4.92 = 737.98: 13%|█▎ | 268/2048 [04:00<21:08, 1.40it/s]
loss 1.75 accuracy 0.31 -- 168.90 + 57.69 + 506.47 + 4.92 = 737.98: 13%|█▎ | 269/2048 [04:00<21:36, 1.37it/s]
loss 1.96 accuracy 0.19 -- 56.49 + 57.56 + 636.88 + 4.93 = 755.85: 13%|█▎ | 269/2048 [04:01<21:36, 1.37it/s]
loss 1.96 accuracy 0.19 -- 56.49 + 57.56 + 636.88 + 4.93 = 755.85: 13%|█▎ | 270/2048 [04:01<22:04, 1.34it/s]
loss 1.86 accuracy 0.25 -- 57.43 + 58.72 + 515.15 + 4.92 = 636.22: 13%|█▎ | 270/2048 [04:02<22:04, 1.34it/s]
loss 1.86 accuracy 0.25 -- 57.43 + 58.72 + 515.15 + 4.92 = 636.22: 13%|█▎ | 271/2048 [04:02<21:20, 1.39it/s]
loss 1.78 accuracy 0.31 -- 56.62 + 57.62 + 630.36 + 4.96 = 749.56: 13%|█▎ | 271/2048 [04:02<21:20, 1.39it/s]
loss 1.78 accuracy 0.31 -- 56.62 + 57.62 + 630.36 + 4.96 = 749.56: 13%|█▎ | 272/2048 [04:02<21:50, 1.36it/s]
loss 2.01 accuracy 0.25 -- 57.35 + 57.14 + 512.02 + 4.91 = 631.41: 13%|█▎ | 272/2048 [04:03<21:50, 1.36it/s]
loss 2.01 accuracy 0.25 -- 57.35 + 57.14 + 512.02 + 4.91 = 631.41: 13%|█▎ | 273/2048 [04:03<21:07, 1.40it/s]
loss 2.58 accuracy 0.25 -- 56.69 + 172.20 + 511.64 + 4.89 = 745.43: 13%|█▎ | 273/2048 [04:04<21:07, 1.40it/s]
loss 2.58 accuracy 0.25 -- 56.69 + 172.20 + 511.64 + 4.89 = 745.43: 13%|█▎ | 274/2048 [04:04<21:38, 1.37it/s]
loss 1.98 accuracy 0.50 -- 56.63 + 56.67 + 507.98 + 4.90 = 626.18: 13%|█▎ | 274/2048 [04:04<21:38, 1.37it/s]
loss 1.98 accuracy 0.50 -- 56.63 + 56.67 + 507.98 + 4.90 = 626.18: 13%|█▎ | 275/2048 [04:04<20:55, 1.41it/s]
loss 1.96 accuracy 0.31 -- 56.76 + 56.39 + 507.06 + 4.90 = 625.11: 13%|█▎ | 275/2048 [04:05<20:55, 1.41it/s]
loss 1.96 accuracy 0.31 -- 56.76 + 56.39 + 507.06 + 4.90 = 625.11: 13%|█▎ | 276/2048 [04:05<21:25, 1.38it/s]
loss 2.47 accuracy 0.06 -- 56.19 + 57.50 + 504.12 + 4.88 = 622.69: 13%|█▎ | 276/2048 [04:06<21:25, 1.38it/s]
loss 2.47 accuracy 0.06 -- 56.19 + 57.50 + 504.12 + 4.88 = 622.69: 14%|█▎ | 277/2048 [04:06<20:45, 1.42it/s]
loss 2.14 accuracy 0.19 -- 163.47 + 57.33 + 499.18 + 4.89 = 724.87: 14%|█▎ | 277/2048 [04:07<20:45, 1.42it/s]
loss 2.14 accuracy 0.19 -- 163.47 + 57.33 + 499.18 + 4.89 = 724.87: 14%|█▎ | 278/2048 [04:07<21:10, 1.39it/s]
loss 2.04 accuracy 0.31 -- 56.69 + 172.04 + 511.21 + 4.90 = 744.83: 14%|█▎ | 278/2048 [04:07<21:10, 1.39it/s]
loss 2.04 accuracy 0.31 -- 56.69 + 172.04 + 511.21 + 4.90 = 744.83: 14%|█▎ | 279/2048 [04:07<21:38, 1.36it/s]
loss 1.98 accuracy 0.12 -- 57.11 + 56.84 + 507.79 + 4.92 = 626.67: 14%|█▎ | 279/2048 [04:08<21:38, 1.36it/s]
loss 1.98 accuracy 0.12 -- 57.11 + 56.84 + 507.79 + 4.92 = 626.67: 14%|█▎ | 280/2048 [04:08<20:55, 1.41it/s]
loss 1.96 accuracy 0.44 -- 169.02 + 57.23 + 506.40 + 4.92 = 737.57: 14%|█▎ | 280/2048 [04:09<20:55, 1.41it/s]
loss 1.96 accuracy 0.44 -- 169.02 + 57.23 + 506.40 + 4.92 = 737.57: 14%|█▎ | 281/2048 [04:09<21:24, 1.38it/s]
loss 2.25 accuracy 0.06 -- 56.56 + 57.74 + 633.22 + 4.89 = 752.41: 14%|█▎ | 281/2048 [04:10<21:24, 1.38it/s]
loss 2.25 accuracy 0.06 -- 56.56 + 57.74 + 633.22 + 4.89 = 752.41: 14%|█▍ | 282/2048 [04:10<21:51, 1.35it/s]
loss 2.28 accuracy 0.19 -- 57.09 + 56.85 + 515.70 + 4.90 = 634.54: 14%|█▍ | 282/2048 [04:10<21:51, 1.35it/s]
loss 2.28 accuracy 0.19 -- 57.09 + 56.85 + 515.70 + 4.90 = 634.54: 14%|█▍ | 283/2048 [04:10<21:08, 1.39it/s]
loss 1.79 accuracy 0.12 -- 56.62 + 57.89 + 631.31 + 4.90 = 750.72: 14%|█▍ | 283/2048 [04:11<21:08, 1.39it/s]
loss 1.79 accuracy 0.12 -- 56.62 + 57.89 + 631.31 + 4.90 = 750.72: 14%|█▍ | 284/2048 [04:11<21:39, 1.36it/s]
loss 2.35 accuracy 0.06 -- 57.50 + 57.05 + 513.52 + 4.93 = 632.99: 14%|█▍ | 284/2048 [04:12<21:39, 1.36it/s]
loss 2.35 accuracy 0.06 -- 57.50 + 57.05 + 513.52 + 4.93 = 632.99: 14%|█▍ | 285/2048 [04:12<20:58, 1.40it/s]
loss 1.89 accuracy 0.25 -- 56.70 + 171.73 + 513.02 + 4.89 = 746.34: 14%|█▍ | 285/2048 [04:12<20:58, 1.40it/s]
loss 1.89 accuracy 0.25 -- 56.70 + 171.73 + 513.02 + 4.89 = 746.34: 14%|█▍ | 286/2048 [04:12<21:29, 1.37it/s]
loss 2.74 accuracy 0.25 -- 57.19 + 56.49 + 507.68 + 4.89 = 626.24: 14%|█▍ | 286/2048 [04:13<21:29, 1.37it/s]
loss 2.74 accuracy 0.25 -- 57.19 + 56.49 + 507.68 + 4.89 = 626.24: 14%|█▍ | 287/2048 [04:13<20:47, 1.41it/s]
loss 2.32 accuracy 0.25 -- 57.33 + 56.96 + 508.47 + 4.93 = 627.70: 14%|█▍ | 287/2048 [04:14<20:47, 1.41it/s]
loss 2.32 accuracy 0.25 -- 57.33 + 56.96 + 508.47 + 4.93 = 627.70: 14%|█▍ | 288/2048 [04:14<21:17, 1.38it/s]
loss 2.05 accuracy 0.19 -- 56.65 + 57.99 + 505.28 + 4.87 = 624.80: 14%|█▍ | 288/2048 [04:14<21:17, 1.38it/s]
loss 2.05 accuracy 0.19 -- 56.65 + 57.99 + 505.28 + 4.87 = 624.80: 14%|█▍ | 289/2048 [04:14<20:38, 1.42it/s]
loss 2.04 accuracy 0.31 -- 163.56 + 57.12 + 498.36 + 4.96 = 723.99: 14%|█▍ | 289/2048 [04:15<20:38, 1.42it/s]
loss 2.04 accuracy 0.31 -- 163.56 + 57.12 + 498.36 + 4.96 = 723.99: 14%|█▍ | 290/2048 [04:15<21:02, 1.39it/s]
loss 2.20 accuracy 0.12 -- 56.24 + 172.13 + 512.34 + 4.89 = 745.61: 14%|█▍ | 290/2048 [04:16<21:02, 1.39it/s]
loss 2.20 accuracy 0.12 -- 56.24 + 172.13 + 512.34 + 4.89 = 745.61: 14%|█▍ | 291/2048 [04:16<21:31, 1.36it/s]
loss 2.05 accuracy 0.12 -- 57.28 + 56.61 + 507.92 + 4.89 = 626.70: 14%|█▍ | 291/2048 [04:17<21:31, 1.36it/s]
loss 2.05 accuracy 0.12 -- 57.28 + 56.61 + 507.92 + 4.89 = 626.70: 14%|█▍ | 292/2048 [04:17<20:48, 1.41it/s]
loss 2.12 accuracy 0.12 -- 169.60 + 57.31 + 507.02 + 4.88 = 738.81: 14%|█▍ | 292/2048 [04:17<20:48, 1.41it/s]
loss 2.12 accuracy 0.12 -- 169.60 + 57.31 + 507.02 + 4.88 = 738.81: 14%|█▍ | 293/2048 [04:17<21:16, 1.37it/s]
loss 2.07 accuracy 0.25 -- 56.97 + 57.98 + 635.99 + 4.90 = 755.84: 14%|█▍ | 293/2048 [04:18<21:16, 1.37it/s]
loss 2.07 accuracy 0.25 -- 56.97 + 57.98 + 635.99 + 4.90 = 755.84: 14%|█▍ | 294/2048 [04:18<21:45, 1.34it/s]
loss 2.18 accuracy 0.12 -- 57.78 + 56.79 + 515.48 + 4.89 = 634.93: 14%|█▍ | 294/2048 [04:19<21:45, 1.34it/s]
loss 2.18 accuracy 0.12 -- 57.78 + 56.79 + 515.48 + 4.89 = 634.93: 14%|█▍ | 295/2048 [04:19<21:01, 1.39it/s]
loss 2.08 accuracy 0.31 -- 56.88 + 57.89 + 631.99 + 4.90 = 751.67: 14%|█▍ | 295/2048 [04:20<21:01, 1.39it/s]
loss 2.08 accuracy 0.31 -- 56.88 + 57.89 + 631.99 + 4.90 = 751.67: 14%|█▍ | 296/2048 [04:20<21:32, 1.36it/s]
loss 1.69 accuracy 0.38 -- 57.00 + 57.11 + 511.18 + 4.88 = 630.18: 14%|█▍ | 296/2048 [04:20<21:32, 1.36it/s]
loss 1.69 accuracy 0.38 -- 57.00 + 57.11 + 511.18 + 4.88 = 630.18: 15%|█▍ | 297/2048 [04:20<20:49, 1.40it/s]
loss 2.20 accuracy 0.25 -- 56.26 + 172.38 + 510.94 + 4.90 = 744.48: 15%|█▍ | 297/2048 [04:21<20:49, 1.40it/s]
loss 2.20 accuracy 0.25 -- 56.26 + 172.38 + 510.94 + 4.90 = 744.48: 15%|█▍ | 298/2048 [04:21<21:19, 1.37it/s]
loss 2.56 accuracy 0.12 -- 56.67 + 56.64 + 511.02 + 4.92 = 629.24: 15%|█▍ | 298/2048 [04:22<21:19, 1.37it/s]
loss 2.56 accuracy 0.12 -- 56.67 + 56.64 + 511.02 + 4.92 = 629.24: 15%|█▍ | 299/2048 [04:22<20:39, 1.41it/s]
loss 2.07 accuracy 0.19 -- 56.82 + 56.43 + 507.58 + 4.89 = 625.71: 15%|█▍ | 299/2048 [04:22<20:39, 1.41it/s]
loss 2.07 accuracy 0.19 -- 56.82 + 56.43 + 507.58 + 4.89 = 625.71: 15%|█▍ | 300/2048 [04:22<21:08, 1.38it/s]
loss 2.14 accuracy 0.25 -- 56.97 + 57.47 + 503.31 + 4.89 = 622.64: 15%|█▍ | 300/2048 [04:23<21:08, 1.38it/s]
loss 2.14 accuracy 0.25 -- 56.97 + 57.47 + 503.31 + 4.89 = 622.64: 15%|█▍ | 301/2048 [04:23<20:28, 1.42it/s]
loss 1.99 accuracy 0.25 -- 163.25 + 56.72 + 497.43 + 4.88 = 722.29: 15%|█▍ | 301/2048 [04:24<20:28, 1.42it/s]
loss 1.99 accuracy 0.25 -- 163.25 + 56.72 + 497.43 + 4.88 = 722.29: 15%|█▍ | 302/2048 [04:24<20:51, 1.39it/s]
loss 2.19 accuracy 0.25 -- 56.39 + 172.01 + 510.66 + 4.91 = 743.98: 15%|█▍ | 302/2048 [04:25<20:51, 1.39it/s]
loss 2.19 accuracy 0.25 -- 56.39 + 172.01 + 510.66 + 4.91 = 743.98: 15%|█▍ | 303/2048 [04:25<21:19, 1.36it/s]
loss 2.18 accuracy 0.19 -- 57.28 + 56.63 + 505.35 + 4.90 = 624.16: 15%|█▍ | 303/2048 [04:25<21:19, 1.36it/s]
loss 2.18 accuracy 0.19 -- 57.28 + 56.63 + 505.35 + 4.90 = 624.16: 15%|█▍ | 304/2048 [04:25<20:36, 1.41it/s]
loss 2.28 accuracy 0.19 -- 168.55 + 57.58 + 504.83 + 4.88 = 735.84: 15%|█▍ | 304/2048 [04:26<20:36, 1.41it/s]
loss 2.28 accuracy 0.19 -- 168.55 + 57.58 + 504.83 + 4.88 = 735.84: 15%|█▍ | 305/2048 [04:26<21:04, 1.38it/s]
loss 2.22 accuracy 0.19 -- 56.66 + 57.48 + 633.45 + 4.91 = 752.50: 15%|█▍ | 305/2048 [04:27<21:04, 1.38it/s]
loss 2.22 accuracy 0.19 -- 56.66 + 57.48 + 633.45 + 4.91 = 752.50: 15%|█▍ | 306/2048 [04:27<21:32, 1.35it/s]
loss 2.22 accuracy 0.25 -- 57.08 + 56.57 + 514.13 + 4.90 = 632.68: 15%|█▍ | 306/2048 [04:28<21:32, 1.35it/s]
loss 2.22 accuracy 0.25 -- 57.08 + 56.57 + 514.13 + 4.90 = 632.68: 15%|█▍ | 307/2048 [04:28<20:48, 1.39it/s]
loss 2.28 accuracy 0.19 -- 56.33 + 57.44 + 629.81 + 4.91 = 748.50: 15%|█▍ | 307/2048 [04:28<20:48, 1.39it/s]
loss 2.28 accuracy 0.19 -- 56.33 + 57.44 + 629.81 + 4.91 = 748.50: 15%|█▌ | 308/2048 [04:28<21:18, 1.36it/s]
loss 2.18 accuracy 0.12 -- 57.33 + 56.75 + 510.54 + 4.89 = 629.50: 15%|█▌ | 308/2048 [04:29<21:18, 1.36it/s]
loss 2.18 accuracy 0.12 -- 57.33 + 56.75 + 510.54 + 4.89 = 629.50: 15%|█▌ | 309/2048 [04:29<20:37, 1.41it/s]
loss 1.78 accuracy 0.31 -- 56.79 + 171.28 + 508.91 + 4.88 = 741.86: 15%|█▌ | 309/2048 [04:30<20:37, 1.41it/s]
loss 1.78 accuracy 0.31 -- 56.79 + 171.28 + 508.91 + 4.88 = 741.86: 15%|█▌ | 310/2048 [04:30<21:06, 1.37it/s]
loss 2.10 accuracy 0.12 -- 56.33 + 56.57 + 507.60 + 4.87 = 625.37: 15%|█▌ | 310/2048 [04:30<21:06, 1.37it/s]
loss 2.10 accuracy 0.12 -- 56.33 + 56.57 + 507.60 + 4.87 = 625.37: 15%|█▌ | 311/2048 [04:30<20:26, 1.42it/s]
loss 1.93 accuracy 0.12 -- 57.36 + 56.32 + 506.38 + 4.90 = 624.95: 15%|█▌ | 311/2048 [04:31<20:26, 1.42it/s]
loss 1.93 accuracy 0.12 -- 57.36 + 56.32 + 506.38 + 4.90 = 624.95: 15%|█▌ | 312/2048 [04:31<20:56, 1.38it/s]
loss 2.14 accuracy 0.12 -- 56.31 + 57.24 + 503.99 + 4.88 = 622.42: 15%|█▌ | 312/2048 [04:32<20:56, 1.38it/s]
loss 2.14 accuracy 0.12 -- 56.31 + 57.24 + 503.99 + 4.88 = 622.42: 15%|█▌ | 313/2048 [04:32<20:16, 1.43it/s]
loss 1.68 accuracy 0.38 -- 163.31 + 56.74 + 497.49 + 4.90 = 722.43: 15%|█▌ | 313/2048 [04:33<20:16, 1.43it/s]
loss 1.68 accuracy 0.38 -- 163.31 + 56.74 + 497.49 + 4.90 = 722.43: 15%|█▌ | 314/2048 [04:33<20:41, 1.40it/s]
loss 1.86 accuracy 0.25 -- 56.18 + 172.05 + 514.18 + 4.94 = 747.35: 15%|█▌ | 314/2048 [04:33<20:41, 1.40it/s]
loss 1.86 accuracy 0.25 -- 56.18 + 172.05 + 514.18 + 4.94 = 747.35: 15%|█▌ | 315/2048 [04:33<21:11, 1.36it/s]
loss 2.54 accuracy 0.19 -- 56.88 + 56.58 + 507.79 + 4.87 = 626.11: 15%|█▌ | 315/2048 [04:34<21:11, 1.36it/s]
loss 2.54 accuracy 0.19 -- 56.88 + 56.58 + 507.79 + 4.87 = 626.11: 15%|█▌ | 316/2048 [04:34<20:29, 1.41it/s]
loss 1.98 accuracy 0.12 -- 168.15 + 57.29 + 504.57 + 4.92 = 734.93: 15%|█▌ | 316/2048 [04:35<20:29, 1.41it/s]
loss 1.98 accuracy 0.12 -- 168.15 + 57.29 + 504.57 + 4.92 = 734.93: 15%|█▌ | 317/2048 [04:35<20:56, 1.38it/s]
loss 1.86 accuracy 0.31 -- 56.57 + 57.21 + 634.13 + 4.92 = 752.84: 15%|█▌ | 317/2048 [04:36<20:56, 1.38it/s]
loss 1.86 accuracy 0.31 -- 56.57 + 57.21 + 634.13 + 4.92 = 752.84: 16%|█▌ | 318/2048 [04:36<21:23, 1.35it/s]
loss 2.16 accuracy 0.44 -- 56.91 + 56.43 + 513.11 + 4.89 = 631.34: 16%|█▌ | 318/2048 [04:36<21:23, 1.35it/s]
loss 2.16 accuracy 0.44 -- 56.91 + 56.43 + 513.11 + 4.89 = 631.34: 16%|█▌ | 319/2048 [04:36<20:39, 1.39it/s]
loss 2.47 accuracy 0.12 -- 56.91 + 57.89 + 630.56 + 4.94 = 750.30: 16%|█▌ | 319/2048 [04:37<20:39, 1.39it/s]
loss 2.47 accuracy 0.12 -- 56.91 + 57.89 + 630.56 + 4.94 = 750.30: 16%|█▌ | 320/2048 [04:37<21:10, 1.36it/s]
loss 2.49 accuracy 0.06 -- 57.43 + 56.78 + 511.17 + 4.90 = 630.28: 16%|█▌ | 320/2048 [04:38<21:10, 1.36it/s]
loss 2.49 accuracy 0.06 -- 57.43 + 56.78 + 511.17 + 4.90 = 630.28: 16%|█▌ | 321/2048 [04:38<20:29, 1.40it/s]
loss 2.09 accuracy 0.12 -- 56.21 + 171.15 + 510.10 + 4.89 = 742.36: 16%|█▌ | 321/2048 [04:38<20:29, 1.40it/s]
loss 2.09 accuracy 0.12 -- 56.21 + 171.15 + 510.10 + 4.89 = 742.36: 16%|█▌ | 322/2048 [04:38<20:58, 1.37it/s]
loss 2.01 accuracy 0.19 -- 56.77 + 56.59 + 510.07 + 4.94 = 628.37: 16%|█▌ | 322/2048 [04:39<20:58, 1.37it/s]
loss 2.01 accuracy 0.19 -- 56.77 + 56.59 + 510.07 + 4.94 = 628.37: 16%|█▌ | 323/2048 [04:39<20:19, 1.41it/s]
loss 2.18 accuracy 0.50 -- 57.45 + 56.89 + 507.60 + 4.92 = 626.87: 16%|█▌ | 323/2048 [04:40<20:19, 1.41it/s]
loss 2.18 accuracy 0.50 -- 57.45 + 56.89 + 507.60 + 4.92 = 626.87: 16%|█▌ | 324/2048 [04:40<20:49, 1.38it/s]
loss 2.48 accuracy 0.06 -- 56.18 + 57.35 + 504.61 + 4.93 = 623.07: 16%|█▌ | 324/2048 [04:40<20:49, 1.38it/s]
loss 2.48 accuracy 0.06 -- 56.18 + 57.35 + 504.61 + 4.93 = 623.07: 16%|█▌ | 325/2048 [04:40<20:10, 1.42it/s]
loss 1.85 accuracy 0.25 -- 163.54 + 57.11 + 499.07 + 4.93 = 724.65: 16%|█▌ | 325/2048 [04:41<20:10, 1.42it/s]
loss 1.85 accuracy 0.25 -- 163.54 + 57.11 + 499.07 + 4.93 = 724.65: 16%|█▌ | 326/2048 [04:41<20:35, 1.39it/s]
loss 1.66 accuracy 0.44 -- 56.43 + 171.68 + 512.81 + 4.89 = 745.82: 16%|█▌ | 326/2048 [04:42<20:35, 1.39it/s]
loss 1.66 accuracy 0.44 -- 56.43 + 171.68 + 512.81 + 4.89 = 745.82: 16%|█▌ | 327/2048 [04:42<21:03, 1.36it/s]
loss 1.89 accuracy 0.25 -- 56.93 + 57.03 + 508.02 + 4.95 = 626.93: 16%|█▌ | 327/2048 [04:43<21:03, 1.36it/s]
loss 1.89 accuracy 0.25 -- 56.93 + 57.03 + 508.02 + 4.95 = 626.93: 16%|█▌ | 328/2048 [04:43<20:21, 1.41it/s]
loss 1.84 accuracy 0.31 -- 168.16 + 57.34 + 507.41 + 4.91 = 737.82: 16%|█▌ | 328/2048 [04:43<20:21, 1.41it/s]
loss 1.84 accuracy 0.31 -- 168.16 + 57.34 + 507.41 + 4.91 = 737.82: 16%|█▌ | 329/2048 [04:43<20:49, 1.38it/s]
loss 2.56 accuracy 0.38 -- 56.69 + 57.44 + 635.50 + 4.90 = 754.54: 16%|█▌ | 329/2048 [04:44<20:49, 1.38it/s]
loss 2.56 accuracy 0.38 -- 56.69 + 57.44 + 635.50 + 4.90 = 754.54: 16%|█▌ | 330/2048 [04:44<21:17, 1.35it/s]
loss 1.76 accuracy 0.31 -- 57.39 + 57.57 + 516.10 + 4.94 = 636.00: 16%|█▌ | 330/2048 [04:45<21:17, 1.35it/s]
loss 1.76 accuracy 0.31 -- 57.39 + 57.57 + 516.10 + 4.94 = 636.00: 16%|█▌ | 331/2048 [04:45<20:35, 1.39it/s]
loss 1.97 accuracy 0.25 -- 56.68 + 57.56 + 631.26 + 4.92 = 750.42: 16%|█▌ | 331/2048 [04:46<20:35, 1.39it/s]
loss 1.97 accuracy 0.25 -- 56.68 + 57.56 + 631.26 + 4.92 = 750.42: 16%|█▌ | 332/2048 [04:46<21:04, 1.36it/s]
loss 2.28 accuracy 0.19 -- 57.55 + 56.57 + 513.04 + 4.92 = 632.08: 16%|█▌ | 332/2048 [04:46<21:04, 1.36it/s]
loss 2.28 accuracy 0.19 -- 57.55 + 56.57 + 513.04 + 4.92 = 632.08: 16%|█▋ | 333/2048 [04:46<20:24, 1.40it/s]
loss 2.08 accuracy 0.38 -- 56.63 + 173.11 + 512.03 + 4.93 = 746.70: 16%|█▋ | 333/2048 [04:47<20:24, 1.40it/s]
loss 2.08 accuracy 0.38 -- 56.63 + 173.11 + 512.03 + 4.93 = 746.70: 16%|█▋ | 334/2048 [04:47<20:54, 1.37it/s]
loss 1.98 accuracy 0.38 -- 56.98 + 56.79 + 508.06 + 4.91 = 626.75: 16%|█▋ | 334/2048 [04:48<20:54, 1.37it/s]
loss 1.98 accuracy 0.38 -- 56.98 + 56.79 + 508.06 + 4.91 = 626.75: 16%|█▋ | 335/2048 [04:48<20:13, 1.41it/s]
loss 2.38 accuracy 0.25 -- 57.43 + 56.61 + 508.51 + 4.93 = 627.48: 16%|█▋ | 335/2048 [04:48<20:13, 1.41it/s]
loss 2.38 accuracy 0.25 -- 57.43 + 56.61 + 508.51 + 4.93 = 627.48: 16%|█▋ | 336/2048 [04:48<20:42, 1.38it/s]
loss 2.36 accuracy 0.25 -- 56.74 + 57.47 + 503.42 + 4.92 = 622.55: 16%|█▋ | 336/2048 [04:49<20:42, 1.38it/s]
loss 2.36 accuracy 0.25 -- 56.74 + 57.47 + 503.42 + 4.92 = 622.55: 16%|█▋ | 337/2048 [04:49<20:03, 1.42it/s]
loss 1.80 accuracy 0.44 -- 163.62 + 57.25 + 498.30 + 4.87 = 724.05: 16%|█▋ | 337/2048 [04:50<20:03, 1.42it/s]
loss 1.80 accuracy 0.44 -- 163.62 + 57.25 + 498.30 + 4.87 = 724.05: 17%|█▋ | 338/2048 [04:50<20:27, 1.39it/s]
loss 1.94 accuracy 0.25 -- 56.17 + 172.15 + 511.16 + 4.95 = 744.42: 17%|█▋ | 338/2048 [04:51<20:27, 1.39it/s]
loss 1.94 accuracy 0.25 -- 56.17 + 172.15 + 511.16 + 4.95 = 744.42: 17%|█▋ | 339/2048 [04:51<20:54, 1.36it/s]
loss 1.68 accuracy 0.38 -- 57.53 + 57.33 + 508.26 + 4.92 = 628.04: 17%|█▋ | 339/2048 [04:51<20:54, 1.36it/s]
loss 1.68 accuracy 0.38 -- 57.53 + 57.33 + 508.26 + 4.92 = 628.04: 17%|█▋ | 340/2048 [04:51<20:13, 1.41it/s]
loss 2.35 accuracy 0.12 -- 168.41 + 57.92 + 506.52 + 4.95 = 737.80: 17%|█▋ | 340/2048 [04:52<20:13, 1.41it/s]
loss 2.35 accuracy 0.12 -- 168.41 + 57.92 + 506.52 + 4.95 = 737.80: 17%|█▋ | 341/2048 [04:52<20:40, 1.38it/s]
loss 2.29 accuracy 0.25 -- 56.78 + 59.62 + 637.24 + 4.93 = 758.56: 17%|█▋ | 341/2048 [04:53<20:40, 1.38it/s]
loss 2.29 accuracy 0.25 -- 56.78 + 59.62 + 637.24 + 4.93 = 758.56: 17%|█▋ | 342/2048 [04:53<21:10, 1.34it/s]
loss 2.23 accuracy 0.25 -- 57.41 + 56.65 + 514.71 + 4.94 = 633.71: 17%|█▋ | 342/2048 [04:54<21:10, 1.34it/s]
loss 2.23 accuracy 0.25 -- 57.41 + 56.65 + 514.71 + 4.94 = 633.71: 17%|█▋ | 343/2048 [04:54<20:45, 1.37it/s]
loss 2.71 accuracy 0.12 -- 56.73 + 57.91 + 631.18 + 4.95 = 750.77: 17%|█▋ | 343/2048 [04:54<20:45, 1.37it/s]
loss 2.71 accuracy 0.12 -- 56.73 + 57.91 + 631.18 + 4.95 = 750.77: 17%|█▋ | 344/2048 [04:54<21:09, 1.34it/s]
loss 1.93 accuracy 0.31 -- 57.38 + 57.18 + 512.71 + 4.89 = 632.17: 17%|█▋ | 344/2048 [04:55<21:09, 1.34it/s]
loss 1.93 accuracy 0.31 -- 57.38 + 57.18 + 512.71 + 4.89 = 632.17: 17%|█▋ | 345/2048 [04:55<20:25, 1.39it/s]
loss 2.16 accuracy 0.31 -- 56.37 + 171.91 + 511.63 + 4.89 = 744.80: 17%|█▋ | 345/2048 [04:56<20:25, 1.39it/s]
loss 2.16 accuracy 0.31 -- 56.37 + 171.91 + 511.63 + 4.89 = 744.80: 17%|█▋ | 346/2048 [04:56<20:51, 1.36it/s]
loss 1.98 accuracy 0.12 -- 57.17 + 57.52 + 510.84 + 4.94 = 630.47: 17%|█▋ | 346/2048 [04:56<20:51, 1.36it/s]
loss 1.98 accuracy 0.12 -- 57.17 + 57.52 + 510.84 + 4.94 = 630.47: 17%|█▋ | 347/2048 [04:56<20:11, 1.40it/s]
loss 2.23 accuracy 0.12 -- 56.89 + 56.89 + 509.18 + 4.93 = 627.89: 17%|█▋ | 347/2048 [04:57<20:11, 1.40it/s]
loss 2.23 accuracy 0.12 -- 56.89 + 56.89 + 509.18 + 4.93 = 627.89: 17%|█▋ | 348/2048 [04:57<20:38, 1.37it/s]
loss 2.10 accuracy 0.31 -- 56.82 + 57.52 + 505.22 + 4.94 = 624.51: 17%|█▋ | 348/2048 [04:58<20:38, 1.37it/s]
loss 2.10 accuracy 0.31 -- 56.82 + 57.52 + 505.22 + 4.94 = 624.51: 17%|█▋ | 349/2048 [04:58<19:58, 1.42it/s]
loss 2.31 accuracy 0.12 -- 163.31 + 57.23 + 498.97 + 4.95 = 724.47: 17%|█▋ | 349/2048 [04:59<19:58, 1.42it/s]
loss 2.31 accuracy 0.12 -- 163.31 + 57.23 + 498.97 + 4.95 = 724.47: 17%|█▋ | 350/2048 [04:59<20:21, 1.39it/s]
loss 2.34 accuracy 0.25 -- 56.55 + 172.68 + 511.37 + 4.95 = 745.56: 17%|█▋ | 350/2048 [04:59<20:21, 1.39it/s]
loss 2.34 accuracy 0.25 -- 56.55 + 172.68 + 511.37 + 4.95 = 745.56: 17%|█▋ | 351/2048 [04:59<20:48, 1.36it/s]
loss 1.95 accuracy 0.38 -- 56.93 + 56.66 + 506.98 + 4.94 = 625.50: 17%|█▋ | 351/2048 [05:00<20:48, 1.36it/s]
loss 1.95 accuracy 0.38 -- 56.93 + 56.66 + 506.98 + 4.94 = 625.50: 17%|█▋ | 352/2048 [05:00<20:05, 1.41it/s]
loss 2.10 accuracy 0.31 -- 168.99 + 57.81 + 508.35 + 4.90 = 740.05: 17%|█▋ | 352/2048 [05:01<20:05, 1.41it/s]
loss 2.10 accuracy 0.31 -- 168.99 + 57.81 + 508.35 + 4.90 = 740.05: 17%|█▋ | 353/2048 [05:01<20:33, 1.37it/s]
loss 2.17 accuracy 0.25 -- 56.60 + 58.04 + 637.37 + 4.95 = 756.96: 17%|█▋ | 353/2048 [05:02<20:33, 1.37it/s]
loss 2.17 accuracy 0.25 -- 56.60 + 58.04 + 637.37 + 4.95 = 756.96: 17%|█▋ | 354/2048 [05:02<21:01, 1.34it/s]
loss 1.92 accuracy 0.25 -- 57.70 + 57.33 + 515.13 + 4.95 = 635.11: 17%|█▋ | 354/2048 [05:02<21:01, 1.34it/s]
loss 1.92 accuracy 0.25 -- 57.70 + 57.33 + 515.13 + 4.95 = 635.11: 17%|█▋ | 355/2048 [05:02<20:19, 1.39it/s]
loss 2.20 accuracy 0.19 -- 56.64 + 57.42 + 632.15 + 4.89 = 751.09: 17%|█▋ | 355/2048 [05:03<20:19, 1.39it/s]
loss 2.20 accuracy 0.19 -- 56.64 + 57.42 + 632.15 + 4.89 = 751.09: 17%|█▋ | 356/2048 [05:03<20:47, 1.36it/s]
loss 2.23 accuracy 0.25 -- 57.05 + 56.80 + 512.16 + 4.91 = 630.93: 17%|█▋ | 356/2048 [05:04<20:47, 1.36it/s]
loss 2.23 accuracy 0.25 -- 57.05 + 56.80 + 512.16 + 4.91 = 630.93: 17%|█▋ | 357/2048 [05:04<20:07, 1.40it/s]
loss 2.05 accuracy 0.12 -- 56.75 + 171.50 + 512.32 + 4.92 = 745.49: 17%|█▋ | 357/2048 [05:04<20:07, 1.40it/s]
loss 2.05 accuracy 0.12 -- 56.75 + 171.50 + 512.32 + 4.92 = 745.49: 17%|█▋ | 358/2048 [05:04<20:36, 1.37it/s]
loss 1.83 accuracy 0.38 -- 57.13 + 56.74 + 509.01 + 4.97 = 627.85: 17%|█▋ | 358/2048 [05:05<20:36, 1.37it/s]
loss 1.83 accuracy 0.38 -- 57.13 + 56.74 + 509.01 + 4.97 = 627.85: 18%|█▊ | 359/2048 [05:05<19:57, 1.41it/s]
loss 2.13 accuracy 0.31 -- 57.29 + 56.69 + 507.47 + 4.93 = 626.37: 18%|█▊ | 359/2048 [05:06<19:57, 1.41it/s]
loss 2.13 accuracy 0.31 -- 57.29 + 56.69 + 507.47 + 4.93 = 626.37: 18%|█▊ | 360/2048 [05:06<20:25, 1.38it/s]
loss 2.14 accuracy 0.12 -- 56.59 + 57.85 + 504.18 + 4.88 = 623.50: 18%|█▊ | 360/2048 [05:07<20:25, 1.38it/s]
loss 2.14 accuracy 0.12 -- 56.59 + 57.85 + 504.18 + 4.88 = 623.50: 18%|█▊ | 361/2048 [05:07<19:46, 1.42it/s]
loss 2.57 accuracy 0.31 -- 164.24 + 56.85 + 497.77 + 4.91 = 723.76: 18%|█▊ | 361/2048 [05:07<19:46, 1.42it/s]
loss 2.57 accuracy 0.31 -- 164.24 + 56.85 + 497.77 + 4.91 = 723.76: 18%|█▊ | 362/2048 [05:07<20:10, 1.39it/s]
loss 2.40 accuracy 0.31 -- 56.44 + 172.03 + 512.91 + 4.91 = 746.28: 18%|█▊ | 362/2048 [05:08<20:10, 1.39it/s]
loss 2.40 accuracy 0.31 -- 56.44 + 172.03 + 512.91 + 4.91 = 746.28: 18%|█▊ | 363/2048 [05:08<20:37, 1.36it/s]
loss 2.48 accuracy 0.31 -- 57.12 + 57.20 + 508.57 + 4.92 = 627.81: 18%|█▊ | 363/2048 [05:09<20:37, 1.36it/s]
loss 2.48 accuracy 0.31 -- 57.12 + 57.20 + 508.57 + 4.92 = 627.81: 18%|█▊ | 364/2048 [05:09<19:57, 1.41it/s]
loss 2.78 accuracy 0.12 -- 169.24 + 57.25 + 505.54 + 4.92 = 736.95: 18%|█▊ | 364/2048 [05:09<19:57, 1.41it/s]
loss 2.78 accuracy 0.12 -- 169.24 + 57.25 + 505.54 + 4.92 = 736.95: 18%|█▊ | 365/2048 [05:09<20:23, 1.38it/s]
loss 1.92 accuracy 0.25 -- 56.65 + 57.50 + 635.00 + 4.94 = 754.09: 18%|█▊ | 365/2048 [05:10<20:23, 1.38it/s]
loss 1.92 accuracy 0.25 -- 56.65 + 57.50 + 635.00 + 4.94 = 754.09: 18%|█▊ | 366/2048 [05:10<20:50, 1.35it/s]
loss 2.32 accuracy 0.06 -- 57.31 + 56.64 + 514.73 + 4.93 = 633.60: 18%|█▊ | 366/2048 [05:11<20:50, 1.35it/s]
loss 2.32 accuracy 0.06 -- 57.31 + 56.64 + 514.73 + 4.93 = 633.60: 18%|█▊ | 367/2048 [05:11<20:07, 1.39it/s]
loss 2.08 accuracy 0.19 -- 56.69 + 57.84 + 629.74 + 4.94 = 749.21: 18%|█▊ | 367/2048 [05:12<20:07, 1.39it/s]
loss 2.08 accuracy 0.19 -- 56.69 + 57.84 + 629.74 + 4.94 = 749.21: 18%|█▊ | 368/2048 [05:12<20:36, 1.36it/s]
loss 2.23 accuracy 0.19 -- 57.03 + 56.53 + 512.06 + 4.92 = 630.53: 18%|█▊ | 368/2048 [05:12<20:36, 1.36it/s]
loss 2.23 accuracy 0.19 -- 57.03 + 56.53 + 512.06 + 4.92 = 630.53: 18%|█▊ | 369/2048 [05:12<19:56, 1.40it/s]
loss 2.17 accuracy 0.06 -- 57.32 + 171.82 + 512.48 + 4.88 = 746.50: 18%|█▊ | 369/2048 [05:13<19:56, 1.40it/s]
loss 2.17 accuracy 0.06 -- 57.32 + 171.82 + 512.48 + 4.88 = 746.50: 18%|█▊ | 370/2048 [05:13<20:26, 1.37it/s]
loss 1.89 accuracy 0.38 -- 56.05 + 56.56 + 508.74 + 4.93 = 626.28: 18%|█▊ | 370/2048 [05:14<20:26, 1.37it/s]
loss 1.89 accuracy 0.38 -- 56.05 + 56.56 + 508.74 + 4.93 = 626.28: 18%|█▊ | 371/2048 [05:14<19:46, 1.41it/s]
loss 1.98 accuracy 0.38 -- 57.64 + 56.95 + 510.40 + 4.90 = 629.89: 18%|█▊ | 371/2048 [05:15<19:46, 1.41it/s]
loss 1.98 accuracy 0.38 -- 57.64 + 56.95 + 510.40 + 4.90 = 629.89: 18%|█▊ | 372/2048 [05:15<20:17, 1.38it/s]
loss 1.88 accuracy 0.12 -- 56.45 + 57.43 + 505.43 + 4.89 = 624.20: 18%|█▊ | 372/2048 [05:15<20:17, 1.38it/s]
loss 1.88 accuracy 0.12 -- 56.45 + 57.43 + 505.43 + 4.89 = 624.20: 18%|█▊ | 373/2048 [05:15<19:38, 1.42it/s]
loss 2.06 accuracy 0.50 -- 162.74 + 56.79 + 497.22 + 4.89 = 721.64: 18%|█▊ | 373/2048 [05:16<19:38, 1.42it/s]
loss 2.06 accuracy 0.50 -- 162.74 + 56.79 + 497.22 + 4.89 = 721.64: 18%|█▊ | 374/2048 [05:16<20:01, 1.39it/s]
loss 2.46 accuracy 0.00 -- 56.10 + 171.22 + 509.83 + 4.90 = 742.05: 18%|█▊ | 374/2048 [05:17<20:01, 1.39it/s]
loss 2.46 accuracy 0.00 -- 56.10 + 171.22 + 509.83 + 4.90 = 742.05: 18%|█▊ | 375/2048 [05:17<20:26, 1.36it/s]
loss 2.17 accuracy 0.25 -- 56.84 + 56.47 + 505.67 + 4.94 = 623.92: 18%|█▊ | 375/2048 [05:17<20:26, 1.36it/s]
loss 2.17 accuracy 0.25 -- 56.84 + 56.47 + 505.67 + 4.94 = 623.92: 18%|█▊ | 376/2048 [05:17<19:44, 1.41it/s]
loss 2.22 accuracy 0.19 -- 168.23 + 57.74 + 507.55 + 4.93 = 738.44: 18%|█▊ | 376/2048 [05:18<19:44, 1.41it/s]
loss 2.22 accuracy 0.19 -- 168.23 + 57.74 + 507.55 + 4.93 = 738.44: 18%|█▊ | 377/2048 [05:18<20:12, 1.38it/s]
loss 1.92 accuracy 0.19 -- 57.38 + 57.94 + 637.82 + 4.93 = 758.07: 18%|█▊ | 377/2048 [05:19<20:12, 1.38it/s]
loss 1.92 accuracy 0.19 -- 57.38 + 57.94 + 637.82 + 4.93 = 758.07: 18%|█▊ | 378/2048 [05:19<20:41, 1.34it/s]
loss 1.87 accuracy 0.38 -- 57.67 + 57.05 + 515.73 + 4.91 = 635.36: 18%|█▊ | 378/2048 [05:20<20:41, 1.34it/s]
loss 1.87 accuracy 0.38 -- 57.67 + 57.05 + 515.73 + 4.91 = 635.36: 19%|█▊ | 379/2048 [05:20<20:00, 1.39it/s]
loss 1.84 accuracy 0.44 -- 56.98 + 57.68 + 632.42 + 4.94 = 752.02: 19%|█▊ | 379/2048 [05:20<20:00, 1.39it/s]
loss 1.84 accuracy 0.44 -- 56.98 + 57.68 + 632.42 + 4.94 = 752.02: 19%|█▊ | 380/2048 [05:20<20:29, 1.36it/s]
loss 2.02 accuracy 0.25 -- 57.73 + 57.42 + 513.67 + 4.93 = 633.75: 19%|█▊ | 380/2048 [05:21<20:29, 1.36it/s]
loss 2.02 accuracy 0.25 -- 57.73 + 57.42 + 513.67 + 4.93 = 633.75: 19%|█▊ | 381/2048 [05:21<19:50, 1.40it/s]
loss 1.85 accuracy 0.31 -- 57.05 + 171.82 + 510.60 + 4.96 = 744.43: 19%|█▊ | 381/2048 [05:22<19:50, 1.40it/s]
loss 1.85 accuracy 0.31 -- 57.05 + 171.82 + 510.60 + 4.96 = 744.43: 19%|█▊ | 382/2048 [05:22<20:19, 1.37it/s]
loss 1.83 accuracy 0.19 -- 56.63 + 57.02 + 509.68 + 4.92 = 628.26: 19%|█▊ | 382/2048 [05:22<20:19, 1.37it/s]
loss 1.83 accuracy 0.19 -- 56.63 + 57.02 + 509.68 + 4.92 = 628.26: 19%|█▊ | 383/2048 [05:22<19:40, 1.41it/s]
loss 1.70 accuracy 0.19 -- 57.46 + 56.80 + 509.08 + 4.93 = 628.28: 19%|█▊ | 383/2048 [05:23<19:40, 1.41it/s]
loss 1.70 accuracy 0.19 -- 57.46 + 56.80 + 509.08 + 4.93 = 628.28: 19%|█▉ | 384/2048 [05:23<20:09, 1.38it/s]
loss 1.74 accuracy 0.38 -- 56.66 + 57.75 + 505.47 + 4.92 = 624.81: 19%|█▉ | 384/2048 [05:24<20:09, 1.38it/s]
loss 1.74 accuracy 0.38 -- 56.66 + 57.75 + 505.47 + 4.92 = 624.81: 19%|█▉ | 385/2048 [05:24<19:31, 1.42it/s]
loss 2.34 accuracy 0.19 -- 163.98 + 57.11 + 500.71 + 4.93 = 726.73: 19%|█▉ | 385/2048 [05:25<19:31, 1.42it/s]
loss 2.34 accuracy 0.19 -- 163.98 + 57.11 + 500.71 + 4.93 = 726.73: 19%|█▉ | 386/2048 [05:25<19:55, 1.39it/s]
loss 2.11 accuracy 0.25 -- 56.34 + 172.12 + 510.85 + 4.95 = 744.27: 19%|█▉ | 386/2048 [05:25<19:55, 1.39it/s]
loss 2.11 accuracy 0.25 -- 56.34 + 172.12 + 510.85 + 4.95 = 744.27: 19%|█▉ | 387/2048 [05:25<20:20, 1.36it/s]
loss 1.95 accuracy 0.25 -- 57.09 + 56.75 + 506.54 + 4.91 = 625.28: 19%|█▉ | 387/2048 [05:26<20:20, 1.36it/s]
loss 1.95 accuracy 0.25 -- 57.09 + 56.75 + 506.54 + 4.91 = 625.28: 19%|█▉ | 388/2048 [05:26<19:39, 1.41it/s]
loss 2.23 accuracy 0.38 -- 169.28 + 57.46 + 506.65 + 4.92 = 738.31: 19%|█▉ | 388/2048 [05:27<19:39, 1.41it/s]
loss 2.23 accuracy 0.38 -- 169.28 + 57.46 + 506.65 + 4.92 = 738.31: 19%|█▉ | 389/2048 [05:27<20:06, 1.38it/s]
loss 1.79 accuracy 0.38 -- 56.72 + 57.43 + 635.45 + 4.93 = 754.52: 19%|█▉ | 389/2048 [05:28<20:06, 1.38it/s]
loss 1.79 accuracy 0.38 -- 56.72 + 57.43 + 635.45 + 4.93 = 754.52: 19%|█▉ | 390/2048 [05:28<20:32, 1.34it/s]
loss 2.49 accuracy 0.31 -- 57.16 + 56.84 + 515.18 + 4.91 = 634.09: 19%|█▉ | 390/2048 [05:28<20:32, 1.34it/s]
loss 2.49 accuracy 0.31 -- 57.16 + 56.84 + 515.18 + 4.91 = 634.09: 19%|█▉ | 391/2048 [05:28<19:51, 1.39it/s]
loss 1.96 accuracy 0.19 -- 57.34 + 58.26 + 631.39 + 4.89 = 751.88: 19%|█▉ | 391/2048 [05:29<19:51, 1.39it/s]
loss 1.96 accuracy 0.19 -- 57.34 + 58.26 + 631.39 + 4.89 = 751.88: 19%|█▉ | 392/2048 [05:29<20:20, 1.36it/s]
loss 2.19 accuracy 0.19 -- 57.60 + 56.70 + 512.06 + 4.93 = 631.28: 19%|█▉ | 392/2048 [05:30<20:20, 1.36it/s]
loss 2.19 accuracy 0.19 -- 57.60 + 56.70 + 512.06 + 4.93 = 631.28: 19%|█▉ | 393/2048 [05:30<19:40, 1.40it/s]
loss 1.77 accuracy 0.31 -- 56.72 + 171.34 + 511.50 + 4.94 = 744.50: 19%|█▉ | 393/2048 [05:30<19:40, 1.40it/s]
loss 1.77 accuracy 0.31 -- 56.72 + 171.34 + 511.50 + 4.94 = 744.50: 19%|█▉ | 394/2048 [05:30<20:09, 1.37it/s]
loss 1.72 accuracy 0.38 -- 56.49 + 56.72 + 508.94 + 4.91 = 627.06: 19%|█▉ | 394/2048 [05:31<20:09, 1.37it/s]
loss 1.72 accuracy 0.38 -- 56.49 + 56.72 + 508.94 + 4.91 = 627.06: 19%|█▉ | 395/2048 [05:31<19:30, 1.41it/s]
loss 1.99 accuracy 0.31 -- 57.38 + 56.50 + 507.55 + 4.93 = 626.36: 19%|█▉ | 395/2048 [05:32<19:30, 1.41it/s]
loss 1.99 accuracy 0.31 -- 57.38 + 56.50 + 507.55 + 4.93 = 626.36: 19%|█▉ | 396/2048 [05:32<19:58, 1.38it/s]
loss 2.34 accuracy 0.06 -- 56.74 + 57.32 + 505.39 + 4.89 = 624.34: 19%|█▉ | 396/2048 [05:33<19:58, 1.38it/s]
loss 2.34 accuracy 0.06 -- 56.74 + 57.32 + 505.39 + 4.89 = 624.34: 19%|█▉ | 397/2048 [05:33<19:21, 1.42it/s]
loss 2.08 accuracy 0.31 -- 164.21 + 57.26 + 499.96 + 4.92 = 726.35: 19%|█▉ | 397/2048 [05:33<19:21, 1.42it/s]
loss 2.08 accuracy 0.31 -- 164.21 + 57.26 + 499.96 + 4.92 = 726.35: 19%|█▉ | 398/2048 [05:33<19:45, 1.39it/s]
loss 2.04 accuracy 0.25 -- 56.15 + 171.81 + 511.96 + 4.92 = 744.84: 19%|█▉ | 398/2048 [05:34<19:45, 1.39it/s]
loss 2.04 accuracy 0.25 -- 56.15 + 171.81 + 511.96 + 4.92 = 744.84: 19%|█▉ | 399/2048 [05:34<20:29, 1.34it/s]
loss 2.87 accuracy 0.12 -- 57.12 + 56.74 + 506.21 + 4.94 = 625.01: 19%|█▉ | 399/2048 [05:35<20:29, 1.34it/s]
loss 2.87 accuracy 0.12 -- 57.12 + 56.74 + 506.21 + 4.94 = 625.01: 20%|█▉ | 400/2048 [05:35<19:42, 1.39it/s]
loss 2.19 accuracy 0.31 -- 168.22 + 57.38 + 507.09 + 4.94 = 737.63: 20%|█▉ | 400/2048 [05:36<19:42, 1.39it/s]
loss 2.19 accuracy 0.31 -- 168.22 + 57.38 + 507.09 + 4.94 = 737.63: 20%|█▉ | 401/2048 [05:36<20:05, 1.37it/s]
loss 1.66 accuracy 0.44 -- 56.62 + 57.80 + 637.74 + 4.95 = 757.11: 20%|█▉ | 401/2048 [05:36<20:05, 1.37it/s]
loss 1.66 accuracy 0.44 -- 56.62 + 57.80 + 637.74 + 4.95 = 757.11: 20%|█▉ | 402/2048 [05:36<20:30, 1.34it/s]
loss 2.80 accuracy 0.06 -- 57.51 + 57.18 + 515.84 + 4.91 = 635.44: 20%|█▉ | 402/2048 [05:37<20:30, 1.34it/s]
loss 2.80 accuracy 0.06 -- 57.51 + 57.18 + 515.84 + 4.91 = 635.44: 20%|█▉ | 403/2048 [05:37<19:48, 1.38it/s]
loss 1.66 accuracy 0.50 -- 56.68 + 57.32 + 628.46 + 4.89 = 747.35: 20%|█▉ | 403/2048 [05:38<19:48, 1.38it/s]
loss 1.66 accuracy 0.50 -- 56.68 + 57.32 + 628.46 + 4.89 = 747.35: 20%|█▉ | 404/2048 [05:38<20:13, 1.36it/s]
loss 2.40 accuracy 0.12 -- 56.86 + 56.46 + 510.57 + 4.94 = 628.83: 20%|█▉ | 404/2048 [05:38<20:13, 1.36it/s]
loss 2.40 accuracy 0.12 -- 56.86 + 56.46 + 510.57 + 4.94 = 628.83: 20%|█▉ | 405/2048 [05:38<19:32, 1.40it/s]
loss 2.19 accuracy 0.12 -- 56.97 + 171.66 + 511.12 + 4.95 = 744.71: 20%|█▉ | 405/2048 [05:39<19:32, 1.40it/s]
loss 2.19 accuracy 0.12 -- 56.97 + 171.66 + 511.12 + 4.95 = 744.71: 20%|█▉ | 406/2048 [05:39<20:17, 1.35it/s]
loss 2.01 accuracy 0.25 -- 56.87 + 56.63 + 509.33 + 4.93 = 627.75: 20%|█▉ | 406/2048 [05:40<20:17, 1.35it/s]
loss 2.01 accuracy 0.25 -- 56.87 + 56.63 + 509.33 + 4.93 = 627.75: 20%|█▉ | 407/2048 [05:40<19:34, 1.40it/s]
loss 2.30 accuracy 0.19 -- 57.09 + 56.82 + 509.81 + 4.94 = 628.66: 20%|█▉ | 407/2048 [05:41<19:34, 1.40it/s]
loss 2.30 accuracy 0.19 -- 57.09 + 56.82 + 509.81 + 4.94 = 628.66: 20%|█▉ | 408/2048 [05:41<19:59, 1.37it/s]
loss 1.97 accuracy 0.19 -- 56.54 + 57.57 + 504.41 + 4.95 = 623.46: 20%|█▉ | 408/2048 [05:41<19:59, 1.37it/s]
loss 1.97 accuracy 0.19 -- 56.54 + 57.57 + 504.41 + 4.95 = 623.46: 20%|█▉ | 409/2048 [05:41<19:19, 1.41it/s]
loss 2.04 accuracy 0.19 -- 164.06 + 57.33 + 499.20 + 4.94 = 725.54: 20%|█▉ | 409/2048 [05:42<19:19, 1.41it/s]
loss 2.04 accuracy 0.19 -- 164.06 + 57.33 + 499.20 + 4.94 = 725.54: 20%|██ | 410/2048 [05:42<19:40, 1.39it/s]
loss 2.54 accuracy 0.06 -- 56.13 + 172.33 + 510.93 + 4.89 = 744.27: 20%|██ | 410/2048 [05:43<19:40, 1.39it/s]
loss 2.54 accuracy 0.06 -- 56.13 + 172.33 + 510.93 + 4.89 = 744.27: 20%|██ | 411/2048 [05:43<20:05, 1.36it/s]
loss 2.37 accuracy 0.25 -- 57.20 + 56.84 + 509.12 + 4.92 = 628.08: 20%|██ | 411/2048 [05:43<20:05, 1.36it/s]
loss 2.37 accuracy 0.25 -- 57.20 + 56.84 + 509.12 + 4.92 = 628.08: 20%|██ | 412/2048 [05:43<19:24, 1.40it/s]
loss 2.81 accuracy 0.00 -- 168.13 + 57.27 + 506.44 + 4.96 = 736.80: 20%|██ | 412/2048 [05:44<19:24, 1.40it/s]
loss 2.81 accuracy 0.00 -- 168.13 + 57.27 + 506.44 + 4.96 = 736.80: 20%|██ | 413/2048 [05:44<20:07, 1.35it/s]
loss 2.43 accuracy 0.38 -- 57.29 + 58.13 + 636.60 + 4.91 = 756.94: 20%|██ | 413/2048 [05:45<20:07, 1.35it/s]
loss 2.43 accuracy 0.38 -- 57.29 + 58.13 + 636.60 + 4.91 = 756.94: 20%|██ | 414/2048 [05:45<20:28, 1.33it/s]
loss 2.02 accuracy 0.25 -- 57.59 + 56.41 + 515.86 + 4.88 = 634.74: 20%|██ | 414/2048 [05:46<20:28, 1.33it/s]
loss 2.02 accuracy 0.25 -- 57.59 + 56.41 + 515.86 + 4.88 = 634.74: 20%|██ | 415/2048 [05:46<19:43, 1.38it/s]
loss 2.88 accuracy 0.19 -- 56.64 + 57.75 + 630.37 + 4.94 = 749.69: 20%|██ | 415/2048 [05:46<19:43, 1.38it/s]
loss 2.88 accuracy 0.19 -- 56.64 + 57.75 + 630.37 + 4.94 = 749.69: 20%|██ | 416/2048 [05:46<20:08, 1.35it/s]
loss 2.02 accuracy 0.31 -- 56.97 + 56.56 + 512.42 + 4.93 = 630.89: 20%|██ | 416/2048 [05:47<20:08, 1.35it/s]
loss 2.02 accuracy 0.31 -- 56.97 + 56.56 + 512.42 + 4.93 = 630.89: 20%|██ | 417/2048 [05:47<19:27, 1.40it/s]
loss 2.18 accuracy 0.12 -- 56.61 + 171.84 + 512.49 + 4.95 = 745.89: 20%|██ | 417/2048 [05:48<19:27, 1.40it/s]
loss 2.18 accuracy 0.12 -- 56.61 + 171.84 + 512.49 + 4.95 = 745.89: 20%|██ | 418/2048 [05:48<19:55, 1.36it/s]
loss 2.32 accuracy 0.25 -- 56.58 + 57.08 + 510.75 + 4.93 = 629.34: 20%|██ | 418/2048 [05:49<19:55, 1.36it/s]
loss 2.32 accuracy 0.25 -- 56.58 + 57.08 + 510.75 + 4.93 = 629.34: 20%|██ | 419/2048 [05:49<19:17, 1.41it/s]
loss 2.29 accuracy 0.12 -- 57.46 + 56.70 + 507.43 + 4.93 = 626.52: 20%|██ | 419/2048 [05:49<19:17, 1.41it/s]
loss 2.29 accuracy 0.12 -- 57.46 + 56.70 + 507.43 + 4.93 = 626.52: 21%|██ | 420/2048 [05:49<20:01, 1.35it/s]
loss 2.14 accuracy 0.25 -- 56.26 + 57.62 + 504.86 + 4.92 = 623.66: 21%|██ | 420/2048 [05:50<20:01, 1.35it/s]
loss 2.14 accuracy 0.25 -- 56.26 + 57.62 + 504.86 + 4.92 = 623.66: 21%|██ | 421/2048 [05:50<19:18, 1.40it/s]
loss 1.79 accuracy 0.44 -- 163.98 + 56.96 + 499.85 + 4.90 = 725.69: 21%|██ | 421/2048 [05:51<19:18, 1.40it/s]
loss 1.79 accuracy 0.44 -- 163.98 + 56.96 + 499.85 + 4.90 = 725.69: 21%|██ | 422/2048 [05:51<19:37, 1.38it/s]
loss 2.08 accuracy 0.31 -- 56.27 + 172.36 + 511.69 + 4.91 = 745.23: 21%|██ | 422/2048 [05:52<19:37, 1.38it/s]
loss 2.08 accuracy 0.31 -- 56.27 + 172.36 + 511.69 + 4.91 = 745.23: 21%|██ | 423/2048 [05:52<20:01, 1.35it/s]
loss 2.12 accuracy 0.19 -- 57.18 + 56.77 + 508.57 + 4.95 = 627.48: 21%|██ | 423/2048 [05:52<20:01, 1.35it/s]
loss 2.12 accuracy 0.19 -- 57.18 + 56.77 + 508.57 + 4.95 = 627.48: 21%|██ | 424/2048 [05:52<19:19, 1.40it/s]
loss 2.41 accuracy 0.12 -- 169.07 + 57.75 + 505.58 + 4.92 = 737.32: 21%|██ | 424/2048 [05:53<19:19, 1.40it/s]
loss 2.41 accuracy 0.12 -- 169.07 + 57.75 + 505.58 + 4.92 = 737.32: 21%|██ | 425/2048 [05:53<19:43, 1.37it/s]
loss 2.08 accuracy 0.25 -- 56.85 + 57.58 + 638.67 + 4.90 = 758.00: 21%|██ | 425/2048 [05:54<19:43, 1.37it/s]
loss 2.08 accuracy 0.25 -- 56.85 + 57.58 + 638.67 + 4.90 = 758.00: 21%|██ | 426/2048 [05:54<20:09, 1.34it/s]
loss 2.10 accuracy 0.12 -- 57.18 + 56.91 + 515.17 + 4.93 = 634.18: 21%|██ | 426/2048 [05:54<20:09, 1.34it/s]
loss 2.10 accuracy 0.12 -- 57.18 + 56.91 + 515.17 + 4.93 = 634.18: 21%|██ | 427/2048 [05:54<19:28, 1.39it/s]
loss 2.35 accuracy 0.19 -- 56.95 + 57.70 + 631.33 + 4.91 = 750.90: 21%|██ | 427/2048 [05:55<19:28, 1.39it/s]
loss 2.35 accuracy 0.19 -- 56.95 + 57.70 + 631.33 + 4.91 = 750.90: 21%|██ | 428/2048 [05:55<19:55, 1.36it/s]
loss 1.74 accuracy 0.44 -- 57.38 + 56.64 + 514.41 + 4.93 = 633.36: 21%|██ | 428/2048 [05:56<19:55, 1.36it/s]
loss 1.74 accuracy 0.44 -- 57.38 + 56.64 + 514.41 + 4.93 = 633.36: 21%|██ | 429/2048 [05:56<19:16, 1.40it/s]
loss 2.39 accuracy 0.25 -- 56.30 + 171.58 + 513.91 + 4.95 = 746.75: 21%|██ | 429/2048 [05:57<19:16, 1.40it/s]
loss 2.39 accuracy 0.25 -- 56.30 + 171.58 + 513.91 + 4.95 = 746.75: 21%|██ | 430/2048 [05:57<19:44, 1.37it/s]
loss 2.13 accuracy 0.38 -- 56.55 + 56.47 + 508.77 + 4.95 = 626.74: 21%|██ | 430/2048 [05:57<19:44, 1.37it/s]
loss 2.13 accuracy 0.38 -- 56.55 + 56.47 + 508.77 + 4.95 = 626.74: 21%|██ | 431/2048 [05:57<19:06, 1.41it/s]
loss 1.89 accuracy 0.38 -- 57.07 + 56.90 + 509.17 + 4.91 = 628.05: 21%|██ | 431/2048 [05:58<19:06, 1.41it/s]
loss 1.89 accuracy 0.38 -- 57.07 + 56.90 + 509.17 + 4.91 = 628.05: 21%|██ | 432/2048 [05:58<19:33, 1.38it/s]
loss 1.86 accuracy 0.31 -- 56.33 + 57.58 + 505.17 + 4.96 = 624.03: 21%|██ | 432/2048 [05:59<19:33, 1.38it/s]
loss 1.86 accuracy 0.31 -- 56.33 + 57.58 + 505.17 + 4.96 = 624.03: 21%|██ | 433/2048 [05:59<18:56, 1.42it/s]
loss 2.00 accuracy 0.19 -- 163.80 + 57.36 + 499.65 + 4.91 = 725.72: 21%|██ | 433/2048 [05:59<18:56, 1.42it/s]
loss 2.00 accuracy 0.19 -- 163.80 + 57.36 + 499.65 + 4.91 = 725.72: 21%|██ | 434/2048 [05:59<19:19, 1.39it/s]
loss 1.81 accuracy 0.25 -- 56.69 + 171.92 + 512.99 + 4.93 = 746.53: 21%|██ | 434/2048 [06:00<19:19, 1.39it/s]
loss 1.81 accuracy 0.25 -- 56.69 + 171.92 + 512.99 + 4.93 = 746.53: 21%|██ | 435/2048 [06:00<19:45, 1.36it/s]
loss 2.04 accuracy 0.25 -- 57.48 + 56.87 + 507.87 + 4.93 = 627.15: 21%|██ | 435/2048 [06:01<19:45, 1.36it/s]
loss 2.04 accuracy 0.25 -- 57.48 + 56.87 + 507.87 + 4.93 = 627.15: 21%|██▏ | 436/2048 [06:01<19:06, 1.41it/s]
loss 2.20 accuracy 0.06 -- 168.72 + 57.84 + 508.53 + 4.93 = 740.02: 21%|██▏ | 436/2048 [06:02<19:06, 1.41it/s]
loss 2.20 accuracy 0.06 -- 168.72 + 57.84 + 508.53 + 4.93 = 740.02: 21%|██▏ | 437/2048 [06:02<19:32, 1.37it/s]
loss 2.25 accuracy 0.19 -- 56.76 + 57.28 + 637.45 + 4.92 = 756.40: 21%|██▏ | 437/2048 [06:02<19:32, 1.37it/s]
loss 2.25 accuracy 0.19 -- 56.76 + 57.28 + 637.45 + 4.92 = 756.40: 21%|██▏ | 438/2048 [06:02<19:58, 1.34it/s]
loss 2.09 accuracy 0.31 -- 57.14 + 56.60 + 518.27 + 4.93 = 636.95: 21%|██▏ | 438/2048 [06:03<19:58, 1.34it/s]
loss 2.09 accuracy 0.31 -- 57.14 + 56.60 + 518.27 + 4.93 = 636.95: 21%|██▏ | 439/2048 [06:03<19:19, 1.39it/s]
loss 1.98 accuracy 0.19 -- 56.48 + 57.46 + 631.64 + 4.94 = 750.52: 21%|██▏ | 439/2048 [06:04<19:19, 1.39it/s]
loss 1.98 accuracy 0.19 -- 56.48 + 57.46 + 631.64 + 4.94 = 750.52: 21%|██▏ | 440/2048 [06:04<19:46, 1.36it/s]
loss 2.19 accuracy 0.19 -- 57.01 + 56.50 + 512.87 + 4.91 = 631.29: 21%|██▏ | 440/2048 [06:05<19:46, 1.36it/s]
loss 2.19 accuracy 0.19 -- 57.01 + 56.50 + 512.87 + 4.91 = 631.29: 22%|██▏ | 441/2048 [06:05<19:07, 1.40it/s]
loss 2.01 accuracy 0.25 -- 56.74 + 171.28 + 511.57 + 4.92 = 744.51: 22%|██▏ | 441/2048 [06:05<19:07, 1.40it/s]
loss 2.01 accuracy 0.25 -- 56.74 + 171.28 + 511.57 + 4.92 = 744.51: 22%|██▏ | 442/2048 [06:05<19:34, 1.37it/s]
loss 2.06 accuracy 0.25 -- 56.68 + 56.80 + 509.52 + 4.94 = 627.94: 22%|██▏ | 442/2048 [06:06<19:34, 1.37it/s]
loss 2.06 accuracy 0.25 -- 56.68 + 56.80 + 509.52 + 4.94 = 627.94: 22%|██▏ | 443/2048 [06:06<18:57, 1.41it/s]
loss 2.70 accuracy 0.38 -- 57.20 + 56.66 + 507.58 + 4.91 = 626.36: 22%|██▏ | 443/2048 [06:07<18:57, 1.41it/s]
loss 2.70 accuracy 0.38 -- 57.20 + 56.66 + 507.58 + 4.91 = 626.36: 22%|██▏ | 444/2048 [06:07<19:24, 1.38it/s]
loss 2.10 accuracy 0.12 -- 56.53 + 57.76 + 504.43 + 4.92 = 623.65: 22%|██▏ | 444/2048 [06:07<19:24, 1.38it/s]
loss 2.10 accuracy 0.12 -- 56.53 + 57.76 + 504.43 + 4.92 = 623.65: 22%|██▏ | 445/2048 [06:07<18:47, 1.42it/s]
loss 2.47 accuracy 0.19 -- 163.85 + 57.28 + 499.32 + 4.93 = 725.38: 22%|██▏ | 445/2048 [06:08<18:47, 1.42it/s]
loss 2.47 accuracy 0.19 -- 163.85 + 57.28 + 499.32 + 4.93 = 725.38: 22%|██▏ | 446/2048 [06:08<19:10, 1.39it/s]
loss 1.63 accuracy 0.56 -- 56.71 + 172.32 + 514.26 + 4.94 = 748.23: 22%|██▏ | 446/2048 [06:09<19:10, 1.39it/s]
loss 1.63 accuracy 0.56 -- 56.71 + 172.32 + 514.26 + 4.94 = 748.23: 22%|██▏ | 447/2048 [06:09<19:37, 1.36it/s]
loss 2.06 accuracy 0.31 -- 57.26 + 56.82 + 507.24 + 4.91 = 626.23: 22%|██▏ | 447/2048 [06:10<19:37, 1.36it/s]
loss 2.06 accuracy 0.31 -- 57.26 + 56.82 + 507.24 + 4.91 = 626.23: 22%|██▏ | 448/2048 [06:10<18:57, 1.41it/s]
loss 2.51 accuracy 0.06 -- 168.00 + 57.79 + 507.40 + 4.94 = 738.14: 22%|██▏ | 448/2048 [06:10<18:57, 1.41it/s]
loss 2.51 accuracy 0.06 -- 168.00 + 57.79 + 507.40 + 4.94 = 738.14: 22%|██▏ | 449/2048 [06:10<19:23, 1.37it/s]
loss 2.75 accuracy 0.06 -- 56.63 + 57.72 + 639.72 + 4.93 = 759.00: 22%|██▏ | 449/2048 [06:11<19:23, 1.37it/s]
loss 2.75 accuracy 0.06 -- 56.63 + 57.72 + 639.72 + 4.93 = 759.00: 22%|██▏ | 450/2048 [06:11<19:50, 1.34it/s]
loss 2.07 accuracy 0.06 -- 57.46 + 56.96 + 515.64 + 4.93 = 634.99: 22%|██▏ | 450/2048 [06:12<19:50, 1.34it/s]
loss 2.07 accuracy 0.06 -- 57.46 + 56.96 + 515.64 + 4.93 = 634.99: 22%|██▏ | 451/2048 [06:12<19:10, 1.39it/s]
loss 2.79 accuracy 0.19 -- 56.53 + 57.63 + 632.74 + 4.96 = 751.87: 22%|██▏ | 451/2048 [06:13<19:10, 1.39it/s]
loss 2.79 accuracy 0.19 -- 56.53 + 57.63 + 632.74 + 4.96 = 751.87: 22%|██▏ | 452/2048 [06:13<19:37, 1.36it/s]
loss 2.31 accuracy 0.25 -- 57.33 + 56.69 + 512.52 + 4.93 = 631.48: 22%|██▏ | 452/2048 [06:13<19:37, 1.36it/s]
loss 2.31 accuracy 0.25 -- 57.33 + 56.69 + 512.52 + 4.93 = 631.48: 22%|██▏ | 453/2048 [06:13<18:59, 1.40it/s]
loss 2.09 accuracy 0.12 -- 56.73 + 185.80 + 511.48 + 4.94 = 758.95: 22%|██▏ | 453/2048 [06:14<18:59, 1.40it/s]
loss 2.09 accuracy 0.12 -- 56.73 + 185.80 + 511.48 + 4.94 = 758.95: 22%|██▏ | 454/2048 [06:14<19:32, 1.36it/s]
loss 2.50 accuracy 0.19 -- 57.43 + 57.43 + 508.14 + 4.89 = 627.89: 22%|██▏ | 454/2048 [06:15<19:32, 1.36it/s]
loss 2.50 accuracy 0.19 -- 57.43 + 57.43 + 508.14 + 4.89 = 627.89: 22%|██▏ | 455/2048 [06:15<18:54, 1.40it/s]
loss 2.06 accuracy 0.38 -- 56.95 + 56.47 + 508.40 + 4.91 = 626.73: 22%|██▏ | 455/2048 [06:15<18:54, 1.40it/s]
loss 2.06 accuracy 0.38 -- 56.95 + 56.47 + 508.40 + 4.91 = 626.73: 22%|██▏ | 456/2048 [06:15<19:20, 1.37it/s]
loss 2.00 accuracy 0.50 -- 56.04 + 57.45 + 503.69 + 4.90 = 622.09: 22%|██▏ | 456/2048 [06:16<19:20, 1.37it/s]
loss 2.00 accuracy 0.50 -- 56.04 + 57.45 + 503.69 + 4.90 = 622.09: 22%|██▏ | 457/2048 [06:16<18:42, 1.42it/s]
loss 1.86 accuracy 0.38 -- 163.91 + 57.21 + 498.78 + 4.89 = 724.80: 22%|██▏ | 457/2048 [06:17<18:42, 1.42it/s]
loss 1.86 accuracy 0.38 -- 163.91 + 57.21 + 498.78 + 4.89 = 724.80: 22%|██▏ | 458/2048 [06:17<19:03, 1.39it/s]
loss 2.12 accuracy 0.38 -- 56.31 + 172.53 + 509.65 + 4.92 = 743.40: 22%|██▏ | 458/2048 [06:18<19:03, 1.39it/s]
loss 2.12 accuracy 0.38 -- 56.31 + 172.53 + 509.65 + 4.92 = 743.40: 22%|██▏ | 459/2048 [06:18<19:27, 1.36it/s]
loss 2.13 accuracy 0.25 -- 56.90 + 56.34 + 507.11 + 4.89 = 625.25: 22%|██▏ | 459/2048 [06:18<19:27, 1.36it/s]
loss 2.13 accuracy 0.25 -- 56.90 + 56.34 + 507.11 + 4.89 = 625.25: 22%|██▏ | 460/2048 [06:18<18:47, 1.41it/s]
loss 1.97 accuracy 0.38 -- 168.91 + 57.53 + 506.28 + 4.89 = 737.60: 22%|██▏ | 460/2048 [06:19<18:47, 1.41it/s]
loss 1.97 accuracy 0.38 -- 168.91 + 57.53 + 506.28 + 4.89 = 737.60: 23%|██▎ | 461/2048 [06:19<19:13, 1.38it/s]
loss 1.95 accuracy 0.25 -- 56.66 + 57.53 + 634.33 + 4.91 = 753.43: 23%|██▎ | 461/2048 [06:20<19:13, 1.38it/s]
loss 1.95 accuracy 0.25 -- 56.66 + 57.53 + 634.33 + 4.91 = 753.43: 23%|██▎ | 462/2048 [06:20<19:38, 1.35it/s]
loss 2.11 accuracy 0.19 -- 57.00 + 56.73 + 515.19 + 4.90 = 633.82: 23%|██▎ | 462/2048 [06:20<19:38, 1.35it/s]
loss 2.11 accuracy 0.19 -- 57.00 + 56.73 + 515.19 + 4.90 = 633.82: 23%|██▎ | 463/2048 [06:20<18:58, 1.39it/s]
loss 2.01 accuracy 0.19 -- 56.35 + 57.33 + 631.29 + 4.90 = 749.88: 23%|██▎ | 463/2048 [06:21<18:58, 1.39it/s]
loss 2.01 accuracy 0.19 -- 56.35 + 57.33 + 631.29 + 4.90 = 749.88: 23%|██▎ | 464/2048 [06:21<19:25, 1.36it/s]
loss 1.89 accuracy 0.31 -- 56.97 + 56.42 + 510.67 + 4.88 = 628.95: 23%|██▎ | 464/2048 [06:22<19:25, 1.36it/s]
loss 1.89 accuracy 0.31 -- 56.97 + 56.42 + 510.67 + 4.88 = 628.95: 23%|██▎ | 465/2048 [06:22<18:47, 1.40it/s]
loss 1.92 accuracy 0.38 -- 56.73 + 171.95 + 510.25 + 4.91 = 743.85: 23%|██▎ | 465/2048 [06:23<18:47, 1.40it/s]
loss 1.92 accuracy 0.38 -- 56.73 + 171.95 + 510.25 + 4.91 = 743.85: 23%|██▎ | 466/2048 [06:23<19:14, 1.37it/s]
loss 1.68 accuracy 0.38 -- 56.71 + 56.50 + 508.54 + 4.88 = 626.64: 23%|██▎ | 466/2048 [06:23<19:14, 1.37it/s]
loss 1.68 accuracy 0.38 -- 56.71 + 56.50 + 508.54 + 4.88 = 626.64: 23%|██▎ | 467/2048 [06:23<18:37, 1.41it/s]
loss 1.68 accuracy 0.31 -- 56.95 + 56.71 + 508.34 + 4.93 = 626.93: 23%|██▎ | 467/2048 [06:24<18:37, 1.41it/s]
loss 1.68 accuracy 0.31 -- 56.95 + 56.71 + 508.34 + 4.93 = 626.93: 23%|██▎ | 468/2048 [06:24<19:05, 1.38it/s]
loss 2.16 accuracy 0.19 -- 56.34 + 57.68 + 506.89 + 4.91 = 625.82: 23%|██▎ | 468/2048 [06:25<19:05, 1.38it/s]
loss 2.16 accuracy 0.19 -- 56.34 + 57.68 + 506.89 + 4.91 = 625.82: 23%|██▎ | 469/2048 [06:25<18:30, 1.42it/s]
loss 1.83 accuracy 0.31 -- 163.60 + 57.42 + 498.30 + 4.91 = 724.22: 23%|██▎ | 469/2048 [06:25<18:30, 1.42it/s]
loss 1.83 accuracy 0.31 -- 163.60 + 57.42 + 498.30 + 4.91 = 724.22: 23%|██▎ | 470/2048 [06:25<18:52, 1.39it/s]
loss 2.06 accuracy 0.25 -- 56.29 + 171.57 + 509.96 + 4.92 = 742.73: 23%|██▎ | 470/2048 [06:26<18:52, 1.39it/s]
loss 2.06 accuracy 0.25 -- 56.29 + 171.57 + 509.96 + 4.92 = 742.73: 23%|██▎ | 471/2048 [06:26<19:33, 1.34it/s]
loss 1.99 accuracy 0.19 -- 57.30 + 56.36 + 506.39 + 4.96 = 625.00: 23%|██▎ | 471/2048 [06:27<19:33, 1.34it/s]
loss 1.99 accuracy 0.19 -- 57.30 + 56.36 + 506.39 + 4.96 = 625.00: 23%|██▎ | 472/2048 [06:27<18:49, 1.40it/s]
loss 1.88 accuracy 0.25 -- 170.36 + 57.60 + 506.43 + 4.88 = 739.27: 23%|██▎ | 472/2048 [06:28<18:49, 1.40it/s]
loss 1.88 accuracy 0.25 -- 170.36 + 57.60 + 506.43 + 4.88 = 739.27: 23%|██▎ | 473/2048 [06:28<19:12, 1.37it/s]
loss 2.26 accuracy 0.19 -- 56.48 + 57.37 + 637.92 + 4.89 = 756.67: 23%|██▎ | 473/2048 [06:28<19:12, 1.37it/s]
loss 2.26 accuracy 0.19 -- 56.48 + 57.37 + 637.92 + 4.89 = 756.67: 23%|██▎ | 474/2048 [06:28<19:36, 1.34it/s]
loss 1.83 accuracy 0.38 -- 57.32 + 56.86 + 514.21 + 4.87 = 633.27: 23%|██▎ | 474/2048 [06:29<19:36, 1.34it/s]
loss 1.83 accuracy 0.38 -- 57.32 + 56.86 + 514.21 + 4.87 = 633.27: 23%|██▎ | 475/2048 [06:29<18:55, 1.39it/s]
loss 1.88 accuracy 0.44 -- 56.39 + 57.61 + 629.80 + 4.92 = 748.72: 23%|██▎ | 475/2048 [06:30<18:55, 1.39it/s]
loss 1.88 accuracy 0.44 -- 56.39 + 57.61 + 629.80 + 4.92 = 748.72: 23%|██▎ | 476/2048 [06:30<19:19, 1.36it/s]
loss 1.90 accuracy 0.31 -- 57.63 + 57.04 + 510.73 + 4.89 = 630.29: 23%|██▎ | 476/2048 [06:31<19:19, 1.36it/s]
loss 1.90 accuracy 0.31 -- 57.63 + 57.04 + 510.73 + 4.89 = 630.29: 23%|██▎ | 477/2048 [06:31<18:41, 1.40it/s]
loss 2.21 accuracy 0.25 -- 56.64 + 171.05 + 510.31 + 4.90 = 742.89: 23%|██▎ | 477/2048 [06:31<18:41, 1.40it/s]
loss 2.21 accuracy 0.25 -- 56.64 + 171.05 + 510.31 + 4.90 = 742.89: 23%|██▎ | 478/2048 [06:31<19:23, 1.35it/s]
loss 2.60 accuracy 0.19 -- 56.39 + 56.37 + 508.48 + 4.90 = 626.14: 23%|██▎ | 478/2048 [06:32<19:23, 1.35it/s]
loss 2.60 accuracy 0.19 -- 56.39 + 56.37 + 508.48 + 4.90 = 626.14: 23%|██▎ | 479/2048 [06:32<18:41, 1.40it/s]
loss 1.80 accuracy 0.12 -- 56.97 + 56.73 + 508.09 + 4.88 = 626.67: 23%|██▎ | 479/2048 [06:33<18:41, 1.40it/s]
loss 1.80 accuracy 0.12 -- 56.97 + 56.73 + 508.09 + 4.88 = 626.67: 23%|██▎ | 480/2048 [06:33<19:05, 1.37it/s]
loss 2.42 accuracy 0.19 -- 56.44 + 57.42 + 503.49 + 4.89 = 622.24: 23%|██▎ | 480/2048 [06:33<19:05, 1.37it/s]
loss 2.42 accuracy 0.19 -- 56.44 + 57.42 + 503.49 + 4.89 = 622.24: 23%|██▎ | 481/2048 [06:33<18:26, 1.42it/s]
loss 2.24 accuracy 0.25 -- 163.47 + 57.00 + 498.69 + 4.93 = 724.09: 23%|██▎ | 481/2048 [06:34<18:26, 1.42it/s]
loss 2.24 accuracy 0.25 -- 163.47 + 57.00 + 498.69 + 4.93 = 724.09: 24%|██▎ | 482/2048 [06:34<18:47, 1.39it/s]
loss 2.12 accuracy 0.19 -- 56.28 + 172.26 + 512.25 + 4.87 = 745.66: 24%|██▎ | 482/2048 [06:35<18:47, 1.39it/s]
loss 2.12 accuracy 0.19 -- 56.28 + 172.26 + 512.25 + 4.87 = 745.66: 24%|██▎ | 483/2048 [06:35<19:11, 1.36it/s]
loss 1.90 accuracy 0.31 -- 57.12 + 56.62 + 504.76 + 4.87 = 623.37: 24%|██▎ | 483/2048 [06:36<19:11, 1.36it/s]
loss 1.90 accuracy 0.31 -- 57.12 + 56.62 + 504.76 + 4.87 = 623.37: 24%|██▎ | 484/2048 [06:36<18:30, 1.41it/s]
loss 1.64 accuracy 0.44 -- 167.77 + 57.06 + 505.33 + 4.89 = 735.05: 24%|██▎ | 484/2048 [06:36<18:30, 1.41it/s]
loss 1.64 accuracy 0.44 -- 167.77 + 57.06 + 505.33 + 4.89 = 735.05: 24%|██▎ | 485/2048 [06:36<19:11, 1.36it/s]
loss 2.03 accuracy 0.25 -- 56.64 + 57.55 + 632.17 + 4.87 = 751.23: 24%|██▎ | 485/2048 [06:37<19:11, 1.36it/s]
loss 2.03 accuracy 0.25 -- 56.64 + 57.55 + 632.17 + 4.87 = 751.23: 24%|██▎ | 486/2048 [06:37<19:30, 1.33it/s]
loss 2.08 accuracy 0.25 -- 57.08 + 56.72 + 512.28 + 4.89 = 630.97: 24%|██▎ | 486/2048 [06:38<19:30, 1.33it/s]
loss 2.08 accuracy 0.25 -- 57.08 + 56.72 + 512.28 + 4.89 = 630.97: 24%|██▍ | 487/2048 [06:38<18:46, 1.39it/s]
loss 2.79 accuracy 0.25 -- 56.26 + 57.31 + 627.79 + 4.89 = 746.25: 24%|██▍ | 487/2048 [06:39<18:46, 1.39it/s]
loss 2.79 accuracy 0.25 -- 56.26 + 57.31 + 627.79 + 4.89 = 746.25: 24%|██▍ | 488/2048 [06:39<19:10, 1.36it/s]
loss 1.96 accuracy 0.25 -- 57.35 + 56.59 + 508.65 + 4.87 = 627.46: 24%|██▍ | 488/2048 [06:39<19:10, 1.36it/s]
loss 1.96 accuracy 0.25 -- 57.35 + 56.59 + 508.65 + 4.87 = 627.46: 24%|██▍ | 489/2048 [06:39<18:30, 1.40it/s]
loss 1.79 accuracy 0.31 -- 56.68 + 171.58 + 509.03 + 4.88 = 742.17: 24%|██▍ | 489/2048 [06:40<18:30, 1.40it/s]
loss 1.79 accuracy 0.31 -- 56.68 + 171.58 + 509.03 + 4.88 = 742.17: 24%|██▍ | 490/2048 [06:40<18:56, 1.37it/s]
loss 1.79 accuracy 0.38 -- 56.32 + 56.83 + 508.21 + 4.88 = 626.25: 24%|██▍ | 490/2048 [06:41<18:56, 1.37it/s]
loss 1.79 accuracy 0.38 -- 56.32 + 56.83 + 508.21 + 4.88 = 626.25: 24%|██▍ | 491/2048 [06:41<18:20, 1.41it/s]
loss 2.10 accuracy 0.19 -- 57.03 + 56.78 + 507.50 + 4.87 = 626.18: 24%|██▍ | 491/2048 [06:42<18:20, 1.41it/s]
loss 2.10 accuracy 0.19 -- 57.03 + 56.78 + 507.50 + 4.87 = 626.18: 24%|██▍ | 492/2048 [06:42<19:03, 1.36it/s]
loss 2.37 accuracy 0.12 -- 56.36 + 57.35 + 502.85 + 4.87 = 621.43: 24%|██▍ | 492/2048 [06:42<19:03, 1.36it/s]
loss 2.37 accuracy 0.12 -- 56.36 + 57.35 + 502.85 + 4.87 = 621.43: 24%|██▍ | 493/2048 [06:42<18:22, 1.41it/s]
loss 1.86 accuracy 0.38 -- 162.86 + 56.78 + 495.88 + 4.89 = 720.41: 24%|██▍ | 493/2048 [06:43<18:22, 1.41it/s]
loss 1.86 accuracy 0.38 -- 162.86 + 56.78 + 495.88 + 4.89 = 720.41: 24%|██▍ | 494/2048 [06:43<18:40, 1.39it/s]
loss 2.28 accuracy 0.12 -- 55.81 + 171.85 + 509.29 + 4.87 = 741.83: 24%|██▍ | 494/2048 [06:44<18:40, 1.39it/s]
loss 2.28 accuracy 0.12 -- 55.81 + 171.85 + 509.29 + 4.87 = 741.83: 24%|██▍ | 495/2048 [06:44<19:02, 1.36it/s]
loss 1.92 accuracy 0.25 -- 56.71 + 56.62 + 504.79 + 4.90 = 623.02: 24%|██▍ | 495/2048 [06:44<19:02, 1.36it/s]
loss 1.92 accuracy 0.25 -- 56.71 + 56.62 + 504.79 + 4.90 = 623.02: 24%|██▍ | 496/2048 [06:44<18:21, 1.41it/s]
loss 2.41 accuracy 0.12 -- 168.09 + 57.14 + 505.20 + 4.84 = 735.26: 24%|██▍ | 496/2048 [06:45<18:21, 1.41it/s]
loss 2.41 accuracy 0.12 -- 168.09 + 57.14 + 505.20 + 4.84 = 735.26: 24%|██▍ | 497/2048 [06:45<18:45, 1.38it/s]
loss 1.64 accuracy 0.44 -- 56.21 + 56.98 + 631.85 + 4.85 = 749.88: 24%|██▍ | 497/2048 [06:46<18:45, 1.38it/s]
loss 1.64 accuracy 0.44 -- 56.21 + 56.98 + 631.85 + 4.85 = 749.88: 24%|██▍ | 498/2048 [06:46<19:08, 1.35it/s]
loss 2.49 accuracy 0.19 -- 57.04 + 56.29 + 511.37 + 4.85 = 629.55: 24%|██▍ | 498/2048 [06:47<19:08, 1.35it/s]
loss 2.49 accuracy 0.19 -- 57.04 + 56.29 + 511.37 + 4.85 = 629.55: 24%|██▍ | 499/2048 [06:47<18:45, 1.38it/s]
loss 1.61 accuracy 0.44 -- 56.31 + 57.35 + 627.44 + 4.83 = 745.92: 24%|██▍ | 499/2048 [06:47<18:45, 1.38it/s]
loss 1.61 accuracy 0.44 -- 56.31 + 57.35 + 627.44 + 4.83 = 745.92: 24%|██▍ | 500/2048 [06:47<19:06, 1.35it/s]
loss 2.44 accuracy 0.12 -- 57.16 + 56.49 + 509.96 + 4.85 = 628.47: 24%|██▍ | 500/2048 [06:48<19:06, 1.35it/s]
loss 2.44 accuracy 0.12 -- 57.16 + 56.49 + 509.96 + 4.85 = 628.47: 24%|██▍ | 501/2048 [06:48<18:26, 1.40it/s]
loss 1.84 accuracy 0.31 -- 56.27 + 171.71 + 508.52 + 4.85 = 741.35: 24%|██▍ | 501/2048 [06:49<18:26, 1.40it/s]
loss 1.84 accuracy 0.31 -- 56.27 + 171.71 + 508.52 + 4.85 = 741.35: 25%|██▍ | 502/2048 [06:49<18:50, 1.37it/s]
loss 2.20 accuracy 0.12 -- 56.12 + 56.63 + 505.56 + 4.82 = 623.15: 25%|██▍ | 502/2048 [06:49<18:50, 1.37it/s]
loss 2.20 accuracy 0.12 -- 56.12 + 56.63 + 505.56 + 4.82 = 623.15: 25%|██▍ | 503/2048 [06:49<18:12, 1.41it/s]
loss 2.28 accuracy 0.38 -- 56.67 + 56.43 + 504.60 + 4.84 = 622.54: 25%|██▍ | 503/2048 [06:50<18:12, 1.41it/s]
loss 2.28 accuracy 0.38 -- 56.67 + 56.43 + 504.60 + 4.84 = 622.54: 25%|██▍ | 504/2048 [06:50<18:37, 1.38it/s]
loss 2.27 accuracy 0.38 -- 56.32 + 57.17 + 500.86 + 4.84 = 619.20: 25%|██▍ | 504/2048 [06:51<18:37, 1.38it/s]
loss 2.27 accuracy 0.38 -- 56.32 + 57.17 + 500.86 + 4.84 = 619.20: 25%|██▍ | 505/2048 [06:51<18:00, 1.43it/s]
loss 1.61 accuracy 0.25 -- 162.72 + 56.95 + 496.47 + 4.85 = 720.99: 25%|██▍ | 505/2048 [06:52<18:00, 1.43it/s]
loss 1.61 accuracy 0.25 -- 162.72 + 56.95 + 496.47 + 4.85 = 720.99: 25%|██▍ | 506/2048 [06:52<18:22, 1.40it/s]
loss 2.30 accuracy 0.06 -- 55.94 + 171.66 + 509.60 + 4.83 = 742.03: 25%|██▍ | 506/2048 [06:52<18:22, 1.40it/s]
loss 2.30 accuracy 0.06 -- 55.94 + 171.66 + 509.60 + 4.83 = 742.03: 25%|██▍ | 507/2048 [06:52<19:03, 1.35it/s]
loss 1.92 accuracy 0.25 -- 56.99 + 56.66 + 503.60 + 4.84 = 622.09: 25%|██▍ | 507/2048 [06:53<19:03, 1.35it/s]
loss 1.92 accuracy 0.25 -- 56.99 + 56.66 + 503.60 + 4.84 = 622.09: 25%|██▍ | 508/2048 [06:53<18:20, 1.40it/s]
loss 2.45 accuracy 0.19 -- 168.10 + 57.07 + 503.05 + 4.84 = 733.05: 25%|██▍ | 508/2048 [06:54<18:20, 1.40it/s]
loss 2.45 accuracy 0.19 -- 168.10 + 57.07 + 503.05 + 4.84 = 733.05: 25%|██▍ | 509/2048 [06:54<18:40, 1.37it/s]
loss 2.01 accuracy 0.25 -- 56.97 + 57.31 + 634.60 + 4.83 = 753.70: 25%|██▍ | 509/2048 [06:55<18:40, 1.37it/s]
loss 2.01 accuracy 0.25 -- 56.97 + 57.31 + 634.60 + 4.83 = 753.70: 25%|██▍ | 510/2048 [06:55<19:04, 1.34it/s]
loss 1.87 accuracy 0.12 -- 57.24 + 56.79 + 515.05 + 4.88 = 633.97: 25%|██▍ | 510/2048 [06:55<19:04, 1.34it/s]
loss 1.87 accuracy 0.12 -- 57.24 + 56.79 + 515.05 + 4.88 = 633.97: 25%|██▍ | 511/2048 [06:55<18:25, 1.39it/s]
loss 1.97 accuracy 0.12 -- 56.56 + 57.40 + 627.91 + 4.82 = 746.68: 25%|██▍ | 511/2048 [06:56<18:25, 1.39it/s]
loss 1.97 accuracy 0.12 -- 56.56 + 57.40 + 627.91 + 4.82 = 746.68: 25%|██▌ | 512/2048 [06:56<18:50, 1.36it/s]
loss 1.64 accuracy 0.38 -- 57.35 + 56.95 + 508.46 + 4.82 = 627.59: 25%|██▌ | 512/2048 [06:57<18:50, 1.36it/s]
loss 1.64 accuracy 0.38 -- 57.35 + 56.95 + 508.46 + 4.82 = 627.59: 25%|██▌ | 513/2048 [06:57<18:12, 1.41it/s]
loss 2.01 accuracy 0.44 -- 56.45 + 170.55 + 507.50 + 4.83 = 739.33: 25%|██▌ | 513/2048 [06:57<18:12, 1.41it/s]
loss 2.01 accuracy 0.44 -- 56.45 + 170.55 + 507.50 + 4.83 = 739.33: 25%|██▌ | 514/2048 [06:57<18:37, 1.37it/s]
loss 2.71 accuracy 0.06 -- 56.05 + 56.38 + 505.05 + 4.82 = 622.29: 25%|██▌ | 514/2048 [06:58<18:37, 1.37it/s]
loss 2.71 accuracy 0.06 -- 56.05 + 56.38 + 505.05 + 4.82 = 622.29: 25%|██▌ | 515/2048 [06:58<18:00, 1.42it/s]
loss 1.82 accuracy 0.25 -- 57.22 + 56.41 + 504.23 + 4.82 = 622.68: 25%|██▌ | 515/2048 [06:59<18:00, 1.42it/s]
loss 1.82 accuracy 0.25 -- 57.22 + 56.41 + 504.23 + 4.82 = 622.68: 25%|██▌ | 516/2048 [06:59<18:26, 1.39it/s]
loss 2.51 accuracy 0.12 -- 56.26 + 57.10 + 500.66 + 4.83 = 618.84: 25%|██▌ | 516/2048 [06:59<18:26, 1.39it/s]
loss 2.51 accuracy 0.12 -- 56.26 + 57.10 + 500.66 + 4.83 = 618.84: 25%|██▌ | 517/2048 [06:59<17:50, 1.43it/s]
loss 2.38 accuracy 0.44 -- 162.64 + 56.88 + 497.07 + 4.84 = 721.44: 25%|██▌ | 517/2048 [07:00<17:50, 1.43it/s]
loss 2.38 accuracy 0.44 -- 162.64 + 56.88 + 497.07 + 4.84 = 721.44: 25%|██▌ | 518/2048 [07:00<18:12, 1.40it/s]
loss 2.12 accuracy 0.12 -- 56.51 + 172.22 + 508.10 + 4.82 = 741.65: 25%|██▌ | 518/2048 [07:01<18:12, 1.40it/s]
loss 2.12 accuracy 0.12 -- 56.51 + 172.22 + 508.10 + 4.82 = 741.65: 25%|██▌ | 519/2048 [07:01<18:37, 1.37it/s]
loss 1.83 accuracy 0.31 -- 57.10 + 56.20 + 505.52 + 4.84 = 623.67: 25%|██▌ | 519/2048 [07:02<18:37, 1.37it/s]
loss 1.83 accuracy 0.31 -- 57.10 + 56.20 + 505.52 + 4.84 = 623.67: 25%|██▌ | 520/2048 [07:02<18:00, 1.41it/s]
loss 2.00 accuracy 0.19 -- 167.69 + 56.97 + 503.00 + 4.83 = 732.48: 25%|██▌ | 520/2048 [07:02<18:00, 1.41it/s]
loss 2.00 accuracy 0.19 -- 167.69 + 56.97 + 503.00 + 4.83 = 732.48: 25%|██▌ | 521/2048 [07:02<18:23, 1.38it/s]
loss 1.83 accuracy 0.31 -- 56.12 + 57.73 + 631.86 + 4.81 = 750.52: 25%|██▌ | 521/2048 [07:03<18:23, 1.38it/s]
loss 1.83 accuracy 0.31 -- 56.12 + 57.73 + 631.86 + 4.81 = 750.52: 25%|██▌ | 522/2048 [07:03<19:04, 1.33it/s]
loss 1.84 accuracy 0.38 -- 57.05 + 56.54 + 511.62 + 4.82 = 630.02: 25%|██▌ | 522/2048 [07:04<19:04, 1.33it/s]
loss 1.84 accuracy 0.38 -- 57.05 + 56.54 + 511.62 + 4.82 = 630.02: 26%|██▌ | 523/2048 [07:04<18:21, 1.38it/s]
loss 2.16 accuracy 0.19 -- 56.35 + 57.33 + 628.02 + 4.83 = 746.53: 26%|██▌ | 523/2048 [07:05<18:21, 1.38it/s]
loss 2.16 accuracy 0.19 -- 56.35 + 57.33 + 628.02 + 4.83 = 746.53: 26%|██▌ | 524/2048 [07:05<18:44, 1.36it/s]
loss 1.68 accuracy 0.38 -- 56.94 + 56.73 + 509.25 + 4.84 = 627.77: 26%|██▌ | 524/2048 [07:05<18:44, 1.36it/s]
loss 1.68 accuracy 0.38 -- 56.94 + 56.73 + 509.25 + 4.84 = 627.77: 26%|██▌ | 525/2048 [07:05<18:05, 1.40it/s]
loss 2.27 accuracy 0.25 -- 56.31 + 171.19 + 507.43 + 4.85 = 739.78: 26%|██▌ | 525/2048 [07:06<18:05, 1.40it/s]
loss 2.27 accuracy 0.25 -- 56.31 + 171.19 + 507.43 + 4.85 = 739.78: 26%|██▌ | 526/2048 [07:06<18:29, 1.37it/s]
loss 2.16 accuracy 0.12 -- 56.55 + 56.66 + 505.14 + 4.82 = 623.16: 26%|██▌ | 526/2048 [07:07<18:29, 1.37it/s]
loss 2.16 accuracy 0.12 -- 56.55 + 56.66 + 505.14 + 4.82 = 623.16: 26%|██▌ | 527/2048 [07:07<17:53, 1.42it/s]
loss 2.04 accuracy 0.25 -- 56.98 + 56.75 + 505.28 + 4.82 = 623.84: 26%|██▌ | 527/2048 [07:07<17:53, 1.42it/s]
loss 2.04 accuracy 0.25 -- 56.98 + 56.75 + 505.28 + 4.82 = 623.84: 26%|██▌ | 528/2048 [07:07<18:18, 1.38it/s]
loss 2.03 accuracy 0.25 -- 56.52 + 57.34 + 501.99 + 4.86 = 620.72: 26%|██▌ | 528/2048 [07:08<18:18, 1.38it/s]
loss 2.03 accuracy 0.25 -- 56.52 + 57.34 + 501.99 + 4.86 = 620.72: 26%|██▌ | 529/2048 [07:08<17:59, 1.41it/s]
loss 1.62 accuracy 0.50 -- 163.41 + 57.20 + 496.01 + 4.83 = 721.45: 26%|██▌ | 529/2048 [07:09<17:59, 1.41it/s]
loss 1.62 accuracy 0.50 -- 163.41 + 57.20 + 496.01 + 4.83 = 721.45: 26%|██▌ | 530/2048 [07:09<18:16, 1.38it/s]
loss 2.07 accuracy 0.25 -- 56.28 + 171.67 + 509.30 + 4.83 = 742.08: 26%|██▌ | 530/2048 [07:10<18:16, 1.38it/s]
loss 2.07 accuracy 0.25 -- 56.28 + 171.67 + 509.30 + 4.83 = 742.08: 26%|██▌ | 531/2048 [07:10<18:37, 1.36it/s]
loss 1.56 accuracy 0.44 -- 57.06 + 56.57 + 503.82 + 4.82 = 622.27: 26%|██▌ | 531/2048 [07:10<18:37, 1.36it/s]
loss 1.56 accuracy 0.44 -- 57.06 + 56.57 + 503.82 + 4.82 = 622.27: 26%|██▌ | 532/2048 [07:10<17:57, 1.41it/s]
loss 2.13 accuracy 0.25 -- 167.54 + 57.13 + 502.52 + 4.83 = 732.03: 26%|██▌ | 532/2048 [07:11<17:57, 1.41it/s]
loss 2.13 accuracy 0.25 -- 167.54 + 57.13 + 502.52 + 4.83 = 732.03: 26%|██▌ | 533/2048 [07:11<18:18, 1.38it/s]
loss 1.84 accuracy 0.44 -- 56.24 + 57.12 + 632.62 + 4.83 = 750.82: 26%|██▌ | 533/2048 [07:12<18:18, 1.38it/s]
loss 1.84 accuracy 0.44 -- 56.24 + 57.12 + 632.62 + 4.83 = 750.82: 26%|██▌ | 534/2048 [07:12<18:42, 1.35it/s]
loss 2.24 accuracy 0.12 -- 56.93 + 56.34 + 513.43 + 4.83 = 631.52: 26%|██▌ | 534/2048 [07:13<18:42, 1.35it/s]
loss 2.24 accuracy 0.12 -- 56.93 + 56.34 + 513.43 + 4.83 = 631.52: 26%|██▌ | 535/2048 [07:13<18:04, 1.40it/s]
loss 2.89 accuracy 0.19 -- 56.22 + 57.27 + 626.85 + 4.84 = 745.17: 26%|██▌ | 535/2048 [07:13<18:04, 1.40it/s]
loss 2.89 accuracy 0.19 -- 56.22 + 57.27 + 626.85 + 4.84 = 745.17: 26%|██▌ | 536/2048 [07:13<18:29, 1.36it/s]
loss 2.13 accuracy 0.25 -- 56.99 + 56.39 + 508.28 + 4.84 = 626.49: 26%|██▌ | 536/2048 [07:14<18:29, 1.36it/s]
loss 2.13 accuracy 0.25 -- 56.99 + 56.39 + 508.28 + 4.84 = 626.49: 26%|██▌ | 537/2048 [07:14<17:52, 1.41it/s]
loss 2.18 accuracy 0.19 -- 56.29 + 171.17 + 508.77 + 4.84 = 741.06: 26%|██▌ | 537/2048 [07:15<17:52, 1.41it/s]
loss 2.18 accuracy 0.19 -- 56.29 + 171.17 + 508.77 + 4.84 = 741.06: 26%|██▋ | 538/2048 [07:15<18:18, 1.37it/s]
loss 3.52 accuracy 0.12 -- 56.51 + 56.44 + 504.93 + 4.82 = 622.71: 26%|██▋ | 538/2048 [07:15<18:18, 1.37it/s]
loss 3.52 accuracy 0.12 -- 56.51 + 56.44 + 504.93 + 4.82 = 622.71: 26%|██▋ | 539/2048 [07:15<17:42, 1.42it/s]
loss 2.46 accuracy 0.19 -- 56.91 + 56.53 + 504.45 + 4.86 = 622.75: 26%|██▋ | 539/2048 [07:16<17:42, 1.42it/s]
loss 2.46 accuracy 0.19 -- 56.91 + 56.53 + 504.45 + 4.86 = 622.75: 26%|██▋ | 540/2048 [07:16<18:08, 1.38it/s]
loss 2.50 accuracy 0.25 -- 56.52 + 57.70 + 501.63 + 4.83 = 620.69: 26%|██▋ | 540/2048 [07:17<18:08, 1.38it/s]
loss 2.50 accuracy 0.25 -- 56.52 + 57.70 + 501.63 + 4.83 = 620.69: 26%|██▋ | 541/2048 [07:17<17:34, 1.43it/s]
loss 2.72 accuracy 0.25 -- 162.27 + 57.07 + 495.34 + 4.82 = 719.51: 26%|██▋ | 541/2048 [07:18<17:34, 1.43it/s]
loss 2.72 accuracy 0.25 -- 162.27 + 57.07 + 495.34 + 4.82 = 719.51: 26%|██▋ | 542/2048 [07:18<17:55, 1.40it/s]
loss 2.48 accuracy 0.12 -- 55.80 + 171.39 + 509.40 + 4.83 = 741.42: 26%|██▋ | 542/2048 [07:18<17:55, 1.40it/s]
loss 2.48 accuracy 0.12 -- 55.80 + 171.39 + 509.40 + 4.83 = 741.42: 27%|██▋ | 543/2048 [07:18<18:35, 1.35it/s]
loss 2.19 accuracy 0.38 -- 56.83 + 56.45 + 503.44 + 4.83 = 621.55: 27%|██▋ | 543/2048 [07:19<18:35, 1.35it/s]
loss 2.19 accuracy 0.38 -- 56.83 + 56.45 + 503.44 + 4.83 = 621.55: 27%|██▋ | 544/2048 [07:19<17:52, 1.40it/s]
loss 1.94 accuracy 0.50 -- 168.21 + 57.13 + 502.94 + 4.82 = 733.10: 27%|██▋ | 544/2048 [07:20<17:52, 1.40it/s]
loss 1.94 accuracy 0.50 -- 168.21 + 57.13 + 502.94 + 4.82 = 733.10: 27%|██▋ | 545/2048 [07:20<18:13, 1.37it/s]
loss 2.00 accuracy 0.25 -- 56.32 + 57.30 + 632.82 + 4.83 = 751.27: 27%|██▋ | 545/2048 [07:21<18:13, 1.37it/s]
loss 2.00 accuracy 0.25 -- 56.32 + 57.30 + 632.82 + 4.83 = 751.27: 27%|██▋ | 546/2048 [07:21<18:35, 1.35it/s]
loss 2.29 accuracy 0.12 -- 57.09 + 56.51 + 511.20 + 4.83 = 629.62: 27%|██▋ | 546/2048 [07:21<18:35, 1.35it/s]
loss 2.29 accuracy 0.12 -- 57.09 + 56.51 + 511.20 + 4.83 = 629.62: 27%|██▋ | 547/2048 [07:21<17:56, 1.39it/s]
loss 1.98 accuracy 0.25 -- 56.67 + 57.62 + 627.88 + 4.86 = 747.01: 27%|██▋ | 547/2048 [07:22<17:56, 1.39it/s]
loss 1.98 accuracy 0.25 -- 56.67 + 57.62 + 627.88 + 4.86 = 747.01: 27%|██▋ | 548/2048 [07:22<18:21, 1.36it/s]
loss 1.91 accuracy 0.25 -- 56.98 + 56.75 + 507.79 + 4.83 = 626.36: 27%|██▋ | 548/2048 [07:23<18:21, 1.36it/s]
loss 1.91 accuracy 0.25 -- 56.98 + 56.75 + 507.79 + 4.83 = 626.36: 27%|██▋ | 549/2048 [07:23<17:44, 1.41it/s]
loss 2.08 accuracy 0.25 -- 56.62 + 171.07 + 508.62 + 4.83 = 741.13: 27%|██▋ | 549/2048 [07:23<17:44, 1.41it/s]
loss 2.08 accuracy 0.25 -- 56.62 + 171.07 + 508.62 + 4.83 = 741.13: 27%|██▋ | 550/2048 [07:23<18:26, 1.35it/s]
loss 1.87 accuracy 0.38 -- 56.15 + 56.79 + 505.88 + 4.82 = 623.63: 27%|██▋ | 550/2048 [07:24<18:26, 1.35it/s]
loss 1.87 accuracy 0.38 -- 56.15 + 56.79 + 505.88 + 4.82 = 623.63: 27%|██▋ | 551/2048 [07:24<17:46, 1.40it/s]
loss 2.41 accuracy 0.25 -- 57.21 + 57.26 + 505.72 + 4.83 = 625.02: 27%|██▋ | 551/2048 [07:25<17:46, 1.40it/s]
loss 2.41 accuracy 0.25 -- 57.21 + 57.26 + 505.72 + 4.83 = 625.02: 27%|██▋ | 552/2048 [07:25<18:08, 1.37it/s]
loss 2.11 accuracy 0.19 -- 56.36 + 57.51 + 501.38 + 4.84 = 620.08: 27%|██▋ | 552/2048 [07:25<18:08, 1.37it/s]
loss 2.11 accuracy 0.19 -- 56.36 + 57.51 + 501.38 + 4.84 = 620.08: 27%|██▋ | 553/2048 [07:25<17:32, 1.42it/s]
loss 2.07 accuracy 0.19 -- 163.49 + 57.97 + 496.01 + 4.82 = 722.29: 27%|██▋ | 553/2048 [07:26<17:32, 1.42it/s]
loss 2.07 accuracy 0.19 -- 163.49 + 57.97 + 496.01 + 4.82 = 722.29: 27%|██▋ | 554/2048 [07:26<17:52, 1.39it/s]
loss 2.13 accuracy 0.25 -- 56.33 + 172.09 + 508.22 + 4.83 = 741.47: 27%|██▋ | 554/2048 [07:27<17:52, 1.39it/s]
loss 2.13 accuracy 0.25 -- 56.33 + 172.09 + 508.22 + 4.83 = 741.47: 27%|██▋ | 555/2048 [07:27<18:14, 1.36it/s]
loss 1.99 accuracy 0.25 -- 56.97 + 56.60 + 504.37 + 4.82 = 622.76: 27%|██▋ | 555/2048 [07:28<18:14, 1.36it/s]
loss 1.99 accuracy 0.25 -- 56.97 + 56.60 + 504.37 + 4.82 = 622.76: 27%|██▋ | 556/2048 [07:28<17:36, 1.41it/s]
loss 1.75 accuracy 0.31 -- 166.92 + 57.50 + 504.55 + 4.82 = 733.78: 27%|██▋ | 556/2048 [07:28<17:36, 1.41it/s]
loss 1.75 accuracy 0.31 -- 166.92 + 57.50 + 504.55 + 4.82 = 733.78: 27%|██▋ | 557/2048 [07:28<18:15, 1.36it/s]
loss 2.44 accuracy 0.25 -- 56.52 + 57.35 + 631.24 + 4.82 = 749.93: 27%|██▋ | 557/2048 [07:29<18:15, 1.36it/s]
loss 2.44 accuracy 0.25 -- 56.52 + 57.35 + 631.24 + 4.82 = 749.93: 27%|██▋ | 558/2048 [07:29<18:33, 1.34it/s]
loss 1.81 accuracy 0.44 -- 57.36 + 56.60 + 511.67 + 4.84 = 630.47: 27%|██▋ | 558/2048 [07:30<18:33, 1.34it/s]
loss 1.81 accuracy 0.44 -- 57.36 + 56.60 + 511.67 + 4.84 = 630.47: 27%|██▋ | 559/2048 [07:30<17:52, 1.39it/s]
loss 2.57 accuracy 0.06 -- 56.53 + 57.74 + 626.47 + 4.82 = 745.55: 27%|██▋ | 559/2048 [07:31<17:52, 1.39it/s]
loss 2.57 accuracy 0.06 -- 56.53 + 57.74 + 626.47 + 4.82 = 745.55: 27%|██▋ | 560/2048 [07:31<18:15, 1.36it/s]
loss 2.18 accuracy 0.19 -- 56.99 + 56.77 + 507.75 + 4.86 = 626.37: 27%|██▋ | 560/2048 [07:31<18:15, 1.36it/s]
loss 2.18 accuracy 0.19 -- 56.99 + 56.77 + 507.75 + 4.86 = 626.37: 27%|██▋ | 561/2048 [07:31<17:38, 1.41it/s]
loss 2.08 accuracy 0.31 -- 56.73 + 171.46 + 507.24 + 4.85 = 740.28: 27%|██▋ | 561/2048 [07:32<17:38, 1.41it/s]
loss 2.08 accuracy 0.31 -- 56.73 + 171.46 + 507.24 + 4.85 = 740.28: 27%|██▋ | 562/2048 [07:32<18:02, 1.37it/s]
loss 1.74 accuracy 0.50 -- 56.94 + 56.74 + 506.91 + 4.83 = 625.41: 27%|██▋ | 562/2048 [07:33<18:02, 1.37it/s]
loss 1.74 accuracy 0.50 -- 56.94 + 56.74 + 506.91 + 4.83 = 625.41: 27%|██▋ | 563/2048 [07:33<17:27, 1.42it/s]
loss 2.17 accuracy 0.06 -- 57.08 + 56.56 + 504.61 + 4.86 = 623.11: 27%|██▋ | 563/2048 [07:34<17:27, 1.42it/s]
loss 2.17 accuracy 0.06 -- 57.08 + 56.56 + 504.61 + 4.86 = 623.11: 28%|██▊ | 564/2048 [07:34<18:08, 1.36it/s]
loss 1.86 accuracy 0.19 -- 56.58 + 56.93 + 500.44 + 4.85 = 618.80: 28%|██▊ | 564/2048 [07:34<18:08, 1.36it/s]
loss 1.86 accuracy 0.19 -- 56.58 + 56.93 + 500.44 + 4.85 = 618.80: 28%|██▊ | 565/2048 [07:34<17:28, 1.41it/s]
loss 2.32 accuracy 0.25 -- 162.70 + 57.00 + 494.58 + 4.82 = 719.11: 28%|██▊ | 565/2048 [07:35<17:28, 1.41it/s]
loss 2.32 accuracy 0.25 -- 162.70 + 57.00 + 494.58 + 4.82 = 719.11: 28%|██▊ | 566/2048 [07:35<17:45, 1.39it/s]
loss 2.66 accuracy 0.12 -- 55.92 + 171.56 + 507.27 + 4.83 = 739.57: 28%|██▊ | 566/2048 [07:36<17:45, 1.39it/s]
loss 2.66 accuracy 0.12 -- 55.92 + 171.56 + 507.27 + 4.83 = 739.57: 28%|██▊ | 567/2048 [07:36<18:05, 1.36it/s]
loss 2.11 accuracy 0.38 -- 56.95 + 56.43 + 505.85 + 4.80 = 624.04: 28%|██▊ | 567/2048 [07:36<18:05, 1.36it/s]
loss 2.11 accuracy 0.38 -- 56.95 + 56.43 + 505.85 + 4.80 = 624.04: 28%|██▊ | 568/2048 [07:36<17:28, 1.41it/s]
loss 2.50 accuracy 0.19 -- 167.86 + 57.04 + 502.96 + 4.87 = 732.74: 28%|██▊ | 568/2048 [07:37<17:28, 1.41it/s]
loss 2.50 accuracy 0.19 -- 167.86 + 57.04 + 502.96 + 4.87 = 732.74: 28%|██▊ | 569/2048 [07:37<17:50, 1.38it/s]
loss 2.30 accuracy 0.25 -- 56.47 + 57.60 + 632.25 + 4.84 = 751.15: 28%|██▊ | 569/2048 [07:38<17:50, 1.38it/s]
loss 2.30 accuracy 0.25 -- 56.47 + 57.60 + 632.25 + 4.84 = 751.15: 28%|██▊ | 570/2048 [07:38<18:14, 1.35it/s]
loss 2.02 accuracy 0.44 -- 57.17 + 56.75 + 510.60 + 4.84 = 629.36: 28%|██▊ | 570/2048 [07:39<18:14, 1.35it/s]
loss 2.02 accuracy 0.44 -- 57.17 + 56.75 + 510.60 + 4.84 = 629.36: 28%|██▊ | 571/2048 [07:39<17:52, 1.38it/s]
loss 2.75 accuracy 0.19 -- 56.36 + 57.16 + 626.04 + 4.84 = 744.39: 28%|██▊ | 571/2048 [07:39<17:52, 1.38it/s]
loss 2.75 accuracy 0.19 -- 56.36 + 57.16 + 626.04 + 4.84 = 744.39: 28%|██▊ | 572/2048 [07:39<18:11, 1.35it/s]
loss 1.93 accuracy 0.25 -- 57.28 + 56.81 + 507.60 + 4.82 = 626.51: 28%|██▊ | 572/2048 [07:40<18:11, 1.35it/s]
loss 1.93 accuracy 0.25 -- 57.28 + 56.81 + 507.60 + 4.82 = 626.51: 28%|██▊ | 573/2048 [07:40<17:33, 1.40it/s]
loss 2.71 accuracy 0.19 -- 56.76 + 171.41 + 510.33 + 4.82 = 743.31: 28%|██▊ | 573/2048 [07:41<17:33, 1.40it/s]
loss 2.71 accuracy 0.19 -- 56.76 + 171.41 + 510.33 + 4.82 = 743.31: 28%|██▊ | 574/2048 [07:41<17:57, 1.37it/s]
loss 1.97 accuracy 0.12 -- 56.43 + 56.47 + 505.95 + 4.81 = 623.66: 28%|██▊ | 574/2048 [07:41<17:57, 1.37it/s]
loss 1.97 accuracy 0.12 -- 56.43 + 56.47 + 505.95 + 4.81 = 623.66: 28%|██▊ | 575/2048 [07:41<17:21, 1.41it/s]
loss 2.22 accuracy 0.12 -- 57.20 + 56.51 + 505.21 + 4.84 = 623.77: 28%|██▊ | 575/2048 [07:42<17:21, 1.41it/s]
loss 2.22 accuracy 0.12 -- 57.20 + 56.51 + 505.21 + 4.84 = 623.77: 28%|██▊ | 576/2048 [07:42<17:45, 1.38it/s]
loss 1.95 accuracy 0.31 -- 56.32 + 57.27 + 502.58 + 4.82 = 620.99: 28%|██▊ | 576/2048 [07:43<17:45, 1.38it/s]
loss 1.95 accuracy 0.31 -- 56.32 + 57.27 + 502.58 + 4.82 = 620.99: 28%|██▊ | 577/2048 [07:43<17:11, 1.43it/s]
loss 2.00 accuracy 0.31 -- 162.49 + 56.98 + 495.67 + 4.81 = 719.94: 28%|██▊ | 577/2048 [07:44<17:11, 1.43it/s]
loss 2.00 accuracy 0.31 -- 162.49 + 56.98 + 495.67 + 4.81 = 719.94: 28%|██▊ | 578/2048 [07:44<17:30, 1.40it/s]
loss 2.02 accuracy 0.12 -- 55.95 + 171.05 + 508.67 + 4.83 = 740.50: 28%|██▊ | 578/2048 [07:44<17:30, 1.40it/s]
loss 2.02 accuracy 0.12 -- 55.95 + 171.05 + 508.67 + 4.83 = 740.50: 28%|██▊ | 579/2048 [07:44<18:09, 1.35it/s]
loss 2.50 accuracy 0.19 -- 57.71 + 56.69 + 502.92 + 4.86 = 622.18: 28%|██▊ | 579/2048 [07:45<18:09, 1.35it/s]
loss 2.50 accuracy 0.19 -- 57.71 + 56.69 + 502.92 + 4.86 = 622.18: 28%|██▊ | 580/2048 [07:45<17:27, 1.40it/s]
loss 2.30 accuracy 0.38 -- 168.37 + 57.40 + 502.13 + 4.82 = 732.73: 28%|██▊ | 580/2048 [07:46<17:27, 1.40it/s]
loss 2.30 accuracy 0.38 -- 168.37 + 57.40 + 502.13 + 4.82 = 732.73: 28%|██▊ | 581/2048 [07:46<17:47, 1.37it/s]
loss 1.89 accuracy 0.19 -- 56.70 + 57.25 + 634.05 + 4.82 = 752.81: 28%|██▊ | 581/2048 [07:47<17:47, 1.37it/s]
loss 1.89 accuracy 0.19 -- 56.70 + 57.25 + 634.05 + 4.82 = 752.81: 28%|██▊ | 582/2048 [07:47<18:09, 1.35it/s]
loss 2.47 accuracy 0.19 -- 56.78 + 56.18 + 510.58 + 4.83 = 628.37: 28%|██▊ | 582/2048 [07:47<18:09, 1.35it/s]
loss 2.47 accuracy 0.19 -- 56.78 + 56.18 + 510.58 + 4.83 = 628.37: 28%|██▊ | 583/2048 [07:47<17:30, 1.39it/s]
loss 2.32 accuracy 0.19 -- 56.33 + 57.35 + 626.43 + 4.84 = 744.94: 28%|██▊ | 583/2048 [07:48<17:30, 1.39it/s]
loss 2.32 accuracy 0.19 -- 56.33 + 57.35 + 626.43 + 4.84 = 744.94: 29%|██▊ | 584/2048 [07:48<17:53, 1.36it/s]
loss 2.17 accuracy 0.31 -- 57.11 + 56.72 + 509.83 + 4.82 = 628.47: 29%|██▊ | 584/2048 [07:49<17:53, 1.36it/s]
loss 2.17 accuracy 0.31 -- 57.11 + 56.72 + 509.83 + 4.82 = 628.47: 29%|██▊ | 585/2048 [07:49<17:18, 1.41it/s]
loss 2.15 accuracy 0.31 -- 56.58 + 170.50 + 506.99 + 4.85 = 738.92: 29%|██▊ | 585/2048 [07:49<17:18, 1.41it/s]
loss 2.15 accuracy 0.31 -- 56.58 + 170.50 + 506.99 + 4.85 = 738.92: 29%|██▊ | 586/2048 [07:49<17:58, 1.36it/s]
loss 2.18 accuracy 0.25 -- 56.52 + 56.35 + 505.09 + 4.82 = 622.78: 29%|██▊ | 586/2048 [07:50<17:58, 1.36it/s]
loss 2.18 accuracy 0.25 -- 56.52 + 56.35 + 505.09 + 4.82 = 622.78: 29%|██▊ | 587/2048 [07:50<17:19, 1.41it/s]
loss 1.87 accuracy 0.31 -- 57.31 + 56.55 + 504.17 + 4.82 = 622.84: 29%|██▊ | 587/2048 [07:51<17:19, 1.41it/s]
loss 1.87 accuracy 0.31 -- 57.31 + 56.55 + 504.17 + 4.82 = 622.84: 29%|██▊ | 588/2048 [07:51<17:40, 1.38it/s]
loss 2.03 accuracy 0.06 -- 56.34 + 57.11 + 500.76 + 4.82 = 619.03: 29%|██▊ | 588/2048 [07:52<17:40, 1.38it/s]
loss 2.03 accuracy 0.06 -- 56.34 + 57.11 + 500.76 + 4.82 = 619.03: 29%|██▉ | 589/2048 [07:52<17:04, 1.42it/s]
loss 2.17 accuracy 0.19 -- 162.41 + 56.89 + 495.86 + 4.82 = 719.98: 29%|██▉ | 589/2048 [07:52<17:04, 1.42it/s]
loss 2.17 accuracy 0.19 -- 162.41 + 56.89 + 495.86 + 4.82 = 719.98: 29%|██▉ | 590/2048 [07:52<17:23, 1.40it/s]
loss 2.01 accuracy 0.31 -- 56.51 + 172.36 + 507.11 + 4.83 = 740.81: 29%|██▉ | 590/2048 [07:53<17:23, 1.40it/s]
loss 2.01 accuracy 0.31 -- 56.51 + 172.36 + 507.11 + 4.83 = 740.81: 29%|██▉ | 591/2048 [07:53<17:45, 1.37it/s]
loss 2.26 accuracy 0.12 -- 56.74 + 56.77 + 503.50 + 4.83 = 621.83: 29%|██▉ | 591/2048 [07:54<17:45, 1.37it/s]
loss 2.26 accuracy 0.12 -- 56.74 + 56.77 + 503.50 + 4.83 = 621.83: 29%|██▉ | 592/2048 [07:54<17:08, 1.41it/s]
loss 1.97 accuracy 0.12 -- 167.37 + 56.64 + 502.75 + 4.82 = 731.58: 29%|██▉ | 592/2048 [07:54<17:08, 1.41it/s]
loss 1.97 accuracy 0.12 -- 167.37 + 56.64 + 502.75 + 4.82 = 731.58: 29%|██▉ | 593/2048 [07:54<17:46, 1.36it/s]
loss 2.21 accuracy 0.12 -- 56.21 + 57.70 + 631.20 + 4.84 = 749.96: 29%|██▉ | 593/2048 [07:55<17:46, 1.36it/s]
loss 2.21 accuracy 0.12 -- 56.21 + 57.70 + 631.20 + 4.84 = 749.96: 29%|██▉ | 594/2048 [07:55<18:04, 1.34it/s]
loss 2.11 accuracy 0.06 -- 57.20 + 56.49 + 512.21 + 4.82 = 630.72: 29%|██▉ | 594/2048 [07:56<18:04, 1.34it/s]
loss 2.11 accuracy 0.06 -- 57.20 + 56.49 + 512.21 + 4.82 = 630.72: 29%|██▉ | 595/2048 [07:56<17:25, 1.39it/s]
loss 2.32 accuracy 0.19 -- 56.16 + 57.28 + 630.51 + 4.82 = 748.77: 29%|██▉ | 595/2048 [07:57<17:25, 1.39it/s]
loss 2.32 accuracy 0.19 -- 56.16 + 57.28 + 630.51 + 4.82 = 748.77: 29%|██▉ | 596/2048 [07:57<17:49, 1.36it/s]
loss 2.30 accuracy 0.19 -- 57.09 + 56.59 + 507.70 + 4.82 = 626.21: 29%|██▉ | 596/2048 [07:57<17:49, 1.36it/s]
loss 2.30 accuracy 0.19 -- 57.09 + 56.59 + 507.70 + 4.82 = 626.21: 29%|██▉ | 597/2048 [07:57<17:12, 1.41it/s]
loss 2.32 accuracy 0.25 -- 56.45 + 171.50 + 506.60 + 4.86 = 739.41: 29%|██▉ | 597/2048 [07:58<17:12, 1.41it/s]
loss 2.32 accuracy 0.25 -- 56.45 + 171.50 + 506.60 + 4.86 = 739.41: 29%|██▉ | 598/2048 [07:58<17:35, 1.37it/s]
loss 2.24 accuracy 0.31 -- 56.75 + 56.69 + 506.08 + 4.81 = 624.34: 29%|██▉ | 598/2048 [07:59<17:35, 1.37it/s]
loss 2.24 accuracy 0.31 -- 56.75 + 56.69 + 506.08 + 4.81 = 624.34: 29%|██▉ | 599/2048 [07:59<17:01, 1.42it/s]
loss 1.73 accuracy 0.44 -- 56.91 + 56.53 + 504.64 + 4.81 = 622.90: 29%|██▉ | 599/2048 [08:00<17:01, 1.42it/s]
loss 1.73 accuracy 0.44 -- 56.91 + 56.53 + 504.64 + 4.81 = 622.90: 29%|██▉ | 600/2048 [08:00<17:26, 1.38it/s]
loss 1.88 accuracy 0.38 -- 56.58 + 57.36 + 501.22 + 4.83 = 619.99: 29%|██▉ | 600/2048 [08:00<17:26, 1.38it/s]
loss 1.88 accuracy 0.38 -- 56.58 + 57.36 + 501.22 + 4.83 = 619.99: 29%|██▉ | 601/2048 [08:00<16:52, 1.43it/s]
loss 1.93 accuracy 0.31 -- 163.11 + 57.22 + 496.37 + 4.82 = 721.52: 29%|██▉ | 601/2048 [08:01<16:52, 1.43it/s]
loss 1.93 accuracy 0.31 -- 163.11 + 57.22 + 496.37 + 4.82 = 721.52: 29%|██▉ | 602/2048 [08:01<17:13, 1.40it/s]
loss 1.89 accuracy 0.38 -- 55.89 + 191.29 + 557.43 + 5.10 = 809.71: 29%|██▉ | 602/2048 [08:02<17:13, 1.40it/s]
loss 1.89 accuracy 0.38 -- 55.89 + 191.29 + 557.43 + 5.10 = 809.71: 29%|██▉ | 603/2048 [08:02<18:05, 1.33it/s]
loss 1.99 accuracy 0.19 -- 57.71 + 59.25 + 542.88 + 5.03 = 664.87: 29%|██▉ | 603/2048 [08:02<18:05, 1.33it/s]
loss 1.99 accuracy 0.19 -- 57.71 + 59.25 + 542.88 + 5.03 = 664.87: 29%|██▉ | 604/2048 [08:02<17:40, 1.36it/s]
loss 2.46 accuracy 0.12 -- 187.78 + 58.21 + 540.41 + 5.03 = 791.44: 29%|██▉ | 604/2048 [08:03<17:40, 1.36it/s]
loss 2.46 accuracy 0.12 -- 187.78 + 58.21 + 540.41 + 5.03 = 791.44: 30%|██▉ | 605/2048 [08:03<18:17, 1.31it/s]
loss 2.19 accuracy 0.31 -- 58.11 + 59.82 + 669.05 + 5.02 = 791.99: 30%|██▉ | 605/2048 [08:04<18:17, 1.31it/s]
loss 2.19 accuracy 0.31 -- 58.11 + 59.82 + 669.05 + 5.02 = 791.99: 30%|██▉ | 606/2048 [08:04<18:43, 1.28it/s]
loss 1.83 accuracy 0.19 -- 58.78 + 57.32 + 547.80 + 5.05 = 668.95: 30%|██▉ | 606/2048 [08:05<18:43, 1.28it/s]
loss 1.83 accuracy 0.19 -- 58.78 + 57.32 + 547.80 + 5.05 = 668.95: 30%|██▉ | 607/2048 [08:05<18:08, 1.32it/s]
loss 2.00 accuracy 0.25 -- 58.88 + 60.82 + 687.05 + 5.01 = 811.77: 30%|██▉ | 607/2048 [08:06<18:08, 1.32it/s]
loss 2.00 accuracy 0.25 -- 58.88 + 60.82 + 687.05 + 5.01 = 811.77: 30%|██▉ | 608/2048 [08:06<18:44, 1.28it/s]
loss 1.80 accuracy 0.31 -- 59.21 + 59.23 + 551.34 + 5.06 = 674.83: 30%|██▉ | 608/2048 [08:06<18:44, 1.28it/s]
loss 1.80 accuracy 0.31 -- 59.21 + 59.23 + 551.34 + 5.06 = 674.83: 30%|██▉ | 609/2048 [08:06<18:11, 1.32it/s]
loss 1.89 accuracy 0.25 -- 58.51 + 188.35 + 546.38 + 4.89 = 798.13: 30%|██▉ | 609/2048 [08:07<18:11, 1.32it/s]
loss 1.89 accuracy 0.25 -- 58.51 + 188.35 + 546.38 + 4.89 = 798.13: 30%|██▉ | 610/2048 [08:07<18:40, 1.28it/s]
loss 2.12 accuracy 0.12 -- 56.53 + 56.99 + 508.78 + 4.83 = 627.13: 30%|██▉ | 610/2048 [08:08<18:40, 1.28it/s]
loss 2.12 accuracy 0.12 -- 56.53 + 56.99 + 508.78 + 4.83 = 627.13: 30%|██▉ | 611/2048 [08:08<17:46, 1.35it/s]
loss 1.94 accuracy 0.44 -- 56.91 + 56.39 + 506.03 + 4.84 = 624.18: 30%|██▉ | 611/2048 [08:09<17:46, 1.35it/s]
loss 1.94 accuracy 0.44 -- 56.91 + 56.39 + 506.03 + 4.84 = 624.18: 30%|██▉ | 612/2048 [08:09<17:56, 1.33it/s]
loss 2.62 accuracy 0.25 -- 56.05 + 57.15 + 501.24 + 4.84 = 619.28: 30%|██▉ | 612/2048 [08:09<17:56, 1.33it/s]
loss 2.62 accuracy 0.25 -- 56.05 + 57.15 + 501.24 + 4.84 = 619.28: 30%|██▉ | 613/2048 [08:09<17:11, 1.39it/s]
loss 1.67 accuracy 0.44 -- 162.55 + 56.92 + 495.63 + 4.81 = 719.91: 30%|██▉ | 613/2048 [08:10<17:11, 1.39it/s]
loss 1.67 accuracy 0.44 -- 162.55 + 56.92 + 495.63 + 4.81 = 719.91: 30%|██▉ | 614/2048 [08:10<17:23, 1.37it/s]
loss 1.98 accuracy 0.06 -- 56.17 + 171.38 + 508.90 + 4.83 = 741.27: 30%|██▉ | 614/2048 [08:11<17:23, 1.37it/s]
loss 1.98 accuracy 0.06 -- 56.17 + 171.38 + 508.90 + 4.83 = 741.27: 30%|███ | 615/2048 [08:11<17:40, 1.35it/s]
loss 2.12 accuracy 0.06 -- 56.62 + 56.66 + 503.69 + 4.82 = 621.79: 30%|███ | 615/2048 [08:11<17:40, 1.35it/s]
loss 2.12 accuracy 0.06 -- 56.62 + 56.66 + 503.69 + 4.82 = 621.79: 30%|███ | 616/2048 [08:11<17:01, 1.40it/s]
loss 1.56 accuracy 0.44 -- 168.16 + 57.16 + 503.86 + 4.83 = 734.02: 30%|███ | 616/2048 [08:12<17:01, 1.40it/s]
loss 1.56 accuracy 0.44 -- 168.16 + 57.16 + 503.86 + 4.83 = 734.02: 30%|███ | 617/2048 [08:12<17:21, 1.37it/s]
loss 2.10 accuracy 0.25 -- 56.12 + 57.50 + 632.01 + 4.82 = 750.45: 30%|███ | 617/2048 [08:13<17:21, 1.37it/s]
loss 2.10 accuracy 0.25 -- 56.12 + 57.50 + 632.01 + 4.82 = 750.45: 30%|███ | 618/2048 [08:13<17:42, 1.35it/s]
loss 2.16 accuracy 0.12 -- 57.09 + 56.32 + 512.64 + 4.81 = 630.86: 30%|███ | 618/2048 [08:14<17:42, 1.35it/s]
loss 2.16 accuracy 0.12 -- 57.09 + 56.32 + 512.64 + 4.81 = 630.86: 30%|███ | 619/2048 [08:14<17:05, 1.39it/s]
loss 2.43 accuracy 0.06 -- 56.60 + 57.42 + 626.97 + 4.83 = 745.82: 30%|███ | 619/2048 [08:14<17:05, 1.39it/s]
loss 2.43 accuracy 0.06 -- 56.60 + 57.42 + 626.97 + 4.83 = 745.82: 30%|███ | 620/2048 [08:14<17:28, 1.36it/s]
loss 2.27 accuracy 0.12 -- 56.74 + 56.66 + 507.89 + 4.82 = 626.11: 30%|███ | 620/2048 [08:15<17:28, 1.36it/s]
loss 2.27 accuracy 0.12 -- 56.74 + 56.66 + 507.89 + 4.82 = 626.11: 30%|███ | 621/2048 [08:15<16:53, 1.41it/s]
loss 1.86 accuracy 0.38 -- 56.27 + 171.09 + 507.02 + 4.82 = 739.20: 30%|███ | 621/2048 [08:16<16:53, 1.41it/s]
loss 1.86 accuracy 0.38 -- 56.27 + 171.09 + 507.02 + 4.82 = 739.20: 30%|███ | 622/2048 [08:16<17:17, 1.37it/s]
loss 1.89 accuracy 0.31 -- 56.15 + 56.39 + 508.44 + 4.82 = 625.81: 30%|███ | 622/2048 [08:16<17:17, 1.37it/s]
loss 1.89 accuracy 0.31 -- 56.15 + 56.39 + 508.44 + 4.82 = 625.81: 30%|███ | 623/2048 [08:16<16:45, 1.42it/s]
loss 1.76 accuracy 0.38 -- 57.01 + 56.43 + 505.48 + 4.82 = 623.74: 30%|███ | 623/2048 [08:17<16:45, 1.42it/s]
loss 1.76 accuracy 0.38 -- 57.01 + 56.43 + 505.48 + 4.82 = 623.74: 30%|███ | 624/2048 [08:17<17:09, 1.38it/s]
loss 1.95 accuracy 0.25 -- 56.54 + 57.13 + 500.71 + 4.81 = 619.19: 30%|███ | 624/2048 [08:18<17:09, 1.38it/s]
loss 1.95 accuracy 0.25 -- 56.54 + 57.13 + 500.71 + 4.81 = 619.19: 31%|███ | 625/2048 [08:18<16:35, 1.43it/s]
loss 2.25 accuracy 0.19 -- 162.57 + 56.92 + 494.94 + 4.80 = 719.24: 31%|███ | 625/2048 [08:19<16:35, 1.43it/s]
loss 2.25 accuracy 0.19 -- 162.57 + 56.92 + 494.94 + 4.80 = 719.24: 31%|███ | 626/2048 [08:19<16:55, 1.40it/s]
loss 1.95 accuracy 0.38 -- 56.02 + 170.75 + 507.07 + 4.83 = 738.67: 31%|███ | 626/2048 [08:19<16:55, 1.40it/s]
loss 1.95 accuracy 0.38 -- 56.02 + 170.75 + 507.07 + 4.83 = 738.67: 31%|███ | 627/2048 [08:19<17:16, 1.37it/s]
loss 2.49 accuracy 0.06 -- 56.86 + 56.52 + 503.57 + 4.81 = 621.77: 31%|███ | 627/2048 [08:20<17:16, 1.37it/s]
loss 2.49 accuracy 0.06 -- 56.86 + 56.52 + 503.57 + 4.81 = 621.77: 31%|███ | 628/2048 [08:20<16:42, 1.42it/s]
loss 1.81 accuracy 0.50 -- 167.39 + 57.03 + 502.37 + 4.81 = 731.61: 31%|███ | 628/2048 [08:21<16:42, 1.42it/s]
loss 1.81 accuracy 0.50 -- 167.39 + 57.03 + 502.37 + 4.81 = 731.61: 31%|███ | 629/2048 [08:21<17:04, 1.39it/s]
loss 1.75 accuracy 0.31 -- 56.08 + 57.07 + 631.59 + 4.81 = 749.56: 31%|███ | 629/2048 [08:22<17:04, 1.39it/s]
loss 1.75 accuracy 0.31 -- 56.08 + 57.07 + 631.59 + 4.81 = 749.56: 31%|███ | 630/2048 [08:22<17:27, 1.35it/s]
loss 2.37 accuracy 0.25 -- 56.71 + 56.28 + 512.52 + 4.84 = 630.35: 31%|███ | 630/2048 [08:22<17:27, 1.35it/s]
loss 2.37 accuracy 0.25 -- 56.71 + 56.28 + 512.52 + 4.84 = 630.35: 31%|███ | 631/2048 [08:22<16:52, 1.40it/s]
loss 2.16 accuracy 0.12 -- 56.47 + 57.36 + 683.41 + 4.82 = 802.06: 31%|███ | 631/2048 [08:23<16:52, 1.40it/s]
loss 2.16 accuracy 0.12 -- 56.47 + 57.36 + 683.41 + 4.82 = 802.06: 31%|███ | 632/2048 [08:23<17:40, 1.33it/s]
loss 2.08 accuracy 0.25 -- 57.19 + 56.49 + 562.20 + 4.80 = 680.68: 31%|███ | 632/2048 [08:24<17:40, 1.33it/s]
loss 2.08 accuracy 0.25 -- 57.19 + 56.49 + 562.20 + 4.80 = 680.68: 31%|███ | 633/2048 [08:24<17:22, 1.36it/s]
loss 1.95 accuracy 0.25 -- 56.36 + 221.20 + 503.43 + 4.79 = 785.78: 31%|███ | 633/2048 [08:25<17:22, 1.36it/s]
loss 1.95 accuracy 0.25 -- 56.36 + 221.20 + 503.43 + 4.79 = 785.78: 31%|███ | 634/2048 [08:25<17:54, 1.32it/s]
loss 2.09 accuracy 0.25 -- 56.18 + 56.50 + 498.83 + 4.77 = 616.28: 31%|███ | 634/2048 [08:25<17:54, 1.32it/s]
loss 2.09 accuracy 0.25 -- 56.18 + 56.50 + 498.83 + 4.77 = 616.28: 31%|███ | 635/2048 [08:25<17:05, 1.38it/s]
loss 1.89 accuracy 0.31 -- 56.62 + 56.21 + 498.20 + 4.81 = 615.83: 31%|███ | 635/2048 [08:26<17:05, 1.38it/s]
loss 1.89 accuracy 0.31 -- 56.62 + 56.21 + 498.20 + 4.81 = 615.83: 31%|███ | 636/2048 [08:26<17:15, 1.36it/s]
loss 2.11 accuracy 0.25 -- 55.97 + 57.44 + 495.70 + 4.79 = 613.91: 31%|███ | 636/2048 [08:27<17:15, 1.36it/s]
loss 2.11 accuracy 0.25 -- 55.97 + 57.44 + 495.70 + 4.79 = 613.91: 31%|███ | 637/2048 [08:27<16:35, 1.42it/s]
loss 1.64 accuracy 0.50 -- 157.50 + 56.86 + 490.79 + 4.78 = 709.93: 31%|███ | 637/2048 [08:27<16:35, 1.42it/s]
loss 1.64 accuracy 0.50 -- 157.50 + 56.86 + 490.79 + 4.78 = 709.93: 31%|███ | 638/2048 [08:27<16:48, 1.40it/s]
loss 2.20 accuracy 0.25 -- 55.86 + 166.91 + 501.83 + 4.79 = 729.38: 31%|███ | 638/2048 [08:28<16:48, 1.40it/s]
loss 2.20 accuracy 0.25 -- 55.86 + 166.91 + 501.83 + 4.79 = 729.38: 31%|███ | 639/2048 [08:28<17:05, 1.37it/s]
loss 1.65 accuracy 0.44 -- 56.45 + 56.47 + 496.86 + 4.77 = 614.55: 31%|███ | 639/2048 [08:29<17:05, 1.37it/s]
loss 1.65 accuracy 0.44 -- 56.45 + 56.47 + 496.86 + 4.77 = 614.55: 31%|███▏ | 640/2048 [08:29<16:29, 1.42it/s]
loss 2.01 accuracy 0.31 -- 162.05 + 57.27 + 497.82 + 4.78 = 721.93: 31%|███▏ | 640/2048 [08:29<16:29, 1.42it/s]
loss 2.01 accuracy 0.31 -- 162.05 + 57.27 + 497.82 + 4.78 = 721.93: 31%|███▏ | 641/2048 [08:29<16:48, 1.40it/s]
loss 1.52 accuracy 0.56 -- 55.92 + 57.11 + 621.34 + 4.79 = 739.16: 31%|███▏ | 641/2048 [08:30<16:48, 1.40it/s]
loss 1.52 accuracy 0.56 -- 55.92 + 57.11 + 621.34 + 4.79 = 739.16: 31%|███▏ | 642/2048 [08:30<17:08, 1.37it/s]
loss 2.08 accuracy 0.06 -- 56.86 + 56.41 + 505.30 + 4.77 = 623.34: 31%|███▏ | 642/2048 [08:31<17:08, 1.37it/s]
loss 2.08 accuracy 0.06 -- 56.86 + 56.41 + 505.30 + 4.77 = 623.34: 31%|███▏ | 643/2048 [08:31<16:33, 1.41it/s]
loss 1.94 accuracy 0.00 -- 55.94 + 57.19 + 615.58 + 4.80 = 733.52: 31%|███▏ | 643/2048 [08:32<16:33, 1.41it/s]
loss 1.94 accuracy 0.00 -- 55.94 + 57.19 + 615.58 + 4.80 = 733.52: 31%|███▏ | 644/2048 [08:32<16:55, 1.38it/s]
loss 1.67 accuracy 0.31 -- 56.63 + 56.97 + 502.47 + 4.86 = 620.93: 31%|███▏ | 644/2048 [08:32<16:55, 1.38it/s]
loss 1.67 accuracy 0.31 -- 56.63 + 56.97 + 502.47 + 4.86 = 620.93: 31%|███▏ | 645/2048 [08:32<16:23, 1.43it/s]
loss 2.07 accuracy 0.44 -- 56.49 + 166.39 + 501.02 + 4.76 = 728.66: 31%|███▏ | 645/2048 [08:33<16:23, 1.43it/s]
loss 2.07 accuracy 0.44 -- 56.49 + 166.39 + 501.02 + 4.76 = 728.66: 32%|███▏ | 646/2048 [08:33<16:45, 1.39it/s]
loss 2.24 accuracy 0.25 -- 56.20 + 56.20 + 498.92 + 4.76 = 616.08: 32%|███▏ | 646/2048 [08:34<16:45, 1.39it/s]
loss 2.24 accuracy 0.25 -- 56.20 + 56.20 + 498.92 + 4.76 = 616.08: 32%|███▏ | 647/2048 [08:34<16:13, 1.44it/s]
loss 1.83 accuracy 0.31 -- 56.92 + 56.21 + 498.46 + 4.77 = 616.37: 32%|███▏ | 647/2048 [08:34<16:13, 1.44it/s]
loss 1.83 accuracy 0.31 -- 56.92 + 56.21 + 498.46 + 4.77 = 616.37: 32%|███▏ | 648/2048 [08:34<16:36, 1.40it/s]
loss 1.61 accuracy 0.44 -- 56.19 + 57.38 + 495.23 + 4.76 = 613.57: 32%|███▏ | 648/2048 [08:35<16:36, 1.40it/s]
loss 1.61 accuracy 0.44 -- 56.19 + 57.38 + 495.23 + 4.76 = 613.57: 32%|███▏ | 649/2048 [08:35<16:06, 1.45it/s]
loss 2.00 accuracy 0.44 -- 158.16 + 56.75 + 490.99 + 4.78 = 710.68: 32%|███▏ | 649/2048 [08:36<16:06, 1.45it/s]
loss 2.00 accuracy 0.44 -- 158.16 + 56.75 + 490.99 + 4.78 = 710.68: 32%|███▏ | 650/2048 [08:36<16:25, 1.42it/s]
loss 2.02 accuracy 0.19 -- 55.67 + 166.22 + 505.11 + 4.77 = 731.78: 32%|███▏ | 650/2048 [08:37<16:25, 1.42it/s]
loss 2.02 accuracy 0.19 -- 55.67 + 166.22 + 505.11 + 4.77 = 731.78: 32%|███▏ | 651/2048 [08:37<16:47, 1.39it/s]
loss 1.81 accuracy 0.31 -- 56.62 + 56.04 + 499.64 + 4.81 = 617.11: 32%|███▏ | 651/2048 [08:37<16:47, 1.39it/s]
loss 1.81 accuracy 0.31 -- 56.62 + 56.04 + 499.64 + 4.81 = 617.11: 32%|███▏ | 652/2048 [08:37<16:14, 1.43it/s]
loss 1.81 accuracy 0.38 -- 162.53 + 57.28 + 497.72 + 4.78 = 722.31: 32%|███▏ | 652/2048 [08:38<16:14, 1.43it/s]
loss 1.81 accuracy 0.38 -- 162.53 + 57.28 + 497.72 + 4.78 = 722.31: 32%|███▏ | 653/2048 [08:38<16:35, 1.40it/s]
loss 2.09 accuracy 0.31 -- 56.10 + 57.52 + 619.55 + 4.77 = 737.94: 32%|███▏ | 653/2048 [08:39<16:35, 1.40it/s]
loss 2.09 accuracy 0.31 -- 56.10 + 57.52 + 619.55 + 4.77 = 737.94: 32%|███▏ | 654/2048 [08:39<16:56, 1.37it/s]
loss 1.65 accuracy 0.31 -- 56.64 + 56.80 + 504.67 + 4.78 = 622.88: 32%|███▏ | 654/2048 [08:39<16:56, 1.37it/s]
loss 1.65 accuracy 0.31 -- 56.64 + 56.80 + 504.67 + 4.78 = 622.88: 32%|███▏ | 655/2048 [08:39<16:22, 1.42it/s]
loss 2.08 accuracy 0.19 -- 56.25 + 57.41 + 616.63 + 4.79 = 735.08: 32%|███▏ | 655/2048 [08:40<16:22, 1.42it/s]
loss 2.08 accuracy 0.19 -- 56.25 + 57.41 + 616.63 + 4.79 = 735.08: 32%|███▏ | 656/2048 [08:40<16:45, 1.38it/s]
loss 2.16 accuracy 0.25 -- 57.13 + 57.16 + 503.06 + 4.77 = 622.13: 32%|███▏ | 656/2048 [08:41<16:45, 1.38it/s]
loss 2.16 accuracy 0.25 -- 57.13 + 57.16 + 503.06 + 4.77 = 622.13: 32%|███▏ | 657/2048 [08:41<16:14, 1.43it/s]
loss 2.49 accuracy 0.19 -- 56.42 + 166.19 + 503.90 + 4.77 = 731.28: 32%|███▏ | 657/2048 [08:42<16:14, 1.43it/s]
loss 2.49 accuracy 0.19 -- 56.42 + 166.19 + 503.90 + 4.77 = 731.28: 32%|███▏ | 658/2048 [08:42<16:37, 1.39it/s]
loss 2.04 accuracy 0.25 -- 56.51 + 56.24 + 500.58 + 4.77 = 618.10: 32%|███▏ | 658/2048 [08:42<16:37, 1.39it/s]
loss 2.04 accuracy 0.25 -- 56.51 + 56.24 + 500.58 + 4.77 = 618.10: 32%|███▏ | 659/2048 [08:42<16:06, 1.44it/s]
loss 1.92 accuracy 0.44 -- 56.53 + 56.25 + 498.66 + 4.77 = 616.22: 32%|███▏ | 659/2048 [08:43<16:06, 1.44it/s]
loss 1.92 accuracy 0.44 -- 56.53 + 56.25 + 498.66 + 4.77 = 616.22: 32%|███▏ | 660/2048 [08:43<16:28, 1.40it/s]
loss 1.65 accuracy 0.50 -- 56.16 + 57.22 + 497.80 + 4.76 = 615.94: 32%|███▏ | 660/2048 [08:44<16:28, 1.40it/s]
loss 1.65 accuracy 0.50 -- 56.16 + 57.22 + 497.80 + 4.76 = 615.94: 32%|███▏ | 661/2048 [08:44<15:59, 1.45it/s]
loss 1.56 accuracy 0.38 -- 157.45 + 57.04 + 489.66 + 4.79 = 708.93: 32%|███▏ | 661/2048 [08:44<15:59, 1.45it/s]
loss 1.56 accuracy 0.38 -- 157.45 + 57.04 + 489.66 + 4.79 = 708.93: 32%|███▏ | 662/2048 [08:44<16:17, 1.42it/s]
loss 2.45 accuracy 0.19 -- 56.27 + 166.72 + 500.84 + 4.76 = 728.58: 32%|███▏ | 662/2048 [08:45<16:17, 1.42it/s]
loss 2.45 accuracy 0.19 -- 56.27 + 166.72 + 500.84 + 4.76 = 728.58: 32%|███▏ | 663/2048 [08:45<16:37, 1.39it/s]
loss 2.27 accuracy 0.12 -- 56.49 + 56.49 + 496.93 + 4.79 = 614.70: 32%|███▏ | 663/2048 [08:46<16:37, 1.39it/s]
loss 2.27 accuracy 0.12 -- 56.49 + 56.49 + 496.93 + 4.79 = 614.70: 32%|███▏ | 664/2048 [08:46<16:04, 1.44it/s]
loss 1.79 accuracy 0.31 -- 162.74 + 56.84 + 497.18 + 4.77 = 721.53: 32%|███▏ | 664/2048 [08:47<16:04, 1.44it/s]
loss 1.79 accuracy 0.31 -- 162.74 + 56.84 + 497.18 + 4.77 = 721.53: 32%|███▏ | 665/2048 [08:47<16:39, 1.38it/s]
loss 2.26 accuracy 0.38 -- 56.06 + 57.34 + 619.75 + 4.78 = 737.94: 32%|███▏ | 665/2048 [08:47<16:39, 1.38it/s]
loss 2.26 accuracy 0.38 -- 56.06 + 57.34 + 619.75 + 4.78 = 737.94: 33%|███▎ | 666/2048 [08:47<16:56, 1.36it/s]
loss 2.23 accuracy 0.31 -- 56.88 + 56.33 + 505.18 + 4.78 = 623.17: 33%|███▎ | 666/2048 [08:48<16:56, 1.36it/s]
loss 2.23 accuracy 0.31 -- 56.88 + 56.33 + 505.18 + 4.78 = 623.17: 33%|███▎ | 667/2048 [08:48<16:20, 1.41it/s]
loss 1.77 accuracy 0.25 -- 56.20 + 57.27 + 616.10 + 4.77 = 734.35: 33%|███▎ | 667/2048 [08:49<16:20, 1.41it/s]
loss 1.77 accuracy 0.25 -- 56.20 + 57.27 + 616.10 + 4.77 = 734.35: 33%|███▎ | 668/2048 [08:49<16:40, 1.38it/s]
loss 1.82 accuracy 0.31 -- 56.63 + 56.65 + 501.83 + 4.78 = 619.89: 33%|███▎ | 668/2048 [08:49<16:40, 1.38it/s]
loss 1.82 accuracy 0.31 -- 56.63 + 56.65 + 501.83 + 4.78 = 619.89: 33%|███▎ | 669/2048 [08:49<16:07, 1.42it/s]
loss 2.02 accuracy 0.38 -- 56.30 + 165.93 + 501.09 + 4.78 = 728.10: 33%|███▎ | 669/2048 [08:50<16:07, 1.42it/s]
loss 2.02 accuracy 0.38 -- 56.30 + 165.93 + 501.09 + 4.78 = 728.10: 33%|███▎ | 670/2048 [08:50<16:29, 1.39it/s]
loss 1.76 accuracy 0.12 -- 55.79 + 56.25 + 499.12 + 4.77 = 615.93: 33%|███▎ | 670/2048 [08:51<16:29, 1.39it/s]
loss 1.76 accuracy 0.12 -- 55.79 + 56.25 + 499.12 + 4.77 = 615.93: 33%|███▎ | 671/2048 [08:51<15:57, 1.44it/s]
loss 1.91 accuracy 0.19 -- 56.48 + 56.43 + 499.61 + 4.77 = 617.30: 33%|███▎ | 671/2048 [08:52<15:57, 1.44it/s]
loss 1.91 accuracy 0.19 -- 56.48 + 56.43 + 499.61 + 4.77 = 617.30: 33%|███▎ | 672/2048 [08:52<16:20, 1.40it/s]
loss 1.54 accuracy 0.50 -- 55.99 + 56.93 + 495.38 + 4.76 = 613.07: 33%|███▎ | 672/2048 [08:52<16:20, 1.40it/s]
loss 1.54 accuracy 0.50 -- 55.99 + 56.93 + 495.38 + 4.76 = 613.07: 33%|███▎ | 673/2048 [08:52<15:49, 1.45it/s]
loss 2.13 accuracy 0.12 -- 157.43 + 57.27 + 490.69 + 4.79 = 710.19: 33%|███▎ | 673/2048 [08:53<15:49, 1.45it/s]
loss 2.13 accuracy 0.12 -- 157.43 + 57.27 + 490.69 + 4.79 = 710.19: 33%|███▎ | 674/2048 [08:53<16:08, 1.42it/s]
loss 2.16 accuracy 0.19 -- 55.62 + 166.29 + 500.51 + 4.78 = 727.20: 33%|███▎ | 674/2048 [08:54<16:08, 1.42it/s]
loss 2.16 accuracy 0.19 -- 55.62 + 166.29 + 500.51 + 4.78 = 727.20: 33%|███▎ | 675/2048 [08:54<16:27, 1.39it/s]
loss 2.35 accuracy 0.19 -- 56.70 + 56.30 + 498.44 + 4.78 = 616.23: 33%|███▎ | 675/2048 [08:54<16:27, 1.39it/s]
loss 2.35 accuracy 0.19 -- 56.70 + 56.30 + 498.44 + 4.78 = 616.23: 33%|███▎ | 676/2048 [08:54<15:56, 1.44it/s]
loss 2.41 accuracy 0.12 -- 162.49 + 57.06 + 497.41 + 4.79 = 721.75: 33%|███▎ | 676/2048 [08:55<15:56, 1.44it/s]
loss 2.41 accuracy 0.12 -- 162.49 + 57.06 + 497.41 + 4.79 = 721.75: 33%|███▎ | 677/2048 [08:55<16:16, 1.40it/s]
loss 2.25 accuracy 0.19 -- 55.98 + 56.97 + 619.89 + 4.79 = 737.63: 33%|███▎ | 677/2048 [08:56<16:16, 1.40it/s]
loss 2.25 accuracy 0.19 -- 55.98 + 56.97 + 619.89 + 4.79 = 737.63: 33%|███▎ | 678/2048 [08:56<16:37, 1.37it/s]
loss 1.83 accuracy 0.44 -- 56.67 + 56.45 + 505.59 + 4.78 = 623.49: 33%|███▎ | 678/2048 [08:56<16:37, 1.37it/s]
loss 1.83 accuracy 0.44 -- 56.67 + 56.45 + 505.59 + 4.78 = 623.49: 33%|███▎ | 679/2048 [08:56<16:05, 1.42it/s]
loss 2.09 accuracy 0.25 -- 56.04 + 57.14 + 616.19 + 4.79 = 734.16: 33%|███▎ | 679/2048 [08:57<16:05, 1.42it/s]
loss 2.09 accuracy 0.25 -- 56.04 + 57.14 + 616.19 + 4.79 = 734.16: 33%|███▎ | 680/2048 [08:57<16:27, 1.39it/s]
loss 1.90 accuracy 0.25 -- 56.51 + 56.48 + 514.24 + 4.85 = 632.07: 33%|███▎ | 680/2048 [08:58<16:27, 1.39it/s]
loss 1.90 accuracy 0.25 -- 56.51 + 56.48 + 514.24 + 4.85 = 632.07: 33%|███▎ | 681/2048 [08:58<16:01, 1.42it/s]
loss 1.99 accuracy 0.19 -- 56.91 + 173.12 + 509.27 + 4.86 = 744.15: 33%|███▎ | 681/2048 [08:59<16:01, 1.42it/s]
loss 1.99 accuracy 0.19 -- 56.91 + 173.12 + 509.27 + 4.86 = 744.15: 33%|███▎ | 682/2048 [08:59<16:31, 1.38it/s]
loss 2.28 accuracy 0.25 -- 56.33 + 56.70 + 507.96 + 4.88 = 625.87: 33%|███▎ | 682/2048 [08:59<16:31, 1.38it/s]
loss 2.28 accuracy 0.25 -- 56.33 + 56.70 + 507.96 + 4.88 = 625.87: 33%|███▎ | 683/2048 [08:59<16:02, 1.42it/s]
loss 1.97 accuracy 0.44 -- 57.66 + 57.24 + 524.21 + 5.00 = 644.11: 33%|███▎ | 683/2048 [09:00<16:02, 1.42it/s]
loss 1.97 accuracy 0.44 -- 57.66 + 57.24 + 524.21 + 5.00 = 644.11: 33%|███▎ | 684/2048 [09:00<16:36, 1.37it/s]
loss 1.98 accuracy 0.31 -- 58.06 + 60.43 + 531.50 + 5.00 = 654.98: 33%|███▎ | 684/2048 [09:01<16:36, 1.37it/s]
loss 1.98 accuracy 0.31 -- 58.06 + 60.43 + 531.50 + 5.00 = 654.98: 33%|███▎ | 685/2048 [09:01<16:17, 1.39it/s]
loss 1.85 accuracy 0.31 -- 170.06 + 57.51 + 521.24 + 5.05 = 753.86: 33%|███▎ | 685/2048 [09:02<16:17, 1.39it/s]
loss 1.85 accuracy 0.31 -- 170.06 + 57.51 + 521.24 + 5.05 = 753.86: 33%|███▎ | 686/2048 [09:02<16:44, 1.36it/s]
loss 2.09 accuracy 0.19 -- 59.14 + 185.63 + 537.29 + 5.08 = 787.14: 33%|███▎ | 686/2048 [09:02<16:44, 1.36it/s]
loss 2.09 accuracy 0.19 -- 59.14 + 185.63 + 537.29 + 5.08 = 787.14: 34%|███▎ | 687/2048 [09:02<17:16, 1.31it/s]
loss 2.50 accuracy 0.19 -- 58.42 + 59.81 + 530.30 + 4.99 = 653.52: 34%|███▎ | 687/2048 [09:03<17:16, 1.31it/s]
loss 2.50 accuracy 0.19 -- 58.42 + 59.81 + 530.30 + 4.99 = 653.52: 34%|███▎ | 688/2048 [09:03<16:43, 1.35it/s]
loss 2.21 accuracy 0.25 -- 175.08 + 59.29 + 522.88 + 4.91 = 762.17: 34%|███▎ | 688/2048 [09:04<16:43, 1.35it/s]
loss 2.21 accuracy 0.25 -- 175.08 + 59.29 + 522.88 + 4.91 = 762.17: 34%|███▎ | 689/2048 [09:04<17:05, 1.33it/s]
loss 2.20 accuracy 0.19 -- 58.13 + 59.56 + 673.00 + 5.01 = 795.70: 34%|███▎ | 689/2048 [09:05<17:05, 1.33it/s]
loss 2.20 accuracy 0.19 -- 58.13 + 59.56 + 673.00 + 5.01 = 795.70: 34%|███▎ | 690/2048 [09:05<17:33, 1.29it/s]
loss 2.84 accuracy 0.19 -- 59.36 + 59.01 + 520.17 + 4.77 = 643.30: 34%|███▎ | 690/2048 [09:05<17:33, 1.29it/s]
loss 2.84 accuracy 0.19 -- 59.36 + 59.01 + 520.17 + 4.77 = 643.30: 34%|███▎ | 691/2048 [09:05<16:50, 1.34it/s]
loss 2.27 accuracy 0.12 -- 56.14 + 57.32 + 615.16 + 4.80 = 733.42: 34%|███▎ | 691/2048 [09:06<16:50, 1.34it/s]
loss 2.27 accuracy 0.12 -- 56.14 + 57.32 + 615.16 + 4.80 = 733.42: 34%|███▍ | 692/2048 [09:06<16:56, 1.33it/s]
loss 2.05 accuracy 0.38 -- 57.02 + 56.67 + 500.75 + 4.78 = 619.21: 34%|███▍ | 692/2048 [09:07<16:56, 1.33it/s]
loss 2.05 accuracy 0.38 -- 57.02 + 56.67 + 500.75 + 4.78 = 619.21: 34%|███▍ | 693/2048 [09:07<16:14, 1.39it/s]
loss 2.42 accuracy 0.19 -- 56.02 + 165.76 + 501.45 + 4.79 = 728.02: 34%|███▍ | 693/2048 [09:08<16:14, 1.39it/s]
loss 2.42 accuracy 0.19 -- 56.02 + 165.76 + 501.45 + 4.79 = 728.02: 34%|███▍ | 694/2048 [09:08<16:28, 1.37it/s]
loss 2.44 accuracy 0.19 -- 56.19 + 56.66 + 499.51 + 4.79 = 617.15: 34%|███▍ | 694/2048 [09:08<16:28, 1.37it/s]
loss 2.44 accuracy 0.19 -- 56.19 + 56.66 + 499.51 + 4.79 = 617.15: 34%|███▍ | 695/2048 [09:08<15:53, 1.42it/s]
loss 2.04 accuracy 0.31 -- 57.08 + 56.84 + 499.99 + 4.82 = 618.72: 34%|███▍ | 695/2048 [09:09<15:53, 1.42it/s]
loss 2.04 accuracy 0.31 -- 57.08 + 56.84 + 499.99 + 4.82 = 618.72: 34%|███▍ | 696/2048 [09:09<16:12, 1.39it/s]
loss 1.67 accuracy 0.31 -- 56.02 + 57.53 + 496.76 + 4.84 = 615.14: 34%|███▍ | 696/2048 [09:10<16:12, 1.39it/s]
loss 1.67 accuracy 0.31 -- 56.02 + 57.53 + 496.76 + 4.84 = 615.14: 34%|███▍ | 697/2048 [09:10<15:40, 1.44it/s]
loss 1.76 accuracy 0.50 -- 157.62 + 57.06 + 489.96 + 4.80 = 709.44: 34%|███▍ | 697/2048 [09:10<15:40, 1.44it/s]
loss 1.76 accuracy 0.50 -- 157.62 + 57.06 + 489.96 + 4.80 = 709.44: 34%|███▍ | 698/2048 [09:10<15:56, 1.41it/s]
loss 1.70 accuracy 0.31 -- 55.98 + 166.38 + 501.76 + 4.80 = 728.92: 34%|███▍ | 698/2048 [09:11<15:56, 1.41it/s]
loss 1.70 accuracy 0.31 -- 55.98 + 166.38 + 501.76 + 4.80 = 728.92: 34%|███▍ | 699/2048 [09:11<16:15, 1.38it/s]
loss 2.33 accuracy 0.38 -- 56.70 + 56.50 + 498.57 + 4.80 = 616.57: 34%|███▍ | 699/2048 [09:12<16:15, 1.38it/s]
loss 2.33 accuracy 0.38 -- 56.70 + 56.50 + 498.57 + 4.80 = 616.57: 34%|███▍ | 700/2048 [09:12<15:42, 1.43it/s]
loss 2.62 accuracy 0.31 -- 163.29 + 57.26 + 500.00 + 4.79 = 725.34: 34%|███▍ | 700/2048 [09:12<15:42, 1.43it/s]
loss 2.62 accuracy 0.31 -- 163.29 + 57.26 + 500.00 + 4.79 = 725.34: 34%|███▍ | 701/2048 [09:12<16:03, 1.40it/s]
loss 1.96 accuracy 0.19 -- 56.38 + 57.36 + 623.80 + 4.84 = 742.37: 34%|███▍ | 701/2048 [09:13<16:03, 1.40it/s]
loss 1.96 accuracy 0.19 -- 56.38 + 57.36 + 623.80 + 4.84 = 742.37: 34%|███▍ | 702/2048 [09:13<16:24, 1.37it/s]
loss 1.65 accuracy 0.56 -- 57.00 + 56.69 + 506.12 + 4.82 = 624.63: 34%|███▍ | 702/2048 [09:14<16:24, 1.37it/s]
loss 1.65 accuracy 0.56 -- 57.00 + 56.69 + 506.12 + 4.82 = 624.63: 34%|███▍ | 703/2048 [09:14<15:51, 1.41it/s]
loss 1.73 accuracy 0.38 -- 56.23 + 57.52 + 616.42 + 4.79 = 734.97: 34%|███▍ | 703/2048 [09:15<15:51, 1.41it/s]
loss 1.73 accuracy 0.38 -- 56.23 + 57.52 + 616.42 + 4.79 = 734.97: 34%|███▍ | 704/2048 [09:15<16:13, 1.38it/s]
loss 1.77 accuracy 0.31 -- 56.69 + 56.77 + 502.89 + 4.82 = 621.16: 34%|███▍ | 704/2048 [09:15<16:13, 1.38it/s]
loss 1.77 accuracy 0.31 -- 56.69 + 56.77 + 502.89 + 4.82 = 621.16: 34%|███▍ | 705/2048 [09:15<15:42, 1.43it/s]
loss 1.90 accuracy 0.25 -- 56.25 + 166.93 + 502.90 + 4.81 = 730.88: 34%|███▍ | 705/2048 [09:16<15:42, 1.43it/s]
loss 1.90 accuracy 0.25 -- 56.25 + 166.93 + 502.90 + 4.81 = 730.88: 34%|███▍ | 706/2048 [09:16<16:04, 1.39it/s]
loss 1.92 accuracy 0.19 -- 56.97 + 57.06 + 501.18 + 4.81 = 620.01: 34%|███▍ | 706/2048 [09:17<16:04, 1.39it/s]
loss 1.92 accuracy 0.19 -- 56.97 + 57.06 + 501.18 + 4.81 = 620.01: 35%|███▍ | 707/2048 [09:17<15:35, 1.43it/s]
loss 2.04 accuracy 0.19 -- 56.58 + 56.52 + 499.87 + 4.78 = 617.75: 35%|███▍ | 707/2048 [09:17<15:35, 1.43it/s]
loss 2.04 accuracy 0.19 -- 56.58 + 56.52 + 499.87 + 4.78 = 617.75: 35%|███▍ | 708/2048 [09:17<15:56, 1.40it/s]
loss 2.84 accuracy 0.19 -- 55.99 + 57.19 + 495.82 + 4.79 = 613.79: 35%|███▍ | 708/2048 [09:18<15:56, 1.40it/s]
loss 2.84 accuracy 0.19 -- 55.99 + 57.19 + 495.82 + 4.79 = 613.79: 35%|███▍ | 709/2048 [09:18<15:26, 1.45it/s]
loss 1.66 accuracy 0.25 -- 157.80 + 57.03 + 490.61 + 4.80 = 710.25: 35%|███▍ | 709/2048 [09:19<15:26, 1.45it/s]
loss 1.66 accuracy 0.25 -- 157.80 + 57.03 + 490.61 + 4.80 = 710.25: 35%|███▍ | 710/2048 [09:19<15:43, 1.42it/s]
loss 2.45 accuracy 0.25 -- 55.89 + 166.98 + 501.57 + 4.78 = 729.21: 35%|███▍ | 710/2048 [09:20<15:43, 1.42it/s]
loss 2.45 accuracy 0.25 -- 55.89 + 166.98 + 501.57 + 4.78 = 729.21: 35%|███▍ | 711/2048 [09:20<16:03, 1.39it/s]
loss 1.90 accuracy 0.44 -- 56.50 + 56.24 + 498.31 + 4.84 = 615.89: 35%|███▍ | 711/2048 [09:20<16:03, 1.39it/s]
loss 1.90 accuracy 0.44 -- 56.50 + 56.24 + 498.31 + 4.84 = 615.89: 35%|███▍ | 712/2048 [09:20<15:31, 1.43it/s]
loss 1.97 accuracy 0.38 -- 163.11 + 57.41 + 496.75 + 4.78 = 722.05: 35%|███▍ | 712/2048 [09:21<15:31, 1.43it/s]
loss 1.97 accuracy 0.38 -- 163.11 + 57.41 + 496.75 + 4.78 = 722.05: 35%|███▍ | 713/2048 [09:21<15:51, 1.40it/s]
loss 1.87 accuracy 0.31 -- 56.13 + 57.48 + 621.72 + 4.79 = 740.11: 35%|███▍ | 713/2048 [09:22<15:51, 1.40it/s]
loss 1.87 accuracy 0.31 -- 56.13 + 57.48 + 621.72 + 4.79 = 740.11: 35%|███▍ | 714/2048 [09:22<16:12, 1.37it/s]
loss 2.07 accuracy 0.31 -- 56.70 + 56.59 + 505.87 + 4.82 = 623.98: 35%|███▍ | 714/2048 [09:22<16:12, 1.37it/s]
loss 2.07 accuracy 0.31 -- 56.70 + 56.59 + 505.87 + 4.82 = 623.98: 35%|███▍ | 715/2048 [09:22<15:41, 1.42it/s]
loss 2.05 accuracy 0.31 -- 55.99 + 57.22 + 616.59 + 4.78 = 734.58: 35%|███▍ | 715/2048 [09:23<15:41, 1.42it/s]
loss 2.05 accuracy 0.31 -- 55.99 + 57.22 + 616.59 + 4.78 = 734.58: 35%|███▍ | 716/2048 [09:23<16:02, 1.38it/s]
loss 1.74 accuracy 0.31 -- 56.84 + 56.31 + 501.08 + 4.80 = 619.03: 35%|███▍ | 716/2048 [09:24<16:02, 1.38it/s]
loss 1.74 accuracy 0.31 -- 56.84 + 56.31 + 501.08 + 4.80 = 619.03: 35%|███▌ | 717/2048 [09:24<15:31, 1.43it/s]
loss 2.16 accuracy 0.19 -- 56.09 + 166.44 + 503.21 + 4.79 = 730.54: 35%|███▌ | 717/2048 [09:25<15:31, 1.43it/s]
loss 2.16 accuracy 0.19 -- 56.09 + 166.44 + 503.21 + 4.79 = 730.54: 35%|███▌ | 718/2048 [09:25<15:54, 1.39it/s]
loss 1.64 accuracy 0.50 -- 55.89 + 56.09 + 498.72 + 4.78 = 615.48: 35%|███▌ | 718/2048 [09:25<15:54, 1.39it/s]
loss 1.64 accuracy 0.50 -- 55.89 + 56.09 + 498.72 + 4.78 = 615.48: 35%|███▌ | 719/2048 [09:25<15:23, 1.44it/s]
loss 1.89 accuracy 0.25 -- 56.65 + 56.58 + 497.86 + 4.77 = 615.86: 35%|███▌ | 719/2048 [09:26<15:23, 1.44it/s]
loss 1.89 accuracy 0.25 -- 56.65 + 56.58 + 497.86 + 4.77 = 615.86: 35%|███▌ | 720/2048 [09:26<15:44, 1.41it/s]
loss 2.41 accuracy 0.06 -- 56.21 + 57.34 + 494.63 + 4.78 = 612.97: 35%|███▌ | 720/2048 [09:27<15:44, 1.41it/s]
loss 2.41 accuracy 0.06 -- 56.21 + 57.34 + 494.63 + 4.78 = 612.97: 35%|███▌ | 721/2048 [09:27<15:15, 1.45it/s]
loss 2.17 accuracy 0.12 -- 157.96 + 56.66 + 489.57 + 4.82 = 709.01: 35%|███▌ | 721/2048 [09:27<15:15, 1.45it/s]
loss 2.17 accuracy 0.12 -- 157.96 + 56.66 + 489.57 + 4.82 = 709.01: 35%|███▌ | 722/2048 [09:27<15:33, 1.42it/s]
loss 2.02 accuracy 0.38 -- 55.86 + 166.27 + 501.03 + 4.78 = 727.93: 35%|███▌ | 722/2048 [09:28<15:33, 1.42it/s]
loss 2.02 accuracy 0.38 -- 55.86 + 166.27 + 501.03 + 4.78 = 727.93: 35%|███▌ | 723/2048 [09:28<15:53, 1.39it/s]
loss 1.88 accuracy 0.19 -- 57.16 + 56.97 + 498.94 + 4.78 = 617.84: 35%|███▌ | 723/2048 [09:29<15:53, 1.39it/s]
loss 1.88 accuracy 0.19 -- 57.16 + 56.97 + 498.94 + 4.78 = 617.84: 35%|███▌ | 724/2048 [09:29<15:22, 1.43it/s]
loss 1.83 accuracy 0.44 -- 162.72 + 56.84 + 497.00 + 4.80 = 721.36: 35%|███▌ | 724/2048 [09:29<15:22, 1.43it/s]
loss 1.83 accuracy 0.44 -- 162.72 + 56.84 + 497.00 + 4.80 = 721.36: 35%|███▌ | 725/2048 [09:29<15:42, 1.40it/s]
loss 1.74 accuracy 0.44 -- 56.54 + 57.64 + 621.47 + 4.81 = 740.46: 35%|███▌ | 725/2048 [09:30<15:42, 1.40it/s]
loss 1.74 accuracy 0.44 -- 56.54 + 57.64 + 621.47 + 4.81 = 740.46: 35%|███▌ | 726/2048 [09:30<16:03, 1.37it/s]
loss 1.91 accuracy 0.38 -- 56.85 + 56.71 + 504.86 + 4.79 = 623.21: 35%|███▌ | 726/2048 [09:31<16:03, 1.37it/s]
loss 1.91 accuracy 0.38 -- 56.85 + 56.71 + 504.86 + 4.79 = 623.21: 35%|███▌ | 727/2048 [09:31<15:32, 1.42it/s]
loss 1.94 accuracy 0.25 -- 56.05 + 57.34 + 616.25 + 4.77 = 734.42: 35%|███▌ | 727/2048 [09:32<15:32, 1.42it/s]
loss 1.94 accuracy 0.25 -- 56.05 + 57.34 + 616.25 + 4.77 = 734.42: 36%|███▌ | 728/2048 [09:32<15:53, 1.38it/s]
loss 1.68 accuracy 0.44 -- 56.64 + 56.65 + 503.49 + 4.79 = 621.57: 36%|███▌ | 728/2048 [09:32<15:53, 1.38it/s]
loss 1.68 accuracy 0.44 -- 56.64 + 56.65 + 503.49 + 4.79 = 621.57: 36%|███▌ | 729/2048 [09:32<15:23, 1.43it/s]
loss 1.81 accuracy 0.12 -- 56.54 + 166.79 + 501.16 + 4.77 = 729.26: 36%|███▌ | 729/2048 [09:33<15:23, 1.43it/s]
loss 1.81 accuracy 0.12 -- 56.54 + 166.79 + 501.16 + 4.77 = 729.26: 36%|███▌ | 730/2048 [09:33<15:45, 1.39it/s]
loss 1.86 accuracy 0.31 -- 56.09 + 56.41 + 499.66 + 4.78 = 616.95: 36%|███▌ | 730/2048 [09:34<15:45, 1.39it/s]
loss 1.86 accuracy 0.31 -- 56.09 + 56.41 + 499.66 + 4.78 = 616.95: 36%|███▌ | 731/2048 [09:34<15:15, 1.44it/s]
loss 2.12 accuracy 0.12 -- 56.36 + 56.45 + 498.28 + 4.79 = 615.89: 36%|███▌ | 731/2048 [09:34<15:15, 1.44it/s]
loss 2.12 accuracy 0.12 -- 56.36 + 56.45 + 498.28 + 4.79 = 615.89: 36%|███▌ | 732/2048 [09:34<15:36, 1.41it/s]
loss 2.19 accuracy 0.25 -- 56.04 + 57.31 + 495.15 + 4.79 = 613.29: 36%|███▌ | 732/2048 [09:35<15:36, 1.41it/s]
loss 2.19 accuracy 0.25 -- 56.04 + 57.31 + 495.15 + 4.79 = 613.29: 36%|███▌ | 733/2048 [09:35<15:07, 1.45it/s]
loss 2.01 accuracy 0.25 -- 156.98 + 56.66 + 490.45 + 4.77 = 708.87: 36%|███▌ | 733/2048 [09:36<15:07, 1.45it/s]
loss 2.01 accuracy 0.25 -- 156.98 + 56.66 + 490.45 + 4.77 = 708.87: 36%|███▌ | 734/2048 [09:36<15:25, 1.42it/s]
loss 2.07 accuracy 0.25 -- 55.90 + 166.42 + 502.91 + 4.76 = 730.00: 36%|███▌ | 734/2048 [09:37<15:25, 1.42it/s]
loss 2.07 accuracy 0.25 -- 55.90 + 166.42 + 502.91 + 4.76 = 730.00: 36%|███▌ | 735/2048 [09:37<15:45, 1.39it/s]
loss 1.44 accuracy 0.44 -- 56.48 + 56.19 + 497.12 + 4.78 = 614.58: 36%|███▌ | 735/2048 [09:37<15:45, 1.39it/s]
loss 1.44 accuracy 0.44 -- 56.48 + 56.19 + 497.12 + 4.78 = 614.58: 36%|███▌ | 736/2048 [09:37<15:13, 1.44it/s]
loss 2.10 accuracy 0.12 -- 162.23 + 56.82 + 496.32 + 4.79 = 720.15: 36%|███▌ | 736/2048 [09:38<15:13, 1.44it/s]
loss 2.10 accuracy 0.12 -- 162.23 + 56.82 + 496.32 + 4.79 = 720.15: 36%|███▌ | 737/2048 [09:38<15:33, 1.40it/s]
loss 1.74 accuracy 0.31 -- 56.28 + 57.64 + 620.88 + 4.82 = 739.61: 36%|███▌ | 737/2048 [09:39<15:33, 1.40it/s]
loss 1.74 accuracy 0.31 -- 56.28 + 57.64 + 620.88 + 4.82 = 739.61: 36%|███▌ | 738/2048 [09:39<15:54, 1.37it/s]
loss 1.70 accuracy 0.25 -- 56.78 + 56.27 + 505.30 + 4.76 = 623.12: 36%|███▌ | 738/2048 [09:39<15:54, 1.37it/s]
loss 1.70 accuracy 0.25 -- 56.78 + 56.27 + 505.30 + 4.76 = 623.12: 36%|███▌ | 739/2048 [09:39<15:36, 1.40it/s]
loss 1.82 accuracy 0.19 -- 56.14 + 57.20 + 617.69 + 4.77 = 735.80: 36%|███▌ | 739/2048 [09:40<15:36, 1.40it/s]
loss 1.82 accuracy 0.19 -- 56.14 + 57.20 + 617.69 + 4.77 = 735.80: 36%|███▌ | 740/2048 [09:40<15:54, 1.37it/s]
loss 2.16 accuracy 0.31 -- 57.82 + 56.84 + 504.10 + 4.78 = 623.55: 36%|███▌ | 740/2048 [09:41<15:54, 1.37it/s]
loss 2.16 accuracy 0.31 -- 57.82 + 56.84 + 504.10 + 4.78 = 623.55: 36%|███▌ | 741/2048 [09:41<15:22, 1.42it/s]
loss 1.78 accuracy 0.25 -- 56.36 + 166.29 + 503.50 + 4.77 = 730.91: 36%|███▌ | 741/2048 [09:42<15:22, 1.42it/s]
loss 1.78 accuracy 0.25 -- 56.36 + 166.29 + 503.50 + 4.77 = 730.91: 36%|███▌ | 742/2048 [09:42<15:42, 1.39it/s]
loss 2.20 accuracy 0.31 -- 56.51 + 56.33 + 500.75 + 4.77 = 618.36: 36%|███▌ | 742/2048 [09:42<15:42, 1.39it/s]
loss 2.20 accuracy 0.31 -- 56.51 + 56.33 + 500.75 + 4.77 = 618.36: 36%|███▋ | 743/2048 [09:42<15:11, 1.43it/s]
loss 1.80 accuracy 0.25 -- 57.19 + 56.84 + 499.47 + 4.77 = 618.26: 36%|███▋ | 743/2048 [09:43<15:11, 1.43it/s]
loss 1.80 accuracy 0.25 -- 57.19 + 56.84 + 499.47 + 4.77 = 618.26: 36%|███▋ | 744/2048 [09:43<15:32, 1.40it/s]
loss 1.98 accuracy 0.19 -- 56.20 + 57.24 + 496.62 + 4.78 = 614.84: 36%|███▋ | 744/2048 [09:44<15:32, 1.40it/s]
loss 1.98 accuracy 0.19 -- 56.20 + 57.24 + 496.62 + 4.78 = 614.84: 36%|███▋ | 745/2048 [09:44<15:02, 1.44it/s]
loss 1.92 accuracy 0.25 -- 157.60 + 57.03 + 490.23 + 4.81 = 709.66: 36%|███▋ | 745/2048 [09:44<15:02, 1.44it/s]
loss 1.92 accuracy 0.25 -- 157.60 + 57.03 + 490.23 + 4.81 = 709.66: 36%|███▋ | 746/2048 [09:44<15:19, 1.42it/s]
loss 2.35 accuracy 0.25 -- 56.05 + 166.99 + 501.39 + 4.79 = 729.22: 36%|███▋ | 746/2048 [09:45<15:19, 1.42it/s]
loss 2.35 accuracy 0.25 -- 56.05 + 166.99 + 501.39 + 4.79 = 729.22: 36%|███▋ | 747/2048 [09:45<15:38, 1.39it/s]
loss 1.72 accuracy 0.38 -- 56.90 + 56.86 + 497.53 + 4.77 = 616.07: 36%|███▋ | 747/2048 [09:46<15:38, 1.39it/s]
loss 1.72 accuracy 0.38 -- 56.90 + 56.86 + 497.53 + 4.77 = 616.07: 37%|███▋ | 748/2048 [09:46<15:07, 1.43it/s]
loss 1.96 accuracy 0.31 -- 162.37 + 56.96 + 498.23 + 4.79 = 722.35: 37%|███▋ | 748/2048 [09:47<15:07, 1.43it/s]
loss 1.96 accuracy 0.31 -- 162.37 + 56.96 + 498.23 + 4.79 = 722.35: 37%|███▋ | 749/2048 [09:47<15:26, 1.40it/s]
loss 2.38 accuracy 0.25 -- 56.06 + 57.17 + 620.85 + 4.80 = 738.88: 37%|███▋ | 749/2048 [09:47<15:26, 1.40it/s]
loss 2.38 accuracy 0.25 -- 56.06 + 57.17 + 620.85 + 4.80 = 738.88: 37%|███▋ | 750/2048 [09:47<15:46, 1.37it/s]
loss 1.78 accuracy 0.31 -- 57.13 + 56.49 + 504.03 + 4.79 = 622.43: 37%|███▋ | 750/2048 [09:48<15:46, 1.37it/s]
loss 1.78 accuracy 0.31 -- 57.13 + 56.49 + 504.03 + 4.79 = 622.43: 37%|███▋ | 751/2048 [09:48<15:14, 1.42it/s]
loss 1.63 accuracy 0.38 -- 56.42 + 57.10 + 617.77 + 4.79 = 736.07: 37%|███▋ | 751/2048 [09:49<15:14, 1.42it/s]
loss 1.63 accuracy 0.38 -- 56.42 + 57.10 + 617.77 + 4.79 = 736.07: 37%|███▋ | 752/2048 [09:49<15:36, 1.38it/s]
loss 2.07 accuracy 0.50 -- 56.56 + 56.29 + 502.39 + 4.79 = 620.02: 37%|███▋ | 752/2048 [09:49<15:36, 1.38it/s]
loss 2.07 accuracy 0.50 -- 56.56 + 56.29 + 502.39 + 4.79 = 620.02: 37%|███▋ | 753/2048 [09:49<15:06, 1.43it/s]
loss 1.86 accuracy 0.19 -- 56.34 + 166.19 + 501.25 + 4.78 = 728.56: 37%|███▋ | 753/2048 [09:50<15:06, 1.43it/s]
loss 1.86 accuracy 0.19 -- 56.34 + 166.19 + 501.25 + 4.78 = 728.56: 37%|███▋ | 754/2048 [09:50<15:27, 1.39it/s]
loss 1.71 accuracy 0.25 -- 56.14 + 58.34 + 531.39 + 4.95 = 650.82: 37%|███▋ | 754/2048 [09:51<15:27, 1.39it/s]
loss 1.71 accuracy 0.25 -- 56.14 + 58.34 + 531.39 + 4.95 = 650.82: 37%|███▋ | 755/2048 [09:51<15:12, 1.42it/s]
loss 1.84 accuracy 0.25 -- 58.40 + 58.79 + 509.60 + 4.79 = 631.59: 37%|███▋ | 755/2048 [09:52<15:12, 1.42it/s]
loss 1.84 accuracy 0.25 -- 58.40 + 58.79 + 509.60 + 4.79 = 631.59: 37%|███▋ | 756/2048 [09:52<15:38, 1.38it/s]
loss 1.95 accuracy 0.19 -- 55.79 + 57.46 + 495.06 + 4.78 = 613.09: 37%|███▋ | 756/2048 [09:52<15:38, 1.38it/s]
loss 1.95 accuracy 0.19 -- 55.79 + 57.46 + 495.06 + 4.78 = 613.09: 37%|███▋ | 757/2048 [09:52<15:04, 1.43it/s]
loss 2.17 accuracy 0.12 -- 156.82 + 56.71 + 489.37 + 4.77 = 707.66: 37%|███▋ | 757/2048 [09:53<15:04, 1.43it/s]
loss 2.17 accuracy 0.12 -- 156.82 + 56.71 + 489.37 + 4.77 = 707.66: 37%|███▋ | 758/2048 [09:53<15:17, 1.41it/s]
loss 1.81 accuracy 0.19 -- 55.90 + 166.73 + 501.84 + 4.78 = 729.25: 37%|███▋ | 758/2048 [09:54<15:17, 1.41it/s]
loss 1.81 accuracy 0.19 -- 55.90 + 166.73 + 501.84 + 4.78 = 729.25: 37%|███▋ | 759/2048 [09:54<15:34, 1.38it/s]
loss 2.70 accuracy 0.12 -- 56.38 + 56.04 + 504.61 + 4.79 = 621.82: 37%|███▋ | 759/2048 [09:54<15:34, 1.38it/s]
loss 2.70 accuracy 0.12 -- 56.38 + 56.04 + 504.61 + 4.79 = 621.82: 37%|███▋ | 760/2048 [09:54<15:04, 1.42it/s]
loss 1.92 accuracy 0.25 -- 162.77 + 57.41 + 497.22 + 4.77 = 722.18: 37%|███▋ | 760/2048 [09:55<15:04, 1.42it/s]
loss 1.92 accuracy 0.25 -- 162.77 + 57.41 + 497.22 + 4.77 = 722.18: 37%|███▋ | 761/2048 [09:55<15:22, 1.40it/s]
loss 1.70 accuracy 0.50 -- 56.30 + 57.83 + 626.48 + 4.77 = 745.37: 37%|███▋ | 761/2048 [09:56<15:22, 1.40it/s]
loss 1.70 accuracy 0.50 -- 56.30 + 57.83 + 626.48 + 4.77 = 745.37: 37%|███▋ | 762/2048 [09:56<15:43, 1.36it/s]
loss 1.99 accuracy 0.31 -- 57.02 + 56.44 + 507.60 + 4.77 = 625.84: 37%|███▋ | 762/2048 [09:57<15:43, 1.36it/s]
loss 1.99 accuracy 0.31 -- 57.02 + 56.44 + 507.60 + 4.77 = 625.84: 37%|███▋ | 763/2048 [09:57<15:11, 1.41it/s]
loss 2.07 accuracy 0.31 -- 56.31 + 57.71 + 615.58 + 4.79 = 734.38: 37%|███▋ | 763/2048 [09:57<15:11, 1.41it/s]
loss 2.07 accuracy 0.31 -- 56.31 + 57.71 + 615.58 + 4.79 = 734.38: 37%|███▋ | 764/2048 [09:57<15:30, 1.38it/s]
loss 2.08 accuracy 0.12 -- 56.87 + 56.56 + 501.64 + 4.78 = 619.85: 37%|███▋ | 764/2048 [09:58<15:30, 1.38it/s]
loss 2.08 accuracy 0.12 -- 56.87 + 56.56 + 501.64 + 4.78 = 619.85: 37%|███▋ | 765/2048 [09:58<15:00, 1.43it/s]
loss 1.90 accuracy 0.25 -- 56.27 + 166.02 + 503.20 + 4.81 = 730.31: 37%|███▋ | 765/2048 [09:59<15:00, 1.43it/s]
loss 1.90 accuracy 0.25 -- 56.27 + 166.02 + 503.20 + 4.81 = 730.31: 37%|███▋ | 766/2048 [09:59<15:20, 1.39it/s]
loss 1.59 accuracy 0.56 -- 56.06 + 56.31 + 500.68 + 4.82 = 617.87: 37%|███▋ | 766/2048 [09:59<15:20, 1.39it/s]
loss 1.59 accuracy 0.56 -- 56.06 + 56.31 + 500.68 + 4.82 = 617.87: 37%|███▋ | 767/2048 [09:59<14:52, 1.44it/s]
loss 1.64 accuracy 0.38 -- 56.80 + 56.14 + 497.95 + 4.77 = 615.67: 37%|███▋ | 767/2048 [10:00<14:52, 1.44it/s]
loss 1.64 accuracy 0.38 -- 56.80 + 56.14 + 497.95 + 4.77 = 615.67: 38%|███▊ | 768/2048 [10:00<15:11, 1.40it/s]
loss 1.84 accuracy 0.31 -- 56.09 + 57.93 + 496.62 + 4.78 = 615.43: 38%|███▊ | 768/2048 [10:01<15:11, 1.40it/s]
loss 1.84 accuracy 0.31 -- 56.09 + 57.93 + 496.62 + 4.78 = 615.43: 38%|███▊ | 769/2048 [10:01<14:44, 1.45it/s]
loss 1.99 accuracy 0.19 -- 157.53 + 56.72 + 497.95 + 4.77 = 716.98: 38%|███▊ | 769/2048 [10:01<14:44, 1.45it/s]
loss 1.99 accuracy 0.19 -- 157.53 + 56.72 + 497.95 + 4.77 = 716.98: 38%|███▊ | 770/2048 [10:01<15:03, 1.41it/s]
loss 2.11 accuracy 0.31 -- 55.91 + 167.27 + 501.64 + 4.78 = 729.60: 38%|███▊ | 770/2048 [10:02<15:03, 1.41it/s]
loss 2.11 accuracy 0.31 -- 55.91 + 167.27 + 501.64 + 4.78 = 729.60: 38%|███▊ | 771/2048 [10:02<15:22, 1.38it/s]
loss 1.80 accuracy 0.38 -- 56.70 + 56.60 + 497.74 + 4.79 = 615.82: 38%|███▊ | 771/2048 [10:03<15:22, 1.38it/s]
loss 1.80 accuracy 0.38 -- 56.70 + 56.60 + 497.74 + 4.79 = 615.82: 38%|███▊ | 772/2048 [10:03<14:51, 1.43it/s]
loss 2.16 accuracy 0.38 -- 162.29 + 57.06 + 497.14 + 4.79 = 721.28: 38%|███▊ | 772/2048 [10:04<14:51, 1.43it/s]
loss 2.16 accuracy 0.38 -- 162.29 + 57.06 + 497.14 + 4.79 = 721.28: 38%|███▊ | 773/2048 [10:04<15:09, 1.40it/s]
loss 2.04 accuracy 0.31 -- 56.01 + 57.35 + 622.45 + 4.82 = 740.64: 38%|███▊ | 773/2048 [10:04<15:09, 1.40it/s]
loss 2.04 accuracy 0.31 -- 56.01 + 57.35 + 622.45 + 4.82 = 740.64: 38%|███▊ | 774/2048 [10:04<15:29, 1.37it/s]
loss 2.03 accuracy 0.25 -- 57.56 + 57.05 + 504.96 + 4.79 = 624.36: 38%|███▊ | 774/2048 [10:05<15:29, 1.37it/s]
loss 2.03 accuracy 0.25 -- 57.56 + 57.05 + 504.96 + 4.79 = 624.36: 38%|███▊ | 775/2048 [10:05<14:59, 1.42it/s]
loss 1.77 accuracy 0.44 -- 56.52 + 57.26 + 616.56 + 4.78 = 735.12: 38%|███▊ | 775/2048 [10:06<14:59, 1.42it/s]
loss 1.77 accuracy 0.44 -- 56.52 + 57.26 + 616.56 + 4.78 = 735.12: 38%|███▊ | 776/2048 [10:06<15:19, 1.38it/s]
loss 2.60 accuracy 0.19 -- 56.98 + 56.69 + 503.09 + 4.78 = 621.54: 38%|███▊ | 776/2048 [10:06<15:19, 1.38it/s]
loss 2.60 accuracy 0.19 -- 56.98 + 56.69 + 503.09 + 4.78 = 621.54: 38%|███▊ | 777/2048 [10:06<14:50, 1.43it/s]
loss 1.74 accuracy 0.31 -- 56.40 + 165.77 + 501.16 + 4.78 = 728.12: 38%|███▊ | 777/2048 [10:07<14:50, 1.43it/s]
loss 1.74 accuracy 0.31 -- 56.40 + 165.77 + 501.16 + 4.78 = 728.12: 38%|███▊ | 778/2048 [10:07<15:10, 1.39it/s]
loss 1.76 accuracy 0.44 -- 56.22 + 56.69 + 501.20 + 4.76 = 618.88: 38%|███▊ | 778/2048 [10:08<15:10, 1.39it/s]
loss 1.76 accuracy 0.44 -- 56.22 + 56.69 + 501.20 + 4.76 = 618.88: 38%|███▊ | 779/2048 [10:08<14:42, 1.44it/s]
loss 2.10 accuracy 0.19 -- 56.53 + 56.36 + 499.89 + 4.75 = 617.53: 38%|███▊ | 779/2048 [10:09<14:42, 1.44it/s]
loss 2.10 accuracy 0.19 -- 56.53 + 56.36 + 499.89 + 4.75 = 617.53: 38%|███▊ | 780/2048 [10:09<15:03, 1.40it/s]
loss 2.16 accuracy 0.12 -- 56.15 + 57.51 + 496.80 + 4.79 = 615.24: 38%|███▊ | 780/2048 [10:09<15:03, 1.40it/s]
loss 2.16 accuracy 0.12 -- 56.15 + 57.51 + 496.80 + 4.79 = 615.24: 38%|███▊ | 781/2048 [10:09<14:36, 1.45it/s]
loss 1.87 accuracy 0.38 -- 157.95 + 56.87 + 490.90 + 4.81 = 710.53: 38%|███▊ | 781/2048 [10:10<14:36, 1.45it/s]
loss 1.87 accuracy 0.38 -- 157.95 + 56.87 + 490.90 + 4.81 = 710.53: 38%|███▊ | 782/2048 [10:10<14:52, 1.42it/s]
loss 1.92 accuracy 0.12 -- 56.14 + 166.82 + 501.48 + 4.80 = 729.24: 38%|███▊ | 782/2048 [10:11<14:52, 1.42it/s]
loss 1.92 accuracy 0.12 -- 56.14 + 166.82 + 501.48 + 4.80 = 729.24: 38%|███▊ | 783/2048 [10:11<15:11, 1.39it/s]
loss 1.69 accuracy 0.56 -- 56.76 + 56.30 + 498.50 + 4.77 = 616.33: 38%|███▊ | 783/2048 [10:11<15:11, 1.39it/s]
loss 1.69 accuracy 0.56 -- 56.76 + 56.30 + 498.50 + 4.77 = 616.33: 38%|███▊ | 784/2048 [10:11<14:41, 1.43it/s]
loss 1.68 accuracy 0.25 -- 162.51 + 57.09 + 496.43 + 4.78 = 720.81: 38%|███▊ | 784/2048 [10:12<14:41, 1.43it/s]
loss 1.68 accuracy 0.25 -- 162.51 + 57.09 + 496.43 + 4.78 = 720.81: 38%|███▊ | 785/2048 [10:12<15:00, 1.40it/s]
loss 2.00 accuracy 0.31 -- 56.59 + 57.70 + 621.74 + 4.78 = 740.81: 38%|███▊ | 785/2048 [10:13<15:00, 1.40it/s]
loss 2.00 accuracy 0.31 -- 56.59 + 57.70 + 621.74 + 4.78 = 740.81: 38%|███▊ | 786/2048 [10:13<15:20, 1.37it/s]
loss 1.63 accuracy 0.50 -- 56.90 + 56.72 + 505.72 + 4.78 = 624.11: 38%|███▊ | 786/2048 [10:14<15:20, 1.37it/s]
loss 1.63 accuracy 0.50 -- 56.90 + 56.72 + 505.72 + 4.78 = 624.11: 38%|███▊ | 787/2048 [10:14<14:50, 1.42it/s]
loss 2.66 accuracy 0.12 -- 56.28 + 57.52 + 616.72 + 4.79 = 735.30: 38%|███▊ | 787/2048 [10:14<14:50, 1.42it/s]
loss 2.66 accuracy 0.12 -- 56.28 + 57.52 + 616.72 + 4.79 = 735.30: 38%|███▊ | 788/2048 [10:14<15:10, 1.38it/s]
loss 1.94 accuracy 0.25 -- 56.96 + 56.91 + 503.10 + 4.78 = 621.74: 38%|███▊ | 788/2048 [10:15<15:10, 1.38it/s]
loss 1.94 accuracy 0.25 -- 56.96 + 56.91 + 503.10 + 4.78 = 621.74: 39%|███▊ | 789/2048 [10:15<14:42, 1.43it/s]
loss 1.88 accuracy 0.31 -- 56.39 + 166.21 + 502.97 + 4.79 = 730.35: 39%|███▊ | 789/2048 [10:16<14:42, 1.43it/s]
loss 1.88 accuracy 0.31 -- 56.39 + 166.21 + 502.97 + 4.79 = 730.35: 39%|███▊ | 790/2048 [10:16<15:02, 1.39it/s]
loss 1.94 accuracy 0.19 -- 56.09 + 56.32 + 501.03 + 4.79 = 618.22: 39%|███▊ | 790/2048 [10:16<15:02, 1.39it/s]
loss 1.94 accuracy 0.19 -- 56.09 + 56.32 + 501.03 + 4.79 = 618.22: 39%|███▊ | 791/2048 [10:16<14:34, 1.44it/s]
loss 1.78 accuracy 0.44 -- 57.16 + 56.36 + 498.48 + 4.79 = 616.79: 39%|███▊ | 791/2048 [10:17<14:34, 1.44it/s]
loss 1.78 accuracy 0.44 -- 57.16 + 56.36 + 498.48 + 4.79 = 616.79: 39%|███▊ | 792/2048 [10:17<14:54, 1.40it/s]
loss 1.88 accuracy 0.19 -- 56.25 + 57.21 + 495.75 + 4.77 = 613.99: 39%|███▊ | 792/2048 [10:18<14:54, 1.40it/s]
loss 1.88 accuracy 0.19 -- 56.25 + 57.21 + 495.75 + 4.77 = 613.99: 39%|███▊ | 793/2048 [10:18<14:27, 1.45it/s]
loss 1.97 accuracy 0.44 -- 157.17 + 57.07 + 491.07 + 4.77 = 710.08: 39%|███▊ | 793/2048 [10:19<14:27, 1.45it/s]
loss 1.97 accuracy 0.44 -- 157.17 + 57.07 + 491.07 + 4.77 = 710.08: 39%|███▉ | 794/2048 [10:19<14:43, 1.42it/s]
loss 1.74 accuracy 0.38 -- 55.96 + 166.21 + 501.24 + 4.77 = 728.18: 39%|███▉ | 794/2048 [10:19<14:43, 1.42it/s]
loss 1.74 accuracy 0.38 -- 55.96 + 166.21 + 501.24 + 4.77 = 728.18: 39%|███▉ | 795/2048 [10:19<15:02, 1.39it/s]
loss 1.78 accuracy 0.31 -- 56.61 + 56.72 + 498.32 + 4.78 = 616.43: 39%|███▉ | 795/2048 [10:20<15:02, 1.39it/s]
loss 1.78 accuracy 0.31 -- 56.61 + 56.72 + 498.32 + 4.78 = 616.43: 39%|███▉ | 796/2048 [10:20<14:32, 1.43it/s]
loss 1.78 accuracy 0.38 -- 162.55 + 57.19 + 497.42 + 4.78 = 721.93: 39%|███▉ | 796/2048 [10:21<14:32, 1.43it/s]
loss 1.78 accuracy 0.38 -- 162.55 + 57.19 + 497.42 + 4.78 = 721.93: 39%|███▉ | 797/2048 [10:21<14:51, 1.40it/s]
loss 1.76 accuracy 0.25 -- 55.83 + 57.15 + 618.63 + 4.80 = 736.41: 39%|███▉ | 797/2048 [10:21<14:51, 1.40it/s]
loss 1.76 accuracy 0.25 -- 55.83 + 57.15 + 618.63 + 4.80 = 736.41: 39%|███▉ | 798/2048 [10:21<15:09, 1.37it/s]
loss 1.57 accuracy 0.44 -- 57.06 + 56.83 + 505.29 + 4.77 = 623.95: 39%|███▉ | 798/2048 [10:22<15:09, 1.37it/s]
loss 1.57 accuracy 0.44 -- 57.06 + 56.83 + 505.29 + 4.77 = 623.95: 39%|███▉ | 799/2048 [10:22<14:40, 1.42it/s]
loss 2.18 accuracy 0.25 -- 56.40 + 57.22 + 616.22 + 4.81 = 734.64: 39%|███▉ | 799/2048 [10:23<14:40, 1.42it/s]
loss 2.18 accuracy 0.25 -- 56.40 + 57.22 + 616.22 + 4.81 = 734.64: 39%|███▉ | 800/2048 [10:23<15:01, 1.39it/s]
loss 2.30 accuracy 0.25 -- 56.70 + 56.73 + 501.64 + 4.78 = 619.86: 39%|███▉ | 800/2048 [10:24<15:01, 1.39it/s]
loss 2.30 accuracy 0.25 -- 56.70 + 56.73 + 501.64 + 4.78 = 619.86: 39%|███▉ | 801/2048 [10:24<14:45, 1.41it/s]
loss 1.53 accuracy 0.56 -- 56.38 + 166.55 + 502.96 + 4.80 = 730.68: 39%|███▉ | 801/2048 [10:24<14:45, 1.41it/s]
loss 1.53 accuracy 0.56 -- 56.38 + 166.55 + 502.96 + 4.80 = 730.68: 39%|███▉ | 802/2048 [10:24<15:02, 1.38it/s]
loss 1.79 accuracy 0.31 -- 56.33 + 56.82 + 498.89 + 4.77 = 616.80: 39%|███▉ | 802/2048 [10:25<15:02, 1.38it/s]
loss 1.79 accuracy 0.31 -- 56.33 + 56.82 + 498.89 + 4.77 = 616.80: 39%|███▉ | 803/2048 [10:25<14:31, 1.43it/s]
loss 1.82 accuracy 0.25 -- 56.79 + 56.18 + 498.49 + 4.77 = 616.23: 39%|███▉ | 803/2048 [10:26<14:31, 1.43it/s]
loss 1.82 accuracy 0.25 -- 56.79 + 56.18 + 498.49 + 4.77 = 616.23: 39%|███▉ | 804/2048 [10:26<14:49, 1.40it/s]
loss 1.79 accuracy 0.38 -- 56.05 + 57.51 + 494.96 + 4.77 = 613.29: 39%|███▉ | 804/2048 [10:26<14:49, 1.40it/s]
loss 1.79 accuracy 0.38 -- 56.05 + 57.51 + 494.96 + 4.77 = 613.29: 39%|███▉ | 805/2048 [10:26<14:21, 1.44it/s]
loss 2.28 accuracy 0.12 -- 157.75 + 57.02 + 488.34 + 4.79 = 707.90: 39%|███▉ | 805/2048 [10:27<14:21, 1.44it/s]
loss 2.28 accuracy 0.12 -- 157.75 + 57.02 + 488.34 + 4.79 = 707.90: 39%|███▉ | 806/2048 [10:27<14:36, 1.42it/s]
loss 1.70 accuracy 0.44 -- 55.83 + 166.65 + 501.78 + 4.78 = 729.03: 39%|███▉ | 806/2048 [10:28<14:36, 1.42it/s]
loss 1.70 accuracy 0.44 -- 55.83 + 166.65 + 501.78 + 4.78 = 729.03: 39%|███▉ | 807/2048 [10:28<14:54, 1.39it/s]
loss 2.17 accuracy 0.44 -- 56.72 + 56.31 + 499.35 + 4.80 = 617.17: 39%|███▉ | 807/2048 [10:28<14:54, 1.39it/s]
loss 2.17 accuracy 0.44 -- 56.72 + 56.31 + 499.35 + 4.80 = 617.17: 39%|███▉ | 808/2048 [10:28<14:25, 1.43it/s]
loss 2.48 accuracy 0.31 -- 162.84 + 57.13 + 497.70 + 4.78 = 722.46: 39%|███▉ | 808/2048 [10:29<14:25, 1.43it/s]
loss 2.48 accuracy 0.31 -- 162.84 + 57.13 + 497.70 + 4.78 = 722.46: 40%|███▉ | 809/2048 [10:29<14:56, 1.38it/s]
loss 2.21 accuracy 0.12 -- 55.93 + 57.03 + 621.32 + 4.79 = 739.07: 40%|███▉ | 809/2048 [10:30<14:56, 1.38it/s]
loss 2.21 accuracy 0.12 -- 55.93 + 57.03 + 621.32 + 4.79 = 739.07: 40%|███▉ | 810/2048 [10:30<15:11, 1.36it/s]
loss 2.33 accuracy 0.19 -- 56.90 + 57.05 + 505.12 + 4.78 = 623.86: 40%|███▉ | 810/2048 [10:31<15:11, 1.36it/s]
loss 2.33 accuracy 0.19 -- 56.90 + 57.05 + 505.12 + 4.78 = 623.86: 40%|███▉ | 811/2048 [10:31<14:39, 1.41it/s]
loss 2.22 accuracy 0.38 -- 56.00 + 57.40 + 615.56 + 4.77 = 733.73: 40%|███▉ | 811/2048 [10:31<14:39, 1.41it/s]
loss 2.22 accuracy 0.38 -- 56.00 + 57.40 + 615.56 + 4.77 = 733.73: 40%|███▉ | 812/2048 [10:31<14:57, 1.38it/s]
loss 2.79 accuracy 0.06 -- 56.67 + 56.72 + 502.91 + 4.76 = 621.06: 40%|███▉ | 812/2048 [10:32<14:57, 1.38it/s]
loss 2.79 accuracy 0.06 -- 56.67 + 56.72 + 502.91 + 4.76 = 621.06: 40%|███▉ | 813/2048 [10:32<14:27, 1.42it/s]
loss 1.69 accuracy 0.38 -- 56.20 + 167.07 + 503.42 + 4.77 = 731.46: 40%|███▉ | 813/2048 [10:33<14:27, 1.42it/s]
loss 1.69 accuracy 0.38 -- 56.20 + 167.07 + 503.42 + 4.77 = 731.46: 40%|███▉ | 814/2048 [10:33<14:47, 1.39it/s]
loss 2.20 accuracy 0.19 -- 56.10 + 56.45 + 498.46 + 4.78 = 615.78: 40%|███▉ | 814/2048 [10:33<14:47, 1.39it/s]
loss 2.20 accuracy 0.19 -- 56.10 + 56.45 + 498.46 + 4.78 = 615.78: 40%|███▉ | 815/2048 [10:33<14:18, 1.44it/s]
loss 2.38 accuracy 0.38 -- 56.65 + 56.38 + 498.15 + 4.78 = 615.95: 40%|███▉ | 815/2048 [10:34<14:18, 1.44it/s]
loss 2.38 accuracy 0.38 -- 56.65 + 56.38 + 498.15 + 4.78 = 615.95: 40%|███▉ | 816/2048 [10:34<14:50, 1.38it/s]
loss 2.17 accuracy 0.25 -- 56.10 + 57.54 + 496.06 + 4.78 = 614.47: 40%|███▉ | 816/2048 [10:35<14:50, 1.38it/s]
loss 2.17 accuracy 0.25 -- 56.10 + 57.54 + 496.06 + 4.78 = 614.47: 40%|███▉ | 817/2048 [10:35<14:20, 1.43it/s]
loss 1.43 accuracy 0.50 -- 157.21 + 56.75 + 489.52 + 4.77 = 708.24: 40%|███▉ | 817/2048 [10:36<14:20, 1.43it/s]
loss 1.43 accuracy 0.50 -- 157.21 + 56.75 + 489.52 + 4.77 = 708.24: 40%|███▉ | 818/2048 [10:36<14:32, 1.41it/s]
loss 1.81 accuracy 0.25 -- 55.72 + 166.37 + 503.13 + 4.77 = 729.99: 40%|███▉ | 818/2048 [10:36<14:32, 1.41it/s]
loss 1.81 accuracy 0.25 -- 55.72 + 166.37 + 503.13 + 4.77 = 729.99: 40%|███▉ | 819/2048 [10:36<14:49, 1.38it/s]
loss 1.81 accuracy 0.38 -- 56.87 + 56.75 + 498.72 + 4.78 = 617.11: 40%|███▉ | 819/2048 [10:37<14:49, 1.38it/s]
loss 1.81 accuracy 0.38 -- 56.87 + 56.75 + 498.72 + 4.78 = 617.11: 40%|████ | 820/2048 [10:37<14:19, 1.43it/s]
loss 1.78 accuracy 0.19 -- 162.40 + 57.04 + 496.44 + 4.77 = 720.66: 40%|████ | 820/2048 [10:38<14:19, 1.43it/s]
loss 1.78 accuracy 0.19 -- 162.40 + 57.04 + 496.44 + 4.77 = 720.66: 40%|████ | 821/2048 [10:38<14:36, 1.40it/s]
loss 1.96 accuracy 0.31 -- 56.16 + 57.17 + 619.66 + 4.78 = 737.78: 40%|████ | 821/2048 [10:39<14:36, 1.40it/s]
loss 1.96 accuracy 0.31 -- 56.16 + 57.17 + 619.66 + 4.78 = 737.78: 40%|████ | 822/2048 [10:39<14:54, 1.37it/s]
loss 2.03 accuracy 0.31 -- 56.92 + 56.89 + 505.71 + 4.79 = 624.30: 40%|████ | 822/2048 [10:39<14:54, 1.37it/s]
loss 2.03 accuracy 0.31 -- 56.92 + 56.89 + 505.71 + 4.79 = 624.30: 40%|████ | 823/2048 [10:39<14:38, 1.40it/s]
loss 2.53 accuracy 0.19 -- 56.17 + 57.23 + 616.05 + 4.78 = 734.22: 40%|████ | 823/2048 [10:40<14:38, 1.40it/s]
loss 2.53 accuracy 0.19 -- 56.17 + 57.23 + 616.05 + 4.78 = 734.22: 40%|████ | 824/2048 [10:40<14:53, 1.37it/s]
loss 1.85 accuracy 0.25 -- 56.82 + 56.68 + 504.45 + 4.77 = 622.71: 40%|████ | 824/2048 [10:41<14:53, 1.37it/s]
loss 1.85 accuracy 0.25 -- 56.82 + 56.68 + 504.45 + 4.77 = 622.71: 40%|████ | 825/2048 [10:41<14:23, 1.42it/s]
loss 1.90 accuracy 0.25 -- 56.07 + 166.23 + 502.10 + 4.78 = 729.18: 40%|████ | 825/2048 [10:41<14:23, 1.42it/s]
loss 1.90 accuracy 0.25 -- 56.07 + 166.23 + 502.10 + 4.78 = 729.18: 40%|████ | 826/2048 [10:41<14:41, 1.39it/s]
loss 2.23 accuracy 0.12 -- 55.87 + 56.53 + 499.77 + 4.77 = 616.93: 40%|████ | 826/2048 [10:42<14:41, 1.39it/s]
loss 2.23 accuracy 0.12 -- 55.87 + 56.53 + 499.77 + 4.77 = 616.93: 40%|████ | 827/2048 [10:42<14:12, 1.43it/s]
loss 2.23 accuracy 0.12 -- 56.84 + 56.36 + 498.65 + 4.79 = 616.64: 40%|████ | 827/2048 [10:43<14:12, 1.43it/s]
loss 2.23 accuracy 0.12 -- 56.84 + 56.36 + 498.65 + 4.79 = 616.64: 40%|████ | 828/2048 [10:43<14:30, 1.40it/s]
loss 1.83 accuracy 0.31 -- 56.35 + 56.92 + 495.17 + 4.78 = 613.23: 40%|████ | 828/2048 [10:43<14:30, 1.40it/s]
loss 1.83 accuracy 0.31 -- 56.35 + 56.92 + 495.17 + 4.78 = 613.23: 40%|████ | 829/2048 [10:43<14:03, 1.45it/s]
loss 1.60 accuracy 0.44 -- 157.36 + 56.75 + 489.16 + 4.79 = 708.06: 40%|████ | 829/2048 [10:44<14:03, 1.45it/s]
loss 1.60 accuracy 0.44 -- 157.36 + 56.75 + 489.16 + 4.79 = 708.06: 41%|████ | 830/2048 [10:44<14:24, 1.41it/s]
loss 1.82 accuracy 0.50 -- 56.08 + 167.04 + 502.14 + 4.77 = 730.03: 41%|████ | 830/2048 [10:45<14:24, 1.41it/s]
loss 1.82 accuracy 0.50 -- 56.08 + 167.04 + 502.14 + 4.77 = 730.03: 41%|████ | 831/2048 [10:45<14:41, 1.38it/s]
loss 1.82 accuracy 0.50 -- 56.55 + 56.58 + 497.01 + 4.78 = 614.92: 41%|████ | 831/2048 [10:46<14:41, 1.38it/s]
loss 1.82 accuracy 0.50 -- 56.55 + 56.58 + 497.01 + 4.78 = 614.92: 41%|████ | 832/2048 [10:46<14:10, 1.43it/s]
loss 1.73 accuracy 0.25 -- 163.02 + 56.95 + 497.65 + 4.77 = 722.39: 41%|████ | 832/2048 [10:46<14:10, 1.43it/s]
loss 1.73 accuracy 0.25 -- 163.02 + 56.95 + 497.65 + 4.77 = 722.39: 41%|████ | 833/2048 [10:46<14:28, 1.40it/s]
loss 1.68 accuracy 0.19 -- 56.02 + 56.86 + 619.63 + 4.78 = 737.29: 41%|████ | 833/2048 [10:47<14:28, 1.40it/s]
loss 1.68 accuracy 0.19 -- 56.02 + 56.86 + 619.63 + 4.78 = 737.29: 41%|████ | 834/2048 [10:47<14:45, 1.37it/s]
loss 1.56 accuracy 0.38 -- 56.94 + 56.80 + 505.11 + 4.78 = 623.63: 41%|████ | 834/2048 [10:48<14:45, 1.37it/s]
loss 1.56 accuracy 0.38 -- 56.94 + 56.80 + 505.11 + 4.78 = 623.63: 41%|████ | 835/2048 [10:48<14:16, 1.42it/s]
loss 1.95 accuracy 0.12 -- 56.21 + 57.66 + 618.80 + 4.80 = 737.47: 41%|████ | 835/2048 [10:49<14:16, 1.42it/s]
loss 1.95 accuracy 0.12 -- 56.21 + 57.66 + 618.80 + 4.80 = 737.47: 41%|████ | 836/2048 [10:49<14:37, 1.38it/s]
loss 2.07 accuracy 0.25 -- 56.81 + 56.50 + 502.67 + 4.78 = 620.77: 41%|████ | 836/2048 [10:49<14:37, 1.38it/s]
loss 2.07 accuracy 0.25 -- 56.81 + 56.50 + 502.67 + 4.78 = 620.77: 41%|████ | 837/2048 [10:49<14:08, 1.43it/s]
loss 1.71 accuracy 0.31 -- 56.18 + 166.46 + 503.71 + 4.83 = 731.18: 41%|████ | 837/2048 [10:50<14:08, 1.43it/s]
loss 1.71 accuracy 0.31 -- 56.18 + 166.46 + 503.71 + 4.83 = 731.18: 41%|████ | 838/2048 [10:50<14:29, 1.39it/s]
loss 1.88 accuracy 0.31 -- 56.23 + 56.78 + 499.70 + 4.78 = 617.49: 41%|████ | 838/2048 [10:51<14:29, 1.39it/s]
loss 1.88 accuracy 0.31 -- 56.23 + 56.78 + 499.70 + 4.78 = 617.49: 41%|████ | 839/2048 [10:51<14:02, 1.44it/s]
loss 1.71 accuracy 0.31 -- 56.71 + 56.30 + 498.30 + 4.78 = 616.09: 41%|████ | 839/2048 [10:51<14:02, 1.44it/s]
loss 1.71 accuracy 0.31 -- 56.71 + 56.30 + 498.30 + 4.78 = 616.09: 41%|████ | 840/2048 [10:51<14:20, 1.40it/s]
loss 2.21 accuracy 0.19 -- 56.28 + 57.25 + 496.71 + 4.78 = 615.01: 41%|████ | 840/2048 [10:52<14:20, 1.40it/s]
loss 2.21 accuracy 0.19 -- 56.28 + 57.25 + 496.71 + 4.78 = 615.01: 41%|████ | 841/2048 [10:52<13:54, 1.45it/s]
loss 1.78 accuracy 0.38 -- 157.69 + 57.17 + 491.34 + 4.77 = 710.97: 41%|████ | 841/2048 [10:53<13:54, 1.45it/s]
loss 1.78 accuracy 0.38 -- 157.69 + 57.17 + 491.34 + 4.77 = 710.97: 41%|████ | 842/2048 [10:53<14:10, 1.42it/s]
loss 2.01 accuracy 0.19 -- 55.83 + 166.30 + 501.28 + 4.78 = 728.19: 41%|████ | 842/2048 [10:53<14:10, 1.42it/s]
loss 2.01 accuracy 0.19 -- 55.83 + 166.30 + 501.28 + 4.78 = 728.19: 41%|████ | 843/2048 [10:53<14:27, 1.39it/s]
loss 1.73 accuracy 0.25 -- 56.34 + 56.38 + 497.59 + 4.78 = 615.10: 41%|████ | 843/2048 [10:54<14:27, 1.39it/s]
loss 1.73 accuracy 0.25 -- 56.34 + 56.38 + 497.59 + 4.78 = 615.10: 41%|████ | 844/2048 [10:54<13:58, 1.44it/s]
loss 2.09 accuracy 0.38 -- 162.94 + 56.92 + 497.03 + 4.77 = 721.65: 41%|████ | 844/2048 [10:55<13:58, 1.44it/s]
loss 2.09 accuracy 0.38 -- 162.94 + 56.92 + 497.03 + 4.77 = 721.65: 41%|████▏ | 845/2048 [10:55<14:16, 1.40it/s]
loss 2.12 accuracy 0.38 -- 56.08 + 57.01 + 621.06 + 4.78 = 738.93: 41%|████▏ | 845/2048 [10:56<14:16, 1.40it/s]
loss 2.12 accuracy 0.38 -- 56.08 + 57.01 + 621.06 + 4.78 = 738.93: 41%|████▏ | 846/2048 [10:56<14:35, 1.37it/s]
loss 1.98 accuracy 0.25 -- 57.32 + 56.55 + 505.52 + 4.83 = 624.21: 41%|████▏ | 846/2048 [10:56<14:35, 1.37it/s]
loss 1.98 accuracy 0.25 -- 57.32 + 56.55 + 505.52 + 4.83 = 624.21: 41%|████▏ | 847/2048 [10:56<14:06, 1.42it/s]
loss 1.81 accuracy 0.44 -- 56.68 + 57.74 + 616.46 + 4.79 = 735.68: 41%|████▏ | 847/2048 [10:57<14:06, 1.42it/s]
loss 1.81 accuracy 0.44 -- 56.68 + 57.74 + 616.46 + 4.79 = 735.68: 41%|████▏ | 848/2048 [10:57<14:26, 1.38it/s]
loss 1.76 accuracy 0.38 -- 56.62 + 56.45 + 503.42 + 4.80 = 621.29: 41%|████▏ | 848/2048 [10:58<14:26, 1.38it/s]
loss 1.76 accuracy 0.38 -- 56.62 + 56.45 + 503.42 + 4.80 = 621.29: 41%|████▏ | 849/2048 [10:58<13:59, 1.43it/s]
loss 1.68 accuracy 0.44 -- 56.40 + 166.36 + 503.49 + 4.78 = 731.03: 41%|████▏ | 849/2048 [10:58<13:59, 1.43it/s]
loss 1.68 accuracy 0.44 -- 56.40 + 166.36 + 503.49 + 4.78 = 731.03: 42%|████▏ | 850/2048 [10:58<14:19, 1.39it/s]
loss 2.47 accuracy 0.19 -- 56.56 + 56.54 + 500.61 + 4.79 = 618.50: 42%|████▏ | 850/2048 [10:59<14:19, 1.39it/s]
loss 2.47 accuracy 0.19 -- 56.56 + 56.54 + 500.61 + 4.79 = 618.50: 42%|████▏ | 851/2048 [10:59<13:52, 1.44it/s]
loss 2.23 accuracy 0.38 -- 56.48 + 56.60 + 498.87 + 4.80 = 616.75: 42%|████▏ | 851/2048 [11:00<13:52, 1.44it/s]
loss 2.23 accuracy 0.38 -- 56.48 + 56.60 + 498.87 + 4.80 = 616.75: 42%|████▏ | 852/2048 [11:00<14:11, 1.40it/s]
loss 2.17 accuracy 0.19 -- 56.94 + 57.58 + 498.15 + 4.78 = 617.44: 42%|████▏ | 852/2048 [11:00<14:11, 1.40it/s]
loss 2.17 accuracy 0.19 -- 56.94 + 57.58 + 498.15 + 4.78 = 617.44: 42%|████▏ | 853/2048 [11:00<13:46, 1.45it/s]
loss 1.92 accuracy 0.12 -- 157.54 + 57.28 + 490.36 + 4.77 = 709.95: 42%|████▏ | 853/2048 [11:01<13:46, 1.45it/s]
loss 1.92 accuracy 0.12 -- 157.54 + 57.28 + 490.36 + 4.77 = 709.95: 42%|████▏ | 854/2048 [11:01<14:02, 1.42it/s]
loss 1.56 accuracy 0.38 -- 55.84 + 166.49 + 501.86 + 4.77 = 728.96: 42%|████▏ | 854/2048 [11:02<14:02, 1.42it/s]
loss 1.56 accuracy 0.38 -- 55.84 + 166.49 + 501.86 + 4.77 = 728.96: 42%|████▏ | 855/2048 [11:02<14:19, 1.39it/s]
loss 2.04 accuracy 0.25 -- 56.61 + 56.87 + 497.35 + 4.77 = 615.59: 42%|████▏ | 855/2048 [11:03<14:19, 1.39it/s]
loss 2.04 accuracy 0.25 -- 56.61 + 56.87 + 497.35 + 4.77 = 615.59: 42%|████▏ | 856/2048 [11:03<13:50, 1.43it/s]
loss 1.99 accuracy 0.19 -- 162.50 + 57.05 + 495.92 + 4.76 = 720.23: 42%|████▏ | 856/2048 [11:03<13:50, 1.43it/s]
loss 1.99 accuracy 0.19 -- 162.50 + 57.05 + 495.92 + 4.76 = 720.23: 42%|████▏ | 857/2048 [11:03<14:08, 1.40it/s]
loss 1.56 accuracy 0.62 -- 56.08 + 57.10 + 621.45 + 4.78 = 739.41: 42%|████▏ | 857/2048 [11:04<14:08, 1.40it/s]
loss 1.56 accuracy 0.62 -- 56.08 + 57.10 + 621.45 + 4.78 = 739.41: 42%|████▏ | 858/2048 [11:04<14:26, 1.37it/s]
loss 2.41 accuracy 0.25 -- 57.04 + 56.93 + 507.49 + 4.81 = 626.27: 42%|████▏ | 858/2048 [11:05<14:26, 1.37it/s]
loss 2.41 accuracy 0.25 -- 57.04 + 56.93 + 507.49 + 4.81 = 626.27: 42%|████▏ | 859/2048 [11:05<13:59, 1.42it/s]
loss 1.67 accuracy 0.31 -- 56.12 + 57.20 + 615.24 + 4.77 = 733.34: 42%|████▏ | 859/2048 [11:06<13:59, 1.42it/s]
loss 1.67 accuracy 0.31 -- 56.12 + 57.20 + 615.24 + 4.77 = 733.34: 42%|████▏ | 860/2048 [11:06<14:17, 1.38it/s]
loss 2.15 accuracy 0.25 -- 56.54 + 56.73 + 501.82 + 4.77 = 619.86: 42%|████▏ | 860/2048 [11:06<14:17, 1.38it/s]
loss 2.15 accuracy 0.25 -- 56.54 + 56.73 + 501.82 + 4.77 = 619.86: 42%|████▏ | 861/2048 [11:06<13:50, 1.43it/s]
loss 1.45 accuracy 0.50 -- 56.03 + 166.19 + 502.01 + 4.79 = 729.01: 42%|████▏ | 861/2048 [11:07<13:50, 1.43it/s]
loss 1.45 accuracy 0.50 -- 56.03 + 166.19 + 502.01 + 4.79 = 729.01: 42%|████▏ | 862/2048 [11:07<14:09, 1.40it/s]
loss 1.87 accuracy 0.44 -- 56.23 + 56.35 + 500.26 + 4.77 = 617.61: 42%|████▏ | 862/2048 [11:08<14:09, 1.40it/s]
loss 1.87 accuracy 0.44 -- 56.23 + 56.35 + 500.26 + 4.77 = 617.61: 42%|████▏ | 863/2048 [11:08<13:43, 1.44it/s]
loss 1.73 accuracy 0.31 -- 56.58 + 56.49 + 499.31 + 4.78 = 617.16: 42%|████▏ | 863/2048 [11:08<13:43, 1.44it/s]
loss 1.73 accuracy 0.31 -- 56.58 + 56.49 + 499.31 + 4.78 = 617.16: 42%|████▏ | 864/2048 [11:08<14:02, 1.40it/s]
loss 1.86 accuracy 0.25 -- 56.39 + 57.59 + 496.32 + 4.79 = 615.08: 42%|████▏ | 864/2048 [11:09<14:02, 1.40it/s]
loss 1.86 accuracy 0.25 -- 56.39 + 57.59 + 496.32 + 4.79 = 615.08: 42%|████▏ | 865/2048 [11:09<13:37, 1.45it/s]
loss 1.45 accuracy 0.44 -- 157.84 + 56.84 + 489.39 + 4.78 = 708.85: 42%|████▏ | 865/2048 [11:10<13:37, 1.45it/s]
loss 1.45 accuracy 0.44 -- 157.84 + 56.84 + 489.39 + 4.78 = 708.85: 42%|████▏ | 866/2048 [11:10<13:52, 1.42it/s]
loss 2.11 accuracy 0.25 -- 55.65 + 166.21 + 501.14 + 4.78 = 727.78: 42%|████▏ | 866/2048 [11:10<13:52, 1.42it/s]
loss 2.11 accuracy 0.25 -- 55.65 + 166.21 + 501.14 + 4.78 = 727.78: 42%|████▏ | 867/2048 [11:10<14:09, 1.39it/s]
loss 1.85 accuracy 0.25 -- 56.95 + 56.59 + 498.15 + 4.78 = 616.48: 42%|████▏ | 867/2048 [11:11<14:09, 1.39it/s]
loss 1.85 accuracy 0.25 -- 56.95 + 56.59 + 498.15 + 4.78 = 616.48: 42%|████▏ | 868/2048 [11:11<13:41, 1.44it/s]
loss 1.68 accuracy 0.25 -- 162.80 + 57.16 + 497.14 + 4.77 = 721.87: 42%|████▏ | 868/2048 [11:12<13:41, 1.44it/s]
loss 1.68 accuracy 0.25 -- 162.80 + 57.16 + 497.14 + 4.77 = 721.87: 42%|████▏ | 869/2048 [11:12<13:59, 1.40it/s]
loss 1.55 accuracy 0.31 -- 55.91 + 57.44 + 620.88 + 4.79 = 739.03: 42%|████▏ | 869/2048 [11:13<13:59, 1.40it/s]
loss 1.55 accuracy 0.31 -- 55.91 + 57.44 + 620.88 + 4.79 = 739.03: 42%|████▏ | 870/2048 [11:13<14:18, 1.37it/s]
loss 1.70 accuracy 0.31 -- 56.60 + 56.55 + 506.13 + 4.78 = 624.06: 42%|████▏ | 870/2048 [11:13<14:18, 1.37it/s]
loss 1.70 accuracy 0.31 -- 56.60 + 56.55 + 506.13 + 4.78 = 624.06: 43%|████▎ | 871/2048 [11:13<13:50, 1.42it/s]
loss 1.95 accuracy 0.19 -- 56.13 + 57.30 + 616.84 + 4.79 = 735.06: 43%|████▎ | 871/2048 [11:14<13:50, 1.42it/s]
loss 1.95 accuracy 0.19 -- 56.13 + 57.30 + 616.84 + 4.79 = 735.06: 43%|████▎ | 872/2048 [11:14<14:09, 1.38it/s]
loss 2.10 accuracy 0.38 -- 56.74 + 56.54 + 501.74 + 4.78 = 619.81: 43%|████▎ | 872/2048 [11:15<14:09, 1.38it/s]
loss 2.10 accuracy 0.38 -- 56.74 + 56.54 + 501.74 + 4.78 = 619.81: 43%|████▎ | 873/2048 [11:15<13:42, 1.43it/s]
loss 2.04 accuracy 0.31 -- 56.34 + 166.89 + 501.48 + 4.77 = 729.48: 43%|████▎ | 873/2048 [11:15<13:42, 1.43it/s]
loss 2.04 accuracy 0.31 -- 56.34 + 166.89 + 501.48 + 4.77 = 729.48: 43%|████▎ | 874/2048 [11:15<14:01, 1.40it/s]
loss 1.70 accuracy 0.31 -- 56.31 + 56.37 + 500.06 + 4.78 = 617.52: 43%|████▎ | 874/2048 [11:16<14:01, 1.40it/s]
loss 1.70 accuracy 0.31 -- 56.31 + 56.37 + 500.06 + 4.78 = 617.52: 43%|████▎ | 875/2048 [11:16<13:35, 1.44it/s]
loss 1.64 accuracy 0.38 -- 56.64 + 56.68 + 498.99 + 4.79 = 617.10: 43%|████▎ | 875/2048 [11:17<13:35, 1.44it/s]
loss 1.64 accuracy 0.38 -- 56.64 + 56.68 + 498.99 + 4.79 = 617.10: 43%|████▎ | 876/2048 [11:17<13:54, 1.40it/s]
loss 1.68 accuracy 0.44 -- 56.22 + 57.16 + 494.71 + 4.77 = 612.85: 43%|████▎ | 876/2048 [11:17<13:54, 1.40it/s]
loss 1.68 accuracy 0.44 -- 56.22 + 57.16 + 494.71 + 4.77 = 612.85: 43%|████▎ | 877/2048 [11:17<13:28, 1.45it/s]
loss 2.25 accuracy 0.19 -- 158.02 + 56.72 + 489.31 + 4.77 = 708.82: 43%|████▎ | 877/2048 [11:18<13:28, 1.45it/s]
loss 2.25 accuracy 0.19 -- 158.02 + 56.72 + 489.31 + 4.77 = 708.82: 43%|████▎ | 878/2048 [11:18<13:43, 1.42it/s]
loss 2.36 accuracy 0.25 -- 55.73 + 166.32 + 501.19 + 4.79 = 728.03: 43%|████▎ | 878/2048 [11:19<13:43, 1.42it/s]
loss 2.36 accuracy 0.25 -- 55.73 + 166.32 + 501.19 + 4.79 = 728.03: 43%|████▎ | 879/2048 [11:19<14:00, 1.39it/s]
loss 1.94 accuracy 0.31 -- 56.58 + 56.64 + 498.34 + 4.77 = 616.33: 43%|████▎ | 879/2048 [11:20<14:00, 1.39it/s]
loss 1.94 accuracy 0.31 -- 56.58 + 56.64 + 498.34 + 4.77 = 616.33: 43%|████▎ | 880/2048 [11:20<13:33, 1.44it/s]
loss 1.89 accuracy 0.38 -- 162.90 + 57.04 + 496.89 + 4.77 = 721.60: 43%|████▎ | 880/2048 [11:20<13:33, 1.44it/s]
loss 1.89 accuracy 0.38 -- 162.90 + 57.04 + 496.89 + 4.77 = 721.60: 43%|████▎ | 881/2048 [11:20<13:51, 1.40it/s]
loss 1.51 accuracy 0.38 -- 56.38 + 57.64 + 620.59 + 4.78 = 739.39: 43%|████▎ | 881/2048 [11:21<13:51, 1.40it/s]
loss 1.51 accuracy 0.38 -- 56.38 + 57.64 + 620.59 + 4.78 = 739.39: 43%|████▎ | 882/2048 [11:21<14:17, 1.36it/s]
loss 1.65 accuracy 0.44 -- 57.07 + 56.70 + 503.63 + 4.78 = 622.18: 43%|████▎ | 882/2048 [11:22<14:17, 1.36it/s]
loss 1.65 accuracy 0.44 -- 57.07 + 56.70 + 503.63 + 4.78 = 622.18: 43%|████▎ | 883/2048 [11:22<13:46, 1.41it/s]
loss 2.39 accuracy 0.31 -- 56.44 + 57.11 + 615.29 + 4.80 = 733.64: 43%|████▎ | 883/2048 [11:23<13:46, 1.41it/s]
loss 2.39 accuracy 0.31 -- 56.44 + 57.11 + 615.29 + 4.80 = 733.64: 43%|████▎ | 884/2048 [11:23<14:03, 1.38it/s]
loss 1.90 accuracy 0.31 -- 56.76 + 56.49 + 502.15 + 4.87 = 620.27: 43%|████▎ | 884/2048 [11:23<14:03, 1.38it/s]
loss 1.90 accuracy 0.31 -- 56.76 + 56.49 + 502.15 + 4.87 = 620.27: 43%|████▎ | 885/2048 [11:23<13:35, 1.43it/s]
loss 1.44 accuracy 0.31 -- 56.18 + 166.45 + 502.51 + 4.79 = 729.94: 43%|████▎ | 885/2048 [11:24<13:35, 1.43it/s]
loss 1.44 accuracy 0.31 -- 56.18 + 166.45 + 502.51 + 4.79 = 729.94: 43%|████▎ | 886/2048 [11:24<13:54, 1.39it/s]
loss 1.93 accuracy 0.38 -- 56.16 + 56.80 + 500.58 + 4.77 = 618.32: 43%|████▎ | 886/2048 [11:25<13:54, 1.39it/s]
loss 1.93 accuracy 0.38 -- 56.16 + 56.80 + 500.58 + 4.77 = 618.32: 43%|████▎ | 887/2048 [11:25<13:28, 1.44it/s]
loss 1.91 accuracy 0.19 -- 56.80 + 56.64 + 497.88 + 4.78 = 616.10: 43%|████▎ | 887/2048 [11:25<13:28, 1.44it/s]
loss 1.91 accuracy 0.19 -- 56.80 + 56.64 + 497.88 + 4.78 = 616.10: 43%|████▎ | 888/2048 [11:25<13:46, 1.40it/s]
loss 1.93 accuracy 0.44 -- 56.32 + 57.52 + 495.49 + 4.78 = 614.11: 43%|████▎ | 888/2048 [11:26<13:46, 1.40it/s]
loss 1.93 accuracy 0.44 -- 56.32 + 57.52 + 495.49 + 4.78 = 614.11: 43%|████▎ | 889/2048 [11:26<13:20, 1.45it/s]
loss 1.81 accuracy 0.38 -- 157.14 + 57.02 + 489.45 + 4.80 = 708.40: 43%|████▎ | 889/2048 [11:27<13:20, 1.45it/s]
loss 1.81 accuracy 0.38 -- 157.14 + 57.02 + 489.45 + 4.80 = 708.40: 43%|████▎ | 890/2048 [11:27<13:35, 1.42it/s]
loss 1.57 accuracy 0.38 -- 55.98 + 166.61 + 501.06 + 4.78 = 728.42: 43%|████▎ | 890/2048 [11:27<13:35, 1.42it/s]
loss 1.57 accuracy 0.38 -- 55.98 + 166.61 + 501.06 + 4.78 = 728.42: 44%|████▎ | 891/2048 [11:27<13:52, 1.39it/s]
loss 1.53 accuracy 0.31 -- 56.48 + 56.43 + 498.67 + 4.80 = 616.38: 44%|████▎ | 891/2048 [11:28<13:52, 1.39it/s]
loss 1.53 accuracy 0.31 -- 56.48 + 56.43 + 498.67 + 4.80 = 616.38: 44%|████▎ | 892/2048 [11:28<13:25, 1.43it/s]
loss 2.04 accuracy 0.25 -- 162.91 + 57.41 + 497.47 + 4.83 = 722.62: 44%|████▎ | 892/2048 [11:29<13:25, 1.43it/s]
loss 2.04 accuracy 0.25 -- 162.91 + 57.41 + 497.47 + 4.83 = 722.62: 44%|████▎ | 893/2048 [11:29<13:43, 1.40it/s]
loss 1.80 accuracy 0.44 -- 56.47 + 57.18 + 620.46 + 4.78 = 738.89: 44%|████▎ | 893/2048 [11:30<13:43, 1.40it/s]
loss 1.80 accuracy 0.44 -- 56.47 + 57.18 + 620.46 + 4.78 = 738.89: 44%|████▎ | 894/2048 [11:30<14:01, 1.37it/s]
loss 1.62 accuracy 0.38 -- 56.59 + 56.58 + 505.19 + 4.80 = 623.17: 44%|████▎ | 894/2048 [11:30<14:01, 1.37it/s]
loss 1.62 accuracy 0.38 -- 56.59 + 56.58 + 505.19 + 4.80 = 623.17: 44%|████▎ | 895/2048 [11:30<13:33, 1.42it/s]
loss 2.17 accuracy 0.25 -- 56.41 + 57.29 + 615.55 + 4.78 = 734.02: 44%|████▎ | 895/2048 [11:31<13:33, 1.42it/s]
loss 2.17 accuracy 0.25 -- 56.41 + 57.29 + 615.55 + 4.78 = 734.02: 44%|████▍ | 896/2048 [11:31<13:51, 1.38it/s]
loss 1.77 accuracy 0.31 -- 56.81 + 56.28 + 501.84 + 4.77 = 619.70: 44%|████▍ | 896/2048 [11:32<13:51, 1.38it/s]
loss 1.77 accuracy 0.31 -- 56.81 + 56.28 + 501.84 + 4.77 = 619.70: 44%|████▍ | 897/2048 [11:32<13:25, 1.43it/s]
loss 1.83 accuracy 0.31 -- 56.35 + 167.34 + 503.20 + 4.82 = 731.71: 44%|████▍ | 897/2048 [11:32<13:25, 1.43it/s]
loss 1.83 accuracy 0.31 -- 56.35 + 167.34 + 503.20 + 4.82 = 731.71: 44%|████▍ | 898/2048 [11:32<13:44, 1.39it/s]
loss 1.65 accuracy 0.38 -- 56.02 + 56.51 + 500.25 + 4.78 = 617.57: 44%|████▍ | 898/2048 [11:33<13:44, 1.39it/s]
loss 1.65 accuracy 0.38 -- 56.02 + 56.51 + 500.25 + 4.78 = 617.57: 44%|████▍ | 899/2048 [11:33<13:19, 1.44it/s]
loss 1.65 accuracy 0.56 -- 56.46 + 56.19 + 497.52 + 4.78 = 614.94: 44%|████▍ | 899/2048 [11:34<13:19, 1.44it/s]
loss 1.65 accuracy 0.56 -- 56.46 + 56.19 + 497.52 + 4.78 = 614.94: 44%|████▍ | 900/2048 [11:34<13:36, 1.41it/s]
loss 1.70 accuracy 0.44 -- 56.08 + 57.04 + 494.78 + 4.77 = 612.66: 44%|████▍ | 900/2048 [11:34<13:36, 1.41it/s]
loss 1.70 accuracy 0.44 -- 56.08 + 57.04 + 494.78 + 4.77 = 612.66: 44%|████▍ | 901/2048 [11:34<13:11, 1.45it/s]
loss 2.05 accuracy 0.12 -- 157.79 + 56.92 + 489.18 + 4.77 = 708.67: 44%|████▍ | 901/2048 [11:35<13:11, 1.45it/s]
loss 2.05 accuracy 0.12 -- 157.79 + 56.92 + 489.18 + 4.77 = 708.67: 44%|████▍ | 902/2048 [11:35<13:26, 1.42it/s]
loss 1.58 accuracy 0.31 -- 55.62 + 166.73 + 502.42 + 4.77 = 729.54: 44%|████▍ | 902/2048 [11:36<13:26, 1.42it/s]
loss 1.58 accuracy 0.31 -- 55.62 + 166.73 + 502.42 + 4.77 = 729.54: 44%|████▍ | 903/2048 [11:36<13:44, 1.39it/s]
loss 1.46 accuracy 0.44 -- 56.63 + 56.23 + 499.25 + 4.77 = 616.88: 44%|████▍ | 903/2048 [11:37<13:44, 1.39it/s]
loss 1.46 accuracy 0.44 -- 56.63 + 56.23 + 499.25 + 4.77 = 616.88: 44%|████▍ | 904/2048 [11:37<13:17, 1.43it/s]
loss 1.70 accuracy 0.38 -- 162.91 + 57.12 + 498.61 + 4.77 = 723.41: 44%|████▍ | 904/2048 [11:37<13:17, 1.43it/s]
loss 1.70 accuracy 0.38 -- 162.91 + 57.12 + 498.61 + 4.77 = 723.41: 44%|████▍ | 905/2048 [11:37<13:35, 1.40it/s]
loss 1.70 accuracy 0.19 -- 56.08 + 57.39 + 620.08 + 4.81 = 738.36: 44%|████▍ | 905/2048 [11:38<13:35, 1.40it/s]
loss 1.70 accuracy 0.19 -- 56.08 + 57.39 + 620.08 + 4.81 = 738.36: 44%|████▍ | 906/2048 [11:38<13:52, 1.37it/s]
loss 1.55 accuracy 0.38 -- 56.52 + 56.37 + 504.63 + 4.78 = 622.30: 44%|████▍ | 906/2048 [11:39<13:52, 1.37it/s]
loss 1.55 accuracy 0.38 -- 56.52 + 56.37 + 504.63 + 4.78 = 622.30: 44%|████▍ | 907/2048 [11:39<13:24, 1.42it/s]
loss 2.29 accuracy 0.06 -- 56.11 + 57.46 + 616.16 + 4.78 = 734.50: 44%|████▍ | 907/2048 [11:40<13:24, 1.42it/s]
loss 2.29 accuracy 0.06 -- 56.11 + 57.46 + 616.16 + 4.78 = 734.50: 44%|████▍ | 908/2048 [11:40<13:43, 1.38it/s]
loss 2.23 accuracy 0.31 -- 56.66 + 56.40 + 503.47 + 4.78 = 621.32: 44%|████▍ | 908/2048 [11:40<13:43, 1.38it/s]
loss 2.23 accuracy 0.31 -- 56.66 + 56.40 + 503.47 + 4.78 = 621.32: 44%|████▍ | 909/2048 [11:40<13:17, 1.43it/s]
loss 2.80 accuracy 0.31 -- 56.49 + 166.76 + 503.17 + 4.78 = 731.19: 44%|████▍ | 909/2048 [11:41<13:17, 1.43it/s]
loss 2.80 accuracy 0.31 -- 56.49 + 166.76 + 503.17 + 4.78 = 731.19: 44%|████▍ | 910/2048 [11:41<13:36, 1.39it/s]
loss 1.68 accuracy 0.44 -- 56.40 + 56.39 + 500.80 + 4.76 = 618.35: 44%|████▍ | 910/2048 [11:42<13:36, 1.39it/s]
loss 1.68 accuracy 0.44 -- 56.40 + 56.39 + 500.80 + 4.76 = 618.35: 44%|████▍ | 911/2048 [11:42<13:11, 1.44it/s]
loss 1.25 accuracy 0.56 -- 57.19 + 56.71 + 499.46 + 4.78 = 618.14: 44%|████▍ | 911/2048 [11:42<13:11, 1.44it/s]
loss 1.25 accuracy 0.56 -- 57.19 + 56.71 + 499.46 + 4.78 = 618.14: 45%|████▍ | 912/2048 [11:42<13:29, 1.40it/s]
loss 1.94 accuracy 0.38 -- 55.87 + 57.13 + 495.48 + 4.77 = 613.25: 45%|████▍ | 912/2048 [11:43<13:29, 1.40it/s]
loss 1.94 accuracy 0.38 -- 55.87 + 57.13 + 495.48 + 4.77 = 613.25: 45%|████▍ | 913/2048 [11:43<13:04, 1.45it/s]
loss 2.16 accuracy 0.31 -- 157.12 + 56.80 + 489.77 + 4.78 = 708.47: 45%|████▍ | 913/2048 [11:44<13:04, 1.45it/s]
loss 2.16 accuracy 0.31 -- 157.12 + 56.80 + 489.77 + 4.78 = 708.47: 45%|████▍ | 914/2048 [11:44<13:19, 1.42it/s]
loss 1.62 accuracy 0.44 -- 55.66 + 166.64 + 503.77 + 4.79 = 730.85: 45%|████▍ | 914/2048 [11:44<13:19, 1.42it/s]
loss 1.62 accuracy 0.44 -- 55.66 + 166.64 + 503.77 + 4.79 = 730.85: 45%|████▍ | 915/2048 [11:44<13:36, 1.39it/s]
loss 1.76 accuracy 0.31 -- 56.82 + 56.44 + 498.21 + 4.77 = 616.24: 45%|████▍ | 915/2048 [11:45<13:36, 1.39it/s]
loss 1.76 accuracy 0.31 -- 56.82 + 56.44 + 498.21 + 4.77 = 616.24: 45%|████▍ | 916/2048 [11:45<13:09, 1.43it/s]
loss 1.32 accuracy 0.38 -- 162.21 + 56.78 + 497.61 + 4.77 = 721.37: 45%|████▍ | 916/2048 [11:46<13:09, 1.43it/s]
loss 1.32 accuracy 0.38 -- 162.21 + 56.78 + 497.61 + 4.77 = 721.37: 45%|████▍ | 917/2048 [11:46<13:26, 1.40it/s]
loss 2.20 accuracy 0.44 -- 56.25 + 57.59 + 620.84 + 4.79 = 739.48: 45%|████▍ | 917/2048 [11:47<13:26, 1.40it/s]
loss 2.20 accuracy 0.44 -- 56.25 + 57.59 + 620.84 + 4.79 = 739.48: 45%|████▍ | 918/2048 [11:47<13:43, 1.37it/s]
loss 1.96 accuracy 0.44 -- 57.04 + 56.62 + 504.89 + 4.78 = 623.33: 45%|████▍ | 918/2048 [11:47<13:43, 1.37it/s]
loss 1.96 accuracy 0.44 -- 57.04 + 56.62 + 504.89 + 4.78 = 623.33: 45%|████▍ | 919/2048 [11:47<13:16, 1.42it/s]
loss 1.64 accuracy 0.31 -- 56.16 + 57.35 + 616.75 + 4.80 = 735.06: 45%|████▍ | 919/2048 [11:48<13:16, 1.42it/s]
loss 1.64 accuracy 0.31 -- 56.16 + 57.35 + 616.75 + 4.80 = 735.06: 45%|████▍ | 920/2048 [11:48<13:35, 1.38it/s]
loss 2.05 accuracy 0.31 -- 56.64 + 57.36 + 502.66 + 4.77 = 621.42: 45%|████▍ | 920/2048 [11:49<13:35, 1.38it/s]
loss 2.05 accuracy 0.31 -- 56.64 + 57.36 + 502.66 + 4.77 = 621.42: 45%|████▍ | 921/2048 [11:49<13:09, 1.43it/s]
loss 1.82 accuracy 0.44 -- 56.38 + 166.28 + 501.82 + 4.77 = 729.24: 45%|████▍ | 921/2048 [11:49<13:09, 1.43it/s]
loss 1.82 accuracy 0.44 -- 56.38 + 166.28 + 501.82 + 4.77 = 729.24: 45%|████▌ | 922/2048 [11:49<13:27, 1.39it/s]
loss 2.01 accuracy 0.25 -- 56.13 + 56.50 + 499.76 + 4.77 = 617.16: 45%|████▌ | 922/2048 [11:50<13:27, 1.39it/s]
loss 2.01 accuracy 0.25 -- 56.13 + 56.50 + 499.76 + 4.77 = 617.16: 45%|████▌ | 923/2048 [11:50<13:02, 1.44it/s]
loss 2.25 accuracy 0.31 -- 56.73 + 56.58 + 498.39 + 4.77 = 616.48: 45%|████▌ | 923/2048 [11:51<13:02, 1.44it/s]
loss 2.25 accuracy 0.31 -- 56.73 + 56.58 + 498.39 + 4.77 = 616.48: 45%|████▌ | 924/2048 [11:51<13:20, 1.40it/s]
loss 1.64 accuracy 0.31 -- 55.75 + 57.72 + 495.39 + 4.80 = 613.65: 45%|████▌ | 924/2048 [11:52<13:20, 1.40it/s]
loss 1.64 accuracy 0.31 -- 55.75 + 57.72 + 495.39 + 4.80 = 613.65: 45%|████▌ | 925/2048 [11:52<12:55, 1.45it/s]
loss 2.17 accuracy 0.19 -- 157.67 + 57.24 + 490.17 + 4.78 = 709.85: 45%|████▌ | 925/2048 [11:52<12:55, 1.45it/s]
loss 2.17 accuracy 0.19 -- 157.67 + 57.24 + 490.17 + 4.78 = 709.85: 45%|████▌ | 926/2048 [11:52<13:10, 1.42it/s]
loss 2.32 accuracy 0.12 -- 56.25 + 167.18 + 502.02 + 4.77 = 730.23: 45%|████▌ | 926/2048 [11:53<13:10, 1.42it/s]
loss 2.32 accuracy 0.12 -- 56.25 + 167.18 + 502.02 + 4.77 = 730.23: 45%|████▌ | 927/2048 [11:53<13:27, 1.39it/s]
loss 1.57 accuracy 0.31 -- 56.67 + 56.55 + 497.52 + 4.80 = 615.54: 45%|████▌ | 927/2048 [11:54<13:27, 1.39it/s]
loss 1.57 accuracy 0.31 -- 56.67 + 56.55 + 497.52 + 4.80 = 615.54: 45%|████▌ | 928/2048 [11:54<13:00, 1.43it/s]
loss 1.88 accuracy 0.31 -- 162.64 + 56.99 + 497.51 + 4.79 = 721.93: 45%|████▌ | 928/2048 [11:54<13:00, 1.43it/s]
loss 1.88 accuracy 0.31 -- 162.64 + 56.99 + 497.51 + 4.79 = 721.93: 45%|████▌ | 929/2048 [11:54<13:17, 1.40it/s]
loss 1.74 accuracy 0.44 -- 56.15 + 56.99 + 620.25 + 4.78 = 738.17: 45%|████▌ | 929/2048 [11:55<13:17, 1.40it/s]
loss 1.74 accuracy 0.44 -- 56.15 + 56.99 + 620.25 + 4.78 = 738.17: 45%|████▌ | 930/2048 [11:55<13:34, 1.37it/s]
loss 1.75 accuracy 0.56 -- 56.57 + 56.75 + 505.13 + 4.77 = 623.22: 45%|████▌ | 930/2048 [11:56<13:34, 1.37it/s]
loss 1.75 accuracy 0.56 -- 56.57 + 56.75 + 505.13 + 4.77 = 623.22: 45%|████▌ | 931/2048 [11:56<13:07, 1.42it/s]
loss 1.80 accuracy 0.25 -- 56.04 + 57.37 + 616.85 + 4.79 = 735.05: 45%|████▌ | 931/2048 [11:57<13:07, 1.42it/s]
loss 1.80 accuracy 0.25 -- 56.04 + 57.37 + 616.85 + 4.79 = 735.05: 46%|████▌ | 932/2048 [11:57<13:25, 1.38it/s]
loss 2.40 accuracy 0.31 -- 56.61 + 56.43 + 502.01 + 4.78 = 619.83: 46%|████▌ | 932/2048 [11:57<13:25, 1.38it/s]
loss 2.40 accuracy 0.31 -- 56.61 + 56.43 + 502.01 + 4.78 = 619.83: 46%|████▌ | 933/2048 [11:57<13:00, 1.43it/s]
loss 2.00 accuracy 0.31 -- 56.28 + 166.27 + 502.32 + 4.78 = 729.64: 46%|████▌ | 933/2048 [11:58<13:00, 1.43it/s]
loss 2.00 accuracy 0.31 -- 56.28 + 166.27 + 502.32 + 4.78 = 729.64: 46%|████▌ | 934/2048 [11:58<13:18, 1.40it/s]
loss 2.14 accuracy 0.19 -- 56.24 + 56.42 + 499.08 + 4.77 = 616.52: 46%|████▌ | 934/2048 [11:59<13:18, 1.40it/s]
loss 2.14 accuracy 0.19 -- 56.24 + 56.42 + 499.08 + 4.77 = 616.52: 46%|████▌ | 935/2048 [11:59<12:53, 1.44it/s]
loss 1.97 accuracy 0.31 -- 56.63 + 56.32 + 497.79 + 4.78 = 615.52: 46%|████▌ | 935/2048 [11:59<12:53, 1.44it/s]
loss 1.97 accuracy 0.31 -- 56.63 + 56.32 + 497.79 + 4.78 = 615.52: 46%|████▌ | 936/2048 [11:59<13:11, 1.41it/s]
loss 1.68 accuracy 0.19 -- 55.90 + 57.39 + 495.81 + 4.79 = 613.89: 46%|████▌ | 936/2048 [12:00<13:11, 1.41it/s]
loss 1.68 accuracy 0.19 -- 55.90 + 57.39 + 495.81 + 4.79 = 613.89: 46%|████▌ | 937/2048 [12:00<12:46, 1.45it/s]
loss 1.80 accuracy 0.38 -- 158.05 + 57.29 + 491.50 + 4.76 = 711.61: 46%|████▌ | 937/2048 [12:01<12:46, 1.45it/s]
loss 1.80 accuracy 0.38 -- 158.05 + 57.29 + 491.50 + 4.76 = 711.61: 46%|████▌ | 938/2048 [12:01<13:02, 1.42it/s]
loss 1.93 accuracy 0.19 -- 55.87 + 166.41 + 502.00 + 4.82 = 729.10: 46%|████▌ | 938/2048 [12:01<13:02, 1.42it/s]
loss 1.93 accuracy 0.19 -- 55.87 + 166.41 + 502.00 + 4.82 = 729.10: 46%|████▌ | 939/2048 [12:01<13:18, 1.39it/s]
loss 1.88 accuracy 0.44 -- 56.87 + 56.87 + 500.20 + 4.77 = 618.70: 46%|████▌ | 939/2048 [12:02<13:18, 1.39it/s]
loss 1.88 accuracy 0.44 -- 56.87 + 56.87 + 500.20 + 4.77 = 618.70: 46%|████▌ | 940/2048 [12:02<12:53, 1.43it/s]
loss 2.05 accuracy 0.31 -- 162.53 + 56.94 + 486.01 + 4.78 = 710.26: 46%|████▌ | 940/2048 [12:03<12:53, 1.43it/s]
loss 2.05 accuracy 0.31 -- 162.53 + 56.94 + 486.01 + 4.78 = 710.26: 46%|████▌ | 941/2048 [12:03<13:05, 1.41it/s]
loss 2.01 accuracy 0.19 -- 56.54 + 57.33 + 623.04 + 4.77 = 741.68: 46%|████▌ | 941/2048 [12:04<13:05, 1.41it/s]
loss 2.01 accuracy 0.19 -- 56.54 + 57.33 + 623.04 + 4.77 = 741.68: 46%|████▌ | 942/2048 [12:04<13:24, 1.37it/s]
loss 1.70 accuracy 0.44 -- 56.77 + 56.55 + 505.42 + 4.78 = 623.51: 46%|████▌ | 942/2048 [12:04<13:24, 1.37it/s]
loss 1.70 accuracy 0.44 -- 56.77 + 56.55 + 505.42 + 4.78 = 623.51: 46%|████▌ | 943/2048 [12:04<12:58, 1.42it/s]
loss 2.05 accuracy 0.12 -- 56.78 + 57.74 + 615.46 + 4.78 = 734.75: 46%|████▌ | 943/2048 [12:05<12:58, 1.42it/s]
loss 2.05 accuracy 0.12 -- 56.78 + 57.74 + 615.46 + 4.78 = 734.75: 46%|████▌ | 944/2048 [12:05<13:16, 1.39it/s]
loss 1.75 accuracy 0.38 -- 56.79 + 56.46 + 501.62 + 4.77 = 619.64: 46%|████▌ | 944/2048 [12:06<13:16, 1.39it/s]
loss 1.75 accuracy 0.38 -- 56.79 + 56.46 + 501.62 + 4.77 = 619.64: 46%|████▌ | 945/2048 [12:06<12:51, 1.43it/s]
loss 2.04 accuracy 0.44 -- 55.94 + 166.39 + 501.82 + 4.77 = 728.92: 46%|████▌ | 945/2048 [12:06<12:51, 1.43it/s]
loss 2.04 accuracy 0.44 -- 55.94 + 166.39 + 501.82 + 4.77 = 728.92: 46%|████▌ | 946/2048 [12:06<13:09, 1.40it/s]
loss 1.83 accuracy 0.38 -- 55.99 + 56.61 + 498.86 + 4.76 = 616.22: 46%|████▌ | 946/2048 [12:07<13:09, 1.40it/s]
loss 1.83 accuracy 0.38 -- 55.99 + 56.61 + 498.86 + 4.76 = 616.22: 46%|████▌ | 947/2048 [12:07<12:44, 1.44it/s]
loss 1.65 accuracy 0.44 -- 56.61 + 56.45 + 498.02 + 4.80 = 615.89: 46%|████▌ | 947/2048 [12:08<12:44, 1.44it/s]
loss 1.65 accuracy 0.44 -- 56.61 + 56.45 + 498.02 + 4.80 = 615.89: 46%|████▋ | 948/2048 [12:08<13:02, 1.41it/s]
loss 1.85 accuracy 0.25 -- 56.31 + 57.41 + 497.46 + 4.78 = 615.97: 46%|████▋ | 948/2048 [12:09<13:02, 1.41it/s]
loss 1.85 accuracy 0.25 -- 56.31 + 57.41 + 497.46 + 4.78 = 615.97: 46%|████▋ | 949/2048 [12:09<12:39, 1.45it/s]
loss 2.04 accuracy 0.38 -- 157.21 + 56.78 + 490.05 + 4.77 = 708.81: 46%|████▋ | 949/2048 [12:09<12:39, 1.45it/s]
loss 2.04 accuracy 0.38 -- 157.21 + 56.78 + 490.05 + 4.77 = 708.81: 46%|████▋ | 950/2048 [12:09<12:53, 1.42it/s]
loss 1.84 accuracy 0.38 -- 55.75 + 166.54 + 500.56 + 4.78 = 727.63: 46%|████▋ | 950/2048 [12:10<12:53, 1.42it/s]
loss 1.84 accuracy 0.38 -- 55.75 + 166.54 + 500.56 + 4.78 = 727.63: 46%|████▋ | 951/2048 [12:10<13:09, 1.39it/s]
loss 1.72 accuracy 0.31 -- 56.61 + 56.56 + 496.97 + 4.78 = 614.92: 46%|████▋ | 951/2048 [12:11<13:09, 1.39it/s]
loss 1.72 accuracy 0.31 -- 56.61 + 56.56 + 496.97 + 4.78 = 614.92: 46%|████▋ | 952/2048 [12:11<12:43, 1.44it/s]
loss 1.73 accuracy 0.38 -- 162.24 + 57.04 + 496.42 + 4.77 = 720.47: 46%|████▋ | 952/2048 [12:11<12:43, 1.44it/s]
loss 1.73 accuracy 0.38 -- 162.24 + 57.04 + 496.42 + 4.77 = 720.47: 47%|████▋ | 953/2048 [12:11<12:59, 1.41it/s]
loss 2.49 accuracy 0.19 -- 55.98 + 57.58 + 620.44 + 4.79 = 738.78: 47%|████▋ | 953/2048 [12:12<12:59, 1.41it/s]
loss 2.49 accuracy 0.19 -- 55.98 + 57.58 + 620.44 + 4.79 = 738.78: 47%|████▋ | 954/2048 [12:12<13:16, 1.37it/s]
loss 2.52 accuracy 0.19 -- 56.99 + 56.88 + 505.27 + 4.77 = 623.92: 47%|████▋ | 954/2048 [12:13<13:16, 1.37it/s]
loss 2.52 accuracy 0.19 -- 56.99 + 56.88 + 505.27 + 4.77 = 623.92: 47%|████▋ | 955/2048 [12:13<12:50, 1.42it/s]
loss 1.87 accuracy 0.31 -- 56.05 + 57.55 + 615.59 + 4.79 = 733.97: 47%|████▋ | 955/2048 [12:14<12:50, 1.42it/s]
loss 1.87 accuracy 0.31 -- 56.05 + 57.55 + 615.59 + 4.79 = 733.97: 47%|████▋ | 956/2048 [12:14<13:08, 1.39it/s]
loss 1.69 accuracy 0.44 -- 56.64 + 56.21 + 502.39 + 4.78 = 620.02: 47%|████▋ | 956/2048 [12:14<13:08, 1.39it/s]
loss 1.69 accuracy 0.44 -- 56.64 + 56.21 + 502.39 + 4.78 = 620.02: 47%|████▋ | 957/2048 [12:14<12:54, 1.41it/s]
loss 2.06 accuracy 0.25 -- 56.21 + 166.01 + 501.60 + 4.78 = 728.60: 47%|████▋ | 957/2048 [12:15<12:54, 1.41it/s]
loss 2.06 accuracy 0.25 -- 56.21 + 166.01 + 501.60 + 4.78 = 728.60: 47%|████▋ | 958/2048 [12:15<13:08, 1.38it/s]
loss 2.17 accuracy 0.38 -- 56.27 + 56.35 + 501.01 + 4.79 = 618.42: 47%|████▋ | 958/2048 [12:16<13:08, 1.38it/s]
loss 2.17 accuracy 0.38 -- 56.27 + 56.35 + 501.01 + 4.79 = 618.42: 47%|████▋ | 959/2048 [12:16<12:42, 1.43it/s]
loss 1.34 accuracy 0.62 -- 56.64 + 56.22 + 500.28 + 4.78 = 617.92: 47%|████▋ | 959/2048 [12:16<12:42, 1.43it/s]
loss 1.34 accuracy 0.62 -- 56.64 + 56.22 + 500.28 + 4.78 = 617.92: 47%|████▋ | 960/2048 [12:16<12:58, 1.40it/s]
loss 2.12 accuracy 0.25 -- 56.28 + 57.88 + 495.74 + 4.78 = 614.67: 47%|████▋ | 960/2048 [12:17<12:58, 1.40it/s]
loss 2.12 accuracy 0.25 -- 56.28 + 57.88 + 495.74 + 4.78 = 614.67: 47%|████▋ | 961/2048 [12:17<12:33, 1.44it/s]
loss 2.11 accuracy 0.25 -- 157.10 + 56.68 + 489.26 + 4.77 = 707.80: 47%|████▋ | 961/2048 [12:18<12:33, 1.44it/s]
loss 2.11 accuracy 0.25 -- 157.10 + 56.68 + 489.26 + 4.77 = 707.80: 47%|████▋ | 962/2048 [12:18<12:46, 1.42it/s]
loss 2.02 accuracy 0.25 -- 55.94 + 166.57 + 501.21 + 4.78 = 728.50: 47%|████▋ | 962/2048 [12:19<12:46, 1.42it/s]
loss 2.02 accuracy 0.25 -- 55.94 + 166.57 + 501.21 + 4.78 = 728.50: 47%|████▋ | 963/2048 [12:19<13:02, 1.39it/s]
loss 1.60 accuracy 0.38 -- 56.76 + 56.43 + 498.43 + 4.78 = 616.40: 47%|████▋ | 963/2048 [12:19<13:02, 1.39it/s]
loss 1.60 accuracy 0.38 -- 56.76 + 56.43 + 498.43 + 4.78 = 616.40: 47%|████▋ | 964/2048 [12:19<12:36, 1.43it/s]
loss 1.67 accuracy 0.31 -- 162.46 + 56.84 + 496.85 + 4.77 = 720.92: 47%|████▋ | 964/2048 [12:20<12:36, 1.43it/s]
loss 1.67 accuracy 0.31 -- 162.46 + 56.84 + 496.85 + 4.77 = 720.92: 47%|████▋ | 965/2048 [12:20<12:52, 1.40it/s]
loss 1.89 accuracy 0.19 -- 56.03 + 57.21 + 621.02 + 4.79 = 739.05: 47%|████▋ | 965/2048 [12:21<12:52, 1.40it/s]
loss 1.89 accuracy 0.19 -- 56.03 + 57.21 + 621.02 + 4.79 = 739.05: 47%|████▋ | 966/2048 [12:21<13:08, 1.37it/s]
loss 1.82 accuracy 0.19 -- 56.77 + 56.47 + 505.35 + 4.79 = 623.39: 47%|████▋ | 966/2048 [12:21<13:08, 1.37it/s]
loss 1.82 accuracy 0.19 -- 56.77 + 56.47 + 505.35 + 4.79 = 623.39: 47%|████▋ | 967/2048 [12:21<12:42, 1.42it/s]
loss 2.26 accuracy 0.19 -- 55.98 + 57.40 + 616.40 + 4.78 = 734.56: 47%|████▋ | 967/2048 [12:22<12:42, 1.42it/s]
loss 2.26 accuracy 0.19 -- 55.98 + 57.40 + 616.40 + 4.78 = 734.56: 47%|████▋ | 968/2048 [12:22<13:00, 1.38it/s]
loss 2.06 accuracy 0.19 -- 56.93 + 56.67 + 501.50 + 4.76 = 619.85: 47%|████▋ | 968/2048 [12:23<13:00, 1.38it/s]
loss 2.06 accuracy 0.19 -- 56.93 + 56.67 + 501.50 + 4.76 = 619.85: 47%|████▋ | 969/2048 [12:23<12:34, 1.43it/s]
loss 1.82 accuracy 0.31 -- 56.20 + 166.19 + 501.33 + 4.78 = 728.50: 47%|████▋ | 969/2048 [12:24<12:34, 1.43it/s]
loss 1.82 accuracy 0.31 -- 56.20 + 166.19 + 501.33 + 4.78 = 728.50: 47%|████▋ | 970/2048 [12:24<12:52, 1.40it/s]
loss 1.94 accuracy 0.25 -- 56.17 + 56.37 + 499.24 + 4.77 = 616.56: 47%|████▋ | 970/2048 [12:24<12:52, 1.40it/s]
loss 1.94 accuracy 0.25 -- 56.17 + 56.37 + 499.24 + 4.77 = 616.56: 47%|████▋ | 971/2048 [12:24<12:28, 1.44it/s]
loss 2.65 accuracy 0.31 -- 57.09 + 56.75 + 499.07 + 4.78 = 617.69: 47%|████▋ | 971/2048 [12:25<12:28, 1.44it/s]
loss 2.65 accuracy 0.31 -- 57.09 + 56.75 + 499.07 + 4.78 = 617.69: 47%|████▋ | 972/2048 [12:25<12:45, 1.40it/s]
loss 1.88 accuracy 0.12 -- 56.04 + 57.33 + 495.12 + 4.77 = 613.26: 47%|████▋ | 972/2048 [12:26<12:45, 1.40it/s]
loss 1.88 accuracy 0.12 -- 56.04 + 57.33 + 495.12 + 4.77 = 613.26: 48%|████▊ | 973/2048 [12:26<12:33, 1.43it/s]
loss 1.92 accuracy 0.19 -- 157.35 + 56.89 + 489.92 + 4.77 = 708.92: 48%|████▊ | 973/2048 [12:26<12:33, 1.43it/s]
loss 1.92 accuracy 0.19 -- 157.35 + 56.89 + 489.92 + 4.77 = 708.92: 48%|████▊ | 974/2048 [12:26<12:44, 1.41it/s]
loss 2.24 accuracy 0.31 -- 55.90 + 166.48 + 501.41 + 4.79 = 728.58: 48%|████▊ | 974/2048 [12:27<12:44, 1.41it/s]
loss 2.24 accuracy 0.31 -- 55.90 + 166.48 + 501.41 + 4.79 = 728.58: 48%|████▊ | 975/2048 [12:27<12:57, 1.38it/s]
loss 1.66 accuracy 0.44 -- 56.54 + 56.69 + 497.66 + 4.78 = 615.67: 48%|████▊ | 975/2048 [12:28<12:57, 1.38it/s]
loss 1.66 accuracy 0.44 -- 56.54 + 56.69 + 497.66 + 4.78 = 615.67: 48%|████▊ | 976/2048 [12:28<12:30, 1.43it/s]
loss 1.85 accuracy 0.50 -- 162.22 + 57.10 + 498.17 + 4.81 = 722.30: 48%|████▊ | 976/2048 [12:28<12:30, 1.43it/s]
loss 1.85 accuracy 0.50 -- 162.22 + 57.10 + 498.17 + 4.81 = 722.30: 48%|████▊ | 977/2048 [12:28<12:45, 1.40it/s]
loss 1.89 accuracy 0.31 -- 56.01 + 57.00 + 621.20 + 4.78 = 739.00: 48%|████▊ | 977/2048 [12:29<12:45, 1.40it/s]
loss 1.89 accuracy 0.31 -- 56.01 + 57.00 + 621.20 + 4.78 = 739.00: 48%|████▊ | 978/2048 [12:29<13:01, 1.37it/s]
loss 2.23 accuracy 0.06 -- 57.04 + 56.34 + 506.23 + 4.80 = 624.40: 48%|████▊ | 978/2048 [12:30<13:01, 1.37it/s]
loss 2.23 accuracy 0.06 -- 57.04 + 56.34 + 506.23 + 4.80 = 624.40: 48%|████▊ | 979/2048 [12:30<12:35, 1.41it/s]
loss 1.90 accuracy 0.44 -- 56.38 + 57.28 + 616.50 + 4.78 = 734.95: 48%|████▊ | 979/2048 [12:31<12:35, 1.41it/s]
loss 1.90 accuracy 0.44 -- 56.38 + 57.28 + 616.50 + 4.78 = 734.95: 48%|████▊ | 980/2048 [12:31<12:52, 1.38it/s]
loss 1.89 accuracy 0.31 -- 56.72 + 57.68 + 503.24 + 4.77 = 622.42: 48%|████▊ | 980/2048 [12:31<12:52, 1.38it/s]
loss 1.89 accuracy 0.31 -- 56.72 + 57.68 + 503.24 + 4.77 = 622.42: 48%|████▊ | 981/2048 [12:31<12:28, 1.43it/s]
loss 2.07 accuracy 0.19 -- 56.39 + 166.27 + 503.18 + 4.78 = 730.62: 48%|████▊ | 981/2048 [12:32<12:28, 1.43it/s]
loss 2.07 accuracy 0.19 -- 56.39 + 166.27 + 503.18 + 4.78 = 730.62: 48%|████▊ | 982/2048 [12:32<12:45, 1.39it/s]
loss 1.34 accuracy 0.56 -- 56.21 + 56.89 + 501.29 + 4.77 = 619.16: 48%|████▊ | 982/2048 [12:33<12:45, 1.39it/s]
loss 1.34 accuracy 0.56 -- 56.21 + 56.89 + 501.29 + 4.77 = 619.16: 48%|████▊ | 983/2048 [12:33<12:21, 1.44it/s]
loss 2.61 accuracy 0.12 -- 56.59 + 56.83 + 498.26 + 4.78 = 616.46: 48%|████▊ | 983/2048 [12:33<12:21, 1.44it/s]
loss 2.61 accuracy 0.12 -- 56.59 + 56.83 + 498.26 + 4.78 = 616.46: 48%|████▊ | 984/2048 [12:33<12:38, 1.40it/s]
loss 1.56 accuracy 0.12 -- 55.90 + 57.33 + 495.46 + 4.78 = 613.47: 48%|████▊ | 984/2048 [12:34<12:38, 1.40it/s]
loss 1.56 accuracy 0.12 -- 55.90 + 57.33 + 495.46 + 4.78 = 613.47: 48%|████▊ | 985/2048 [12:34<12:14, 1.45it/s]
loss 1.57 accuracy 0.50 -- 157.18 + 57.05 + 489.99 + 4.79 = 709.01: 48%|████▊ | 985/2048 [12:35<12:14, 1.45it/s]
loss 1.57 accuracy 0.50 -- 157.18 + 57.05 + 489.99 + 4.79 = 709.01: 48%|████▊ | 986/2048 [12:35<12:28, 1.42it/s]
loss 1.60 accuracy 0.38 -- 56.03 + 166.30 + 503.10 + 4.78 = 730.20: 48%|████▊ | 986/2048 [12:36<12:28, 1.42it/s]
loss 1.60 accuracy 0.38 -- 56.03 + 166.30 + 503.10 + 4.78 = 730.20: 48%|████▊ | 987/2048 [12:36<12:44, 1.39it/s]
loss 1.87 accuracy 0.31 -- 56.81 + 56.38 + 499.90 + 4.84 = 617.93: 48%|████▊ | 987/2048 [12:36<12:44, 1.39it/s]
loss 1.87 accuracy 0.31 -- 56.81 + 56.38 + 499.90 + 4.84 = 617.93: 48%|████▊ | 988/2048 [12:36<12:19, 1.43it/s]
loss 1.93 accuracy 0.25 -- 163.47 + 57.67 + 498.55 + 4.77 = 724.47: 48%|████▊ | 988/2048 [12:37<12:19, 1.43it/s]
loss 1.93 accuracy 0.25 -- 163.47 + 57.67 + 498.55 + 4.77 = 724.47: 48%|████▊ | 989/2048 [12:37<12:36, 1.40it/s]
loss 1.24 accuracy 0.44 -- 56.28 + 57.35 + 620.90 + 4.78 = 739.31: 48%|████▊ | 989/2048 [12:38<12:36, 1.40it/s]
loss 1.24 accuracy 0.44 -- 56.28 + 57.35 + 620.90 + 4.78 = 739.31: 48%|████▊ | 990/2048 [12:38<12:52, 1.37it/s]
loss 1.65 accuracy 0.50 -- 56.46 + 56.47 + 505.17 + 4.78 = 622.88: 48%|████▊ | 990/2048 [12:38<12:52, 1.37it/s]
loss 1.65 accuracy 0.50 -- 56.46 + 56.47 + 505.17 + 4.78 = 622.88: 48%|████▊ | 991/2048 [12:38<12:26, 1.42it/s]
loss 1.47 accuracy 0.50 -- 56.19 + 57.27 + 615.52 + 4.79 = 733.77: 48%|████▊ | 991/2048 [12:39<12:26, 1.42it/s]
loss 1.47 accuracy 0.50 -- 56.19 + 57.27 + 615.52 + 4.79 = 733.77: 48%|████▊ | 992/2048 [12:39<12:42, 1.38it/s]
loss 1.80 accuracy 0.19 -- 56.74 + 56.93 + 501.77 + 4.77 = 620.21: 48%|████▊ | 992/2048 [12:40<12:42, 1.38it/s]
loss 1.80 accuracy 0.19 -- 56.74 + 56.93 + 501.77 + 4.77 = 620.21: 48%|████▊ | 993/2048 [12:40<12:18, 1.43it/s]
loss 1.81 accuracy 0.12 -- 56.00 + 166.25 + 503.48 + 4.78 = 730.51: 48%|████▊ | 993/2048 [12:41<12:18, 1.43it/s]
loss 1.81 accuracy 0.12 -- 56.00 + 166.25 + 503.48 + 4.78 = 730.51: 49%|████▊ | 994/2048 [12:41<12:35, 1.39it/s]
loss 1.83 accuracy 0.44 -- 55.91 + 56.19 + 498.27 + 4.78 = 615.14: 49%|████▊ | 994/2048 [12:41<12:35, 1.39it/s]
loss 1.83 accuracy 0.44 -- 55.91 + 56.19 + 498.27 + 4.78 = 615.14: 49%|████▊ | 995/2048 [12:41<12:11, 1.44it/s]
loss 1.94 accuracy 0.25 -- 57.07 + 56.52 + 498.62 + 4.78 = 616.99: 49%|████▊ | 995/2048 [12:42<12:11, 1.44it/s]
loss 1.94 accuracy 0.25 -- 57.07 + 56.52 + 498.62 + 4.78 = 616.99: 49%|████▊ | 996/2048 [12:42<12:28, 1.41it/s]
loss 2.04 accuracy 0.31 -- 55.88 + 56.89 + 495.00 + 4.78 = 612.56: 49%|████▊ | 996/2048 [12:43<12:28, 1.41it/s]
loss 2.04 accuracy 0.31 -- 55.88 + 56.89 + 495.00 + 4.78 = 612.56: 49%|████▊ | 997/2048 [12:43<12:05, 1.45it/s]
loss 2.38 accuracy 0.06 -- 156.77 + 56.81 + 489.32 + 4.77 = 707.66: 49%|████▊ | 997/2048 [12:43<12:05, 1.45it/s]
loss 2.38 accuracy 0.06 -- 156.77 + 56.81 + 489.32 + 4.77 = 707.66: 49%|████▊ | 998/2048 [12:43<12:18, 1.42it/s]
loss 1.70 accuracy 0.38 -- 55.79 + 166.36 + 502.30 + 4.78 = 729.23: 49%|████▊ | 998/2048 [12:44<12:18, 1.42it/s]
loss 1.70 accuracy 0.38 -- 55.79 + 166.36 + 502.30 + 4.78 = 729.23: 49%|████▉ | 999/2048 [12:44<12:34, 1.39it/s]
loss 1.59 accuracy 0.31 -- 56.90 + 56.75 + 498.45 + 4.79 = 616.89: 49%|████▉ | 999/2048 [12:45<12:34, 1.39it/s]
loss 1.59 accuracy 0.31 -- 56.90 + 56.75 + 498.45 + 4.79 = 616.89: 49%|████▉ | 1000/2048 [12:45<12:10, 1.44it/s]
loss 1.73 accuracy 0.44 -- 162.71 + 56.96 + 497.72 + 4.76 = 722.15: 49%|████▉ | 1000/2048 [12:45<12:10, 1.44it/s]
loss 1.73 accuracy 0.44 -- 162.71 + 56.96 + 497.72 + 4.76 = 722.15: 49%|████▉ | 1001/2048 [12:45<12:26, 1.40it/s]
loss 2.03 accuracy 0.38 -- 56.41 + 57.14 + 619.66 + 4.78 = 737.98: 49%|████▉ | 1001/2048 [12:46<12:26, 1.40it/s]
loss 2.03 accuracy 0.38 -- 56.41 + 57.14 + 619.66 + 4.78 = 737.98: 49%|████▉ | 1002/2048 [12:46<12:41, 1.37it/s]
loss 1.93 accuracy 0.19 -- 56.70 + 56.52 + 504.79 + 4.79 = 622.79: 49%|████▉ | 1002/2048 [12:47<12:41, 1.37it/s]
loss 1.93 accuracy 0.19 -- 56.70 + 56.52 + 504.79 + 4.79 = 622.79: 49%|████▉ | 1003/2048 [12:47<12:16, 1.42it/s]
loss 1.89 accuracy 0.25 -- 56.31 + 57.46 + 616.15 + 4.77 = 734.70: 49%|████▉ | 1003/2048 [12:48<12:16, 1.42it/s]
loss 1.89 accuracy 0.25 -- 56.31 + 57.46 + 616.15 + 4.77 = 734.70: 49%|████▉ | 1004/2048 [12:48<12:33, 1.39it/s]
loss 1.81 accuracy 0.44 -- 56.83 + 56.49 + 503.59 + 4.78 = 621.71: 49%|████▉ | 1004/2048 [12:48<12:33, 1.39it/s]
loss 1.81 accuracy 0.44 -- 56.83 + 56.49 + 503.59 + 4.78 = 621.71: 49%|████▉ | 1005/2048 [12:48<12:21, 1.41it/s]
loss 2.68 accuracy 0.06 -- 56.49 + 166.63 + 502.43 + 4.78 = 730.33: 49%|████▉ | 1005/2048 [12:49<12:21, 1.41it/s]
loss 2.68 accuracy 0.06 -- 56.49 + 166.63 + 502.43 + 4.78 = 730.33: 49%|████▉ | 1006/2048 [12:49<12:34, 1.38it/s]
loss 2.08 accuracy 0.31 -- 56.19 + 56.36 + 499.60 + 4.80 = 616.94: 49%|████▉ | 1006/2048 [12:50<12:34, 1.38it/s]
loss 2.08 accuracy 0.31 -- 56.19 + 56.36 + 499.60 + 4.80 = 616.94: 49%|████▉ | 1007/2048 [12:50<12:09, 1.43it/s]
loss 2.01 accuracy 0.38 -- 56.74 + 56.53 + 497.70 + 4.77 = 615.74: 49%|████▉ | 1007/2048 [12:50<12:09, 1.43it/s]
loss 2.01 accuracy 0.38 -- 56.74 + 56.53 + 497.70 + 4.77 = 615.74: 49%|████▉ | 1008/2048 [12:50<12:23, 1.40it/s]
loss 2.05 accuracy 0.31 -- 56.12 + 57.45 + 495.70 + 4.78 = 614.05: 49%|████▉ | 1008/2048 [12:51<12:23, 1.40it/s]
loss 2.05 accuracy 0.31 -- 56.12 + 57.45 + 495.70 + 4.78 = 614.05: 49%|████▉ | 1009/2048 [12:51<12:00, 1.44it/s]
loss 1.76 accuracy 0.19 -- 157.67 + 57.20 + 489.91 + 4.77 = 709.55: 49%|████▉ | 1009/2048 [12:52<12:00, 1.44it/s]
loss 1.76 accuracy 0.19 -- 157.67 + 57.20 + 489.91 + 4.77 = 709.55: 49%|████▉ | 1010/2048 [12:52<12:12, 1.42it/s]
loss 2.20 accuracy 0.25 -- 55.67 + 166.59 + 502.65 + 4.76 = 729.68: 49%|████▉ | 1010/2048 [12:53<12:12, 1.42it/s]
loss 2.20 accuracy 0.25 -- 55.67 + 166.59 + 502.65 + 4.76 = 729.68: 49%|████▉ | 1011/2048 [12:53<12:27, 1.39it/s]
loss 1.74 accuracy 0.31 -- 56.78 + 56.35 + 498.26 + 4.78 = 616.17: 49%|████▉ | 1011/2048 [12:53<12:27, 1.39it/s]
loss 1.74 accuracy 0.31 -- 56.78 + 56.35 + 498.26 + 4.78 = 616.17: 49%|████▉ | 1012/2048 [12:53<12:02, 1.43it/s]
loss 2.29 accuracy 0.25 -- 162.27 + 56.83 + 497.01 + 4.77 = 720.87: 49%|████▉ | 1012/2048 [12:54<12:02, 1.43it/s]
loss 2.29 accuracy 0.25 -- 162.27 + 56.83 + 497.01 + 4.77 = 720.87: 49%|████▉ | 1013/2048 [12:54<12:17, 1.40it/s]
loss 1.48 accuracy 0.56 -- 56.28 + 57.26 + 620.80 + 4.79 = 739.13: 49%|████▉ | 1013/2048 [12:55<12:17, 1.40it/s]
loss 1.48 accuracy 0.56 -- 56.28 + 57.26 + 620.80 + 4.79 = 739.13: 50%|████▉ | 1014/2048 [12:55<12:33, 1.37it/s]
loss 1.96 accuracy 0.25 -- 56.77 + 56.22 + 506.15 + 4.78 = 623.91: 50%|████▉ | 1014/2048 [12:55<12:33, 1.37it/s]
loss 1.96 accuracy 0.25 -- 56.77 + 56.22 + 506.15 + 4.78 = 623.91: 50%|████▉ | 1015/2048 [12:55<12:08, 1.42it/s]
loss 1.70 accuracy 0.25 -- 56.40 + 57.23 + 615.14 + 4.86 = 733.64: 50%|████▉ | 1015/2048 [12:56<12:08, 1.42it/s]
loss 1.70 accuracy 0.25 -- 56.40 + 57.23 + 615.14 + 4.86 = 733.64: 50%|████▉ | 1016/2048 [12:56<12:25, 1.38it/s]
loss 1.87 accuracy 0.25 -- 57.02 + 56.71 + 503.02 + 4.78 = 621.54: 50%|████▉ | 1016/2048 [12:57<12:25, 1.38it/s]
loss 1.87 accuracy 0.25 -- 57.02 + 56.71 + 503.02 + 4.78 = 621.54: 50%|████▉ | 1017/2048 [12:57<12:01, 1.43it/s]
loss 1.89 accuracy 0.31 -- 56.36 + 166.43 + 502.57 + 4.76 = 730.13: 50%|████▉ | 1017/2048 [12:58<12:01, 1.43it/s]
loss 1.89 accuracy 0.31 -- 56.36 + 166.43 + 502.57 + 4.76 = 730.13: 50%|████▉ | 1018/2048 [12:58<12:18, 1.39it/s]
loss 1.86 accuracy 0.19 -- 56.12 + 56.28 + 500.01 + 4.78 = 617.19: 50%|████▉ | 1018/2048 [12:58<12:18, 1.39it/s]
loss 1.86 accuracy 0.19 -- 56.12 + 56.28 + 500.01 + 4.78 = 617.19: 50%|████▉ | 1019/2048 [12:58<11:55, 1.44it/s]
loss 2.00 accuracy 0.38 -- 56.40 + 56.77 + 499.56 + 4.78 = 617.51: 50%|████▉ | 1019/2048 [12:59<11:55, 1.44it/s]
loss 2.00 accuracy 0.38 -- 56.40 + 56.77 + 499.56 + 4.78 = 617.51: 50%|████▉ | 1020/2048 [12:59<12:12, 1.40it/s]
loss 1.62 accuracy 0.50 -- 55.93 + 57.49 + 495.61 + 4.78 = 613.82: 50%|████▉ | 1020/2048 [13:00<12:12, 1.40it/s]
loss 1.62 accuracy 0.50 -- 55.93 + 57.49 + 495.61 + 4.78 = 613.82: 50%|████▉ | 1021/2048 [13:00<11:49, 1.45it/s]
loss 2.06 accuracy 0.12 -- 157.21 + 56.80 + 490.41 + 4.82 = 709.23: 50%|████▉ | 1021/2048 [13:00<11:49, 1.45it/s]
loss 2.06 accuracy 0.12 -- 157.21 + 56.80 + 490.41 + 4.82 = 709.23: 50%|████▉ | 1022/2048 [13:00<12:02, 1.42it/s]
loss 1.49 accuracy 0.31 -- 56.14 + 166.58 + 501.47 + 4.78 = 728.97: 50%|████▉ | 1022/2048 [13:01<12:02, 1.42it/s]
loss 1.49 accuracy 0.31 -- 56.14 + 166.58 + 501.47 + 4.78 = 728.97: 50%|████▉ | 1023/2048 [13:01<12:18, 1.39it/s]
loss 1.94 accuracy 0.31 -- 56.87 + 56.42 + 497.66 + 4.85 = 615.80: 50%|████▉ | 1023/2048 [13:02<12:18, 1.39it/s]
loss 1.94 accuracy 0.31 -- 56.87 + 56.42 + 497.66 + 4.85 = 615.80: 50%|█████ | 1024/2048 [13:02<11:53, 1.43it/s]
loss 1.80 accuracy 0.31 -- 162.36 + 57.05 + 496.55 + 4.77 = 720.73: 50%|█████ | 1024/2048 [13:03<11:53, 1.43it/s]
loss 1.80 accuracy 0.31 -- 162.36 + 57.05 + 496.55 + 4.77 = 720.73: 50%|█████ | 1025/2048 [13:03<12:08, 1.40it/s]
loss 1.76 accuracy 0.44 -- 56.57 + 57.39 + 620.30 + 4.78 = 739.04: 50%|█████ | 1025/2048 [13:03<12:08, 1.40it/s]
loss 1.76 accuracy 0.44 -- 56.57 + 57.39 + 620.30 + 4.78 = 739.04: 50%|█████ | 1026/2048 [13:03<12:24, 1.37it/s]
loss 1.93 accuracy 0.19 -- 56.91 + 56.66 + 505.92 + 4.76 = 624.25: 50%|█████ | 1026/2048 [13:04<12:24, 1.37it/s]
loss 1.93 accuracy 0.19 -- 56.91 + 56.66 + 505.92 + 4.76 = 624.25: 50%|█████ | 1027/2048 [13:04<12:00, 1.42it/s]
loss 1.97 accuracy 0.25 -- 56.15 + 57.41 + 617.38 + 4.79 = 735.73: 50%|█████ | 1027/2048 [13:05<12:00, 1.42it/s]
loss 1.97 accuracy 0.25 -- 56.15 + 57.41 + 617.38 + 4.79 = 735.73: 50%|█████ | 1028/2048 [13:05<12:16, 1.38it/s]
loss 1.64 accuracy 0.38 -- 56.78 + 56.31 + 502.19 + 4.79 = 620.07: 50%|█████ | 1028/2048 [13:05<12:16, 1.38it/s]
loss 1.64 accuracy 0.38 -- 56.78 + 56.31 + 502.19 + 4.79 = 620.07: 50%|█████ | 1029/2048 [13:05<11:53, 1.43it/s]
loss 1.93 accuracy 0.19 -- 56.52 + 166.32 + 501.80 + 4.78 = 729.42: 50%|█████ | 1029/2048 [13:06<11:53, 1.43it/s]
loss 1.93 accuracy 0.19 -- 56.52 + 166.32 + 501.80 + 4.78 = 729.42: 50%|█████ | 1030/2048 [13:06<12:09, 1.39it/s]
loss 1.74 accuracy 0.38 -- 56.21 + 56.32 + 498.44 + 4.77 = 615.73: 50%|█████ | 1030/2048 [13:07<12:09, 1.39it/s]
loss 1.74 accuracy 0.38 -- 56.21 + 56.32 + 498.44 + 4.77 = 615.73: 50%|█████ | 1031/2048 [13:07<11:46, 1.44it/s]
loss 2.09 accuracy 0.25 -- 56.78 + 56.54 + 499.31 + 4.79 = 617.41: 50%|█████ | 1031/2048 [13:07<11:46, 1.44it/s]
loss 2.09 accuracy 0.25 -- 56.78 + 56.54 + 499.31 + 4.79 = 617.41: 50%|█████ | 1032/2048 [13:07<12:03, 1.40it/s]
loss 2.12 accuracy 0.25 -- 56.27 + 57.38 + 495.24 + 4.79 = 613.68: 50%|█████ | 1032/2048 [13:08<12:03, 1.40it/s]
loss 2.12 accuracy 0.25 -- 56.27 + 57.38 + 495.24 + 4.79 = 613.68: 50%|█████ | 1033/2048 [13:08<11:40, 1.45it/s]
loss 2.00 accuracy 0.12 -- 158.51 + 57.50 + 489.82 + 4.76 = 710.59: 50%|█████ | 1033/2048 [13:09<11:40, 1.45it/s]
loss 2.00 accuracy 0.12 -- 158.51 + 57.50 + 489.82 + 4.76 = 710.59: 50%|█████ | 1034/2048 [13:09<11:54, 1.42it/s]
loss 2.34 accuracy 0.12 -- 55.71 + 166.21 + 502.53 + 4.76 = 729.22: 50%|█████ | 1034/2048 [13:10<11:54, 1.42it/s]
loss 2.34 accuracy 0.12 -- 55.71 + 166.21 + 502.53 + 4.76 = 729.22: 51%|█████ | 1035/2048 [13:10<12:09, 1.39it/s]
loss 2.29 accuracy 0.06 -- 56.72 + 56.90 + 497.94 + 4.79 = 616.35: 51%|█████ | 1035/2048 [13:10<12:09, 1.39it/s]
loss 2.29 accuracy 0.06 -- 56.72 + 56.90 + 497.94 + 4.79 = 616.35: 51%|█████ | 1036/2048 [13:10<11:45, 1.43it/s]
loss 1.90 accuracy 0.38 -- 162.54 + 57.27 + 497.92 + 4.78 = 722.50: 51%|█████ | 1036/2048 [13:11<11:45, 1.43it/s]
loss 1.90 accuracy 0.38 -- 162.54 + 57.27 + 497.92 + 4.78 = 722.50: 51%|█████ | 1037/2048 [13:11<12:00, 1.40it/s]
loss 2.09 accuracy 0.25 -- 56.21 + 57.34 + 621.69 + 4.78 = 740.01: 51%|█████ | 1037/2048 [13:12<12:00, 1.40it/s]
loss 2.09 accuracy 0.25 -- 56.21 + 57.34 + 621.69 + 4.78 = 740.01: 51%|█████ | 1038/2048 [13:12<12:16, 1.37it/s]
loss 2.13 accuracy 0.25 -- 57.22 + 56.56 + 509.24 + 4.78 = 627.80: 51%|█████ | 1038/2048 [13:12<12:16, 1.37it/s]
loss 2.13 accuracy 0.25 -- 57.22 + 56.56 + 509.24 + 4.78 = 627.80: 51%|█████ | 1039/2048 [13:12<11:53, 1.41it/s]
loss 1.76 accuracy 0.31 -- 56.79 + 57.70 + 619.05 + 4.81 = 738.36: 51%|█████ | 1039/2048 [13:13<11:53, 1.41it/s]
loss 1.76 accuracy 0.31 -- 56.79 + 57.70 + 619.05 + 4.81 = 738.36: 51%|█████ | 1040/2048 [13:13<12:10, 1.38it/s]
loss 1.62 accuracy 0.56 -- 56.96 + 56.48 + 502.29 + 4.77 = 620.50: 51%|█████ | 1040/2048 [13:14<12:10, 1.38it/s]
loss 1.62 accuracy 0.56 -- 56.96 + 56.48 + 502.29 + 4.77 = 620.50: 51%|█████ | 1041/2048 [13:14<11:46, 1.43it/s]
loss 1.60 accuracy 0.25 -- 56.45 + 166.09 + 502.72 + 4.79 = 730.05: 51%|█████ | 1041/2048 [13:15<11:46, 1.43it/s]
loss 1.60 accuracy 0.25 -- 56.45 + 166.09 + 502.72 + 4.79 = 730.05: 51%|█████ | 1042/2048 [13:15<12:02, 1.39it/s]
loss 2.55 accuracy 0.31 -- 56.40 + 56.64 + 499.67 + 4.77 = 617.48: 51%|█████ | 1042/2048 [13:15<12:02, 1.39it/s]
loss 2.55 accuracy 0.31 -- 56.40 + 56.64 + 499.67 + 4.77 = 617.48: 51%|█████ | 1043/2048 [13:15<11:39, 1.44it/s]
loss 1.84 accuracy 0.25 -- 56.75 + 56.80 + 500.30 + 4.79 = 618.64: 51%|█████ | 1043/2048 [13:16<11:39, 1.44it/s]
loss 1.84 accuracy 0.25 -- 56.75 + 56.80 + 500.30 + 4.79 = 618.64: 51%|█████ | 1044/2048 [13:16<11:55, 1.40it/s]
loss 1.69 accuracy 0.38 -- 56.59 + 57.25 + 497.58 + 4.78 = 616.20: 51%|█████ | 1044/2048 [13:17<11:55, 1.40it/s]
loss 1.69 accuracy 0.38 -- 56.59 + 57.25 + 497.58 + 4.78 = 616.20: 51%|█████ | 1045/2048 [13:17<11:34, 1.44it/s]
loss 2.06 accuracy 0.25 -- 158.03 + 57.84 + 491.04 + 4.79 = 711.70: 51%|█████ | 1045/2048 [13:17<11:34, 1.44it/s]
loss 2.06 accuracy 0.25 -- 158.03 + 57.84 + 491.04 + 4.79 = 711.70: 51%|█████ | 1046/2048 [13:17<11:47, 1.42it/s]
loss 2.08 accuracy 0.31 -- 55.86 + 166.33 + 501.34 + 4.82 = 728.35: 51%|█████ | 1046/2048 [13:18<11:47, 1.42it/s]
loss 2.08 accuracy 0.31 -- 55.86 + 166.33 + 501.34 + 4.82 = 728.35: 51%|█████ | 1047/2048 [13:18<12:01, 1.39it/s]
loss 2.10 accuracy 0.31 -- 56.52 + 56.37 + 497.77 + 4.76 = 615.42: 51%|█████ | 1047/2048 [13:19<12:01, 1.39it/s]
loss 2.10 accuracy 0.31 -- 56.52 + 56.37 + 497.77 + 4.76 = 615.42: 51%|█████ | 1048/2048 [13:19<11:37, 1.43it/s]
loss 1.52 accuracy 0.38 -- 162.49 + 57.34 + 497.11 + 4.77 = 721.71: 51%|█████ | 1048/2048 [13:20<11:37, 1.43it/s]
loss 1.52 accuracy 0.38 -- 162.49 + 57.34 + 497.11 + 4.77 = 721.71: 51%|█████ | 1049/2048 [13:20<11:52, 1.40it/s]
loss 1.64 accuracy 0.38 -- 56.34 + 57.37 + 622.58 + 4.78 = 741.07: 51%|█████ | 1049/2048 [13:20<11:52, 1.40it/s]
loss 1.64 accuracy 0.38 -- 56.34 + 57.37 + 622.58 + 4.78 = 741.07: 51%|█████▏ | 1050/2048 [13:20<12:08, 1.37it/s]
loss 2.06 accuracy 0.38 -- 57.34 + 56.75 + 505.95 + 4.76 = 624.81: 51%|█████▏ | 1050/2048 [13:21<12:08, 1.37it/s]
loss 2.06 accuracy 0.38 -- 57.34 + 56.75 + 505.95 + 4.76 = 624.81: 51%|█████▏ | 1051/2048 [13:21<11:44, 1.42it/s]
loss 1.70 accuracy 0.44 -- 56.20 + 57.52 + 616.46 + 4.84 = 735.01: 51%|█████▏ | 1051/2048 [13:22<11:44, 1.42it/s]
loss 1.70 accuracy 0.44 -- 56.20 + 57.52 + 616.46 + 4.84 = 735.01: 51%|█████▏ | 1052/2048 [13:22<12:00, 1.38it/s]
loss 1.72 accuracy 0.38 -- 56.94 + 56.42 + 502.54 + 4.76 = 620.65: 51%|█████▏ | 1052/2048 [13:22<12:00, 1.38it/s]
loss 1.72 accuracy 0.38 -- 56.94 + 56.42 + 502.54 + 4.76 = 620.65: 51%|█████▏ | 1053/2048 [13:22<11:37, 1.43it/s]
loss 1.70 accuracy 0.38 -- 56.16 + 166.32 + 501.69 + 4.79 = 728.97: 51%|█████▏ | 1053/2048 [13:23<11:37, 1.43it/s]
loss 1.70 accuracy 0.38 -- 56.16 + 166.32 + 501.69 + 4.79 = 728.97: 51%|█████▏ | 1054/2048 [13:23<11:52, 1.39it/s]
loss 1.89 accuracy 0.38 -- 56.21 + 56.49 + 499.69 + 4.77 = 617.16: 51%|█████▏ | 1054/2048 [13:24<11:52, 1.39it/s]
loss 1.89 accuracy 0.38 -- 56.21 + 56.49 + 499.69 + 4.77 = 617.16: 52%|█████▏ | 1055/2048 [13:24<11:30, 1.44it/s]
loss 1.57 accuracy 0.50 -- 56.36 + 56.22 + 499.61 + 4.77 = 616.96: 52%|█████▏ | 1055/2048 [13:25<11:30, 1.44it/s]
loss 1.57 accuracy 0.50 -- 56.36 + 56.22 + 499.61 + 4.77 = 616.96: 52%|█████▏ | 1056/2048 [13:25<11:46, 1.40it/s]
loss 2.17 accuracy 0.19 -- 56.33 + 57.18 + 495.42 + 4.82 = 613.74: 52%|█████▏ | 1056/2048 [13:25<11:46, 1.40it/s]
loss 2.17 accuracy 0.19 -- 56.33 + 57.18 + 495.42 + 4.82 = 613.74: 52%|█████▏ | 1057/2048 [13:25<11:24, 1.45it/s]
loss 1.81 accuracy 0.31 -- 157.89 + 56.87 + 489.74 + 4.78 = 709.28: 52%|█████▏ | 1057/2048 [13:26<11:24, 1.45it/s]
loss 1.81 accuracy 0.31 -- 157.89 + 56.87 + 489.74 + 4.78 = 709.28: 52%|█████▏ | 1058/2048 [13:26<11:37, 1.42it/s]
loss 2.38 accuracy 0.19 -- 55.91 + 166.96 + 502.13 + 4.78 = 729.78: 52%|█████▏ | 1058/2048 [13:27<11:37, 1.42it/s]
loss 2.38 accuracy 0.19 -- 55.91 + 166.96 + 502.13 + 4.78 = 729.78: 52%|█████▏ | 1059/2048 [13:27<11:52, 1.39it/s]
loss 1.73 accuracy 0.44 -- 56.43 + 56.54 + 496.79 + 4.77 = 614.53: 52%|█████▏ | 1059/2048 [13:27<11:52, 1.39it/s]
loss 1.73 accuracy 0.44 -- 56.43 + 56.54 + 496.79 + 4.77 = 614.53: 52%|█████▏ | 1060/2048 [13:27<11:28, 1.44it/s]
loss 1.90 accuracy 0.25 -- 162.84 + 57.54 + 499.84 + 4.78 = 724.99: 52%|█████▏ | 1060/2048 [13:28<11:28, 1.44it/s]
loss 1.90 accuracy 0.25 -- 162.84 + 57.54 + 499.84 + 4.78 = 724.99: 52%|█████▏ | 1061/2048 [13:28<11:44, 1.40it/s]
loss 1.62 accuracy 0.38 -- 56.66 + 57.59 + 620.47 + 4.79 = 739.51: 52%|█████▏ | 1061/2048 [13:29<11:44, 1.40it/s]
loss 1.62 accuracy 0.38 -- 56.66 + 57.59 + 620.47 + 4.79 = 739.51: 52%|█████▏ | 1062/2048 [13:29<11:59, 1.37it/s]
loss 1.83 accuracy 0.31 -- 56.65 + 56.57 + 504.25 + 4.78 = 622.25: 52%|█████▏ | 1062/2048 [13:29<11:59, 1.37it/s]
loss 1.83 accuracy 0.31 -- 56.65 + 56.57 + 504.25 + 4.78 = 622.25: 52%|█████▏ | 1063/2048 [13:29<11:34, 1.42it/s]
loss 2.42 accuracy 0.06 -- 56.16 + 57.28 + 615.82 + 4.77 = 734.03: 52%|█████▏ | 1063/2048 [13:30<11:34, 1.42it/s]
loss 2.42 accuracy 0.06 -- 56.16 + 57.28 + 615.82 + 4.77 = 734.03: 52%|█████▏ | 1064/2048 [13:30<11:50, 1.38it/s]
loss 1.54 accuracy 0.38 -- 56.64 + 56.43 + 502.22 + 4.78 = 620.07: 52%|█████▏ | 1064/2048 [13:31<11:50, 1.38it/s]
loss 1.54 accuracy 0.38 -- 56.64 + 56.43 + 502.22 + 4.78 = 620.07: 52%|█████▏ | 1065/2048 [13:31<11:27, 1.43it/s]
loss 2.40 accuracy 0.12 -- 56.21 + 165.86 + 501.31 + 4.78 = 728.16: 52%|█████▏ | 1065/2048 [13:32<11:27, 1.43it/s]
loss 2.40 accuracy 0.12 -- 56.21 + 165.86 + 501.31 + 4.78 = 728.16: 52%|█████▏ | 1066/2048 [13:32<11:43, 1.40it/s]
loss 1.76 accuracy 0.38 -- 55.99 + 56.41 + 500.16 + 4.77 = 617.33: 52%|█████▏ | 1066/2048 [13:32<11:43, 1.40it/s]
loss 1.76 accuracy 0.38 -- 55.99 + 56.41 + 500.16 + 4.77 = 617.33: 52%|█████▏ | 1067/2048 [13:32<11:21, 1.44it/s]
loss 1.86 accuracy 0.38 -- 56.90 + 56.45 + 498.78 + 4.78 = 616.91: 52%|█████▏ | 1067/2048 [13:33<11:21, 1.44it/s]
loss 1.86 accuracy 0.38 -- 56.90 + 56.45 + 498.78 + 4.78 = 616.91: 52%|█████▏ | 1068/2048 [13:33<11:37, 1.41it/s]
loss 1.43 accuracy 0.31 -- 56.06 + 56.89 + 494.68 + 4.76 = 612.40: 52%|█████▏ | 1068/2048 [13:34<11:37, 1.41it/s]
loss 1.43 accuracy 0.31 -- 56.06 + 56.89 + 494.68 + 4.76 = 612.40: 52%|█████▏ | 1069/2048 [13:34<11:15, 1.45it/s]
loss 1.78 accuracy 0.38 -- 157.63 + 57.07 + 490.42 + 4.78 = 709.90: 52%|█████▏ | 1069/2048 [13:34<11:15, 1.45it/s]
loss 1.78 accuracy 0.38 -- 157.63 + 57.07 + 490.42 + 4.78 = 709.90: 52%|█████▏ | 1070/2048 [13:34<11:28, 1.42it/s]
loss 2.38 accuracy 0.06 -- 55.67 + 166.35 + 502.85 + 4.78 = 729.64: 52%|█████▏ | 1070/2048 [13:35<11:28, 1.42it/s]
loss 2.38 accuracy 0.06 -- 55.67 + 166.35 + 502.85 + 4.78 = 729.64: 52%|█████▏ | 1071/2048 [13:35<11:43, 1.39it/s]
loss 1.73 accuracy 0.44 -- 56.70 + 56.77 + 498.21 + 4.78 = 616.47: 52%|█████▏ | 1071/2048 [13:36<11:43, 1.39it/s]
loss 1.73 accuracy 0.44 -- 56.70 + 56.77 + 498.21 + 4.78 = 616.47: 52%|█████▏ | 1072/2048 [13:36<11:20, 1.43it/s]
loss 1.56 accuracy 0.44 -- 162.64 + 57.00 + 497.08 + 4.77 = 721.50: 52%|█████▏ | 1072/2048 [13:37<11:20, 1.43it/s]
loss 1.56 accuracy 0.44 -- 162.64 + 57.00 + 497.08 + 4.77 = 721.50: 52%|█████▏ | 1073/2048 [13:37<11:34, 1.40it/s]
loss 1.70 accuracy 0.38 -- 56.33 + 57.07 + 619.90 + 4.80 = 738.09: 52%|█████▏ | 1073/2048 [13:37<11:34, 1.40it/s]
loss 1.70 accuracy 0.38 -- 56.33 + 57.07 + 619.90 + 4.80 = 738.09: 52%|█████▏ | 1074/2048 [13:37<11:49, 1.37it/s]
loss 1.98 accuracy 0.25 -- 56.79 + 56.55 + 505.38 + 4.82 = 623.55: 52%|█████▏ | 1074/2048 [13:38<11:49, 1.37it/s]
loss 1.98 accuracy 0.25 -- 56.79 + 56.55 + 505.38 + 4.82 = 623.55: 52%|█████▏ | 1075/2048 [13:38<11:26, 1.42it/s]
loss 2.16 accuracy 0.31 -- 56.37 + 58.17 + 617.49 + 4.79 = 736.82: 52%|█████▏ | 1075/2048 [13:39<11:26, 1.42it/s]
loss 2.16 accuracy 0.31 -- 56.37 + 58.17 + 617.49 + 4.79 = 736.82: 53%|█████▎ | 1076/2048 [13:39<11:42, 1.38it/s]
loss 1.91 accuracy 0.31 -- 56.59 + 56.65 + 502.74 + 4.78 = 620.76: 53%|█████▎ | 1076/2048 [13:39<11:42, 1.38it/s]
loss 1.91 accuracy 0.31 -- 56.59 + 56.65 + 502.74 + 4.78 = 620.76: 53%|█████▎ | 1077/2048 [13:39<11:19, 1.43it/s]
loss 1.63 accuracy 0.38 -- 56.28 + 166.20 + 501.56 + 4.79 = 728.84: 53%|█████▎ | 1077/2048 [13:40<11:19, 1.43it/s]
loss 1.63 accuracy 0.38 -- 56.28 + 166.20 + 501.56 + 4.79 = 728.84: 53%|█████▎ | 1078/2048 [13:40<11:35, 1.39it/s]
loss 1.70 accuracy 0.31 -- 56.34 + 56.61 + 500.28 + 4.77 = 618.00: 53%|█████▎ | 1078/2048 [13:41<11:35, 1.39it/s]
loss 1.70 accuracy 0.31 -- 56.34 + 56.61 + 500.28 + 4.77 = 618.00: 53%|█████▎ | 1079/2048 [13:41<11:13, 1.44it/s]
loss 1.60 accuracy 0.19 -- 56.30 + 56.35 + 498.46 + 4.77 = 615.88: 53%|█████▎ | 1079/2048 [13:42<11:13, 1.44it/s]
loss 1.60 accuracy 0.19 -- 56.30 + 56.35 + 498.46 + 4.77 = 615.88: 53%|█████▎ | 1080/2048 [13:42<11:29, 1.40it/s]
loss 1.29 accuracy 0.44 -- 56.22 + 57.31 + 495.64 + 4.78 = 613.95: 53%|█████▎ | 1080/2048 [13:42<11:29, 1.40it/s]
loss 1.29 accuracy 0.44 -- 56.22 + 57.31 + 495.64 + 4.78 = 613.95: 53%|█████▎ | 1081/2048 [13:42<11:07, 1.45it/s]
loss 2.30 accuracy 0.19 -- 158.45 + 56.88 + 490.93 + 4.79 = 711.05: 53%|█████▎ | 1081/2048 [13:43<11:07, 1.45it/s]
loss 2.30 accuracy 0.19 -- 158.45 + 56.88 + 490.93 + 4.79 = 711.05: 53%|█████▎ | 1082/2048 [13:43<11:20, 1.42it/s]
loss 1.47 accuracy 0.50 -- 55.96 + 166.11 + 501.33 + 4.81 = 728.20: 53%|█████▎ | 1082/2048 [13:44<11:20, 1.42it/s]
loss 1.47 accuracy 0.50 -- 55.96 + 166.11 + 501.33 + 4.81 = 728.20: 53%|█████▎ | 1083/2048 [13:44<11:34, 1.39it/s]
loss 1.46 accuracy 0.50 -- 56.53 + 56.63 + 499.56 + 4.80 = 617.51: 53%|█████▎ | 1083/2048 [13:44<11:34, 1.39it/s]
loss 1.46 accuracy 0.50 -- 56.53 + 56.63 + 499.56 + 4.80 = 617.51: 53%|█████▎ | 1084/2048 [13:44<11:12, 1.43it/s]
loss 1.86 accuracy 0.38 -- 163.14 + 56.80 + 497.86 + 4.78 = 722.58: 53%|█████▎ | 1084/2048 [13:45<11:12, 1.43it/s]
loss 1.86 accuracy 0.38 -- 163.14 + 56.80 + 497.86 + 4.78 = 722.58: 53%|█████▎ | 1085/2048 [13:45<11:26, 1.40it/s]
loss 1.55 accuracy 0.38 -- 56.09 + 57.33 + 620.82 + 4.77 = 739.00: 53%|█████▎ | 1085/2048 [13:46<11:26, 1.40it/s]
loss 1.55 accuracy 0.38 -- 56.09 + 57.33 + 620.82 + 4.77 = 739.00: 53%|█████▎ | 1086/2048 [13:46<11:41, 1.37it/s]
loss 1.67 accuracy 0.44 -- 56.56 + 56.21 + 504.01 + 4.76 = 621.54: 53%|█████▎ | 1086/2048 [13:46<11:41, 1.37it/s]
loss 1.67 accuracy 0.44 -- 56.56 + 56.21 + 504.01 + 4.76 = 621.54: 53%|█████▎ | 1087/2048 [13:46<11:17, 1.42it/s]
loss 1.84 accuracy 0.19 -- 55.84 + 57.18 + 613.84 + 4.78 = 731.64: 53%|█████▎ | 1087/2048 [13:47<11:17, 1.42it/s]
loss 1.84 accuracy 0.19 -- 55.84 + 57.18 + 613.84 + 4.78 = 731.64: 53%|█████▎ | 1088/2048 [13:47<11:32, 1.39it/s]
loss 2.07 accuracy 0.31 -- 56.45 + 56.36 + 500.81 + 4.78 = 618.40: 53%|█████▎ | 1088/2048 [13:48<11:32, 1.39it/s]
loss 2.07 accuracy 0.31 -- 56.45 + 56.36 + 500.81 + 4.78 = 618.40: 53%|█████▎ | 1089/2048 [13:48<11:09, 1.43it/s]
loss 2.41 accuracy 0.19 -- 56.05 + 165.99 + 501.76 + 4.79 = 728.59: 53%|█████▎ | 1089/2048 [13:49<11:09, 1.43it/s]
loss 2.41 accuracy 0.19 -- 56.05 + 165.99 + 501.76 + 4.79 = 728.59: 53%|█████▎ | 1090/2048 [13:49<11:25, 1.40it/s]
loss 2.21 accuracy 0.25 -- 55.87 + 56.04 + 497.96 + 4.78 = 614.65: 53%|█████▎ | 1090/2048 [13:49<11:25, 1.40it/s]
loss 2.21 accuracy 0.25 -- 55.87 + 56.04 + 497.96 + 4.78 = 614.65: 53%|█████▎ | 1091/2048 [13:49<11:03, 1.44it/s]
loss 2.19 accuracy 0.12 -- 56.33 + 56.34 + 497.50 + 4.78 = 614.96: 53%|█████▎ | 1091/2048 [13:50<11:03, 1.44it/s]
loss 2.19 accuracy 0.12 -- 56.33 + 56.34 + 497.50 + 4.78 = 614.96: 53%|█████▎ | 1092/2048 [13:50<11:18, 1.41it/s]
loss 1.39 accuracy 0.69 -- 55.77 + 57.11 + 494.26 + 4.78 = 611.92: 53%|█████▎ | 1092/2048 [13:51<11:18, 1.41it/s]
loss 1.39 accuracy 0.69 -- 55.77 + 57.11 + 494.26 + 4.78 = 611.92: 53%|█████▎ | 1093/2048 [13:51<10:57, 1.45it/s]
loss 1.99 accuracy 0.31 -- 157.57 + 56.76 + 488.87 + 4.77 = 707.98: 53%|█████▎ | 1093/2048 [13:51<10:57, 1.45it/s]
loss 1.99 accuracy 0.31 -- 157.57 + 56.76 + 488.87 + 4.77 = 707.98: 53%|█████▎ | 1094/2048 [13:51<11:10, 1.42it/s]
loss 1.92 accuracy 0.25 -- 55.51 + 166.02 + 502.20 + 4.77 = 728.50: 53%|█████▎ | 1094/2048 [13:52<11:10, 1.42it/s]
loss 1.92 accuracy 0.25 -- 55.51 + 166.02 + 502.20 + 4.77 = 728.50: 53%|█████▎ | 1095/2048 [13:52<11:24, 1.39it/s]
loss 2.26 accuracy 0.12 -- 56.54 + 56.36 + 497.54 + 4.78 = 615.22: 53%|█████▎ | 1095/2048 [13:53<11:24, 1.39it/s]
loss 2.26 accuracy 0.12 -- 56.54 + 56.36 + 497.54 + 4.78 = 615.22: 54%|█████▎ | 1096/2048 [13:53<11:02, 1.44it/s]
loss 1.91 accuracy 0.31 -- 162.28 + 57.02 + 495.56 + 4.76 = 719.63: 54%|█████▎ | 1096/2048 [13:54<11:02, 1.44it/s]
loss 1.91 accuracy 0.31 -- 162.28 + 57.02 + 495.56 + 4.76 = 719.63: 54%|█████▎ | 1097/2048 [13:54<11:16, 1.41it/s]
loss 1.74 accuracy 0.44 -- 55.95 + 56.98 + 618.88 + 4.78 = 736.59: 54%|█████▎ | 1097/2048 [13:54<11:16, 1.41it/s]
loss 1.74 accuracy 0.44 -- 55.95 + 56.98 + 618.88 + 4.78 = 736.59: 54%|█████▎ | 1098/2048 [13:54<11:30, 1.38it/s]
loss 1.70 accuracy 0.38 -- 56.60 + 56.55 + 504.09 + 4.78 = 622.02: 54%|█████▎ | 1098/2048 [13:55<11:30, 1.38it/s]
loss 1.70 accuracy 0.38 -- 56.60 + 56.55 + 504.09 + 4.78 = 622.02: 54%|█████▎ | 1099/2048 [13:55<11:07, 1.42it/s]
loss 1.53 accuracy 0.38 -- 56.19 + 57.18 + 616.87 + 4.82 = 735.06: 54%|█████▎ | 1099/2048 [13:56<11:07, 1.42it/s]
loss 1.53 accuracy 0.38 -- 56.19 + 57.18 + 616.87 + 4.82 = 735.06: 54%|█████▎ | 1100/2048 [13:56<11:23, 1.39it/s]
loss 1.90 accuracy 0.25 -- 56.89 + 56.62 + 504.03 + 4.81 = 622.35: 54%|█████▎ | 1100/2048 [13:56<11:23, 1.39it/s]
loss 1.90 accuracy 0.25 -- 56.89 + 56.62 + 504.03 + 4.81 = 622.35: 54%|█████▍ | 1101/2048 [13:56<11:02, 1.43it/s]
loss 1.47 accuracy 0.69 -- 56.70 + 166.81 + 502.77 + 4.81 = 731.09: 54%|█████▍ | 1101/2048 [13:57<11:02, 1.43it/s]
loss 1.47 accuracy 0.69 -- 56.70 + 166.81 + 502.77 + 4.81 = 731.09: 54%|█████▍ | 1102/2048 [13:57<11:18, 1.39it/s]
loss 1.75 accuracy 0.31 -- 56.03 + 56.36 + 500.21 + 4.78 = 617.38: 54%|█████▍ | 1102/2048 [13:58<11:18, 1.39it/s]
loss 1.75 accuracy 0.31 -- 56.03 + 56.36 + 500.21 + 4.78 = 617.38: 54%|█████▍ | 1103/2048 [13:58<10:57, 1.44it/s]
loss 1.69 accuracy 0.38 -- 56.68 + 56.33 + 500.38 + 4.86 = 618.25: 54%|█████▍ | 1103/2048 [13:59<10:57, 1.44it/s]
loss 1.69 accuracy 0.38 -- 56.68 + 56.33 + 500.38 + 4.86 = 618.25: 54%|█████▍ | 1104/2048 [13:59<11:12, 1.40it/s]
loss 1.84 accuracy 0.19 -- 56.07 + 57.01 + 495.67 + 4.82 = 613.57: 54%|█████▍ | 1104/2048 [13:59<11:12, 1.40it/s]
loss 1.84 accuracy 0.19 -- 56.07 + 57.01 + 495.67 + 4.82 = 613.57: 54%|█████▍ | 1105/2048 [13:59<10:51, 1.45it/s]
loss 1.77 accuracy 0.31 -- 157.72 + 56.98 + 490.52 + 4.76 = 709.98: 54%|█████▍ | 1105/2048 [14:00<10:51, 1.45it/s]
loss 1.77 accuracy 0.31 -- 157.72 + 56.98 + 490.52 + 4.76 = 709.98: 54%|█████▍ | 1106/2048 [14:00<11:04, 1.42it/s]
loss 1.51 accuracy 0.31 -- 56.04 + 166.14 + 503.51 + 4.77 = 730.46: 54%|█████▍ | 1106/2048 [14:01<11:04, 1.42it/s]
loss 1.51 accuracy 0.31 -- 56.04 + 166.14 + 503.51 + 4.77 = 730.46: 54%|█████▍ | 1107/2048 [14:01<11:18, 1.39it/s]
loss 1.67 accuracy 0.50 -- 56.93 + 56.78 + 497.85 + 4.79 = 616.35: 54%|█████▍ | 1107/2048 [14:01<11:18, 1.39it/s]
loss 1.67 accuracy 0.50 -- 56.93 + 56.78 + 497.85 + 4.79 = 616.35: 54%|█████▍ | 1108/2048 [14:01<10:55, 1.43it/s]
loss 1.80 accuracy 0.12 -- 162.51 + 56.91 + 496.40 + 4.79 = 720.60: 54%|█████▍ | 1108/2048 [14:02<10:55, 1.43it/s]
loss 1.80 accuracy 0.12 -- 162.51 + 56.91 + 496.40 + 4.79 = 720.60: 54%|█████▍ | 1109/2048 [14:02<11:09, 1.40it/s]
loss 2.10 accuracy 0.19 -- 56.20 + 57.30 + 622.64 + 4.78 = 740.92: 54%|█████▍ | 1109/2048 [14:03<11:09, 1.40it/s]
loss 2.10 accuracy 0.19 -- 56.20 + 57.30 + 622.64 + 4.78 = 740.92: 54%|█████▍ | 1110/2048 [14:03<11:23, 1.37it/s]
loss 2.11 accuracy 0.44 -- 56.87 + 56.17 + 504.08 + 4.78 = 621.89: 54%|█████▍ | 1110/2048 [14:03<11:23, 1.37it/s]
loss 2.11 accuracy 0.44 -- 56.87 + 56.17 + 504.08 + 4.78 = 621.89: 54%|█████▍ | 1111/2048 [14:03<11:00, 1.42it/s]
loss 1.28 accuracy 0.50 -- 56.08 + 57.45 + 616.20 + 4.78 = 734.51: 54%|█████▍ | 1111/2048 [14:04<11:00, 1.42it/s]
loss 1.28 accuracy 0.50 -- 56.08 + 57.45 + 616.20 + 4.78 = 734.51: 54%|█████▍ | 1112/2048 [14:04<11:15, 1.38it/s]
loss 1.63 accuracy 0.31 -- 56.91 + 56.90 + 502.86 + 4.80 = 621.48: 54%|█████▍ | 1112/2048 [14:05<11:15, 1.38it/s]
loss 1.63 accuracy 0.31 -- 56.91 + 56.90 + 502.86 + 4.80 = 621.48: 54%|█████▍ | 1113/2048 [14:05<10:54, 1.43it/s]
loss 1.59 accuracy 0.44 -- 56.15 + 166.12 + 501.45 + 4.78 = 728.50: 54%|█████▍ | 1113/2048 [14:06<10:54, 1.43it/s]
loss 1.59 accuracy 0.44 -- 56.15 + 166.12 + 501.45 + 4.78 = 728.50: 54%|█████▍ | 1114/2048 [14:06<11:09, 1.40it/s]
loss 1.77 accuracy 0.31 -- 55.93 + 56.55 + 499.13 + 4.77 = 616.37: 54%|█████▍ | 1114/2048 [14:06<11:09, 1.40it/s]
loss 1.77 accuracy 0.31 -- 55.93 + 56.55 + 499.13 + 4.77 = 616.37: 54%|█████▍ | 1115/2048 [14:06<10:48, 1.44it/s]
loss 1.78 accuracy 0.25 -- 56.66 + 56.21 + 498.24 + 4.77 = 615.89: 54%|█████▍ | 1115/2048 [14:07<10:48, 1.44it/s]
loss 1.78 accuracy 0.25 -- 56.66 + 56.21 + 498.24 + 4.77 = 615.89: 54%|█████▍ | 1116/2048 [14:07<11:02, 1.41it/s]
loss 1.69 accuracy 0.38 -- 55.72 + 57.58 + 494.81 + 4.75 = 612.86: 54%|█████▍ | 1116/2048 [14:08<11:02, 1.41it/s]
loss 1.69 accuracy 0.38 -- 55.72 + 57.58 + 494.81 + 4.75 = 612.86: 55%|█████▍ | 1117/2048 [14:08<10:42, 1.45it/s]
loss 1.89 accuracy 0.38 -- 157.12 + 57.22 + 490.86 + 4.80 = 710.00: 55%|█████▍ | 1117/2048 [14:08<10:42, 1.45it/s]
loss 1.89 accuracy 0.38 -- 157.12 + 57.22 + 490.86 + 4.80 = 710.00: 55%|█████▍ | 1118/2048 [14:08<10:54, 1.42it/s]
loss 1.80 accuracy 0.25 -- 55.90 + 166.54 + 501.19 + 4.78 = 728.40: 55%|█████▍ | 1118/2048 [14:09<10:54, 1.42it/s]
loss 1.80 accuracy 0.25 -- 55.90 + 166.54 + 501.19 + 4.78 = 728.40: 55%|█████▍ | 1119/2048 [14:09<11:08, 1.39it/s]
loss 1.79 accuracy 0.31 -- 56.44 + 56.17 + 497.25 + 4.78 = 614.64: 55%|█████▍ | 1119/2048 [14:10<11:08, 1.39it/s]
loss 1.79 accuracy 0.31 -- 56.44 + 56.17 + 497.25 + 4.78 = 614.64: 55%|█████▍ | 1120/2048 [14:10<10:46, 1.44it/s]
loss 1.61 accuracy 0.62 -- 162.65 + 57.37 + 495.40 + 4.78 = 720.19: 55%|█████▍ | 1120/2048 [14:11<10:46, 1.44it/s]
loss 1.61 accuracy 0.62 -- 162.65 + 57.37 + 495.40 + 4.78 = 720.19: 55%|█████▍ | 1121/2048 [14:11<10:59, 1.41it/s]
loss 1.58 accuracy 0.44 -- 56.26 + 57.05 + 619.54 + 4.78 = 737.63: 55%|█████▍ | 1121/2048 [14:11<10:59, 1.41it/s]
loss 1.58 accuracy 0.44 -- 56.26 + 57.05 + 619.54 + 4.78 = 737.63: 55%|█████▍ | 1122/2048 [14:11<11:13, 1.37it/s]
loss 2.62 accuracy 0.19 -- 56.63 + 56.60 + 504.88 + 4.78 = 622.90: 55%|█████▍ | 1122/2048 [14:12<11:13, 1.37it/s]
loss 2.62 accuracy 0.19 -- 56.63 + 56.60 + 504.88 + 4.78 = 622.90: 55%|█████▍ | 1123/2048 [14:12<10:51, 1.42it/s]
loss 1.75 accuracy 0.38 -- 56.16 + 57.38 + 616.45 + 4.78 = 734.76: 55%|█████▍ | 1123/2048 [14:13<10:51, 1.42it/s]
loss 1.75 accuracy 0.38 -- 56.16 + 57.38 + 616.45 + 4.78 = 734.76: 55%|█████▍ | 1124/2048 [14:13<11:06, 1.39it/s]
loss 2.24 accuracy 0.25 -- 56.60 + 56.66 + 501.85 + 4.80 = 619.91: 55%|█████▍ | 1124/2048 [14:13<11:06, 1.39it/s]
loss 2.24 accuracy 0.25 -- 56.60 + 56.66 + 501.85 + 4.80 = 619.91: 55%|█████▍ | 1125/2048 [14:13<10:45, 1.43it/s]
loss 1.70 accuracy 0.50 -- 56.46 + 167.03 + 503.91 + 4.80 = 732.20: 55%|█████▍ | 1125/2048 [14:14<10:45, 1.43it/s]
loss 1.70 accuracy 0.50 -- 56.46 + 167.03 + 503.91 + 4.80 = 732.20: 55%|█████▍ | 1126/2048 [14:14<11:01, 1.39it/s]
loss 1.56 accuracy 0.50 -- 56.47 + 56.80 + 501.24 + 4.77 = 619.28: 55%|█████▍ | 1126/2048 [14:15<11:01, 1.39it/s]
loss 1.56 accuracy 0.50 -- 56.47 + 56.80 + 501.24 + 4.77 = 619.28: 55%|█████▌ | 1127/2048 [14:15<10:41, 1.44it/s]
loss 2.13 accuracy 0.31 -- 57.09 + 56.92 + 498.78 + 4.78 = 617.57: 55%|█████▌ | 1127/2048 [14:16<10:41, 1.44it/s]
loss 2.13 accuracy 0.31 -- 57.09 + 56.92 + 498.78 + 4.78 = 617.57: 55%|█████▌ | 1128/2048 [14:16<10:55, 1.40it/s]
loss 1.65 accuracy 0.38 -- 56.37 + 57.51 + 496.30 + 4.78 = 614.96: 55%|█████▌ | 1128/2048 [14:16<10:55, 1.40it/s]
loss 1.65 accuracy 0.38 -- 56.37 + 57.51 + 496.30 + 4.78 = 614.96: 55%|█████▌ | 1129/2048 [14:16<10:35, 1.45it/s]
loss 1.66 accuracy 0.38 -- 157.84 + 57.33 + 490.51 + 4.77 = 710.45: 55%|█████▌ | 1129/2048 [14:17<10:35, 1.45it/s]
loss 1.66 accuracy 0.38 -- 157.84 + 57.33 + 490.51 + 4.77 = 710.45: 55%|█████▌ | 1130/2048 [14:17<10:47, 1.42it/s]
loss 1.50 accuracy 0.38 -- 55.74 + 166.70 + 501.18 + 4.77 = 728.39: 55%|█████▌ | 1130/2048 [14:18<10:47, 1.42it/s]
loss 1.50 accuracy 0.38 -- 55.74 + 166.70 + 501.18 + 4.77 = 728.39: 55%|█████▌ | 1131/2048 [14:18<11:00, 1.39it/s]
loss 1.89 accuracy 0.19 -- 56.48 + 56.32 + 498.51 + 4.77 = 616.07: 55%|█████▌ | 1131/2048 [14:18<11:00, 1.39it/s]
loss 1.89 accuracy 0.19 -- 56.48 + 56.32 + 498.51 + 4.77 = 616.07: 55%|█████▌ | 1132/2048 [14:18<10:38, 1.43it/s]
loss 2.22 accuracy 0.19 -- 162.80 + 57.04 + 496.69 + 4.77 = 721.30: 55%|█████▌ | 1132/2048 [14:19<10:38, 1.43it/s]
loss 2.22 accuracy 0.19 -- 162.80 + 57.04 + 496.69 + 4.77 = 721.30: 55%|█████▌ | 1133/2048 [14:19<10:52, 1.40it/s]
loss 1.70 accuracy 0.44 -- 56.01 + 57.19 + 619.39 + 4.80 = 737.39: 55%|█████▌ | 1133/2048 [14:20<10:52, 1.40it/s]
loss 1.70 accuracy 0.44 -- 56.01 + 57.19 + 619.39 + 4.80 = 737.39: 55%|█████▌ | 1134/2048 [14:20<11:05, 1.37it/s]
loss 1.91 accuracy 0.44 -- 56.70 + 56.21 + 505.95 + 4.78 = 623.64: 55%|█████▌ | 1134/2048 [14:20<11:05, 1.37it/s]
loss 1.91 accuracy 0.44 -- 56.70 + 56.21 + 505.95 + 4.78 = 623.64: 55%|█████▌ | 1135/2048 [14:20<10:43, 1.42it/s]
loss 1.71 accuracy 0.50 -- 56.09 + 57.43 + 615.98 + 4.78 = 734.29: 55%|█████▌ | 1135/2048 [14:21<10:43, 1.42it/s]
loss 1.71 accuracy 0.50 -- 56.09 + 57.43 + 615.98 + 4.78 = 734.29: 55%|█████▌ | 1136/2048 [14:21<10:58, 1.38it/s]
loss 1.75 accuracy 0.44 -- 56.69 + 56.75 + 502.41 + 4.77 = 620.62: 55%|█████▌ | 1136/2048 [14:22<10:58, 1.38it/s]
loss 1.75 accuracy 0.44 -- 56.69 + 56.75 + 502.41 + 4.77 = 620.62: 56%|█████▌ | 1137/2048 [14:22<10:37, 1.43it/s]
loss 1.84 accuracy 0.38 -- 56.42 + 166.59 + 501.82 + 4.79 = 729.61: 56%|█████▌ | 1137/2048 [14:23<10:37, 1.43it/s]
loss 1.84 accuracy 0.38 -- 56.42 + 166.59 + 501.82 + 4.79 = 729.61: 56%|█████▌ | 1138/2048 [14:23<10:52, 1.39it/s]
loss 1.48 accuracy 0.25 -- 56.35 + 56.64 + 498.66 + 4.78 = 616.42: 56%|█████▌ | 1138/2048 [14:23<10:52, 1.39it/s]
loss 1.48 accuracy 0.25 -- 56.35 + 56.64 + 498.66 + 4.78 = 616.42: 56%|█████▌ | 1139/2048 [14:23<10:31, 1.44it/s]
loss 1.53 accuracy 0.44 -- 56.73 + 56.86 + 499.27 + 4.77 = 617.63: 56%|█████▌ | 1139/2048 [14:24<10:31, 1.44it/s]
loss 1.53 accuracy 0.44 -- 56.73 + 56.86 + 499.27 + 4.77 = 617.63: 56%|█████▌ | 1140/2048 [14:24<10:46, 1.40it/s]
loss 1.69 accuracy 0.31 -- 56.11 + 57.70 + 496.10 + 4.77 = 614.67: 56%|█████▌ | 1140/2048 [14:25<10:46, 1.40it/s]
loss 1.69 accuracy 0.31 -- 56.11 + 57.70 + 496.10 + 4.77 = 614.67: 56%|█████▌ | 1141/2048 [14:25<10:26, 1.45it/s]
loss 1.87 accuracy 0.44 -- 157.29 + 57.03 + 488.66 + 4.78 = 707.75: 56%|█████▌ | 1141/2048 [14:25<10:26, 1.45it/s]
loss 1.87 accuracy 0.44 -- 157.29 + 57.03 + 488.66 + 4.78 = 707.75: 56%|█████▌ | 1142/2048 [14:25<10:37, 1.42it/s]
loss 1.75 accuracy 0.31 -- 55.65 + 166.98 + 501.56 + 4.77 = 728.95: 56%|█████▌ | 1142/2048 [14:26<10:37, 1.42it/s]
loss 1.75 accuracy 0.31 -- 55.65 + 166.98 + 501.56 + 4.77 = 728.95: 56%|█████▌ | 1143/2048 [14:26<10:51, 1.39it/s]
loss 1.61 accuracy 0.38 -- 56.69 + 56.23 + 496.81 + 4.79 = 614.52: 56%|█████▌ | 1143/2048 [14:27<10:51, 1.39it/s]
loss 1.61 accuracy 0.38 -- 56.69 + 56.23 + 496.81 + 4.79 = 614.52: 56%|█████▌ | 1144/2048 [14:27<10:29, 1.44it/s]
loss 2.19 accuracy 0.31 -- 162.58 + 57.08 + 496.84 + 4.78 = 721.28: 56%|█████▌ | 1144/2048 [14:28<10:29, 1.44it/s]
loss 2.19 accuracy 0.31 -- 162.58 + 57.08 + 496.84 + 4.78 = 721.28: 56%|█████▌ | 1145/2048 [14:28<10:42, 1.40it/s]
loss 1.64 accuracy 0.25 -- 56.25 + 57.58 + 620.76 + 4.82 = 739.41: 56%|█████▌ | 1145/2048 [14:28<10:42, 1.40it/s]
loss 1.64 accuracy 0.25 -- 56.25 + 57.58 + 620.76 + 4.82 = 739.41: 56%|█████▌ | 1146/2048 [14:28<10:56, 1.37it/s]
loss 2.08 accuracy 0.38 -- 56.92 + 56.87 + 505.42 + 4.77 = 623.98: 56%|█████▌ | 1146/2048 [14:29<10:56, 1.37it/s]
loss 2.08 accuracy 0.38 -- 56.92 + 56.87 + 505.42 + 4.77 = 623.98: 56%|█████▌ | 1147/2048 [14:29<10:35, 1.42it/s]
loss 1.83 accuracy 0.38 -- 56.06 + 57.62 + 616.36 + 4.79 = 734.84: 56%|█████▌ | 1147/2048 [14:30<10:35, 1.42it/s]
loss 1.83 accuracy 0.38 -- 56.06 + 57.62 + 616.36 + 4.79 = 734.84: 56%|█████▌ | 1148/2048 [14:30<10:50, 1.38it/s]
loss 1.77 accuracy 0.25 -- 56.72 + 56.55 + 502.33 + 4.77 = 620.37: 56%|█████▌ | 1148/2048 [14:30<10:50, 1.38it/s]
loss 1.77 accuracy 0.25 -- 56.72 + 56.55 + 502.33 + 4.77 = 620.37: 56%|█████▌ | 1149/2048 [14:30<10:29, 1.43it/s]
loss 1.63 accuracy 0.31 -- 55.99 + 166.46 + 502.01 + 4.79 = 729.24: 56%|█████▌ | 1149/2048 [14:31<10:29, 1.43it/s]
loss 1.63 accuracy 0.31 -- 55.99 + 166.46 + 502.01 + 4.79 = 729.24: 56%|█████▌ | 1150/2048 [14:31<10:43, 1.39it/s]
loss 1.67 accuracy 0.38 -- 55.95 + 56.48 + 499.08 + 4.77 = 616.28: 56%|█████▌ | 1150/2048 [14:32<10:43, 1.39it/s]
loss 1.67 accuracy 0.38 -- 55.95 + 56.48 + 499.08 + 4.77 = 616.28: 56%|█████▌ | 1151/2048 [14:32<10:23, 1.44it/s]
loss 1.73 accuracy 0.44 -- 56.76 + 56.75 + 499.36 + 4.78 = 617.64: 56%|█████▌ | 1151/2048 [14:33<10:23, 1.44it/s]
loss 1.73 accuracy 0.44 -- 56.76 + 56.75 + 499.36 + 4.78 = 617.64: 56%|█████▋ | 1152/2048 [14:33<10:37, 1.40it/s]
loss 1.96 accuracy 0.19 -- 56.09 + 57.33 + 494.87 + 4.79 = 613.07: 56%|█████▋ | 1152/2048 [14:33<10:37, 1.40it/s]
loss 1.96 accuracy 0.19 -- 56.09 + 57.33 + 494.87 + 4.79 = 613.07: 56%|█████▋ | 1153/2048 [14:33<10:17, 1.45it/s]
loss 1.49 accuracy 0.31 -- 157.99 + 56.67 + 489.12 + 4.77 = 708.56: 56%|█████▋ | 1153/2048 [14:34<10:17, 1.45it/s]
loss 1.49 accuracy 0.31 -- 157.99 + 56.67 + 489.12 + 4.77 = 708.56: 56%|█████▋ | 1154/2048 [14:34<10:29, 1.42it/s]
loss 2.22 accuracy 0.44 -- 56.20 + 166.90 + 501.66 + 4.79 = 729.55: 56%|█████▋ | 1154/2048 [14:35<10:29, 1.42it/s]
loss 2.22 accuracy 0.44 -- 56.20 + 166.90 + 501.66 + 4.79 = 729.55: 56%|█████▋ | 1155/2048 [14:35<10:42, 1.39it/s]
loss 1.51 accuracy 0.50 -- 56.85 + 56.17 + 497.60 + 4.77 = 615.38: 56%|█████▋ | 1155/2048 [14:35<10:42, 1.39it/s]
loss 1.51 accuracy 0.50 -- 56.85 + 56.17 + 497.60 + 4.77 = 615.38: 56%|█████▋ | 1156/2048 [14:35<10:21, 1.44it/s]
loss 2.21 accuracy 0.19 -- 162.59 + 57.20 + 497.95 + 4.77 = 722.51: 56%|█████▋ | 1156/2048 [14:36<10:21, 1.44it/s]
loss 2.21 accuracy 0.19 -- 162.59 + 57.20 + 497.95 + 4.77 = 722.51: 56%|█████▋ | 1157/2048 [14:36<10:34, 1.40it/s]
loss 1.57 accuracy 0.44 -- 56.19 + 57.74 + 622.09 + 4.79 = 740.81: 56%|█████▋ | 1157/2048 [14:37<10:34, 1.40it/s]
loss 1.57 accuracy 0.44 -- 56.19 + 57.74 + 622.09 + 4.79 = 740.81: 57%|█████▋ | 1158/2048 [14:37<10:48, 1.37it/s]
loss 2.05 accuracy 0.25 -- 56.53 + 56.89 + 504.98 + 4.78 = 623.18: 57%|█████▋ | 1158/2048 [14:37<10:48, 1.37it/s]
loss 2.05 accuracy 0.25 -- 56.53 + 56.89 + 504.98 + 4.78 = 623.18: 57%|█████▋ | 1159/2048 [14:37<10:27, 1.42it/s]
loss 2.09 accuracy 0.31 -- 56.24 + 57.23 + 615.40 + 4.80 = 733.66: 57%|█████▋ | 1159/2048 [14:38<10:27, 1.42it/s]
loss 2.09 accuracy 0.31 -- 56.24 + 57.23 + 615.40 + 4.80 = 733.66: 57%|█████▋ | 1160/2048 [14:38<10:41, 1.38it/s]
loss 1.80 accuracy 0.38 -- 56.81 + 56.63 + 502.08 + 4.78 = 620.30: 57%|█████▋ | 1160/2048 [14:39<10:41, 1.38it/s]
loss 1.80 accuracy 0.38 -- 56.81 + 56.63 + 502.08 + 4.78 = 620.30: 57%|█████▋ | 1161/2048 [14:39<10:20, 1.43it/s]
loss 1.56 accuracy 0.31 -- 56.29 + 166.28 + 502.43 + 4.77 = 729.77: 57%|█████▋ | 1161/2048 [14:40<10:20, 1.43it/s]
loss 1.56 accuracy 0.31 -- 56.29 + 166.28 + 502.43 + 4.77 = 729.77: 57%|█████▋ | 1162/2048 [14:40<10:35, 1.40it/s]
loss 1.70 accuracy 0.50 -- 56.51 + 56.34 + 500.00 + 4.78 = 617.63: 57%|█████▋ | 1162/2048 [14:40<10:35, 1.40it/s]
loss 1.70 accuracy 0.50 -- 56.51 + 56.34 + 500.00 + 4.78 = 617.63: 57%|█████▋ | 1163/2048 [14:40<10:15, 1.44it/s]
loss 1.39 accuracy 0.38 -- 57.14 + 56.62 + 498.37 + 4.79 = 616.92: 57%|█████▋ | 1163/2048 [14:41<10:15, 1.44it/s]
loss 1.39 accuracy 0.38 -- 57.14 + 56.62 + 498.37 + 4.79 = 616.92: 57%|█████▋ | 1164/2048 [14:41<10:29, 1.40it/s]
loss 2.52 accuracy 0.19 -- 55.81 + 57.27 + 493.36 + 4.77 = 611.22: 57%|█████▋ | 1164/2048 [14:42<10:29, 1.40it/s]
loss 2.52 accuracy 0.19 -- 55.81 + 57.27 + 493.36 + 4.77 = 611.22: 57%|█████▋ | 1165/2048 [14:42<10:09, 1.45it/s]
loss 1.75 accuracy 0.31 -- 157.26 + 57.24 + 490.47 + 4.77 = 709.74: 57%|█████▋ | 1165/2048 [14:42<10:09, 1.45it/s]
loss 1.75 accuracy 0.31 -- 157.26 + 57.24 + 490.47 + 4.77 = 709.74: 57%|█████▋ | 1166/2048 [14:42<10:20, 1.42it/s]
loss 1.60 accuracy 0.19 -- 55.79 + 166.71 + 502.06 + 4.78 = 729.34: 57%|█████▋ | 1166/2048 [14:43<10:20, 1.42it/s]
loss 1.60 accuracy 0.19 -- 55.79 + 166.71 + 502.06 + 4.78 = 729.34: 57%|█████▋ | 1167/2048 [14:43<10:34, 1.39it/s]
loss 2.02 accuracy 0.38 -- 56.81 + 56.62 + 497.89 + 4.78 = 616.10: 57%|█████▋ | 1167/2048 [14:44<10:34, 1.39it/s]
loss 2.02 accuracy 0.38 -- 56.81 + 56.62 + 497.89 + 4.78 = 616.10: 57%|█████▋ | 1168/2048 [14:44<10:13, 1.44it/s]
loss 1.44 accuracy 0.50 -- 162.30 + 57.54 + 498.18 + 4.78 = 722.79: 57%|█████▋ | 1168/2048 [14:45<10:13, 1.44it/s]
loss 1.44 accuracy 0.50 -- 162.30 + 57.54 + 498.18 + 4.78 = 722.79: 57%|█████▋ | 1169/2048 [14:45<10:26, 1.40it/s]
loss 1.96 accuracy 0.19 -- 55.85 + 57.17 + 620.99 + 4.79 = 738.79: 57%|█████▋ | 1169/2048 [14:45<10:26, 1.40it/s]
loss 1.96 accuracy 0.19 -- 55.85 + 57.17 + 620.99 + 4.79 = 738.79: 57%|█████▋ | 1170/2048 [14:45<10:39, 1.37it/s]
loss 1.88 accuracy 0.19 -- 56.72 + 56.30 + 504.45 + 4.77 = 622.24: 57%|█████▋ | 1170/2048 [14:46<10:39, 1.37it/s]
loss 1.88 accuracy 0.19 -- 56.72 + 56.30 + 504.45 + 4.77 = 622.24: 57%|█████▋ | 1171/2048 [14:46<10:18, 1.42it/s]
loss 1.76 accuracy 0.31 -- 56.57 + 57.17 + 616.78 + 4.79 = 735.31: 57%|█████▋ | 1171/2048 [14:47<10:18, 1.42it/s]
loss 1.76 accuracy 0.31 -- 56.57 + 57.17 + 616.78 + 4.79 = 735.31: 57%|█████▋ | 1172/2048 [14:47<10:32, 1.38it/s]
loss 1.72 accuracy 0.38 -- 56.75 + 56.68 + 501.55 + 4.79 = 619.77: 57%|█████▋ | 1172/2048 [14:47<10:32, 1.38it/s]
loss 1.72 accuracy 0.38 -- 56.75 + 56.68 + 501.55 + 4.79 = 619.77: 57%|█████▋ | 1173/2048 [14:47<10:12, 1.43it/s]
loss 1.78 accuracy 0.25 -- 56.17 + 166.15 + 502.08 + 4.79 = 729.19: 57%|█████▋ | 1173/2048 [14:48<10:12, 1.43it/s]
loss 1.78 accuracy 0.25 -- 56.17 + 166.15 + 502.08 + 4.79 = 729.19: 57%|█████▋ | 1174/2048 [14:48<10:26, 1.40it/s]
loss 1.51 accuracy 0.38 -- 56.39 + 56.77 + 498.90 + 4.76 = 616.81: 57%|█████▋ | 1174/2048 [14:49<10:26, 1.40it/s]
loss 1.51 accuracy 0.38 -- 56.39 + 56.77 + 498.90 + 4.76 = 616.81: 57%|█████▋ | 1175/2048 [14:49<10:06, 1.44it/s]
loss 1.70 accuracy 0.44 -- 56.72 + 56.84 + 498.10 + 4.78 = 616.44: 57%|█████▋ | 1175/2048 [14:50<10:06, 1.44it/s]
loss 1.70 accuracy 0.44 -- 56.72 + 56.84 + 498.10 + 4.78 = 616.44: 57%|█████▋ | 1176/2048 [14:50<10:20, 1.41it/s]
loss 1.66 accuracy 0.44 -- 55.82 + 57.05 + 494.51 + 4.77 = 612.15: 57%|█████▋ | 1176/2048 [14:50<10:20, 1.41it/s]
loss 1.66 accuracy 0.44 -- 55.82 + 57.05 + 494.51 + 4.77 = 612.15: 57%|█████▋ | 1177/2048 [14:50<10:00, 1.45it/s]
loss 1.65 accuracy 0.56 -- 157.48 + 56.72 + 489.24 + 4.78 = 708.22: 57%|█████▋ | 1177/2048 [14:51<10:00, 1.45it/s]
loss 1.65 accuracy 0.56 -- 157.48 + 56.72 + 489.24 + 4.78 = 708.22: 58%|█████▊ | 1178/2048 [14:51<10:12, 1.42it/s]
loss 2.26 accuracy 0.25 -- 55.72 + 166.80 + 500.24 + 4.77 = 727.52: 58%|█████▊ | 1178/2048 [14:52<10:12, 1.42it/s]
loss 2.26 accuracy 0.25 -- 55.72 + 166.80 + 500.24 + 4.77 = 727.52: 58%|█████▊ | 1179/2048 [14:52<10:24, 1.39it/s]
loss 1.46 accuracy 0.50 -- 56.59 + 56.46 + 499.49 + 4.81 = 617.35: 58%|█████▊ | 1179/2048 [14:52<10:24, 1.39it/s]
loss 1.46 accuracy 0.50 -- 56.59 + 56.46 + 499.49 + 4.81 = 617.35: 58%|█████▊ | 1180/2048 [14:52<10:04, 1.44it/s]
loss 2.42 accuracy 0.19 -- 162.66 + 57.23 + 497.73 + 4.78 = 722.40: 58%|█████▊ | 1180/2048 [14:53<10:04, 1.44it/s]
loss 2.42 accuracy 0.19 -- 162.66 + 57.23 + 497.73 + 4.78 = 722.40: 58%|█████▊ | 1181/2048 [14:53<10:17, 1.40it/s]
loss 2.08 accuracy 0.38 -- 56.20 + 57.51 + 622.35 + 4.79 = 740.85: 58%|█████▊ | 1181/2048 [14:54<10:17, 1.40it/s]
loss 2.08 accuracy 0.38 -- 56.20 + 57.51 + 622.35 + 4.79 = 740.85: 58%|█████▊ | 1182/2048 [14:54<10:31, 1.37it/s]
loss 1.66 accuracy 0.31 -- 56.75 + 56.43 + 505.73 + 4.77 = 623.68: 58%|█████▊ | 1182/2048 [14:54<10:31, 1.37it/s]
loss 1.66 accuracy 0.31 -- 56.75 + 56.43 + 505.73 + 4.77 = 623.68: 58%|█████▊ | 1183/2048 [14:54<10:10, 1.42it/s]
loss 2.11 accuracy 0.31 -- 56.02 + 57.26 + 615.63 + 4.80 = 733.70: 58%|█████▊ | 1183/2048 [14:55<10:10, 1.42it/s]
loss 2.11 accuracy 0.31 -- 56.02 + 57.26 + 615.63 + 4.80 = 733.70: 58%|█████▊ | 1184/2048 [14:55<10:24, 1.38it/s]
loss 1.76 accuracy 0.56 -- 56.73 + 56.67 + 501.54 + 4.77 = 619.70: 58%|█████▊ | 1184/2048 [14:56<10:24, 1.38it/s]
loss 1.76 accuracy 0.56 -- 56.73 + 56.67 + 501.54 + 4.77 = 619.70: 58%|█████▊ | 1185/2048 [14:56<10:03, 1.43it/s]
loss 2.02 accuracy 0.25 -- 56.37 + 166.72 + 503.05 + 4.77 = 730.90: 58%|█████▊ | 1185/2048 [14:57<10:03, 1.43it/s]
loss 2.02 accuracy 0.25 -- 56.37 + 166.72 + 503.05 + 4.77 = 730.90: 58%|█████▊ | 1186/2048 [14:57<10:18, 1.39it/s]
loss 1.73 accuracy 0.38 -- 56.18 + 56.80 + 500.11 + 4.77 = 617.86: 58%|█████▊ | 1186/2048 [14:57<10:18, 1.39it/s]
loss 1.73 accuracy 0.38 -- 56.18 + 56.80 + 500.11 + 4.77 = 617.86: 58%|█████▊ | 1187/2048 [14:57<09:58, 1.44it/s]
loss 2.42 accuracy 0.25 -- 56.80 + 56.35 + 498.32 + 4.79 = 616.26: 58%|█████▊ | 1187/2048 [14:58<09:58, 1.44it/s]
loss 2.42 accuracy 0.25 -- 56.80 + 56.35 + 498.32 + 4.79 = 616.26: 58%|█████▊ | 1188/2048 [14:58<10:12, 1.40it/s]
loss 2.19 accuracy 0.19 -- 56.35 + 57.54 + 495.94 + 4.77 = 614.59: 58%|█████▊ | 1188/2048 [14:59<10:12, 1.40it/s]
loss 2.19 accuracy 0.19 -- 56.35 + 57.54 + 495.94 + 4.77 = 614.59: 58%|█████▊ | 1189/2048 [14:59<09:53, 1.45it/s]
loss 1.48 accuracy 0.38 -- 157.02 + 56.92 + 491.58 + 4.78 = 710.30: 58%|█████▊ | 1189/2048 [14:59<09:53, 1.45it/s]
loss 1.48 accuracy 0.38 -- 157.02 + 56.92 + 491.58 + 4.78 = 710.30: 58%|█████▊ | 1190/2048 [14:59<10:04, 1.42it/s]
loss 1.55 accuracy 0.38 -- 55.78 + 166.47 + 501.96 + 4.77 = 728.98: 58%|█████▊ | 1190/2048 [15:00<10:04, 1.42it/s]
loss 1.55 accuracy 0.38 -- 55.78 + 166.47 + 501.96 + 4.77 = 728.98: 58%|█████▊ | 1191/2048 [15:00<10:17, 1.39it/s]
loss 1.62 accuracy 0.44 -- 57.01 + 56.83 + 498.19 + 4.78 = 616.81: 58%|█████▊ | 1191/2048 [15:01<10:17, 1.39it/s]
loss 1.62 accuracy 0.44 -- 57.01 + 56.83 + 498.19 + 4.78 = 616.81: 58%|█████▊ | 1192/2048 [15:01<09:56, 1.43it/s]
loss 2.06 accuracy 0.38 -- 162.73 + 57.17 + 496.49 + 4.77 = 721.16: 58%|█████▊ | 1192/2048 [15:02<09:56, 1.43it/s]
loss 2.06 accuracy 0.38 -- 162.73 + 57.17 + 496.49 + 4.77 = 721.16: 58%|█████▊ | 1193/2048 [15:02<10:09, 1.40it/s]
loss 2.45 accuracy 0.12 -- 56.02 + 57.06 + 620.77 + 4.78 = 738.63: 58%|█████▊ | 1193/2048 [15:02<10:09, 1.40it/s]
loss 2.45 accuracy 0.12 -- 56.02 + 57.06 + 620.77 + 4.78 = 738.63: 58%|█████▊ | 1194/2048 [15:02<10:22, 1.37it/s]
loss 1.63 accuracy 0.38 -- 56.66 + 56.52 + 504.31 + 4.76 = 622.25: 58%|█████▊ | 1194/2048 [15:03<10:22, 1.37it/s]
loss 1.63 accuracy 0.38 -- 56.66 + 56.52 + 504.31 + 4.76 = 622.25: 58%|█████▊ | 1195/2048 [15:03<10:01, 1.42it/s]
loss 1.68 accuracy 0.38 -- 56.11 + 57.28 + 614.84 + 4.78 = 733.01: 58%|█████▊ | 1195/2048 [15:04<10:01, 1.42it/s]
loss 1.68 accuracy 0.38 -- 56.11 + 57.28 + 614.84 + 4.78 = 733.01: 58%|█████▊ | 1196/2048 [15:04<10:14, 1.39it/s]
loss 1.75 accuracy 0.19 -- 56.75 + 56.41 + 503.25 + 4.78 = 621.18: 58%|█████▊ | 1196/2048 [15:04<10:14, 1.39it/s]
loss 1.75 accuracy 0.19 -- 56.75 + 56.41 + 503.25 + 4.78 = 621.18: 58%|█████▊ | 1197/2048 [15:04<09:55, 1.43it/s]
loss 1.90 accuracy 0.25 -- 56.41 + 166.50 + 501.15 + 4.77 = 728.83: 58%|█████▊ | 1197/2048 [15:05<09:55, 1.43it/s]
loss 1.90 accuracy 0.25 -- 56.41 + 166.50 + 501.15 + 4.77 = 728.83: 58%|█████▊ | 1198/2048 [15:05<10:09, 1.40it/s]
loss 3.22 accuracy 0.06 -- 56.11 + 56.54 + 499.24 + 4.82 = 616.71: 58%|█████▊ | 1198/2048 [15:06<10:09, 1.40it/s]
loss 3.22 accuracy 0.06 -- 56.11 + 56.54 + 499.24 + 4.82 = 616.71: 59%|█████▊ | 1199/2048 [15:06<09:49, 1.44it/s]
loss 2.45 accuracy 0.06 -- 56.69 + 56.40 + 498.93 + 4.80 = 616.82: 59%|█████▊ | 1199/2048 [15:07<09:49, 1.44it/s]
loss 2.45 accuracy 0.06 -- 56.69 + 56.40 + 498.93 + 4.80 = 616.82: 59%|█████▊ | 1200/2048 [15:07<10:03, 1.41it/s]
loss 1.69 accuracy 0.44 -- 56.13 + 57.46 + 495.59 + 4.82 = 614.00: 59%|█████▊ | 1200/2048 [15:07<10:03, 1.41it/s]
loss 1.69 accuracy 0.44 -- 56.13 + 57.46 + 495.59 + 4.82 = 614.00: 59%|█████▊ | 1201/2048 [15:07<09:44, 1.45it/s]
loss 1.94 accuracy 0.44 -- 157.63 + 57.11 + 488.96 + 4.76 = 708.46: 59%|█████▊ | 1201/2048 [15:08<09:44, 1.45it/s]
loss 1.94 accuracy 0.44 -- 157.63 + 57.11 + 488.96 + 4.76 = 708.46: 59%|█████▊ | 1202/2048 [15:08<09:55, 1.42it/s]
loss 1.97 accuracy 0.25 -- 55.74 + 166.55 + 503.03 + 4.77 = 730.10: 59%|█████▊ | 1202/2048 [15:09<09:55, 1.42it/s]
loss 1.97 accuracy 0.25 -- 55.74 + 166.55 + 503.03 + 4.77 = 730.10: 59%|█████▊ | 1203/2048 [15:09<10:08, 1.39it/s]
loss 1.61 accuracy 0.38 -- 56.76 + 56.34 + 498.26 + 4.78 = 616.14: 59%|█████▊ | 1203/2048 [15:09<10:08, 1.39it/s]
loss 1.61 accuracy 0.38 -- 56.76 + 56.34 + 498.26 + 4.78 = 616.14: 59%|█████▉ | 1204/2048 [15:09<09:48, 1.43it/s]
loss 2.34 accuracy 0.31 -- 162.84 + 57.15 + 497.75 + 4.79 = 722.53: 59%|█████▉ | 1204/2048 [15:10<09:48, 1.43it/s]
loss 2.34 accuracy 0.31 -- 162.84 + 57.15 + 497.75 + 4.79 = 722.53: 59%|█████▉ | 1205/2048 [15:10<10:00, 1.40it/s]
loss 1.86 accuracy 0.06 -- 56.23 + 57.30 + 620.28 + 4.78 = 738.59: 59%|█████▉ | 1205/2048 [15:11<10:00, 1.40it/s]
loss 1.86 accuracy 0.06 -- 56.23 + 57.30 + 620.28 + 4.78 = 738.59: 59%|█████▉ | 1206/2048 [15:11<10:13, 1.37it/s]
loss 2.01 accuracy 0.25 -- 56.67 + 56.59 + 504.66 + 4.78 = 622.70: 59%|█████▉ | 1206/2048 [15:11<10:13, 1.37it/s]
loss 2.01 accuracy 0.25 -- 56.67 + 56.59 + 504.66 + 4.78 = 622.70: 59%|█████▉ | 1207/2048 [15:11<09:52, 1.42it/s]
loss 1.92 accuracy 0.44 -- 56.12 + 57.30 + 616.94 + 4.80 = 735.16: 59%|█████▉ | 1207/2048 [15:12<09:52, 1.42it/s]
loss 1.92 accuracy 0.44 -- 56.12 + 57.30 + 616.94 + 4.80 = 735.16: 59%|█████▉ | 1208/2048 [15:12<10:06, 1.38it/s]
loss 1.67 accuracy 0.44 -- 57.25 + 56.69 + 502.45 + 4.78 = 621.17: 59%|█████▉ | 1208/2048 [15:13<10:06, 1.38it/s]
loss 1.67 accuracy 0.44 -- 57.25 + 56.69 + 502.45 + 4.78 = 621.17: 59%|█████▉ | 1209/2048 [15:13<09:47, 1.43it/s]
loss 1.81 accuracy 0.50 -- 55.96 + 166.43 + 502.07 + 4.79 = 729.26: 59%|█████▉ | 1209/2048 [15:14<09:47, 1.43it/s]
loss 1.81 accuracy 0.50 -- 55.96 + 166.43 + 502.07 + 4.79 = 729.26: 59%|█████▉ | 1210/2048 [15:14<10:00, 1.39it/s]
loss 2.18 accuracy 0.31 -- 55.84 + 56.05 + 498.20 + 4.78 = 614.87: 59%|█████▉ | 1210/2048 [15:14<10:00, 1.39it/s]
loss 2.18 accuracy 0.31 -- 55.84 + 56.05 + 498.20 + 4.78 = 614.87: 59%|█████▉ | 1211/2048 [15:14<09:41, 1.44it/s]
loss 1.89 accuracy 0.19 -- 56.68 + 56.55 + 498.03 + 4.77 = 616.03: 59%|█████▉ | 1211/2048 [15:15<09:41, 1.44it/s]
loss 1.89 accuracy 0.19 -- 56.68 + 56.55 + 498.03 + 4.77 = 616.03: 59%|█████▉ | 1212/2048 [15:15<09:54, 1.41it/s]
loss 2.36 accuracy 0.31 -- 55.91 + 57.35 + 496.49 + 4.79 = 614.54: 59%|█████▉ | 1212/2048 [15:16<09:54, 1.41it/s]
loss 2.36 accuracy 0.31 -- 55.91 + 57.35 + 496.49 + 4.79 = 614.54: 59%|█████▉ | 1213/2048 [15:16<09:36, 1.45it/s]
loss 2.00 accuracy 0.25 -- 157.53 + 56.92 + 491.55 + 4.77 = 710.77: 59%|█████▉ | 1213/2048 [15:16<09:36, 1.45it/s]
loss 2.00 accuracy 0.25 -- 157.53 + 56.92 + 491.55 + 4.77 = 710.77: 59%|█████▉ | 1214/2048 [15:16<09:47, 1.42it/s]
loss 1.96 accuracy 0.31 -- 56.04 + 166.55 + 501.55 + 4.79 = 728.93: 59%|█████▉ | 1214/2048 [15:17<09:47, 1.42it/s]
loss 1.96 accuracy 0.31 -- 56.04 + 166.55 + 501.55 + 4.79 = 728.93: 59%|█████▉ | 1215/2048 [15:17<10:01, 1.38it/s]
loss 1.99 accuracy 0.25 -- 56.80 + 56.51 + 497.80 + 4.78 = 615.88: 59%|█████▉ | 1215/2048 [15:18<10:01, 1.38it/s]
loss 1.99 accuracy 0.25 -- 56.80 + 56.51 + 497.80 + 4.78 = 615.88: 59%|█████▉ | 1216/2048 [15:18<09:41, 1.43it/s]
loss 1.86 accuracy 0.25 -- 162.92 + 57.24 + 496.08 + 4.78 = 721.03: 59%|█████▉ | 1216/2048 [15:19<09:41, 1.43it/s]
loss 1.86 accuracy 0.25 -- 162.92 + 57.24 + 496.08 + 4.78 = 721.03: 59%|█████▉ | 1217/2048 [15:19<09:52, 1.40it/s]
loss 1.57 accuracy 0.44 -- 55.85 + 56.98 + 618.83 + 4.83 = 736.50: 59%|█████▉ | 1217/2048 [15:19<09:52, 1.40it/s]
loss 1.57 accuracy 0.44 -- 55.85 + 56.98 + 618.83 + 4.83 = 736.50: 59%|█████▉ | 1218/2048 [15:19<10:04, 1.37it/s]
loss 1.65 accuracy 0.31 -- 56.60 + 56.20 + 504.84 + 4.78 = 622.42: 59%|█████▉ | 1218/2048 [15:20<10:04, 1.37it/s]
loss 1.65 accuracy 0.31 -- 56.60 + 56.20 + 504.84 + 4.78 = 622.42: 60%|█████▉ | 1219/2048 [15:20<09:44, 1.42it/s]
loss 2.32 accuracy 0.19 -- 56.34 + 57.57 + 617.80 + 4.80 = 736.50: 60%|█████▉ | 1219/2048 [15:21<09:44, 1.42it/s]
loss 2.32 accuracy 0.19 -- 56.34 + 57.57 + 617.80 + 4.80 = 736.50: 60%|█████▉ | 1220/2048 [15:21<09:58, 1.38it/s]
loss 2.22 accuracy 0.38 -- 56.72 + 56.38 + 502.04 + 4.78 = 619.93: 60%|█████▉ | 1220/2048 [15:21<09:58, 1.38it/s]
loss 2.22 accuracy 0.38 -- 56.72 + 56.38 + 502.04 + 4.78 = 619.93: 60%|█████▉ | 1221/2048 [15:21<09:38, 1.43it/s]
loss 2.30 accuracy 0.19 -- 56.13 + 166.14 + 502.50 + 4.79 = 729.56: 60%|█████▉ | 1221/2048 [15:22<09:38, 1.43it/s]
loss 2.30 accuracy 0.19 -- 56.13 + 166.14 + 502.50 + 4.79 = 729.56: 60%|█████▉ | 1222/2048 [15:22<09:52, 1.39it/s]
loss 2.07 accuracy 0.19 -- 56.35 + 56.43 + 498.82 + 4.77 = 616.37: 60%|█████▉ | 1222/2048 [15:23<09:52, 1.39it/s]
loss 2.07 accuracy 0.19 -- 56.35 + 56.43 + 498.82 + 4.77 = 616.37: 60%|█████▉ | 1223/2048 [15:23<09:33, 1.44it/s]
loss 1.61 accuracy 0.38 -- 56.48 + 56.39 + 498.90 + 4.79 = 616.56: 60%|█████▉ | 1223/2048 [15:24<09:33, 1.44it/s]
loss 1.61 accuracy 0.38 -- 56.48 + 56.39 + 498.90 + 4.79 = 616.56: 60%|█████▉ | 1224/2048 [15:24<09:46, 1.41it/s]
loss 2.11 accuracy 0.25 -- 55.97 + 57.30 + 496.45 + 4.84 = 614.55: 60%|█████▉ | 1224/2048 [15:24<09:46, 1.41it/s]
loss 2.11 accuracy 0.25 -- 55.97 + 57.30 + 496.45 + 4.84 = 614.55: 60%|█████▉ | 1225/2048 [15:24<09:28, 1.45it/s]
loss 2.02 accuracy 0.19 -- 158.35 + 57.24 + 489.39 + 4.78 = 709.76: 60%|█████▉ | 1225/2048 [15:25<09:28, 1.45it/s]
loss 2.02 accuracy 0.19 -- 158.35 + 57.24 + 489.39 + 4.78 = 709.76: 60%|█████▉ | 1226/2048 [15:25<09:39, 1.42it/s]
loss 2.08 accuracy 0.44 -- 55.72 + 166.74 + 501.88 + 4.78 = 729.12: 60%|█████▉ | 1226/2048 [15:26<09:39, 1.42it/s]
loss 2.08 accuracy 0.44 -- 55.72 + 166.74 + 501.88 + 4.78 = 729.12: 60%|█████▉ | 1227/2048 [15:26<09:51, 1.39it/s]
loss 1.59 accuracy 0.31 -- 56.65 + 56.61 + 497.64 + 4.82 = 615.72: 60%|█████▉ | 1227/2048 [15:26<09:51, 1.39it/s]
loss 1.59 accuracy 0.31 -- 56.65 + 56.61 + 497.64 + 4.82 = 615.72: 60%|█████▉ | 1228/2048 [15:26<09:31, 1.43it/s]
loss 2.06 accuracy 0.31 -- 162.90 + 57.14 + 496.44 + 4.80 = 721.28: 60%|█████▉ | 1228/2048 [15:27<09:31, 1.43it/s]
loss 2.06 accuracy 0.31 -- 162.90 + 57.14 + 496.44 + 4.80 = 721.28: 60%|██████ | 1229/2048 [15:27<09:43, 1.40it/s]
loss 1.85 accuracy 0.31 -- 55.90 + 57.01 + 619.95 + 4.79 = 737.65: 60%|██████ | 1229/2048 [15:28<09:43, 1.40it/s]
loss 1.85 accuracy 0.31 -- 55.90 + 57.01 + 619.95 + 4.79 = 737.65: 60%|██████ | 1230/2048 [15:28<09:55, 1.37it/s]
loss 2.39 accuracy 0.12 -- 56.92 + 56.37 + 506.41 + 4.77 = 624.47: 60%|██████ | 1230/2048 [15:29<09:55, 1.37it/s]
loss 2.39 accuracy 0.12 -- 56.92 + 56.37 + 506.41 + 4.77 = 624.47: 60%|██████ | 1231/2048 [15:29<09:36, 1.42it/s]
loss 1.55 accuracy 0.56 -- 56.21 + 57.12 + 616.03 + 4.79 = 734.15: 60%|██████ | 1231/2048 [15:29<09:36, 1.42it/s]
loss 1.55 accuracy 0.56 -- 56.21 + 57.12 + 616.03 + 4.79 = 734.15: 60%|██████ | 1232/2048 [15:29<09:49, 1.38it/s]
loss 2.06 accuracy 0.25 -- 56.76 + 56.55 + 501.73 + 4.78 = 619.81: 60%|██████ | 1232/2048 [15:30<09:49, 1.38it/s]
loss 2.06 accuracy 0.25 -- 56.76 + 56.55 + 501.73 + 4.78 = 619.81: 60%|██████ | 1233/2048 [15:30<09:30, 1.43it/s]
loss 1.76 accuracy 0.19 -- 55.94 + 166.59 + 502.10 + 4.79 = 729.43: 60%|██████ | 1233/2048 [15:31<09:30, 1.43it/s]
loss 1.76 accuracy 0.19 -- 55.94 + 166.59 + 502.10 + 4.79 = 729.43: 60%|██████ | 1234/2048 [15:31<09:43, 1.40it/s]
loss 2.06 accuracy 0.31 -- 55.91 + 56.55 + 499.30 + 4.78 = 616.54: 60%|██████ | 1234/2048 [15:31<09:43, 1.40it/s]
loss 2.06 accuracy 0.31 -- 55.91 + 56.55 + 499.30 + 4.78 = 616.54: 60%|██████ | 1235/2048 [15:31<09:24, 1.44it/s]
loss 1.76 accuracy 0.25 -- 56.57 + 56.46 + 498.32 + 4.79 = 616.14: 60%|██████ | 1235/2048 [15:32<09:24, 1.44it/s]
loss 1.76 accuracy 0.25 -- 56.57 + 56.46 + 498.32 + 4.79 = 616.14: 60%|██████ | 1236/2048 [15:32<09:37, 1.41it/s]
loss 2.11 accuracy 0.25 -- 56.25 + 57.68 + 496.73 + 4.78 = 615.43: 60%|██████ | 1236/2048 [15:33<09:37, 1.41it/s]
loss 2.11 accuracy 0.25 -- 56.25 + 57.68 + 496.73 + 4.78 = 615.43: 60%|██████ | 1237/2048 [15:33<09:20, 1.45it/s]
loss 2.04 accuracy 0.31 -- 157.62 + 56.82 + 489.20 + 4.77 = 708.42: 60%|██████ | 1237/2048 [15:33<09:20, 1.45it/s]
loss 2.04 accuracy 0.31 -- 157.62 + 56.82 + 489.20 + 4.77 = 708.42: 60%|██████ | 1238/2048 [15:33<09:30, 1.42it/s]
loss 2.11 accuracy 0.31 -- 55.78 + 166.48 + 500.87 + 4.77 = 727.90: 60%|██████ | 1238/2048 [15:34<09:30, 1.42it/s]
loss 2.11 accuracy 0.31 -- 55.78 + 166.48 + 500.87 + 4.77 = 727.90: 60%|██████ | 1239/2048 [15:34<09:42, 1.39it/s]
loss 1.92 accuracy 0.38 -- 56.45 + 56.61 + 498.66 + 4.78 = 616.50: 60%|██████ | 1239/2048 [15:35<09:42, 1.39it/s]
loss 1.92 accuracy 0.38 -- 56.45 + 56.61 + 498.66 + 4.78 = 616.50: 61%|██████ | 1240/2048 [15:35<09:23, 1.44it/s]
loss 1.87 accuracy 0.25 -- 162.27 + 57.18 + 497.10 + 4.79 = 721.35: 61%|██████ | 1240/2048 [15:36<09:23, 1.44it/s]
loss 1.87 accuracy 0.25 -- 162.27 + 57.18 + 497.10 + 4.79 = 721.35: 61%|██████ | 1241/2048 [15:36<09:34, 1.40it/s]
loss 1.78 accuracy 0.38 -- 55.91 + 57.22 + 622.50 + 4.79 = 740.41: 61%|██████ | 1241/2048 [15:36<09:34, 1.40it/s]
loss 1.78 accuracy 0.38 -- 55.91 + 57.22 + 622.50 + 4.79 = 740.41: 61%|██████ | 1242/2048 [15:36<09:47, 1.37it/s]
loss 1.88 accuracy 0.25 -- 57.04 + 56.84 + 504.16 + 4.78 = 622.82: 61%|██████ | 1242/2048 [15:37<09:47, 1.37it/s]
loss 1.88 accuracy 0.25 -- 57.04 + 56.84 + 504.16 + 4.78 = 622.82: 61%|██████ | 1243/2048 [15:37<09:27, 1.42it/s]
loss 1.99 accuracy 0.25 -- 56.10 + 57.08 + 615.96 + 4.78 = 733.92: 61%|██████ | 1243/2048 [15:38<09:27, 1.42it/s]
loss 1.99 accuracy 0.25 -- 56.10 + 57.08 + 615.96 + 4.78 = 733.92: 61%|██████ | 1244/2048 [15:38<09:40, 1.38it/s]
loss 1.86 accuracy 0.25 -- 56.62 + 56.69 + 502.68 + 4.77 = 620.76: 61%|██████ | 1244/2048 [15:38<09:40, 1.38it/s]
loss 1.86 accuracy 0.25 -- 56.62 + 56.69 + 502.68 + 4.77 = 620.76: 61%|██████ | 1245/2048 [15:38<09:21, 1.43it/s]
loss 1.88 accuracy 0.31 -- 56.33 + 165.97 + 501.80 + 4.79 = 728.88: 61%|██████ | 1245/2048 [15:39<09:21, 1.43it/s]
loss 1.88 accuracy 0.31 -- 56.33 + 165.97 + 501.80 + 4.79 = 728.88: 61%|██████ | 1246/2048 [15:39<09:34, 1.40it/s]
loss 1.84 accuracy 0.31 -- 56.46 + 56.59 + 499.66 + 4.77 = 617.49: 61%|██████ | 1246/2048 [15:40<09:34, 1.40it/s]
loss 1.84 accuracy 0.31 -- 56.46 + 56.59 + 499.66 + 4.77 = 617.49: 61%|██████ | 1247/2048 [15:40<09:16, 1.44it/s]
loss 1.82 accuracy 0.31 -- 56.63 + 56.73 + 498.93 + 4.76 = 617.05: 61%|██████ | 1247/2048 [15:41<09:16, 1.44it/s]
loss 1.82 accuracy 0.31 -- 56.63 + 56.73 + 498.93 + 4.76 = 617.05: 61%|██████ | 1248/2048 [15:41<09:29, 1.40it/s]
loss 1.92 accuracy 0.31 -- 55.85 + 57.01 + 494.28 + 4.78 = 611.92: 61%|██████ | 1248/2048 [15:41<09:29, 1.40it/s]
loss 1.92 accuracy 0.31 -- 55.85 + 57.01 + 494.28 + 4.78 = 611.92: 61%|██████ | 1249/2048 [15:41<09:11, 1.45it/s]
loss 1.64 accuracy 0.44 -- 157.74 + 56.76 + 490.07 + 4.78 = 709.35: 61%|██████ | 1249/2048 [15:42<09:11, 1.45it/s]
loss 1.64 accuracy 0.44 -- 157.74 + 56.76 + 490.07 + 4.78 = 709.35: 61%|██████ | 1250/2048 [15:42<09:21, 1.42it/s]
loss 1.55 accuracy 0.44 -- 56.04 + 167.43 + 503.39 + 4.78 = 731.63: 61%|██████ | 1250/2048 [15:43<09:21, 1.42it/s]
loss 1.55 accuracy 0.44 -- 56.04 + 167.43 + 503.39 + 4.78 = 731.63: 61%|██████ | 1251/2048 [15:43<09:34, 1.39it/s]
loss 2.22 accuracy 0.31 -- 56.62 + 56.37 + 496.60 + 4.79 = 614.38: 61%|██████ | 1251/2048 [15:43<09:34, 1.39it/s]
loss 2.22 accuracy 0.31 -- 56.62 + 56.37 + 496.60 + 4.79 = 614.38: 61%|██████ | 1252/2048 [15:43<09:14, 1.44it/s]
loss 1.82 accuracy 0.38 -- 162.69 + 56.92 + 496.45 + 4.77 = 720.82: 61%|██████ | 1252/2048 [15:44<09:14, 1.44it/s]
loss 1.82 accuracy 0.38 -- 162.69 + 56.92 + 496.45 + 4.77 = 720.82: 61%|██████ | 1253/2048 [15:44<09:26, 1.40it/s]
loss 1.98 accuracy 0.25 -- 56.28 + 57.63 + 621.04 + 4.81 = 739.75: 61%|██████ | 1253/2048 [15:45<09:26, 1.40it/s]
loss 1.98 accuracy 0.25 -- 56.28 + 57.63 + 621.04 + 4.81 = 739.75: 61%|██████ | 1254/2048 [15:45<09:38, 1.37it/s]
loss 1.78 accuracy 0.31 -- 56.79 + 56.48 + 505.03 + 4.80 = 623.11: 61%|██████ | 1254/2048 [15:46<09:38, 1.37it/s]
loss 1.78 accuracy 0.31 -- 56.79 + 56.48 + 505.03 + 4.80 = 623.11: 61%|██████▏ | 1255/2048 [15:46<09:19, 1.42it/s]
loss 1.74 accuracy 0.38 -- 56.24 + 57.17 + 615.21 + 4.78 = 733.41: 61%|██████▏ | 1255/2048 [15:46<09:19, 1.42it/s]
loss 1.74 accuracy 0.38 -- 56.24 + 57.17 + 615.21 + 4.78 = 733.41: 61%|██████▏ | 1256/2048 [15:46<09:31, 1.39it/s]
loss 1.97 accuracy 0.12 -- 56.59 + 56.77 + 502.24 + 4.78 = 620.38: 61%|██████▏ | 1256/2048 [15:47<09:31, 1.39it/s]
loss 1.97 accuracy 0.12 -- 56.59 + 56.77 + 502.24 + 4.78 = 620.38: 61%|██████▏ | 1257/2048 [15:47<09:13, 1.43it/s]
loss 1.76 accuracy 0.38 -- 56.18 + 166.40 + 503.26 + 4.78 = 730.62: 61%|██████▏ | 1257/2048 [15:48<09:13, 1.43it/s]
loss 1.76 accuracy 0.38 -- 56.18 + 166.40 + 503.26 + 4.78 = 730.62: 61%|██████▏ | 1258/2048 [15:48<09:26, 1.39it/s]
loss 1.96 accuracy 0.38 -- 56.05 + 56.20 + 501.11 + 4.76 = 618.13: 61%|██████▏ | 1258/2048 [15:48<09:26, 1.39it/s]
loss 1.96 accuracy 0.38 -- 56.05 + 56.20 + 501.11 + 4.76 = 618.13: 61%|██████▏ | 1259/2048 [15:48<09:08, 1.44it/s]
loss 1.88 accuracy 0.50 -- 57.31 + 56.83 + 498.38 + 4.79 = 617.31: 61%|██████▏ | 1259/2048 [15:49<09:08, 1.44it/s]
loss 1.88 accuracy 0.50 -- 57.31 + 56.83 + 498.38 + 4.79 = 617.31: 62%|██████▏ | 1260/2048 [15:49<09:21, 1.40it/s]
loss 2.08 accuracy 0.25 -- 56.37 + 57.16 + 495.38 + 4.78 = 613.69: 62%|██████▏ | 1260/2048 [15:50<09:21, 1.40it/s]
loss 2.08 accuracy 0.25 -- 56.37 + 57.16 + 495.38 + 4.78 = 613.69: 62%|██████▏ | 1261/2048 [15:50<09:03, 1.45it/s]
loss 2.16 accuracy 0.25 -- 157.34 + 56.91 + 488.94 + 4.76 = 707.95: 62%|██████▏ | 1261/2048 [15:50<09:03, 1.45it/s]
loss 2.16 accuracy 0.25 -- 157.34 + 56.91 + 488.94 + 4.76 = 707.95: 62%|██████▏ | 1262/2048 [15:50<09:13, 1.42it/s]
loss 1.64 accuracy 0.50 -- 55.92 + 166.51 + 501.39 + 4.78 = 728.60: 62%|██████▏ | 1262/2048 [15:51<09:13, 1.42it/s]
loss 1.64 accuracy 0.50 -- 55.92 + 166.51 + 501.39 + 4.78 = 728.60: 62%|██████▏ | 1263/2048 [15:51<09:24, 1.39it/s]
loss 2.11 accuracy 0.12 -- 56.55 + 56.71 + 497.58 + 4.78 = 615.62: 62%|██████▏ | 1263/2048 [15:52<09:24, 1.39it/s]
loss 2.11 accuracy 0.12 -- 56.55 + 56.71 + 497.58 + 4.78 = 615.62: 62%|██████▏ | 1264/2048 [15:52<09:06, 1.44it/s]
loss 1.54 accuracy 0.44 -- 162.62 + 56.82 + 499.04 + 4.76 = 723.24: 62%|██████▏ | 1264/2048 [15:53<09:06, 1.44it/s]
loss 1.54 accuracy 0.44 -- 162.62 + 56.82 + 499.04 + 4.76 = 723.24: 62%|██████▏ | 1265/2048 [15:53<09:18, 1.40it/s]
loss 2.46 accuracy 0.12 -- 56.23 + 57.17 + 620.45 + 4.79 = 738.64: 62%|██████▏ | 1265/2048 [15:53<09:18, 1.40it/s]
loss 2.46 accuracy 0.12 -- 56.23 + 57.17 + 620.45 + 4.79 = 738.64: 62%|██████▏ | 1266/2048 [15:53<09:29, 1.37it/s]
loss 2.24 accuracy 0.12 -- 57.06 + 56.65 + 506.71 + 4.77 = 625.19: 62%|██████▏ | 1266/2048 [15:54<09:29, 1.37it/s]
loss 2.24 accuracy 0.12 -- 57.06 + 56.65 + 506.71 + 4.77 = 625.19: 62%|██████▏ | 1267/2048 [15:54<09:11, 1.42it/s]
loss 2.23 accuracy 0.12 -- 56.39 + 57.19 + 617.11 + 4.79 = 735.47: 62%|██████▏ | 1267/2048 [15:55<09:11, 1.42it/s]
loss 2.23 accuracy 0.12 -- 56.39 + 57.19 + 617.11 + 4.79 = 735.47: 62%|██████▏ | 1268/2048 [15:55<09:23, 1.38it/s]
loss 1.53 accuracy 0.50 -- 56.93 + 56.76 + 503.22 + 4.78 = 621.69: 62%|██████▏ | 1268/2048 [15:55<09:23, 1.38it/s]
loss 1.53 accuracy 0.50 -- 56.93 + 56.76 + 503.22 + 4.78 = 621.69: 62%|██████▏ | 1269/2048 [15:55<09:05, 1.43it/s]
loss 1.48 accuracy 0.50 -- 56.27 + 166.30 + 502.13 + 4.80 = 729.50: 62%|██████▏ | 1269/2048 [15:56<09:05, 1.43it/s]
loss 1.48 accuracy 0.50 -- 56.27 + 166.30 + 502.13 + 4.80 = 729.50: 62%|██████▏ | 1270/2048 [15:56<09:18, 1.39it/s]
loss 1.61 accuracy 0.44 -- 56.89 + 57.08 + 499.78 + 4.78 = 618.53: 62%|██████▏ | 1270/2048 [15:57<09:18, 1.39it/s]
loss 1.61 accuracy 0.44 -- 56.89 + 57.08 + 499.78 + 4.78 = 618.53: 62%|██████▏ | 1271/2048 [15:57<09:00, 1.44it/s]
loss 1.53 accuracy 0.50 -- 56.53 + 56.71 + 499.77 + 4.77 = 617.77: 62%|██████▏ | 1271/2048 [15:58<09:00, 1.44it/s]
loss 1.53 accuracy 0.50 -- 56.53 + 56.71 + 499.77 + 4.77 = 617.77: 62%|██████▏ | 1272/2048 [15:58<09:13, 1.40it/s]
loss 2.23 accuracy 0.19 -- 56.11 + 57.17 + 494.81 + 4.78 = 612.88: 62%|██████▏ | 1272/2048 [15:58<09:13, 1.40it/s]
loss 2.23 accuracy 0.19 -- 56.11 + 57.17 + 494.81 + 4.78 = 612.88: 62%|██████▏ | 1273/2048 [15:58<09:03, 1.43it/s]
loss 2.02 accuracy 0.38 -- 157.40 + 57.08 + 489.56 + 4.77 = 708.82: 62%|██████▏ | 1273/2048 [15:59<09:03, 1.43it/s]
loss 2.02 accuracy 0.38 -- 157.40 + 57.08 + 489.56 + 4.77 = 708.82: 62%|██████▏ | 1274/2048 [15:59<09:10, 1.40it/s]
loss 1.92 accuracy 0.31 -- 55.92 + 166.65 + 501.67 + 4.77 = 729.01: 62%|██████▏ | 1274/2048 [16:00<09:10, 1.40it/s]
loss 1.92 accuracy 0.31 -- 55.92 + 166.65 + 501.67 + 4.77 = 729.01: 62%|██████▏ | 1275/2048 [16:00<09:20, 1.38it/s]
loss 2.22 accuracy 0.25 -- 56.47 + 56.32 + 500.03 + 4.80 = 617.62: 62%|██████▏ | 1275/2048 [16:00<09:20, 1.38it/s]
loss 2.22 accuracy 0.25 -- 56.47 + 56.32 + 500.03 + 4.80 = 617.62: 62%|██████▏ | 1276/2048 [16:00<09:01, 1.43it/s]
loss 1.95 accuracy 0.25 -- 163.36 + 56.90 + 496.25 + 4.81 = 721.32: 62%|██████▏ | 1276/2048 [16:01<09:01, 1.43it/s]
loss 1.95 accuracy 0.25 -- 163.36 + 56.90 + 496.25 + 4.81 = 721.32: 62%|██████▏ | 1277/2048 [16:01<09:11, 1.40it/s]
loss 1.71 accuracy 0.44 -- 56.04 + 57.40 + 619.89 + 4.77 = 738.10: 62%|██████▏ | 1277/2048 [16:02<09:11, 1.40it/s]
loss 1.71 accuracy 0.44 -- 56.04 + 57.40 + 619.89 + 4.77 = 738.10: 62%|██████▏ | 1278/2048 [16:02<09:22, 1.37it/s]
loss 2.01 accuracy 0.44 -- 56.67 + 56.54 + 505.59 + 4.78 = 623.58: 62%|██████▏ | 1278/2048 [16:03<09:22, 1.37it/s]
loss 2.01 accuracy 0.44 -- 56.67 + 56.54 + 505.59 + 4.78 = 623.58: 62%|██████▏ | 1279/2048 [16:03<09:03, 1.42it/s]
loss 1.47 accuracy 0.25 -- 56.26 + 57.33 + 616.04 + 4.83 = 734.45: 62%|██████▏ | 1279/2048 [16:03<09:03, 1.42it/s]
loss 1.47 accuracy 0.25 -- 56.26 + 57.33 + 616.04 + 4.83 = 734.45: 62%|██████▎ | 1280/2048 [16:03<09:15, 1.38it/s]
loss 1.65 accuracy 0.56 -- 56.85 + 56.64 + 501.34 + 4.77 = 619.60: 62%|██████▎ | 1280/2048 [16:04<09:15, 1.38it/s]
loss 1.65 accuracy 0.56 -- 56.85 + 56.64 + 501.34 + 4.77 = 619.60: 63%|██████▎ | 1281/2048 [16:04<08:56, 1.43it/s]
loss 1.48 accuracy 0.44 -- 56.19 + 166.49 + 502.46 + 4.79 = 729.94: 63%|██████▎ | 1281/2048 [16:05<08:56, 1.43it/s]
loss 1.48 accuracy 0.44 -- 56.19 + 166.49 + 502.46 + 4.79 = 729.94: 63%|██████▎ | 1282/2048 [16:05<09:09, 1.39it/s]
loss 1.64 accuracy 0.38 -- 56.15 + 56.41 + 498.83 + 4.78 = 616.16: 63%|██████▎ | 1282/2048 [16:05<09:09, 1.39it/s]
loss 1.64 accuracy 0.38 -- 56.15 + 56.41 + 498.83 + 4.78 = 616.16: 63%|██████▎ | 1283/2048 [16:05<08:51, 1.44it/s]
loss 1.69 accuracy 0.25 -- 56.53 + 56.39 + 497.71 + 4.78 = 615.41: 63%|██████▎ | 1283/2048 [16:06<08:51, 1.44it/s]
loss 1.69 accuracy 0.25 -- 56.53 + 56.39 + 497.71 + 4.78 = 615.41: 63%|██████▎ | 1284/2048 [16:06<09:03, 1.41it/s]
loss 2.55 accuracy 0.06 -- 55.86 + 57.31 + 494.39 + 4.78 = 612.33: 63%|██████▎ | 1284/2048 [16:07<09:03, 1.41it/s]
loss 2.55 accuracy 0.06 -- 55.86 + 57.31 + 494.39 + 4.78 = 612.33: 63%|██████▎ | 1285/2048 [16:07<08:46, 1.45it/s]
loss 1.74 accuracy 0.31 -- 157.40 + 56.83 + 489.29 + 4.84 = 708.37: 63%|██████▎ | 1285/2048 [16:07<08:46, 1.45it/s]
loss 1.74 accuracy 0.31 -- 157.40 + 56.83 + 489.29 + 4.84 = 708.37: 63%|██████▎ | 1286/2048 [16:07<08:56, 1.42it/s]
loss 1.52 accuracy 0.44 -- 55.79 + 166.30 + 502.22 + 4.80 = 729.11: 63%|██████▎ | 1286/2048 [16:08<08:56, 1.42it/s]
loss 1.52 accuracy 0.44 -- 55.79 + 166.30 + 502.22 + 4.80 = 729.11: 63%|██████▎ | 1287/2048 [16:08<09:07, 1.39it/s]
loss 2.08 accuracy 0.19 -- 57.09 + 56.84 + 497.13 + 4.77 = 615.82: 63%|██████▎ | 1287/2048 [16:09<09:07, 1.39it/s]
loss 2.08 accuracy 0.19 -- 57.09 + 56.84 + 497.13 + 4.77 = 615.82: 63%|██████▎ | 1288/2048 [16:09<08:49, 1.44it/s]
loss 1.81 accuracy 0.31 -- 162.64 + 56.84 + 496.59 + 4.80 = 720.86: 63%|██████▎ | 1288/2048 [16:10<08:49, 1.44it/s]
loss 1.81 accuracy 0.31 -- 162.64 + 56.84 + 496.59 + 4.80 = 720.86: 63%|██████▎ | 1289/2048 [16:10<09:00, 1.40it/s]
loss 2.04 accuracy 0.25 -- 56.09 + 57.36 + 619.70 + 4.77 = 737.92: 63%|██████▎ | 1289/2048 [16:10<09:00, 1.40it/s]
loss 2.04 accuracy 0.25 -- 56.09 + 57.36 + 619.70 + 4.77 = 737.92: 63%|██████▎ | 1290/2048 [16:10<09:11, 1.37it/s]
loss 1.77 accuracy 0.19 -- 56.66 + 56.35 + 503.85 + 4.77 = 621.62: 63%|██████▎ | 1290/2048 [16:11<09:11, 1.37it/s]
loss 1.77 accuracy 0.19 -- 56.66 + 56.35 + 503.85 + 4.77 = 621.62: 63%|██████▎ | 1291/2048 [16:11<08:53, 1.42it/s]
loss 2.08 accuracy 0.31 -- 56.15 + 57.18 + 615.18 + 4.79 = 733.30: 63%|██████▎ | 1291/2048 [16:12<08:53, 1.42it/s]
loss 2.08 accuracy 0.31 -- 56.15 + 57.18 + 615.18 + 4.79 = 733.30: 63%|██████▎ | 1292/2048 [16:12<09:05, 1.39it/s]
loss 1.94 accuracy 0.31 -- 56.65 + 56.46 + 504.89 + 4.80 = 622.80: 63%|██████▎ | 1292/2048 [16:12<09:05, 1.39it/s]
loss 1.94 accuracy 0.31 -- 56.65 + 56.46 + 504.89 + 4.80 = 622.80: 63%|██████▎ | 1293/2048 [16:12<08:48, 1.43it/s]
loss 2.05 accuracy 0.19 -- 56.57 + 166.65 + 502.56 + 4.79 = 730.56: 63%|██████▎ | 1293/2048 [16:13<08:48, 1.43it/s]
loss 2.05 accuracy 0.19 -- 56.57 + 166.65 + 502.56 + 4.79 = 730.56: 63%|██████▎ | 1294/2048 [16:13<09:00, 1.39it/s]
loss 1.68 accuracy 0.44 -- 56.51 + 56.39 + 500.72 + 4.77 = 618.39: 63%|██████▎ | 1294/2048 [16:14<09:00, 1.39it/s]
loss 1.68 accuracy 0.44 -- 56.51 + 56.39 + 500.72 + 4.77 = 618.39: 63%|██████▎ | 1295/2048 [16:14<08:43, 1.44it/s]
loss 2.10 accuracy 0.38 -- 56.75 + 56.64 + 500.06 + 4.81 = 618.26: 63%|██████▎ | 1295/2048 [16:15<08:43, 1.44it/s]
loss 2.10 accuracy 0.38 -- 56.75 + 56.64 + 500.06 + 4.81 = 618.26: 63%|██████▎ | 1296/2048 [16:15<08:55, 1.40it/s]
loss 1.89 accuracy 0.19 -- 56.15 + 57.20 + 495.55 + 4.77 = 613.67: 63%|██████▎ | 1296/2048 [16:15<08:55, 1.40it/s]
loss 1.89 accuracy 0.19 -- 56.15 + 57.20 + 495.55 + 4.77 = 613.67: 63%|██████▎ | 1297/2048 [16:15<08:46, 1.43it/s]
loss 2.01 accuracy 0.19 -- 158.30 + 56.96 + 490.52 + 4.77 = 710.56: 63%|██████▎ | 1297/2048 [16:16<08:46, 1.43it/s]
loss 2.01 accuracy 0.19 -- 158.30 + 56.96 + 490.52 + 4.77 = 710.56: 63%|██████▎ | 1298/2048 [16:16<08:54, 1.40it/s]
loss 1.84 accuracy 0.25 -- 55.79 + 166.46 + 502.14 + 4.77 = 729.15: 63%|██████▎ | 1298/2048 [16:17<08:54, 1.40it/s]
loss 1.84 accuracy 0.25 -- 55.79 + 166.46 + 502.14 + 4.77 = 729.15: 63%|██████▎ | 1299/2048 [16:17<09:03, 1.38it/s]
loss 1.41 accuracy 0.38 -- 56.47 + 56.46 + 497.03 + 4.77 = 614.73: 63%|██████▎ | 1299/2048 [16:17<09:03, 1.38it/s]
loss 1.41 accuracy 0.38 -- 56.47 + 56.46 + 497.03 + 4.77 = 614.73: 63%|██████▎ | 1300/2048 [16:17<08:43, 1.43it/s]
loss 1.97 accuracy 0.38 -- 162.94 + 57.14 + 496.63 + 4.78 = 721.50: 63%|██████▎ | 1300/2048 [16:18<08:43, 1.43it/s]
loss 1.97 accuracy 0.38 -- 162.94 + 57.14 + 496.63 + 4.78 = 721.50: 64%|██████▎ | 1301/2048 [16:18<08:54, 1.40it/s]
loss 1.56 accuracy 0.31 -- 56.02 + 57.19 + 621.41 + 4.78 = 739.40: 64%|██████▎ | 1301/2048 [16:19<08:54, 1.40it/s]
loss 1.56 accuracy 0.31 -- 56.02 + 57.19 + 621.41 + 4.78 = 739.40: 64%|██████▎ | 1302/2048 [16:19<09:04, 1.37it/s]
loss 2.12 accuracy 0.38 -- 57.04 + 56.80 + 505.99 + 4.77 = 624.60: 64%|██████▎ | 1302/2048 [16:20<09:04, 1.37it/s]
loss 2.12 accuracy 0.38 -- 57.04 + 56.80 + 505.99 + 4.77 = 624.60: 64%|██████▎ | 1303/2048 [16:20<08:46, 1.41it/s]
loss 2.23 accuracy 0.38 -- 56.18 + 56.95 + 616.56 + 4.80 = 734.49: 64%|██████▎ | 1303/2048 [16:20<08:46, 1.41it/s]
loss 2.23 accuracy 0.38 -- 56.18 + 56.95 + 616.56 + 4.80 = 734.49: 64%|██████▎ | 1304/2048 [16:20<08:58, 1.38it/s]
loss 1.74 accuracy 0.38 -- 57.15 + 56.57 + 501.18 + 4.78 = 619.69: 64%|██████▎ | 1304/2048 [16:21<08:58, 1.38it/s]
loss 1.74 accuracy 0.38 -- 57.15 + 56.57 + 501.18 + 4.78 = 619.69: 64%|██████▎ | 1305/2048 [16:21<08:40, 1.43it/s]
loss 1.92 accuracy 0.38 -- 56.17 + 165.97 + 500.42 + 4.77 = 727.32: 64%|██████▎ | 1305/2048 [16:22<08:40, 1.43it/s]
loss 1.92 accuracy 0.38 -- 56.17 + 165.97 + 500.42 + 4.77 = 727.32: 64%|██████▍ | 1306/2048 [16:22<08:51, 1.40it/s]
loss 1.70 accuracy 0.38 -- 56.05 + 56.16 + 499.13 + 4.76 = 616.10: 64%|██████▍ | 1306/2048 [16:22<08:51, 1.40it/s]
loss 1.70 accuracy 0.38 -- 56.05 + 56.16 + 499.13 + 4.76 = 616.10: 64%|██████▍ | 1307/2048 [16:22<08:34, 1.44it/s]
loss 1.82 accuracy 0.31 -- 56.56 + 56.43 + 497.95 + 4.78 = 615.72: 64%|██████▍ | 1307/2048 [16:23<08:34, 1.44it/s]
loss 1.82 accuracy 0.31 -- 56.56 + 56.43 + 497.95 + 4.78 = 615.72: 64%|██████▍ | 1308/2048 [16:23<08:46, 1.41it/s]
loss 1.09 accuracy 0.75 -- 56.32 + 57.18 + 495.20 + 4.78 = 613.48: 64%|██████▍ | 1308/2048 [16:24<08:46, 1.41it/s]
loss 1.09 accuracy 0.75 -- 56.32 + 57.18 + 495.20 + 4.78 = 613.48: 64%|██████▍ | 1309/2048 [16:24<08:29, 1.45it/s]
loss 1.92 accuracy 0.31 -- 158.14 + 56.72 + 491.00 + 4.78 = 710.64: 64%|██████▍ | 1309/2048 [16:25<08:29, 1.45it/s]
loss 1.92 accuracy 0.31 -- 158.14 + 56.72 + 491.00 + 4.78 = 710.64: 64%|██████▍ | 1310/2048 [16:25<08:39, 1.42it/s]
loss 1.62 accuracy 0.56 -- 56.14 + 166.94 + 500.98 + 4.77 = 728.83: 64%|██████▍ | 1310/2048 [16:25<08:39, 1.42it/s]
loss 1.62 accuracy 0.56 -- 56.14 + 166.94 + 500.98 + 4.77 = 728.83: 64%|██████▍ | 1311/2048 [16:25<08:50, 1.39it/s]
loss 1.82 accuracy 0.25 -- 56.37 + 56.51 + 498.22 + 4.79 = 615.89: 64%|██████▍ | 1311/2048 [16:26<08:50, 1.39it/s]
loss 1.82 accuracy 0.25 -- 56.37 + 56.51 + 498.22 + 4.79 = 615.89: 64%|██████▍ | 1312/2048 [16:26<08:32, 1.44it/s]
loss 1.96 accuracy 0.25 -- 163.14 + 56.73 + 498.00 + 4.82 = 722.69: 64%|██████▍ | 1312/2048 [16:27<08:32, 1.44it/s]
loss 1.96 accuracy 0.25 -- 163.14 + 56.73 + 498.00 + 4.82 = 722.69: 64%|██████▍ | 1313/2048 [16:27<08:43, 1.40it/s]
loss 1.93 accuracy 0.31 -- 56.16 + 57.07 + 621.25 + 4.78 = 739.25: 64%|██████▍ | 1313/2048 [16:27<08:43, 1.40it/s]
loss 1.93 accuracy 0.31 -- 56.16 + 57.07 + 621.25 + 4.78 = 739.25: 64%|██████▍ | 1314/2048 [16:27<08:54, 1.37it/s]
loss 1.53 accuracy 0.38 -- 56.89 + 56.70 + 506.25 + 4.79 = 624.63: 64%|██████▍ | 1314/2048 [16:28<08:54, 1.37it/s]
loss 1.53 accuracy 0.38 -- 56.89 + 56.70 + 506.25 + 4.79 = 624.63: 64%|██████▍ | 1315/2048 [16:28<08:37, 1.42it/s]
loss 2.15 accuracy 0.06 -- 56.26 + 57.56 + 616.91 + 4.80 = 735.53: 64%|██████▍ | 1315/2048 [16:29<08:37, 1.42it/s]
loss 2.15 accuracy 0.06 -- 56.26 + 57.56 + 616.91 + 4.80 = 735.53: 64%|██████▍ | 1316/2048 [16:29<08:49, 1.38it/s]
loss 2.51 accuracy 0.25 -- 57.07 + 56.45 + 502.72 + 4.77 = 621.01: 64%|██████▍ | 1316/2048 [16:30<08:49, 1.38it/s]
loss 2.51 accuracy 0.25 -- 57.07 + 56.45 + 502.72 + 4.77 = 621.01: 64%|██████▍ | 1317/2048 [16:30<08:31, 1.43it/s]
loss 2.04 accuracy 0.19 -- 55.92 + 166.59 + 501.35 + 4.76 = 728.62: 64%|██████▍ | 1317/2048 [16:30<08:31, 1.43it/s]
loss 2.04 accuracy 0.19 -- 55.92 + 166.59 + 501.35 + 4.76 = 728.62: 64%|██████▍ | 1318/2048 [16:30<08:43, 1.39it/s]
loss 2.36 accuracy 0.19 -- 56.01 + 56.69 + 498.72 + 4.78 = 616.20: 64%|██████▍ | 1318/2048 [16:31<08:43, 1.39it/s]
loss 2.36 accuracy 0.19 -- 56.01 + 56.69 + 498.72 + 4.78 = 616.20: 64%|██████▍ | 1319/2048 [16:31<08:26, 1.44it/s]
loss 1.83 accuracy 0.31 -- 56.75 + 56.29 + 497.55 + 4.78 = 615.38: 64%|██████▍ | 1319/2048 [16:32<08:26, 1.44it/s]
loss 1.83 accuracy 0.31 -- 56.75 + 56.29 + 497.55 + 4.78 = 615.38: 64%|██████▍ | 1320/2048 [16:32<08:37, 1.41it/s]
loss 1.54 accuracy 0.31 -- 55.98 + 56.98 + 495.81 + 4.79 = 613.57: 64%|██████▍ | 1320/2048 [16:32<08:37, 1.41it/s]
loss 1.54 accuracy 0.31 -- 55.98 + 56.98 + 495.81 + 4.79 = 613.57: 65%|██████▍ | 1321/2048 [16:32<08:21, 1.45it/s]
loss 2.01 accuracy 0.38 -- 158.54 + 56.72 + 489.26 + 4.78 = 709.30: 65%|██████▍ | 1321/2048 [16:33<08:21, 1.45it/s]
loss 2.01 accuracy 0.38 -- 158.54 + 56.72 + 489.26 + 4.78 = 709.30: 65%|██████▍ | 1322/2048 [16:33<08:31, 1.42it/s]
loss 1.79 accuracy 0.44 -- 55.58 + 166.32 + 501.41 + 4.77 = 728.07: 65%|██████▍ | 1322/2048 [16:34<08:31, 1.42it/s]
loss 1.79 accuracy 0.44 -- 55.58 + 166.32 + 501.41 + 4.77 = 728.07: 65%|██████▍ | 1323/2048 [16:34<08:41, 1.39it/s]
loss 1.44 accuracy 0.56 -- 56.75 + 56.31 + 498.30 + 4.78 = 616.14: 65%|██████▍ | 1323/2048 [16:34<08:41, 1.39it/s]
loss 1.44 accuracy 0.56 -- 56.75 + 56.31 + 498.30 + 4.78 = 616.14: 65%|██████▍ | 1324/2048 [16:34<08:24, 1.44it/s]
loss 1.55 accuracy 0.50 -- 162.58 + 56.97 + 496.36 + 4.80 = 720.71: 65%|██████▍ | 1324/2048 [16:35<08:24, 1.44it/s]
loss 1.55 accuracy 0.50 -- 162.58 + 56.97 + 496.36 + 4.80 = 720.71: 65%|██████▍ | 1325/2048 [16:35<08:34, 1.40it/s]
loss 1.67 accuracy 0.38 -- 56.03 + 57.36 + 619.81 + 4.78 = 737.98: 65%|██████▍ | 1325/2048 [16:36<08:34, 1.40it/s]
loss 1.67 accuracy 0.38 -- 56.03 + 57.36 + 619.81 + 4.78 = 737.98: 65%|██████▍ | 1326/2048 [16:36<08:45, 1.37it/s]
loss 1.67 accuracy 0.31 -- 56.81 + 56.47 + 505.71 + 4.84 = 623.84: 65%|██████▍ | 1326/2048 [16:37<08:45, 1.37it/s]
loss 1.67 accuracy 0.31 -- 56.81 + 56.47 + 505.71 + 4.84 = 623.84: 65%|██████▍ | 1327/2048 [16:37<08:28, 1.42it/s]
loss 2.77 accuracy 0.25 -- 56.18 + 57.24 + 615.05 + 4.79 = 733.25: 65%|██████▍ | 1327/2048 [16:37<08:28, 1.42it/s]
loss 2.77 accuracy 0.25 -- 56.18 + 57.24 + 615.05 + 4.79 = 733.25: 65%|██████▍ | 1328/2048 [16:37<08:39, 1.39it/s]
loss 1.52 accuracy 0.38 -- 56.68 + 56.48 + 502.06 + 4.77 = 619.99: 65%|██████▍ | 1328/2048 [16:38<08:39, 1.39it/s]
loss 1.52 accuracy 0.38 -- 56.68 + 56.48 + 502.06 + 4.77 = 619.99: 65%|██████▍ | 1329/2048 [16:38<08:22, 1.43it/s]
loss 1.76 accuracy 0.31 -- 56.21 + 166.21 + 501.49 + 4.78 = 728.69: 65%|██████▍ | 1329/2048 [16:39<08:22, 1.43it/s]
loss 1.76 accuracy 0.31 -- 56.21 + 166.21 + 501.49 + 4.78 = 728.69: 65%|██████▍ | 1330/2048 [16:39<08:34, 1.40it/s]
loss 1.57 accuracy 0.56 -- 56.30 + 56.46 + 498.90 + 4.77 = 616.43: 65%|██████▍ | 1330/2048 [16:39<08:34, 1.40it/s]
loss 1.57 accuracy 0.56 -- 56.30 + 56.46 + 498.90 + 4.77 = 616.43: 65%|██████▍ | 1331/2048 [16:39<08:17, 1.44it/s]
loss 2.98 accuracy 0.25 -- 56.76 + 56.59 + 499.08 + 4.80 = 617.23: 65%|██████▍ | 1331/2048 [16:40<08:17, 1.44it/s]
loss 2.98 accuracy 0.25 -- 56.76 + 56.59 + 499.08 + 4.80 = 617.23: 65%|██████▌ | 1332/2048 [16:40<08:29, 1.41it/s]
loss 1.74 accuracy 0.38 -- 56.31 + 57.82 + 497.52 + 4.77 = 616.42: 65%|██████▌ | 1332/2048 [16:41<08:29, 1.41it/s]
loss 1.74 accuracy 0.38 -- 56.31 + 57.82 + 497.52 + 4.77 = 616.42: 65%|██████▌ | 1333/2048 [16:41<08:14, 1.45it/s]
loss 2.14 accuracy 0.31 -- 157.73 + 56.92 + 489.59 + 4.78 = 709.01: 65%|██████▌ | 1333/2048 [16:42<08:14, 1.45it/s]
loss 2.14 accuracy 0.31 -- 157.73 + 56.92 + 489.59 + 4.78 = 709.01: 65%|██████▌ | 1334/2048 [16:42<08:23, 1.42it/s]
loss 2.10 accuracy 0.25 -- 55.81 + 166.95 + 501.31 + 4.77 = 728.83: 65%|██████▌ | 1334/2048 [16:42<08:23, 1.42it/s]
loss 2.10 accuracy 0.25 -- 55.81 + 166.95 + 501.31 + 4.77 = 728.83: 65%|██████▌ | 1335/2048 [16:42<08:33, 1.39it/s]
loss 1.58 accuracy 0.44 -- 56.56 + 56.20 + 498.27 + 4.76 = 615.80: 65%|██████▌ | 1335/2048 [16:43<08:33, 1.39it/s]
loss 1.58 accuracy 0.44 -- 56.56 + 56.20 + 498.27 + 4.76 = 615.80: 65%|██████▌ | 1336/2048 [16:43<08:16, 1.43it/s]
loss 2.06 accuracy 0.12 -- 163.05 + 57.35 + 496.91 + 4.81 = 722.11: 65%|██████▌ | 1336/2048 [16:44<08:16, 1.43it/s]
loss 2.06 accuracy 0.12 -- 163.05 + 57.35 + 496.91 + 4.81 = 722.11: 65%|██████▌ | 1337/2048 [16:44<08:26, 1.40it/s]
loss 1.60 accuracy 0.38 -- 56.22 + 56.97 + 621.37 + 4.78 = 739.34: 65%|██████▌ | 1337/2048 [16:44<08:26, 1.40it/s]
loss 1.60 accuracy 0.38 -- 56.22 + 56.97 + 621.37 + 4.78 = 739.34: 65%|██████▌ | 1338/2048 [16:44<08:37, 1.37it/s]
loss 1.89 accuracy 0.25 -- 57.05 + 56.18 + 505.27 + 4.78 = 623.28: 65%|██████▌ | 1338/2048 [16:45<08:37, 1.37it/s]
loss 1.89 accuracy 0.25 -- 57.05 + 56.18 + 505.27 + 4.78 = 623.28: 65%|██████▌ | 1339/2048 [16:45<08:20, 1.42it/s]
loss 2.00 accuracy 0.25 -- 56.01 + 57.23 + 615.06 + 4.78 = 733.08: 65%|██████▌ | 1339/2048 [16:46<08:20, 1.42it/s]
loss 2.00 accuracy 0.25 -- 56.01 + 57.23 + 615.06 + 4.78 = 733.08: 65%|██████▌ | 1340/2048 [16:46<08:31, 1.39it/s]
loss 1.87 accuracy 0.25 -- 56.30 + 56.35 + 501.25 + 4.77 = 618.67: 65%|██████▌ | 1340/2048 [16:47<08:31, 1.39it/s]
loss 1.87 accuracy 0.25 -- 56.30 + 56.35 + 501.25 + 4.77 = 618.67: 65%|██████▌ | 1341/2048 [16:47<08:14, 1.43it/s]
loss 1.80 accuracy 0.44 -- 56.23 + 166.49 + 502.23 + 4.78 = 729.74: 65%|██████▌ | 1341/2048 [16:47<08:14, 1.43it/s]
loss 1.80 accuracy 0.44 -- 56.23 + 166.49 + 502.23 + 4.78 = 729.74: 66%|██████▌ | 1342/2048 [16:47<08:25, 1.40it/s]
loss 1.99 accuracy 0.19 -- 56.30 + 56.25 + 499.53 + 4.78 = 616.86: 66%|██████▌ | 1342/2048 [16:48<08:25, 1.40it/s]
loss 1.99 accuracy 0.19 -- 56.30 + 56.25 + 499.53 + 4.78 = 616.86: 66%|██████▌ | 1343/2048 [16:48<08:09, 1.44it/s]
loss 1.76 accuracy 0.19 -- 56.68 + 56.66 + 498.84 + 4.77 = 616.96: 66%|██████▌ | 1343/2048 [16:49<08:09, 1.44it/s]
loss 1.76 accuracy 0.19 -- 56.68 + 56.66 + 498.84 + 4.77 = 616.96: 66%|██████▌ | 1344/2048 [16:49<08:21, 1.41it/s]
loss 2.04 accuracy 0.25 -- 56.09 + 57.97 + 495.37 + 4.79 = 614.22: 66%|██████▌ | 1344/2048 [16:49<08:21, 1.41it/s]
loss 2.04 accuracy 0.25 -- 56.09 + 57.97 + 495.37 + 4.79 = 614.22: 66%|██████▌ | 1345/2048 [16:49<08:12, 1.43it/s]
loss 2.18 accuracy 0.25 -- 157.45 + 56.85 + 491.28 + 4.77 = 710.36: 66%|██████▌ | 1345/2048 [16:50<08:12, 1.43it/s]
loss 2.18 accuracy 0.25 -- 157.45 + 56.85 + 491.28 + 4.77 = 710.36: 66%|██████▌ | 1346/2048 [16:50<08:19, 1.40it/s]
loss 2.09 accuracy 0.31 -- 55.98 + 166.76 + 501.93 + 4.77 = 729.44: 66%|██████▌ | 1346/2048 [16:51<08:19, 1.40it/s]
loss 2.09 accuracy 0.31 -- 55.98 + 166.76 + 501.93 + 4.77 = 729.44: 66%|██████▌ | 1347/2048 [16:51<08:28, 1.38it/s]
loss 1.85 accuracy 0.25 -- 56.77 + 56.44 + 497.64 + 4.77 = 615.62: 66%|██████▌ | 1347/2048 [16:51<08:28, 1.38it/s]
loss 1.85 accuracy 0.25 -- 56.77 + 56.44 + 497.64 + 4.77 = 615.62: 66%|██████▌ | 1348/2048 [16:51<08:10, 1.43it/s]
loss 1.42 accuracy 0.50 -- 162.87 + 57.14 + 500.13 + 4.80 = 724.93: 66%|██████▌ | 1348/2048 [16:52<08:10, 1.43it/s]
loss 1.42 accuracy 0.50 -- 162.87 + 57.14 + 500.13 + 4.80 = 724.93: 66%|██████▌ | 1349/2048 [16:52<08:20, 1.40it/s]
loss 2.26 accuracy 0.19 -- 56.30 + 57.62 + 622.68 + 4.78 = 741.38: 66%|██████▌ | 1349/2048 [16:53<08:20, 1.40it/s]
loss 2.26 accuracy 0.19 -- 56.30 + 57.62 + 622.68 + 4.78 = 741.38: 66%|██████▌ | 1350/2048 [16:53<08:30, 1.37it/s]
loss 2.34 accuracy 0.31 -- 56.93 + 56.18 + 506.01 + 4.76 = 623.88: 66%|██████▌ | 1350/2048 [16:54<08:30, 1.37it/s]
loss 2.34 accuracy 0.31 -- 56.93 + 56.18 + 506.01 + 4.76 = 623.88: 66%|██████▌ | 1351/2048 [16:54<08:13, 1.41it/s]
loss 2.05 accuracy 0.38 -- 56.42 + 57.44 + 616.97 + 4.79 = 735.61: 66%|██████▌ | 1351/2048 [16:54<08:13, 1.41it/s]
loss 2.05 accuracy 0.38 -- 56.42 + 57.44 + 616.97 + 4.79 = 735.61: 66%|██████▌ | 1352/2048 [16:54<08:23, 1.38it/s]
loss 1.78 accuracy 0.31 -- 56.87 + 56.56 + 503.39 + 4.78 = 621.59: 66%|██████▌ | 1352/2048 [16:55<08:23, 1.38it/s]
loss 1.78 accuracy 0.31 -- 56.87 + 56.56 + 503.39 + 4.78 = 621.59: 66%|██████▌ | 1353/2048 [16:55<08:07, 1.43it/s]
loss 2.17 accuracy 0.25 -- 56.53 + 166.80 + 503.08 + 4.78 = 731.20: 66%|██████▌ | 1353/2048 [16:56<08:07, 1.43it/s]
loss 2.17 accuracy 0.25 -- 56.53 + 166.80 + 503.08 + 4.78 = 731.20: 66%|██████▌ | 1354/2048 [16:56<08:18, 1.39it/s]
loss 1.94 accuracy 0.50 -- 56.20 + 56.03 + 501.52 + 4.78 = 618.53: 66%|██████▌ | 1354/2048 [16:56<08:18, 1.39it/s]
loss 1.94 accuracy 0.50 -- 56.20 + 56.03 + 501.52 + 4.78 = 618.53: 66%|██████▌ | 1355/2048 [16:56<08:02, 1.44it/s]
loss 1.85 accuracy 0.25 -- 56.79 + 56.46 + 498.95 + 4.77 = 616.97: 66%|██████▌ | 1355/2048 [16:57<08:02, 1.44it/s]
loss 1.85 accuracy 0.25 -- 56.79 + 56.46 + 498.95 + 4.77 = 616.97: 66%|██████▌ | 1356/2048 [16:57<08:13, 1.40it/s]
loss 1.67 accuracy 0.56 -- 55.98 + 57.25 + 494.95 + 4.78 = 612.95: 66%|██████▌ | 1356/2048 [16:58<08:13, 1.40it/s]
loss 1.67 accuracy 0.56 -- 55.98 + 57.25 + 494.95 + 4.78 = 612.95: 66%|██████▋ | 1357/2048 [16:58<07:57, 1.45it/s]
loss 1.35 accuracy 0.56 -- 157.67 + 57.00 + 489.45 + 4.77 = 708.90: 66%|██████▋ | 1357/2048 [16:59<07:57, 1.45it/s]
loss 1.35 accuracy 0.56 -- 157.67 + 57.00 + 489.45 + 4.77 = 708.90: 66%|██████▋ | 1358/2048 [16:59<08:06, 1.42it/s]
loss 2.05 accuracy 0.25 -- 55.57 + 166.02 + 501.32 + 4.78 = 727.68: 66%|██████▋ | 1358/2048 [16:59<08:06, 1.42it/s]
loss 2.05 accuracy 0.25 -- 55.57 + 166.02 + 501.32 + 4.78 = 727.68: 66%|██████▋ | 1359/2048 [16:59<08:15, 1.39it/s]
loss 1.76 accuracy 0.44 -- 56.39 + 56.36 + 498.25 + 4.77 = 615.76: 66%|██████▋ | 1359/2048 [17:00<08:15, 1.39it/s]
loss 1.76 accuracy 0.44 -- 56.39 + 56.36 + 498.25 + 4.77 = 615.76: 66%|██████▋ | 1360/2048 [17:00<07:59, 1.44it/s]
loss 1.96 accuracy 0.19 -- 162.61 + 57.32 + 497.92 + 4.76 = 722.61: 66%|██████▋ | 1360/2048 [17:01<07:59, 1.44it/s]
loss 1.96 accuracy 0.19 -- 162.61 + 57.32 + 497.92 + 4.76 = 722.61: 66%|██████▋ | 1361/2048 [17:01<08:09, 1.40it/s]
loss 1.98 accuracy 0.31 -- 56.06 + 57.15 + 620.92 + 4.79 = 738.92: 66%|██████▋ | 1361/2048 [17:02<08:09, 1.40it/s]
loss 1.98 accuracy 0.31 -- 56.06 + 57.15 + 620.92 + 4.79 = 738.92: 67%|██████▋ | 1362/2048 [17:02<08:19, 1.37it/s]
loss 1.78 accuracy 0.31 -- 56.80 + 56.30 + 505.53 + 4.76 = 623.39: 67%|██████▋ | 1362/2048 [17:02<08:19, 1.37it/s]
loss 1.78 accuracy 0.31 -- 56.80 + 56.30 + 505.53 + 4.76 = 623.39: 67%|██████▋ | 1363/2048 [17:02<08:03, 1.42it/s]
loss 1.97 accuracy 0.19 -- 55.92 + 56.99 + 616.19 + 4.78 = 733.88: 67%|██████▋ | 1363/2048 [17:03<08:03, 1.42it/s]
loss 1.97 accuracy 0.19 -- 55.92 + 56.99 + 616.19 + 4.78 = 733.88: 67%|██████▋ | 1364/2048 [17:03<08:13, 1.39it/s]
loss 2.25 accuracy 0.06 -- 56.74 + 56.34 + 503.07 + 4.77 = 620.92: 67%|██████▋ | 1364/2048 [17:04<08:13, 1.39it/s]
loss 2.25 accuracy 0.06 -- 56.74 + 56.34 + 503.07 + 4.77 = 620.92: 67%|██████▋ | 1365/2048 [17:04<07:57, 1.43it/s]
loss 1.46 accuracy 0.44 -- 56.38 + 166.73 + 504.01 + 4.83 = 731.96: 67%|██████▋ | 1365/2048 [17:04<07:57, 1.43it/s]
loss 1.46 accuracy 0.44 -- 56.38 + 166.73 + 504.01 + 4.83 = 731.96: 67%|██████▋ | 1366/2048 [17:04<08:09, 1.39it/s]
loss 1.65 accuracy 0.38 -- 56.34 + 56.63 + 499.71 + 4.76 = 617.43: 67%|██████▋ | 1366/2048 [17:05<08:09, 1.39it/s]
loss 1.65 accuracy 0.38 -- 56.34 + 56.63 + 499.71 + 4.76 = 617.43: 67%|██████▋ | 1367/2048 [17:05<07:53, 1.44it/s]
loss 1.48 accuracy 0.50 -- 56.72 + 56.71 + 498.12 + 4.78 = 616.33: 67%|██████▋ | 1367/2048 [17:06<07:53, 1.44it/s]
loss 1.48 accuracy 0.50 -- 56.72 + 56.71 + 498.12 + 4.78 = 616.33: 67%|██████▋ | 1368/2048 [17:06<08:04, 1.40it/s]
loss 2.04 accuracy 0.50 -- 55.99 + 57.21 + 494.90 + 4.77 = 612.86: 67%|██████▋ | 1368/2048 [17:06<08:04, 1.40it/s]
loss 2.04 accuracy 0.50 -- 55.99 + 57.21 + 494.90 + 4.77 = 612.86: 67%|██████▋ | 1369/2048 [17:06<07:55, 1.43it/s]
loss 1.59 accuracy 0.44 -- 157.57 + 56.95 + 491.30 + 4.79 = 710.61: 67%|██████▋ | 1369/2048 [17:07<07:55, 1.43it/s]
loss 1.59 accuracy 0.44 -- 157.57 + 56.95 + 491.30 + 4.79 = 710.61: 67%|██████▋ | 1370/2048 [17:07<08:02, 1.40it/s]
loss 1.80 accuracy 0.31 -- 55.90 + 166.35 + 501.66 + 4.77 = 728.69: 67%|██████▋ | 1370/2048 [17:08<08:02, 1.40it/s]
loss 1.80 accuracy 0.31 -- 55.90 + 166.35 + 501.66 + 4.77 = 728.69: 67%|██████▋ | 1371/2048 [17:08<08:10, 1.38it/s]
loss 1.87 accuracy 0.31 -- 56.71 + 57.83 + 499.35 + 4.77 = 618.66: 67%|██████▋ | 1371/2048 [17:09<08:10, 1.38it/s]
loss 1.87 accuracy 0.31 -- 56.71 + 57.83 + 499.35 + 4.77 = 618.66: 67%|██████▋ | 1372/2048 [17:09<07:54, 1.43it/s]
loss 2.07 accuracy 0.19 -- 162.60 + 56.95 + 497.27 + 4.78 = 721.60: 67%|██████▋ | 1372/2048 [17:09<07:54, 1.43it/s]
loss 2.07 accuracy 0.19 -- 162.60 + 56.95 + 497.27 + 4.78 = 721.60: 67%|██████▋ | 1373/2048 [17:09<08:02, 1.40it/s]
loss 1.89 accuracy 0.38 -- 56.23 + 57.41 + 621.06 + 4.78 = 739.48: 67%|██████▋ | 1373/2048 [17:10<08:02, 1.40it/s]
loss 1.89 accuracy 0.38 -- 56.23 + 57.41 + 621.06 + 4.78 = 739.48: 67%|██████▋ | 1374/2048 [17:10<08:12, 1.37it/s]
loss 1.60 accuracy 0.38 -- 56.35 + 56.24 + 504.41 + 4.78 = 621.78: 67%|██████▋ | 1374/2048 [17:11<08:12, 1.37it/s]
loss 1.60 accuracy 0.38 -- 56.35 + 56.24 + 504.41 + 4.78 = 621.78: 67%|██████▋ | 1375/2048 [17:11<07:55, 1.42it/s]
loss 1.63 accuracy 0.50 -- 56.41 + 57.29 + 615.75 + 4.78 = 734.24: 67%|██████▋ | 1375/2048 [17:11<07:55, 1.42it/s]
loss 1.63 accuracy 0.50 -- 56.41 + 57.29 + 615.75 + 4.78 = 734.24: 67%|██████▋ | 1376/2048 [17:11<08:05, 1.38it/s]
loss 1.68 accuracy 0.31 -- 56.47 + 56.61 + 502.03 + 4.76 = 619.87: 67%|██████▋ | 1376/2048 [17:12<08:05, 1.38it/s]
loss 1.68 accuracy 0.31 -- 56.47 + 56.61 + 502.03 + 4.76 = 619.87: 67%|██████▋ | 1377/2048 [17:12<07:49, 1.43it/s]
loss 1.42 accuracy 0.44 -- 56.48 + 166.77 + 502.69 + 4.82 = 730.75: 67%|██████▋ | 1377/2048 [17:13<07:49, 1.43it/s]
loss 1.42 accuracy 0.44 -- 56.48 + 166.77 + 502.69 + 4.82 = 730.75: 67%|██████▋ | 1378/2048 [17:13<08:00, 1.39it/s]
loss 1.82 accuracy 0.19 -- 55.99 + 56.34 + 499.36 + 4.78 = 616.47: 67%|██████▋ | 1378/2048 [17:14<08:00, 1.39it/s]
loss 1.82 accuracy 0.19 -- 55.99 + 56.34 + 499.36 + 4.78 = 616.47: 67%|██████▋ | 1379/2048 [17:14<07:45, 1.44it/s]
loss 1.55 accuracy 0.62 -- 56.74 + 56.67 + 499.33 + 4.78 = 617.53: 67%|██████▋ | 1379/2048 [17:14<07:45, 1.44it/s]
loss 1.55 accuracy 0.62 -- 56.74 + 56.67 + 499.33 + 4.78 = 617.53: 67%|██████▋ | 1380/2048 [17:14<07:55, 1.40it/s]
loss 1.76 accuracy 0.44 -- 56.09 + 57.35 + 495.20 + 4.80 = 613.44: 67%|██████▋ | 1380/2048 [17:15<07:55, 1.40it/s]
loss 1.76 accuracy 0.44 -- 56.09 + 57.35 + 495.20 + 4.80 = 613.44: 67%|██████▋ | 1381/2048 [17:15<07:40, 1.45it/s]
loss 1.33 accuracy 0.50 -- 156.95 + 56.96 + 489.38 + 4.78 = 708.07: 67%|██████▋ | 1381/2048 [17:16<07:40, 1.45it/s]
loss 1.33 accuracy 0.50 -- 156.95 + 56.96 + 489.38 + 4.78 = 708.07: 67%|██████▋ | 1382/2048 [17:16<07:49, 1.42it/s]
loss 1.87 accuracy 0.19 -- 55.75 + 166.77 + 502.82 + 4.78 = 730.13: 67%|██████▋ | 1382/2048 [17:16<07:49, 1.42it/s]
loss 1.87 accuracy 0.19 -- 55.75 + 166.77 + 502.82 + 4.78 = 730.13: 68%|██████▊ | 1383/2048 [17:16<07:58, 1.39it/s]
loss 1.66 accuracy 0.25 -- 57.04 + 56.76 + 498.40 + 4.76 = 616.96: 68%|██████▊ | 1383/2048 [17:17<07:58, 1.39it/s]
loss 1.66 accuracy 0.25 -- 57.04 + 56.76 + 498.40 + 4.76 = 616.96: 68%|██████▊ | 1384/2048 [17:17<07:43, 1.43it/s]
loss 1.73 accuracy 0.50 -- 162.93 + 56.98 + 497.03 + 4.78 = 721.72: 68%|██████▊ | 1384/2048 [17:18<07:43, 1.43it/s]
loss 1.73 accuracy 0.50 -- 162.93 + 56.98 + 497.03 + 4.78 = 721.72: 68%|██████▊ | 1385/2048 [17:18<07:52, 1.40it/s]
loss 2.07 accuracy 0.25 -- 55.93 + 57.56 + 620.56 + 4.78 = 738.83: 68%|██████▊ | 1385/2048 [17:19<07:52, 1.40it/s]
loss 2.07 accuracy 0.25 -- 55.93 + 57.56 + 620.56 + 4.78 = 738.83: 68%|██████▊ | 1386/2048 [17:19<08:02, 1.37it/s]
loss 1.62 accuracy 0.44 -- 57.05 + 56.35 + 504.98 + 4.78 = 623.17: 68%|██████▊ | 1386/2048 [17:19<08:02, 1.37it/s]
loss 1.62 accuracy 0.44 -- 57.05 + 56.35 + 504.98 + 4.78 = 623.17: 68%|██████▊ | 1387/2048 [17:19<07:46, 1.42it/s]
loss 1.58 accuracy 0.38 -- 56.24 + 57.18 + 616.62 + 4.80 = 734.84: 68%|██████▊ | 1387/2048 [17:20<07:46, 1.42it/s]
loss 1.58 accuracy 0.38 -- 56.24 + 57.18 + 616.62 + 4.80 = 734.84: 68%|██████▊ | 1388/2048 [17:20<07:56, 1.38it/s]
loss 1.78 accuracy 0.25 -- 56.71 + 56.50 + 503.80 + 4.78 = 621.79: 68%|██████▊ | 1388/2048 [17:21<07:56, 1.38it/s]
loss 1.78 accuracy 0.25 -- 56.71 + 56.50 + 503.80 + 4.78 = 621.79: 68%|██████▊ | 1389/2048 [17:21<07:41, 1.43it/s]
loss 1.99 accuracy 0.19 -- 56.27 + 166.72 + 502.43 + 4.79 = 730.20: 68%|██████▊ | 1389/2048 [17:21<07:41, 1.43it/s]
loss 1.99 accuracy 0.19 -- 56.27 + 166.72 + 502.43 + 4.79 = 730.20: 68%|██████▊ | 1390/2048 [17:21<07:52, 1.39it/s]
loss 1.78 accuracy 0.31 -- 56.13 + 56.25 + 499.09 + 4.78 = 616.24: 68%|██████▊ | 1390/2048 [17:22<07:52, 1.39it/s]
loss 1.78 accuracy 0.31 -- 56.13 + 56.25 + 499.09 + 4.78 = 616.24: 68%|██████▊ | 1391/2048 [17:22<07:36, 1.44it/s]
loss 1.78 accuracy 0.25 -- 56.67 + 56.62 + 498.09 + 4.79 = 616.17: 68%|██████▊ | 1391/2048 [17:23<07:36, 1.44it/s]
loss 1.78 accuracy 0.25 -- 56.67 + 56.62 + 498.09 + 4.79 = 616.17: 68%|██████▊ | 1392/2048 [17:23<07:46, 1.40it/s]
loss 2.47 accuracy 0.19 -- 56.02 + 57.09 + 494.90 + 4.77 = 612.79: 68%|██████▊ | 1392/2048 [17:23<07:46, 1.40it/s]
loss 2.47 accuracy 0.19 -- 56.02 + 57.09 + 494.90 + 4.77 = 612.79: 68%|██████▊ | 1393/2048 [17:23<07:39, 1.43it/s]
loss 1.67 accuracy 0.19 -- 158.16 + 56.44 + 490.33 + 4.78 = 709.70: 68%|██████▊ | 1393/2048 [17:24<07:39, 1.43it/s]
loss 1.67 accuracy 0.19 -- 158.16 + 56.44 + 490.33 + 4.78 = 709.70: 68%|██████▊ | 1394/2048 [17:24<07:45, 1.41it/s]
loss 1.78 accuracy 0.38 -- 55.99 + 167.25 + 502.37 + 4.79 = 730.40: 68%|██████▊ | 1394/2048 [17:25<07:45, 1.41it/s]
loss 1.78 accuracy 0.38 -- 55.99 + 167.25 + 502.37 + 4.79 = 730.40: 68%|██████▊ | 1395/2048 [17:25<07:53, 1.38it/s]
loss 2.17 accuracy 0.19 -- 56.58 + 56.26 + 497.82 + 4.76 = 615.42: 68%|██████▊ | 1395/2048 [17:26<07:53, 1.38it/s]
loss 2.17 accuracy 0.19 -- 56.58 + 56.26 + 497.82 + 4.76 = 615.42: 68%|██████▊ | 1396/2048 [17:26<07:36, 1.43it/s]
loss 1.66 accuracy 0.19 -- 162.87 + 57.17 + 497.32 + 4.79 = 722.15: 68%|██████▊ | 1396/2048 [17:26<07:36, 1.43it/s]
loss 1.66 accuracy 0.19 -- 162.87 + 57.17 + 497.32 + 4.79 = 722.15: 68%|██████▊ | 1397/2048 [17:26<07:45, 1.40it/s]
loss 1.54 accuracy 0.50 -- 56.10 + 57.33 + 621.19 + 4.80 = 739.42: 68%|██████▊ | 1397/2048 [17:27<07:45, 1.40it/s]
loss 1.54 accuracy 0.50 -- 56.10 + 57.33 + 621.19 + 4.80 = 739.42: 68%|██████▊ | 1398/2048 [17:27<07:54, 1.37it/s]
loss 1.79 accuracy 0.31 -- 56.74 + 56.52 + 504.80 + 4.78 = 622.83: 68%|██████▊ | 1398/2048 [17:28<07:54, 1.37it/s]
loss 1.79 accuracy 0.31 -- 56.74 + 56.52 + 504.80 + 4.78 = 622.83: 68%|██████▊ | 1399/2048 [17:28<07:38, 1.42it/s]
loss 1.83 accuracy 0.44 -- 56.21 + 57.09 + 617.55 + 4.78 = 735.64: 68%|██████▊ | 1399/2048 [17:29<07:38, 1.42it/s]
loss 1.83 accuracy 0.44 -- 56.21 + 57.09 + 617.55 + 4.78 = 735.64: 68%|██████▊ | 1400/2048 [17:29<07:48, 1.38it/s]
loss 2.09 accuracy 0.19 -- 56.80 + 56.49 + 501.94 + 4.77 = 620.00: 68%|██████▊ | 1400/2048 [17:29<07:48, 1.38it/s]
loss 2.09 accuracy 0.19 -- 56.80 + 56.49 + 501.94 + 4.77 = 620.00: 68%|██████▊ | 1401/2048 [17:29<07:40, 1.41it/s]
loss 1.82 accuracy 0.38 -- 56.30 + 166.11 + 501.72 + 4.79 = 728.92: 68%|██████▊ | 1401/2048 [17:30<07:40, 1.41it/s]
loss 1.82 accuracy 0.38 -- 56.30 + 166.11 + 501.72 + 4.79 = 728.92: 68%|██████▊ | 1402/2048 [17:30<07:48, 1.38it/s]
loss 1.56 accuracy 0.38 -- 56.36 + 56.22 + 498.65 + 4.79 = 616.02: 68%|██████▊ | 1402/2048 [17:31<07:48, 1.38it/s]
loss 1.56 accuracy 0.38 -- 56.36 + 56.22 + 498.65 + 4.79 = 616.02: 69%|██████▊ | 1403/2048 [17:31<07:31, 1.43it/s]
loss 1.94 accuracy 0.19 -- 56.60 + 56.42 + 498.41 + 4.77 = 616.20: 69%|██████▊ | 1403/2048 [17:31<07:31, 1.43it/s]
loss 1.94 accuracy 0.19 -- 56.60 + 56.42 + 498.41 + 4.77 = 616.20: 69%|██████▊ | 1404/2048 [17:31<07:40, 1.40it/s]
loss 1.55 accuracy 0.25 -- 56.11 + 57.39 + 495.71 + 4.76 = 613.97: 69%|██████▊ | 1404/2048 [17:32<07:40, 1.40it/s]
loss 1.55 accuracy 0.25 -- 56.11 + 57.39 + 495.71 + 4.76 = 613.97: 69%|██████▊ | 1405/2048 [17:32<07:25, 1.44it/s]
loss 1.69 accuracy 0.38 -- 157.63 + 57.48 + 491.33 + 4.77 = 711.21: 69%|██████▊ | 1405/2048 [17:33<07:25, 1.44it/s]
loss 1.69 accuracy 0.38 -- 157.63 + 57.48 + 491.33 + 4.77 = 711.21: 69%|██████▊ | 1406/2048 [17:33<07:33, 1.42it/s]
loss 1.96 accuracy 0.44 -- 56.20 + 167.01 + 503.48 + 4.77 = 731.46: 69%|██████▊ | 1406/2048 [17:33<07:33, 1.42it/s]
loss 1.96 accuracy 0.44 -- 56.20 + 167.01 + 503.48 + 4.77 = 731.46: 69%|██████▊ | 1407/2048 [17:33<07:42, 1.38it/s]
loss 1.65 accuracy 0.44 -- 56.71 + 56.27 + 499.46 + 4.77 = 617.21: 69%|██████▊ | 1407/2048 [17:34<07:42, 1.38it/s]
loss 1.65 accuracy 0.44 -- 56.71 + 56.27 + 499.46 + 4.77 = 617.21: 69%|██████▉ | 1408/2048 [17:34<07:27, 1.43it/s]
loss 2.61 accuracy 0.12 -- 163.18 + 57.15 + 497.24 + 4.83 = 722.40: 69%|██████▉ | 1408/2048 [17:35<07:27, 1.43it/s]
loss 2.61 accuracy 0.12 -- 163.18 + 57.15 + 497.24 + 4.83 = 722.40: 69%|██████▉ | 1409/2048 [17:35<07:36, 1.40it/s]
loss 2.18 accuracy 0.19 -- 56.08 + 57.48 + 619.61 + 4.78 = 737.95: 69%|██████▉ | 1409/2048 [17:36<07:36, 1.40it/s]
loss 2.18 accuracy 0.19 -- 56.08 + 57.48 + 619.61 + 4.78 = 737.95: 69%|██████▉ | 1410/2048 [17:36<07:45, 1.37it/s]
loss 2.30 accuracy 0.12 -- 56.70 + 56.46 + 504.96 + 4.81 = 622.93: 69%|██████▉ | 1410/2048 [17:36<07:45, 1.37it/s]
loss 2.30 accuracy 0.12 -- 56.70 + 56.46 + 504.96 + 4.81 = 622.93: 69%|██████▉ | 1411/2048 [17:36<07:29, 1.42it/s]
loss 1.94 accuracy 0.25 -- 56.46 + 57.49 + 615.97 + 4.77 = 734.69: 69%|██████▉ | 1411/2048 [17:37<07:29, 1.42it/s]
loss 1.94 accuracy 0.25 -- 56.46 + 57.49 + 615.97 + 4.77 = 734.69: 69%|██████▉ | 1412/2048 [17:37<07:39, 1.38it/s]
loss 1.61 accuracy 0.56 -- 56.44 + 56.31 + 503.96 + 4.81 = 621.52: 69%|██████▉ | 1412/2048 [17:38<07:39, 1.38it/s]
loss 1.61 accuracy 0.56 -- 56.44 + 56.31 + 503.96 + 4.81 = 621.52: 69%|██████▉ | 1413/2048 [17:38<07:24, 1.43it/s]
loss 1.46 accuracy 0.44 -- 56.05 + 165.96 + 501.42 + 4.78 = 728.21: 69%|██████▉ | 1413/2048 [17:38<07:24, 1.43it/s]
loss 1.46 accuracy 0.44 -- 56.05 + 165.96 + 501.42 + 4.78 = 728.21: 69%|██████▉ | 1414/2048 [17:38<07:34, 1.39it/s]
loss 1.98 accuracy 0.31 -- 56.09 + 56.25 + 499.52 + 4.78 = 616.65: 69%|██████▉ | 1414/2048 [17:39<07:34, 1.39it/s]
loss 1.98 accuracy 0.31 -- 56.09 + 56.25 + 499.52 + 4.78 = 616.65: 69%|██████▉ | 1415/2048 [17:39<07:20, 1.44it/s]
loss 2.28 accuracy 0.31 -- 56.48 + 56.42 + 497.24 + 4.78 = 614.92: 69%|██████▉ | 1415/2048 [17:40<07:20, 1.44it/s]
loss 2.28 accuracy 0.31 -- 56.48 + 56.42 + 497.24 + 4.78 = 614.92: 69%|██████▉ | 1416/2048 [17:40<07:29, 1.41it/s]
loss 1.71 accuracy 0.44 -- 56.34 + 57.25 + 496.41 + 4.80 = 614.80: 69%|██████▉ | 1416/2048 [17:41<07:29, 1.41it/s]
loss 1.71 accuracy 0.44 -- 56.34 + 57.25 + 496.41 + 4.80 = 614.80: 69%|██████▉ | 1417/2048 [17:41<07:22, 1.43it/s]
loss 1.65 accuracy 0.50 -- 157.81 + 56.83 + 489.57 + 4.77 = 708.98: 69%|██████▉ | 1417/2048 [17:41<07:22, 1.43it/s]
loss 1.65 accuracy 0.50 -- 157.81 + 56.83 + 489.57 + 4.77 = 708.98: 69%|██████▉ | 1418/2048 [17:41<07:28, 1.41it/s]
loss 2.15 accuracy 0.25 -- 55.91 + 166.34 + 501.36 + 4.78 = 728.39: 69%|██████▉ | 1418/2048 [17:42<07:28, 1.41it/s]
loss 2.15 accuracy 0.25 -- 55.91 + 166.34 + 501.36 + 4.78 = 728.39: 69%|██████▉ | 1419/2048 [17:42<07:35, 1.38it/s]
loss 2.25 accuracy 0.12 -- 56.60 + 56.53 + 497.87 + 4.78 = 615.78: 69%|██████▉ | 1419/2048 [17:43<07:35, 1.38it/s]
loss 2.25 accuracy 0.12 -- 56.60 + 56.53 + 497.87 + 4.78 = 615.78: 69%|██████▉ | 1420/2048 [17:43<07:19, 1.43it/s]
loss 2.10 accuracy 0.19 -- 162.64 + 57.21 + 497.81 + 4.77 = 722.43: 69%|██████▉ | 1420/2048 [17:43<07:19, 1.43it/s]
loss 2.10 accuracy 0.19 -- 162.64 + 57.21 + 497.81 + 4.77 = 722.43: 69%|██████▉ | 1421/2048 [17:43<07:28, 1.40it/s]
loss 1.43 accuracy 0.50 -- 56.15 + 57.36 + 621.38 + 4.79 = 739.67: 69%|██████▉ | 1421/2048 [17:44<07:28, 1.40it/s]
loss 1.43 accuracy 0.50 -- 56.15 + 57.36 + 621.38 + 4.79 = 739.67: 69%|██████▉ | 1422/2048 [17:44<07:37, 1.37it/s]
loss 1.96 accuracy 0.25 -- 56.99 + 56.61 + 505.62 + 4.77 = 623.99: 69%|██████▉ | 1422/2048 [17:45<07:37, 1.37it/s]
loss 1.96 accuracy 0.25 -- 56.99 + 56.61 + 505.62 + 4.77 = 623.99: 69%|██████▉ | 1423/2048 [17:45<07:21, 1.42it/s]
loss 1.65 accuracy 0.38 -- 56.40 + 57.20 + 616.52 + 4.79 = 734.91: 69%|██████▉ | 1423/2048 [17:46<07:21, 1.42it/s]
loss 1.65 accuracy 0.38 -- 56.40 + 57.20 + 616.52 + 4.79 = 734.91: 70%|██████▉ | 1424/2048 [17:46<07:31, 1.38it/s]
loss 1.40 accuracy 0.56 -- 57.05 + 56.54 + 502.32 + 4.77 = 620.67: 70%|██████▉ | 1424/2048 [17:46<07:31, 1.38it/s]
loss 1.40 accuracy 0.56 -- 57.05 + 56.54 + 502.32 + 4.77 = 620.67: 70%|██████▉ | 1425/2048 [17:46<07:22, 1.41it/s]
loss 1.76 accuracy 0.25 -- 55.94 + 165.84 + 502.74 + 4.79 = 729.31: 70%|██████▉ | 1425/2048 [17:47<07:22, 1.41it/s]
loss 1.76 accuracy 0.25 -- 55.94 + 165.84 + 502.74 + 4.79 = 729.31: 70%|██████▉ | 1426/2048 [17:47<07:30, 1.38it/s]
loss 1.58 accuracy 0.25 -- 56.00 + 56.35 + 499.86 + 4.77 = 616.98: 70%|██████▉ | 1426/2048 [17:48<07:30, 1.38it/s]
loss 1.58 accuracy 0.25 -- 56.00 + 56.35 + 499.86 + 4.77 = 616.98: 70%|██████▉ | 1427/2048 [17:48<07:14, 1.43it/s]
loss 2.22 accuracy 0.38 -- 56.80 + 56.51 + 500.32 + 4.78 = 618.41: 70%|██████▉ | 1427/2048 [17:48<07:14, 1.43it/s]
loss 2.22 accuracy 0.38 -- 56.80 + 56.51 + 500.32 + 4.78 = 618.41: 70%|██████▉ | 1428/2048 [17:48<07:23, 1.40it/s]
loss 1.82 accuracy 0.31 -- 56.32 + 57.34 + 495.77 + 4.78 = 614.22: 70%|██████▉ | 1428/2048 [17:49<07:23, 1.40it/s]
loss 1.82 accuracy 0.31 -- 56.32 + 57.34 + 495.77 + 4.78 = 614.22: 70%|██████▉ | 1429/2048 [17:49<07:09, 1.44it/s]
loss 1.57 accuracy 0.50 -- 158.07 + 57.12 + 489.84 + 4.79 = 709.82: 70%|██████▉ | 1429/2048 [17:50<07:09, 1.44it/s]
loss 1.57 accuracy 0.50 -- 158.07 + 57.12 + 489.84 + 4.79 = 709.82: 70%|██████▉ | 1430/2048 [17:50<07:16, 1.42it/s]
loss 2.43 accuracy 0.25 -- 55.70 + 166.19 + 501.54 + 4.78 = 728.21: 70%|██████▉ | 1430/2048 [17:51<07:16, 1.42it/s]
loss 2.43 accuracy 0.25 -- 55.70 + 166.19 + 501.54 + 4.78 = 728.21: 70%|██████▉ | 1431/2048 [17:51<07:25, 1.39it/s]
loss 2.03 accuracy 0.25 -- 56.52 + 56.58 + 497.10 + 4.77 = 614.97: 70%|██████▉ | 1431/2048 [17:51<07:25, 1.39it/s]
loss 2.03 accuracy 0.25 -- 56.52 + 56.58 + 497.10 + 4.77 = 614.97: 70%|██████▉ | 1432/2048 [17:51<07:09, 1.43it/s]
loss 2.17 accuracy 0.19 -- 162.22 + 57.17 + 496.76 + 4.77 = 720.92: 70%|██████▉ | 1432/2048 [17:52<07:09, 1.43it/s]
loss 2.17 accuracy 0.19 -- 162.22 + 57.17 + 496.76 + 4.77 = 720.92: 70%|██████▉ | 1433/2048 [17:52<07:18, 1.40it/s]
loss 2.74 accuracy 0.19 -- 56.06 + 57.42 + 621.27 + 4.77 = 739.52: 70%|██████▉ | 1433/2048 [17:53<07:18, 1.40it/s]
loss 2.74 accuracy 0.19 -- 56.06 + 57.42 + 621.27 + 4.77 = 739.52: 70%|███████ | 1434/2048 [17:53<07:27, 1.37it/s]
loss 1.78 accuracy 0.25 -- 57.02 + 56.41 + 504.41 + 4.78 = 622.63: 70%|███████ | 1434/2048 [17:53<07:27, 1.37it/s]
loss 1.78 accuracy 0.25 -- 57.02 + 56.41 + 504.41 + 4.78 = 622.63: 70%|███████ | 1435/2048 [17:53<07:12, 1.42it/s]
loss 2.06 accuracy 0.44 -- 56.29 + 57.06 + 615.78 + 4.79 = 733.92: 70%|███████ | 1435/2048 [17:54<07:12, 1.42it/s]
loss 2.06 accuracy 0.44 -- 56.29 + 57.06 + 615.78 + 4.79 = 733.92: 70%|███████ | 1436/2048 [17:54<07:21, 1.39it/s]
loss 1.74 accuracy 0.31 -- 56.76 + 56.39 + 501.08 + 4.77 = 618.99: 70%|███████ | 1436/2048 [17:55<07:21, 1.39it/s]
loss 1.74 accuracy 0.31 -- 56.76 + 56.39 + 501.08 + 4.77 = 618.99: 70%|███████ | 1437/2048 [17:55<07:07, 1.43it/s]
loss 1.93 accuracy 0.19 -- 56.18 + 166.42 + 502.03 + 4.80 = 729.42: 70%|███████ | 1437/2048 [17:56<07:07, 1.43it/s]
loss 1.93 accuracy 0.19 -- 56.18 + 166.42 + 502.03 + 4.80 = 729.42: 70%|███████ | 1438/2048 [17:56<07:16, 1.40it/s]
loss 1.78 accuracy 0.38 -- 56.13 + 56.48 + 499.95 + 4.77 = 617.33: 70%|███████ | 1438/2048 [17:56<07:16, 1.40it/s]
loss 1.78 accuracy 0.38 -- 56.13 + 56.48 + 499.95 + 4.77 = 617.33: 70%|███████ | 1439/2048 [17:56<07:02, 1.44it/s]
loss 1.70 accuracy 0.25 -- 56.70 + 56.77 + 497.65 + 4.78 = 615.90: 70%|███████ | 1439/2048 [17:57<07:02, 1.44it/s]
loss 1.70 accuracy 0.25 -- 56.70 + 56.77 + 497.65 + 4.78 = 615.90: 70%|███████ | 1440/2048 [17:57<07:12, 1.41it/s]
loss 2.03 accuracy 0.19 -- 55.93 + 57.35 + 495.11 + 4.79 = 613.17: 70%|███████ | 1440/2048 [17:58<07:12, 1.41it/s]
loss 2.03 accuracy 0.19 -- 55.93 + 57.35 + 495.11 + 4.79 = 613.17: 70%|███████ | 1441/2048 [17:58<06:58, 1.45it/s]
loss 1.74 accuracy 0.31 -- 157.87 + 57.03 + 490.31 + 4.78 = 709.99: 70%|███████ | 1441/2048 [17:58<06:58, 1.45it/s]
loss 1.74 accuracy 0.31 -- 157.87 + 57.03 + 490.31 + 4.78 = 709.99: 70%|███████ | 1442/2048 [17:58<07:06, 1.42it/s]
loss 2.06 accuracy 0.19 -- 55.78 + 166.88 + 501.72 + 4.77 = 729.15: 70%|███████ | 1442/2048 [17:59<07:06, 1.42it/s]
loss 2.06 accuracy 0.19 -- 55.78 + 166.88 + 501.72 + 4.77 = 729.15: 70%|███████ | 1443/2048 [17:59<07:15, 1.39it/s]
loss 1.97 accuracy 0.31 -- 56.81 + 56.73 + 498.45 + 4.77 = 616.76: 70%|███████ | 1443/2048 [18:00<07:15, 1.39it/s]
loss 1.97 accuracy 0.31 -- 56.81 + 56.73 + 498.45 + 4.77 = 616.76: 71%|███████ | 1444/2048 [18:00<07:01, 1.43it/s]
loss 1.82 accuracy 0.38 -- 162.92 + 57.25 + 498.41 + 4.84 = 723.41: 71%|███████ | 1444/2048 [18:00<07:01, 1.43it/s]
loss 1.82 accuracy 0.38 -- 162.92 + 57.25 + 498.41 + 4.84 = 723.41: 71%|███████ | 1445/2048 [18:00<07:09, 1.40it/s]
loss 2.08 accuracy 0.56 -- 55.97 + 57.45 + 621.90 + 4.80 = 740.12: 71%|███████ | 1445/2048 [18:01<07:09, 1.40it/s]
loss 2.08 accuracy 0.56 -- 55.97 + 57.45 + 621.90 + 4.80 = 740.12: 71%|███████ | 1446/2048 [18:01<07:18, 1.37it/s]
loss 1.59 accuracy 0.50 -- 57.20 + 56.77 + 506.18 + 4.77 = 624.91: 71%|███████ | 1446/2048 [18:02<07:18, 1.37it/s]
loss 1.59 accuracy 0.50 -- 57.20 + 56.77 + 506.18 + 4.77 = 624.91: 71%|███████ | 1447/2048 [18:02<07:04, 1.42it/s]
loss 2.02 accuracy 0.19 -- 56.57 + 57.62 + 617.39 + 4.79 = 736.37: 71%|███████ | 1447/2048 [18:03<07:04, 1.42it/s]
loss 2.02 accuracy 0.19 -- 56.57 + 57.62 + 617.39 + 4.79 = 736.37: 71%|███████ | 1448/2048 [18:03<07:13, 1.38it/s]
loss 1.84 accuracy 0.12 -- 56.85 + 56.23 + 501.94 + 4.78 = 619.80: 71%|███████ | 1448/2048 [18:03<07:13, 1.38it/s]
loss 1.84 accuracy 0.12 -- 56.85 + 56.23 + 501.94 + 4.78 = 619.80: 71%|███████ | 1449/2048 [18:03<07:05, 1.41it/s]
loss 1.77 accuracy 0.50 -- 56.32 + 166.28 + 501.54 + 4.78 = 728.92: 71%|███████ | 1449/2048 [18:04<07:05, 1.41it/s]
loss 1.77 accuracy 0.50 -- 56.32 + 166.28 + 501.54 + 4.78 = 728.92: 71%|███████ | 1450/2048 [18:04<07:13, 1.38it/s]
loss 2.00 accuracy 0.50 -- 56.15 + 56.80 + 499.43 + 4.77 = 617.15: 71%|███████ | 1450/2048 [18:05<07:13, 1.38it/s]
loss 2.00 accuracy 0.50 -- 56.15 + 56.80 + 499.43 + 4.77 = 617.15: 71%|███████ | 1451/2048 [18:05<06:58, 1.43it/s]
loss 2.06 accuracy 0.25 -- 56.69 + 56.28 + 498.45 + 4.80 = 616.21: 71%|███████ | 1451/2048 [18:05<06:58, 1.43it/s]
loss 2.06 accuracy 0.25 -- 56.69 + 56.28 + 498.45 + 4.80 = 616.21: 71%|███████ | 1452/2048 [18:05<07:06, 1.40it/s]
loss 1.42 accuracy 0.50 -- 56.24 + 57.04 + 495.13 + 4.81 = 613.22: 71%|███████ | 1452/2048 [18:06<07:06, 1.40it/s]
loss 1.42 accuracy 0.50 -- 56.24 + 57.04 + 495.13 + 4.81 = 613.22: 71%|███████ | 1453/2048 [18:06<06:52, 1.44it/s]
loss 1.62 accuracy 0.44 -- 157.53 + 56.98 + 489.24 + 4.76 = 708.51: 71%|███████ | 1453/2048 [18:07<06:52, 1.44it/s]
loss 1.62 accuracy 0.44 -- 157.53 + 56.98 + 489.24 + 4.76 = 708.51: 71%|███████ | 1454/2048 [18:07<06:59, 1.42it/s]
loss 1.86 accuracy 0.31 -- 55.53 + 166.88 + 501.57 + 4.78 = 728.76: 71%|███████ | 1454/2048 [18:08<06:59, 1.42it/s]
loss 1.86 accuracy 0.31 -- 55.53 + 166.88 + 501.57 + 4.78 = 728.76: 71%|███████ | 1455/2048 [18:08<07:07, 1.39it/s]
loss 1.64 accuracy 0.31 -- 56.43 + 56.72 + 498.55 + 4.82 = 616.52: 71%|███████ | 1455/2048 [18:08<07:07, 1.39it/s]
loss 1.64 accuracy 0.31 -- 56.43 + 56.72 + 498.55 + 4.82 = 616.52: 71%|███████ | 1456/2048 [18:08<06:52, 1.43it/s]
loss 1.91 accuracy 0.25 -- 163.55 + 57.38 + 497.09 + 4.76 = 722.78: 71%|███████ | 1456/2048 [18:09<06:52, 1.43it/s]
loss 1.91 accuracy 0.25 -- 163.55 + 57.38 + 497.09 + 4.76 = 722.78: 71%|███████ | 1457/2048 [18:09<07:01, 1.40it/s]
loss 1.71 accuracy 0.38 -- 56.03 + 57.05 + 620.54 + 4.80 = 738.43: 71%|███████ | 1457/2048 [18:10<07:01, 1.40it/s]
loss 1.71 accuracy 0.38 -- 56.03 + 57.05 + 620.54 + 4.80 = 738.43: 71%|███████ | 1458/2048 [18:10<07:09, 1.37it/s]
loss 2.09 accuracy 0.25 -- 57.02 + 56.78 + 504.87 + 4.81 = 623.48: 71%|███████ | 1458/2048 [18:10<07:09, 1.37it/s]
loss 2.09 accuracy 0.25 -- 57.02 + 56.78 + 504.87 + 4.81 = 623.48: 71%|███████ | 1459/2048 [18:10<06:55, 1.42it/s]
loss 1.96 accuracy 0.06 -- 55.92 + 57.38 + 615.63 + 4.78 = 733.71: 71%|███████ | 1459/2048 [18:11<06:55, 1.42it/s]
loss 1.96 accuracy 0.06 -- 55.92 + 57.38 + 615.63 + 4.78 = 733.71: 71%|███████▏ | 1460/2048 [18:11<07:04, 1.39it/s]
loss 2.01 accuracy 0.31 -- 56.69 + 56.87 + 501.03 + 4.78 = 619.38: 71%|███████▏ | 1460/2048 [18:12<07:04, 1.39it/s]
loss 2.01 accuracy 0.31 -- 56.69 + 56.87 + 501.03 + 4.78 = 619.38: 71%|███████▏ | 1461/2048 [18:12<06:50, 1.43it/s]
loss 2.11 accuracy 0.38 -- 56.00 + 166.38 + 504.07 + 4.81 = 731.26: 71%|███████▏ | 1461/2048 [18:13<06:50, 1.43it/s]
loss 2.11 accuracy 0.38 -- 56.00 + 166.38 + 504.07 + 4.81 = 731.26: 71%|███████▏ | 1462/2048 [18:13<07:00, 1.39it/s]
loss 2.24 accuracy 0.38 -- 56.08 + 56.33 + 498.62 + 4.76 = 615.80: 71%|███████▏ | 1462/2048 [18:13<07:00, 1.39it/s]
loss 2.24 accuracy 0.38 -- 56.08 + 56.33 + 498.62 + 4.76 = 615.80: 71%|███████▏ | 1463/2048 [18:13<06:46, 1.44it/s]
loss 1.82 accuracy 0.25 -- 56.49 + 56.23 + 497.41 + 4.78 = 614.91: 71%|███████▏ | 1463/2048 [18:14<06:46, 1.44it/s]
loss 1.82 accuracy 0.25 -- 56.49 + 56.23 + 497.41 + 4.78 = 614.91: 71%|███████▏ | 1464/2048 [18:14<06:55, 1.41it/s]
loss 1.49 accuracy 0.44 -- 56.15 + 57.57 + 494.96 + 4.77 = 613.45: 71%|███████▏ | 1464/2048 [18:15<06:55, 1.41it/s]
loss 1.49 accuracy 0.44 -- 56.15 + 57.57 + 494.96 + 4.77 = 613.45: 72%|███████▏ | 1465/2048 [18:15<06:48, 1.43it/s]
loss 1.85 accuracy 0.25 -- 158.14 + 56.63 + 489.61 + 4.77 = 709.15: 72%|███████▏ | 1465/2048 [18:15<06:48, 1.43it/s]
loss 1.85 accuracy 0.25 -- 158.14 + 56.63 + 489.61 + 4.77 = 709.15: 72%|███████▏ | 1466/2048 [18:15<06:53, 1.41it/s]
loss 1.79 accuracy 0.19 -- 55.67 + 165.99 + 499.95 + 4.77 = 726.38: 72%|███████▏ | 1466/2048 [18:16<06:53, 1.41it/s]
loss 1.79 accuracy 0.19 -- 55.67 + 165.99 + 499.95 + 4.77 = 726.38: 72%|███████▏ | 1467/2048 [18:16<07:00, 1.38it/s]
loss 1.41 accuracy 0.50 -- 56.90 + 57.11 + 498.53 + 4.77 = 617.31: 72%|███████▏ | 1467/2048 [18:17<07:00, 1.38it/s]
loss 1.41 accuracy 0.50 -- 56.90 + 57.11 + 498.53 + 4.77 = 617.31: 72%|███████▏ | 1468/2048 [18:17<06:46, 1.43it/s]
loss 1.96 accuracy 0.12 -- 162.26 + 56.85 + 496.85 + 4.77 = 720.74: 72%|███████▏ | 1468/2048 [18:18<06:46, 1.43it/s]
loss 1.96 accuracy 0.12 -- 162.26 + 56.85 + 496.85 + 4.77 = 720.74: 72%|███████▏ | 1469/2048 [18:18<06:53, 1.40it/s]
loss 1.73 accuracy 0.38 -- 56.00 + 57.32 + 620.24 + 4.79 = 738.35: 72%|███████▏ | 1469/2048 [18:18<06:53, 1.40it/s]
loss 1.73 accuracy 0.38 -- 56.00 + 57.32 + 620.24 + 4.79 = 738.35: 72%|███████▏ | 1470/2048 [18:18<07:01, 1.37it/s]
loss 1.87 accuracy 0.31 -- 56.76 + 56.54 + 504.27 + 4.82 = 622.38: 72%|███████▏ | 1470/2048 [18:19<07:01, 1.37it/s]
loss 1.87 accuracy 0.31 -- 56.76 + 56.54 + 504.27 + 4.82 = 622.38: 72%|███████▏ | 1471/2048 [18:19<06:47, 1.42it/s]
loss 1.71 accuracy 0.44 -- 56.16 + 57.25 + 616.81 + 4.78 = 735.01: 72%|███████▏ | 1471/2048 [18:20<06:47, 1.42it/s]
loss 1.71 accuracy 0.44 -- 56.16 + 57.25 + 616.81 + 4.78 = 735.01: 72%|███████▏ | 1472/2048 [18:20<06:56, 1.38it/s]
loss 1.61 accuracy 0.44 -- 56.72 + 56.77 + 504.25 + 4.79 = 622.53: 72%|███████▏ | 1472/2048 [18:20<06:56, 1.38it/s]
loss 1.61 accuracy 0.44 -- 56.72 + 56.77 + 504.25 + 4.79 = 622.53: 72%|███████▏ | 1473/2048 [18:20<06:48, 1.41it/s]
loss 1.57 accuracy 0.38 -- 56.53 + 167.05 + 501.91 + 4.79 = 730.28: 72%|███████▏ | 1473/2048 [18:21<06:48, 1.41it/s]
loss 1.57 accuracy 0.38 -- 56.53 + 167.05 + 501.91 + 4.79 = 730.28: 72%|███████▏ | 1474/2048 [18:21<06:56, 1.38it/s]
loss 1.85 accuracy 0.25 -- 56.14 + 56.63 + 499.52 + 4.76 = 617.05: 72%|███████▏ | 1474/2048 [18:22<06:56, 1.38it/s]
loss 1.85 accuracy 0.25 -- 56.14 + 56.63 + 499.52 + 4.76 = 617.05: 72%|███████▏ | 1475/2048 [18:22<06:41, 1.43it/s]
loss 1.85 accuracy 0.19 -- 56.97 + 56.42 + 498.90 + 4.78 = 617.07: 72%|███████▏ | 1475/2048 [18:23<06:41, 1.43it/s]
loss 1.85 accuracy 0.19 -- 56.97 + 56.42 + 498.90 + 4.78 = 617.07: 72%|███████▏ | 1476/2048 [18:23<06:49, 1.40it/s]
loss 1.80 accuracy 0.38 -- 56.09 + 57.51 + 495.44 + 4.77 = 613.81: 72%|███████▏ | 1476/2048 [18:23<06:49, 1.40it/s]
loss 1.80 accuracy 0.38 -- 56.09 + 57.51 + 495.44 + 4.77 = 613.81: 72%|███████▏ | 1477/2048 [18:23<06:35, 1.44it/s]
loss 1.53 accuracy 0.44 -- 158.27 + 57.14 + 489.68 + 4.77 = 709.85: 72%|███████▏ | 1477/2048 [18:24<06:35, 1.44it/s]
loss 1.53 accuracy 0.44 -- 158.27 + 57.14 + 489.68 + 4.77 = 709.85: 72%|███████▏ | 1478/2048 [18:24<06:42, 1.42it/s]
loss 1.92 accuracy 0.38 -- 56.01 + 166.73 + 503.56 + 4.77 = 731.08: 72%|███████▏ | 1478/2048 [18:25<06:42, 1.42it/s]
loss 1.92 accuracy 0.38 -- 56.01 + 166.73 + 503.56 + 4.77 = 731.08: 72%|███████▏ | 1479/2048 [18:25<06:50, 1.39it/s]
loss 1.81 accuracy 0.25 -- 56.42 + 56.47 + 497.54 + 4.77 = 615.21: 72%|███████▏ | 1479/2048 [18:25<06:50, 1.39it/s]
loss 1.81 accuracy 0.25 -- 56.42 + 56.47 + 497.54 + 4.77 = 615.21: 72%|███████▏ | 1480/2048 [18:25<06:36, 1.43it/s]
loss 1.47 accuracy 0.56 -- 162.87 + 57.09 + 496.74 + 4.77 = 721.48: 72%|███████▏ | 1480/2048 [18:26<06:36, 1.43it/s]
loss 1.47 accuracy 0.56 -- 162.87 + 57.09 + 496.74 + 4.77 = 721.48: 72%|███████▏ | 1481/2048 [18:26<06:44, 1.40it/s]
loss 1.61 accuracy 0.25 -- 55.99 + 57.21 + 620.59 + 4.78 = 738.56: 72%|███████▏ | 1481/2048 [18:27<06:44, 1.40it/s]
loss 1.61 accuracy 0.25 -- 55.99 + 57.21 + 620.59 + 4.78 = 738.56: 72%|███████▏ | 1482/2048 [18:27<06:52, 1.37it/s]
loss 1.55 accuracy 0.50 -- 57.13 + 56.77 + 506.70 + 4.78 = 625.38: 72%|███████▏ | 1482/2048 [18:27<06:52, 1.37it/s]
loss 1.55 accuracy 0.50 -- 57.13 + 56.77 + 506.70 + 4.78 = 625.38: 72%|███████▏ | 1483/2048 [18:27<06:38, 1.42it/s]
loss 2.01 accuracy 0.31 -- 56.13 + 57.72 + 617.59 + 4.79 = 736.23: 72%|███████▏ | 1483/2048 [18:28<06:38, 1.42it/s]
loss 2.01 accuracy 0.31 -- 56.13 + 57.72 + 617.59 + 4.79 = 736.23: 72%|███████▏ | 1484/2048 [18:28<06:47, 1.38it/s]
loss 1.89 accuracy 0.44 -- 57.22 + 57.30 + 503.17 + 4.78 = 622.47: 72%|███████▏ | 1484/2048 [18:29<06:47, 1.38it/s]
loss 1.89 accuracy 0.44 -- 57.22 + 57.30 + 503.17 + 4.78 = 622.47: 73%|███████▎ | 1485/2048 [18:29<06:34, 1.43it/s]
loss 2.18 accuracy 0.12 -- 56.33 + 166.48 + 503.02 + 4.78 = 730.60: 73%|███████▎ | 1485/2048 [18:30<06:34, 1.43it/s]
loss 2.18 accuracy 0.12 -- 56.33 + 166.48 + 503.02 + 4.78 = 730.60: 73%|███████▎ | 1486/2048 [18:30<06:43, 1.39it/s]
loss 1.49 accuracy 0.50 -- 56.24 + 56.31 + 500.04 + 4.78 = 617.37: 73%|███████▎ | 1486/2048 [18:30<06:43, 1.39it/s]
loss 1.49 accuracy 0.50 -- 56.24 + 56.31 + 500.04 + 4.78 = 617.37: 73%|███████▎ | 1487/2048 [18:30<06:30, 1.44it/s]
loss 2.22 accuracy 0.12 -- 56.83 + 56.49 + 497.87 + 4.79 = 615.97: 73%|███████▎ | 1487/2048 [18:31<06:30, 1.44it/s]
loss 2.22 accuracy 0.12 -- 56.83 + 56.49 + 497.87 + 4.79 = 615.97: 73%|███████▎ | 1488/2048 [18:31<06:38, 1.40it/s]
loss 1.81 accuracy 0.50 -- 56.02 + 57.27 + 494.49 + 4.83 = 612.60: 73%|███████▎ | 1488/2048 [18:32<06:38, 1.40it/s]
loss 1.81 accuracy 0.50 -- 56.02 + 57.27 + 494.49 + 4.83 = 612.60: 73%|███████▎ | 1489/2048 [18:32<06:26, 1.45it/s]
loss 1.67 accuracy 0.56 -- 157.26 + 56.92 + 492.08 + 4.78 = 711.03: 73%|███████▎ | 1489/2048 [18:32<06:26, 1.45it/s]
loss 1.67 accuracy 0.56 -- 157.26 + 56.92 + 492.08 + 4.78 = 711.03: 73%|███████▎ | 1490/2048 [18:32<06:33, 1.42it/s]
loss 2.06 accuracy 0.44 -- 55.87 + 166.66 + 501.10 + 4.79 = 728.43: 73%|███████▎ | 1490/2048 [18:33<06:33, 1.42it/s]
loss 2.06 accuracy 0.44 -- 55.87 + 166.66 + 501.10 + 4.79 = 728.43: 73%|███████▎ | 1491/2048 [18:33<06:41, 1.39it/s]
loss 2.49 accuracy 0.19 -- 56.66 + 56.41 + 498.10 + 4.76 = 615.92: 73%|███████▎ | 1491/2048 [18:34<06:41, 1.39it/s]
loss 2.49 accuracy 0.19 -- 56.66 + 56.41 + 498.10 + 4.76 = 615.92: 73%|███████▎ | 1492/2048 [18:34<06:27, 1.43it/s]
loss 1.88 accuracy 0.25 -- 163.00 + 57.11 + 497.13 + 4.78 = 722.01: 73%|███████▎ | 1492/2048 [18:35<06:27, 1.43it/s]
loss 1.88 accuracy 0.25 -- 163.00 + 57.11 + 497.13 + 4.78 = 722.01: 73%|███████▎ | 1493/2048 [18:35<06:35, 1.40it/s]
loss 1.58 accuracy 0.44 -- 56.13 + 57.22 + 619.77 + 4.80 = 737.93: 73%|███████▎ | 1493/2048 [18:35<06:35, 1.40it/s]
loss 1.58 accuracy 0.44 -- 56.13 + 57.22 + 619.77 + 4.80 = 737.93: 73%|███████▎ | 1494/2048 [18:35<06:43, 1.37it/s]
loss 1.30 accuracy 0.56 -- 56.98 + 56.54 + 505.20 + 4.79 = 623.52: 73%|███████▎ | 1494/2048 [18:36<06:43, 1.37it/s]
loss 1.30 accuracy 0.56 -- 56.98 + 56.54 + 505.20 + 4.79 = 623.52: 73%|███████▎ | 1495/2048 [18:36<06:29, 1.42it/s]
loss 1.61 accuracy 0.50 -- 56.27 + 57.31 + 617.13 + 4.78 = 735.50: 73%|███████▎ | 1495/2048 [18:37<06:29, 1.42it/s]
loss 1.61 accuracy 0.50 -- 56.27 + 57.31 + 617.13 + 4.78 = 735.50: 73%|███████▎ | 1496/2048 [18:37<06:38, 1.38it/s]
loss 1.66 accuracy 0.31 -- 57.02 + 56.99 + 504.50 + 4.78 = 623.28: 73%|███████▎ | 1496/2048 [18:37<06:38, 1.38it/s]
loss 1.66 accuracy 0.31 -- 57.02 + 56.99 + 504.50 + 4.78 = 623.28: 73%|███████▎ | 1497/2048 [18:37<06:31, 1.41it/s]
loss 1.58 accuracy 0.50 -- 56.22 + 166.58 + 501.88 + 4.77 = 729.45: 73%|███████▎ | 1497/2048 [18:38<06:31, 1.41it/s]
loss 1.58 accuracy 0.50 -- 56.22 + 166.58 + 501.88 + 4.77 = 729.45: 73%|███████▎ | 1498/2048 [18:38<06:38, 1.38it/s]
loss 2.05 accuracy 0.25 -- 56.23 + 56.63 + 499.90 + 4.76 = 617.52: 73%|███████▎ | 1498/2048 [18:39<06:38, 1.38it/s]
loss 2.05 accuracy 0.25 -- 56.23 + 56.63 + 499.90 + 4.76 = 617.52: 73%|███████▎ | 1499/2048 [18:39<06:24, 1.43it/s]
loss 1.83 accuracy 0.19 -- 56.86 + 56.29 + 498.22 + 4.78 = 616.14: 73%|███████▎ | 1499/2048 [18:40<06:24, 1.43it/s]
loss 1.83 accuracy 0.19 -- 56.86 + 56.29 + 498.22 + 4.78 = 616.14: 73%|███████▎ | 1500/2048 [18:40<06:32, 1.40it/s]
loss 1.86 accuracy 0.56 -- 55.86 + 57.21 + 495.40 + 4.80 = 613.27: 73%|███████▎ | 1500/2048 [18:40<06:32, 1.40it/s]
loss 1.86 accuracy 0.56 -- 55.86 + 57.21 + 495.40 + 4.80 = 613.27: 73%|███████▎ | 1501/2048 [18:40<06:19, 1.44it/s]
loss 2.50 accuracy 0.25 -- 157.96 + 57.17 + 488.15 + 4.78 = 708.05: 73%|███████▎ | 1501/2048 [18:41<06:19, 1.44it/s]
loss 2.50 accuracy 0.25 -- 157.96 + 57.17 + 488.15 + 4.78 = 708.05: 73%|███████▎ | 1502/2048 [18:41<06:25, 1.42it/s]
loss 1.70 accuracy 0.19 -- 55.68 + 166.04 + 501.59 + 4.78 = 728.09: 73%|███████▎ | 1502/2048 [18:42<06:25, 1.42it/s]
loss 1.70 accuracy 0.19 -- 55.68 + 166.04 + 501.59 + 4.78 = 728.09: 73%|███████▎ | 1503/2048 [18:42<06:32, 1.39it/s]
loss 2.46 accuracy 0.12 -- 56.48 + 56.46 + 496.68 + 4.80 = 614.41: 73%|███████▎ | 1503/2048 [18:42<06:32, 1.39it/s]
loss 2.46 accuracy 0.12 -- 56.48 + 56.46 + 496.68 + 4.80 = 614.41: 73%|███████▎ | 1504/2048 [18:42<06:18, 1.44it/s]
loss 2.05 accuracy 0.38 -- 163.05 + 56.88 + 496.50 + 4.78 = 721.21: 73%|███████▎ | 1504/2048 [18:43<06:18, 1.44it/s]
loss 2.05 accuracy 0.38 -- 163.05 + 56.88 + 496.50 + 4.78 = 721.21: 73%|███████▎ | 1505/2048 [18:43<06:26, 1.40it/s]
loss 1.77 accuracy 0.31 -- 56.05 + 57.37 + 620.68 + 4.80 = 738.90: 73%|███████▎ | 1505/2048 [18:44<06:26, 1.40it/s]
loss 1.77 accuracy 0.31 -- 56.05 + 57.37 + 620.68 + 4.80 = 738.90: 74%|███████▎ | 1506/2048 [18:44<06:34, 1.37it/s]
loss 1.57 accuracy 0.38 -- 56.65 + 56.44 + 507.47 + 4.78 = 625.33: 74%|███████▎ | 1506/2048 [18:45<06:34, 1.37it/s]
loss 1.57 accuracy 0.38 -- 56.65 + 56.44 + 507.47 + 4.78 = 625.33: 74%|███████▎ | 1507/2048 [18:45<06:21, 1.42it/s]
loss 1.76 accuracy 0.19 -- 56.18 + 58.43 + 617.34 + 4.80 = 736.75: 74%|███████▎ | 1507/2048 [18:45<06:21, 1.42it/s]
loss 1.76 accuracy 0.19 -- 56.18 + 58.43 + 617.34 + 4.80 = 736.75: 74%|███████▎ | 1508/2048 [18:45<06:30, 1.38it/s]
loss 1.83 accuracy 0.31 -- 56.69 + 56.58 + 502.28 + 4.79 = 620.34: 74%|███████▎ | 1508/2048 [18:46<06:30, 1.38it/s]
loss 1.83 accuracy 0.31 -- 56.69 + 56.58 + 502.28 + 4.79 = 620.34: 74%|███████▎ | 1509/2048 [18:46<06:17, 1.43it/s]
loss 1.23 accuracy 0.56 -- 56.00 + 167.02 + 501.65 + 4.78 = 729.45: 74%|███████▎ | 1509/2048 [18:47<06:17, 1.43it/s]
loss 1.23 accuracy 0.56 -- 56.00 + 167.02 + 501.65 + 4.78 = 729.45: 74%|███████▎ | 1510/2048 [18:47<06:25, 1.39it/s]
loss 1.94 accuracy 0.31 -- 56.10 + 56.53 + 499.61 + 4.79 = 617.03: 74%|███████▎ | 1510/2048 [18:47<06:25, 1.39it/s]
loss 1.94 accuracy 0.31 -- 56.10 + 56.53 + 499.61 + 4.79 = 617.03: 74%|███████▍ | 1511/2048 [18:47<06:13, 1.44it/s]
loss 1.48 accuracy 0.44 -- 56.84 + 56.27 + 498.74 + 4.78 = 616.63: 74%|███████▍ | 1511/2048 [18:48<06:13, 1.44it/s]
loss 1.48 accuracy 0.44 -- 56.84 + 56.27 + 498.74 + 4.78 = 616.63: 74%|███████▍ | 1512/2048 [18:48<06:21, 1.40it/s]
loss 2.18 accuracy 0.25 -- 56.16 + 57.38 + 496.34 + 4.82 = 614.69: 74%|███████▍ | 1512/2048 [18:49<06:21, 1.40it/s]
loss 2.18 accuracy 0.25 -- 56.16 + 57.38 + 496.34 + 4.82 = 614.69: 74%|███████▍ | 1513/2048 [18:49<06:09, 1.45it/s]
loss 1.80 accuracy 0.38 -- 157.41 + 56.95 + 489.89 + 4.77 = 709.03: 74%|███████▍ | 1513/2048 [18:49<06:09, 1.45it/s]
loss 1.80 accuracy 0.38 -- 157.41 + 56.95 + 489.89 + 4.77 = 709.03: 74%|███████▍ | 1514/2048 [18:49<06:16, 1.42it/s]
loss 1.79 accuracy 0.31 -- 55.96 + 166.42 + 501.70 + 4.79 = 728.88: 74%|███████▍ | 1514/2048 [18:50<06:16, 1.42it/s]
loss 1.79 accuracy 0.31 -- 55.96 + 166.42 + 501.70 + 4.79 = 728.88: 74%|███████▍ | 1515/2048 [18:50<06:23, 1.39it/s]
loss 2.34 accuracy 0.12 -- 56.71 + 56.50 + 496.97 + 4.78 = 614.96: 74%|███████▍ | 1515/2048 [18:51<06:23, 1.39it/s]
loss 2.34 accuracy 0.12 -- 56.71 + 56.50 + 496.97 + 4.78 = 614.96: 74%|███████▍ | 1516/2048 [18:51<06:10, 1.44it/s]
loss 2.30 accuracy 0.06 -- 162.62 + 56.78 + 496.92 + 4.77 = 721.10: 74%|███████▍ | 1516/2048 [18:52<06:10, 1.44it/s]
loss 2.30 accuracy 0.06 -- 162.62 + 56.78 + 496.92 + 4.77 = 721.10: 74%|███████▍ | 1517/2048 [18:52<06:18, 1.40it/s]
loss 1.86 accuracy 0.44 -- 56.19 + 57.05 + 623.85 + 4.87 = 741.97: 74%|███████▍ | 1517/2048 [18:52<06:18, 1.40it/s]
loss 1.86 accuracy 0.44 -- 56.19 + 57.05 + 623.85 + 4.87 = 741.97: 74%|███████▍ | 1518/2048 [18:52<06:26, 1.37it/s]
loss 1.93 accuracy 0.31 -- 57.44 + 56.81 + 505.77 + 4.78 = 624.80: 74%|███████▍ | 1518/2048 [18:53<06:26, 1.37it/s]
loss 1.93 accuracy 0.31 -- 57.44 + 56.81 + 505.77 + 4.78 = 624.80: 74%|███████▍ | 1519/2048 [18:53<06:13, 1.42it/s]
loss 1.99 accuracy 0.56 -- 56.20 + 57.24 + 616.11 + 4.77 = 734.32: 74%|███████▍ | 1519/2048 [18:54<06:13, 1.42it/s]
loss 1.99 accuracy 0.56 -- 56.20 + 57.24 + 616.11 + 4.77 = 734.32: 74%|███████▍ | 1520/2048 [18:54<06:21, 1.38it/s]
loss 1.64 accuracy 0.31 -- 56.94 + 56.76 + 504.02 + 4.78 = 622.50: 74%|███████▍ | 1520/2048 [18:54<06:21, 1.38it/s]
loss 1.64 accuracy 0.31 -- 56.94 + 56.76 + 504.02 + 4.78 = 622.50: 74%|███████▍ | 1521/2048 [18:54<06:09, 1.43it/s]
loss 1.76 accuracy 0.31 -- 56.47 + 166.21 + 503.64 + 4.78 = 731.10: 74%|███████▍ | 1521/2048 [18:55<06:09, 1.43it/s]
loss 1.76 accuracy 0.31 -- 56.47 + 166.21 + 503.64 + 4.78 = 731.10: 74%|███████▍ | 1522/2048 [18:55<06:17, 1.39it/s]
loss 1.49 accuracy 0.56 -- 56.33 + 56.34 + 498.96 + 4.77 = 616.39: 74%|███████▍ | 1522/2048 [18:56<06:17, 1.39it/s]
loss 1.49 accuracy 0.56 -- 56.33 + 56.34 + 498.96 + 4.77 = 616.39: 74%|███████▍ | 1523/2048 [18:56<06:05, 1.44it/s]
loss 2.63 accuracy 0.19 -- 56.68 + 56.55 + 499.75 + 4.78 = 617.76: 74%|███████▍ | 1523/2048 [18:57<06:05, 1.44it/s]
loss 2.63 accuracy 0.19 -- 56.68 + 56.55 + 499.75 + 4.78 = 617.76: 74%|███████▍ | 1524/2048 [18:57<06:13, 1.40it/s]
loss 1.77 accuracy 0.25 -- 55.80 + 57.22 + 494.14 + 4.78 = 611.94: 74%|███████▍ | 1524/2048 [18:57<06:13, 1.40it/s]
loss 1.77 accuracy 0.25 -- 55.80 + 57.22 + 494.14 + 4.78 = 611.94: 74%|███████▍ | 1525/2048 [18:57<06:01, 1.45it/s]
loss 1.64 accuracy 0.38 -- 157.43 + 56.73 + 488.55 + 4.78 = 707.49: 74%|███████▍ | 1525/2048 [18:58<06:01, 1.45it/s]
loss 1.64 accuracy 0.38 -- 157.43 + 56.73 + 488.55 + 4.78 = 707.49: 75%|███████▍ | 1526/2048 [18:58<06:07, 1.42it/s]
loss 2.20 accuracy 0.19 -- 55.84 + 166.38 + 501.13 + 4.77 = 728.12: 75%|███████▍ | 1526/2048 [18:59<06:07, 1.42it/s]
loss 2.20 accuracy 0.19 -- 55.84 + 166.38 + 501.13 + 4.77 = 728.12: 75%|███████▍ | 1527/2048 [18:59<06:14, 1.39it/s]
loss 1.83 accuracy 0.31 -- 56.61 + 56.41 + 498.22 + 4.79 = 616.04: 75%|███████▍ | 1527/2048 [18:59<06:14, 1.39it/s]
loss 1.83 accuracy 0.31 -- 56.61 + 56.41 + 498.22 + 4.79 = 616.04: 75%|███████▍ | 1528/2048 [18:59<06:02, 1.44it/s]
loss 1.47 accuracy 0.50 -- 163.17 + 57.04 + 496.56 + 4.76 = 721.53: 75%|███████▍ | 1528/2048 [19:00<06:02, 1.44it/s]
loss 1.47 accuracy 0.50 -- 163.17 + 57.04 + 496.56 + 4.76 = 721.53: 75%|███████▍ | 1529/2048 [19:00<06:09, 1.40it/s]
loss 1.56 accuracy 0.50 -- 56.48 + 57.57 + 621.19 + 4.79 = 740.03: 75%|███████▍ | 1529/2048 [19:01<06:09, 1.40it/s]
loss 1.56 accuracy 0.50 -- 56.48 + 57.57 + 621.19 + 4.79 = 740.03: 75%|███████▍ | 1530/2048 [19:01<06:17, 1.37it/s]
loss 1.33 accuracy 0.50 -- 56.80 + 56.36 + 504.84 + 4.80 = 622.80: 75%|███████▍ | 1530/2048 [19:02<06:17, 1.37it/s]
loss 1.33 accuracy 0.50 -- 56.80 + 56.36 + 504.84 + 4.80 = 622.80: 75%|███████▍ | 1531/2048 [19:02<06:04, 1.42it/s]
loss 1.89 accuracy 0.25 -- 56.44 + 57.55 + 618.01 + 4.81 = 736.81: 75%|███████▍ | 1531/2048 [19:02<06:04, 1.42it/s]
loss 1.89 accuracy 0.25 -- 56.44 + 57.55 + 618.01 + 4.81 = 736.81: 75%|███████▍ | 1532/2048 [19:02<06:12, 1.38it/s]
loss 1.53 accuracy 0.62 -- 57.02 + 56.71 + 504.95 + 4.86 = 623.54: 75%|███████▍ | 1532/2048 [19:03<06:12, 1.38it/s]
loss 1.53 accuracy 0.62 -- 57.02 + 56.71 + 504.95 + 4.86 = 623.54: 75%|███████▍ | 1533/2048 [19:03<06:00, 1.43it/s]
loss 2.03 accuracy 0.44 -- 56.48 + 166.88 + 501.89 + 4.79 = 730.04: 75%|███████▍ | 1533/2048 [19:04<06:00, 1.43it/s]
loss 2.03 accuracy 0.44 -- 56.48 + 166.88 + 501.89 + 4.79 = 730.04: 75%|███████▍ | 1534/2048 [19:04<06:08, 1.39it/s]
loss 2.01 accuracy 0.25 -- 56.15 + 56.41 + 503.02 + 4.77 = 620.34: 75%|███████▍ | 1534/2048 [19:04<06:08, 1.39it/s]
loss 2.01 accuracy 0.25 -- 56.15 + 56.41 + 503.02 + 4.77 = 620.34: 75%|███████▍ | 1535/2048 [19:04<05:57, 1.44it/s]
loss 1.89 accuracy 0.31 -- 56.85 + 56.46 + 498.40 + 4.78 = 616.49: 75%|███████▍ | 1535/2048 [19:05<05:57, 1.44it/s]
loss 1.89 accuracy 0.31 -- 56.85 + 56.46 + 498.40 + 4.78 = 616.49: 75%|███████▌ | 1536/2048 [19:05<06:05, 1.40it/s]
loss 1.68 accuracy 0.25 -- 56.04 + 57.27 + 500.10 + 4.78 = 618.19: 75%|███████▌ | 1536/2048 [19:06<06:05, 1.40it/s]
loss 1.68 accuracy 0.25 -- 56.04 + 57.27 + 500.10 + 4.78 = 618.19: 75%|███████▌ | 1537/2048 [19:06<05:54, 1.44it/s]
loss 1.78 accuracy 0.44 -- 157.58 + 56.97 + 491.09 + 4.77 = 710.41: 75%|███████▌ | 1537/2048 [19:06<05:54, 1.44it/s]
loss 1.78 accuracy 0.44 -- 157.58 + 56.97 + 491.09 + 4.77 = 710.41: 75%|███████▌ | 1538/2048 [19:06<06:00, 1.42it/s]
loss 2.10 accuracy 0.38 -- 55.86 + 166.23 + 501.18 + 4.77 = 728.04: 75%|███████▌ | 1538/2048 [19:07<06:00, 1.42it/s]
loss 2.10 accuracy 0.38 -- 55.86 + 166.23 + 501.18 + 4.77 = 728.04: 75%|███████▌ | 1539/2048 [19:07<06:07, 1.39it/s]
loss 2.30 accuracy 0.19 -- 56.46 + 56.48 + 496.79 + 4.79 = 614.53: 75%|███████▌ | 1539/2048 [19:08<06:07, 1.39it/s]
loss 2.30 accuracy 0.19 -- 56.46 + 56.48 + 496.79 + 4.79 = 614.53: 75%|███████▌ | 1540/2048 [19:08<05:54, 1.43it/s]
loss 1.92 accuracy 0.31 -- 162.12 + 56.94 + 497.96 + 4.77 = 721.80: 75%|███████▌ | 1540/2048 [19:09<05:54, 1.43it/s]
loss 1.92 accuracy 0.31 -- 162.12 + 56.94 + 497.96 + 4.77 = 721.80: 75%|███████▌ | 1541/2048 [19:09<06:01, 1.40it/s]
loss 2.12 accuracy 0.19 -- 56.16 + 57.28 + 619.77 + 4.78 = 737.99: 75%|███████▌ | 1541/2048 [19:09<06:01, 1.40it/s]
loss 2.12 accuracy 0.19 -- 56.16 + 57.28 + 619.77 + 4.78 = 737.99: 75%|███████▌ | 1542/2048 [19:09<06:08, 1.37it/s]
loss 1.83 accuracy 0.44 -- 56.64 + 56.41 + 504.67 + 4.82 = 622.54: 75%|███████▌ | 1542/2048 [19:10<06:08, 1.37it/s]
loss 1.83 accuracy 0.44 -- 56.64 + 56.41 + 504.67 + 4.82 = 622.54: 75%|███████▌ | 1543/2048 [19:10<05:55, 1.42it/s]
loss 1.48 accuracy 0.44 -- 56.28 + 57.50 + 616.67 + 4.78 = 735.23: 75%|███████▌ | 1543/2048 [19:11<05:55, 1.42it/s]
loss 1.48 accuracy 0.44 -- 56.28 + 57.50 + 616.67 + 4.78 = 735.23: 75%|███████▌ | 1544/2048 [19:11<06:03, 1.39it/s]
loss 1.71 accuracy 0.50 -- 56.56 + 56.49 + 501.78 + 4.76 = 619.58: 75%|███████▌ | 1544/2048 [19:11<06:03, 1.39it/s]
loss 1.71 accuracy 0.50 -- 56.56 + 56.49 + 501.78 + 4.76 = 619.58: 75%|███████▌ | 1545/2048 [19:11<05:51, 1.43it/s]
loss 1.72 accuracy 0.56 -- 56.22 + 166.31 + 502.26 + 4.80 = 729.58: 75%|███████▌ | 1545/2048 [19:12<05:51, 1.43it/s]
loss 1.72 accuracy 0.56 -- 56.22 + 166.31 + 502.26 + 4.80 = 729.58: 75%|███████▌ | 1546/2048 [19:12<05:59, 1.40it/s]
loss 1.47 accuracy 0.56 -- 56.40 + 56.79 + 500.48 + 4.77 = 618.43: 75%|███████▌ | 1546/2048 [19:13<05:59, 1.40it/s]
loss 1.47 accuracy 0.56 -- 56.40 + 56.79 + 500.48 + 4.77 = 618.43: 76%|███████▌ | 1547/2048 [19:13<05:48, 1.44it/s]
loss 1.88 accuracy 0.19 -- 56.73 + 56.39 + 498.32 + 4.78 = 616.22: 76%|███████▌ | 1547/2048 [19:14<05:48, 1.44it/s]
loss 1.88 accuracy 0.19 -- 56.73 + 56.39 + 498.32 + 4.78 = 616.22: 76%|███████▌ | 1548/2048 [19:14<05:55, 1.41it/s]
loss 1.80 accuracy 0.38 -- 56.14 + 57.17 + 496.50 + 4.78 = 614.59: 76%|███████▌ | 1548/2048 [19:14<05:55, 1.41it/s]
loss 1.80 accuracy 0.38 -- 56.14 + 57.17 + 496.50 + 4.78 = 614.59: 76%|███████▌ | 1549/2048 [19:14<05:44, 1.45it/s]
loss 1.86 accuracy 0.38 -- 157.46 + 56.99 + 489.75 + 4.77 = 708.97: 76%|███████▌ | 1549/2048 [19:15<05:44, 1.45it/s]
loss 1.86 accuracy 0.38 -- 157.46 + 56.99 + 489.75 + 4.77 = 708.97: 76%|███████▌ | 1550/2048 [19:15<05:50, 1.42it/s]
loss 1.71 accuracy 0.44 -- 55.82 + 166.37 + 502.64 + 4.77 = 729.60: 76%|███████▌ | 1550/2048 [19:16<05:50, 1.42it/s]
loss 1.71 accuracy 0.44 -- 55.82 + 166.37 + 502.64 + 4.77 = 729.60: 76%|███████▌ | 1551/2048 [19:16<05:57, 1.39it/s]
loss 1.63 accuracy 0.25 -- 56.68 + 56.26 + 499.53 + 4.78 = 617.24: 76%|███████▌ | 1551/2048 [19:16<05:57, 1.39it/s]
loss 1.63 accuracy 0.25 -- 56.68 + 56.26 + 499.53 + 4.78 = 617.24: 76%|███████▌ | 1552/2048 [19:16<05:45, 1.43it/s]
loss 1.65 accuracy 0.31 -- 162.87 + 56.93 + 498.05 + 4.82 = 722.67: 76%|███████▌ | 1552/2048 [19:17<05:45, 1.43it/s]
loss 1.65 accuracy 0.31 -- 162.87 + 56.93 + 498.05 + 4.82 = 722.67: 76%|███████▌ | 1553/2048 [19:17<05:53, 1.40it/s]
loss 1.90 accuracy 0.25 -- 56.16 + 57.48 + 621.48 + 4.81 = 739.93: 76%|███████▌ | 1553/2048 [19:18<05:53, 1.40it/s]
loss 1.90 accuracy 0.25 -- 56.16 + 57.48 + 621.48 + 4.81 = 739.93: 76%|███████▌ | 1554/2048 [19:18<06:00, 1.37it/s]
loss 1.92 accuracy 0.19 -- 56.79 + 56.95 + 506.86 + 4.80 = 625.40: 76%|███████▌ | 1554/2048 [19:19<06:00, 1.37it/s]
loss 1.92 accuracy 0.19 -- 56.79 + 56.95 + 506.86 + 4.80 = 625.40: 76%|███████▌ | 1555/2048 [19:19<05:48, 1.42it/s]
loss 2.55 accuracy 0.38 -- 56.38 + 57.50 + 616.09 + 4.79 = 734.75: 76%|███████▌ | 1555/2048 [19:19<05:48, 1.42it/s]
loss 2.55 accuracy 0.38 -- 56.38 + 57.50 + 616.09 + 4.79 = 734.75: 76%|███████▌ | 1556/2048 [19:19<05:55, 1.38it/s]
loss 1.91 accuracy 0.38 -- 56.68 + 56.57 + 501.45 + 4.78 = 619.48: 76%|███████▌ | 1556/2048 [19:20<05:55, 1.38it/s]
loss 1.91 accuracy 0.38 -- 56.68 + 56.57 + 501.45 + 4.78 = 619.48: 76%|███████▌ | 1557/2048 [19:20<05:43, 1.43it/s]
loss 1.79 accuracy 0.38 -- 55.98 + 166.51 + 502.93 + 4.78 = 730.19: 76%|███████▌ | 1557/2048 [19:21<05:43, 1.43it/s]
loss 1.79 accuracy 0.38 -- 55.98 + 166.51 + 502.93 + 4.78 = 730.19: 76%|███████▌ | 1558/2048 [19:21<05:51, 1.39it/s]
loss 1.70 accuracy 0.31 -- 56.14 + 56.16 + 498.61 + 4.77 = 615.67: 76%|███████▌ | 1558/2048 [19:21<05:51, 1.39it/s]
loss 1.70 accuracy 0.31 -- 56.14 + 56.16 + 498.61 + 4.77 = 615.67: 76%|███████▌ | 1559/2048 [19:21<05:39, 1.44it/s]
loss 1.58 accuracy 0.25 -- 56.53 + 56.36 + 497.77 + 4.79 = 615.45: 76%|███████▌ | 1559/2048 [19:22<05:39, 1.44it/s]
loss 1.58 accuracy 0.25 -- 56.53 + 56.36 + 497.77 + 4.79 = 615.45: 76%|███████▌ | 1560/2048 [19:22<05:46, 1.41it/s]
loss 2.09 accuracy 0.25 -- 56.15 + 57.06 + 496.49 + 4.78 = 614.48: 76%|███████▌ | 1560/2048 [19:23<05:46, 1.41it/s]
loss 2.09 accuracy 0.25 -- 56.15 + 57.06 + 496.49 + 4.78 = 614.48: 76%|███████▌ | 1561/2048 [19:23<05:36, 1.45it/s]
loss 1.81 accuracy 0.31 -- 157.15 + 56.88 + 488.44 + 4.80 = 707.27: 76%|███████▌ | 1561/2048 [19:23<05:36, 1.45it/s]
loss 1.81 accuracy 0.31 -- 157.15 + 56.88 + 488.44 + 4.80 = 707.27: 76%|███████▋ | 1562/2048 [19:23<05:41, 1.42it/s]
loss 1.62 accuracy 0.50 -- 56.04 + 166.19 + 502.49 + 4.79 = 729.51: 76%|███████▋ | 1562/2048 [19:24<05:41, 1.42it/s]
loss 1.62 accuracy 0.50 -- 56.04 + 166.19 + 502.49 + 4.79 = 729.51: 76%|███████▋ | 1563/2048 [19:24<05:48, 1.39it/s]
loss 1.56 accuracy 0.38 -- 56.82 + 56.92 + 499.35 + 4.78 = 617.88: 76%|███████▋ | 1563/2048 [19:25<05:48, 1.39it/s]
loss 1.56 accuracy 0.38 -- 56.82 + 56.92 + 499.35 + 4.78 = 617.88: 76%|███████▋ | 1564/2048 [19:25<05:37, 1.43it/s]
loss 2.05 accuracy 0.31 -- 162.41 + 57.45 + 496.37 + 4.92 = 721.16: 76%|███████▋ | 1564/2048 [19:26<05:37, 1.43it/s]
loss 2.05 accuracy 0.31 -- 162.41 + 57.45 + 496.37 + 4.92 = 721.16: 76%|███████▋ | 1565/2048 [19:26<05:44, 1.40it/s]
loss 1.73 accuracy 0.50 -- 56.31 + 57.09 + 621.36 + 4.78 = 739.54: 76%|███████▋ | 1565/2048 [19:26<05:44, 1.40it/s]
loss 1.73 accuracy 0.50 -- 56.31 + 57.09 + 621.36 + 4.78 = 739.54: 76%|███████▋ | 1566/2048 [19:26<05:51, 1.37it/s]
loss 2.24 accuracy 0.19 -- 56.68 + 56.47 + 504.54 + 4.77 = 622.47: 76%|███████▋ | 1566/2048 [19:27<05:51, 1.37it/s]
loss 2.24 accuracy 0.19 -- 56.68 + 56.47 + 504.54 + 4.77 = 622.47: 77%|███████▋ | 1567/2048 [19:27<05:39, 1.42it/s]
loss 1.42 accuracy 0.44 -- 55.97 + 57.23 + 615.03 + 4.78 = 733.01: 77%|███████▋ | 1567/2048 [19:28<05:39, 1.42it/s]
loss 1.42 accuracy 0.44 -- 55.97 + 57.23 + 615.03 + 4.78 = 733.01: 77%|███████▋ | 1568/2048 [19:28<05:46, 1.39it/s]
loss 1.92 accuracy 0.44 -- 56.47 + 56.36 + 502.83 + 4.77 = 620.43: 77%|███████▋ | 1568/2048 [19:28<05:46, 1.39it/s]
loss 1.92 accuracy 0.44 -- 56.47 + 56.36 + 502.83 + 4.77 = 620.43: 77%|███████▋ | 1569/2048 [19:28<05:34, 1.43it/s]
loss 2.27 accuracy 0.38 -- 56.19 + 165.75 + 502.30 + 4.78 = 729.02: 77%|███████▋ | 1569/2048 [19:29<05:34, 1.43it/s]
loss 2.27 accuracy 0.38 -- 56.19 + 165.75 + 502.30 + 4.78 = 729.02: 77%|███████▋ | 1570/2048 [19:29<05:42, 1.40it/s]
loss 1.91 accuracy 0.12 -- 56.32 + 56.45 + 499.35 + 4.76 = 616.89: 77%|███████▋ | 1570/2048 [19:30<05:42, 1.40it/s]
loss 1.91 accuracy 0.12 -- 56.32 + 56.45 + 499.35 + 4.76 = 616.89: 77%|███████▋ | 1571/2048 [19:30<05:31, 1.44it/s]
loss 2.22 accuracy 0.12 -- 56.44 + 56.41 + 497.82 + 4.76 = 615.44: 77%|███████▋ | 1571/2048 [19:31<05:31, 1.44it/s]
loss 2.22 accuracy 0.12 -- 56.44 + 56.41 + 497.82 + 4.76 = 615.44: 77%|███████▋ | 1572/2048 [19:31<05:38, 1.41it/s]
loss 1.99 accuracy 0.25 -- 55.89 + 57.46 + 495.08 + 4.77 = 613.21: 77%|███████▋ | 1572/2048 [19:31<05:38, 1.41it/s]
loss 1.99 accuracy 0.25 -- 55.89 + 57.46 + 495.08 + 4.77 = 613.21: 77%|███████▋ | 1573/2048 [19:31<05:27, 1.45it/s]
loss 1.57 accuracy 0.25 -- 156.76 + 57.11 + 488.99 + 4.76 = 707.62: 77%|███████▋ | 1573/2048 [19:32<05:27, 1.45it/s]
loss 1.57 accuracy 0.25 -- 156.76 + 57.11 + 488.99 + 4.76 = 707.62: 77%|███████▋ | 1574/2048 [19:32<05:33, 1.42it/s]
loss 1.76 accuracy 0.38 -- 55.63 + 166.53 + 502.25 + 4.79 = 729.20: 77%|███████▋ | 1574/2048 [19:33<05:33, 1.42it/s]
loss 1.76 accuracy 0.38 -- 55.63 + 166.53 + 502.25 + 4.79 = 729.20: 77%|███████▋ | 1575/2048 [19:33<05:40, 1.39it/s]
loss 1.70 accuracy 0.25 -- 56.45 + 56.81 + 496.75 + 4.76 = 614.77: 77%|███████▋ | 1575/2048 [19:33<05:40, 1.39it/s]
loss 1.70 accuracy 0.25 -- 56.45 + 56.81 + 496.75 + 4.76 = 614.77: 77%|███████▋ | 1576/2048 [19:33<05:28, 1.44it/s]
loss 1.93 accuracy 0.19 -- 162.46 + 56.71 + 496.66 + 4.77 = 720.60: 77%|███████▋ | 1576/2048 [19:34<05:28, 1.44it/s]
loss 1.93 accuracy 0.19 -- 162.46 + 56.71 + 496.66 + 4.77 = 720.60: 77%|███████▋ | 1577/2048 [19:34<05:35, 1.41it/s]
loss 1.53 accuracy 0.56 -- 56.05 + 57.30 + 620.57 + 4.78 = 738.70: 77%|███████▋ | 1577/2048 [19:35<05:35, 1.41it/s]
loss 1.53 accuracy 0.56 -- 56.05 + 57.30 + 620.57 + 4.78 = 738.70: 77%|███████▋ | 1578/2048 [19:35<05:42, 1.37it/s]
loss 1.83 accuracy 0.38 -- 56.62 + 56.51 + 504.72 + 4.78 = 622.63: 77%|███████▋ | 1578/2048 [19:36<05:42, 1.37it/s]
loss 1.83 accuracy 0.38 -- 56.62 + 56.51 + 504.72 + 4.78 = 622.63: 77%|███████▋ | 1579/2048 [19:36<05:30, 1.42it/s]
loss 1.68 accuracy 0.25 -- 55.97 + 57.18 + 616.35 + 4.79 = 734.29: 77%|███████▋ | 1579/2048 [19:36<05:30, 1.42it/s]
loss 1.68 accuracy 0.25 -- 55.97 + 57.18 + 616.35 + 4.79 = 734.29: 77%|███████▋ | 1580/2048 [19:36<05:37, 1.39it/s]
loss 1.60 accuracy 0.31 -- 56.91 + 56.93 + 502.29 + 4.79 = 620.92: 77%|███████▋ | 1580/2048 [19:37<05:37, 1.39it/s]
loss 1.60 accuracy 0.31 -- 56.91 + 56.93 + 502.29 + 4.79 = 620.92: 77%|███████▋ | 1581/2048 [19:37<05:26, 1.43it/s]
loss 1.93 accuracy 0.44 -- 56.36 + 166.22 + 502.40 + 4.78 = 729.75: 77%|███████▋ | 1581/2048 [19:38<05:26, 1.43it/s]
loss 1.93 accuracy 0.44 -- 56.36 + 166.22 + 502.40 + 4.78 = 729.75: 77%|███████▋ | 1582/2048 [19:38<05:33, 1.40it/s]
loss 1.85 accuracy 0.19 -- 56.15 + 56.46 + 499.43 + 4.80 = 616.84: 77%|███████▋ | 1582/2048 [19:38<05:33, 1.40it/s]
loss 1.85 accuracy 0.19 -- 56.15 + 56.46 + 499.43 + 4.80 = 616.84: 77%|███████▋ | 1583/2048 [19:38<05:23, 1.44it/s]
loss 2.07 accuracy 0.31 -- 56.63 + 56.86 + 500.14 + 4.79 = 618.43: 77%|███████▋ | 1583/2048 [19:39<05:23, 1.44it/s]
loss 2.07 accuracy 0.31 -- 56.63 + 56.86 + 500.14 + 4.79 = 618.43: 77%|███████▋ | 1584/2048 [19:39<05:30, 1.40it/s]
loss 1.45 accuracy 0.50 -- 55.92 + 57.08 + 496.01 + 4.80 = 613.81: 77%|███████▋ | 1584/2048 [19:40<05:30, 1.40it/s]
loss 1.45 accuracy 0.50 -- 55.92 + 57.08 + 496.01 + 4.80 = 613.81: 77%|███████▋ | 1585/2048 [19:40<05:24, 1.43it/s]
loss 1.70 accuracy 0.44 -- 157.17 + 56.87 + 490.97 + 4.75 = 709.77: 77%|███████▋ | 1585/2048 [19:41<05:24, 1.43it/s]
loss 1.70 accuracy 0.44 -- 157.17 + 56.87 + 490.97 + 4.75 = 709.77: 77%|███████▋ | 1586/2048 [19:41<05:28, 1.40it/s]
loss 2.05 accuracy 0.31 -- 56.06 + 166.51 + 501.51 + 4.78 = 728.85: 77%|███████▋ | 1586/2048 [19:41<05:28, 1.40it/s]
loss 2.05 accuracy 0.31 -- 56.06 + 166.51 + 501.51 + 4.78 = 728.85: 77%|███████▋ | 1587/2048 [19:41<05:34, 1.38it/s]
loss 1.58 accuracy 0.50 -- 56.60 + 56.37 + 499.02 + 4.78 = 616.77: 77%|███████▋ | 1587/2048 [19:42<05:34, 1.38it/s]
loss 1.58 accuracy 0.50 -- 56.60 + 56.37 + 499.02 + 4.78 = 616.77: 78%|███████▊ | 1588/2048 [19:42<05:22, 1.43it/s]
loss 1.90 accuracy 0.12 -- 162.81 + 57.03 + 497.35 + 4.80 = 721.99: 78%|███████▊ | 1588/2048 [19:43<05:22, 1.43it/s]
loss 1.90 accuracy 0.12 -- 162.81 + 57.03 + 497.35 + 4.80 = 721.99: 78%|███████▊ | 1589/2048 [19:43<05:28, 1.40it/s]
loss 1.62 accuracy 0.31 -- 56.22 + 57.34 + 619.69 + 4.77 = 738.01: 78%|███████▊ | 1589/2048 [19:43<05:28, 1.40it/s]
loss 1.62 accuracy 0.31 -- 56.22 + 57.34 + 619.69 + 4.77 = 738.01: 78%|███████▊ | 1590/2048 [19:43<05:34, 1.37it/s]
loss 1.94 accuracy 0.25 -- 56.70 + 56.68 + 505.75 + 4.78 = 623.90: 78%|███████▊ | 1590/2048 [19:44<05:34, 1.37it/s]
loss 1.94 accuracy 0.25 -- 56.70 + 56.68 + 505.75 + 4.78 = 623.90: 78%|███████▊ | 1591/2048 [19:44<05:22, 1.42it/s]
loss 1.60 accuracy 0.38 -- 56.47 + 57.40 + 615.43 + 4.84 = 734.14: 78%|███████▊ | 1591/2048 [19:45<05:22, 1.42it/s]
loss 1.60 accuracy 0.38 -- 56.47 + 57.40 + 615.43 + 4.84 = 734.14: 78%|███████▊ | 1592/2048 [19:45<05:29, 1.38it/s]
loss 1.79 accuracy 0.44 -- 56.66 + 56.79 + 503.91 + 4.78 = 622.14: 78%|███████▊ | 1592/2048 [19:46<05:29, 1.38it/s]
loss 1.79 accuracy 0.44 -- 56.66 + 56.79 + 503.91 + 4.78 = 622.14: 78%|███████▊ | 1593/2048 [19:46<05:23, 1.41it/s]
loss 2.14 accuracy 0.12 -- 56.48 + 166.17 + 501.80 + 4.78 = 729.24: 78%|███████▊ | 1593/2048 [19:46<05:23, 1.41it/s]
loss 2.14 accuracy 0.12 -- 56.48 + 166.17 + 501.80 + 4.78 = 729.24: 78%|███████▊ | 1594/2048 [19:46<05:29, 1.38it/s]
loss 2.12 accuracy 0.25 -- 56.10 + 56.34 + 498.63 + 4.77 = 615.85: 78%|███████▊ | 1594/2048 [19:47<05:29, 1.38it/s]
loss 2.12 accuracy 0.25 -- 56.10 + 56.34 + 498.63 + 4.77 = 615.85: 78%|███████▊ | 1595/2048 [19:47<05:17, 1.43it/s]
loss 1.78 accuracy 0.31 -- 56.65 + 56.31 + 499.04 + 4.79 = 616.79: 78%|███████▊ | 1595/2048 [19:48<05:17, 1.43it/s]
loss 1.78 accuracy 0.31 -- 56.65 + 56.31 + 499.04 + 4.79 = 616.79: 78%|███████▊ | 1596/2048 [19:48<05:23, 1.40it/s]
loss 1.98 accuracy 0.38 -- 56.04 + 57.62 + 496.33 + 4.78 = 614.78: 78%|███████▊ | 1596/2048 [19:48<05:23, 1.40it/s]
loss 1.98 accuracy 0.38 -- 56.04 + 57.62 + 496.33 + 4.78 = 614.78: 78%|███████▊ | 1597/2048 [19:48<05:12, 1.44it/s]
loss 2.13 accuracy 0.31 -- 157.08 + 56.91 + 488.65 + 4.77 = 707.40: 78%|███████▊ | 1597/2048 [19:49<05:12, 1.44it/s]
loss 2.13 accuracy 0.31 -- 157.08 + 56.91 + 488.65 + 4.77 = 707.40: 78%|███████▊ | 1598/2048 [19:49<05:17, 1.42it/s]
loss 1.96 accuracy 0.25 -- 55.78 + 166.65 + 501.76 + 4.79 = 728.98: 78%|███████▊ | 1598/2048 [19:50<05:17, 1.42it/s]
loss 1.96 accuracy 0.25 -- 55.78 + 166.65 + 501.76 + 4.79 = 728.98: 78%|███████▊ | 1599/2048 [19:50<05:23, 1.39it/s]
loss 1.54 accuracy 0.44 -- 56.82 + 56.90 + 497.97 + 4.79 = 616.47: 78%|███████▊ | 1599/2048 [19:50<05:23, 1.39it/s]
loss 1.54 accuracy 0.44 -- 56.82 + 56.90 + 497.97 + 4.79 = 616.47: 78%|███████▊ | 1600/2048 [19:50<05:12, 1.43it/s]
loss 1.84 accuracy 0.31 -- 162.13 + 57.11 + 496.49 + 4.77 = 720.49: 78%|███████▊ | 1600/2048 [19:51<05:12, 1.43it/s]
loss 1.84 accuracy 0.31 -- 162.13 + 57.11 + 496.49 + 4.77 = 720.49: 78%|███████▊ | 1601/2048 [19:51<05:23, 1.38it/s]
loss 1.86 accuracy 0.25 -- 55.99 + 57.60 + 621.08 + 4.77 = 739.44: 78%|███████▊ | 1601/2048 [19:52<05:23, 1.38it/s]
loss 1.86 accuracy 0.25 -- 55.99 + 57.60 + 621.08 + 4.77 = 739.44: 78%|███████▊ | 1602/2048 [19:52<05:28, 1.36it/s]
loss 2.26 accuracy 0.38 -- 56.68 + 56.69 + 506.93 + 4.80 = 625.09: 78%|███████▊ | 1602/2048 [19:53<05:28, 1.36it/s]
loss 2.26 accuracy 0.38 -- 56.68 + 56.69 + 506.93 + 4.80 = 625.09: 78%|███████▊ | 1603/2048 [19:53<05:16, 1.41it/s]
loss 1.28 accuracy 0.69 -- 56.03 + 57.42 + 615.63 + 4.77 = 733.86: 78%|███████▊ | 1603/2048 [19:53<05:16, 1.41it/s]
loss 1.28 accuracy 0.69 -- 56.03 + 57.42 + 615.63 + 4.77 = 733.86: 78%|███████▊ | 1604/2048 [19:53<05:22, 1.38it/s]
loss 1.95 accuracy 0.25 -- 56.74 + 56.68 + 502.69 + 4.75 = 620.87: 78%|███████▊ | 1604/2048 [19:54<05:22, 1.38it/s]
loss 1.95 accuracy 0.25 -- 56.74 + 56.68 + 502.69 + 4.75 = 620.87: 78%|███████▊ | 1605/2048 [19:54<05:11, 1.42it/s]
loss 1.99 accuracy 0.19 -- 56.44 + 165.92 + 500.30 + 4.78 = 727.43: 78%|███████▊ | 1605/2048 [19:55<05:11, 1.42it/s]
loss 1.99 accuracy 0.19 -- 56.44 + 165.92 + 500.30 + 4.78 = 727.43: 78%|███████▊ | 1606/2048 [19:55<05:17, 1.39it/s]
loss 1.80 accuracy 0.38 -- 56.00 + 56.35 + 498.68 + 4.78 = 615.81: 78%|███████▊ | 1606/2048 [19:55<05:17, 1.39it/s]
loss 1.80 accuracy 0.38 -- 56.00 + 56.35 + 498.68 + 4.78 = 615.81: 78%|███████▊ | 1607/2048 [19:55<05:06, 1.44it/s]
loss 1.72 accuracy 0.56 -- 56.50 + 56.47 + 498.02 + 4.78 = 615.77: 78%|███████▊ | 1607/2048 [19:56<05:06, 1.44it/s]
loss 1.72 accuracy 0.56 -- 56.50 + 56.47 + 498.02 + 4.78 = 615.77: 79%|███████▊ | 1608/2048 [19:56<05:17, 1.38it/s]
loss 1.49 accuracy 0.56 -- 56.30 + 57.81 + 495.62 + 4.77 = 614.51: 79%|███████▊ | 1608/2048 [19:57<05:17, 1.38it/s]
loss 1.49 accuracy 0.56 -- 56.30 + 57.81 + 495.62 + 4.77 = 614.51: 79%|███████▊ | 1609/2048 [19:57<05:06, 1.43it/s]
loss 1.75 accuracy 0.25 -- 157.02 + 56.76 + 488.55 + 4.81 = 707.15: 79%|███████▊ | 1609/2048 [19:58<05:06, 1.43it/s]
loss 1.75 accuracy 0.25 -- 157.02 + 56.76 + 488.55 + 4.81 = 707.15: 79%|███████▊ | 1610/2048 [19:58<05:10, 1.41it/s]
loss 1.58 accuracy 0.38 -- 55.73 + 166.16 + 501.80 + 4.77 = 728.45: 79%|███████▊ | 1610/2048 [19:58<05:10, 1.41it/s]
loss 1.58 accuracy 0.38 -- 55.73 + 166.16 + 501.80 + 4.77 = 728.45: 79%|███████▊ | 1611/2048 [19:58<05:15, 1.38it/s]
loss 1.55 accuracy 0.25 -- 56.68 + 56.34 + 498.76 + 4.80 = 616.59: 79%|███████▊ | 1611/2048 [19:59<05:15, 1.38it/s]
loss 1.55 accuracy 0.25 -- 56.68 + 56.34 + 498.76 + 4.80 = 616.59: 79%|███████▊ | 1612/2048 [19:59<05:04, 1.43it/s]
loss 1.81 accuracy 0.25 -- 162.06 + 57.42 + 496.43 + 4.77 = 720.68: 79%|███████▊ | 1612/2048 [20:00<05:04, 1.43it/s]
loss 1.81 accuracy 0.25 -- 162.06 + 57.42 + 496.43 + 4.77 = 720.68: 79%|███████▉ | 1613/2048 [20:00<05:10, 1.40it/s]
loss 2.45 accuracy 0.19 -- 56.18 + 57.45 + 622.21 + 4.78 = 740.62: 79%|███████▉ | 1613/2048 [20:01<05:10, 1.40it/s]
loss 2.45 accuracy 0.19 -- 56.18 + 57.45 + 622.21 + 4.78 = 740.62: 79%|███████▉ | 1614/2048 [20:01<05:16, 1.37it/s]
loss 1.71 accuracy 0.25 -- 56.90 + 56.58 + 506.51 + 4.79 = 624.76: 79%|███████▉ | 1614/2048 [20:01<05:16, 1.37it/s]
loss 1.71 accuracy 0.25 -- 56.90 + 56.58 + 506.51 + 4.79 = 624.76: 79%|███████▉ | 1615/2048 [20:01<05:10, 1.39it/s]
loss 1.60 accuracy 0.50 -- 56.02 + 57.52 + 616.04 + 4.78 = 734.36: 79%|███████▉ | 1615/2048 [20:02<05:10, 1.39it/s]
loss 1.60 accuracy 0.50 -- 56.02 + 57.52 + 616.04 + 4.78 = 734.36: 79%|███████▉ | 1616/2048 [20:02<05:15, 1.37it/s]
loss 1.96 accuracy 0.25 -- 56.51 + 56.54 + 502.37 + 4.78 = 620.21: 79%|███████▉ | 1616/2048 [20:03<05:15, 1.37it/s]
loss 1.96 accuracy 0.25 -- 56.51 + 56.54 + 502.37 + 4.78 = 620.21: 79%|███████▉ | 1617/2048 [20:03<05:04, 1.42it/s]
loss 1.71 accuracy 0.19 -- 55.99 + 165.98 + 500.94 + 4.77 = 727.68: 79%|███████▉ | 1617/2048 [20:03<05:04, 1.42it/s]
loss 1.71 accuracy 0.19 -- 55.99 + 165.98 + 500.94 + 4.77 = 727.68: 79%|███████▉ | 1618/2048 [20:03<05:09, 1.39it/s]
loss 1.55 accuracy 0.38 -- 55.92 + 56.44 + 498.24 + 4.78 = 615.38: 79%|███████▉ | 1618/2048 [20:04<05:09, 1.39it/s]
loss 1.55 accuracy 0.38 -- 55.92 + 56.44 + 498.24 + 4.78 = 615.38: 79%|███████▉ | 1619/2048 [20:04<04:58, 1.43it/s]
loss 1.73 accuracy 0.38 -- 56.64 + 56.31 + 498.47 + 4.78 = 616.20: 79%|███████▉ | 1619/2048 [20:05<04:58, 1.43it/s]
loss 1.73 accuracy 0.38 -- 56.64 + 56.31 + 498.47 + 4.78 = 616.20: 79%|███████▉ | 1620/2048 [20:05<05:05, 1.40it/s]
loss 1.27 accuracy 0.50 -- 56.02 + 57.63 + 494.58 + 4.78 = 613.02: 79%|███████▉ | 1620/2048 [20:05<05:05, 1.40it/s]
loss 1.27 accuracy 0.50 -- 56.02 + 57.63 + 494.58 + 4.78 = 613.02: 79%|███████▉ | 1621/2048 [20:05<04:55, 1.45it/s]
loss 1.54 accuracy 0.44 -- 156.57 + 56.68 + 488.88 + 4.76 = 706.89: 79%|███████▉ | 1621/2048 [20:06<04:55, 1.45it/s]
loss 1.54 accuracy 0.44 -- 156.57 + 56.68 + 488.88 + 4.76 = 706.89: 79%|███████▉ | 1622/2048 [20:06<04:59, 1.42it/s]
loss 1.86 accuracy 0.19 -- 55.88 + 166.46 + 502.34 + 4.79 = 729.48: 79%|███████▉ | 1622/2048 [20:07<04:59, 1.42it/s]
loss 1.86 accuracy 0.19 -- 55.88 + 166.46 + 502.34 + 4.79 = 729.48: 79%|███████▉ | 1623/2048 [20:07<05:05, 1.39it/s]
loss 1.64 accuracy 0.50 -- 56.66 + 56.44 + 497.66 + 4.78 = 615.54: 79%|███████▉ | 1623/2048 [20:08<05:05, 1.39it/s]
loss 1.64 accuracy 0.50 -- 56.66 + 56.44 + 497.66 + 4.78 = 615.54: 79%|███████▉ | 1624/2048 [20:08<04:55, 1.44it/s]
loss 1.69 accuracy 0.19 -- 162.70 + 57.08 + 497.73 + 4.80 = 722.33: 79%|███████▉ | 1624/2048 [20:08<04:55, 1.44it/s]
loss 1.69 accuracy 0.19 -- 162.70 + 57.08 + 497.73 + 4.80 = 722.33: 79%|███████▉ | 1625/2048 [20:08<05:01, 1.40it/s]
loss 1.59 accuracy 0.50 -- 56.23 + 57.87 + 619.65 + 4.79 = 738.54: 79%|███████▉ | 1625/2048 [20:09<05:01, 1.40it/s]
loss 1.59 accuracy 0.50 -- 56.23 + 57.87 + 619.65 + 4.79 = 738.54: 79%|███████▉ | 1626/2048 [20:09<05:07, 1.37it/s]
loss 1.84 accuracy 0.31 -- 56.67 + 56.92 + 504.03 + 4.78 = 622.40: 79%|███████▉ | 1626/2048 [20:10<05:07, 1.37it/s]
loss 1.84 accuracy 0.31 -- 56.67 + 56.92 + 504.03 + 4.78 = 622.40: 79%|███████▉ | 1627/2048 [20:10<04:56, 1.42it/s]
loss 1.55 accuracy 0.50 -- 56.16 + 57.11 + 616.03 + 4.80 = 734.10: 79%|███████▉ | 1627/2048 [20:10<04:56, 1.42it/s]
loss 1.55 accuracy 0.50 -- 56.16 + 57.11 + 616.03 + 4.80 = 734.10: 79%|███████▉ | 1628/2048 [20:10<05:03, 1.39it/s]
loss 1.49 accuracy 0.25 -- 56.87 + 56.49 + 501.89 + 4.78 = 620.03: 79%|███████▉ | 1628/2048 [20:11<05:03, 1.39it/s]
loss 1.49 accuracy 0.25 -- 56.87 + 56.49 + 501.89 + 4.78 = 620.03: 80%|███████▉ | 1629/2048 [20:11<04:53, 1.43it/s]
loss 2.06 accuracy 0.31 -- 56.08 + 167.37 + 501.28 + 4.78 = 729.51: 80%|███████▉ | 1629/2048 [20:12<04:53, 1.43it/s]
loss 2.06 accuracy 0.31 -- 56.08 + 167.37 + 501.28 + 4.78 = 729.51: 80%|███████▉ | 1630/2048 [20:12<04:59, 1.40it/s]
loss 1.83 accuracy 0.44 -- 56.05 + 56.42 + 499.94 + 4.79 = 617.19: 80%|███████▉ | 1630/2048 [20:13<04:59, 1.40it/s]
loss 1.83 accuracy 0.44 -- 56.05 + 56.42 + 499.94 + 4.79 = 617.19: 80%|███████▉ | 1631/2048 [20:13<04:49, 1.44it/s]
loss 2.01 accuracy 0.31 -- 56.54 + 56.47 + 497.81 + 4.77 = 615.59: 80%|███████▉ | 1631/2048 [20:13<04:49, 1.44it/s]
loss 2.01 accuracy 0.31 -- 56.54 + 56.47 + 497.81 + 4.77 = 615.59: 80%|███████▉ | 1632/2048 [20:13<04:55, 1.41it/s]
loss 2.11 accuracy 0.31 -- 55.86 + 57.27 + 494.79 + 4.79 = 612.72: 80%|███████▉ | 1632/2048 [20:14<04:55, 1.41it/s]
loss 2.11 accuracy 0.31 -- 55.86 + 57.27 + 494.79 + 4.79 = 612.72: 80%|███████▉ | 1633/2048 [20:14<04:46, 1.45it/s]
loss 1.66 accuracy 0.31 -- 157.53 + 56.70 + 488.54 + 4.80 = 707.58: 80%|███████▉ | 1633/2048 [20:15<04:46, 1.45it/s]
loss 1.66 accuracy 0.31 -- 157.53 + 56.70 + 488.54 + 4.80 = 707.58: 80%|███████▉ | 1634/2048 [20:15<04:51, 1.42it/s]
loss 1.70 accuracy 0.31 -- 55.59 + 165.84 + 499.97 + 4.78 = 726.18: 80%|███████▉ | 1634/2048 [20:15<04:51, 1.42it/s]
loss 1.70 accuracy 0.31 -- 55.59 + 165.84 + 499.97 + 4.78 = 726.18: 80%|███████▉ | 1635/2048 [20:15<04:56, 1.39it/s]
loss 1.27 accuracy 0.69 -- 56.43 + 56.56 + 497.48 + 4.79 = 615.26: 80%|███████▉ | 1635/2048 [20:16<04:56, 1.39it/s]
loss 1.27 accuracy 0.69 -- 56.43 + 56.56 + 497.48 + 4.79 = 615.26: 80%|███████▉ | 1636/2048 [20:16<04:46, 1.44it/s]
loss 1.83 accuracy 0.50 -- 162.07 + 57.72 + 497.80 + 4.77 = 722.36: 80%|███████▉ | 1636/2048 [20:17<04:46, 1.44it/s]
loss 1.83 accuracy 0.50 -- 162.07 + 57.72 + 497.80 + 4.77 = 722.36: 80%|███████▉ | 1637/2048 [20:17<04:52, 1.41it/s]
loss 1.43 accuracy 0.50 -- 56.03 + 57.06 + 619.95 + 4.83 = 737.86: 80%|███████▉ | 1637/2048 [20:18<04:52, 1.41it/s]
loss 1.43 accuracy 0.50 -- 56.03 + 57.06 + 619.95 + 4.83 = 737.86: 80%|███████▉ | 1638/2048 [20:18<04:58, 1.37it/s]
loss 1.72 accuracy 0.38 -- 56.75 + 56.24 + 505.05 + 4.77 = 622.81: 80%|███████▉ | 1638/2048 [20:18<04:58, 1.37it/s]
loss 1.72 accuracy 0.38 -- 56.75 + 56.24 + 505.05 + 4.77 = 622.81: 80%|████████ | 1639/2048 [20:18<04:52, 1.40it/s]
loss 2.23 accuracy 0.12 -- 56.11 + 57.29 + 615.28 + 4.78 = 733.45: 80%|████████ | 1639/2048 [20:19<04:52, 1.40it/s]
loss 2.23 accuracy 0.12 -- 56.11 + 57.29 + 615.28 + 4.78 = 733.45: 80%|████████ | 1640/2048 [20:19<04:57, 1.37it/s]
loss 1.89 accuracy 0.50 -- 56.75 + 56.24 + 502.17 + 4.78 = 619.94: 80%|████████ | 1640/2048 [20:20<04:57, 1.37it/s]
loss 1.89 accuracy 0.50 -- 56.75 + 56.24 + 502.17 + 4.78 = 619.94: 80%|████████ | 1641/2048 [20:20<04:46, 1.42it/s]
loss 2.06 accuracy 0.31 -- 56.18 + 166.01 + 502.95 + 4.78 = 729.92: 80%|████████ | 1641/2048 [20:20<04:46, 1.42it/s]
loss 2.06 accuracy 0.31 -- 56.18 + 166.01 + 502.95 + 4.78 = 729.92: 80%|████████ | 1642/2048 [20:20<04:52, 1.39it/s]
loss 2.20 accuracy 0.31 -- 56.37 + 56.41 + 499.39 + 4.78 = 616.95: 80%|████████ | 1642/2048 [20:21<04:52, 1.39it/s]
loss 2.20 accuracy 0.31 -- 56.37 + 56.41 + 499.39 + 4.78 = 616.95: 80%|████████ | 1643/2048 [20:21<04:42, 1.43it/s]
loss 2.14 accuracy 0.12 -- 56.78 + 56.48 + 498.18 + 4.79 = 616.23: 80%|████████ | 1643/2048 [20:22<04:42, 1.43it/s]
loss 2.14 accuracy 0.12 -- 56.78 + 56.48 + 498.18 + 4.79 = 616.23: 80%|████████ | 1644/2048 [20:22<04:48, 1.40it/s]
loss 2.09 accuracy 0.19 -- 55.96 + 57.04 + 495.33 + 4.77 = 613.09: 80%|████████ | 1644/2048 [20:22<04:48, 1.40it/s]
loss 2.09 accuracy 0.19 -- 55.96 + 57.04 + 495.33 + 4.77 = 613.09: 80%|████████ | 1645/2048 [20:22<04:38, 1.45it/s]
loss 1.74 accuracy 0.50 -- 157.65 + 56.86 + 489.11 + 4.78 = 708.39: 80%|████████ | 1645/2048 [20:23<04:38, 1.45it/s]
loss 1.74 accuracy 0.50 -- 157.65 + 56.86 + 489.11 + 4.78 = 708.39: 80%|████████ | 1646/2048 [20:23<04:43, 1.42it/s]
loss 1.89 accuracy 0.38 -- 55.83 + 166.38 + 501.34 + 4.83 = 728.37: 80%|████████ | 1646/2048 [20:24<04:43, 1.42it/s]
loss 1.89 accuracy 0.38 -- 55.83 + 166.38 + 501.34 + 4.83 = 728.37: 80%|████████ | 1647/2048 [20:24<04:52, 1.37it/s]
loss 1.62 accuracy 0.50 -- 56.58 + 56.21 + 499.44 + 4.78 = 617.02: 80%|████████ | 1647/2048 [20:25<04:52, 1.37it/s]
loss 1.62 accuracy 0.50 -- 56.58 + 56.21 + 499.44 + 4.78 = 617.02: 80%|████████ | 1648/2048 [20:25<04:41, 1.42it/s]
loss 1.67 accuracy 0.44 -- 162.08 + 57.19 + 497.17 + 4.77 = 721.21: 80%|████████ | 1648/2048 [20:25<04:41, 1.42it/s]
loss 1.67 accuracy 0.44 -- 162.08 + 57.19 + 497.17 + 4.77 = 721.21: 81%|████████ | 1649/2048 [20:25<04:46, 1.39it/s]
loss 1.61 accuracy 0.44 -- 56.37 + 57.22 + 620.64 + 4.78 = 739.01: 81%|████████ | 1649/2048 [20:26<04:46, 1.39it/s]
loss 1.61 accuracy 0.44 -- 56.37 + 57.22 + 620.64 + 4.78 = 739.01: 81%|████████ | 1650/2048 [20:26<04:51, 1.37it/s]
loss 1.84 accuracy 0.31 -- 56.91 + 56.30 + 504.28 + 4.76 = 622.25: 81%|████████ | 1650/2048 [20:27<04:51, 1.37it/s]
loss 1.84 accuracy 0.31 -- 56.91 + 56.30 + 504.28 + 4.76 = 622.25: 81%|████████ | 1651/2048 [20:27<04:40, 1.41it/s]
loss 1.51 accuracy 0.44 -- 56.07 + 57.69 + 615.21 + 4.77 = 733.73: 81%|████████ | 1651/2048 [20:28<04:40, 1.41it/s]
loss 1.51 accuracy 0.44 -- 56.07 + 57.69 + 615.21 + 4.77 = 733.73: 81%|████████ | 1652/2048 [20:28<04:46, 1.38it/s]
loss 1.56 accuracy 0.44 -- 56.69 + 56.32 + 503.10 + 4.79 = 620.91: 81%|████████ | 1652/2048 [20:28<04:46, 1.38it/s]
loss 1.56 accuracy 0.44 -- 56.69 + 56.32 + 503.10 + 4.79 = 620.91: 81%|████████ | 1653/2048 [20:28<04:36, 1.43it/s]
loss 1.87 accuracy 0.12 -- 56.61 + 166.75 + 501.04 + 4.76 = 729.15: 81%|████████ | 1653/2048 [20:29<04:36, 1.43it/s]
loss 1.87 accuracy 0.12 -- 56.61 + 166.75 + 501.04 + 4.76 = 729.15: 81%|████████ | 1654/2048 [20:29<04:46, 1.37it/s]
loss 1.59 accuracy 0.44 -- 56.26 + 56.88 + 499.06 + 4.78 = 616.98: 81%|████████ | 1654/2048 [20:30<04:46, 1.37it/s]
loss 1.59 accuracy 0.44 -- 56.26 + 56.88 + 499.06 + 4.78 = 616.98: 81%|████████ | 1655/2048 [20:30<04:36, 1.42it/s]
loss 1.35 accuracy 0.44 -- 56.68 + 56.15 + 498.51 + 4.76 = 616.12: 81%|████████ | 1655/2048 [20:30<04:36, 1.42it/s]
loss 1.35 accuracy 0.44 -- 56.68 + 56.15 + 498.51 + 4.76 = 616.12: 81%|████████ | 1656/2048 [20:30<04:41, 1.39it/s]
loss 1.90 accuracy 0.25 -- 56.09 + 57.27 + 495.18 + 4.84 = 613.38: 81%|████████ | 1656/2048 [20:31<04:41, 1.39it/s]
loss 1.90 accuracy 0.25 -- 56.09 + 57.27 + 495.18 + 4.84 = 613.38: 81%|████████ | 1657/2048 [20:31<04:31, 1.44it/s]
loss 1.28 accuracy 0.50 -- 157.88 + 56.67 + 490.38 + 4.77 = 709.71: 81%|████████ | 1657/2048 [20:32<04:31, 1.44it/s]
loss 1.28 accuracy 0.50 -- 157.88 + 56.67 + 490.38 + 4.77 = 709.71: 81%|████████ | 1658/2048 [20:32<04:35, 1.41it/s]
loss 1.90 accuracy 0.19 -- 55.79 + 166.29 + 504.09 + 4.78 = 730.96: 81%|████████ | 1658/2048 [20:33<04:35, 1.41it/s]
loss 1.90 accuracy 0.19 -- 55.79 + 166.29 + 504.09 + 4.78 = 730.96: 81%|████████ | 1659/2048 [20:33<04:40, 1.38it/s]
loss 2.00 accuracy 0.31 -- 56.69 + 56.61 + 498.23 + 4.77 = 616.31: 81%|████████ | 1659/2048 [20:33<04:40, 1.38it/s]
loss 2.00 accuracy 0.31 -- 56.69 + 56.61 + 498.23 + 4.77 = 616.31: 81%|████████ | 1660/2048 [20:33<04:31, 1.43it/s]
loss 1.41 accuracy 0.50 -- 162.12 + 57.72 + 497.00 + 4.77 = 721.60: 81%|████████ | 1660/2048 [20:34<04:31, 1.43it/s]
loss 1.41 accuracy 0.50 -- 162.12 + 57.72 + 497.00 + 4.77 = 721.60: 81%|████████ | 1661/2048 [20:34<04:36, 1.40it/s]
loss 2.63 accuracy 0.25 -- 56.12 + 57.75 + 619.83 + 4.78 = 738.48: 81%|████████ | 1661/2048 [20:35<04:36, 1.40it/s]
loss 2.63 accuracy 0.25 -- 56.12 + 57.75 + 619.83 + 4.78 = 738.48: 81%|████████ | 1662/2048 [20:35<04:45, 1.35it/s]
loss 1.65 accuracy 0.44 -- 56.67 + 56.33 + 505.09 + 4.77 = 622.86: 81%|████████ | 1662/2048 [20:35<04:45, 1.35it/s]
loss 1.65 accuracy 0.44 -- 56.67 + 56.33 + 505.09 + 4.77 = 622.86: 81%|████████ | 1663/2048 [20:35<04:34, 1.40it/s]
loss 1.96 accuracy 0.44 -- 56.17 + 57.25 + 614.80 + 4.78 = 733.00: 81%|████████ | 1663/2048 [20:36<04:34, 1.40it/s]
loss 1.96 accuracy 0.44 -- 56.17 + 57.25 + 614.80 + 4.78 = 733.00: 81%|████████▏ | 1664/2048 [20:36<04:39, 1.38it/s]
loss 1.50 accuracy 0.38 -- 57.11 + 57.06 + 502.72 + 4.76 = 621.65: 81%|████████▏ | 1664/2048 [20:37<04:39, 1.38it/s]
loss 1.50 accuracy 0.38 -- 57.11 + 57.06 + 502.72 + 4.76 = 621.65: 81%|████████▏ | 1665/2048 [20:37<04:29, 1.42it/s]
loss 1.85 accuracy 0.31 -- 56.40 + 166.17 + 502.35 + 4.79 = 729.71: 81%|████████▏ | 1665/2048 [20:38<04:29, 1.42it/s]
loss 1.85 accuracy 0.31 -- 56.40 + 166.17 + 502.35 + 4.79 = 729.71: 81%|████████▏ | 1666/2048 [20:38<04:34, 1.39it/s]
loss 2.34 accuracy 0.25 -- 56.06 + 56.37 + 499.75 + 4.77 = 616.96: 81%|████████▏ | 1666/2048 [20:38<04:34, 1.39it/s]
loss 2.34 accuracy 0.25 -- 56.06 + 56.37 + 499.75 + 4.77 = 616.96: 81%|████████▏ | 1667/2048 [20:38<04:25, 1.44it/s]
loss 1.54 accuracy 0.44 -- 56.63 + 56.49 + 497.81 + 4.77 = 615.70: 81%|████████▏ | 1667/2048 [20:39<04:25, 1.44it/s]
loss 1.54 accuracy 0.44 -- 56.63 + 56.49 + 497.81 + 4.77 = 615.70: 81%|████████▏ | 1668/2048 [20:39<04:30, 1.40it/s]
loss 1.67 accuracy 0.31 -- 55.99 + 57.48 + 494.44 + 4.77 = 612.69: 81%|████████▏ | 1668/2048 [20:40<04:30, 1.40it/s]
loss 1.67 accuracy 0.31 -- 55.99 + 57.48 + 494.44 + 4.77 = 612.69: 81%|████████▏ | 1669/2048 [20:40<04:25, 1.43it/s]
loss 2.53 accuracy 0.31 -- 157.38 + 56.86 + 489.40 + 4.78 = 708.43: 81%|████████▏ | 1669/2048 [20:40<04:25, 1.43it/s]
loss 2.53 accuracy 0.31 -- 157.38 + 56.86 + 489.40 + 4.78 = 708.43: 82%|████████▏ | 1670/2048 [20:40<04:28, 1.41it/s]
loss 2.16 accuracy 0.31 -- 55.85 + 166.09 + 499.90 + 4.79 = 726.63: 82%|████████▏ | 1670/2048 [20:41<04:28, 1.41it/s]
loss 2.16 accuracy 0.31 -- 55.85 + 166.09 + 499.90 + 4.79 = 726.63: 82%|████████▏ | 1671/2048 [20:41<04:32, 1.38it/s]
loss 1.87 accuracy 0.25 -- 56.76 + 56.52 + 496.67 + 4.79 = 614.74: 82%|████████▏ | 1671/2048 [20:42<04:32, 1.38it/s]
loss 1.87 accuracy 0.25 -- 56.76 + 56.52 + 496.67 + 4.79 = 614.74: 82%|████████▏ | 1672/2048 [20:42<04:22, 1.43it/s]
loss 1.43 accuracy 0.69 -- 162.36 + 57.05 + 496.15 + 4.77 = 720.34: 82%|████████▏ | 1672/2048 [20:42<04:22, 1.43it/s]
loss 1.43 accuracy 0.69 -- 162.36 + 57.05 + 496.15 + 4.77 = 720.34: 82%|████████▏ | 1673/2048 [20:42<04:27, 1.40it/s]
loss 1.78 accuracy 0.31 -- 56.14 + 57.07 + 619.95 + 4.78 = 737.94: 82%|████████▏ | 1673/2048 [20:43<04:27, 1.40it/s]
loss 1.78 accuracy 0.31 -- 56.14 + 57.07 + 619.95 + 4.78 = 737.94: 82%|████████▏ | 1674/2048 [20:43<04:32, 1.37it/s]
loss 1.90 accuracy 0.44 -- 56.71 + 56.50 + 504.29 + 4.77 = 622.27: 82%|████████▏ | 1674/2048 [20:44<04:32, 1.37it/s]
loss 1.90 accuracy 0.44 -- 56.71 + 56.50 + 504.29 + 4.77 = 622.27: 82%|████████▏ | 1675/2048 [20:44<04:23, 1.42it/s]
loss 1.62 accuracy 0.62 -- 55.94 + 57.19 + 615.51 + 4.76 = 733.40: 82%|████████▏ | 1675/2048 [20:45<04:23, 1.42it/s]
loss 1.62 accuracy 0.62 -- 55.94 + 57.19 + 615.51 + 4.76 = 733.40: 82%|████████▏ | 1676/2048 [20:45<04:28, 1.39it/s]
loss 1.69 accuracy 0.19 -- 56.56 + 56.49 + 502.05 + 4.77 = 619.87: 82%|████████▏ | 1676/2048 [20:45<04:28, 1.39it/s]
loss 1.69 accuracy 0.19 -- 56.56 + 56.49 + 502.05 + 4.77 = 619.87: 82%|████████▏ | 1677/2048 [20:45<04:23, 1.41it/s]
loss 1.47 accuracy 0.44 -- 56.33 + 166.24 + 501.76 + 4.78 = 729.11: 82%|████████▏ | 1677/2048 [20:46<04:23, 1.41it/s]
loss 1.47 accuracy 0.44 -- 56.33 + 166.24 + 501.76 + 4.78 = 729.11: 82%|████████▏ | 1678/2048 [20:46<04:27, 1.38it/s]
loss 1.60 accuracy 0.38 -- 56.14 + 56.54 + 499.03 + 4.78 = 616.49: 82%|████████▏ | 1678/2048 [20:47<04:27, 1.38it/s]
loss 1.60 accuracy 0.38 -- 56.14 + 56.54 + 499.03 + 4.78 = 616.49: 82%|████████▏ | 1679/2048 [20:47<04:18, 1.43it/s]
loss 1.68 accuracy 0.50 -- 56.71 + 56.66 + 498.86 + 4.77 = 617.01: 82%|████████▏ | 1679/2048 [20:47<04:18, 1.43it/s]
loss 1.68 accuracy 0.50 -- 56.71 + 56.66 + 498.86 + 4.77 = 617.01: 82%|████████▏ | 1680/2048 [20:47<04:23, 1.40it/s]
loss 1.70 accuracy 0.38 -- 55.93 + 57.27 + 494.98 + 4.76 = 612.94: 82%|████████▏ | 1680/2048 [20:48<04:23, 1.40it/s]
loss 1.70 accuracy 0.38 -- 55.93 + 57.27 + 494.98 + 4.76 = 612.94: 82%|████████▏ | 1681/2048 [20:48<04:14, 1.44it/s]
loss 1.63 accuracy 0.38 -- 158.07 + 57.16 + 489.60 + 4.77 = 709.60: 82%|████████▏ | 1681/2048 [20:49<04:14, 1.44it/s]
loss 1.63 accuracy 0.38 -- 158.07 + 57.16 + 489.60 + 4.77 = 709.60: 82%|████████▏ | 1682/2048 [20:49<04:18, 1.42it/s]
loss 1.81 accuracy 0.25 -- 55.60 + 166.02 + 501.86 + 4.77 = 728.25: 82%|████████▏ | 1682/2048 [20:50<04:18, 1.42it/s]
loss 1.81 accuracy 0.25 -- 55.60 + 166.02 + 501.86 + 4.77 = 728.25: 82%|████████▏ | 1683/2048 [20:50<04:23, 1.39it/s]
loss 1.52 accuracy 0.50 -- 57.02 + 56.57 + 498.86 + 4.79 = 617.23: 82%|████████▏ | 1683/2048 [20:50<04:23, 1.39it/s]
loss 1.52 accuracy 0.50 -- 57.02 + 56.57 + 498.86 + 4.79 = 617.23: 82%|████████▏ | 1684/2048 [20:50<04:13, 1.43it/s]
loss 1.96 accuracy 0.25 -- 162.31 + 57.15 + 496.60 + 4.79 = 720.86: 82%|████████▏ | 1684/2048 [20:51<04:13, 1.43it/s]
loss 1.96 accuracy 0.25 -- 162.31 + 57.15 + 496.60 + 4.79 = 720.86: 82%|████████▏ | 1685/2048 [20:51<04:18, 1.40it/s]
loss 1.84 accuracy 0.25 -- 56.21 + 57.46 + 618.48 + 4.77 = 736.93: 82%|████████▏ | 1685/2048 [20:52<04:18, 1.40it/s]
loss 1.84 accuracy 0.25 -- 56.21 + 57.46 + 618.48 + 4.77 = 736.93: 82%|████████▏ | 1686/2048 [20:52<04:23, 1.37it/s]
loss 1.84 accuracy 0.38 -- 56.68 + 56.25 + 505.59 + 4.80 = 623.33: 82%|████████▏ | 1686/2048 [20:52<04:23, 1.37it/s]
loss 1.84 accuracy 0.38 -- 56.68 + 56.25 + 505.59 + 4.80 = 623.33: 82%|████████▏ | 1687/2048 [20:52<04:14, 1.42it/s]
loss 1.18 accuracy 0.62 -- 56.60 + 57.65 + 617.13 + 4.78 = 736.16: 82%|████████▏ | 1687/2048 [20:53<04:14, 1.42it/s]
loss 1.18 accuracy 0.62 -- 56.60 + 57.65 + 617.13 + 4.78 = 736.16: 82%|████████▏ | 1688/2048 [20:53<04:20, 1.38it/s]
loss 1.81 accuracy 0.44 -- 56.69 + 56.33 + 500.99 + 4.78 = 618.80: 82%|████████▏ | 1688/2048 [20:54<04:20, 1.38it/s]
loss 1.81 accuracy 0.44 -- 56.69 + 56.33 + 500.99 + 4.78 = 618.80: 82%|████████▏ | 1689/2048 [20:54<04:11, 1.43it/s]
loss 1.88 accuracy 0.31 -- 56.33 + 166.18 + 500.63 + 4.79 = 727.92: 82%|████████▏ | 1689/2048 [20:55<04:11, 1.43it/s]
loss 1.88 accuracy 0.31 -- 56.33 + 166.18 + 500.63 + 4.79 = 727.92: 83%|████████▎ | 1690/2048 [20:55<04:16, 1.40it/s]
loss 2.12 accuracy 0.31 -- 55.99 + 56.74 + 499.08 + 4.79 = 616.60: 83%|████████▎ | 1690/2048 [20:55<04:16, 1.40it/s]
loss 2.12 accuracy 0.31 -- 55.99 + 56.74 + 499.08 + 4.79 = 616.60: 83%|████████▎ | 1691/2048 [20:55<04:07, 1.44it/s]
loss 2.04 accuracy 0.25 -- 56.69 + 56.45 + 497.90 + 4.80 = 615.84: 83%|████████▎ | 1691/2048 [20:56<04:07, 1.44it/s]
loss 2.04 accuracy 0.25 -- 56.69 + 56.45 + 497.90 + 4.80 = 615.84: 83%|████████▎ | 1692/2048 [20:56<04:16, 1.39it/s]
loss 1.82 accuracy 0.38 -- 56.07 + 57.70 + 496.92 + 4.76 = 615.44: 83%|████████▎ | 1692/2048 [20:57<04:16, 1.39it/s]
loss 1.82 accuracy 0.38 -- 56.07 + 57.70 + 496.92 + 4.76 = 615.44: 83%|████████▎ | 1693/2048 [20:57<04:07, 1.43it/s]
loss 1.92 accuracy 0.38 -- 157.39 + 56.82 + 489.23 + 4.76 = 708.20: 83%|████████▎ | 1693/2048 [20:57<04:07, 1.43it/s]
loss 1.92 accuracy 0.38 -- 157.39 + 56.82 + 489.23 + 4.76 = 708.20: 83%|████████▎ | 1694/2048 [20:57<04:10, 1.41it/s]
loss 1.55 accuracy 0.50 -- 55.87 + 166.41 + 501.82 + 4.78 = 728.88: 83%|████████▎ | 1694/2048 [20:58<04:10, 1.41it/s]
loss 1.55 accuracy 0.50 -- 55.87 + 166.41 + 501.82 + 4.78 = 728.88: 83%|████████▎ | 1695/2048 [20:58<04:15, 1.38it/s]
loss 2.16 accuracy 0.19 -- 56.73 + 56.57 + 497.65 + 4.77 = 615.72: 83%|████████▎ | 1695/2048 [20:59<04:15, 1.38it/s]
loss 2.16 accuracy 0.19 -- 56.73 + 56.57 + 497.65 + 4.77 = 615.72: 83%|████████▎ | 1696/2048 [20:59<04:05, 1.43it/s]
loss 1.74 accuracy 0.50 -- 162.44 + 57.01 + 497.21 + 4.77 = 721.43: 83%|████████▎ | 1696/2048 [21:00<04:05, 1.43it/s]
loss 1.74 accuracy 0.50 -- 162.44 + 57.01 + 497.21 + 4.77 = 721.43: 83%|████████▎ | 1697/2048 [21:00<04:10, 1.40it/s]
loss 1.65 accuracy 0.56 -- 56.00 + 57.03 + 621.60 + 4.79 = 739.42: 83%|████████▎ | 1697/2048 [21:00<04:10, 1.40it/s]
loss 1.65 accuracy 0.56 -- 56.00 + 57.03 + 621.60 + 4.79 = 739.42: 83%|████████▎ | 1698/2048 [21:00<04:15, 1.37it/s]
loss 1.42 accuracy 0.56 -- 57.16 + 57.53 + 507.31 + 4.82 = 626.82: 83%|████████▎ | 1698/2048 [21:01<04:15, 1.37it/s]
loss 1.42 accuracy 0.56 -- 57.16 + 57.53 + 507.31 + 4.82 = 626.82: 83%|████████▎ | 1699/2048 [21:01<04:06, 1.41it/s]
loss 2.24 accuracy 0.38 -- 56.45 + 57.70 + 615.81 + 4.78 = 734.74: 83%|████████▎ | 1699/2048 [21:02<04:06, 1.41it/s]
loss 2.24 accuracy 0.38 -- 56.45 + 57.70 + 615.81 + 4.78 = 734.74: 83%|████████▎ | 1700/2048 [21:02<04:11, 1.38it/s]
loss 1.80 accuracy 0.31 -- 56.80 + 56.25 + 503.12 + 4.81 = 620.99: 83%|████████▎ | 1700/2048 [21:02<04:11, 1.38it/s]
loss 1.80 accuracy 0.31 -- 56.80 + 56.25 + 503.12 + 4.81 = 620.99: 83%|████████▎ | 1701/2048 [21:02<04:06, 1.41it/s]
loss 1.78 accuracy 0.38 -- 56.03 + 166.56 + 502.28 + 4.79 = 729.65: 83%|████████▎ | 1701/2048 [21:03<04:06, 1.41it/s]
loss 1.78 accuracy 0.38 -- 56.03 + 166.56 + 502.28 + 4.79 = 729.65: 83%|████████▎ | 1702/2048 [21:03<04:10, 1.38it/s]
loss 1.58 accuracy 0.44 -- 56.31 + 56.54 + 499.13 + 4.77 = 616.74: 83%|████████▎ | 1702/2048 [21:04<04:10, 1.38it/s]
loss 1.58 accuracy 0.44 -- 56.31 + 56.54 + 499.13 + 4.77 = 616.74: 83%|████████▎ | 1703/2048 [21:04<04:01, 1.43it/s]
loss 1.47 accuracy 0.25 -- 56.85 + 56.50 + 499.93 + 4.78 = 618.06: 83%|████████▎ | 1703/2048 [21:05<04:01, 1.43it/s]
loss 1.47 accuracy 0.25 -- 56.85 + 56.50 + 499.93 + 4.78 = 618.06: 83%|████████▎ | 1704/2048 [21:05<04:06, 1.40it/s]
loss 1.45 accuracy 0.31 -- 56.08 + 57.12 + 495.78 + 4.77 = 613.74: 83%|████████▎ | 1704/2048 [21:05<04:06, 1.40it/s]
loss 1.45 accuracy 0.31 -- 56.08 + 57.12 + 495.78 + 4.77 = 613.74: 83%|████████▎ | 1705/2048 [21:05<03:57, 1.44it/s]
loss 1.65 accuracy 0.31 -- 157.54 + 56.97 + 488.78 + 4.77 = 708.06: 83%|████████▎ | 1705/2048 [21:06<03:57, 1.44it/s]
loss 1.65 accuracy 0.31 -- 157.54 + 56.97 + 488.78 + 4.77 = 708.06: 83%|████████▎ | 1706/2048 [21:06<04:01, 1.42it/s]
loss 1.74 accuracy 0.25 -- 55.78 + 166.09 + 500.25 + 4.76 = 726.88: 83%|████████▎ | 1706/2048 [21:07<04:01, 1.42it/s]
loss 1.74 accuracy 0.25 -- 55.78 + 166.09 + 500.25 + 4.76 = 726.88: 83%|████████▎ | 1707/2048 [21:07<04:05, 1.39it/s]
loss 1.64 accuracy 0.31 -- 56.36 + 56.33 + 497.17 + 4.78 = 614.64: 83%|████████▎ | 1707/2048 [21:07<04:05, 1.39it/s]
loss 1.64 accuracy 0.31 -- 56.36 + 56.33 + 497.17 + 4.78 = 614.64: 83%|████████▎ | 1708/2048 [21:07<03:56, 1.44it/s]
loss 2.26 accuracy 0.25 -- 162.40 + 57.09 + 495.53 + 4.76 = 719.77: 83%|████████▎ | 1708/2048 [21:08<03:56, 1.44it/s]
loss 2.26 accuracy 0.25 -- 162.40 + 57.09 + 495.53 + 4.76 = 719.77: 83%|████████▎ | 1709/2048 [21:08<04:01, 1.40it/s]
loss 1.72 accuracy 0.44 -- 56.06 + 57.79 + 621.54 + 4.79 = 740.17: 83%|████████▎ | 1709/2048 [21:09<04:01, 1.40it/s]
loss 1.72 accuracy 0.44 -- 56.06 + 57.79 + 621.54 + 4.79 = 740.17: 83%|████████▎ | 1710/2048 [21:09<04:06, 1.37it/s]
loss 1.99 accuracy 0.38 -- 56.92 + 56.42 + 506.27 + 4.78 = 624.39: 83%|████████▎ | 1710/2048 [21:09<04:06, 1.37it/s]
loss 1.99 accuracy 0.38 -- 56.92 + 56.42 + 506.27 + 4.78 = 624.39: 84%|████████▎ | 1711/2048 [21:09<03:57, 1.42it/s]
loss 1.80 accuracy 0.38 -- 56.42 + 57.30 + 615.65 + 4.78 = 734.15: 84%|████████▎ | 1711/2048 [21:10<03:57, 1.42it/s]
loss 1.80 accuracy 0.38 -- 56.42 + 57.30 + 615.65 + 4.78 = 734.15: 84%|████████▎ | 1712/2048 [21:10<04:02, 1.39it/s]
loss 1.51 accuracy 0.44 -- 56.88 + 56.35 + 502.47 + 4.78 = 620.48: 84%|████████▎ | 1712/2048 [21:11<04:02, 1.39it/s]
loss 1.51 accuracy 0.44 -- 56.88 + 56.35 + 502.47 + 4.78 = 620.48: 84%|████████▎ | 1713/2048 [21:11<03:54, 1.43it/s]
loss 2.39 accuracy 0.25 -- 56.14 + 165.70 + 501.58 + 4.77 = 728.19: 84%|████████▎ | 1713/2048 [21:12<03:54, 1.43it/s]
loss 2.39 accuracy 0.25 -- 56.14 + 165.70 + 501.58 + 4.77 = 728.19: 84%|████████▎ | 1714/2048 [21:12<03:59, 1.40it/s]
loss 1.76 accuracy 0.38 -- 56.15 + 56.55 + 499.44 + 4.81 = 616.96: 84%|████████▎ | 1714/2048 [21:12<03:59, 1.40it/s]
loss 1.76 accuracy 0.38 -- 56.15 + 56.55 + 499.44 + 4.81 = 616.96: 84%|████████▎ | 1715/2048 [21:12<03:51, 1.44it/s]
loss 1.53 accuracy 0.44 -- 56.99 + 56.55 + 498.75 + 4.77 = 617.05: 84%|████████▎ | 1715/2048 [21:13<03:51, 1.44it/s]
loss 1.53 accuracy 0.44 -- 56.99 + 56.55 + 498.75 + 4.77 = 617.05: 84%|████████▍ | 1716/2048 [21:13<03:56, 1.41it/s]
loss 2.31 accuracy 0.38 -- 56.31 + 57.14 + 495.76 + 4.77 = 613.98: 84%|████████▍ | 1716/2048 [21:14<03:56, 1.41it/s]
loss 2.31 accuracy 0.38 -- 56.31 + 57.14 + 495.76 + 4.77 = 613.98: 84%|████████▍ | 1717/2048 [21:14<03:52, 1.43it/s]
loss 1.67 accuracy 0.38 -- 157.71 + 56.78 + 488.99 + 4.77 = 708.26: 84%|████████▍ | 1717/2048 [21:14<03:52, 1.43it/s]
loss 1.67 accuracy 0.38 -- 157.71 + 56.78 + 488.99 + 4.77 = 708.26: 84%|████████▍ | 1718/2048 [21:14<03:54, 1.41it/s]
loss 1.68 accuracy 0.38 -- 55.78 + 166.39 + 501.19 + 4.76 = 728.12: 84%|████████▍ | 1718/2048 [21:15<03:54, 1.41it/s]
loss 1.68 accuracy 0.38 -- 55.78 + 166.39 + 501.19 + 4.76 = 728.12: 84%|████████▍ | 1719/2048 [21:15<03:58, 1.38it/s]
loss 2.04 accuracy 0.38 -- 56.82 + 56.77 + 496.93 + 4.78 = 615.30: 84%|████████▍ | 1719/2048 [21:16<03:58, 1.38it/s]
loss 2.04 accuracy 0.38 -- 56.82 + 56.77 + 496.93 + 4.78 = 615.30: 84%|████████▍ | 1720/2048 [21:16<03:49, 1.43it/s]
loss 1.64 accuracy 0.56 -- 162.34 + 57.11 + 497.96 + 4.78 = 722.20: 84%|████████▍ | 1720/2048 [21:17<03:49, 1.43it/s]
loss 1.64 accuracy 0.56 -- 162.34 + 57.11 + 497.96 + 4.78 = 722.20: 84%|████████▍ | 1721/2048 [21:17<03:53, 1.40it/s]
loss 1.69 accuracy 0.25 -- 55.97 + 56.90 + 620.84 + 4.78 = 738.48: 84%|████████▍ | 1721/2048 [21:17<03:53, 1.40it/s]
loss 1.69 accuracy 0.25 -- 55.97 + 56.90 + 620.84 + 4.78 = 738.48: 84%|████████▍ | 1722/2048 [21:17<03:57, 1.37it/s]
loss 1.77 accuracy 0.50 -- 56.74 + 56.48 + 506.19 + 4.76 = 624.18: 84%|████████▍ | 1722/2048 [21:18<03:57, 1.37it/s]
loss 1.77 accuracy 0.50 -- 56.74 + 56.48 + 506.19 + 4.76 = 624.18: 84%|████████▍ | 1723/2048 [21:18<03:49, 1.42it/s]
loss 1.74 accuracy 0.38 -- 56.14 + 56.88 + 614.59 + 4.78 = 732.39: 84%|████████▍ | 1723/2048 [21:19<03:49, 1.42it/s]
loss 1.74 accuracy 0.38 -- 56.14 + 56.88 + 614.59 + 4.78 = 732.39: 84%|████████▍ | 1724/2048 [21:19<03:53, 1.38it/s]
loss 1.48 accuracy 0.44 -- 56.99 + 56.51 + 502.36 + 4.77 = 620.64: 84%|████████▍ | 1724/2048 [21:19<03:53, 1.38it/s]
loss 1.48 accuracy 0.44 -- 56.99 + 56.51 + 502.36 + 4.77 = 620.64: 84%|████████▍ | 1725/2048 [21:19<03:49, 1.41it/s]
loss 1.28 accuracy 0.44 -- 56.43 + 165.84 + 500.94 + 4.83 = 728.04: 84%|████████▍ | 1725/2048 [21:20<03:49, 1.41it/s]
loss 1.28 accuracy 0.44 -- 56.43 + 165.84 + 500.94 + 4.83 = 728.04: 84%|████████▍ | 1726/2048 [21:20<03:53, 1.38it/s]
loss 1.83 accuracy 0.38 -- 56.46 + 56.64 + 498.92 + 4.76 = 616.79: 84%|████████▍ | 1726/2048 [21:21<03:53, 1.38it/s]
loss 1.83 accuracy 0.38 -- 56.46 + 56.64 + 498.92 + 4.76 = 616.79: 84%|████████▍ | 1727/2048 [21:21<03:44, 1.43it/s]
loss 1.39 accuracy 0.31 -- 56.66 + 56.41 + 497.09 + 4.76 = 614.92: 84%|████████▍ | 1727/2048 [21:22<03:44, 1.43it/s]
loss 1.39 accuracy 0.31 -- 56.66 + 56.41 + 497.09 + 4.76 = 614.92: 84%|████████▍ | 1728/2048 [21:22<03:48, 1.40it/s]
loss 1.67 accuracy 0.38 -- 55.71 + 57.13 + 494.83 + 4.78 = 612.45: 84%|████████▍ | 1728/2048 [21:22<03:48, 1.40it/s]
loss 1.67 accuracy 0.38 -- 55.71 + 57.13 + 494.83 + 4.78 = 612.45: 84%|████████▍ | 1729/2048 [21:22<03:40, 1.45it/s]
loss 1.87 accuracy 0.19 -- 157.57 + 56.85 + 488.65 + 4.77 = 707.85: 84%|████████▍ | 1729/2048 [21:23<03:40, 1.45it/s]
loss 1.87 accuracy 0.19 -- 157.57 + 56.85 + 488.65 + 4.77 = 707.85: 84%|████████▍ | 1730/2048 [21:23<03:44, 1.42it/s]
loss 1.80 accuracy 0.38 -- 55.73 + 166.30 + 500.98 + 4.77 = 727.78: 84%|████████▍ | 1730/2048 [21:24<03:44, 1.42it/s]
loss 1.80 accuracy 0.38 -- 55.73 + 166.30 + 500.98 + 4.77 = 727.78: 85%|████████▍ | 1731/2048 [21:24<03:48, 1.39it/s]
loss 1.56 accuracy 0.38 -- 56.66 + 56.50 + 498.53 + 4.77 = 616.46: 85%|████████▍ | 1731/2048 [21:24<03:48, 1.39it/s]
loss 1.56 accuracy 0.38 -- 56.66 + 56.50 + 498.53 + 4.77 = 616.46: 85%|████████▍ | 1732/2048 [21:24<03:40, 1.43it/s]
loss 1.37 accuracy 0.50 -- 162.44 + 56.84 + 495.60 + 4.78 = 719.65: 85%|████████▍ | 1732/2048 [21:25<03:40, 1.43it/s]
loss 1.37 accuracy 0.50 -- 162.44 + 56.84 + 495.60 + 4.78 = 719.65: 85%|████████▍ | 1733/2048 [21:25<03:47, 1.38it/s]
loss 1.28 accuracy 0.62 -- 55.94 + 56.98 + 620.00 + 4.77 = 737.69: 85%|████████▍ | 1733/2048 [21:26<03:47, 1.38it/s]
loss 1.28 accuracy 0.62 -- 55.94 + 56.98 + 620.00 + 4.77 = 737.69: 85%|████████▍ | 1734/2048 [21:26<03:50, 1.36it/s]
loss 1.32 accuracy 0.50 -- 56.56 + 56.54 + 505.89 + 4.77 = 623.76: 85%|████████▍ | 1734/2048 [21:27<03:50, 1.36it/s]
loss 1.32 accuracy 0.50 -- 56.56 + 56.54 + 505.89 + 4.77 = 623.76: 85%|████████▍ | 1735/2048 [21:27<03:42, 1.41it/s]
loss 2.11 accuracy 0.31 -- 56.23 + 57.14 + 615.42 + 4.84 = 733.63: 85%|████████▍ | 1735/2048 [21:27<03:42, 1.41it/s]
loss 2.11 accuracy 0.31 -- 56.23 + 57.14 + 615.42 + 4.84 = 733.63: 85%|████████▍ | 1736/2048 [21:27<03:46, 1.38it/s]
loss 1.91 accuracy 0.12 -- 56.76 + 56.48 + 501.98 + 4.80 = 620.01: 85%|████████▍ | 1736/2048 [21:28<03:46, 1.38it/s]
loss 1.91 accuracy 0.12 -- 56.76 + 56.48 + 501.98 + 4.80 = 620.01: 85%|████████▍ | 1737/2048 [21:28<03:38, 1.43it/s]
loss 1.43 accuracy 0.50 -- 56.32 + 166.48 + 503.45 + 4.78 = 731.04: 85%|████████▍ | 1737/2048 [21:29<03:38, 1.43it/s]
loss 1.43 accuracy 0.50 -- 56.32 + 166.48 + 503.45 + 4.78 = 731.04: 85%|████████▍ | 1738/2048 [21:29<03:42, 1.39it/s]
loss 1.85 accuracy 0.31 -- 56.16 + 56.40 + 499.39 + 4.80 = 616.74: 85%|████████▍ | 1738/2048 [21:29<03:42, 1.39it/s]
loss 1.85 accuracy 0.31 -- 56.16 + 56.40 + 499.39 + 4.80 = 616.74: 85%|████████▍ | 1739/2048 [21:29<03:35, 1.44it/s]
loss 2.12 accuracy 0.25 -- 56.77 + 56.44 + 497.55 + 4.78 = 615.53: 85%|████████▍ | 1739/2048 [21:30<03:35, 1.44it/s]
loss 2.12 accuracy 0.25 -- 56.77 + 56.44 + 497.55 + 4.78 = 615.53: 85%|████████▍ | 1740/2048 [21:30<03:39, 1.40it/s]
loss 1.74 accuracy 0.31 -- 55.86 + 57.27 + 495.41 + 4.77 = 613.31: 85%|████████▍ | 1740/2048 [21:31<03:39, 1.40it/s]
loss 1.74 accuracy 0.31 -- 55.86 + 57.27 + 495.41 + 4.77 = 613.31: 85%|████████▌ | 1741/2048 [21:31<03:32, 1.45it/s]
loss 1.93 accuracy 0.44 -- 157.65 + 56.88 + 489.27 + 4.78 = 708.58: 85%|████████▌ | 1741/2048 [21:32<03:32, 1.45it/s]
loss 1.93 accuracy 0.44 -- 157.65 + 56.88 + 489.27 + 4.78 = 708.58: 85%|████████▌ | 1742/2048 [21:32<03:35, 1.42it/s]
loss 2.33 accuracy 0.25 -- 56.05 + 166.49 + 501.01 + 4.77 = 728.32: 85%|████████▌ | 1742/2048 [21:32<03:35, 1.42it/s]
loss 2.33 accuracy 0.25 -- 56.05 + 166.49 + 501.01 + 4.77 = 728.32: 85%|████████▌ | 1743/2048 [21:32<03:39, 1.39it/s]
loss 2.04 accuracy 0.19 -- 56.75 + 56.83 + 498.28 + 4.78 = 616.64: 85%|████████▌ | 1743/2048 [21:33<03:39, 1.39it/s]
loss 2.04 accuracy 0.19 -- 56.75 + 56.83 + 498.28 + 4.78 = 616.64: 85%|████████▌ | 1744/2048 [21:33<03:31, 1.44it/s]
loss 1.99 accuracy 0.25 -- 162.88 + 57.01 + 496.24 + 4.78 = 720.91: 85%|████████▌ | 1744/2048 [21:34<03:31, 1.44it/s]
loss 1.99 accuracy 0.25 -- 162.88 + 57.01 + 496.24 + 4.78 = 720.91: 85%|████████▌ | 1745/2048 [21:34<03:35, 1.40it/s]
loss 1.86 accuracy 0.25 -- 56.09 + 57.31 + 619.76 + 4.80 = 737.97: 85%|████████▌ | 1745/2048 [21:34<03:35, 1.40it/s]
loss 1.86 accuracy 0.25 -- 56.09 + 57.31 + 619.76 + 4.80 = 737.97: 85%|████████▌ | 1746/2048 [21:34<03:39, 1.37it/s]
loss 1.47 accuracy 0.44 -- 56.79 + 56.53 + 503.49 + 4.78 = 621.59: 85%|████████▌ | 1746/2048 [21:35<03:39, 1.37it/s]
loss 1.47 accuracy 0.44 -- 56.79 + 56.53 + 503.49 + 4.78 = 621.59: 85%|████████▌ | 1747/2048 [21:35<03:31, 1.42it/s]
loss 1.43 accuracy 0.38 -- 55.78 + 57.14 + 615.07 + 4.79 = 732.77: 85%|████████▌ | 1747/2048 [21:36<03:31, 1.42it/s]
loss 1.43 accuracy 0.38 -- 55.78 + 57.14 + 615.07 + 4.79 = 732.77: 85%|████████▌ | 1748/2048 [21:36<03:36, 1.39it/s]
loss 2.18 accuracy 0.31 -- 56.80 + 56.35 + 503.25 + 4.77 = 621.17: 85%|████████▌ | 1748/2048 [21:36<03:36, 1.39it/s]
loss 2.18 accuracy 0.31 -- 56.80 + 56.35 + 503.25 + 4.77 = 621.17: 85%|████████▌ | 1749/2048 [21:36<03:29, 1.43it/s]
loss 2.23 accuracy 0.31 -- 56.03 + 165.88 + 501.06 + 4.79 = 727.75: 85%|████████▌ | 1749/2048 [21:37<03:29, 1.43it/s]
loss 2.23 accuracy 0.31 -- 56.03 + 165.88 + 501.06 + 4.79 = 727.75: 85%|████████▌ | 1750/2048 [21:37<03:33, 1.40it/s]
loss 1.77 accuracy 0.25 -- 56.07 + 56.45 + 498.33 + 4.77 = 615.63: 85%|████████▌ | 1750/2048 [21:38<03:33, 1.40it/s]
loss 1.77 accuracy 0.25 -- 56.07 + 56.45 + 498.33 + 4.77 = 615.63: 85%|████████▌ | 1751/2048 [21:38<03:26, 1.44it/s]
loss 2.52 accuracy 0.38 -- 56.89 + 56.51 + 498.68 + 4.77 = 616.85: 85%|████████▌ | 1751/2048 [21:39<03:26, 1.44it/s]
loss 2.52 accuracy 0.38 -- 56.89 + 56.51 + 498.68 + 4.77 = 616.85: 86%|████████▌ | 1752/2048 [21:39<03:30, 1.41it/s]
loss 1.58 accuracy 0.38 -- 55.84 + 57.02 + 495.04 + 4.77 = 612.68: 86%|████████▌ | 1752/2048 [21:39<03:30, 1.41it/s]
loss 1.58 accuracy 0.38 -- 55.84 + 57.02 + 495.04 + 4.77 = 612.68: 86%|████████▌ | 1753/2048 [21:39<03:23, 1.45it/s]
loss 1.76 accuracy 0.25 -- 157.70 + 56.99 + 489.21 + 4.77 = 708.67: 86%|████████▌ | 1753/2048 [21:40<03:23, 1.45it/s]
loss 1.76 accuracy 0.25 -- 157.70 + 56.99 + 489.21 + 4.77 = 708.67: 86%|████████▌ | 1754/2048 [21:40<03:26, 1.42it/s]
loss 1.59 accuracy 0.38 -- 55.96 + 166.35 + 502.01 + 4.78 = 729.10: 86%|████████▌ | 1754/2048 [21:41<03:26, 1.42it/s]
loss 1.59 accuracy 0.38 -- 55.96 + 166.35 + 502.01 + 4.78 = 729.10: 86%|████████▌ | 1755/2048 [21:41<03:30, 1.39it/s]
loss 1.63 accuracy 0.38 -- 56.50 + 56.50 + 496.74 + 4.80 = 614.55: 86%|████████▌ | 1755/2048 [21:41<03:30, 1.39it/s]
loss 1.63 accuracy 0.38 -- 56.50 + 56.50 + 496.74 + 4.80 = 614.55: 86%|████████▌ | 1756/2048 [21:41<03:23, 1.44it/s]
loss 1.90 accuracy 0.12 -- 162.65 + 57.22 + 496.02 + 4.77 = 720.66: 86%|████████▌ | 1756/2048 [21:42<03:23, 1.44it/s]
loss 1.90 accuracy 0.12 -- 162.65 + 57.22 + 496.02 + 4.77 = 720.66: 86%|████████▌ | 1757/2048 [21:42<03:27, 1.41it/s]
loss 2.66 accuracy 0.25 -- 55.71 + 56.83 + 619.25 + 4.78 = 736.57: 86%|████████▌ | 1757/2048 [21:43<03:27, 1.41it/s]
loss 2.66 accuracy 0.25 -- 55.71 + 56.83 + 619.25 + 4.78 = 736.57: 86%|████████▌ | 1758/2048 [21:43<03:30, 1.38it/s]
loss 1.98 accuracy 0.38 -- 56.87 + 56.49 + 505.05 + 4.77 = 623.18: 86%|████████▌ | 1758/2048 [21:44<03:30, 1.38it/s]
loss 1.98 accuracy 0.38 -- 56.87 + 56.49 + 505.05 + 4.77 = 623.18: 86%|████████▌ | 1759/2048 [21:44<03:23, 1.42it/s]
loss 1.70 accuracy 0.44 -- 55.94 + 57.23 + 615.31 + 4.78 = 733.25: 86%|████████▌ | 1759/2048 [21:44<03:23, 1.42it/s]
loss 1.70 accuracy 0.44 -- 55.94 + 57.23 + 615.31 + 4.78 = 733.25: 86%|████████▌ | 1760/2048 [21:44<03:27, 1.39it/s]
loss 1.60 accuracy 0.50 -- 57.12 + 56.75 + 501.41 + 4.77 = 620.06: 86%|████████▌ | 1760/2048 [21:45<03:27, 1.39it/s]
loss 1.60 accuracy 0.50 -- 57.12 + 56.75 + 501.41 + 4.77 = 620.06: 86%|████████▌ | 1761/2048 [21:45<03:20, 1.43it/s]
loss 1.77 accuracy 0.31 -- 56.17 + 166.09 + 501.96 + 4.77 = 729.00: 86%|████████▌ | 1761/2048 [21:46<03:20, 1.43it/s]
loss 1.77 accuracy 0.31 -- 56.17 + 166.09 + 501.96 + 4.77 = 729.00: 86%|████████▌ | 1762/2048 [21:46<03:24, 1.40it/s]
loss 2.09 accuracy 0.25 -- 56.09 + 55.95 + 498.50 + 4.76 = 615.31: 86%|████████▌ | 1762/2048 [21:46<03:24, 1.40it/s]
loss 2.09 accuracy 0.25 -- 56.09 + 55.95 + 498.50 + 4.76 = 615.31: 86%|████████▌ | 1763/2048 [21:46<03:17, 1.44it/s]
loss 1.53 accuracy 0.38 -- 56.50 + 56.44 + 496.17 + 4.77 = 613.87: 86%|████████▌ | 1763/2048 [21:47<03:17, 1.44it/s]
loss 1.53 accuracy 0.38 -- 56.50 + 56.44 + 496.17 + 4.77 = 613.87: 86%|████████▌ | 1764/2048 [21:47<03:21, 1.41it/s]
loss 1.52 accuracy 0.50 -- 56.02 + 57.41 + 494.65 + 4.77 = 612.85: 86%|████████▌ | 1764/2048 [21:48<03:21, 1.41it/s]
loss 1.52 accuracy 0.50 -- 56.02 + 57.41 + 494.65 + 4.77 = 612.85: 86%|████████▌ | 1765/2048 [21:48<03:14, 1.45it/s]
loss 1.97 accuracy 0.19 -- 157.19 + 56.67 + 490.84 + 4.77 = 709.47: 86%|████████▌ | 1765/2048 [21:48<03:14, 1.45it/s]
loss 1.97 accuracy 0.19 -- 157.19 + 56.67 + 490.84 + 4.77 = 709.47: 86%|████████▌ | 1766/2048 [21:48<03:18, 1.42it/s]
loss 1.67 accuracy 0.56 -- 55.98 + 166.36 + 500.33 + 4.77 = 727.44: 86%|████████▌ | 1766/2048 [21:49<03:18, 1.42it/s]
loss 1.67 accuracy 0.56 -- 55.98 + 166.36 + 500.33 + 4.77 = 727.44: 86%|████████▋ | 1767/2048 [21:49<03:21, 1.39it/s]
loss 1.28 accuracy 0.62 -- 56.44 + 56.59 + 497.35 + 4.77 = 615.16: 86%|████████▋ | 1767/2048 [21:50<03:21, 1.39it/s]
loss 1.28 accuracy 0.62 -- 56.44 + 56.59 + 497.35 + 4.77 = 615.16: 86%|████████▋ | 1768/2048 [21:50<03:14, 1.44it/s]
loss 1.53 accuracy 0.38 -- 162.07 + 56.99 + 496.86 + 4.76 = 720.68: 86%|████████▋ | 1768/2048 [21:51<03:14, 1.44it/s]
loss 1.53 accuracy 0.38 -- 162.07 + 56.99 + 496.86 + 4.76 = 720.68: 86%|████████▋ | 1769/2048 [21:51<03:18, 1.41it/s]
loss 1.74 accuracy 0.31 -- 56.03 + 57.10 + 619.37 + 4.78 = 737.29: 86%|████████▋ | 1769/2048 [21:51<03:18, 1.41it/s]
loss 1.74 accuracy 0.31 -- 56.03 + 57.10 + 619.37 + 4.78 = 737.29: 86%|████████▋ | 1770/2048 [21:51<03:25, 1.36it/s]
loss 1.41 accuracy 0.38 -- 56.70 + 56.59 + 503.63 + 4.78 = 621.70: 86%|████████▋ | 1770/2048 [21:52<03:25, 1.36it/s]
loss 1.41 accuracy 0.38 -- 56.70 + 56.59 + 503.63 + 4.78 = 621.70: 86%|████████▋ | 1771/2048 [21:52<03:16, 1.41it/s]
loss 2.15 accuracy 0.38 -- 56.34 + 57.75 + 616.42 + 4.78 = 735.29: 86%|████████▋ | 1771/2048 [21:53<03:16, 1.41it/s]
loss 2.15 accuracy 0.38 -- 56.34 + 57.75 + 616.42 + 4.78 = 735.29: 87%|████████▋ | 1772/2048 [21:53<03:20, 1.38it/s]
loss 2.24 accuracy 0.06 -- 56.61 + 56.63 + 502.19 + 4.78 = 620.21: 87%|████████▋ | 1772/2048 [21:53<03:20, 1.38it/s]
loss 2.24 accuracy 0.06 -- 56.61 + 56.63 + 502.19 + 4.78 = 620.21: 87%|████████▋ | 1773/2048 [21:53<03:13, 1.42it/s]
loss 2.85 accuracy 0.12 -- 56.25 + 166.58 + 501.29 + 4.77 = 728.89: 87%|████████▋ | 1773/2048 [21:54<03:13, 1.42it/s]
loss 2.85 accuracy 0.12 -- 56.25 + 166.58 + 501.29 + 4.77 = 728.89: 87%|████████▋ | 1774/2048 [21:54<03:16, 1.39it/s]
loss 1.66 accuracy 0.38 -- 55.78 + 56.16 + 499.36 + 4.76 = 616.06: 87%|████████▋ | 1774/2048 [21:55<03:16, 1.39it/s]
loss 1.66 accuracy 0.38 -- 55.78 + 56.16 + 499.36 + 4.76 = 616.06: 87%|████████▋ | 1775/2048 [21:55<03:09, 1.44it/s]
loss 1.74 accuracy 0.19 -- 56.53 + 56.47 + 497.09 + 4.82 = 614.91: 87%|████████▋ | 1775/2048 [21:56<03:09, 1.44it/s]
loss 1.74 accuracy 0.19 -- 56.53 + 56.47 + 497.09 + 4.82 = 614.91: 87%|████████▋ | 1776/2048 [21:56<03:13, 1.40it/s]
loss 2.12 accuracy 0.25 -- 56.04 + 57.28 + 495.75 + 4.77 = 613.84: 87%|████████▋ | 1776/2048 [21:56<03:13, 1.40it/s]
loss 2.12 accuracy 0.25 -- 56.04 + 57.28 + 495.75 + 4.77 = 613.84: 87%|████████▋ | 1777/2048 [21:56<03:07, 1.45it/s]
loss 2.00 accuracy 0.19 -- 158.10 + 56.80 + 489.14 + 4.76 = 708.80: 87%|████████▋ | 1777/2048 [21:57<03:07, 1.45it/s]
loss 2.00 accuracy 0.19 -- 158.10 + 56.80 + 489.14 + 4.76 = 708.80: 87%|████████▋ | 1778/2048 [21:57<03:10, 1.42it/s]
loss 1.85 accuracy 0.25 -- 55.64 + 166.59 + 502.03 + 4.78 = 729.03: 87%|████████▋ | 1778/2048 [21:58<03:10, 1.42it/s]
loss 1.85 accuracy 0.25 -- 55.64 + 166.59 + 502.03 + 4.78 = 729.03: 87%|████████▋ | 1779/2048 [21:58<03:13, 1.39it/s]
loss 1.43 accuracy 0.31 -- 56.89 + 56.85 + 498.64 + 4.77 = 617.15: 87%|████████▋ | 1779/2048 [21:58<03:13, 1.39it/s]
loss 1.43 accuracy 0.31 -- 56.89 + 56.85 + 498.64 + 4.77 = 617.15: 87%|████████▋ | 1780/2048 [21:58<03:06, 1.43it/s]
loss 1.88 accuracy 0.31 -- 162.49 + 57.18 + 498.37 + 4.77 = 722.81: 87%|████████▋ | 1780/2048 [21:59<03:06, 1.43it/s]
loss 1.88 accuracy 0.31 -- 162.49 + 57.18 + 498.37 + 4.77 = 722.81: 87%|████████▋ | 1781/2048 [21:59<03:10, 1.40it/s]
loss 1.76 accuracy 0.38 -- 56.11 + 57.11 + 619.71 + 4.78 = 737.71: 87%|████████▋ | 1781/2048 [22:00<03:10, 1.40it/s]
loss 1.76 accuracy 0.38 -- 56.11 + 57.11 + 619.71 + 4.78 = 737.71: 87%|████████▋ | 1782/2048 [22:00<03:13, 1.37it/s]
loss 2.45 accuracy 0.31 -- 56.79 + 56.65 + 506.34 + 4.77 = 624.55: 87%|████████▋ | 1782/2048 [22:01<03:13, 1.37it/s]
loss 2.45 accuracy 0.31 -- 56.79 + 56.65 + 506.34 + 4.77 = 624.55: 87%|████████▋ | 1783/2048 [22:01<03:06, 1.42it/s]
loss 1.21 accuracy 0.62 -- 56.09 + 57.18 + 614.21 + 4.78 = 732.26: 87%|████████▋ | 1783/2048 [22:01<03:06, 1.42it/s]
loss 1.21 accuracy 0.62 -- 56.09 + 57.18 + 614.21 + 4.78 = 732.26: 87%|████████▋ | 1784/2048 [22:01<03:10, 1.39it/s]
loss 2.00 accuracy 0.19 -- 56.68 + 56.74 + 501.06 + 4.76 = 619.23: 87%|████████▋ | 1784/2048 [22:02<03:10, 1.39it/s]
loss 2.00 accuracy 0.19 -- 56.68 + 56.74 + 501.06 + 4.76 = 619.23: 87%|████████▋ | 1785/2048 [22:02<03:03, 1.43it/s]
loss 1.68 accuracy 0.44 -- 56.31 + 166.41 + 502.55 + 4.78 = 730.05: 87%|████████▋ | 1785/2048 [22:03<03:03, 1.43it/s]
loss 1.68 accuracy 0.44 -- 56.31 + 166.41 + 502.55 + 4.78 = 730.05: 87%|████████▋ | 1786/2048 [22:03<03:07, 1.40it/s]
loss 1.86 accuracy 0.25 -- 56.30 + 56.48 + 499.19 + 4.77 = 616.74: 87%|████████▋ | 1786/2048 [22:03<03:07, 1.40it/s]
loss 1.86 accuracy 0.25 -- 56.30 + 56.48 + 499.19 + 4.77 = 616.74: 87%|████████▋ | 1787/2048 [22:03<03:01, 1.44it/s]
loss 2.10 accuracy 0.31 -- 56.77 + 56.67 + 498.14 + 4.77 = 616.34: 87%|████████▋ | 1787/2048 [22:04<03:01, 1.44it/s]
loss 2.10 accuracy 0.31 -- 56.77 + 56.67 + 498.14 + 4.77 = 616.34: 87%|████████▋ | 1788/2048 [22:04<03:04, 1.41it/s]
loss 1.49 accuracy 0.50 -- 56.11 + 57.50 + 495.19 + 4.77 = 613.57: 87%|████████▋ | 1788/2048 [22:05<03:04, 1.41it/s]
loss 1.49 accuracy 0.50 -- 56.11 + 57.50 + 495.19 + 4.77 = 613.57: 87%|████████▋ | 1789/2048 [22:05<02:58, 1.45it/s]
loss 1.96 accuracy 0.31 -- 157.09 + 57.03 + 489.67 + 4.77 = 708.56: 87%|████████▋ | 1789/2048 [22:06<02:58, 1.45it/s]
loss 1.96 accuracy 0.31 -- 157.09 + 57.03 + 489.67 + 4.77 = 708.56: 87%|████████▋ | 1790/2048 [22:06<03:01, 1.42it/s]
loss 1.79 accuracy 0.25 -- 55.92 + 166.38 + 499.93 + 4.79 = 727.02: 87%|████████▋ | 1790/2048 [22:06<03:01, 1.42it/s]
loss 1.79 accuracy 0.25 -- 55.92 + 166.38 + 499.93 + 4.79 = 727.02: 87%|████████▋ | 1791/2048 [22:06<03:04, 1.39it/s]
loss 1.30 accuracy 0.62 -- 56.42 + 56.88 + 497.04 + 4.78 = 615.13: 87%|████████▋ | 1791/2048 [22:07<03:04, 1.39it/s]
loss 1.30 accuracy 0.62 -- 56.42 + 56.88 + 497.04 + 4.78 = 615.13: 88%|████████▊ | 1792/2048 [22:07<02:58, 1.44it/s]
loss 1.81 accuracy 0.38 -- 162.22 + 56.86 + 496.88 + 4.76 = 720.72: 88%|████████▊ | 1792/2048 [22:08<02:58, 1.44it/s]
loss 1.81 accuracy 0.38 -- 162.22 + 56.86 + 496.88 + 4.76 = 720.72: 88%|████████▊ | 1793/2048 [22:08<03:04, 1.39it/s]
loss 1.93 accuracy 0.38 -- 55.84 + 57.33 + 621.74 + 4.78 = 739.68: 88%|████████▊ | 1793/2048 [22:08<03:04, 1.39it/s]
loss 1.93 accuracy 0.38 -- 55.84 + 57.33 + 621.74 + 4.78 = 739.68: 88%|████████▊ | 1794/2048 [22:08<03:06, 1.36it/s]
loss 1.85 accuracy 0.31 -- 57.00 + 56.48 + 504.83 + 4.78 = 623.09: 88%|████████▊ | 1794/2048 [22:09<03:06, 1.36it/s]
loss 1.85 accuracy 0.31 -- 57.00 + 56.48 + 504.83 + 4.78 = 623.09: 88%|████████▊ | 1795/2048 [22:09<02:59, 1.41it/s]
loss 1.69 accuracy 0.38 -- 56.02 + 57.29 + 614.72 + 4.77 = 732.80: 88%|████████▊ | 1795/2048 [22:10<02:59, 1.41it/s]
loss 1.69 accuracy 0.38 -- 56.02 + 57.29 + 614.72 + 4.77 = 732.80: 88%|████████▊ | 1796/2048 [22:10<03:02, 1.38it/s]
loss 1.78 accuracy 0.50 -- 56.82 + 56.50 + 501.24 + 4.77 = 619.33: 88%|████████▊ | 1796/2048 [22:11<03:02, 1.38it/s]
loss 1.78 accuracy 0.50 -- 56.82 + 56.50 + 501.24 + 4.77 = 619.33: 88%|████████▊ | 1797/2048 [22:11<02:56, 1.43it/s]
loss 1.64 accuracy 0.31 -- 56.01 + 166.14 + 501.02 + 4.77 = 727.95: 88%|████████▊ | 1797/2048 [22:11<02:56, 1.43it/s]
loss 1.64 accuracy 0.31 -- 56.01 + 166.14 + 501.02 + 4.77 = 727.95: 88%|████████▊ | 1798/2048 [22:11<02:59, 1.39it/s]
loss 1.76 accuracy 0.44 -- 56.04 + 56.32 + 499.59 + 4.77 = 616.72: 88%|████████▊ | 1798/2048 [22:12<02:59, 1.39it/s]
loss 1.76 accuracy 0.44 -- 56.04 + 56.32 + 499.59 + 4.77 = 616.72: 88%|████████▊ | 1799/2048 [22:12<02:53, 1.44it/s]
loss 2.25 accuracy 0.38 -- 56.60 + 56.95 + 498.98 + 4.79 = 617.32: 88%|████████▊ | 1799/2048 [22:13<02:53, 1.44it/s]
loss 2.25 accuracy 0.38 -- 56.60 + 56.95 + 498.98 + 4.79 = 617.32: 88%|████████▊ | 1800/2048 [22:13<02:59, 1.38it/s]
loss 1.96 accuracy 0.31 -- 55.96 + 57.42 + 494.79 + 4.76 = 612.92: 88%|████████▊ | 1800/2048 [22:13<02:59, 1.38it/s]
loss 1.96 accuracy 0.31 -- 55.96 + 57.42 + 494.79 + 4.76 = 612.92: 88%|████████▊ | 1801/2048 [22:13<02:52, 1.43it/s]
loss 1.53 accuracy 0.38 -- 157.60 + 57.07 + 489.13 + 4.76 = 708.56: 88%|████████▊ | 1801/2048 [22:14<02:52, 1.43it/s]
loss 1.53 accuracy 0.38 -- 157.60 + 57.07 + 489.13 + 4.76 = 708.56: 88%|████████▊ | 1802/2048 [22:14<02:54, 1.41it/s]
loss 1.54 accuracy 0.38 -- 55.69 + 166.92 + 500.72 + 4.79 = 728.12: 88%|████████▊ | 1802/2048 [22:15<02:54, 1.41it/s]
loss 1.54 accuracy 0.38 -- 55.69 + 166.92 + 500.72 + 4.79 = 728.12: 88%|████████▊ | 1803/2048 [22:15<02:57, 1.38it/s]
loss 2.07 accuracy 0.31 -- 56.79 + 56.31 + 496.73 + 4.77 = 614.60: 88%|████████▊ | 1803/2048 [22:15<02:57, 1.38it/s]
loss 2.07 accuracy 0.31 -- 56.79 + 56.31 + 496.73 + 4.77 = 614.60: 88%|████████▊ | 1804/2048 [22:15<02:50, 1.43it/s]
loss 1.87 accuracy 0.38 -- 162.83 + 56.83 + 497.14 + 4.78 = 721.58: 88%|████████▊ | 1804/2048 [22:16<02:50, 1.43it/s]
loss 1.87 accuracy 0.38 -- 162.83 + 56.83 + 497.14 + 4.78 = 721.58: 88%|████████▊ | 1805/2048 [22:16<02:53, 1.40it/s]
loss 1.94 accuracy 0.12 -- 56.33 + 57.56 + 620.88 + 4.79 = 739.56: 88%|████████▊ | 1805/2048 [22:17<02:53, 1.40it/s]
loss 1.94 accuracy 0.12 -- 56.33 + 57.56 + 620.88 + 4.79 = 739.56: 88%|████████▊ | 1806/2048 [22:17<02:56, 1.37it/s]
loss 1.89 accuracy 0.31 -- 56.83 + 56.49 + 504.31 + 4.76 = 622.39: 88%|████████▊ | 1806/2048 [22:18<02:56, 1.37it/s]
loss 1.89 accuracy 0.31 -- 56.83 + 56.49 + 504.31 + 4.76 = 622.39: 88%|████████▊ | 1807/2048 [22:18<02:52, 1.40it/s]
loss 1.70 accuracy 0.25 -- 56.52 + 57.22 + 614.54 + 4.79 = 733.06: 88%|████████▊ | 1807/2048 [22:18<02:52, 1.40it/s]
loss 1.70 accuracy 0.25 -- 56.52 + 57.22 + 614.54 + 4.79 = 733.06: 88%|████████▊ | 1808/2048 [22:18<02:54, 1.37it/s]
loss 1.51 accuracy 0.31 -- 57.04 + 56.86 + 503.60 + 4.79 = 622.30: 88%|████████▊ | 1808/2048 [22:19<02:54, 1.37it/s]
loss 1.51 accuracy 0.31 -- 57.04 + 56.86 + 503.60 + 4.79 = 622.30: 88%|████████▊ | 1809/2048 [22:19<02:48, 1.42it/s]
loss 2.17 accuracy 0.12 -- 56.26 + 166.12 + 502.58 + 4.80 = 729.76: 88%|████████▊ | 1809/2048 [22:20<02:48, 1.42it/s]
loss 2.17 accuracy 0.12 -- 56.26 + 166.12 + 502.58 + 4.80 = 729.76: 88%|████████▊ | 1810/2048 [22:20<02:51, 1.39it/s]
loss 1.82 accuracy 0.25 -- 56.05 + 56.16 + 500.96 + 4.77 = 617.94: 88%|████████▊ | 1810/2048 [22:20<02:51, 1.39it/s]
loss 1.82 accuracy 0.25 -- 56.05 + 56.16 + 500.96 + 4.77 = 617.94: 88%|████████▊ | 1811/2048 [22:20<02:45, 1.43it/s]
loss 2.29 accuracy 0.31 -- 56.72 + 56.62 + 499.25 + 4.76 = 617.36: 88%|████████▊ | 1811/2048 [22:21<02:45, 1.43it/s]
loss 2.29 accuracy 0.31 -- 56.72 + 56.62 + 499.25 + 4.76 = 617.36: 88%|████████▊ | 1812/2048 [22:21<02:48, 1.40it/s]
loss 1.66 accuracy 0.31 -- 55.97 + 57.30 + 494.98 + 4.76 = 613.01: 88%|████████▊ | 1812/2048 [22:22<02:48, 1.40it/s]
loss 1.66 accuracy 0.31 -- 55.97 + 57.30 + 494.98 + 4.76 = 613.01: 89%|████████▊ | 1813/2048 [22:22<02:42, 1.45it/s]
loss 1.86 accuracy 0.19 -- 157.29 + 56.81 + 489.77 + 4.77 = 708.64: 89%|████████▊ | 1813/2048 [22:23<02:42, 1.45it/s]
loss 1.86 accuracy 0.19 -- 157.29 + 56.81 + 489.77 + 4.77 = 708.64: 89%|████████▊ | 1814/2048 [22:23<02:44, 1.42it/s]
loss 1.45 accuracy 0.50 -- 55.81 + 166.16 + 501.19 + 4.77 = 727.93: 89%|████████▊ | 1814/2048 [22:23<02:44, 1.42it/s]
loss 1.45 accuracy 0.50 -- 55.81 + 166.16 + 501.19 + 4.77 = 727.93: 89%|████████▊ | 1815/2048 [22:23<02:50, 1.37it/s]
loss 1.54 accuracy 0.44 -- 56.68 + 56.39 + 497.53 + 4.79 = 615.39: 89%|████████▊ | 1815/2048 [22:24<02:50, 1.37it/s]
loss 1.54 accuracy 0.44 -- 56.68 + 56.39 + 497.53 + 4.79 = 615.39: 89%|████████▊ | 1816/2048 [22:24<02:43, 1.42it/s]
loss 2.04 accuracy 0.31 -- 162.28 + 57.25 + 496.72 + 4.76 = 721.01: 89%|████████▊ | 1816/2048 [22:25<02:43, 1.42it/s]
loss 2.04 accuracy 0.31 -- 162.28 + 57.25 + 496.72 + 4.76 = 721.01: 89%|████████▊ | 1817/2048 [22:25<02:45, 1.39it/s]
loss 1.38 accuracy 0.50 -- 56.33 + 57.10 + 620.27 + 4.77 = 738.47: 89%|████████▊ | 1817/2048 [22:26<02:45, 1.39it/s]
loss 1.38 accuracy 0.50 -- 56.33 + 57.10 + 620.27 + 4.77 = 738.47: 89%|████████▉ | 1818/2048 [22:26<02:48, 1.37it/s]
loss 1.91 accuracy 0.38 -- 56.55 + 56.33 + 505.28 + 4.76 = 622.92: 89%|████████▉ | 1818/2048 [22:26<02:48, 1.37it/s]
loss 1.91 accuracy 0.38 -- 56.55 + 56.33 + 505.28 + 4.76 = 622.92: 89%|████████▉ | 1819/2048 [22:26<02:41, 1.41it/s]
loss 2.27 accuracy 0.19 -- 56.30 + 57.24 + 615.49 + 4.78 = 733.81: 89%|████████▉ | 1819/2048 [22:27<02:41, 1.41it/s]
loss 2.27 accuracy 0.19 -- 56.30 + 57.24 + 615.49 + 4.78 = 733.81: 89%|████████▉ | 1820/2048 [22:27<02:44, 1.38it/s]
loss 2.42 accuracy 0.12 -- 56.89 + 56.45 + 500.49 + 4.78 = 618.60: 89%|████████▉ | 1820/2048 [22:28<02:44, 1.38it/s]
loss 2.42 accuracy 0.12 -- 56.89 + 56.45 + 500.49 + 4.78 = 618.60: 89%|████████▉ | 1821/2048 [22:28<02:38, 1.43it/s]
loss 2.04 accuracy 0.38 -- 56.47 + 166.00 + 502.46 + 4.79 = 729.71: 89%|████████▉ | 1821/2048 [22:28<02:38, 1.43it/s]
loss 2.04 accuracy 0.38 -- 56.47 + 166.00 + 502.46 + 4.79 = 729.71: 89%|████████▉ | 1822/2048 [22:28<02:44, 1.37it/s]
loss 1.73 accuracy 0.44 -- 56.20 + 56.43 + 498.42 + 4.78 = 615.82: 89%|████████▉ | 1822/2048 [22:29<02:44, 1.37it/s]
loss 1.73 accuracy 0.44 -- 56.20 + 56.43 + 498.42 + 4.78 = 615.82: 89%|████████▉ | 1823/2048 [22:29<02:37, 1.42it/s]
loss 1.76 accuracy 0.38 -- 56.76 + 56.49 + 498.49 + 4.79 = 616.54: 89%|████████▉ | 1823/2048 [22:30<02:37, 1.42it/s]
loss 1.76 accuracy 0.38 -- 56.76 + 56.49 + 498.49 + 4.79 = 616.54: 89%|████████▉ | 1824/2048 [22:30<02:40, 1.40it/s]
loss 1.88 accuracy 0.25 -- 56.00 + 57.31 + 495.47 + 4.78 = 613.57: 89%|████████▉ | 1824/2048 [22:30<02:40, 1.40it/s]
loss 1.88 accuracy 0.25 -- 56.00 + 57.31 + 495.47 + 4.78 = 613.57: 89%|████████▉ | 1825/2048 [22:30<02:34, 1.44it/s]
loss 1.60 accuracy 0.38 -- 157.83 + 56.80 + 490.37 + 4.76 = 709.76: 89%|████████▉ | 1825/2048 [22:31<02:34, 1.44it/s]
loss 1.60 accuracy 0.38 -- 157.83 + 56.80 + 490.37 + 4.76 = 709.76: 89%|████████▉ | 1826/2048 [22:31<02:36, 1.42it/s]
loss 1.79 accuracy 0.31 -- 55.65 + 166.52 + 502.12 + 4.76 = 729.06: 89%|████████▉ | 1826/2048 [22:32<02:36, 1.42it/s]
loss 1.79 accuracy 0.31 -- 55.65 + 166.52 + 502.12 + 4.76 = 729.06: 89%|████████▉ | 1827/2048 [22:32<02:39, 1.39it/s]
loss 2.11 accuracy 0.31 -- 56.32 + 56.35 + 499.37 + 4.77 = 616.81: 89%|████████▉ | 1827/2048 [22:33<02:39, 1.39it/s]
loss 2.11 accuracy 0.31 -- 56.32 + 56.35 + 499.37 + 4.77 = 616.81: 89%|████████▉ | 1828/2048 [22:33<02:33, 1.43it/s]
loss 1.56 accuracy 0.56 -- 162.75 + 57.07 + 496.36 + 4.78 = 720.95: 89%|████████▉ | 1828/2048 [22:33<02:33, 1.43it/s]
loss 1.56 accuracy 0.56 -- 162.75 + 57.07 + 496.36 + 4.78 = 720.95: 89%|████████▉ | 1829/2048 [22:33<02:36, 1.40it/s]
loss 1.74 accuracy 0.38 -- 55.99 + 57.33 + 620.28 + 4.78 = 738.38: 89%|████████▉ | 1829/2048 [22:34<02:36, 1.40it/s]
loss 1.74 accuracy 0.38 -- 55.99 + 57.33 + 620.28 + 4.78 = 738.38: 89%|████████▉ | 1830/2048 [22:34<02:38, 1.37it/s]
loss 1.67 accuracy 0.38 -- 56.92 + 56.48 + 504.03 + 4.78 = 622.21: 89%|████████▉ | 1830/2048 [22:35<02:38, 1.37it/s]
loss 1.67 accuracy 0.38 -- 56.92 + 56.48 + 504.03 + 4.78 = 622.21: 89%|████████▉ | 1831/2048 [22:35<02:32, 1.42it/s]
loss 1.85 accuracy 0.31 -- 55.99 + 57.08 + 615.74 + 4.78 = 733.59: 89%|████████▉ | 1831/2048 [22:35<02:32, 1.42it/s]
loss 1.85 accuracy 0.31 -- 55.99 + 57.08 + 615.74 + 4.78 = 733.59: 89%|████████▉ | 1832/2048 [22:35<02:35, 1.39it/s]
loss 1.81 accuracy 0.31 -- 56.69 + 56.60 + 502.39 + 4.76 = 620.44: 89%|████████▉ | 1832/2048 [22:36<02:35, 1.39it/s]
loss 1.81 accuracy 0.31 -- 56.69 + 56.60 + 502.39 + 4.76 = 620.44: 90%|████████▉ | 1833/2048 [22:36<02:30, 1.43it/s]
loss 1.66 accuracy 0.50 -- 56.54 + 166.51 + 501.35 + 4.78 = 729.18: 90%|████████▉ | 1833/2048 [22:37<02:30, 1.43it/s]
loss 1.66 accuracy 0.50 -- 56.54 + 166.51 + 501.35 + 4.78 = 729.18: 90%|████████▉ | 1834/2048 [22:37<02:33, 1.40it/s]
loss 2.19 accuracy 0.25 -- 56.02 + 56.53 + 498.61 + 4.77 = 615.94: 90%|████████▉ | 1834/2048 [22:38<02:33, 1.40it/s]
loss 2.19 accuracy 0.25 -- 56.02 + 56.53 + 498.61 + 4.77 = 615.94: 90%|████████▉ | 1835/2048 [22:38<02:27, 1.44it/s]
loss 2.09 accuracy 0.38 -- 56.85 + 56.55 + 497.13 + 4.78 = 615.30: 90%|████████▉ | 1835/2048 [22:38<02:27, 1.44it/s]
loss 2.09 accuracy 0.38 -- 56.85 + 56.55 + 497.13 + 4.78 = 615.30: 90%|████████▉ | 1836/2048 [22:38<02:30, 1.41it/s]
loss 1.56 accuracy 0.56 -- 56.04 + 57.49 + 495.43 + 4.80 = 613.77: 90%|████████▉ | 1836/2048 [22:39<02:30, 1.41it/s]
loss 1.56 accuracy 0.56 -- 56.04 + 57.49 + 495.43 + 4.80 = 613.77: 90%|████████▉ | 1837/2048 [22:39<02:25, 1.45it/s]
loss 1.93 accuracy 0.38 -- 157.49 + 56.69 + 488.67 + 4.77 = 707.63: 90%|████████▉ | 1837/2048 [22:40<02:25, 1.45it/s]
loss 1.93 accuracy 0.38 -- 157.49 + 56.69 + 488.67 + 4.77 = 707.63: 90%|████████▉ | 1838/2048 [22:40<02:29, 1.40it/s]
loss 1.65 accuracy 0.31 -- 55.92 + 166.38 + 502.10 + 4.85 = 729.25: 90%|████████▉ | 1838/2048 [22:40<02:29, 1.40it/s]
loss 1.65 accuracy 0.31 -- 55.92 + 166.38 + 502.10 + 4.85 = 729.25: 90%|████████▉ | 1839/2048 [22:40<02:31, 1.38it/s]
loss 1.48 accuracy 0.56 -- 56.76 + 56.24 + 497.60 + 4.77 = 615.37: 90%|████████▉ | 1839/2048 [22:41<02:31, 1.38it/s]
loss 1.48 accuracy 0.56 -- 56.76 + 56.24 + 497.60 + 4.77 = 615.37: 90%|████████▉ | 1840/2048 [22:41<02:25, 1.43it/s]
loss 2.01 accuracy 0.31 -- 162.24 + 57.29 + 497.24 + 4.80 = 721.57: 90%|████████▉ | 1840/2048 [22:42<02:25, 1.43it/s]
loss 2.01 accuracy 0.31 -- 162.24 + 57.29 + 497.24 + 4.80 = 721.57: 90%|████████▉ | 1841/2048 [22:42<02:28, 1.40it/s]
loss 2.13 accuracy 0.31 -- 56.05 + 57.12 + 619.42 + 4.79 = 737.39: 90%|████████▉ | 1841/2048 [22:43<02:28, 1.40it/s]
loss 2.13 accuracy 0.31 -- 56.05 + 57.12 + 619.42 + 4.79 = 737.39: 90%|████████▉ | 1842/2048 [22:43<02:30, 1.37it/s]
loss 2.11 accuracy 0.38 -- 56.74 + 56.61 + 505.54 + 4.77 = 623.67: 90%|████████▉ | 1842/2048 [22:43<02:30, 1.37it/s]
loss 2.11 accuracy 0.38 -- 56.74 + 56.61 + 505.54 + 4.77 = 623.67: 90%|████████▉ | 1843/2048 [22:43<02:24, 1.42it/s]
loss 1.58 accuracy 0.38 -- 56.33 + 57.43 + 615.01 + 4.78 = 733.55: 90%|████████▉ | 1843/2048 [22:44<02:24, 1.42it/s]
loss 1.58 accuracy 0.38 -- 56.33 + 57.43 + 615.01 + 4.78 = 733.55: 90%|█████████ | 1844/2048 [22:44<02:27, 1.38it/s]
loss 1.83 accuracy 0.31 -- 56.66 + 56.74 + 502.44 + 4.79 = 620.62: 90%|█████████ | 1844/2048 [22:45<02:27, 1.38it/s]
loss 1.83 accuracy 0.31 -- 56.66 + 56.74 + 502.44 + 4.79 = 620.62: 90%|█████████ | 1845/2048 [22:45<02:24, 1.41it/s]
loss 1.27 accuracy 0.56 -- 56.32 + 166.21 + 500.79 + 4.78 = 728.09: 90%|█████████ | 1845/2048 [22:45<02:24, 1.41it/s]
loss 1.27 accuracy 0.56 -- 56.32 + 166.21 + 500.79 + 4.78 = 728.09: 90%|█████████ | 1846/2048 [22:45<02:26, 1.38it/s]
loss 1.91 accuracy 0.19 -- 56.25 + 56.76 + 498.54 + 4.78 = 616.32: 90%|█████████ | 1846/2048 [22:46<02:26, 1.38it/s]
loss 1.91 accuracy 0.19 -- 56.25 + 56.76 + 498.54 + 4.78 = 616.32: 90%|█████████ | 1847/2048 [22:46<02:20, 1.43it/s]
loss 1.50 accuracy 0.38 -- 56.67 + 56.47 + 499.78 + 4.77 = 617.69: 90%|█████████ | 1847/2048 [22:47<02:20, 1.43it/s]
loss 1.50 accuracy 0.38 -- 56.67 + 56.47 + 499.78 + 4.77 = 617.69: 90%|█████████ | 1848/2048 [22:47<02:23, 1.40it/s]
loss 1.53 accuracy 0.50 -- 56.13 + 57.06 + 494.63 + 4.76 = 612.59: 90%|█████████ | 1848/2048 [22:47<02:23, 1.40it/s]
loss 1.53 accuracy 0.50 -- 56.13 + 57.06 + 494.63 + 4.76 = 612.59: 90%|█████████ | 1849/2048 [22:47<02:17, 1.44it/s]
loss 1.66 accuracy 0.25 -- 156.93 + 57.20 + 490.14 + 4.78 = 709.04: 90%|█████████ | 1849/2048 [22:48<02:17, 1.44it/s]
loss 1.66 accuracy 0.25 -- 156.93 + 57.20 + 490.14 + 4.78 = 709.04: 90%|█████████ | 1850/2048 [22:48<02:19, 1.42it/s]
loss 1.98 accuracy 0.25 -- 56.15 + 166.76 + 501.81 + 4.78 = 729.49: 90%|█████████ | 1850/2048 [22:49<02:19, 1.42it/s]
loss 1.98 accuracy 0.25 -- 56.15 + 166.76 + 501.81 + 4.78 = 729.49: 90%|█████████ | 1851/2048 [22:49<02:21, 1.39it/s]
loss 1.85 accuracy 0.25 -- 56.61 + 56.58 + 497.13 + 4.77 = 615.09: 90%|█████████ | 1851/2048 [22:50<02:21, 1.39it/s]
loss 1.85 accuracy 0.25 -- 56.61 + 56.58 + 497.13 + 4.77 = 615.09: 90%|█████████ | 1852/2048 [22:50<02:16, 1.43it/s]
loss 1.80 accuracy 0.50 -- 162.26 + 57.29 + 496.68 + 4.78 = 721.01: 90%|█████████ | 1852/2048 [22:50<02:16, 1.43it/s]
loss 1.80 accuracy 0.50 -- 162.26 + 57.29 + 496.68 + 4.78 = 721.01: 90%|█████████ | 1853/2048 [22:50<02:18, 1.40it/s]
loss 2.31 accuracy 0.25 -- 56.49 + 57.19 + 620.70 + 4.77 = 739.15: 90%|█████████ | 1853/2048 [22:51<02:18, 1.40it/s]
loss 2.31 accuracy 0.25 -- 56.49 + 57.19 + 620.70 + 4.77 = 739.15: 91%|█████████ | 1854/2048 [22:51<02:21, 1.37it/s]
loss 1.67 accuracy 0.44 -- 56.34 + 56.14 + 504.24 + 4.77 = 621.49: 91%|█████████ | 1854/2048 [22:52<02:21, 1.37it/s]
loss 1.67 accuracy 0.44 -- 56.34 + 56.14 + 504.24 + 4.77 = 621.49: 91%|█████████ | 1855/2048 [22:52<02:15, 1.42it/s]
loss 1.64 accuracy 0.25 -- 56.65 + 57.22 + 616.54 + 4.77 = 735.19: 91%|█████████ | 1855/2048 [22:53<02:15, 1.42it/s]
loss 1.64 accuracy 0.25 -- 56.65 + 57.22 + 616.54 + 4.77 = 735.19: 91%|█████████ | 1856/2048 [22:53<02:18, 1.39it/s]
loss 1.59 accuracy 0.44 -- 56.68 + 56.47 + 501.91 + 4.78 = 619.83: 91%|█████████ | 1856/2048 [22:53<02:18, 1.39it/s]
loss 1.59 accuracy 0.44 -- 56.68 + 56.47 + 501.91 + 4.78 = 619.83: 91%|█████████ | 1857/2048 [22:53<02:13, 1.43it/s]
loss 1.33 accuracy 0.50 -- 56.29 + 166.30 + 502.02 + 4.79 = 729.40: 91%|█████████ | 1857/2048 [22:54<02:13, 1.43it/s]
loss 1.33 accuracy 0.50 -- 56.29 + 166.30 + 502.02 + 4.79 = 729.40: 91%|█████████ | 1858/2048 [22:54<02:16, 1.40it/s]
loss 2.36 accuracy 0.31 -- 55.87 + 56.26 + 498.44 + 4.75 = 615.32: 91%|█████████ | 1858/2048 [22:55<02:16, 1.40it/s]
loss 2.36 accuracy 0.31 -- 55.87 + 56.26 + 498.44 + 4.75 = 615.32: 91%|█████████ | 1859/2048 [22:55<02:11, 1.44it/s]
loss 1.46 accuracy 0.44 -- 56.74 + 56.32 + 499.38 + 4.78 = 617.22: 91%|█████████ | 1859/2048 [22:55<02:11, 1.44it/s]
loss 1.46 accuracy 0.44 -- 56.74 + 56.32 + 499.38 + 4.78 = 617.22: 91%|█████████ | 1860/2048 [22:55<02:13, 1.41it/s]
loss 2.09 accuracy 0.31 -- 56.07 + 57.36 + 494.31 + 4.78 = 612.51: 91%|█████████ | 1860/2048 [22:56<02:13, 1.41it/s]
loss 2.09 accuracy 0.31 -- 56.07 + 57.36 + 494.31 + 4.78 = 612.51: 91%|█████████ | 1861/2048 [22:56<02:08, 1.45it/s]
loss 1.65 accuracy 0.38 -- 157.49 + 57.00 + 490.85 + 4.77 = 710.10: 91%|█████████ | 1861/2048 [22:57<02:08, 1.45it/s]
loss 1.65 accuracy 0.38 -- 157.49 + 57.00 + 490.85 + 4.77 = 710.10: 91%|█████████ | 1862/2048 [22:57<02:10, 1.42it/s]
loss 1.29 accuracy 0.44 -- 55.97 + 166.28 + 501.80 + 4.79 = 728.84: 91%|█████████ | 1862/2048 [22:57<02:10, 1.42it/s]
loss 1.29 accuracy 0.44 -- 55.97 + 166.28 + 501.80 + 4.79 = 728.84: 91%|█████████ | 1863/2048 [22:57<02:13, 1.39it/s]
loss 2.09 accuracy 0.19 -- 56.75 + 56.74 + 498.15 + 4.78 = 616.42: 91%|█████████ | 1863/2048 [22:58<02:13, 1.39it/s]
loss 2.09 accuracy 0.19 -- 56.75 + 56.74 + 498.15 + 4.78 = 616.42: 91%|█████████ | 1864/2048 [22:58<02:08, 1.44it/s]
loss 1.54 accuracy 0.38 -- 162.01 + 57.28 + 497.18 + 4.79 = 721.26: 91%|█████████ | 1864/2048 [22:59<02:08, 1.44it/s]
loss 1.54 accuracy 0.38 -- 162.01 + 57.28 + 497.18 + 4.79 = 721.26: 91%|█████████ | 1865/2048 [22:59<02:10, 1.40it/s]
loss 1.48 accuracy 0.44 -- 56.26 + 57.27 + 620.08 + 4.78 = 738.39: 91%|█████████ | 1865/2048 [23:00<02:10, 1.40it/s]
loss 1.48 accuracy 0.44 -- 56.26 + 57.27 + 620.08 + 4.78 = 738.39: 91%|█████████ | 1866/2048 [23:00<02:12, 1.37it/s]
loss 1.87 accuracy 0.12 -- 56.71 + 56.80 + 505.46 + 4.78 = 623.75: 91%|█████████ | 1866/2048 [23:00<02:12, 1.37it/s]
loss 1.87 accuracy 0.12 -- 56.71 + 56.80 + 505.46 + 4.78 = 623.75: 91%|█████████ | 1867/2048 [23:00<02:07, 1.42it/s]
loss 1.66 accuracy 0.38 -- 56.59 + 57.62 + 617.13 + 4.78 = 736.12: 91%|█████████ | 1867/2048 [23:01<02:07, 1.42it/s]
loss 1.66 accuracy 0.38 -- 56.59 + 57.62 + 617.13 + 4.78 = 736.12: 91%|█████████ | 1868/2048 [23:01<02:10, 1.38it/s]
loss 1.53 accuracy 0.44 -- 56.65 + 56.70 + 501.35 + 4.78 = 619.47: 91%|█████████ | 1868/2048 [23:02<02:10, 1.38it/s]
loss 1.53 accuracy 0.44 -- 56.65 + 56.70 + 501.35 + 4.78 = 619.47: 91%|█████████▏| 1869/2048 [23:02<02:05, 1.43it/s]
loss 2.12 accuracy 0.19 -- 56.09 + 166.29 + 501.16 + 4.82 = 728.36: 91%|█████████▏| 1869/2048 [23:02<02:05, 1.43it/s]
loss 2.12 accuracy 0.19 -- 56.09 + 166.29 + 501.16 + 4.82 = 728.36: 91%|█████████▏| 1870/2048 [23:02<02:07, 1.40it/s]
loss 1.87 accuracy 0.19 -- 56.38 + 56.56 + 499.10 + 4.76 = 616.80: 91%|█████████▏| 1870/2048 [23:03<02:07, 1.40it/s]
loss 1.87 accuracy 0.19 -- 56.38 + 56.56 + 499.10 + 4.76 = 616.80: 91%|█████████▏| 1871/2048 [23:03<02:02, 1.44it/s]
loss 1.38 accuracy 0.44 -- 56.50 + 56.26 + 498.25 + 4.77 = 615.77: 91%|█████████▏| 1871/2048 [23:04<02:02, 1.44it/s]
loss 1.38 accuracy 0.44 -- 56.50 + 56.26 + 498.25 + 4.77 = 615.77: 91%|█████████▏| 1872/2048 [23:04<02:05, 1.41it/s]
loss 2.05 accuracy 0.31 -- 55.99 + 57.08 + 496.43 + 4.79 = 614.28: 91%|█████████▏| 1872/2048 [23:04<02:05, 1.41it/s]
loss 2.05 accuracy 0.31 -- 55.99 + 57.08 + 496.43 + 4.79 = 614.28: 91%|█████████▏| 1873/2048 [23:04<02:00, 1.45it/s]
loss 1.59 accuracy 0.50 -- 157.46 + 56.96 + 489.00 + 4.76 = 708.18: 91%|█████████▏| 1873/2048 [23:05<02:00, 1.45it/s]
loss 1.59 accuracy 0.50 -- 157.46 + 56.96 + 489.00 + 4.76 = 708.18: 92%|█████████▏| 1874/2048 [23:05<02:02, 1.42it/s]
loss 1.86 accuracy 0.19 -- 55.65 + 166.39 + 500.72 + 4.76 = 727.53: 92%|█████████▏| 1874/2048 [23:06<02:02, 1.42it/s]
loss 1.86 accuracy 0.19 -- 55.65 + 166.39 + 500.72 + 4.76 = 727.53: 92%|█████████▏| 1875/2048 [23:06<02:04, 1.39it/s]
loss 1.55 accuracy 0.38 -- 56.44 + 56.23 + 496.90 + 4.86 = 614.42: 92%|█████████▏| 1875/2048 [23:07<02:04, 1.39it/s]
loss 1.55 accuracy 0.38 -- 56.44 + 56.23 + 496.90 + 4.86 = 614.42: 92%|█████████▏| 1876/2048 [23:07<01:59, 1.44it/s]
loss 1.68 accuracy 0.44 -- 162.16 + 56.86 + 498.17 + 4.78 = 721.96: 92%|█████████▏| 1876/2048 [23:07<01:59, 1.44it/s]
loss 1.68 accuracy 0.44 -- 162.16 + 56.86 + 498.17 + 4.78 = 721.96: 92%|█████████▏| 1877/2048 [23:07<02:01, 1.40it/s]
loss 1.65 accuracy 0.50 -- 56.16 + 57.12 + 620.32 + 4.77 = 738.36: 92%|█████████▏| 1877/2048 [23:08<02:01, 1.40it/s]
loss 1.65 accuracy 0.50 -- 56.16 + 57.12 + 620.32 + 4.77 = 738.36: 92%|█████████▏| 1878/2048 [23:08<02:03, 1.37it/s]
loss 2.03 accuracy 0.19 -- 56.96 + 56.64 + 505.58 + 4.78 = 623.96: 92%|█████████▏| 1878/2048 [23:09<02:03, 1.37it/s]
loss 2.03 accuracy 0.19 -- 56.96 + 56.64 + 505.58 + 4.78 = 623.96: 92%|█████████▏| 1879/2048 [23:09<01:59, 1.42it/s]
loss 1.64 accuracy 0.31 -- 56.09 + 56.98 + 615.48 + 4.78 = 733.33: 92%|█████████▏| 1879/2048 [23:10<01:59, 1.42it/s]
loss 1.64 accuracy 0.31 -- 56.09 + 56.98 + 615.48 + 4.78 = 733.33: 92%|█████████▏| 1880/2048 [23:10<02:01, 1.39it/s]
loss 1.35 accuracy 0.44 -- 56.80 + 56.57 + 502.02 + 4.76 = 620.15: 92%|█████████▏| 1880/2048 [23:10<02:01, 1.39it/s]
loss 1.35 accuracy 0.44 -- 56.80 + 56.57 + 502.02 + 4.76 = 620.15: 92%|█████████▏| 1881/2048 [23:10<01:56, 1.43it/s]
loss 1.52 accuracy 0.50 -- 55.91 + 165.85 + 501.72 + 4.80 = 728.29: 92%|█████████▏| 1881/2048 [23:11<01:56, 1.43it/s]
loss 1.52 accuracy 0.50 -- 55.91 + 165.85 + 501.72 + 4.80 = 728.29: 92%|█████████▏| 1882/2048 [23:11<01:58, 1.40it/s]
loss 1.67 accuracy 0.38 -- 56.39 + 56.32 + 499.78 + 4.76 = 617.25: 92%|█████████▏| 1882/2048 [23:12<01:58, 1.40it/s]
loss 1.67 accuracy 0.38 -- 56.39 + 56.32 + 499.78 + 4.76 = 617.25: 92%|█████████▏| 1883/2048 [23:12<01:54, 1.44it/s]
loss 2.12 accuracy 0.19 -- 56.90 + 56.74 + 499.23 + 4.77 = 617.63: 92%|█████████▏| 1883/2048 [23:12<01:54, 1.44it/s]
loss 2.12 accuracy 0.19 -- 56.90 + 56.74 + 499.23 + 4.77 = 617.63: 92%|█████████▏| 1884/2048 [23:12<01:56, 1.41it/s]
loss 1.39 accuracy 0.50 -- 56.40 + 57.76 + 494.85 + 4.77 = 613.78: 92%|█████████▏| 1884/2048 [23:13<01:56, 1.41it/s]
loss 1.39 accuracy 0.50 -- 56.40 + 57.76 + 494.85 + 4.77 = 613.78: 92%|█████████▏| 1885/2048 [23:13<01:52, 1.45it/s]
loss 1.69 accuracy 0.31 -- 157.18 + 56.86 + 490.16 + 4.76 = 708.97: 92%|█████████▏| 1885/2048 [23:14<01:52, 1.45it/s]
loss 1.69 accuracy 0.31 -- 157.18 + 56.86 + 490.16 + 4.76 = 708.97: 92%|█████████▏| 1886/2048 [23:14<01:54, 1.42it/s]
loss 1.70 accuracy 0.56 -- 56.01 + 166.90 + 500.62 + 4.79 = 728.31: 92%|█████████▏| 1886/2048 [23:14<01:54, 1.42it/s]
loss 1.70 accuracy 0.56 -- 56.01 + 166.90 + 500.62 + 4.79 = 728.31: 92%|█████████▏| 1887/2048 [23:14<01:55, 1.39it/s]
loss 1.83 accuracy 0.38 -- 56.49 + 56.67 + 497.44 + 4.77 = 615.37: 92%|█████████▏| 1887/2048 [23:15<01:55, 1.39it/s]
loss 1.83 accuracy 0.38 -- 56.49 + 56.67 + 497.44 + 4.77 = 615.37: 92%|█████████▏| 1888/2048 [23:15<01:51, 1.44it/s]
loss 1.42 accuracy 0.31 -- 162.19 + 56.99 + 496.43 + 4.79 = 720.40: 92%|█████████▏| 1888/2048 [23:16<01:51, 1.44it/s]
loss 1.42 accuracy 0.31 -- 162.19 + 56.99 + 496.43 + 4.79 = 720.40: 92%|█████████▏| 1889/2048 [23:16<01:53, 1.41it/s]
loss 1.71 accuracy 0.25 -- 56.23 + 57.53 + 622.44 + 4.78 = 740.99: 92%|█████████▏| 1889/2048 [23:17<01:53, 1.41it/s]
loss 1.71 accuracy 0.25 -- 56.23 + 57.53 + 622.44 + 4.78 = 740.99: 92%|█████████▏| 1890/2048 [23:17<01:55, 1.37it/s]
loss 1.53 accuracy 0.31 -- 56.73 + 56.21 + 504.62 + 4.76 = 622.31: 92%|█████████▏| 1890/2048 [23:17<01:55, 1.37it/s]
loss 1.53 accuracy 0.31 -- 56.73 + 56.21 + 504.62 + 4.76 = 622.31: 92%|█████████▏| 1891/2048 [23:17<01:50, 1.42it/s]
loss 2.01 accuracy 0.19 -- 56.03 + 57.48 + 615.69 + 4.77 = 733.99: 92%|█████████▏| 1891/2048 [23:18<01:50, 1.42it/s]
loss 2.01 accuracy 0.19 -- 56.03 + 57.48 + 615.69 + 4.77 = 733.99: 92%|█████████▏| 1892/2048 [23:18<01:52, 1.39it/s]
loss 1.32 accuracy 0.50 -- 56.63 + 56.57 + 500.82 + 4.78 = 618.80: 92%|█████████▏| 1892/2048 [23:19<01:52, 1.39it/s]
loss 1.32 accuracy 0.50 -- 56.63 + 56.57 + 500.82 + 4.78 = 618.80: 92%|█████████▏| 1893/2048 [23:19<01:49, 1.41it/s]
loss 1.45 accuracy 0.50 -- 56.19 + 166.06 + 501.25 + 4.78 = 728.28: 92%|█████████▏| 1893/2048 [23:19<01:49, 1.41it/s]
loss 1.45 accuracy 0.50 -- 56.19 + 166.06 + 501.25 + 4.78 = 728.28: 92%|█████████▏| 1894/2048 [23:19<01:51, 1.38it/s]
loss 1.99 accuracy 0.56 -- 56.46 + 56.44 + 498.93 + 4.76 = 616.58: 92%|█████████▏| 1894/2048 [23:20<01:51, 1.38it/s]
loss 1.99 accuracy 0.56 -- 56.46 + 56.44 + 498.93 + 4.76 = 616.58: 93%|█████████▎| 1895/2048 [23:20<01:46, 1.43it/s]
loss 1.50 accuracy 0.31 -- 56.75 + 56.74 + 498.27 + 4.77 = 616.54: 93%|█████████▎| 1895/2048 [23:21<01:46, 1.43it/s]
loss 1.50 accuracy 0.31 -- 56.75 + 56.74 + 498.27 + 4.77 = 616.54: 93%|█████████▎| 1896/2048 [23:21<01:48, 1.40it/s]
loss 1.73 accuracy 0.25 -- 56.11 + 57.32 + 495.11 + 4.76 = 613.30: 93%|█████████▎| 1896/2048 [23:22<01:48, 1.40it/s]
loss 1.73 accuracy 0.25 -- 56.11 + 57.32 + 495.11 + 4.76 = 613.30: 93%|█████████▎| 1897/2048 [23:22<01:44, 1.44it/s]
loss 1.47 accuracy 0.31 -- 157.14 + 56.82 + 489.79 + 4.77 = 708.52: 93%|█████████▎| 1897/2048 [23:22<01:44, 1.44it/s]
loss 1.47 accuracy 0.31 -- 157.14 + 56.82 + 489.79 + 4.77 = 708.52: 93%|█████████▎| 1898/2048 [23:22<01:45, 1.42it/s]
loss 1.63 accuracy 0.44 -- 55.80 + 166.87 + 500.35 + 4.78 = 727.80: 93%|█████████▎| 1898/2048 [23:23<01:45, 1.42it/s]
loss 1.63 accuracy 0.44 -- 55.80 + 166.87 + 500.35 + 4.78 = 727.80: 93%|█████████▎| 1899/2048 [23:23<01:47, 1.39it/s]
loss 1.58 accuracy 0.38 -- 56.86 + 56.44 + 497.50 + 4.81 = 615.61: 93%|█████████▎| 1899/2048 [23:24<01:47, 1.39it/s]
loss 1.58 accuracy 0.38 -- 56.86 + 56.44 + 497.50 + 4.81 = 615.61: 93%|█████████▎| 1900/2048 [23:24<01:43, 1.44it/s]
loss 1.66 accuracy 0.38 -- 162.35 + 57.17 + 496.87 + 4.79 = 721.19: 93%|█████████▎| 1900/2048 [23:24<01:43, 1.44it/s]
loss 1.66 accuracy 0.38 -- 162.35 + 57.17 + 496.87 + 4.79 = 721.19: 93%|█████████▎| 1901/2048 [23:24<01:44, 1.40it/s]
loss 2.17 accuracy 0.19 -- 56.39 + 56.98 + 618.91 + 4.80 = 737.07: 93%|█████████▎| 1901/2048 [23:25<01:44, 1.40it/s]
loss 2.17 accuracy 0.19 -- 56.39 + 56.98 + 618.91 + 4.80 = 737.07: 93%|█████████▎| 1902/2048 [23:25<01:46, 1.37it/s]
loss 1.29 accuracy 0.50 -- 56.80 + 56.28 + 505.15 + 4.76 = 622.98: 93%|█████████▎| 1902/2048 [23:26<01:46, 1.37it/s]
loss 1.29 accuracy 0.50 -- 56.80 + 56.28 + 505.15 + 4.76 = 622.98: 93%|█████████▎| 1903/2048 [23:26<01:42, 1.42it/s]
loss 1.71 accuracy 0.56 -- 56.02 + 57.12 + 616.93 + 4.79 = 734.85: 93%|█████████▎| 1903/2048 [23:27<01:42, 1.42it/s]
loss 1.71 accuracy 0.56 -- 56.02 + 57.12 + 616.93 + 4.79 = 734.85: 93%|█████████▎| 1904/2048 [23:27<01:43, 1.39it/s]
loss 1.78 accuracy 0.38 -- 56.83 + 56.80 + 502.72 + 4.78 = 621.13: 93%|█████████▎| 1904/2048 [23:27<01:43, 1.39it/s]
loss 1.78 accuracy 0.38 -- 56.83 + 56.80 + 502.72 + 4.78 = 621.13: 93%|█████████▎| 1905/2048 [23:27<01:40, 1.43it/s]
loss 1.79 accuracy 0.31 -- 56.20 + 166.05 + 501.29 + 4.79 = 728.33: 93%|█████████▎| 1905/2048 [23:28<01:40, 1.43it/s]
loss 1.79 accuracy 0.31 -- 56.20 + 166.05 + 501.29 + 4.79 = 728.33: 93%|█████████▎| 1906/2048 [23:28<01:41, 1.40it/s]
loss 1.68 accuracy 0.19 -- 56.49 + 56.46 + 500.27 + 4.77 = 617.99: 93%|█████████▎| 1906/2048 [23:29<01:41, 1.40it/s]
loss 1.68 accuracy 0.19 -- 56.49 + 56.46 + 500.27 + 4.77 = 617.99: 93%|█████████▎| 1907/2048 [23:29<01:37, 1.44it/s]
loss 1.49 accuracy 0.44 -- 56.48 + 56.42 + 497.29 + 4.79 = 614.98: 93%|█████████▎| 1907/2048 [23:29<01:37, 1.44it/s]
loss 1.49 accuracy 0.44 -- 56.48 + 56.42 + 497.29 + 4.79 = 614.98: 93%|█████████▎| 1908/2048 [23:29<01:39, 1.41it/s]
loss 1.56 accuracy 0.38 -- 55.79 + 57.45 + 494.80 + 4.77 = 612.80: 93%|█████████▎| 1908/2048 [23:30<01:39, 1.41it/s]
loss 1.56 accuracy 0.38 -- 55.79 + 57.45 + 494.80 + 4.77 = 612.80: 93%|█████████▎| 1909/2048 [23:30<01:37, 1.43it/s]
loss 1.55 accuracy 0.44 -- 157.60 + 56.73 + 488.66 + 4.76 = 707.74: 93%|█████████▎| 1909/2048 [23:31<01:37, 1.43it/s]
loss 1.55 accuracy 0.44 -- 157.60 + 56.73 + 488.66 + 4.76 = 707.74: 93%|█████████▎| 1910/2048 [23:31<01:38, 1.41it/s]
loss 1.45 accuracy 0.50 -- 55.50 + 166.04 + 500.31 + 4.76 = 726.61: 93%|█████████▎| 1910/2048 [23:32<01:38, 1.41it/s]
loss 1.45 accuracy 0.50 -- 55.50 + 166.04 + 500.31 + 4.76 = 726.61: 93%|█████████▎| 1911/2048 [23:32<01:39, 1.38it/s]
loss 1.80 accuracy 0.31 -- 56.17 + 56.42 + 497.51 + 4.81 = 614.91: 93%|█████████▎| 1911/2048 [23:32<01:39, 1.38it/s]
loss 1.80 accuracy 0.31 -- 56.17 + 56.42 + 497.51 + 4.81 = 614.91: 93%|█████████▎| 1912/2048 [23:32<01:35, 1.43it/s]
loss 1.70 accuracy 0.31 -- 163.43 + 57.26 + 496.59 + 4.78 = 722.06: 93%|█████████▎| 1912/2048 [23:33<01:35, 1.43it/s]
loss 1.70 accuracy 0.31 -- 163.43 + 57.26 + 496.59 + 4.78 = 722.06: 93%|█████████▎| 1913/2048 [23:33<01:36, 1.40it/s]
loss 1.98 accuracy 0.12 -- 56.12 + 57.38 + 619.92 + 4.79 = 738.22: 93%|█████████▎| 1913/2048 [23:34<01:36, 1.40it/s]
loss 1.98 accuracy 0.12 -- 56.12 + 57.38 + 619.92 + 4.79 = 738.22: 93%|█████████▎| 1914/2048 [23:34<01:37, 1.37it/s]
loss 1.71 accuracy 0.38 -- 56.87 + 56.50 + 505.39 + 4.82 = 623.57: 93%|█████████▎| 1914/2048 [23:34<01:37, 1.37it/s]
loss 1.71 accuracy 0.38 -- 56.87 + 56.50 + 505.39 + 4.82 = 623.57: 94%|█████████▎| 1915/2048 [23:34<01:33, 1.42it/s]
loss 2.05 accuracy 0.12 -- 55.98 + 56.93 + 615.62 + 4.78 = 733.31: 94%|█████████▎| 1915/2048 [23:35<01:33, 1.42it/s]
loss 2.05 accuracy 0.12 -- 55.98 + 56.93 + 615.62 + 4.78 = 733.31: 94%|█████████▎| 1916/2048 [23:35<01:35, 1.39it/s]
loss 1.55 accuracy 0.44 -- 56.71 + 56.78 + 501.05 + 4.77 = 619.32: 94%|█████████▎| 1916/2048 [23:36<01:35, 1.39it/s]
loss 1.55 accuracy 0.44 -- 56.71 + 56.78 + 501.05 + 4.77 = 619.32: 94%|█████████▎| 1917/2048 [23:36<01:31, 1.43it/s]
loss 1.91 accuracy 0.19 -- 56.01 + 165.96 + 503.64 + 4.78 = 730.40: 94%|█████████▎| 1917/2048 [23:36<01:31, 1.43it/s]
loss 1.91 accuracy 0.19 -- 56.01 + 165.96 + 503.64 + 4.78 = 730.40: 94%|█████████▎| 1918/2048 [23:36<01:33, 1.40it/s]
loss 1.40 accuracy 0.38 -- 56.21 + 56.31 + 499.72 + 4.76 = 617.00: 94%|█████████▎| 1918/2048 [23:37<01:33, 1.40it/s]
loss 1.40 accuracy 0.38 -- 56.21 + 56.31 + 499.72 + 4.76 = 617.00: 94%|█████████▎| 1919/2048 [23:37<01:29, 1.44it/s]
loss 1.57 accuracy 0.38 -- 56.83 + 56.74 + 498.58 + 4.78 = 616.93: 94%|█████████▎| 1919/2048 [23:38<01:29, 1.44it/s]
loss 1.57 accuracy 0.38 -- 56.83 + 56.74 + 498.58 + 4.78 = 616.93: 94%|█████████▍| 1920/2048 [23:38<01:31, 1.41it/s]
loss 1.94 accuracy 0.38 -- 56.03 + 57.11 + 495.75 + 4.80 = 613.70: 94%|█████████▍| 1920/2048 [23:39<01:31, 1.41it/s]
loss 1.94 accuracy 0.38 -- 56.03 + 57.11 + 495.75 + 4.80 = 613.70: 94%|█████████▍| 1921/2048 [23:39<01:27, 1.45it/s]
loss 1.62 accuracy 0.25 -- 156.96 + 57.24 + 489.00 + 4.77 = 707.97: 94%|█████████▍| 1921/2048 [23:39<01:27, 1.45it/s]
loss 1.62 accuracy 0.25 -- 156.96 + 57.24 + 489.00 + 4.77 = 707.97: 94%|█████████▍| 1922/2048 [23:39<01:28, 1.42it/s]
loss 1.72 accuracy 0.25 -- 55.93 + 166.35 + 501.53 + 4.81 = 728.61: 94%|█████████▍| 1922/2048 [23:40<01:28, 1.42it/s]
loss 1.72 accuracy 0.25 -- 55.93 + 166.35 + 501.53 + 4.81 = 728.61: 94%|█████████▍| 1923/2048 [23:40<01:29, 1.39it/s]
loss 1.86 accuracy 0.44 -- 56.61 + 57.00 + 497.90 + 4.78 = 616.29: 94%|█████████▍| 1923/2048 [23:41<01:29, 1.39it/s]
loss 1.86 accuracy 0.44 -- 56.61 + 57.00 + 497.90 + 4.78 = 616.29: 94%|█████████▍| 1924/2048 [23:41<01:26, 1.44it/s]
loss 1.81 accuracy 0.25 -- 162.77 + 56.91 + 497.24 + 4.77 = 721.69: 94%|█████████▍| 1924/2048 [23:41<01:26, 1.44it/s]
loss 1.81 accuracy 0.25 -- 162.77 + 56.91 + 497.24 + 4.77 = 721.69: 94%|█████████▍| 1925/2048 [23:41<01:27, 1.40it/s]
loss 1.51 accuracy 0.19 -- 56.20 + 57.18 + 620.94 + 4.78 = 739.09: 94%|█████████▍| 1925/2048 [23:42<01:27, 1.40it/s]
loss 1.51 accuracy 0.19 -- 56.20 + 57.18 + 620.94 + 4.78 = 739.09: 94%|█████████▍| 1926/2048 [23:42<01:28, 1.37it/s]
loss 1.61 accuracy 0.44 -- 56.59 + 56.40 + 505.06 + 4.77 = 622.82: 94%|█████████▍| 1926/2048 [23:43<01:28, 1.37it/s]
loss 1.61 accuracy 0.44 -- 56.59 + 56.40 + 505.06 + 4.77 = 622.82: 94%|█████████▍| 1927/2048 [23:43<01:25, 1.42it/s]
loss 1.59 accuracy 0.31 -- 55.97 + 57.22 + 616.41 + 4.79 = 734.38: 94%|█████████▍| 1927/2048 [23:44<01:25, 1.42it/s]
loss 1.59 accuracy 0.31 -- 55.97 + 57.22 + 616.41 + 4.79 = 734.38: 94%|█████████▍| 1928/2048 [23:44<01:26, 1.39it/s]
loss 2.30 accuracy 0.31 -- 56.51 + 56.37 + 502.28 + 4.79 = 619.95: 94%|█████████▍| 1928/2048 [23:44<01:26, 1.39it/s]
loss 2.30 accuracy 0.31 -- 56.51 + 56.37 + 502.28 + 4.79 = 619.95: 94%|█████████▍| 1929/2048 [23:44<01:23, 1.43it/s]
loss 1.54 accuracy 0.50 -- 56.46 + 166.28 + 500.17 + 4.77 = 727.68: 94%|█████████▍| 1929/2048 [23:45<01:23, 1.43it/s]
loss 1.54 accuracy 0.50 -- 56.46 + 166.28 + 500.17 + 4.77 = 727.68: 94%|█████████▍| 1930/2048 [23:45<01:24, 1.40it/s]
loss 1.56 accuracy 0.44 -- 56.05 + 56.74 + 498.90 + 4.78 = 616.47: 94%|█████████▍| 1930/2048 [23:46<01:24, 1.40it/s]
loss 1.56 accuracy 0.44 -- 56.05 + 56.74 + 498.90 + 4.78 = 616.47: 94%|█████████▍| 1931/2048 [23:46<01:21, 1.44it/s]
loss 1.85 accuracy 0.44 -- 56.64 + 56.35 + 498.60 + 4.77 = 616.36: 94%|█████████▍| 1931/2048 [23:46<01:21, 1.44it/s]
loss 1.85 accuracy 0.44 -- 56.64 + 56.35 + 498.60 + 4.77 = 616.36: 94%|█████████▍| 1932/2048 [23:46<01:22, 1.41it/s]
loss 2.34 accuracy 0.25 -- 56.16 + 57.23 + 494.89 + 4.79 = 613.06: 94%|█████████▍| 1932/2048 [23:47<01:22, 1.41it/s]
loss 2.34 accuracy 0.25 -- 56.16 + 57.23 + 494.89 + 4.79 = 613.06: 94%|█████████▍| 1933/2048 [23:47<01:20, 1.43it/s]
loss 1.97 accuracy 0.12 -- 158.05 + 56.83 + 489.65 + 4.77 = 709.30: 94%|█████████▍| 1933/2048 [23:48<01:20, 1.43it/s]
loss 1.97 accuracy 0.12 -- 158.05 + 56.83 + 489.65 + 4.77 = 709.30: 94%|█████████▍| 1934/2048 [23:48<01:21, 1.41it/s]
loss 1.72 accuracy 0.44 -- 56.01 + 166.59 + 502.26 + 4.76 = 729.63: 94%|█████████▍| 1934/2048 [23:49<01:21, 1.41it/s]
loss 1.72 accuracy 0.44 -- 56.01 + 166.59 + 502.26 + 4.76 = 729.63: 94%|█████████▍| 1935/2048 [23:49<01:21, 1.38it/s]
loss 1.51 accuracy 0.44 -- 56.53 + 56.30 + 497.17 + 4.78 = 614.78: 94%|█████████▍| 1935/2048 [23:49<01:21, 1.38it/s]
loss 1.51 accuracy 0.44 -- 56.53 + 56.30 + 497.17 + 4.78 = 614.78: 95%|█████████▍| 1936/2048 [23:49<01:18, 1.43it/s]
loss 1.56 accuracy 0.31 -- 162.40 + 57.20 + 497.17 + 4.77 = 721.54: 95%|█████████▍| 1936/2048 [23:50<01:18, 1.43it/s]
loss 1.56 accuracy 0.31 -- 162.40 + 57.20 + 497.17 + 4.77 = 721.54: 95%|█████████▍| 1937/2048 [23:50<01:19, 1.40it/s]
loss 1.93 accuracy 0.31 -- 56.17 + 57.19 + 620.67 + 4.77 = 738.80: 95%|█████████▍| 1937/2048 [23:51<01:19, 1.40it/s]
loss 1.93 accuracy 0.31 -- 56.17 + 57.19 + 620.67 + 4.77 = 738.80: 95%|█████████▍| 1938/2048 [23:51<01:20, 1.37it/s]
loss 1.58 accuracy 0.38 -- 57.07 + 56.68 + 506.53 + 4.77 = 625.05: 95%|█████████▍| 1938/2048 [23:51<01:20, 1.37it/s]
loss 1.58 accuracy 0.38 -- 57.07 + 56.68 + 506.53 + 4.77 = 625.05: 95%|█████████▍| 1939/2048 [23:51<01:16, 1.42it/s]
loss 1.86 accuracy 0.25 -- 56.22 + 57.30 + 616.67 + 4.78 = 734.98: 95%|█████████▍| 1939/2048 [23:52<01:16, 1.42it/s]
loss 1.86 accuracy 0.25 -- 56.22 + 57.30 + 616.67 + 4.78 = 734.98: 95%|█████████▍| 1940/2048 [23:52<01:18, 1.38it/s]
loss 2.07 accuracy 0.31 -- 57.45 + 57.01 + 502.78 + 4.85 = 622.09: 95%|█████████▍| 1940/2048 [23:53<01:18, 1.38it/s]
loss 2.07 accuracy 0.31 -- 57.45 + 57.01 + 502.78 + 4.85 = 622.09: 95%|█████████▍| 1941/2048 [23:53<01:14, 1.43it/s]
loss 1.61 accuracy 0.38 -- 56.24 + 166.46 + 502.37 + 4.78 = 729.85: 95%|█████████▍| 1941/2048 [23:54<01:14, 1.43it/s]
loss 1.61 accuracy 0.38 -- 56.24 + 166.46 + 502.37 + 4.78 = 729.85: 95%|█████████▍| 1942/2048 [23:54<01:16, 1.39it/s]
loss 1.71 accuracy 0.38 -- 56.17 + 56.28 + 498.94 + 4.79 = 616.18: 95%|█████████▍| 1942/2048 [23:54<01:16, 1.39it/s]
loss 1.71 accuracy 0.38 -- 56.17 + 56.28 + 498.94 + 4.79 = 616.18: 95%|█████████▍| 1943/2048 [23:54<01:12, 1.44it/s]
loss 1.77 accuracy 0.25 -- 56.81 + 56.48 + 497.99 + 4.80 = 616.08: 95%|█████████▍| 1943/2048 [23:55<01:12, 1.44it/s]
loss 1.77 accuracy 0.25 -- 56.81 + 56.48 + 497.99 + 4.80 = 616.08: 95%|█████████▍| 1944/2048 [23:55<01:14, 1.41it/s]
loss 1.95 accuracy 0.50 -- 55.88 + 57.15 + 495.12 + 4.81 = 612.95: 95%|█████████▍| 1944/2048 [23:56<01:14, 1.41it/s]
loss 1.95 accuracy 0.50 -- 55.88 + 57.15 + 495.12 + 4.81 = 612.95: 95%|█████████▍| 1945/2048 [23:56<01:11, 1.45it/s]
loss 1.50 accuracy 0.31 -- 157.94 + 56.70 + 490.52 + 4.80 = 709.96: 95%|█████████▍| 1945/2048 [23:56<01:11, 1.45it/s]
loss 1.50 accuracy 0.31 -- 157.94 + 56.70 + 490.52 + 4.80 = 709.96: 95%|█████████▌| 1946/2048 [23:56<01:11, 1.42it/s]
loss 1.57 accuracy 0.44 -- 56.14 + 167.10 + 502.77 + 4.78 = 730.78: 95%|█████████▌| 1946/2048 [23:57<01:11, 1.42it/s]
loss 1.57 accuracy 0.44 -- 56.14 + 167.10 + 502.77 + 4.78 = 730.78: 95%|█████████▌| 1947/2048 [23:57<01:12, 1.39it/s]
loss 1.76 accuracy 0.31 -- 56.53 + 56.62 + 498.15 + 4.78 = 616.08: 95%|█████████▌| 1947/2048 [23:58<01:12, 1.39it/s]
loss 1.76 accuracy 0.31 -- 56.53 + 56.62 + 498.15 + 4.78 = 616.08: 95%|█████████▌| 1948/2048 [23:58<01:09, 1.43it/s]
loss 2.18 accuracy 0.25 -- 162.31 + 56.88 + 497.33 + 4.76 = 721.28: 95%|█████████▌| 1948/2048 [23:58<01:09, 1.43it/s]
loss 2.18 accuracy 0.25 -- 162.31 + 56.88 + 497.33 + 4.76 = 721.28: 95%|█████████▌| 1949/2048 [23:58<01:10, 1.40it/s]
loss 1.63 accuracy 0.25 -- 55.85 + 57.18 + 618.98 + 4.78 = 736.79: 95%|█████████▌| 1949/2048 [23:59<01:10, 1.40it/s]
loss 1.63 accuracy 0.25 -- 55.85 + 57.18 + 618.98 + 4.78 = 736.79: 95%|█████████▌| 1950/2048 [23:59<01:11, 1.37it/s]
loss 1.88 accuracy 0.38 -- 56.76 + 56.71 + 504.03 + 4.80 = 622.30: 95%|█████████▌| 1950/2048 [24:00<01:11, 1.37it/s]
loss 1.88 accuracy 0.38 -- 56.76 + 56.71 + 504.03 + 4.80 = 622.30: 95%|█████████▌| 1951/2048 [24:00<01:08, 1.42it/s]
loss 1.56 accuracy 0.38 -- 55.99 + 57.17 + 617.36 + 4.77 = 735.29: 95%|█████████▌| 1951/2048 [24:01<01:08, 1.42it/s]
loss 1.56 accuracy 0.38 -- 55.99 + 57.17 + 617.36 + 4.77 = 735.29: 95%|█████████▌| 1952/2048 [24:01<01:09, 1.39it/s]
loss 2.12 accuracy 0.25 -- 56.39 + 56.25 + 500.92 + 4.76 = 618.32: 95%|█████████▌| 1952/2048 [24:01<01:09, 1.39it/s]
loss 2.12 accuracy 0.25 -- 56.39 + 56.25 + 500.92 + 4.76 = 618.32: 95%|█████████▌| 1953/2048 [24:01<01:06, 1.43it/s]
loss 1.71 accuracy 0.38 -- 56.11 + 166.02 + 500.83 + 4.82 = 727.78: 95%|█████████▌| 1953/2048 [24:02<01:06, 1.43it/s]
loss 1.71 accuracy 0.38 -- 56.11 + 166.02 + 500.83 + 4.82 = 727.78: 95%|█████████▌| 1954/2048 [24:02<01:07, 1.40it/s]
loss 1.58 accuracy 0.38 -- 56.08 + 56.39 + 498.51 + 4.77 = 615.75: 95%|█████████▌| 1954/2048 [24:03<01:07, 1.40it/s]
loss 1.58 accuracy 0.38 -- 56.08 + 56.39 + 498.51 + 4.77 = 615.75: 95%|█████████▌| 1955/2048 [24:03<01:04, 1.44it/s]
loss 1.59 accuracy 0.44 -- 56.56 + 56.49 + 497.88 + 4.78 = 615.71: 95%|█████████▌| 1955/2048 [24:03<01:04, 1.44it/s]
loss 1.59 accuracy 0.44 -- 56.56 + 56.49 + 497.88 + 4.78 = 615.71: 96%|█████████▌| 1956/2048 [24:03<01:05, 1.41it/s]
loss 1.88 accuracy 0.44 -- 56.08 + 57.50 + 495.64 + 4.81 = 614.04: 96%|█████████▌| 1956/2048 [24:04<01:05, 1.41it/s]
loss 1.88 accuracy 0.44 -- 56.08 + 57.50 + 495.64 + 4.81 = 614.04: 96%|█████████▌| 1957/2048 [24:04<01:03, 1.43it/s]
loss 1.77 accuracy 0.19 -- 157.37 + 57.17 + 490.24 + 4.76 = 709.54: 96%|█████████▌| 1957/2048 [24:05<01:03, 1.43it/s]
loss 1.77 accuracy 0.19 -- 157.37 + 57.17 + 490.24 + 4.76 = 709.54: 96%|█████████▌| 1958/2048 [24:05<01:03, 1.41it/s]
loss 1.67 accuracy 0.50 -- 55.89 + 166.48 + 501.74 + 4.78 = 728.89: 96%|█████████▌| 1958/2048 [24:06<01:03, 1.41it/s]
loss 1.67 accuracy 0.50 -- 55.89 + 166.48 + 501.74 + 4.78 = 728.89: 96%|█████████▌| 1959/2048 [24:06<01:04, 1.38it/s]
loss 1.67 accuracy 0.44 -- 56.54 + 56.08 + 498.17 + 4.77 = 615.55: 96%|█████████▌| 1959/2048 [24:06<01:04, 1.38it/s]
loss 1.67 accuracy 0.44 -- 56.54 + 56.08 + 498.17 + 4.77 = 615.55: 96%|█████████▌| 1960/2048 [24:06<01:01, 1.43it/s]
loss 1.77 accuracy 0.38 -- 162.83 + 56.77 + 496.27 + 4.81 = 720.68: 96%|█████████▌| 1960/2048 [24:07<01:01, 1.43it/s]
loss 1.77 accuracy 0.38 -- 162.83 + 56.77 + 496.27 + 4.81 = 720.68: 96%|█████████▌| 1961/2048 [24:07<01:02, 1.40it/s]
loss 1.73 accuracy 0.50 -- 56.25 + 57.34 + 619.59 + 4.77 = 737.95: 96%|█████████▌| 1961/2048 [24:08<01:02, 1.40it/s]
loss 1.73 accuracy 0.50 -- 56.25 + 57.34 + 619.59 + 4.77 = 737.95: 96%|█████████▌| 1962/2048 [24:08<01:02, 1.37it/s]
loss 2.39 accuracy 0.12 -- 56.56 + 56.53 + 506.81 + 4.76 = 624.65: 96%|█████████▌| 1962/2048 [24:08<01:02, 1.37it/s]
loss 2.39 accuracy 0.12 -- 56.56 + 56.53 + 506.81 + 4.76 = 624.65: 96%|█████████▌| 1963/2048 [24:08<01:00, 1.42it/s]
loss 1.77 accuracy 0.38 -- 56.38 + 57.03 + 615.52 + 4.78 = 733.70: 96%|█████████▌| 1963/2048 [24:09<01:00, 1.42it/s]
loss 1.77 accuracy 0.38 -- 56.38 + 57.03 + 615.52 + 4.78 = 733.70: 96%|█████████▌| 1964/2048 [24:09<01:00, 1.38it/s]
loss 1.94 accuracy 0.31 -- 56.63 + 56.61 + 501.49 + 4.79 = 619.52: 96%|█████████▌| 1964/2048 [24:10<01:00, 1.38it/s]
loss 1.94 accuracy 0.31 -- 56.63 + 56.61 + 501.49 + 4.79 = 619.52: 96%|█████████▌| 1965/2048 [24:10<00:58, 1.41it/s]
loss 1.76 accuracy 0.38 -- 56.04 + 166.16 + 501.56 + 4.78 = 728.53: 96%|█████████▌| 1965/2048 [24:11<00:58, 1.41it/s]
loss 1.76 accuracy 0.38 -- 56.04 + 166.16 + 501.56 + 4.78 = 728.53: 96%|█████████▌| 1966/2048 [24:11<00:59, 1.38it/s]
loss 1.94 accuracy 0.12 -- 56.03 + 56.52 + 499.38 + 4.79 = 616.72: 96%|█████████▌| 1966/2048 [24:11<00:59, 1.38it/s]
loss 1.94 accuracy 0.12 -- 56.03 + 56.52 + 499.38 + 4.79 = 616.72: 96%|█████████▌| 1967/2048 [24:11<00:56, 1.43it/s]
loss 1.29 accuracy 0.69 -- 56.83 + 56.39 + 498.90 + 4.78 = 616.90: 96%|█████████▌| 1967/2048 [24:12<00:56, 1.43it/s]
loss 1.29 accuracy 0.69 -- 56.83 + 56.39 + 498.90 + 4.78 = 616.90: 96%|█████████▌| 1968/2048 [24:12<00:57, 1.40it/s]
loss 1.70 accuracy 0.31 -- 56.16 + 57.22 + 498.71 + 4.78 = 616.86: 96%|█████████▌| 1968/2048 [24:13<00:57, 1.40it/s]
loss 1.70 accuracy 0.31 -- 56.16 + 57.22 + 498.71 + 4.78 = 616.86: 96%|█████████▌| 1969/2048 [24:13<00:54, 1.44it/s]
loss 2.07 accuracy 0.19 -- 158.12 + 57.03 + 491.39 + 4.78 = 711.31: 96%|█████████▌| 1969/2048 [24:13<00:54, 1.44it/s]
loss 2.07 accuracy 0.19 -- 158.12 + 57.03 + 491.39 + 4.78 = 711.31: 96%|█████████▌| 1970/2048 [24:13<00:55, 1.41it/s]
loss 2.17 accuracy 0.12 -- 55.92 + 166.04 + 502.01 + 4.77 = 728.75: 96%|█████████▌| 1970/2048 [24:14<00:55, 1.41it/s]
loss 2.17 accuracy 0.12 -- 55.92 + 166.04 + 502.01 + 4.77 = 728.75: 96%|█████████▌| 1971/2048 [24:14<00:55, 1.39it/s]
loss 1.28 accuracy 0.69 -- 56.70 + 56.50 + 497.82 + 4.76 = 615.79: 96%|█████████▌| 1971/2048 [24:15<00:55, 1.39it/s]
loss 1.28 accuracy 0.69 -- 56.70 + 56.50 + 497.82 + 4.76 = 615.79: 96%|█████████▋| 1972/2048 [24:15<00:53, 1.43it/s]
loss 1.77 accuracy 0.25 -- 162.25 + 57.01 + 496.86 + 4.77 = 720.88: 96%|█████████▋| 1972/2048 [24:16<00:53, 1.43it/s]
loss 1.77 accuracy 0.25 -- 162.25 + 57.01 + 496.86 + 4.77 = 720.88: 96%|█████████▋| 1973/2048 [24:16<00:54, 1.38it/s]
loss 1.80 accuracy 0.31 -- 55.89 + 57.05 + 620.75 + 4.80 = 738.50: 96%|█████████▋| 1973/2048 [24:16<00:54, 1.38it/s]
loss 1.80 accuracy 0.31 -- 55.89 + 57.05 + 620.75 + 4.80 = 738.50: 96%|█████████▋| 1974/2048 [24:16<00:54, 1.36it/s]
loss 2.15 accuracy 0.25 -- 57.07 + 56.90 + 505.86 + 4.77 = 624.60: 96%|█████████▋| 1974/2048 [24:17<00:54, 1.36it/s]
loss 2.15 accuracy 0.25 -- 57.07 + 56.90 + 505.86 + 4.77 = 624.60: 96%|█████████▋| 1975/2048 [24:17<00:51, 1.41it/s]
loss 1.86 accuracy 0.25 -- 56.19 + 57.25 + 616.42 + 4.78 = 734.64: 96%|█████████▋| 1975/2048 [24:18<00:51, 1.41it/s]
loss 1.86 accuracy 0.25 -- 56.19 + 57.25 + 616.42 + 4.78 = 734.64: 96%|█████████▋| 1976/2048 [24:18<00:52, 1.38it/s]
loss 1.58 accuracy 0.31 -- 56.71 + 56.60 + 502.06 + 4.76 = 620.14: 96%|█████████▋| 1976/2048 [24:18<00:52, 1.38it/s]
loss 1.58 accuracy 0.31 -- 56.71 + 56.60 + 502.06 + 4.76 = 620.14: 97%|█████████▋| 1977/2048 [24:18<00:49, 1.42it/s]
loss 1.43 accuracy 0.38 -- 56.05 + 165.99 + 501.88 + 4.78 = 728.70: 97%|█████████▋| 1977/2048 [24:19<00:49, 1.42it/s]
loss 1.43 accuracy 0.38 -- 56.05 + 165.99 + 501.88 + 4.78 = 728.70: 97%|█████████▋| 1978/2048 [24:19<00:50, 1.39it/s]
loss 1.52 accuracy 0.56 -- 56.18 + 56.74 + 499.49 + 4.76 = 617.17: 97%|█████████▋| 1978/2048 [24:20<00:50, 1.39it/s]
loss 1.52 accuracy 0.56 -- 56.18 + 56.74 + 499.49 + 4.76 = 617.17: 97%|█████████▋| 1979/2048 [24:20<00:48, 1.44it/s]
loss 1.64 accuracy 0.31 -- 56.68 + 56.69 + 499.48 + 4.78 = 617.62: 97%|█████████▋| 1979/2048 [24:21<00:48, 1.44it/s]
loss 1.64 accuracy 0.31 -- 56.68 + 56.69 + 499.48 + 4.78 = 617.62: 97%|█████████▋| 1980/2048 [24:21<00:48, 1.40it/s]
loss 1.33 accuracy 0.56 -- 55.91 + 57.13 + 495.66 + 4.78 = 613.48: 97%|█████████▋| 1980/2048 [24:21<00:48, 1.40it/s]
loss 1.33 accuracy 0.56 -- 55.91 + 57.13 + 495.66 + 4.78 = 613.48: 97%|█████████▋| 1981/2048 [24:21<00:46, 1.45it/s]
loss 1.84 accuracy 0.38 -- 158.02 + 56.95 + 488.92 + 4.77 = 708.66: 97%|█████████▋| 1981/2048 [24:22<00:46, 1.45it/s]
loss 1.84 accuracy 0.38 -- 158.02 + 56.95 + 488.92 + 4.77 = 708.66: 97%|█████████▋| 1982/2048 [24:22<00:46, 1.42it/s]
loss 2.22 accuracy 0.38 -- 55.91 + 166.38 + 502.58 + 4.78 = 729.65: 97%|█████████▋| 1982/2048 [24:23<00:46, 1.42it/s]
loss 2.22 accuracy 0.38 -- 55.91 + 166.38 + 502.58 + 4.78 = 729.65: 97%|█████████▋| 1983/2048 [24:23<00:46, 1.39it/s]
loss 1.66 accuracy 0.44 -- 56.52 + 56.29 + 498.61 + 4.77 = 616.19: 97%|█████████▋| 1983/2048 [24:23<00:46, 1.39it/s]
loss 1.66 accuracy 0.44 -- 56.52 + 56.29 + 498.61 + 4.77 = 616.19: 97%|█████████▋| 1984/2048 [24:23<00:44, 1.43it/s]
loss 2.49 accuracy 0.19 -- 162.95 + 56.96 + 496.99 + 4.79 = 721.69: 97%|█████████▋| 1984/2048 [24:24<00:44, 1.43it/s]
loss 2.49 accuracy 0.19 -- 162.95 + 56.96 + 496.99 + 4.79 = 721.69: 97%|█████████▋| 1985/2048 [24:24<00:44, 1.40it/s]
loss 1.57 accuracy 0.19 -- 56.20 + 57.63 + 621.01 + 4.80 = 739.63: 97%|█████████▋| 1985/2048 [24:25<00:44, 1.40it/s]
loss 1.57 accuracy 0.19 -- 56.20 + 57.63 + 621.01 + 4.80 = 739.63: 97%|█████████▋| 1986/2048 [24:25<00:45, 1.37it/s]
loss 1.46 accuracy 0.31 -- 56.74 + 56.50 + 504.57 + 4.78 = 622.59: 97%|█████████▋| 1986/2048 [24:25<00:45, 1.37it/s]
loss 1.46 accuracy 0.31 -- 56.74 + 56.50 + 504.57 + 4.78 = 622.59: 97%|█████████▋| 1987/2048 [24:25<00:43, 1.42it/s]
loss 1.56 accuracy 0.56 -- 56.20 + 57.09 + 615.49 + 4.78 = 733.55: 97%|█████████▋| 1987/2048 [24:26<00:43, 1.42it/s]
loss 1.56 accuracy 0.56 -- 56.20 + 57.09 + 615.49 + 4.78 = 733.55: 97%|█████████▋| 1988/2048 [24:26<00:43, 1.39it/s]
loss 1.72 accuracy 0.31 -- 56.64 + 56.42 + 501.84 + 4.78 = 619.68: 97%|█████████▋| 1988/2048 [24:27<00:43, 1.39it/s]
loss 1.72 accuracy 0.31 -- 56.64 + 56.42 + 501.84 + 4.78 = 619.68: 97%|█████████▋| 1989/2048 [24:27<00:41, 1.43it/s]
loss 2.08 accuracy 0.19 -- 56.17 + 166.04 + 501.76 + 4.78 = 728.74: 97%|█████████▋| 1989/2048 [24:28<00:41, 1.43it/s]
loss 2.08 accuracy 0.19 -- 56.17 + 166.04 + 501.76 + 4.78 = 728.74: 97%|█████████▋| 1990/2048 [24:28<00:41, 1.40it/s]
loss 1.61 accuracy 0.31 -- 56.26 + 56.41 + 498.92 + 4.79 = 616.38: 97%|█████████▋| 1990/2048 [24:28<00:41, 1.40it/s]
loss 1.61 accuracy 0.31 -- 56.26 + 56.41 + 498.92 + 4.79 = 616.38: 97%|█████████▋| 1991/2048 [24:28<00:39, 1.44it/s]
loss 1.66 accuracy 0.38 -- 57.30 + 56.50 + 498.46 + 4.79 = 617.04: 97%|█████████▋| 1991/2048 [24:29<00:39, 1.44it/s]
loss 1.66 accuracy 0.38 -- 57.30 + 56.50 + 498.46 + 4.79 = 617.04: 97%|█████████▋| 1992/2048 [24:29<00:39, 1.41it/s]
loss 2.12 accuracy 0.25 -- 56.29 + 57.77 + 496.10 + 4.79 = 614.95: 97%|█████████▋| 1992/2048 [24:30<00:39, 1.41it/s]
loss 2.12 accuracy 0.25 -- 56.29 + 57.77 + 496.10 + 4.79 = 614.95: 97%|█████████▋| 1993/2048 [24:30<00:37, 1.45it/s]
loss 1.92 accuracy 0.50 -- 157.71 + 57.14 + 489.33 + 4.76 = 708.94: 97%|█████████▋| 1993/2048 [24:30<00:37, 1.45it/s]
loss 1.92 accuracy 0.50 -- 157.71 + 57.14 + 489.33 + 4.76 = 708.94: 97%|█████████▋| 1994/2048 [24:30<00:38, 1.42it/s]
loss 1.68 accuracy 0.75 -- 55.79 + 166.28 + 501.62 + 4.77 = 728.46: 97%|█████████▋| 1994/2048 [24:31<00:38, 1.42it/s]
loss 1.68 accuracy 0.75 -- 55.79 + 166.28 + 501.62 + 4.77 = 728.46: 97%|█████████▋| 1995/2048 [24:31<00:38, 1.39it/s]
loss 1.84 accuracy 0.31 -- 56.59 + 56.41 + 498.12 + 4.76 = 615.89: 97%|█████████▋| 1995/2048 [24:32<00:38, 1.39it/s]
loss 1.84 accuracy 0.31 -- 56.59 + 56.41 + 498.12 + 4.76 = 615.89: 97%|█████████▋| 1996/2048 [24:32<00:36, 1.44it/s]
loss 1.52 accuracy 0.44 -- 162.06 + 57.05 + 498.47 + 4.78 = 722.35: 97%|█████████▋| 1996/2048 [24:33<00:36, 1.44it/s]
loss 1.52 accuracy 0.44 -- 162.06 + 57.05 + 498.47 + 4.78 = 722.35: 98%|█████████▊| 1997/2048 [24:33<00:36, 1.40it/s]
loss 1.56 accuracy 0.31 -- 56.01 + 57.14 + 620.52 + 4.78 = 738.45: 98%|█████████▊| 1997/2048 [24:33<00:36, 1.40it/s]
loss 1.56 accuracy 0.31 -- 56.01 + 57.14 + 620.52 + 4.78 = 738.45: 98%|█████████▊| 1998/2048 [24:33<00:36, 1.37it/s]
loss 1.65 accuracy 0.38 -- 56.83 + 56.93 + 505.05 + 4.79 = 623.60: 98%|█████████▊| 1998/2048 [24:34<00:36, 1.37it/s]
loss 1.65 accuracy 0.38 -- 56.83 + 56.93 + 505.05 + 4.79 = 623.60: 98%|█████████▊| 1999/2048 [24:34<00:34, 1.42it/s]
loss 2.13 accuracy 0.25 -- 56.23 + 57.24 + 615.72 + 4.77 = 733.97: 98%|█████████▊| 1999/2048 [24:35<00:34, 1.42it/s]
loss 2.13 accuracy 0.25 -- 56.23 + 57.24 + 615.72 + 4.77 = 733.97: 98%|█████████▊| 2000/2048 [24:35<00:34, 1.39it/s]
loss 2.11 accuracy 0.44 -- 56.69 + 56.44 + 501.61 + 4.82 = 619.55: 98%|█████████▊| 2000/2048 [24:35<00:34, 1.39it/s]
loss 2.11 accuracy 0.44 -- 56.69 + 56.44 + 501.61 + 4.82 = 619.55: 98%|█████████▊| 2001/2048 [24:35<00:32, 1.43it/s]
loss 1.81 accuracy 0.56 -- 56.73 + 166.69 + 501.31 + 4.83 = 729.57: 98%|█████████▊| 2001/2048 [24:36<00:32, 1.43it/s]
loss 1.81 accuracy 0.56 -- 56.73 + 166.69 + 501.31 + 4.83 = 729.57: 98%|█████████▊| 2002/2048 [24:36<00:32, 1.40it/s]
loss 1.61 accuracy 0.25 -- 56.59 + 56.98 + 500.52 + 4.76 = 618.86: 98%|█████████▊| 2002/2048 [24:37<00:32, 1.40it/s]
loss 1.61 accuracy 0.25 -- 56.59 + 56.98 + 500.52 + 4.76 = 618.86: 98%|█████████▊| 2003/2048 [24:37<00:31, 1.44it/s]
loss 1.57 accuracy 0.38 -- 56.47 + 56.36 + 498.06 + 4.79 = 615.67: 98%|█████████▊| 2003/2048 [24:38<00:31, 1.44it/s]
loss 1.57 accuracy 0.38 -- 56.47 + 56.36 + 498.06 + 4.79 = 615.67: 98%|█████████▊| 2004/2048 [24:38<00:31, 1.41it/s]
loss 2.23 accuracy 0.31 -- 55.92 + 57.22 + 494.63 + 4.81 = 612.57: 98%|█████████▊| 2004/2048 [24:38<00:31, 1.41it/s]
loss 2.23 accuracy 0.31 -- 55.92 + 57.22 + 494.63 + 4.81 = 612.57: 98%|█████████▊| 2005/2048 [24:38<00:29, 1.45it/s]
loss 1.38 accuracy 0.44 -- 157.40 + 56.73 + 488.64 + 4.77 = 707.53: 98%|█████████▊| 2005/2048 [24:39<00:29, 1.45it/s]
loss 1.38 accuracy 0.44 -- 157.40 + 56.73 + 488.64 + 4.77 = 707.53: 98%|█████████▊| 2006/2048 [24:39<00:29, 1.42it/s]
loss 1.78 accuracy 0.38 -- 55.61 + 166.31 + 500.78 + 4.76 = 727.46: 98%|█████████▊| 2006/2048 [24:40<00:29, 1.42it/s]
loss 1.78 accuracy 0.38 -- 55.61 + 166.31 + 500.78 + 4.76 = 727.46: 98%|█████████▊| 2007/2048 [24:40<00:29, 1.39it/s]
loss 1.99 accuracy 0.25 -- 56.40 + 56.53 + 497.99 + 4.76 = 615.68: 98%|█████████▊| 2007/2048 [24:40<00:29, 1.39it/s]
loss 1.99 accuracy 0.25 -- 56.40 + 56.53 + 497.99 + 4.76 = 615.68: 98%|█████████▊| 2008/2048 [24:40<00:27, 1.44it/s]
loss 1.82 accuracy 0.31 -- 162.53 + 57.26 + 496.41 + 4.77 = 720.97: 98%|█████████▊| 2008/2048 [24:41<00:27, 1.44it/s]
loss 1.82 accuracy 0.31 -- 162.53 + 57.26 + 496.41 + 4.77 = 720.97: 98%|█████████▊| 2009/2048 [24:41<00:27, 1.41it/s]
loss 2.01 accuracy 0.44 -- 56.10 + 57.40 + 620.57 + 4.80 = 738.87: 98%|█████████▊| 2009/2048 [24:42<00:27, 1.41it/s]
loss 2.01 accuracy 0.44 -- 56.10 + 57.40 + 620.57 + 4.80 = 738.87: 98%|█████████▊| 2010/2048 [24:42<00:28, 1.35it/s]
loss 1.76 accuracy 0.31 -- 56.70 + 56.79 + 504.95 + 4.78 = 623.22: 98%|█████████▊| 2010/2048 [24:43<00:28, 1.35it/s]
loss 1.76 accuracy 0.31 -- 56.70 + 56.79 + 504.95 + 4.78 = 623.22: 98%|█████████▊| 2011/2048 [24:43<00:26, 1.40it/s]
loss 1.58 accuracy 0.25 -- 56.41 + 57.08 + 616.61 + 4.78 = 734.87: 98%|█████████▊| 2011/2048 [24:43<00:26, 1.40it/s]
loss 1.58 accuracy 0.25 -- 56.41 + 57.08 + 616.61 + 4.78 = 734.87: 98%|█████████▊| 2012/2048 [24:43<00:26, 1.38it/s]
loss 1.85 accuracy 0.31 -- 56.68 + 56.54 + 502.52 + 4.77 = 620.51: 98%|█████████▊| 2012/2048 [24:44<00:26, 1.38it/s]
loss 1.85 accuracy 0.31 -- 56.68 + 56.54 + 502.52 + 4.77 = 620.51: 98%|█████████▊| 2013/2048 [24:44<00:24, 1.42it/s]
loss 1.47 accuracy 0.44 -- 56.18 + 166.61 + 503.71 + 4.78 = 731.29: 98%|█████████▊| 2013/2048 [24:45<00:24, 1.42it/s]
loss 1.47 accuracy 0.44 -- 56.18 + 166.61 + 503.71 + 4.78 = 731.29: 98%|█████████▊| 2014/2048 [24:45<00:24, 1.39it/s]
loss 1.98 accuracy 0.31 -- 56.20 + 56.43 + 498.72 + 4.78 = 616.13: 98%|█████████▊| 2014/2048 [24:45<00:24, 1.39it/s]
loss 1.98 accuracy 0.31 -- 56.20 + 56.43 + 498.72 + 4.78 = 616.13: 98%|█████████▊| 2015/2048 [24:45<00:22, 1.44it/s]
loss 1.71 accuracy 0.38 -- 56.45 + 56.62 + 497.17 + 4.78 = 615.03: 98%|█████████▊| 2015/2048 [24:46<00:22, 1.44it/s]
loss 1.71 accuracy 0.38 -- 56.45 + 56.62 + 497.17 + 4.78 = 615.03: 98%|█████████▊| 2016/2048 [24:46<00:22, 1.40it/s]
loss 1.51 accuracy 0.50 -- 56.00 + 57.15 + 494.89 + 4.77 = 612.80: 98%|█████████▊| 2016/2048 [24:47<00:22, 1.40it/s]
loss 1.51 accuracy 0.50 -- 56.00 + 57.15 + 494.89 + 4.77 = 612.80: 98%|█████████▊| 2017/2048 [24:47<00:21, 1.43it/s]
loss 1.81 accuracy 0.38 -- 157.20 + 56.76 + 488.64 + 4.76 = 707.37: 98%|█████████▊| 2017/2048 [24:47<00:21, 1.43it/s]
loss 1.81 accuracy 0.38 -- 157.20 + 56.76 + 488.64 + 4.76 = 707.37: 99%|█████████▊| 2018/2048 [24:47<00:21, 1.41it/s]
loss 2.18 accuracy 0.31 -- 55.84 + 166.45 + 500.72 + 4.78 = 727.79: 99%|█████████▊| 2018/2048 [24:48<00:21, 1.41it/s]
loss 2.18 accuracy 0.31 -- 55.84 + 166.45 + 500.72 + 4.78 = 727.79: 99%|█████████▊| 2019/2048 [24:48<00:21, 1.38it/s]
loss 2.55 accuracy 0.31 -- 56.78 + 56.80 + 498.30 + 4.79 = 616.66: 99%|█████████▊| 2019/2048 [24:49<00:21, 1.38it/s]
loss 2.55 accuracy 0.31 -- 56.78 + 56.80 + 498.30 + 4.79 = 616.66: 99%|█████████▊| 2020/2048 [24:49<00:19, 1.43it/s]
loss 1.77 accuracy 0.25 -- 161.82 + 57.20 + 496.47 + 4.78 = 720.28: 99%|█████████▊| 2020/2048 [24:50<00:19, 1.43it/s]
loss 1.77 accuracy 0.25 -- 161.82 + 57.20 + 496.47 + 4.78 = 720.28: 99%|█████████▊| 2021/2048 [24:50<00:19, 1.40it/s]
loss 1.74 accuracy 0.25 -- 55.85 + 57.30 + 619.05 + 4.77 = 736.97: 99%|█████████▊| 2021/2048 [24:50<00:19, 1.40it/s]
loss 1.74 accuracy 0.25 -- 55.85 + 57.30 + 619.05 + 4.77 = 736.97: 99%|█████████▊| 2022/2048 [24:50<00:18, 1.37it/s]
loss 2.06 accuracy 0.12 -- 56.61 + 56.79 + 504.93 + 4.79 = 623.12: 99%|█████████▊| 2022/2048 [24:51<00:18, 1.37it/s]
loss 2.06 accuracy 0.12 -- 56.61 + 56.79 + 504.93 + 4.79 = 623.12: 99%|█████████▉| 2023/2048 [24:51<00:17, 1.42it/s]
loss 1.67 accuracy 0.38 -- 56.29 + 57.44 + 615.80 + 4.78 = 734.31: 99%|█████████▉| 2023/2048 [24:52<00:17, 1.42it/s]
loss 1.67 accuracy 0.38 -- 56.29 + 57.44 + 615.80 + 4.78 = 734.31: 99%|█████████▉| 2024/2048 [24:52<00:17, 1.38it/s]
loss 1.61 accuracy 0.44 -- 56.66 + 56.68 + 503.46 + 4.79 = 621.60: 99%|█████████▉| 2024/2048 [24:52<00:17, 1.38it/s]
loss 1.61 accuracy 0.44 -- 56.66 + 56.68 + 503.46 + 4.79 = 621.60: 99%|█████████▉| 2025/2048 [24:52<00:16, 1.41it/s]
loss 1.72 accuracy 0.25 -- 56.36 + 166.03 + 501.16 + 4.76 = 728.31: 99%|█████████▉| 2025/2048 [24:53<00:16, 1.41it/s]
loss 1.72 accuracy 0.25 -- 56.36 + 166.03 + 501.16 + 4.76 = 728.31: 99%|█████████▉| 2026/2048 [24:53<00:15, 1.38it/s]
loss 1.74 accuracy 0.31 -- 55.79 + 56.33 + 499.28 + 4.77 = 616.17: 99%|█████████▉| 2026/2048 [24:54<00:15, 1.38it/s]
loss 1.74 accuracy 0.31 -- 55.79 + 56.33 + 499.28 + 4.77 = 616.17: 99%|█████████▉| 2027/2048 [24:54<00:14, 1.43it/s]
loss 1.73 accuracy 0.25 -- 56.88 + 56.44 + 499.05 + 4.78 = 617.15: 99%|█████████▉| 2027/2048 [24:55<00:14, 1.43it/s]
loss 1.73 accuracy 0.25 -- 56.88 + 56.44 + 499.05 + 4.78 = 617.15: 99%|█████████▉| 2028/2048 [24:55<00:14, 1.40it/s]
loss 1.61 accuracy 0.38 -- 55.88 + 56.99 + 495.97 + 4.77 = 613.62: 99%|█████████▉| 2028/2048 [24:55<00:14, 1.40it/s]
loss 1.61 accuracy 0.38 -- 55.88 + 56.99 + 495.97 + 4.77 = 613.62: 99%|█████████▉| 2029/2048 [24:55<00:13, 1.44it/s]
loss 1.88 accuracy 0.06 -- 157.32 + 57.33 + 491.26 + 4.77 = 710.68: 99%|█████████▉| 2029/2048 [24:56<00:13, 1.44it/s]
loss 1.88 accuracy 0.06 -- 157.32 + 57.33 + 491.26 + 4.77 = 710.68: 99%|█████████▉| 2030/2048 [24:56<00:12, 1.42it/s]
loss 1.67 accuracy 0.31 -- 55.85 + 166.31 + 502.82 + 4.77 = 729.74: 99%|█████████▉| 2030/2048 [24:57<00:12, 1.42it/s]
loss 1.67 accuracy 0.31 -- 55.85 + 166.31 + 502.82 + 4.77 = 729.74: 99%|█████████▉| 2031/2048 [24:57<00:12, 1.39it/s]
loss 1.96 accuracy 0.31 -- 56.92 + 56.50 + 498.44 + 4.77 = 616.63: 99%|█████████▉| 2031/2048 [24:57<00:12, 1.39it/s]
loss 1.96 accuracy 0.31 -- 56.92 + 56.50 + 498.44 + 4.77 = 616.63: 99%|█████████▉| 2032/2048 [24:57<00:11, 1.43it/s]
loss 2.01 accuracy 0.31 -- 162.34 + 56.89 + 497.02 + 4.77 = 721.02: 99%|█████████▉| 2032/2048 [24:58<00:11, 1.43it/s]
loss 2.01 accuracy 0.31 -- 162.34 + 56.89 + 497.02 + 4.77 = 721.02: 99%|█████████▉| 2033/2048 [24:58<00:10, 1.38it/s]
loss 1.79 accuracy 0.31 -- 56.19 + 57.56 + 620.20 + 4.77 = 738.72: 99%|█████████▉| 2033/2048 [24:59<00:10, 1.38it/s]
loss 1.79 accuracy 0.31 -- 56.19 + 57.56 + 620.20 + 4.77 = 738.72: 99%|█████████▉| 2034/2048 [24:59<00:10, 1.36it/s]
loss 1.76 accuracy 0.31 -- 56.60 + 56.60 + 505.16 + 4.77 = 623.12: 99%|█████████▉| 2034/2048 [25:00<00:10, 1.36it/s]
loss 1.76 accuracy 0.31 -- 56.60 + 56.60 + 505.16 + 4.77 = 623.12: 99%|█████████▉| 2035/2048 [25:00<00:09, 1.41it/s]
loss 1.64 accuracy 0.31 -- 56.19 + 57.43 + 617.11 + 4.78 = 735.52: 99%|█████████▉| 2035/2048 [25:00<00:09, 1.41it/s]
loss 1.64 accuracy 0.31 -- 56.19 + 57.43 + 617.11 + 4.78 = 735.52: 99%|█████████▉| 2036/2048 [25:00<00:08, 1.38it/s]
loss 1.40 accuracy 0.56 -- 57.21 + 56.55 + 502.67 + 4.77 = 621.20: 99%|█████████▉| 2036/2048 [25:01<00:08, 1.38it/s]
loss 1.40 accuracy 0.56 -- 57.21 + 56.55 + 502.67 + 4.77 = 621.20: 99%|█████████▉| 2037/2048 [25:01<00:07, 1.42it/s]
loss 1.91 accuracy 0.31 -- 56.08 + 165.77 + 500.97 + 4.77 = 727.58: 99%|█████████▉| 2037/2048 [25:02<00:07, 1.42it/s]
loss 1.91 accuracy 0.31 -- 56.08 + 165.77 + 500.97 + 4.77 = 727.58: 100%|█████████▉| 2038/2048 [25:02<00:07, 1.39it/s]
loss 1.33 accuracy 0.38 -- 56.22 + 56.21 + 499.04 + 4.78 = 616.25: 100%|█████████▉| 2038/2048 [25:02<00:07, 1.39it/s]
loss 1.33 accuracy 0.38 -- 56.22 + 56.21 + 499.04 + 4.78 = 616.25: 100%|█████████▉| 2039/2048 [25:02<00:06, 1.44it/s]
loss 1.88 accuracy 0.31 -- 57.04 + 56.43 + 498.63 + 4.78 = 616.87: 100%|█████████▉| 2039/2048 [25:03<00:06, 1.44it/s]
loss 1.88 accuracy 0.31 -- 57.04 + 56.43 + 498.63 + 4.78 = 616.87: 100%|█████████▉| 2040/2048 [25:03<00:05, 1.38it/s]
loss 1.63 accuracy 0.50 -- 56.24 + 57.27 + 496.35 + 4.79 = 614.65: 100%|█████████▉| 2040/2048 [25:04<00:05, 1.38it/s]
loss 1.63 accuracy 0.50 -- 56.24 + 57.27 + 496.35 + 4.79 = 614.65: 100%|█████████▉| 2041/2048 [25:04<00:04, 1.43it/s]
loss 1.51 accuracy 0.44 -- 157.50 + 57.04 + 491.51 + 4.76 = 710.81: 100%|█████████▉| 2041/2048 [25:05<00:04, 1.43it/s]
loss 1.51 accuracy 0.44 -- 157.50 + 57.04 + 491.51 + 4.76 = 710.81: 100%|█████████▉| 2042/2048 [25:05<00:04, 1.41it/s]
loss 1.77 accuracy 0.25 -- 56.06 + 166.03 + 501.05 + 4.79 = 727.93: 100%|█████████▉| 2042/2048 [25:05<00:04, 1.41it/s]
loss 1.77 accuracy 0.25 -- 56.06 + 166.03 + 501.05 + 4.79 = 727.93: 100%|█████████▉| 2043/2048 [25:05<00:03, 1.38it/s]
loss 1.90 accuracy 0.38 -- 56.68 + 56.67 + 497.25 + 4.80 = 615.41: 100%|█████████▉| 2043/2048 [25:06<00:03, 1.38it/s]
loss 1.90 accuracy 0.38 -- 56.68 + 56.67 + 497.25 + 4.80 = 615.41: 100%|█████████▉| 2044/2048 [25:06<00:02, 1.43it/s]
loss 1.90 accuracy 0.50 -- 162.28 + 57.17 + 496.46 + 4.78 = 720.68: 100%|█████████▉| 2044/2048 [25:07<00:02, 1.43it/s]
loss 1.90 accuracy 0.50 -- 162.28 + 57.17 + 496.46 + 4.78 = 720.68: 100%|█████████▉| 2045/2048 [25:07<00:02, 1.40it/s]
loss 1.73 accuracy 0.44 -- 55.81 + 57.19 + 619.30 + 4.77 = 737.06: 100%|█████████▉| 2045/2048 [25:07<00:02, 1.40it/s]
loss 1.73 accuracy 0.44 -- 55.81 + 57.19 + 619.30 + 4.77 = 737.06: 100%|█████████▉| 2046/2048 [25:07<00:01, 1.37it/s]
loss 2.41 accuracy 0.12 -- 56.75 + 56.62 + 505.55 + 4.77 = 623.70: 100%|█████████▉| 2046/2048 [25:08<00:01, 1.37it/s]
loss 2.41 accuracy 0.12 -- 56.75 + 56.62 + 505.55 + 4.77 = 623.70: 100%|█████████▉| 2047/2048 [25:08<00:00, 1.40it/s]
loss 1.83 accuracy 0.38 -- 56.69 + 57.95 + 617.37 + 4.77 = 736.78: 100%|█████████▉| 2047/2048 [25:09<00:00, 1.40it/s]
loss 1.83 accuracy 0.38 -- 56.69 + 57.95 + 617.37 + 4.77 = 736.78: 100%|██████████| 2048/2048 [25:09<00:00, 1.37it/s]
loss 1.83 accuracy 0.38 -- 56.69 + 57.95 + 617.37 + 4.77 = 736.78: 100%|██████████| 2048/2048 [25:09<00:00, 1.36it/s]
train_resnet.py
0%| | 0/100 [00:00<?, ?it/s]
loss 2.36 accuracy 0.03: 0%| | 0/100 [00:19<?, ?it/s]
loss 2.36 accuracy 0.03: 1%| | 1/100 [00:19<32:43, 19.83s/it]
loss 2.33 accuracy 0.22: 1%| | 1/100 [00:27<32:43, 19.83s/it]
loss 2.33 accuracy 0.22: 2%|▏ | 2/100 [00:27<21:06, 12.93s/it]
loss 2.16 accuracy 0.28: 2%|▏ | 2/100 [00:27<21:06, 12.93s/it]
loss 1.81 accuracy 0.47: 2%|▏ | 2/100 [00:27<21:06, 12.93s/it]
loss 1.85 accuracy 0.41: 2%|▏ | 2/100 [00:27<21:06, 12.93s/it]
loss 1.82 accuracy 0.44: 2%|▏ | 2/100 [00:27<21:06, 12.93s/it]
loss 1.41 accuracy 0.59: 2%|▏ | 2/100 [00:27<21:06, 12.93s/it]
loss 1.37 accuracy 0.59: 2%|▏ | 2/100 [00:28<21:06, 12.93s/it]
loss 1.34 accuracy 0.59: 2%|▏ | 2/100 [00:28<21:06, 12.93s/it]
loss 0.76 accuracy 0.91: 2%|▏ | 2/100 [00:28<21:06, 12.93s/it]
loss 0.76 accuracy 0.91: 10%|█ | 10/100 [00:28<02:31, 1.69s/it]
loss 0.70 accuracy 0.88: 10%|█ | 10/100 [00:28<02:31, 1.69s/it]
loss 0.65 accuracy 0.78: 10%|█ | 10/100 [00:28<02:31, 1.69s/it]
loss 0.90 accuracy 0.75: 10%|█ | 10/100 [00:28<02:31, 1.69s/it]
loss 0.73 accuracy 0.78: 10%|█ | 10/100 [00:28<02:31, 1.69s/it]
loss 0.49 accuracy 0.88: 10%|█ | 10/100 [00:28<02:31, 1.69s/it]
loss 0.45 accuracy 0.81: 10%|█ | 10/100 [00:28<02:31, 1.69s/it]
loss 0.44 accuracy 0.88: 10%|█ | 10/100 [00:28<02:31, 1.69s/it]
loss 0.43 accuracy 0.84: 10%|█ | 10/100 [00:28<02:31, 1.69s/it]
loss 0.43 accuracy 0.84: 18%|█▊ | 18/100 [00:28<01:02, 1.32it/s]
loss 0.97 accuracy 0.75: 18%|█▊ | 18/100 [00:28<01:02, 1.32it/s]
loss 0.38 accuracy 0.91: 18%|█▊ | 18/100 [00:28<01:02, 1.32it/s]
loss 0.44 accuracy 0.81: 18%|█▊ | 18/100 [00:28<01:02, 1.32it/s]
loss 0.32 accuracy 0.94: 18%|█▊ | 18/100 [00:28<01:02, 1.32it/s]
loss 0.33 accuracy 0.88: 18%|█▊ | 18/100 [00:28<01:02, 1.32it/s]
loss 0.44 accuracy 0.81: 18%|█▊ | 18/100 [00:28<01:02, 1.32it/s]
loss 0.43 accuracy 0.91: 18%|█▊ | 18/100 [00:28<01:02, 1.32it/s]
loss 0.58 accuracy 0.91: 18%|█▊ | 18/100 [00:28<01:02, 1.32it/s]
loss 0.58 accuracy 0.91: 26%|██▌ | 26/100 [00:28<00:31, 2.33it/s]
loss 0.26 accuracy 0.88: 26%|██▌ | 26/100 [00:28<00:31, 2.33it/s]
loss 0.41 accuracy 0.91: 26%|██▌ | 26/100 [00:28<00:31, 2.33it/s]
loss 0.44 accuracy 0.88: 26%|██▌ | 26/100 [00:28<00:31, 2.33it/s]
loss 0.27 accuracy 0.91: 26%|██▌ | 26/100 [00:28<00:31, 2.33it/s]
loss 0.49 accuracy 0.81: 26%|██▌ | 26/100 [00:28<00:31, 2.33it/s]
loss 0.91 accuracy 0.72: 26%|██▌ | 26/100 [00:28<00:31, 2.33it/s]
loss 0.27 accuracy 0.94: 26%|██▌ | 26/100 [00:28<00:31, 2.33it/s]
loss 0.24 accuracy 0.91: 26%|██▌ | 26/100 [00:28<00:31, 2.33it/s]
loss 0.24 accuracy 0.91: 34%|███▍ | 34/100 [00:28<00:17, 3.73it/s]
loss 0.26 accuracy 0.94: 34%|███▍ | 34/100 [00:28<00:17, 3.73it/s]
loss 0.45 accuracy 0.88: 34%|███▍ | 34/100 [00:28<00:17, 3.73it/s]
loss 0.33 accuracy 0.91: 34%|███▍ | 34/100 [00:28<00:17, 3.73it/s]
loss 0.23 accuracy 0.94: 34%|███▍ | 34/100 [00:28<00:17, 3.73it/s]
loss 0.25 accuracy 0.91: 34%|███▍ | 34/100 [00:28<00:17, 3.73it/s]
loss 0.22 accuracy 0.94: 34%|███▍ | 34/100 [00:28<00:17, 3.73it/s]
loss 0.22 accuracy 0.94: 34%|███▍ | 34/100 [00:28<00:17, 3.73it/s]
loss 0.36 accuracy 0.91: 34%|███▍ | 34/100 [00:28<00:17, 3.73it/s]
loss 0.36 accuracy 0.91: 42%|████▏ | 42/100 [00:28<00:10, 5.64it/s]
loss 0.28 accuracy 0.91: 42%|████▏ | 42/100 [00:28<00:10, 5.64it/s]
loss 0.61 accuracy 0.81: 42%|████▏ | 42/100 [00:28<00:10, 5.64it/s]
loss 0.15 accuracy 0.97: 42%|████▏ | 42/100 [00:28<00:10, 5.64it/s]
loss 0.17 accuracy 0.97: 42%|████▏ | 42/100 [00:28<00:10, 5.64it/s]
loss 0.28 accuracy 0.91: 42%|████▏ | 42/100 [00:28<00:10, 5.64it/s]
loss 0.62 accuracy 0.81: 42%|████▏ | 42/100 [00:28<00:10, 5.64it/s]
loss 0.85 accuracy 0.78: 42%|████▏ | 42/100 [00:28<00:10, 5.64it/s]
loss 1.09 accuracy 0.75: 42%|████▏ | 42/100 [00:28<00:10, 5.64it/s]
loss 1.09 accuracy 0.75: 50%|█████ | 50/100 [00:28<00:06, 8.21it/s]
loss 0.36 accuracy 0.88: 50%|█████ | 50/100 [00:28<00:06, 8.21it/s]
loss 0.16 accuracy 0.97: 50%|█████ | 50/100 [00:28<00:06, 8.21it/s]
loss 0.22 accuracy 0.91: 50%|█████ | 50/100 [00:28<00:06, 8.21it/s]
loss 0.20 accuracy 0.97: 50%|█████ | 50/100 [00:28<00:06, 8.21it/s]
loss 0.43 accuracy 0.88: 50%|█████ | 50/100 [00:28<00:06, 8.21it/s]
loss 0.64 accuracy 0.75: 50%|█████ | 50/100 [00:28<00:06, 8.21it/s]
loss 0.38 accuracy 0.91: 50%|█████ | 50/100 [00:28<00:06, 8.21it/s]
loss 0.47 accuracy 0.91: 50%|█████ | 50/100 [00:28<00:06, 8.21it/s]
loss 0.47 accuracy 0.91: 58%|█████▊ | 58/100 [00:28<00:03, 11.57it/s]
loss 0.33 accuracy 0.91: 58%|█████▊ | 58/100 [00:28<00:03, 11.57it/s]
loss 0.42 accuracy 0.88: 58%|█████▊ | 58/100 [00:28<00:03, 11.57it/s]
loss 0.20 accuracy 0.91: 58%|█████▊ | 58/100 [00:28<00:03, 11.57it/s]
loss 0.33 accuracy 0.88: 58%|█████▊ | 58/100 [00:28<00:03, 11.57it/s]
loss 0.24 accuracy 0.88: 58%|█████▊ | 58/100 [00:28<00:03, 11.57it/s]
loss 0.11 accuracy 0.94: 58%|█████▊ | 58/100 [00:28<00:03, 11.57it/s]
loss 0.24 accuracy 0.88: 58%|█████▊ | 58/100 [00:28<00:03, 11.57it/s]
loss 0.52 accuracy 0.88: 58%|█████▊ | 58/100 [00:28<00:03, 11.57it/s]
loss 0.52 accuracy 0.88: 66%|██████▌ | 66/100 [00:28<00:02, 15.85it/s]
loss 0.14 accuracy 0.97: 66%|██████▌ | 66/100 [00:28<00:02, 15.85it/s]
loss 0.29 accuracy 0.97: 66%|██████▌ | 66/100 [00:28<00:02, 15.85it/s]
loss 0.39 accuracy 0.84: 66%|██████▌ | 66/100 [00:28<00:02, 15.85it/s]
loss 0.17 accuracy 0.97: 66%|██████▌ | 66/100 [00:28<00:02, 15.85it/s]
loss 0.18 accuracy 0.94: 66%|██████▌ | 66/100 [00:28<00:02, 15.85it/s]
loss 0.12 accuracy 0.97: 66%|██████▌ | 66/100 [00:28<00:02, 15.85it/s]
loss 0.26 accuracy 0.94: 66%|██████▌ | 66/100 [00:28<00:02, 15.85it/s]
loss 0.61 accuracy 0.81: 66%|██████▌ | 66/100 [00:28<00:02, 15.85it/s]
loss 0.61 accuracy 0.81: 74%|███████▍ | 74/100 [00:28<00:01, 21.07it/s]
loss 0.41 accuracy 0.88: 74%|███████▍ | 74/100 [00:28<00:01, 21.07it/s]
loss 0.02 accuracy 1.00: 74%|███████▍ | 74/100 [00:28<00:01, 21.07it/s]
loss 0.13 accuracy 0.94: 74%|███████▍ | 74/100 [00:28<00:01, 21.07it/s]
loss 0.03 accuracy 1.00: 74%|███████▍ | 74/100 [00:28<00:01, 21.07it/s]
loss 0.13 accuracy 0.97: 74%|███████▍ | 74/100 [00:28<00:01, 21.07it/s]
loss 0.31 accuracy 0.94: 74%|███████▍ | 74/100 [00:28<00:01, 21.07it/s]
loss 0.31 accuracy 0.84: 74%|███████▍ | 74/100 [00:28<00:01, 21.07it/s]
loss 0.08 accuracy 0.97: 74%|███████▍ | 74/100 [00:28<00:01, 21.07it/s]
loss 0.08 accuracy 0.97: 82%|████████▏ | 82/100 [00:28<00:00, 27.17it/s]
loss 0.30 accuracy 0.94: 82%|████████▏ | 82/100 [00:28<00:00, 27.17it/s]
loss 0.43 accuracy 0.81: 82%|████████▏ | 82/100 [00:28<00:00, 27.17it/s]
loss 0.05 accuracy 1.00: 82%|████████▏ | 82/100 [00:29<00:00, 27.17it/s]
loss 0.07 accuracy 0.97: 82%|████████▏ | 82/100 [00:29<00:00, 27.17it/s]
loss 0.29 accuracy 0.94: 82%|████████▏ | 82/100 [00:29<00:00, 27.17it/s]
loss 0.31 accuracy 0.91: 82%|████████▏ | 82/100 [00:29<00:00, 27.17it/s]
loss 0.31 accuracy 0.91: 82%|████████▏ | 82/100 [00:29<00:00, 27.17it/s]
loss 0.18 accuracy 0.94: 82%|████████▏ | 82/100 [00:29<00:00, 27.17it/s]
loss 0.18 accuracy 0.94: 90%|█████████ | 90/100 [00:29<00:00, 33.88it/s]
loss 0.14 accuracy 0.97: 90%|█████████ | 90/100 [00:29<00:00, 33.88it/s]
loss 0.08 accuracy 0.97: 90%|█████████ | 90/100 [00:29<00:00, 33.88it/s]
loss 0.06 accuracy 0.97: 90%|█████████ | 90/100 [00:29<00:00, 33.88it/s]
loss 0.03 accuracy 1.00: 90%|█████████ | 90/100 [00:29<00:00, 33.88it/s]
loss 0.03 accuracy 1.00: 90%|█████████ | 90/100 [00:29<00:00, 33.88it/s]
loss 0.05 accuracy 0.97: 90%|█████████ | 90/100 [00:29<00:00, 33.88it/s]
loss 0.03 accuracy 1.00: 90%|█████████ | 90/100 [00:29<00:00, 33.88it/s]
loss 0.24 accuracy 0.91: 90%|█████████ | 90/100 [00:29<00:00, 33.88it/s]
loss 0.24 accuracy 0.91: 98%|█████████▊| 98/100 [00:29<00:00, 40.87it/s]
loss 0.13 accuracy 0.97: 98%|█████████▊| 98/100 [00:29<00:00, 40.87it/s]
loss 0.27 accuracy 0.91: 98%|█████████▊| 98/100 [00:29<00:00, 40.87it/s]
loss 0.27 accuracy 0.91: 100%|██████████| 100/100 [00:29<00:00, 3.43it/s]
0%| | 0/79 [00:00<?, ?it/s]
1%|▏ | 1/79 [00:02<03:51, 2.97s/it]
4%|▍ | 3/79 [00:03<01:02, 1.22it/s]
9%|▉ | 7/79 [00:03<00:20, 3.50it/s]
14%|█▍ | 11/79 [00:03<00:10, 6.22it/s]
19%|█▉ | 15/79 [00:03<00:06, 9.30it/s]
24%|██▍ | 19/79 [00:03<00:04, 12.58it/s]
28%|██▊ | 22/79 [00:03<00:04, 13.88it/s]
33%|███▎ | 26/79 [00:03<00:03, 17.25it/s]
38%|███▊ | 30/79 [00:04<00:02, 20.26it/s]
43%|████▎ | 34/79 [00:04<00:01, 22.82it/s]
48%|████▊ | 38/79 [00:04<00:01, 24.95it/s]
52%|█████▏ | 41/79 [00:04<00:01, 23.05it/s]
57%|█████▋ | 45/79 [00:04<00:01, 25.18it/s]
62%|██████▏ | 49/79 [00:04<00:01, 26.77it/s]
67%|██████▋ | 53/79 [00:04<00:00, 28.01it/s]
72%|███████▏ | 57/79 [00:04<00:00, 28.91it/s]
77%|███████▋ | 61/79 [00:05<00:00, 25.89it/s]
82%|████████▏ | 65/79 [00:05<00:00, 27.29it/s]
87%|████████▋ | 69/79 [00:05<00:00, 28.30it/s]
92%|█████████▏| 73/79 [00:05<00:00, 29.08it/s]
97%|█████████▋| 77/79 [00:05<00:00, 29.72it/s]
100%|██████████| 79/79 [00:08<00:00, 8.94it/s]
test set accuracy is 0.938300
reducing lr to 0.0041667
0%| | 0/100 [00:00<?, ?it/s]
loss 0.07 accuracy 1.00: 0%| | 0/100 [00:00<?, ?it/s]
loss 0.07 accuracy 1.00: 1%| | 1/100 [00:00<00:13, 7.33it/s]
loss 0.25 accuracy 0.94: 1%| | 1/100 [00:00<00:13, 7.33it/s]
loss 0.25 accuracy 0.94: 2%|▏ | 2/100 [00:00<00:14, 6.59it/s]
loss 0.26 accuracy 0.94: 2%|▏ | 2/100 [00:00<00:14, 6.59it/s]
loss 0.14 accuracy 0.97: 2%|▏ | 2/100 [00:00<00:14, 6.59it/s]
loss 0.13 accuracy 0.94: 2%|▏ | 2/100 [00:00<00:14, 6.59it/s]
loss 0.19 accuracy 0.94: 2%|▏ | 2/100 [00:00<00:14, 6.59it/s]
loss 0.16 accuracy 0.94: 2%|▏ | 2/100 [00:00<00:14, 6.59it/s]
loss 0.10 accuracy 0.94: 2%|▏ | 2/100 [00:00<00:14, 6.59it/s]
loss 0.10 accuracy 0.97: 2%|▏ | 2/100 [00:00<00:14, 6.59it/s]
loss 0.37 accuracy 0.88: 2%|▏ | 2/100 [00:00<00:14, 6.59it/s]
loss 0.37 accuracy 0.88: 10%|█ | 10/100 [00:00<00:02, 32.49it/s]
loss 0.19 accuracy 0.94: 10%|█ | 10/100 [00:00<00:02, 32.49it/s]
loss 0.10 accuracy 0.94: 10%|█ | 10/100 [00:00<00:02, 32.49it/s]
loss 0.15 accuracy 0.94: 10%|█ | 10/100 [00:00<00:02, 32.49it/s]
loss 0.53 accuracy 0.91: 10%|█ | 10/100 [00:00<00:02, 32.49it/s]
loss 0.02 accuracy 1.00: 10%|█ | 10/100 [00:00<00:02, 32.49it/s]
loss 0.11 accuracy 0.94: 10%|█ | 10/100 [00:00<00:02, 32.49it/s]
loss 0.12 accuracy 0.97: 10%|█ | 10/100 [00:00<00:02, 32.49it/s]
loss 0.03 accuracy 1.00: 10%|█ | 10/100 [00:00<00:02, 32.49it/s]
loss 0.03 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 48.32it/s]
loss 0.19 accuracy 0.94: 18%|█▊ | 18/100 [00:00<00:01, 48.32it/s]
loss 0.19 accuracy 0.94: 18%|█▊ | 18/100 [00:00<00:01, 48.32it/s]
loss 0.10 accuracy 0.97: 18%|█▊ | 18/100 [00:00<00:01, 48.32it/s]
loss 0.05 accuracy 0.97: 18%|█▊ | 18/100 [00:00<00:01, 48.32it/s]
loss 0.14 accuracy 0.97: 18%|█▊ | 18/100 [00:00<00:01, 48.32it/s]
loss 0.06 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 48.32it/s]
loss 0.16 accuracy 0.97: 18%|█▊ | 18/100 [00:00<00:01, 48.32it/s]
loss 0.15 accuracy 0.97: 18%|█▊ | 18/100 [00:00<00:01, 48.32it/s]
loss 0.15 accuracy 0.97: 26%|██▌ | 26/100 [00:00<00:01, 58.40it/s]
loss 0.44 accuracy 0.84: 26%|██▌ | 26/100 [00:00<00:01, 58.40it/s]
loss 0.06 accuracy 1.00: 26%|██▌ | 26/100 [00:00<00:01, 58.40it/s]
loss 0.24 accuracy 0.94: 26%|██▌ | 26/100 [00:00<00:01, 58.40it/s]
loss 0.08 accuracy 0.97: 26%|██▌ | 26/100 [00:00<00:01, 58.40it/s]
loss 0.04 accuracy 1.00: 26%|██▌ | 26/100 [00:00<00:01, 58.40it/s]
loss 0.18 accuracy 0.94: 26%|██▌ | 26/100 [00:00<00:01, 58.40it/s]
loss 0.02 accuracy 1.00: 26%|██▌ | 26/100 [00:00<00:01, 58.40it/s]
loss 0.10 accuracy 0.97: 26%|██▌ | 26/100 [00:00<00:01, 58.40it/s]
loss 0.10 accuracy 0.97: 34%|███▍ | 34/100 [00:00<00:01, 65.11it/s]
loss 0.21 accuracy 0.91: 34%|███▍ | 34/100 [00:00<00:01, 65.11it/s]
loss 0.16 accuracy 0.97: 34%|███▍ | 34/100 [00:00<00:01, 65.11it/s]
loss 0.17 accuracy 0.94: 34%|███▍ | 34/100 [00:00<00:01, 65.11it/s]
loss 0.02 accuracy 1.00: 34%|███▍ | 34/100 [00:00<00:01, 65.11it/s]
loss 0.03 accuracy 1.00: 34%|███▍ | 34/100 [00:00<00:01, 65.11it/s]
loss 0.10 accuracy 0.97: 34%|███▍ | 34/100 [00:00<00:01, 65.11it/s]
loss 0.19 accuracy 0.94: 34%|███▍ | 34/100 [00:00<00:01, 65.11it/s]
loss 0.12 accuracy 0.94: 34%|███▍ | 34/100 [00:00<00:01, 65.11it/s]
loss 0.12 accuracy 0.94: 42%|████▏ | 42/100 [00:00<00:00, 69.56it/s]
loss 0.09 accuracy 0.94: 42%|████▏ | 42/100 [00:00<00:00, 69.56it/s]
loss 0.08 accuracy 0.97: 42%|████▏ | 42/100 [00:00<00:00, 69.56it/s]
loss 0.26 accuracy 0.97: 42%|████▏ | 42/100 [00:00<00:00, 69.56it/s]
loss 0.04 accuracy 1.00: 42%|████▏ | 42/100 [00:00<00:00, 69.56it/s]
loss 0.18 accuracy 0.94: 42%|████▏ | 42/100 [00:00<00:00, 69.56it/s]
loss 0.05 accuracy 1.00: 42%|████▏ | 42/100 [00:00<00:00, 69.56it/s]
loss 0.26 accuracy 0.91: 42%|████▏ | 42/100 [00:00<00:00, 69.56it/s]
loss 0.05 accuracy 1.00: 42%|████▏ | 42/100 [00:00<00:00, 69.56it/s]
loss 0.05 accuracy 1.00: 50%|█████ | 50/100 [00:00<00:00, 72.58it/s]
loss 0.02 accuracy 1.00: 50%|█████ | 50/100 [00:00<00:00, 72.58it/s]
loss 0.14 accuracy 0.94: 50%|█████ | 50/100 [00:00<00:00, 72.58it/s]
loss 0.08 accuracy 0.97: 50%|█████ | 50/100 [00:00<00:00, 72.58it/s]
loss 0.25 accuracy 0.91: 50%|█████ | 50/100 [00:00<00:00, 72.58it/s]
loss 0.31 accuracy 0.94: 50%|█████ | 50/100 [00:00<00:00, 72.58it/s]
loss 0.16 accuracy 0.94: 50%|█████ | 50/100 [00:00<00:00, 72.58it/s]
loss 0.29 accuracy 0.88: 50%|█████ | 50/100 [00:00<00:00, 72.58it/s]
loss 0.21 accuracy 0.97: 50%|█████ | 50/100 [00:01<00:00, 72.58it/s]
loss 0.21 accuracy 0.97: 58%|█████▊ | 58/100 [00:01<00:00, 74.59it/s]
loss 0.11 accuracy 0.97: 58%|█████▊ | 58/100 [00:01<00:00, 74.59it/s]
loss 0.21 accuracy 0.94: 58%|█████▊ | 58/100 [00:01<00:00, 74.59it/s]
loss 0.08 accuracy 0.97: 58%|█████▊ | 58/100 [00:01<00:00, 74.59it/s]
loss 0.38 accuracy 0.91: 58%|█████▊ | 58/100 [00:01<00:00, 74.59it/s]
loss 0.04 accuracy 1.00: 58%|█████▊ | 58/100 [00:01<00:00, 74.59it/s]
loss 0.15 accuracy 0.97: 58%|█████▊ | 58/100 [00:01<00:00, 74.59it/s]
loss 0.26 accuracy 0.94: 58%|█████▊ | 58/100 [00:01<00:00, 74.59it/s]
loss 0.05 accuracy 1.00: 58%|█████▊ | 58/100 [00:01<00:00, 74.59it/s]
loss 0.05 accuracy 1.00: 66%|██████▌ | 66/100 [00:01<00:00, 76.01it/s]
loss 0.03 accuracy 1.00: 66%|██████▌ | 66/100 [00:01<00:00, 76.01it/s]
loss 0.27 accuracy 0.94: 66%|██████▌ | 66/100 [00:01<00:00, 76.01it/s]
loss 0.17 accuracy 0.94: 66%|██████▌ | 66/100 [00:01<00:00, 76.01it/s]
loss 0.04 accuracy 1.00: 66%|██████▌ | 66/100 [00:01<00:00, 76.01it/s]
loss 0.04 accuracy 1.00: 66%|██████▌ | 66/100 [00:01<00:00, 76.01it/s]
loss 0.04 accuracy 1.00: 66%|██████▌ | 66/100 [00:01<00:00, 76.01it/s]
loss 0.12 accuracy 0.94: 66%|██████▌ | 66/100 [00:01<00:00, 76.01it/s]
loss 0.38 accuracy 0.78: 66%|██████▌ | 66/100 [00:01<00:00, 76.01it/s]
loss 0.38 accuracy 0.78: 74%|███████▍ | 74/100 [00:01<00:00, 77.05it/s]
loss 0.09 accuracy 0.94: 74%|███████▍ | 74/100 [00:01<00:00, 77.05it/s]
loss 0.07 accuracy 0.97: 74%|███████▍ | 74/100 [00:01<00:00, 77.05it/s]
loss 0.02 accuracy 1.00: 74%|███████▍ | 74/100 [00:01<00:00, 77.05it/s]
loss 0.02 accuracy 1.00: 74%|███████▍ | 74/100 [00:01<00:00, 77.05it/s]
loss 0.08 accuracy 0.97: 74%|███████▍ | 74/100 [00:01<00:00, 77.05it/s]
loss 0.34 accuracy 0.91: 74%|███████▍ | 74/100 [00:01<00:00, 77.05it/s]
loss 0.03 accuracy 1.00: 74%|███████▍ | 74/100 [00:01<00:00, 77.05it/s]
loss 0.05 accuracy 0.97: 74%|███████▍ | 74/100 [00:01<00:00, 77.05it/s]
loss 0.05 accuracy 0.97: 82%|████████▏ | 82/100 [00:01<00:00, 77.68it/s]
loss 0.07 accuracy 0.97: 82%|████████▏ | 82/100 [00:01<00:00, 77.68it/s]
loss 0.08 accuracy 0.94: 82%|████████▏ | 82/100 [00:01<00:00, 77.68it/s]
loss 0.04 accuracy 1.00: 82%|████████▏ | 82/100 [00:01<00:00, 77.68it/s]
loss 0.16 accuracy 0.94: 82%|████████▏ | 82/100 [00:01<00:00, 77.68it/s]
loss 0.45 accuracy 0.91: 82%|████████▏ | 82/100 [00:01<00:00, 77.68it/s]
loss 0.37 accuracy 0.91: 82%|████████▏ | 82/100 [00:01<00:00, 77.68it/s]
loss 0.07 accuracy 0.97: 82%|████████▏ | 82/100 [00:01<00:00, 77.68it/s]
loss 0.03 accuracy 1.00: 82%|████████▏ | 82/100 [00:01<00:00, 77.68it/s]
loss 0.03 accuracy 1.00: 90%|█████████ | 90/100 [00:01<00:00, 78.14it/s]
loss 0.08 accuracy 0.97: 90%|█████████ | 90/100 [00:01<00:00, 78.14it/s]
loss 0.05 accuracy 1.00: 90%|█████████ | 90/100 [00:01<00:00, 78.14it/s]
loss 0.16 accuracy 0.97: 90%|█████████ | 90/100 [00:01<00:00, 78.14it/s]
loss 0.09 accuracy 0.94: 90%|█████████ | 90/100 [00:01<00:00, 78.14it/s]
loss 0.24 accuracy 0.94: 90%|█████████ | 90/100 [00:01<00:00, 78.14it/s]
loss 0.03 accuracy 1.00: 90%|█████████ | 90/100 [00:01<00:00, 78.14it/s]
loss 0.07 accuracy 0.97: 90%|█████████ | 90/100 [00:01<00:00, 78.14it/s]
loss 0.15 accuracy 0.91: 90%|█████████ | 90/100 [00:01<00:00, 78.14it/s]
loss 0.15 accuracy 0.91: 98%|█████████▊| 98/100 [00:01<00:00, 78.40it/s]
loss 0.13 accuracy 0.91: 98%|█████████▊| 98/100 [00:01<00:00, 78.40it/s]
loss 0.17 accuracy 0.97: 98%|█████████▊| 98/100 [00:01<00:00, 78.40it/s]
loss 0.17 accuracy 0.97: 100%|██████████| 100/100 [00:01<00:00, 65.08it/s]
0%| | 0/79 [00:00<?, ?it/s]
5%|▌ | 4/79 [00:00<00:02, 30.67it/s]
10%|█ | 8/79 [00:00<00:03, 23.22it/s]
15%|█▌ | 12/79 [00:00<00:02, 26.17it/s]
20%|██ | 16/79 [00:00<00:02, 27.85it/s]
25%|██▌ | 20/79 [00:00<00:02, 28.96it/s]
30%|███ | 24/79 [00:00<00:01, 29.58it/s]
35%|███▌ | 28/79 [00:01<00:01, 25.65it/s]
41%|████ | 32/79 [00:01<00:01, 27.14it/s]
46%|████▌ | 36/79 [00:01<00:01, 28.18it/s]
51%|█████ | 40/79 [00:01<00:01, 28.94it/s]
54%|█████▍ | 43/79 [00:01<00:01, 25.03it/s]
59%|█████▉ | 47/79 [00:01<00:01, 26.64it/s]
65%|██████▍ | 51/79 [00:01<00:01, 27.83it/s]
70%|██████▉ | 55/79 [00:01<00:00, 28.67it/s]
75%|███████▍ | 59/79 [00:02<00:00, 29.31it/s]
78%|███████▊ | 62/79 [00:02<00:00, 25.27it/s]
84%|████████▎ | 66/79 [00:02<00:00, 26.83it/s]
89%|████████▊ | 70/79 [00:02<00:00, 28.02it/s]
94%|█████████▎| 74/79 [00:02<00:00, 28.80it/s]
99%|█████████▊| 78/79 [00:02<00:00, 29.40it/s]
100%|██████████| 79/79 [00:02<00:00, 27.87it/s]
test set accuracy is 0.960200
reducing lr to 0.0034722
0%| | 0/100 [00:00<?, ?it/s]
loss 0.33 accuracy 0.97: 0%| | 0/100 [00:00<?, ?it/s]
loss 0.33 accuracy 0.97: 1%| | 1/100 [00:00<00:20, 4.88it/s]
loss 0.10 accuracy 0.97: 1%| | 1/100 [00:00<00:20, 4.88it/s]
loss 0.10 accuracy 0.97: 2%|▏ | 2/100 [00:00<00:17, 5.56it/s]
loss 0.08 accuracy 0.94: 2%|▏ | 2/100 [00:00<00:17, 5.56it/s]
loss 0.13 accuracy 0.94: 2%|▏ | 2/100 [00:00<00:17, 5.56it/s]
loss 0.19 accuracy 0.97: 2%|▏ | 2/100 [00:00<00:17, 5.56it/s]
loss 0.09 accuracy 0.97: 2%|▏ | 2/100 [00:00<00:17, 5.56it/s]
loss 0.01 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.56it/s]
loss 0.22 accuracy 0.88: 2%|▏ | 2/100 [00:00<00:17, 5.56it/s]
loss 0.05 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.56it/s]
loss 0.16 accuracy 0.91: 2%|▏ | 2/100 [00:00<00:17, 5.56it/s]
loss 0.16 accuracy 0.91: 10%|█ | 10/100 [00:00<00:03, 29.21it/s]
loss 0.03 accuracy 0.97: 10%|█ | 10/100 [00:00<00:03, 29.21it/s]
loss 0.12 accuracy 0.94: 10%|█ | 10/100 [00:00<00:03, 29.21it/s]
loss 0.02 accuracy 1.00: 10%|█ | 10/100 [00:00<00:03, 29.21it/s]
loss 0.04 accuracy 0.97: 10%|█ | 10/100 [00:00<00:03, 29.21it/s]
loss 0.08 accuracy 0.97: 10%|█ | 10/100 [00:00<00:03, 29.21it/s]
loss 0.06 accuracy 0.97: 10%|█ | 10/100 [00:00<00:03, 29.21it/s]
loss 0.08 accuracy 0.97: 10%|█ | 10/100 [00:00<00:03, 29.21it/s]
loss 0.00 accuracy 1.00: 10%|█ | 10/100 [00:00<00:03, 29.21it/s]
loss 0.14 accuracy 0.91: 10%|█ | 10/100 [00:00<00:03, 29.21it/s]
loss 0.14 accuracy 0.91: 19%|█▉ | 19/100 [00:00<00:01, 46.48it/s]
loss 0.08 accuracy 0.97: 19%|█▉ | 19/100 [00:00<00:01, 46.48it/s]
loss 0.02 accuracy 1.00: 19%|█▉ | 19/100 [00:00<00:01, 46.48it/s]
loss 0.07 accuracy 0.97: 19%|█▉ | 19/100 [00:00<00:01, 46.48it/s]
loss 0.21 accuracy 0.94: 19%|█▉ | 19/100 [00:00<00:01, 46.48it/s]
loss 0.07 accuracy 0.94: 19%|█▉ | 19/100 [00:00<00:01, 46.48it/s]
loss 0.03 accuracy 1.00: 19%|█▉ | 19/100 [00:00<00:01, 46.48it/s]
loss 0.01 accuracy 1.00: 19%|█▉ | 19/100 [00:00<00:01, 46.48it/s]
loss 0.24 accuracy 0.94: 19%|█▉ | 19/100 [00:00<00:01, 46.48it/s]
loss 0.29 accuracy 0.94: 19%|█▉ | 19/100 [00:00<00:01, 46.48it/s]
loss 0.29 accuracy 0.94: 28%|██▊ | 28/100 [00:00<00:01, 57.46it/s]
loss 0.16 accuracy 0.94: 28%|██▊ | 28/100 [00:00<00:01, 57.46it/s]
loss 0.01 accuracy 1.00: 28%|██▊ | 28/100 [00:00<00:01, 57.46it/s]
loss 0.21 accuracy 0.91: 28%|██▊ | 28/100 [00:00<00:01, 57.46it/s]
loss 0.12 accuracy 0.97: 28%|██▊ | 28/100 [00:00<00:01, 57.46it/s]
loss 0.21 accuracy 0.94: 28%|██▊ | 28/100 [00:00<00:01, 57.46it/s]
loss 0.05 accuracy 0.97: 28%|██▊ | 28/100 [00:00<00:01, 57.46it/s]
loss 0.01 accuracy 1.00: 28%|██▊ | 28/100 [00:00<00:01, 57.46it/s]
loss 0.04 accuracy 1.00: 28%|██▊ | 28/100 [00:00<00:01, 57.46it/s]
loss 0.02 accuracy 1.00: 28%|██▊ | 28/100 [00:00<00:01, 57.46it/s]
loss 0.02 accuracy 1.00: 37%|███▋ | 37/100 [00:00<00:00, 64.70it/s]
loss 0.31 accuracy 0.94: 37%|███▋ | 37/100 [00:00<00:00, 64.70it/s]
loss 0.15 accuracy 0.97: 37%|███▋ | 37/100 [00:00<00:00, 64.70it/s]
loss 0.04 accuracy 1.00: 37%|███▋ | 37/100 [00:00<00:00, 64.70it/s]
loss 0.01 accuracy 1.00: 37%|███▋ | 37/100 [00:00<00:00, 64.70it/s]
loss 0.01 accuracy 1.00: 37%|███▋ | 37/100 [00:00<00:00, 64.70it/s]
loss 0.10 accuracy 0.97: 37%|███▋ | 37/100 [00:00<00:00, 64.70it/s]
loss 0.07 accuracy 0.94: 37%|███▋ | 37/100 [00:00<00:00, 64.70it/s]
loss 0.21 accuracy 0.97: 37%|███▋ | 37/100 [00:00<00:00, 64.70it/s]
loss 0.12 accuracy 0.97: 37%|███▋ | 37/100 [00:00<00:00, 64.70it/s]
loss 0.12 accuracy 0.97: 46%|████▌ | 46/100 [00:00<00:00, 69.56it/s]
loss 0.12 accuracy 0.97: 46%|████▌ | 46/100 [00:00<00:00, 69.56it/s]
loss 0.08 accuracy 0.97: 46%|████▌ | 46/100 [00:00<00:00, 69.56it/s]
loss 0.11 accuracy 0.97: 46%|████▌ | 46/100 [00:00<00:00, 69.56it/s]
loss 0.05 accuracy 1.00: 46%|████▌ | 46/100 [00:00<00:00, 69.56it/s]
loss 0.02 accuracy 1.00: 46%|████▌ | 46/100 [00:00<00:00, 69.56it/s]
loss 0.01 accuracy 1.00: 46%|████▌ | 46/100 [00:00<00:00, 69.56it/s]
loss 0.01 accuracy 1.00: 46%|████▌ | 46/100 [00:01<00:00, 69.56it/s]
loss 0.14 accuracy 0.94: 46%|████▌ | 46/100 [00:01<00:00, 69.56it/s]
loss 0.04 accuracy 1.00: 46%|████▌ | 46/100 [00:01<00:00, 69.56it/s]
loss 0.04 accuracy 1.00: 55%|█████▌ | 55/100 [00:01<00:00, 72.91it/s]
loss 0.17 accuracy 0.97: 55%|█████▌ | 55/100 [00:01<00:00, 72.91it/s]
loss 0.01 accuracy 1.00: 55%|█████▌ | 55/100 [00:01<00:00, 72.91it/s]
loss 0.30 accuracy 0.88: 55%|█████▌ | 55/100 [00:01<00:00, 72.91it/s]
loss 0.25 accuracy 0.94: 55%|█████▌ | 55/100 [00:01<00:00, 72.91it/s]
loss 0.03 accuracy 1.00: 55%|█████▌ | 55/100 [00:01<00:00, 72.91it/s]
loss 0.08 accuracy 0.97: 55%|█████▌ | 55/100 [00:01<00:00, 72.91it/s]
loss 0.25 accuracy 0.94: 55%|█████▌ | 55/100 [00:01<00:00, 72.91it/s]
loss 0.16 accuracy 0.94: 55%|█████▌ | 55/100 [00:01<00:00, 72.91it/s]
loss 0.11 accuracy 0.97: 55%|█████▌ | 55/100 [00:01<00:00, 72.91it/s]
loss 0.11 accuracy 0.97: 64%|██████▍ | 64/100 [00:01<00:00, 75.23it/s]
loss 0.14 accuracy 0.94: 64%|██████▍ | 64/100 [00:01<00:00, 75.23it/s]
loss 0.14 accuracy 0.94: 64%|██████▍ | 64/100 [00:01<00:00, 75.23it/s]
loss 0.07 accuracy 0.97: 64%|██████▍ | 64/100 [00:01<00:00, 75.23it/s]
loss 0.08 accuracy 0.97: 64%|██████▍ | 64/100 [00:01<00:00, 75.23it/s]
loss 0.15 accuracy 0.94: 64%|██████▍ | 64/100 [00:01<00:00, 75.23it/s]
loss 0.03 accuracy 1.00: 64%|██████▍ | 64/100 [00:01<00:00, 75.23it/s]
loss 0.22 accuracy 0.88: 64%|██████▍ | 64/100 [00:01<00:00, 75.23it/s]
loss 0.13 accuracy 0.94: 64%|██████▍ | 64/100 [00:01<00:00, 75.23it/s]
loss 0.09 accuracy 0.94: 64%|██████▍ | 64/100 [00:01<00:00, 75.23it/s]
loss 0.09 accuracy 0.94: 73%|███████▎ | 73/100 [00:01<00:00, 76.81it/s]
loss 0.16 accuracy 0.97: 73%|███████▎ | 73/100 [00:01<00:00, 76.81it/s]
loss 0.30 accuracy 0.94: 73%|███████▎ | 73/100 [00:01<00:00, 76.81it/s]
loss 0.16 accuracy 0.97: 73%|███████▎ | 73/100 [00:01<00:00, 76.81it/s]
loss 0.05 accuracy 0.97: 73%|███████▎ | 73/100 [00:01<00:00, 76.81it/s]
loss 0.05 accuracy 0.97: 73%|███████▎ | 73/100 [00:01<00:00, 76.81it/s]
loss 0.03 accuracy 1.00: 73%|███████▎ | 73/100 [00:01<00:00, 76.81it/s]
loss 0.16 accuracy 0.97: 73%|███████▎ | 73/100 [00:01<00:00, 76.81it/s]
loss 0.18 accuracy 0.94: 73%|███████▎ | 73/100 [00:01<00:00, 76.81it/s]
loss 0.08 accuracy 0.97: 73%|███████▎ | 73/100 [00:01<00:00, 76.81it/s]
loss 0.08 accuracy 0.97: 82%|████████▏ | 82/100 [00:01<00:00, 77.88it/s]
loss 0.02 accuracy 1.00: 82%|████████▏ | 82/100 [00:01<00:00, 77.88it/s]
loss 0.00 accuracy 1.00: 82%|████████▏ | 82/100 [00:01<00:00, 77.88it/s]
loss 0.33 accuracy 0.97: 82%|████████▏ | 82/100 [00:01<00:00, 77.88it/s]
loss 0.03 accuracy 1.00: 82%|████████▏ | 82/100 [00:01<00:00, 77.88it/s]
loss 0.03 accuracy 1.00: 82%|████████▏ | 82/100 [00:01<00:00, 77.88it/s]
loss 0.03 accuracy 1.00: 82%|████████▏ | 82/100 [00:01<00:00, 77.88it/s]
loss 0.16 accuracy 0.97: 82%|████████▏ | 82/100 [00:01<00:00, 77.88it/s]
loss 0.04 accuracy 1.00: 82%|████████▏ | 82/100 [00:01<00:00, 77.88it/s]
loss 0.07 accuracy 0.97: 82%|████████▏ | 82/100 [00:01<00:00, 77.88it/s]
loss 0.07 accuracy 0.97: 91%|█████████ | 91/100 [00:01<00:00, 78.61it/s]
loss 0.02 accuracy 1.00: 91%|█████████ | 91/100 [00:01<00:00, 78.61it/s]
loss 0.06 accuracy 1.00: 91%|█████████ | 91/100 [00:01<00:00, 78.61it/s]
loss 0.10 accuracy 0.97: 91%|█████████ | 91/100 [00:01<00:00, 78.61it/s]
loss 0.16 accuracy 0.97: 91%|█████████ | 91/100 [00:01<00:00, 78.61it/s]
loss 0.07 accuracy 0.97: 91%|█████████ | 91/100 [00:01<00:00, 78.61it/s]
loss 0.09 accuracy 0.97: 91%|█████████ | 91/100 [00:01<00:00, 78.61it/s]
loss 0.08 accuracy 0.97: 91%|█████████ | 91/100 [00:01<00:00, 78.61it/s]
loss 0.16 accuracy 0.97: 91%|█████████ | 91/100 [00:01<00:00, 78.61it/s]
loss 0.16 accuracy 0.94: 91%|█████████ | 91/100 [00:01<00:00, 78.61it/s]
loss 0.16 accuracy 0.94: 100%|██████████| 100/100 [00:01<00:00, 79.15it/s]
loss 0.16 accuracy 0.94: 100%|██████████| 100/100 [00:01<00:00, 62.98it/s]
0%| | 0/79 [00:00<?, ?it/s]
5%|▌ | 4/79 [00:00<00:02, 31.09it/s]
10%|█ | 8/79 [00:00<00:03, 23.47it/s]
15%|█▌ | 12/79 [00:00<00:02, 26.45it/s]
20%|██ | 16/79 [00:00<00:02, 28.05it/s]
25%|██▌ | 20/79 [00:00<00:02, 29.07it/s]
30%|███ | 24/79 [00:00<00:01, 29.75it/s]
35%|███▌ | 28/79 [00:01<00:01, 25.65it/s]
41%|████ | 32/79 [00:01<00:01, 27.20it/s]
46%|████▌ | 36/79 [00:01<00:01, 28.32it/s]
51%|█████ | 40/79 [00:01<00:01, 29.09it/s]
56%|█████▌ | 44/79 [00:01<00:01, 29.66it/s]
61%|██████ | 48/79 [00:01<00:01, 25.95it/s]
66%|██████▌ | 52/79 [00:01<00:00, 27.28it/s]
71%|███████ | 56/79 [00:02<00:00, 28.32it/s]
76%|███████▌ | 60/79 [00:02<00:00, 29.08it/s]
81%|████████ | 64/79 [00:02<00:00, 25.51it/s]
86%|████████▌ | 68/79 [00:02<00:00, 26.90it/s]
91%|█████████ | 72/79 [00:02<00:00, 28.04it/s]
96%|█████████▌| 76/79 [00:02<00:00, 28.85it/s]
100%|██████████| 79/79 [00:02<00:00, 28.03it/s]
test set accuracy is 0.971000
reducing lr to 0.0028935
0%| | 0/100 [00:00<?, ?it/s]
loss 0.07 accuracy 0.97: 0%| | 0/100 [00:00<?, ?it/s]
loss 0.07 accuracy 0.97: 1%| | 1/100 [00:00<00:20, 4.81it/s]
loss 0.16 accuracy 0.97: 1%| | 1/100 [00:00<00:20, 4.81it/s]
loss 0.16 accuracy 0.97: 2%|▏ | 2/100 [00:00<00:17, 5.51it/s]
loss 0.02 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.51it/s]
loss 0.03 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.51it/s]
loss 0.01 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.51it/s]
loss 0.04 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.51it/s]
loss 0.01 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.51it/s]
loss 0.04 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.51it/s]
loss 0.21 accuracy 0.91: 2%|▏ | 2/100 [00:00<00:17, 5.51it/s]
loss 0.03 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.51it/s]
loss 0.03 accuracy 1.00: 10%|█ | 10/100 [00:00<00:03, 28.97it/s]
loss 0.07 accuracy 0.97: 10%|█ | 10/100 [00:00<00:03, 28.97it/s]
loss 0.12 accuracy 0.97: 10%|█ | 10/100 [00:00<00:03, 28.97it/s]
loss 0.25 accuracy 0.94: 10%|█ | 10/100 [00:00<00:03, 28.97it/s]
loss 0.01 accuracy 1.00: 10%|█ | 10/100 [00:00<00:03, 28.97it/s]
loss 0.46 accuracy 0.94: 10%|█ | 10/100 [00:00<00:03, 28.97it/s]
loss 0.08 accuracy 0.97: 10%|█ | 10/100 [00:00<00:03, 28.97it/s]
loss 0.08 accuracy 0.97: 10%|█ | 10/100 [00:00<00:03, 28.97it/s]
loss 0.11 accuracy 0.97: 10%|█ | 10/100 [00:00<00:03, 28.97it/s]
loss 0.11 accuracy 0.97: 18%|█▊ | 18/100 [00:00<00:01, 44.81it/s]
loss 0.02 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 44.81it/s]
loss 0.01 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 44.81it/s]
loss 0.21 accuracy 0.94: 18%|█▊ | 18/100 [00:00<00:01, 44.81it/s]
loss 0.03 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 44.81it/s]
loss 0.05 accuracy 0.97: 18%|█▊ | 18/100 [00:00<00:01, 44.81it/s]
loss 0.02 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 44.81it/s]
loss 0.02 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 44.81it/s]
loss 0.02 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 44.81it/s]
loss 0.02 accuracy 1.00: 26%|██▌ | 26/100 [00:00<00:01, 55.59it/s]
loss 0.11 accuracy 0.97: 26%|██▌ | 26/100 [00:00<00:01, 55.59it/s]
loss 0.17 accuracy 0.94: 26%|██▌ | 26/100 [00:00<00:01, 55.59it/s]
loss 0.03 accuracy 1.00: 26%|██▌ | 26/100 [00:00<00:01, 55.59it/s]
loss 0.04 accuracy 1.00: 26%|██▌ | 26/100 [00:00<00:01, 55.59it/s]
loss 0.06 accuracy 0.97: 26%|██▌ | 26/100 [00:00<00:01, 55.59it/s]
loss 0.05 accuracy 0.97: 26%|██▌ | 26/100 [00:00<00:01, 55.59it/s]
loss 0.03 accuracy 1.00: 26%|██▌ | 26/100 [00:00<00:01, 55.59it/s]
loss 0.02 accuracy 1.00: 26%|██▌ | 26/100 [00:00<00:01, 55.59it/s]
loss 0.02 accuracy 1.00: 34%|███▍ | 34/100 [00:00<00:01, 63.01it/s]
loss 0.07 accuracy 0.97: 34%|███▍ | 34/100 [00:00<00:01, 63.01it/s]
loss 0.12 accuracy 0.97: 34%|███▍ | 34/100 [00:00<00:01, 63.01it/s]
loss 0.19 accuracy 0.97: 34%|███▍ | 34/100 [00:00<00:01, 63.01it/s]
loss 0.09 accuracy 0.97: 34%|███▍ | 34/100 [00:00<00:01, 63.01it/s]
loss 0.01 accuracy 1.00: 34%|███▍ | 34/100 [00:00<00:01, 63.01it/s]
loss 0.01 accuracy 1.00: 34%|███▍ | 34/100 [00:00<00:01, 63.01it/s]
loss 0.13 accuracy 0.97: 34%|███▍ | 34/100 [00:00<00:01, 63.01it/s]
loss 0.09 accuracy 0.97: 34%|███▍ | 34/100 [00:00<00:01, 63.01it/s]
loss 0.09 accuracy 0.97: 42%|████▏ | 42/100 [00:00<00:00, 68.10it/s]
loss 0.02 accuracy 1.00: 42%|████▏ | 42/100 [00:00<00:00, 68.10it/s]
loss 0.03 accuracy 1.00: 42%|████▏ | 42/100 [00:00<00:00, 68.10it/s]
loss 0.06 accuracy 0.97: 42%|████▏ | 42/100 [00:00<00:00, 68.10it/s]
loss 0.09 accuracy 0.97: 42%|████▏ | 42/100 [00:00<00:00, 68.10it/s]
loss 0.09 accuracy 0.94: 42%|████▏ | 42/100 [00:00<00:00, 68.10it/s]
loss 0.06 accuracy 0.97: 42%|████▏ | 42/100 [00:00<00:00, 68.10it/s]
loss 0.04 accuracy 0.97: 42%|████▏ | 42/100 [00:00<00:00, 68.10it/s]
loss 0.00 accuracy 1.00: 42%|████▏ | 42/100 [00:00<00:00, 68.10it/s]
loss 0.15 accuracy 0.97: 42%|████▏ | 42/100 [00:00<00:00, 68.10it/s]
loss 0.15 accuracy 0.97: 51%|█████ | 51/100 [00:00<00:00, 72.04it/s]
loss 0.14 accuracy 0.97: 51%|█████ | 51/100 [00:00<00:00, 72.04it/s]
loss 0.02 accuracy 1.00: 51%|█████ | 51/100 [00:01<00:00, 72.04it/s]
loss 0.03 accuracy 0.97: 51%|█████ | 51/100 [00:01<00:00, 72.04it/s]
loss 0.08 accuracy 0.97: 51%|█████ | 51/100 [00:01<00:00, 72.04it/s]
loss 0.06 accuracy 0.97: 51%|█████ | 51/100 [00:01<00:00, 72.04it/s]
loss 0.04 accuracy 1.00: 51%|█████ | 51/100 [00:01<00:00, 72.04it/s]
loss 0.01 accuracy 1.00: 51%|█████ | 51/100 [00:01<00:00, 72.04it/s]
loss 0.30 accuracy 0.94: 51%|█████ | 51/100 [00:01<00:00, 72.04it/s]
loss 0.30 accuracy 0.94: 59%|█████▉ | 59/100 [00:01<00:00, 74.26it/s]
loss 0.02 accuracy 1.00: 59%|█████▉ | 59/100 [00:01<00:00, 74.26it/s]
loss 0.05 accuracy 0.97: 59%|█████▉ | 59/100 [00:01<00:00, 74.26it/s]
loss 0.05 accuracy 1.00: 59%|█████▉ | 59/100 [00:01<00:00, 74.26it/s]
loss 0.20 accuracy 0.94: 59%|█████▉ | 59/100 [00:01<00:00, 74.26it/s]
loss 0.02 accuracy 1.00: 59%|█████▉ | 59/100 [00:01<00:00, 74.26it/s]
loss 0.13 accuracy 0.94: 59%|█████▉ | 59/100 [00:01<00:00, 74.26it/s]
loss 0.04 accuracy 0.97: 59%|█████▉ | 59/100 [00:01<00:00, 74.26it/s]
loss 0.24 accuracy 0.88: 59%|█████▉ | 59/100 [00:01<00:00, 74.26it/s]
loss 0.24 accuracy 0.88: 67%|██████▋ | 67/100 [00:01<00:00, 75.93it/s]
loss 0.02 accuracy 1.00: 67%|██████▋ | 67/100 [00:01<00:00, 75.93it/s]
loss 0.21 accuracy 0.97: 67%|██████▋ | 67/100 [00:01<00:00, 75.93it/s]
loss 0.02 accuracy 1.00: 67%|██████▋ | 67/100 [00:01<00:00, 75.93it/s]
loss 0.08 accuracy 1.00: 67%|██████▋ | 67/100 [00:01<00:00, 75.93it/s]
loss 0.06 accuracy 0.97: 67%|██████▋ | 67/100 [00:01<00:00, 75.93it/s]
loss 0.22 accuracy 0.91: 67%|██████▋ | 67/100 [00:01<00:00, 75.93it/s]
loss 0.12 accuracy 0.94: 67%|██████▋ | 67/100 [00:01<00:00, 75.93it/s]
loss 0.05 accuracy 1.00: 67%|██████▋ | 67/100 [00:01<00:00, 75.93it/s]
loss 0.05 accuracy 1.00: 75%|███████▌ | 75/100 [00:01<00:00, 72.21it/s]
loss 0.09 accuracy 0.94: 75%|███████▌ | 75/100 [00:01<00:00, 72.21it/s]
loss 0.10 accuracy 0.97: 75%|███████▌ | 75/100 [00:01<00:00, 72.21it/s]
loss 0.16 accuracy 0.97: 75%|███████▌ | 75/100 [00:01<00:00, 72.21it/s]
loss 0.11 accuracy 0.97: 75%|███████▌ | 75/100 [00:01<00:00, 72.21it/s]
loss 0.06 accuracy 0.97: 75%|███████▌ | 75/100 [00:01<00:00, 72.21it/s]
loss 0.27 accuracy 0.94: 75%|███████▌ | 75/100 [00:01<00:00, 72.21it/s]
loss 0.09 accuracy 0.97: 75%|███████▌ | 75/100 [00:01<00:00, 72.21it/s]
loss 0.04 accuracy 0.97: 75%|███████▌ | 75/100 [00:01<00:00, 72.21it/s]
loss 0.04 accuracy 0.97: 83%|████████▎ | 83/100 [00:01<00:00, 74.34it/s]
loss 0.03 accuracy 1.00: 83%|████████▎ | 83/100 [00:01<00:00, 74.34it/s]
loss 0.05 accuracy 0.97: 83%|████████▎ | 83/100 [00:01<00:00, 74.34it/s]
loss 0.05 accuracy 1.00: 83%|████████▎ | 83/100 [00:01<00:00, 74.34it/s]
loss 0.03 accuracy 1.00: 83%|████████▎ | 83/100 [00:01<00:00, 74.34it/s]
loss 0.03 accuracy 1.00: 83%|████████▎ | 83/100 [00:01<00:00, 74.34it/s]
loss 0.01 accuracy 1.00: 83%|████████▎ | 83/100 [00:01<00:00, 74.34it/s]
loss 0.06 accuracy 0.97: 83%|████████▎ | 83/100 [00:01<00:00, 74.34it/s]
loss 0.00 accuracy 1.00: 83%|████████▎ | 83/100 [00:01<00:00, 74.34it/s]
loss 0.00 accuracy 1.00: 91%|█████████ | 91/100 [00:01<00:00, 75.95it/s]
loss 0.03 accuracy 1.00: 91%|█████████ | 91/100 [00:01<00:00, 75.95it/s]
loss 0.11 accuracy 0.97: 91%|█████████ | 91/100 [00:01<00:00, 75.95it/s]
loss 0.14 accuracy 0.94: 91%|█████████ | 91/100 [00:01<00:00, 75.95it/s]
loss 0.18 accuracy 0.94: 91%|█████████ | 91/100 [00:01<00:00, 75.95it/s]
loss 0.12 accuracy 0.94: 91%|█████████ | 91/100 [00:01<00:00, 75.95it/s]
loss 0.02 accuracy 1.00: 91%|█████████ | 91/100 [00:01<00:00, 75.95it/s]
loss 0.01 accuracy 1.00: 91%|█████████ | 91/100 [00:01<00:00, 75.95it/s]
loss 0.05 accuracy 0.97: 91%|█████████ | 91/100 [00:01<00:00, 75.95it/s]
loss 0.19 accuracy 0.97: 91%|█████████ | 91/100 [00:01<00:00, 75.95it/s]
loss 0.19 accuracy 0.97: 100%|██████████| 100/100 [00:01<00:00, 77.27it/s]
loss 0.19 accuracy 0.97: 100%|██████████| 100/100 [00:01<00:00, 61.66it/s]
0%| | 0/79 [00:00<?, ?it/s]
5%|▌ | 4/79 [00:00<00:02, 31.03it/s]
10%|█ | 8/79 [00:00<00:02, 30.96it/s]
15%|█▌ | 12/79 [00:00<00:02, 24.77it/s]
20%|██ | 16/79 [00:00<00:02, 26.86it/s]
25%|██▌ | 20/79 [00:00<00:02, 28.15it/s]
30%|███ | 24/79 [00:00<00:01, 29.01it/s]
35%|███▌ | 28/79 [00:00<00:01, 29.59it/s]
39%|███▉ | 31/79 [00:01<00:01, 25.24it/s]
44%|████▍ | 35/79 [00:01<00:01, 26.92it/s]
49%|████▉ | 39/79 [00:01<00:01, 28.03it/s]
54%|█████▍ | 43/79 [00:01<00:01, 28.90it/s]
59%|█████▉ | 47/79 [00:01<00:01, 29.47it/s]
63%|██████▎ | 50/79 [00:01<00:01, 25.34it/s]
68%|██████▊ | 54/79 [00:01<00:00, 26.80it/s]
73%|███████▎ | 58/79 [00:02<00:00, 27.93it/s]
78%|███████▊ | 62/79 [00:02<00:00, 28.80it/s]
84%|████████▎ | 66/79 [00:02<00:00, 29.41it/s]
87%|████████▋ | 69/79 [00:02<00:00, 25.41it/s]
92%|█████████▏| 73/79 [00:02<00:00, 26.92it/s]
97%|█████████▋| 77/79 [00:02<00:00, 28.07it/s]
100%|██████████| 79/79 [00:02<00:00, 27.88it/s]
test set accuracy is 0.978200
reducing lr to 0.0024113
0%| | 0/100 [00:00<?, ?it/s]
loss 0.01 accuracy 1.00: 0%| | 0/100 [00:00<?, ?it/s]
loss 0.01 accuracy 1.00: 1%| | 1/100 [00:00<00:20, 4.82it/s]
loss 0.01 accuracy 1.00: 1%| | 1/100 [00:00<00:20, 4.82it/s]
loss 0.01 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.52it/s]
loss 0.02 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.52it/s]
loss 0.21 accuracy 0.94: 2%|▏ | 2/100 [00:00<00:17, 5.52it/s]
loss 0.01 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.52it/s]
loss 0.02 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.52it/s]
loss 0.11 accuracy 0.97: 2%|▏ | 2/100 [00:00<00:17, 5.52it/s]
loss 0.03 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.52it/s]
loss 0.16 accuracy 0.94: 2%|▏ | 2/100 [00:00<00:17, 5.52it/s]
loss 0.01 accuracy 1.00: 2%|▏ | 2/100 [00:00<00:17, 5.52it/s]
loss 0.01 accuracy 1.00: 10%|█ | 10/100 [00:00<00:03, 28.87it/s]
loss 0.03 accuracy 1.00: 10%|█ | 10/100 [00:00<00:03, 28.87it/s]
loss 0.11 accuracy 0.97: 10%|█ | 10/100 [00:00<00:03, 28.87it/s]
loss 0.00 accuracy 1.00: 10%|█ | 10/100 [00:00<00:03, 28.87it/s]
loss 0.08 accuracy 0.97: 10%|█ | 10/100 [00:00<00:03, 28.87it/s]
loss 0.10 accuracy 0.97: 10%|█ | 10/100 [00:00<00:03, 28.87it/s]
loss 0.26 accuracy 0.94: 10%|█ | 10/100 [00:00<00:03, 28.87it/s]
loss 0.01 accuracy 1.00: 10%|█ | 10/100 [00:00<00:03, 28.87it/s]
loss 0.01 accuracy 1.00: 10%|█ | 10/100 [00:00<00:03, 28.87it/s]
loss 0.01 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 44.48it/s]
loss 0.03 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 44.48it/s]
loss 0.07 accuracy 0.94: 18%|█▊ | 18/100 [00:00<00:01, 44.48it/s]
loss 0.02 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 44.48it/s]
loss 0.02 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 44.48it/s]
loss 0.01 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 44.48it/s]
loss 0.04 accuracy 1.00: 18%|█▊ | 18/100 [00:00<00:01, 44.48it/s]
loss 0.12 accuracy 0.91: 18%|█▊ | 18/100 [00:00<00:01, 44.48it/s]
loss 0.24 accuracy 0.94: 18%|█▊ | 18/100 [00:00<00:01, 44.48it/s]
loss 0.24 accuracy 0.94: 26%|██▌ | 26/100 [00:00<00:01, 55.00it/s]
loss 0.02 accuracy 1.00: 26%|██▌ | 26/100 [00:00<00:01, 55.00it/s]
loss 0.20 accuracy 0.94: 26%|██▌ | 26/100 [00:00<00:01, 55.00it/s]
loss 0.00 accuracy 1.00: 26%|██▌ | 26/100 [00:00<00:01, 55.00it/s]
loss 0.01 accuracy 1.00: 26%|██▌ | 26/100 [00:00<00:01, 55.00it/s]
loss 0.21 accuracy 0.94: 26%|██▌ | 26/100 [00:00<00:01, 55.00it/s]
loss 0.04 accuracy 1.00: 26%|██▌ | 26/100 [00:00<00:01, 55.00it/s]
loss 0.09 accuracy 0.97: 26%|██▌ | 26/100 [00:00<00:01, 55.00it/s]
loss 0.04 accuracy 1.00: 26%|██▌ | 26/100 [00:00<00:01, 55.00it/s]
loss 0.04 accuracy 1.00: 34%|███▍ | 34/100 [00:00<00:01, 62.23it/s]
loss 0.04 accuracy 0.97: 34%|███▍ | 34/100 [00:00<00:01, 62.23it/s]
loss 0.14 accuracy 0.94: 34%|███▍ | 34/100 [00:00<00:01, 62.23it/s]
loss 0.01 accuracy 1.00: 34%|███▍ | 34/100 [00:00<00:01, 62.23it/s]
loss 0.05 accuracy 1.00: 34%|███▍ | 34/100 [00:00<00:01, 62.23it/s]
loss 0.20 accuracy 0.97: 34%|███▍ | 34/100 [00:00<00:01, 62.23it/s]
loss 0.01 accuracy 1.00: 34%|███▍ | 34/100 [00:00<00:01, 62.23it/s]
loss 0.17 accuracy 0.97: 34%|███▍ | 34/100 [00:00<00:01, 62.23it/s]
loss 0.00 accuracy 1.00: 34%|███▍ | 34/100 [00:00<00:01, 62.23it/s]
loss 0.00 accuracy 1.00: 42%|████▏ | 42/100 [00:00<00:00, 67.12it/s]
loss 0.02 accuracy 1.00: 42%|████▏ | 42/100 [00:00<00:00, 67.12it/s]
loss 0.02 accuracy 1.00: 42%|████▏ | 42/100 [00:00<00:00, 67.12it/s]
loss 0.03 accuracy 1.00: 42%|████▏ | 42/100 [00:00<00:00, 67.12it/s]
loss 0.07 accuracy 0.94: 42%|████▏ | 42/100 [00:00<00:00, 67.12it/s]
loss 0.01 accuracy 1.00: 42%|████▏ | 42/100 [00:00<00:00, 67.12it/s]
loss 0.02 accuracy 1.00: 42%|████▏ | 42/100 [00:00<00:00, 67.12it/s]
loss 0.03 accuracy 1.00: 42%|████▏ | 42/100 [00:00<00:00, 67.12it/s]
loss 0.03 accuracy 0.97: 42%|████▏ | 42/100 [00:00<00:00, 67.12it/s]
loss 0.03 accuracy 0.97: 50%|█████ | 50/100 [00:00<00:00, 70.50it/s]
loss 0.20 accuracy 0.97: 50%|█████ | 50/100 [00:00<00:00, 70.50it/s]
loss 0.03 accuracy 1.00: 50%|█████ | 50/100 [00:01<00:00, 70.50it/s]
loss 0.04 accuracy 1.00: 50%|█████ | 50/100 [00:01<00:00, 70.50it/s]
loss 0.05 accuracy 0.97: 50%|█████ | 50/100 [00:01<00:00, 70.50it/s]
loss 0.10 accuracy 0.94: 50%|█████ | 50/100 [00:01<00:00, 70.50it/s]
loss 0.04 accuracy 1.00: 50%|█████ | 50/100 [00:01<00:00, 70.50it/s]
loss 0.04 accuracy 0.97: 50%|█████ | 50/100 [00:01<00:00, 70.50it/s]
loss 0.09 accuracy 0.94: 50%|█████ | 50/100 [00:01<00:00, 70.50it/s]
loss 0.09 accuracy 0.94: 58%|█████▊ | 58/100 [00:01<00:00, 72.76it/s]
loss 0.11 accuracy 0.97: 58%|█████▊ | 58/100 [00:01<00:00, 72.76it/s]
loss 0.01 accuracy 1.00: 58%|█████▊ | 58/100 [00:01<00:00, 72.76it/s]
loss 0.27 accuracy 0.94: 58%|█████▊ | 58/100 [00:01<00:00, 72.76it/s]
loss 0.10 accuracy 0.97: 58%|█████▊ | 58/100 [00:01<00:00, 72.76it/s]
loss 0.16 accuracy 0.97: 58%|█████▊ | 58/100 [00:01<00:00, 72.76it/s]
loss 0.08 accuracy 0.94: 58%|█████▊ | 58/100 [00:01<00:00, 72.76it/s]
loss 0.00 accuracy 1.00: 58%|█████▊ | 58/100 [00:01<00:00, 72.76it/s]
loss 0.34 accuracy 0.94: 58%|█████▊ | 58/100 [00:01<00:00, 72.76it/s]
loss 0.34 accuracy 0.94: 66%|██████▌ | 66/100 [00:01<00:00, 74.26it/s]
loss 0.11 accuracy 0.97: 66%|██████▌ | 66/100 [00:01<00:00, 74.26it/s]
loss 0.03 accuracy 1.00: 66%|██████▌ | 66/100 [00:01<00:00, 74.26it/s]
loss 0.01 accuracy 1.00: 66%|██████▌ | 66/100 [00:01<00:00, 74.26it/s]
loss 0.09 accuracy 1.00: 66%|██████▌ | 66/100 [00:01<00:00, 74.26it/s]
loss 0.07 accuracy 0.94: 66%|██████▌ | 66/100 [00:01<00:00, 74.26it/s]
loss 0.01 accuracy 1.00: 66%|██████▌ | 66/100 [00:01<00:00, 74.26it/s]
loss 0.00 accuracy 1.00: 66%|██████▌ | 66/100 [00:01<00:00, 74.26it/s]
loss 0.00 accuracy 1.00: 66%|██████▌ | 66/100 [00:01<00:00, 74.26it/s]
loss 0.00 accuracy 1.00: 74%|███████▍ | 74/100 [00:01<00:00, 75.31it/s]
loss 0.01 accuracy 1.00: 74%|███████▍ | 74/100 [00:01<00:00, 75.31it/s]
loss 0.06 accuracy 0.97: 74%|███████▍ | 74/100 [00:01<00:00, 75.31it/s]
loss 0.07 accuracy 0.97: 74%|███████▍ | 74/100 [00:01<00:00, 75.31it/s]
loss 0.01 accuracy 1.00: 74%|███████▍ | 74/100 [00:01<00:00, 75.31it/s]
loss 0.07 accuracy 0.97: 74%|███████▍ | 74/100 [00:01<00:00, 75.31it/s]
loss 0.15 accuracy 0.97: 74%|███████▍ | 74/100 [00:01<00:00, 75.31it/s]
loss 0.02 accuracy 1.00: 74%|███████▍ | 74/100 [00:01<00:00, 75.31it/s]
loss 0.05 accuracy 0.97: 74%|███████▍ | 74/100 [00:01<00:00, 75.31it/s]
loss 0.05 accuracy 0.97: 82%|████████▏ | 82/100 [00:01<00:00, 76.06it/s]
loss 0.08 accuracy 0.97: 82%|████████▏ | 82/100 [00:01<00:00, 76.06it/s]
loss 0.01 accuracy 1.00: 82%|████████▏ | 82/100 [00:01<00:00, 76.06it/s]
loss 0.03 accuracy 1.00: 82%|████████▏ | 82/100 [00:01<00:00, 76.06it/s]
loss 0.05 accuracy 0.97: 82%|████████▏ | 82/100 [00:01<00:00, 76.06it/s]
loss 0.02 accuracy 1.00: 82%|████████▏ | 82/100 [00:01<00:00, 76.06it/s]
loss 0.03 accuracy 1.00: 82%|████████▏ | 82/100 [00:01<00:00, 76.06it/s]
loss 0.02 accuracy 1.00: 82%|████████▏ | 82/100 [00:01<00:00, 76.06it/s]
loss 0.11 accuracy 0.97: 82%|████████▏ | 82/100 [00:01<00:00, 76.06it/s]
loss 0.11 accuracy 0.97: 90%|█████████ | 90/100 [00:01<00:00, 76.59it/s]
loss 0.01 accuracy 1.00: 90%|█████████ | 90/100 [00:01<00:00, 76.59it/s]
loss 0.04 accuracy 1.00: 90%|█████████ | 90/100 [00:01<00:00, 76.59it/s]
loss 0.09 accuracy 0.97: 90%|█████████ | 90/100 [00:01<00:00, 76.59it/s]
loss 0.02 accuracy 1.00: 90%|█████████ | 90/100 [00:01<00:00, 76.59it/s]
loss 0.05 accuracy 0.97: 90%|█████████ | 90/100 [00:01<00:00, 76.59it/s]
loss 0.03 accuracy 0.97: 90%|█████████ | 90/100 [00:01<00:00, 76.59it/s]
loss 0.02 accuracy 1.00: 90%|█████████ | 90/100 [00:01<00:00, 76.59it/s]
loss 0.02 accuracy 1.00: 90%|█████████ | 90/100 [00:01<00:00, 76.59it/s]
loss 0.02 accuracy 1.00: 98%|█████████▊| 98/100 [00:01<00:00, 77.01it/s]
loss 0.00 accuracy 1.00: 98%|█████████▊| 98/100 [00:01<00:00, 77.01it/s]
loss 0.01 accuracy 1.00: 98%|█████████▊| 98/100 [00:01<00:00, 77.01it/s]
loss 0.01 accuracy 1.00: 100%|██████████| 100/100 [00:01<00:00, 61.53it/s]
0%| | 0/79 [00:00<?, ?it/s]
5%|▌ | 4/79 [00:00<00:02, 30.90it/s]
10%|█ | 8/79 [00:00<00:02, 30.91it/s]
15%|█▌ | 12/79 [00:00<00:02, 24.69it/s]
20%|██ | 16/79 [00:00<00:02, 26.87it/s]
25%|██▌ | 20/79 [00:00<00:02, 28.18it/s]
30%|███ | 24/79 [00:00<00:01, 29.09it/s]
35%|███▌ | 28/79 [00:00<00:01, 29.69it/s]
41%|████ | 32/79 [00:01<00:01, 25.74it/s]
46%|████▌ | 36/79 [00:01<00:01, 27.18it/s]
51%|█████ | 40/79 [00:01<00:01, 28.12it/s]
56%|█████▌ | 44/79 [00:01<00:01, 28.88it/s]
61%|██████ | 48/79 [00:01<00:01, 29.47it/s]
65%|██████▍ | 51/79 [00:01<00:01, 25.34it/s]
70%|██████▉ | 55/79 [00:01<00:00, 26.91it/s]
75%|███████▍ | 59/79 [00:02<00:00, 28.00it/s]
80%|███████▉ | 63/79 [00:02<00:00, 28.86it/s]
85%|████████▍ | 67/79 [00:02<00:00, 29.49it/s]
89%|████████▊ | 70/79 [00:02<00:00, 25.34it/s]
94%|█████████▎| 74/79 [00:02<00:00, 26.94it/s]
99%|█████████▊| 78/79 [00:02<00:00, 28.03it/s]
100%|██████████| 79/79 [00:02<00:00, 27.90it/s]
test set accuracy is 0.981400
reducing lr to 0.0020094
transformer.py
0%| | 0/50 [00:00<?, ?it/s]
loss 2.35 accuracy 0.07: 0%| | 0/50 [00:04<?, ?it/s]
loss 2.35 accuracy 0.07: 2%|▏ | 1/50 [00:04<03:58, 4.86s/it]
loss 2.23 accuracy 0.23: 2%|▏ | 1/50 [00:06<03:58, 4.86s/it]
loss 2.23 accuracy 0.23: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 2.20 accuracy 0.19: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 2.15 accuracy 0.24: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 2.12 accuracy 0.24: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 2.09 accuracy 0.20: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 2.03 accuracy 0.29: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 1.96 accuracy 0.31: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 1.92 accuracy 0.33: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 1.88 accuracy 0.34: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 1.79 accuracy 0.39: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 1.76 accuracy 0.41: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 1.65 accuracy 0.47: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 1.64 accuracy 0.46: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 1.43 accuracy 0.65: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 1.38 accuracy 0.72: 4%|▍ | 2/50 [00:06<02:15, 2.83s/it]
loss 1.38 accuracy 0.72: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 1.26 accuracy 0.72: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 1.13 accuracy 0.81: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 1.03 accuracy 0.83: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 0.89 accuracy 0.85: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 0.78 accuracy 0.87: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 0.82 accuracy 0.85: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 0.70 accuracy 0.87: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 0.65 accuracy 0.86: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 0.66 accuracy 0.85: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 0.60 accuracy 0.86: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 0.57 accuracy 0.85: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 0.60 accuracy 0.84: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 0.54 accuracy 0.85: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 0.52 accuracy 0.86: 32%|███▏ | 16/50 [00:06<00:07, 4.37it/s]
loss 0.52 accuracy 0.86: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.50 accuracy 0.87: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.49 accuracy 0.87: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.58 accuracy 0.85: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.49 accuracy 0.87: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.45 accuracy 0.89: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.51 accuracy 0.86: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.46 accuracy 0.86: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.48 accuracy 0.85: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.45 accuracy 0.87: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.44 accuracy 0.86: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.41 accuracy 0.87: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.45 accuracy 0.87: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.42 accuracy 0.87: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.42 accuracy 0.86: 60%|██████ | 30/50 [00:06<00:02, 9.73it/s]
loss 0.42 accuracy 0.86: 88%|████████▊ | 44/50 [00:06<00:00, 16.68it/s]
loss 0.43 accuracy 0.85: 88%|████████▊ | 44/50 [00:06<00:00, 16.68it/s]
loss 0.39 accuracy 0.87: 88%|████████▊ | 44/50 [00:06<00:00, 16.68it/s]
loss 0.38 accuracy 0.88: 88%|████████▊ | 44/50 [00:06<00:00, 16.68it/s]
loss 0.39 accuracy 0.86: 88%|████████▊ | 44/50 [00:06<00:00, 16.68it/s]
loss 0.35 accuracy 0.89: 88%|████████▊ | 44/50 [00:06<00:00, 16.68it/s]
loss 0.38 accuracy 0.87: 88%|████████▊ | 44/50 [00:06<00:00, 16.68it/s]
loss 0.38 accuracy 0.87: 100%|██████████| 50/50 [00:06<00:00, 7.55it/s]
0%| | 0/16 [00:00<?, ?it/s]
6%|▋ | 1/16 [00:01<00:17, 1.15s/it]
62%|██████▎ | 10/16 [00:01<00:00, 10.61it/s]
100%|██████████| 16/16 [00:02<00:00, 6.73it/s]
100%|██████████| 16/16 [00:02<00:00, 6.35it/s]
test set accuracy is 0.867750
reducing lr to 0.0025
0%| | 0/50 [00:00<?, ?it/s]
loss 0.36 accuracy 0.88: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.39 accuracy 0.87: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.39 accuracy 0.87: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.36 accuracy 0.88: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.43 accuracy 0.86: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.40 accuracy 0.87: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.40 accuracy 0.87: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.38 accuracy 0.87: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.38 accuracy 0.86: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.39 accuracy 0.88: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.40 accuracy 0.85: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.38 accuracy 0.86: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.38 accuracy 0.87: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.39 accuracy 0.86: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.36 accuracy 0.88: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.34 accuracy 0.88: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.34 accuracy 0.88: 4%|▍ | 2/50 [00:00<00:04, 10.36it/s]
loss 0.34 accuracy 0.88: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.34 accuracy 0.88: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.38 accuracy 0.85: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.34 accuracy 0.88: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.38 accuracy 0.85: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.33 accuracy 0.88: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.40 accuracy 0.86: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.33 accuracy 0.88: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.36 accuracy 0.86: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.36 accuracy 0.87: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.34 accuracy 0.87: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.33 accuracy 0.89: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.36 accuracy 0.86: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.34 accuracy 0.88: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.33 accuracy 0.89: 32%|███▏ | 16/50 [00:00<00:00, 64.82it/s]
loss 0.33 accuracy 0.89: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.29 accuracy 0.90: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.35 accuracy 0.87: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.32 accuracy 0.88: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.35 accuracy 0.87: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.33 accuracy 0.88: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.30 accuracy 0.89: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.32 accuracy 0.89: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.32 accuracy 0.89: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.29 accuracy 0.90: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.30 accuracy 0.89: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.29 accuracy 0.90: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.30 accuracy 0.89: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.31 accuracy 0.88: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.30 accuracy 0.89: 60%|██████ | 30/50 [00:00<00:00, 92.64it/s]
loss 0.30 accuracy 0.89: 88%|████████▊ | 44/50 [00:00<00:00, 108.77it/s]
loss 0.27 accuracy 0.90: 88%|████████▊ | 44/50 [00:00<00:00, 108.77it/s]
loss 0.31 accuracy 0.88: 88%|████████▊ | 44/50 [00:00<00:00, 108.77it/s]
loss 0.28 accuracy 0.90: 88%|████████▊ | 44/50 [00:00<00:00, 108.77it/s]
loss 0.28 accuracy 0.89: 88%|████████▊ | 44/50 [00:00<00:00, 108.77it/s]
loss 0.27 accuracy 0.90: 88%|████████▊ | 44/50 [00:00<00:00, 108.77it/s]
loss 0.25 accuracy 0.91: 88%|████████▊ | 44/50 [00:00<00:00, 108.77it/s]
loss 0.25 accuracy 0.91: 100%|██████████| 50/50 [00:00<00:00, 92.45it/s]
0%| | 0/16 [00:00<?, ?it/s]
38%|███▊ | 6/16 [00:00<00:00, 56.07it/s]
94%|█████████▍| 15/16 [00:00<00:00, 74.99it/s]
100%|██████████| 16/16 [00:00<00:00, 72.89it/s]
test set accuracy is 0.898583
reducing lr to 0.0021
0%| | 0/50 [00:00<?, ?it/s]
loss 0.32 accuracy 0.87: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.35 accuracy 0.87: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.35 accuracy 0.87: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.31 accuracy 0.88: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.26 accuracy 0.91: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.28 accuracy 0.90: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.25 accuracy 0.91: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.28 accuracy 0.90: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.28 accuracy 0.89: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.29 accuracy 0.89: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.29 accuracy 0.89: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.27 accuracy 0.91: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.28 accuracy 0.89: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.29 accuracy 0.89: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.26 accuracy 0.91: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.30 accuracy 0.90: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.30 accuracy 0.89: 4%|▍ | 2/50 [00:00<00:04, 10.42it/s]
loss 0.30 accuracy 0.89: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.25 accuracy 0.90: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.25 accuracy 0.91: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.28 accuracy 0.90: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.23 accuracy 0.90: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.26 accuracy 0.89: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.25 accuracy 0.90: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.26 accuracy 0.90: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.23 accuracy 0.91: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.24 accuracy 0.91: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.22 accuracy 0.93: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.24 accuracy 0.90: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.25 accuracy 0.90: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.24 accuracy 0.90: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.23 accuracy 0.91: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.19 accuracy 0.93: 32%|███▏ | 16/50 [00:00<00:00, 65.34it/s]
loss 0.19 accuracy 0.93: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.19 accuracy 0.93: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.22 accuracy 0.92: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.20 accuracy 0.93: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.20 accuracy 0.91: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.22 accuracy 0.91: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.24 accuracy 0.92: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.22 accuracy 0.90: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.21 accuracy 0.91: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.19 accuracy 0.92: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.22 accuracy 0.91: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.16 accuracy 0.93: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.20 accuracy 0.94: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.22 accuracy 0.91: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.21 accuracy 0.92: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.21 accuracy 0.92: 62%|██████▏ | 31/50 [00:00<00:00, 95.09it/s]
loss 0.21 accuracy 0.92: 92%|█████████▏| 46/50 [00:00<00:00, 111.79it/s]
loss 0.19 accuracy 0.92: 92%|█████████▏| 46/50 [00:00<00:00, 111.79it/s]
loss 0.19 accuracy 0.92: 92%|█████████▏| 46/50 [00:00<00:00, 111.79it/s]
loss 0.21 accuracy 0.92: 92%|█████████▏| 46/50 [00:00<00:00, 111.79it/s]
loss 0.22 accuracy 0.92: 92%|█████████▏| 46/50 [00:00<00:00, 111.79it/s]
loss 0.22 accuracy 0.92: 100%|██████████| 50/50 [00:00<00:00, 93.71it/s]
0%| | 0/16 [00:00<?, ?it/s]
56%|█████▋ | 9/16 [00:00<00:00, 86.95it/s]
100%|██████████| 16/16 [00:00<00:00, 73.27it/s]
test set accuracy is 0.929500
reducing lr to 0.0017
0%| | 0/50 [00:00<?, ?it/s]
loss 0.19 accuracy 0.92: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.21 accuracy 0.92: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.21 accuracy 0.92: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.15 accuracy 0.96: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.21 accuracy 0.91: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.19 accuracy 0.94: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.20 accuracy 0.93: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.19 accuracy 0.92: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.19 accuracy 0.92: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.24 accuracy 0.92: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.15 accuracy 0.94: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.20 accuracy 0.92: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.18 accuracy 0.93: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.15 accuracy 0.95: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.18 accuracy 0.93: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.18 accuracy 0.92: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.19 accuracy 0.92: 4%|▍ | 2/50 [00:00<00:04, 10.44it/s]
loss 0.19 accuracy 0.92: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.17 accuracy 0.94: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.18 accuracy 0.93: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.15 accuracy 0.94: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.16 accuracy 0.94: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.17 accuracy 0.94: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.17 accuracy 0.92: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.18 accuracy 0.93: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.16 accuracy 0.94: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.12 accuracy 0.95: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.14 accuracy 0.94: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.14 accuracy 0.95: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.12 accuracy 0.96: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.11 accuracy 0.96: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.12 accuracy 0.95: 32%|███▏ | 16/50 [00:00<00:00, 65.19it/s]
loss 0.12 accuracy 0.95: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.14 accuracy 0.93: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.13 accuracy 0.95: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.12 accuracy 0.95: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.11 accuracy 0.95: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.13 accuracy 0.95: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.12 accuracy 0.96: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.15 accuracy 0.93: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.08 accuracy 0.98: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.08 accuracy 0.98: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.10 accuracy 0.96: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.13 accuracy 0.95: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.11 accuracy 0.97: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.11 accuracy 0.96: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.09 accuracy 0.98: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.08 accuracy 0.97: 60%|██████ | 30/50 [00:00<00:00, 92.90it/s]
loss 0.08 accuracy 0.97: 90%|█████████ | 45/50 [00:00<00:00, 110.13it/s]
loss 0.10 accuracy 0.96: 90%|█████████ | 45/50 [00:00<00:00, 110.13it/s]
loss 0.08 accuracy 0.98: 90%|█████████ | 45/50 [00:00<00:00, 110.13it/s]
loss 0.09 accuracy 0.97: 90%|█████████ | 45/50 [00:00<00:00, 110.13it/s]
loss 0.08 accuracy 0.98: 90%|█████████ | 45/50 [00:00<00:00, 110.13it/s]
loss 0.07 accuracy 0.98: 90%|█████████ | 45/50 [00:00<00:00, 110.13it/s]
loss 0.07 accuracy 0.98: 100%|██████████| 50/50 [00:00<00:00, 93.05it/s]
0%| | 0/16 [00:00<?, ?it/s]
56%|█████▋ | 9/16 [00:00<00:00, 87.34it/s]
100%|██████████| 16/16 [00:00<00:00, 73.32it/s]
test set accuracy is 0.979917
reducing lr to 0.0014
0%| | 0/50 [00:00<?, ?it/s]
loss 0.09 accuracy 0.97: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.22 accuracy 0.93: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.22 accuracy 0.93: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.13 accuracy 0.95: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.19 accuracy 0.93: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.17 accuracy 0.93: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.09 accuracy 0.97: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.17 accuracy 0.95: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.10 accuracy 0.97: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.12 accuracy 0.96: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.11 accuracy 0.96: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.15 accuracy 0.95: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.16 accuracy 0.93: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.08 accuracy 0.98: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.11 accuracy 0.95: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.10 accuracy 0.97: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.09 accuracy 0.97: 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]
loss 0.09 accuracy 0.97: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.12 accuracy 0.95: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.08 accuracy 0.97: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.10 accuracy 0.97: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.10 accuracy 0.96: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.09 accuracy 0.96: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.08 accuracy 0.97: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.10 accuracy 0.97: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.08 accuracy 0.98: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.10 accuracy 0.97: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.10 accuracy 0.97: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.08 accuracy 0.98: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.09 accuracy 0.97: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.09 accuracy 0.97: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.08 accuracy 0.97: 32%|███▏ | 16/50 [00:00<00:00, 65.06it/s]
loss 0.08 accuracy 0.97: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.10 accuracy 0.98: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.06 accuracy 0.98: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.08 accuracy 0.97: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.08 accuracy 0.96: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.08 accuracy 0.97: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.06 accuracy 0.98: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.08 accuracy 0.98: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.07 accuracy 0.98: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.06 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.08 accuracy 0.98: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.06 accuracy 0.98: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.05 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.06 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.10 accuracy 0.97: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.06 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.63it/s]
loss 0.06 accuracy 0.99: 90%|█████████ | 45/50 [00:00<00:00, 109.96it/s]
loss 0.05 accuracy 0.99: 90%|█████████ | 45/50 [00:00<00:00, 109.96it/s]
loss 0.06 accuracy 0.99: 90%|█████████ | 45/50 [00:00<00:00, 109.96it/s]
loss 0.09 accuracy 0.98: 90%|█████████ | 45/50 [00:00<00:00, 109.96it/s]
loss 0.06 accuracy 0.98: 90%|█████████ | 45/50 [00:00<00:00, 109.96it/s]
loss 0.06 accuracy 0.98: 90%|█████████ | 45/50 [00:00<00:00, 109.96it/s]
loss 0.06 accuracy 0.98: 100%|██████████| 50/50 [00:00<00:00, 92.77it/s]
0%| | 0/16 [00:00<?, ?it/s]
56%|█████▋ | 9/16 [00:00<00:00, 86.92it/s]
100%|██████████| 16/16 [00:00<00:00, 87.32it/s]
test set accuracy is 0.988917
reducing lr to 0.0012
0%| | 0/50 [00:00<?, ?it/s]
loss 0.05 accuracy 0.99: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.05 accuracy 0.99: 2%|▏ | 1/50 [00:00<00:06, 8.05it/s]
loss 0.13 accuracy 0.95: 2%|▏ | 1/50 [00:00<00:06, 8.05it/s]
loss 0.13 accuracy 0.95: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.07 accuracy 0.98: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.08 accuracy 0.97: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.11 accuracy 0.96: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.09 accuracy 0.97: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.09 accuracy 0.97: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.06 accuracy 0.98: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.07 accuracy 0.98: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.04 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.07 accuracy 0.98: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.06 accuracy 0.98: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.05 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.05 accuracy 0.98: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.08 accuracy 0.97: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.05 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.86it/s]
loss 0.05 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.05 accuracy 0.98: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.04 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.06 accuracy 0.98: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.05 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.06 accuracy 0.98: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.04 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.05 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.04 accuracy 0.98: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.05 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.05 accuracy 0.98: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.05 accuracy 0.98: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.04 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.03 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.04 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.18it/s]
loss 0.04 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.06 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.04 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.03 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.04 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.05 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.03 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.05 accuracy 0.98: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.03 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.04 accuracy 0.98: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.03 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.07 accuracy 0.98: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.03 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.03 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.03 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 92.25it/s]
loss 0.03 accuracy 0.99: 88%|████████▊ | 44/50 [00:00<00:00, 108.73it/s]
loss 0.02 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 108.73it/s]
loss 0.06 accuracy 0.99: 88%|████████▊ | 44/50 [00:00<00:00, 108.73it/s]
loss 0.03 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 108.73it/s]
loss 0.03 accuracy 0.99: 88%|████████▊ | 44/50 [00:00<00:00, 108.73it/s]
loss 0.03 accuracy 0.99: 88%|████████▊ | 44/50 [00:00<00:00, 108.73it/s]
loss 0.04 accuracy 0.99: 88%|████████▊ | 44/50 [00:00<00:00, 108.73it/s]
loss 0.04 accuracy 0.99: 100%|██████████| 50/50 [00:00<00:00, 86.77it/s]
0%| | 0/16 [00:00<?, ?it/s]
56%|█████▋ | 9/16 [00:00<00:00, 87.70it/s]
100%|██████████| 16/16 [00:00<00:00, 87.74it/s]
test set accuracy is 0.995000
reducing lr to 0.0010
0%| | 0/50 [00:00<?, ?it/s]
loss 0.04 accuracy 1.00: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.04 accuracy 1.00: 2%|▏ | 1/50 [00:00<00:06, 7.91it/s]
loss 0.03 accuracy 0.99: 2%|▏ | 1/50 [00:00<00:06, 7.91it/s]
loss 0.03 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.04 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.05 accuracy 0.98: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.04 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.06 accuracy 0.98: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.03 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.02 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.03 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.04 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.07 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.05 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.03 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.03 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.03 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.03 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.81it/s]
loss 0.03 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.03 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.03 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.02 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.02 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.03 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.03 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.02 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.02 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.03 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.02 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.03 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.03 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.02 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.02 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 64.03it/s]
loss 0.02 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.03 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.02 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.02 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.02 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.02 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 91.97it/s]
loss 0.01 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 108.20it/s]
loss 0.02 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 108.20it/s]
loss 0.03 accuracy 0.99: 88%|████████▊ | 44/50 [00:00<00:00, 108.20it/s]
loss 0.01 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 108.20it/s]
loss 0.02 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 108.20it/s]
loss 0.02 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 108.20it/s]
loss 0.01 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 108.20it/s]
loss 0.01 accuracy 1.00: 100%|██████████| 50/50 [00:00<00:00, 86.26it/s]
0%| | 0/16 [00:00<?, ?it/s]
56%|█████▋ | 9/16 [00:00<00:00, 86.56it/s]
100%|██████████| 16/16 [00:00<00:00, 87.15it/s]
test set accuracy is 0.999583
reducing lr to 0.0008
0%| | 0/50 [00:00<?, ?it/s]
loss 0.01 accuracy 1.00: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.02 accuracy 0.99: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.02 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.03 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.02 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.02 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.02 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.02 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.02 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.02 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.03 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.02 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.69it/s]
loss 0.02 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.53it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.53it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.53it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.53it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.53it/s]
loss 0.02 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 58.53it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.53it/s]
loss 0.02 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 58.53it/s]
loss 0.03 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.53it/s]
loss 0.03 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.53it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.53it/s]
loss 0.02 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 58.53it/s]
loss 0.02 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.53it/s]
loss 0.02 accuracy 1.00: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.02 accuracy 0.99: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.01 accuracy 1.00: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.01 accuracy 1.00: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.01 accuracy 0.99: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.01 accuracy 1.00: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.01 accuracy 0.99: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.01 accuracy 1.00: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.01 accuracy 1.00: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.01 accuracy 1.00: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.01 accuracy 1.00: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.01 accuracy 1.00: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.02 accuracy 0.99: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.01 accuracy 0.99: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.01 accuracy 1.00: 56%|█████▌ | 28/50 [00:00<00:00, 76.19it/s]
loss 0.01 accuracy 1.00: 84%|████████▍ | 42/50 [00:00<00:00, 96.43it/s]
loss 0.01 accuracy 1.00: 84%|████████▍ | 42/50 [00:00<00:00, 96.43it/s]
loss 0.01 accuracy 1.00: 84%|████████▍ | 42/50 [00:00<00:00, 96.43it/s]
loss 0.01 accuracy 1.00: 84%|████████▍ | 42/50 [00:00<00:00, 96.43it/s]
loss 0.02 accuracy 1.00: 84%|████████▍ | 42/50 [00:00<00:00, 96.43it/s]
loss 0.01 accuracy 1.00: 84%|████████▍ | 42/50 [00:00<00:00, 96.43it/s]
loss 0.02 accuracy 0.99: 84%|████████▍ | 42/50 [00:00<00:00, 96.43it/s]
loss 0.06 accuracy 0.99: 84%|████████▍ | 42/50 [00:00<00:00, 96.43it/s]
loss 0.01 accuracy 0.99: 84%|████████▍ | 42/50 [00:00<00:00, 96.43it/s]
loss 0.01 accuracy 0.99: 100%|██████████| 50/50 [00:00<00:00, 82.55it/s]
0%| | 0/16 [00:00<?, ?it/s]
56%|█████▋ | 9/16 [00:00<00:00, 86.23it/s]
100%|██████████| 16/16 [00:00<00:00, 87.04it/s]
test set accuracy is 1.000000
reducing lr to 0.0007
0%| | 0/50 [00:00<?, ?it/s]
loss 0.01 accuracy 1.00: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.02 accuracy 0.99: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.02 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.01 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.02 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.00 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.01 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.03 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.63it/s]
loss 0.03 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.02 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.02 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.01 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.01 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.01 accuracy 0.99: 32%|███▏ | 16/50 [00:00<00:00, 58.20it/s]
loss 0.01 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.02 accuracy 0.99: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.38it/s]
loss 0.00 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 103.89it/s]
loss 0.02 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 103.89it/s]
loss 0.00 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 103.89it/s]
loss 0.00 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 103.89it/s]
loss 0.01 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 103.89it/s]
loss 0.00 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 103.89it/s]
loss 0.00 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 103.89it/s]
loss 0.00 accuracy 1.00: 100%|██████████| 50/50 [00:00<00:00, 86.30it/s]
0%| | 0/16 [00:00<?, ?it/s]
56%|█████▋ | 9/16 [00:00<00:00, 87.82it/s]
100%|██████████| 16/16 [00:00<00:00, 88.10it/s]
test set accuracy is 1.000000
reducing lr to 0.0006
0%| | 0/50 [00:00<?, ?it/s]
loss 0.00 accuracy 1.00: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.01 accuracy 1.00: 0%| | 0/50 [00:00<?, ?it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.00 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.00 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.01 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.01 accuracy 0.99: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.00 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.00 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.00 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.00 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.00 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.02 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.01 accuracy 1.00: 4%|▍ | 2/50 [00:00<00:05, 8.75it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.00 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.00 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.00 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.00 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.00 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.00 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.00 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.00 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.01 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.00 accuracy 1.00: 32%|███▏ | 16/50 [00:00<00:00, 58.80it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.01 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.02 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.00 accuracy 1.00: 60%|██████ | 30/50 [00:00<00:00, 86.77it/s]
loss 0.00 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 104.25it/s]
loss 0.00 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 104.25it/s]
loss 0.00 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 104.25it/s]
loss 0.00 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 104.25it/s]
loss 0.00 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 104.25it/s]
loss 0.00 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 104.25it/s]
loss 0.00 accuracy 1.00: 88%|████████▊ | 44/50 [00:00<00:00, 104.25it/s]
loss 0.00 accuracy 1.00: 100%|██████████| 50/50 [00:00<00:00, 86.80it/s]
0%| | 0/16 [00:00<?, ?it/s]
56%|█████▋ | 9/16 [00:00<00:00, 87.44it/s]
100%|██████████| 16/16 [00:00<00:00, 87.77it/s]
test set accuracy is 1.000000
reducing lr to 0.0005
Wrong predictions: 0, acc = 1.0000
vgg7.py
python3 -m examples.vgg7 import MODELJSON MODEL
imports a waifu2x JSON vgg_7 model, i.e. waifu2x/models/vgg_7/art/scale2.0x_model.json
into a safetensors file
weight tensors are ordered in tinygrad/ncnn form, as so: (outC,inC,H,W)
*this format is used by most other commands in this program*
python3 -m examples.vgg7 import_kinne MODEL_KINNE MODEL_SAFETENSORS
imports a model in 'KINNE' format (raw floats: used by older versions of this example) into safetensors
python3 -m examples.vgg7 execute MODEL IMG_IN IMG_OUT
given an already-nearest-neighbour-scaled image, runs vgg7 on it
output image has 7 pixels removed on all edges
do not run on large images, will have *hilarious* RAM use
python3 -m examples.vgg7 execute_full MODEL IMG_IN IMG_OUT
does the 'whole thing' (padding, tiling)
safe for large images, etc.
python3 -m examples.vgg7 new MODEL
creates a new model (experimental)
python3 -m examples.vgg7 train MODEL SAMPLES_DIR ROUNDS ROUNDS_SAVE
trains a model (experimental)
(how experimental? well, every time I tried it, it flooded w/ NaNs)
note: ROUNDS < 0 means 'forever'. ROUNDS_SAVE <= 0 is not a good idea.
expects roughly execute's input as SAMPLES_DIR/IDXa.png
expects roughly execute's output as SAMPLES_DIR/IDXb.png
(i.e. my_samples/0a.png is the first pre-nearest-scaled image,
my_samples/0b.png is the first original image)
in addition, SAMPLES_DIR/samples_count.txt indicates sample count
won't pad or tile, so keep image sizes sane
python3 -m examples.vgg7 samplify IMG_A IMG_B SAMPLES_DIR SIZE
creates overlapping micropatches (SIZExSIZE w/ 7-pixel border) for training
maintains/creates samples_count.txt automatically
unlike training, IMG_A must be exactly half the size of IMG_B
vit.py
(1, 1000) 208 16.274183 Labrador retriever
vits.py
INFO:root:Model has 109 speakers
INFO:root:You selected speaker 6 (name: ?)
Traceback (most recent call last):
File "/home/jebba/devel/tinygrad/tinygrad/examples/vits.py", line 723, in <module>
net_g = load_model(text_mapper.symbols, hps, model_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/examples/vits.py", line 535, in load_model
_ = load_checkpoint(fetch(model[1]), net_g, None)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/examples/vits.py", line 540, in load_checkpoint
checkpoint_dict = torch_load(checkpoint_path)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/tinygrad/nn/state.py", line 145, in torch_load
_, _, _, rwd, _, ids, base_offset = pkl.load(), pkl.load(), pkl.load(), f.tell(), pkl.load(), pkl.load(), f.tell()
^^^^^^^^^^
_pickle.UnpicklingError: invalid load key, '<'.
whisper.py
0%| | 0/168 [00:00<?, ?it/s]
ram used: 0.00 GB, encoder.conv1.weight : 0%| | 0/168 [00:00<?, ?it/s]
ram used: 0.00 GB, encoder.conv1.bias : 0%| | 0/168 [00:00<?, ?it/s]
ram used: 0.00 GB, encoder.conv2.weight : 0%| | 0/168 [00:00<?, ?it/s]
ram used: 0.00 GB, encoder.conv2.bias : 0%| | 0/168 [00:00<?, ?it/s]
ram used: 0.00 GB, encoder.blocks.0.attn.query.weight : 0%| | 0/168 [00:00<?, ?it/s]
ram used: 0.00 GB, encoder.blocks.0.attn.query.bias : 0%| | 0/168 [00:00<?, ?it/s]
ram used: 0.00 GB, encoder.blocks.0.attn.key.weight : 0%| | 0/168 [00:00<?, ?it/s]
ram used: 0.00 GB, encoder.blocks.0.attn.value.weight : 0%| | 0/168 [00:00<?, ?it/s]
ram used: 0.00 GB, encoder.blocks.0.attn.value.bias : 0%| | 0/168 [00:00<?, ?it/s]
ram used: 0.00 GB, encoder.blocks.0.attn.out.weight : 0%| | 0/168 [00:00<?, ?it/s]
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loaded weights in 175.99 ms, 0.11 GB loaded at 0.62 GB/s
ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory
ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:1334:(snd_func_refer) error evaluating name
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:5703:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM sysdefault
ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory
ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:1334:(snd_func_refer) error evaluating name
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:5703:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM sysdefault
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.front
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.rear
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.center_lfe
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.side
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround21
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround21
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround40
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround41
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround50
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround51
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.surround71
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.iec958
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.iec958
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.iec958
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.hdmi
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.hdmi
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.modem
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.modem
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.phoneline
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.phoneline
ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory
ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:1334:(snd_func_refer) error evaluating name
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:5703:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM default
ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory
ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:1334:(snd_func_refer) error evaluating name
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:5703:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM default
ALSA lib confmisc.c:855:(parse_card) cannot find card '0'
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_card_id returned error: No such file or directory
ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
ALSA lib confmisc.c:1334:(snd_func_refer) error evaluating name
ALSA lib conf.c:5180:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:5703:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2666:(snd_pcm_open_noupdate) Unknown PCM dmix
Cannot connect to server socket err = No such file or directory
Cannot connect to server request channel
jack server is not running or cannot be started
JackShmReadWritePtr::~JackShmReadWritePtr - Init not done for -1, skipping unlock
JackShmReadWritePtr::~JackShmReadWritePtr - Init not done for -1, skipping unlock
Process Process-1:
Traceback (most recent call last):
File "/usr/lib/python3.11/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.11/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/jebba/devel/tinygrad/tinygrad/examples/whisper.py", line 313, in listener
stream = p.open(format=pyaudio.paInt16, channels=1, rate=RATE, input=True, frames_per_buffer=CHUNK)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/venv/lib/python3.11/site-packages/pyaudio/__init__.py", line 639, in open
stream = PyAudio.Stream(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/venv/lib/python3.11/site-packages/pyaudio/__init__.py", line 441, in __init__
self._stream = pa.open(**arguments)
^^^^^^^^^^^^^^^^^^^^
OSError: [Errno -9996] Invalid input device (no default output device)
Traceback (most recent call last):
File "/home/jebba/devel/tinygrad/tinygrad/examples/whisper.py", line 338, in <module>
waveform = q.get()
^^^^^^^
File "/usr/lib/python3.11/multiprocessing/queues.py", line 103, in get
res = self._recv_bytes()
^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/multiprocessing/connection.py", line 215, in recv_bytes
buf = self._recv_bytes(maxlength)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/multiprocessing/connection.py", line 413, in _recv_bytes
buf = self._recv(4)
^^^^^^^^^^^^^
File "/usr/lib/python3.11/multiprocessing/connection.py", line 378, in _recv
chunk = read(handle, remaining)
^^^^^^^^^^^^^^^^^^^^^^^
KeyboardInterrupt
yolov3.py
Modules length: 107
Loading weights file (237MB). This might take a while…
running inference…
did inference in 3.367573s
Detected bicycle 98.95
Detected truck 99.00
Detected dog 94.23
yolov8-onnx.py
{'images': (1, 3, 480, 640)}
0: op Conv shape [(1, 3, 480, 640), (16, 3, 3, 3), (16,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (2, 2)}
1: op Sigmoid shape [(1, 16, 240, 320)] opt {}
2: op Mul shape [(1, 16, 240, 320), (1, 16, 240, 320)] opt {}
3: op Conv shape [(1, 16, 240, 320), (32, 16, 3, 3), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (2, 2)}
4: op Sigmoid shape [(1, 32, 120, 160)] opt {}
5: op Mul shape [(1, 32, 120, 160), (1, 32, 120, 160)] opt {}
6: op Conv shape [(1, 32, 120, 160), (32, 32, 1, 1), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
7: op Sigmoid shape [(1, 32, 120, 160)] opt {}
8: op Mul shape [(1, 32, 120, 160), (1, 32, 120, 160)] opt {}
9: op Constant shape [] opt {'value': <Tensor <LB HIP (2,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
10: op Split shape [(1, 32, 120, 160), (2,)] opt {'axis': 1}
11: op Conv shape [(1, 16, 120, 160), (16, 16, 3, 3), (16,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
12: op Sigmoid shape [(1, 16, 120, 160)] opt {}
13: op Mul shape [(1, 16, 120, 160), (1, 16, 120, 160)] opt {}
14: op Conv shape [(1, 16, 120, 160), (16, 16, 3, 3), (16,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
15: op Sigmoid shape [(1, 16, 120, 160)] opt {}
16: op Mul shape [(1, 16, 120, 160), (1, 16, 120, 160)] opt {}
17: op Add shape [(1, 16, 120, 160), (1, 16, 120, 160)] opt {}
18: op Concat shape [(1, 16, 120, 160), (1, 16, 120, 160), (1, 16, 120, 160)] opt {'axis': 1}
19: op Conv shape [(1, 48, 120, 160), (32, 48, 1, 1), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
20: op Sigmoid shape [(1, 32, 120, 160)] opt {}
21: op Mul shape [(1, 32, 120, 160), (1, 32, 120, 160)] opt {}
22: op Conv shape [(1, 32, 120, 160), (64, 32, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (2, 2)}
23: op Sigmoid shape [(1, 64, 60, 80)] opt {}
24: op Mul shape [(1, 64, 60, 80), (1, 64, 60, 80)] opt {}
25: op Conv shape [(1, 64, 60, 80), (64, 64, 1, 1), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
26: op Sigmoid shape [(1, 64, 60, 80)] opt {}
27: op Mul shape [(1, 64, 60, 80), (1, 64, 60, 80)] opt {}
28: op Constant shape [] opt {'value': <Tensor <LB HIP (2,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
29: op Split shape [(1, 64, 60, 80), (2,)] opt {'axis': 1}
30: op Conv shape [(1, 32, 60, 80), (32, 32, 3, 3), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
31: op Sigmoid shape [(1, 32, 60, 80)] opt {}
32: op Mul shape [(1, 32, 60, 80), (1, 32, 60, 80)] opt {}
33: op Conv shape [(1, 32, 60, 80), (32, 32, 3, 3), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
34: op Sigmoid shape [(1, 32, 60, 80)] opt {}
35: op Mul shape [(1, 32, 60, 80), (1, 32, 60, 80)] opt {}
36: op Add shape [(1, 32, 60, 80), (1, 32, 60, 80)] opt {}
37: op Conv shape [(1, 32, 60, 80), (32, 32, 3, 3), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
38: op Sigmoid shape [(1, 32, 60, 80)] opt {}
39: op Mul shape [(1, 32, 60, 80), (1, 32, 60, 80)] opt {}
40: op Conv shape [(1, 32, 60, 80), (32, 32, 3, 3), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
41: op Sigmoid shape [(1, 32, 60, 80)] opt {}
42: op Mul shape [(1, 32, 60, 80), (1, 32, 60, 80)] opt {}
43: op Add shape [(1, 32, 60, 80), (1, 32, 60, 80)] opt {}
44: op Concat shape [(1, 32, 60, 80), (1, 32, 60, 80), (1, 32, 60, 80), (1, 32, 60, 80)] opt {'axis': 1}
45: op Conv shape [(1, 128, 60, 80), (64, 128, 1, 1), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
46: op Sigmoid shape [(1, 64, 60, 80)] opt {}
47: op Mul shape [(1, 64, 60, 80), (1, 64, 60, 80)] opt {}
48: op Conv shape [(1, 64, 60, 80), (128, 64, 3, 3), (128,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (2, 2)}
49: op Sigmoid shape [(1, 128, 30, 40)] opt {}
50: op Mul shape [(1, 128, 30, 40), (1, 128, 30, 40)] opt {}
51: op Conv shape [(1, 128, 30, 40), (128, 128, 1, 1), (128,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
52: op Sigmoid shape [(1, 128, 30, 40)] opt {}
53: op Mul shape [(1, 128, 30, 40), (1, 128, 30, 40)] opt {}
54: op Constant shape [] opt {'value': <Tensor <LB HIP (2,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
55: op Split shape [(1, 128, 30, 40), (2,)] opt {'axis': 1}
56: op Conv shape [(1, 64, 30, 40), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
57: op Sigmoid shape [(1, 64, 30, 40)] opt {}
58: op Mul shape [(1, 64, 30, 40), (1, 64, 30, 40)] opt {}
59: op Conv shape [(1, 64, 30, 40), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
60: op Sigmoid shape [(1, 64, 30, 40)] opt {}
61: op Mul shape [(1, 64, 30, 40), (1, 64, 30, 40)] opt {}
62: op Add shape [(1, 64, 30, 40), (1, 64, 30, 40)] opt {}
63: op Conv shape [(1, 64, 30, 40), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
64: op Sigmoid shape [(1, 64, 30, 40)] opt {}
65: op Mul shape [(1, 64, 30, 40), (1, 64, 30, 40)] opt {}
66: op Conv shape [(1, 64, 30, 40), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
67: op Sigmoid shape [(1, 64, 30, 40)] opt {}
68: op Mul shape [(1, 64, 30, 40), (1, 64, 30, 40)] opt {}
69: op Add shape [(1, 64, 30, 40), (1, 64, 30, 40)] opt {}
70: op Concat shape [(1, 64, 30, 40), (1, 64, 30, 40), (1, 64, 30, 40), (1, 64, 30, 40)] opt {'axis': 1}
71: op Conv shape [(1, 256, 30, 40), (128, 256, 1, 1), (128,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
72: op Sigmoid shape [(1, 128, 30, 40)] opt {}
73: op Mul shape [(1, 128, 30, 40), (1, 128, 30, 40)] opt {}
74: op Conv shape [(1, 128, 30, 40), (256, 128, 3, 3), (256,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (2, 2)}
75: op Sigmoid shape [(1, 256, 15, 20)] opt {}
76: op Mul shape [(1, 256, 15, 20), (1, 256, 15, 20)] opt {}
77: op Conv shape [(1, 256, 15, 20), (256, 256, 1, 1), (256,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
78: op Sigmoid shape [(1, 256, 15, 20)] opt {}
79: op Mul shape [(1, 256, 15, 20), (1, 256, 15, 20)] opt {}
80: op Constant shape [] opt {'value': <Tensor <LB HIP (2,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
81: op Split shape [(1, 256, 15, 20), (2,)] opt {'axis': 1}
82: op Conv shape [(1, 128, 15, 20), (128, 128, 3, 3), (128,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
83: op Sigmoid shape [(1, 128, 15, 20)] opt {}
84: op Mul shape [(1, 128, 15, 20), (1, 128, 15, 20)] opt {}
85: op Conv shape [(1, 128, 15, 20), (128, 128, 3, 3), (128,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
86: op Sigmoid shape [(1, 128, 15, 20)] opt {}
87: op Mul shape [(1, 128, 15, 20), (1, 128, 15, 20)] opt {}
88: op Add shape [(1, 128, 15, 20), (1, 128, 15, 20)] opt {}
89: op Concat shape [(1, 128, 15, 20), (1, 128, 15, 20), (1, 128, 15, 20)] opt {'axis': 1}
90: op Conv shape [(1, 384, 15, 20), (256, 384, 1, 1), (256,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
91: op Sigmoid shape [(1, 256, 15, 20)] opt {}
92: op Mul shape [(1, 256, 15, 20), (1, 256, 15, 20)] opt {}
93: op Conv shape [(1, 256, 15, 20), (128, 256, 1, 1), (128,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
94: op Sigmoid shape [(1, 128, 15, 20)] opt {}
95: op Mul shape [(1, 128, 15, 20), (1, 128, 15, 20)] opt {}
96: op MaxPool shape [(1, 128, 15, 20)] opt {'ceil_mode': 0, 'dilations': (1, 1), 'kernel_shape': (5, 5), 'pads': (2, 2, 2, 2), 'strides': (1, 1)}
97: op MaxPool shape [(1, 128, 15, 20)] opt {'ceil_mode': 0, 'dilations': (1, 1), 'kernel_shape': (5, 5), 'pads': (2, 2, 2, 2), 'strides': (1, 1)}
98: op MaxPool shape [(1, 128, 15, 20)] opt {'ceil_mode': 0, 'dilations': (1, 1), 'kernel_shape': (5, 5), 'pads': (2, 2, 2, 2), 'strides': (1, 1)}
99: op Concat shape [(1, 128, 15, 20), (1, 128, 15, 20), (1, 128, 15, 20), (1, 128, 15, 20)] opt {'axis': 1}
100: op Conv shape [(1, 512, 15, 20), (256, 512, 1, 1), (256,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
101: op Sigmoid shape [(1, 256, 15, 20)] opt {}
102: op Mul shape [(1, 256, 15, 20), (1, 256, 15, 20)] opt {}
103: op Constant shape [] opt {'value': <Tensor <LB HIP (4,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
104: op Resize shape [(1, 256, 15, 20), None, (4,)] opt {'coordinate_transformation_mode': 'asymmetric', 'cubic_coeff_a': -0.75, 'mode': 'nearest', 'nearest_mode': 'floor'}
105: op Concat shape [(1, 256, 30, 40), (1, 128, 30, 40)] opt {'axis': 1}
106: op Conv shape [(1, 384, 30, 40), (128, 384, 1, 1), (128,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
107: op Sigmoid shape [(1, 128, 30, 40)] opt {}
108: op Mul shape [(1, 128, 30, 40), (1, 128, 30, 40)] opt {}
109: op Split shape [(1, 128, 30, 40), (2,)] opt {'axis': 1}
110: op Conv shape [(1, 64, 30, 40), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
111: op Sigmoid shape [(1, 64, 30, 40)] opt {}
112: op Mul shape [(1, 64, 30, 40), (1, 64, 30, 40)] opt {}
113: op Conv shape [(1, 64, 30, 40), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
114: op Sigmoid shape [(1, 64, 30, 40)] opt {}
115: op Mul shape [(1, 64, 30, 40), (1, 64, 30, 40)] opt {}
116: op Concat shape [(1, 64, 30, 40), (1, 64, 30, 40), (1, 64, 30, 40)] opt {'axis': 1}
117: op Conv shape [(1, 192, 30, 40), (128, 192, 1, 1), (128,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
118: op Sigmoid shape [(1, 128, 30, 40)] opt {}
119: op Mul shape [(1, 128, 30, 40), (1, 128, 30, 40)] opt {}
120: op Constant shape [] opt {'value': <Tensor <LB HIP (4,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
121: op Resize shape [(1, 128, 30, 40), None, (4,)] opt {'coordinate_transformation_mode': 'asymmetric', 'cubic_coeff_a': -0.75, 'mode': 'nearest', 'nearest_mode': 'floor'}
122: op Concat shape [(1, 128, 60, 80), (1, 64, 60, 80)] opt {'axis': 1}
123: op Conv shape [(1, 192, 60, 80), (64, 192, 1, 1), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
124: op Sigmoid shape [(1, 64, 60, 80)] opt {}
125: op Mul shape [(1, 64, 60, 80), (1, 64, 60, 80)] opt {}
126: op Split shape [(1, 64, 60, 80), (2,)] opt {'axis': 1}
127: op Conv shape [(1, 32, 60, 80), (32, 32, 3, 3), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
128: op Sigmoid shape [(1, 32, 60, 80)] opt {}
129: op Mul shape [(1, 32, 60, 80), (1, 32, 60, 80)] opt {}
130: op Conv shape [(1, 32, 60, 80), (32, 32, 3, 3), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
131: op Sigmoid shape [(1, 32, 60, 80)] opt {}
132: op Mul shape [(1, 32, 60, 80), (1, 32, 60, 80)] opt {}
133: op Concat shape [(1, 32, 60, 80), (1, 32, 60, 80), (1, 32, 60, 80)] opt {'axis': 1}
134: op Conv shape [(1, 96, 60, 80), (64, 96, 1, 1), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
135: op Sigmoid shape [(1, 64, 60, 80)] opt {}
136: op Mul shape [(1, 64, 60, 80), (1, 64, 60, 80)] opt {}
137: op Conv shape [(1, 64, 60, 80), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (2, 2)}
138: op Sigmoid shape [(1, 64, 30, 40)] opt {}
139: op Mul shape [(1, 64, 30, 40), (1, 64, 30, 40)] opt {}
140: op Concat shape [(1, 64, 30, 40), (1, 128, 30, 40)] opt {'axis': 1}
141: op Conv shape [(1, 192, 30, 40), (128, 192, 1, 1), (128,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
142: op Sigmoid shape [(1, 128, 30, 40)] opt {}
143: op Mul shape [(1, 128, 30, 40), (1, 128, 30, 40)] opt {}
144: op Split shape [(1, 128, 30, 40), (2,)] opt {'axis': 1}
145: op Conv shape [(1, 64, 30, 40), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
146: op Sigmoid shape [(1, 64, 30, 40)] opt {}
147: op Mul shape [(1, 64, 30, 40), (1, 64, 30, 40)] opt {}
148: op Conv shape [(1, 64, 30, 40), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
149: op Sigmoid shape [(1, 64, 30, 40)] opt {}
150: op Mul shape [(1, 64, 30, 40), (1, 64, 30, 40)] opt {}
151: op Concat shape [(1, 64, 30, 40), (1, 64, 30, 40), (1, 64, 30, 40)] opt {'axis': 1}
152: op Conv shape [(1, 192, 30, 40), (128, 192, 1, 1), (128,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
153: op Sigmoid shape [(1, 128, 30, 40)] opt {}
154: op Mul shape [(1, 128, 30, 40), (1, 128, 30, 40)] opt {}
155: op Conv shape [(1, 128, 30, 40), (128, 128, 3, 3), (128,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (2, 2)}
156: op Sigmoid shape [(1, 128, 15, 20)] opt {}
157: op Mul shape [(1, 128, 15, 20), (1, 128, 15, 20)] opt {}
158: op Concat shape [(1, 128, 15, 20), (1, 256, 15, 20)] opt {'axis': 1}
159: op Conv shape [(1, 384, 15, 20), (256, 384, 1, 1), (256,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
160: op Sigmoid shape [(1, 256, 15, 20)] opt {}
161: op Mul shape [(1, 256, 15, 20), (1, 256, 15, 20)] opt {}
162: op Split shape [(1, 256, 15, 20), (2,)] opt {'axis': 1}
163: op Conv shape [(1, 128, 15, 20), (128, 128, 3, 3), (128,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
164: op Sigmoid shape [(1, 128, 15, 20)] opt {}
165: op Mul shape [(1, 128, 15, 20), (1, 128, 15, 20)] opt {}
166: op Conv shape [(1, 128, 15, 20), (128, 128, 3, 3), (128,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
167: op Sigmoid shape [(1, 128, 15, 20)] opt {}
168: op Mul shape [(1, 128, 15, 20), (1, 128, 15, 20)] opt {}
169: op Concat shape [(1, 128, 15, 20), (1, 128, 15, 20), (1, 128, 15, 20)] opt {'axis': 1}
170: op Conv shape [(1, 384, 15, 20), (256, 384, 1, 1), (256,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
171: op Sigmoid shape [(1, 256, 15, 20)] opt {}
172: op Mul shape [(1, 256, 15, 20), (1, 256, 15, 20)] opt {}
173: op Conv shape [(1, 64, 60, 80), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
174: op Sigmoid shape [(1, 64, 60, 80)] opt {}
175: op Mul shape [(1, 64, 60, 80), (1, 64, 60, 80)] opt {}
176: op ConvTranspose shape [(1, 64, 60, 80), (64, 64, 2, 2), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (2, 2), 'pads': (0, 0, 0, 0), 'strides': (2, 2)}
177: op Conv shape [(1, 64, 120, 160), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
178: op Sigmoid shape [(1, 64, 120, 160)] opt {}
179: op Mul shape [(1, 64, 120, 160), (1, 64, 120, 160)] opt {}
180: op Conv shape [(1, 64, 120, 160), (32, 64, 1, 1), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
181: op Sigmoid shape [(1, 32, 120, 160)] opt {}
182: op Mul shape [(1, 32, 120, 160), (1, 32, 120, 160)] opt {}
183: op Conv shape [(1, 64, 60, 80), (32, 64, 3, 3), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
184: op Sigmoid shape [(1, 32, 60, 80)] opt {}
185: op Mul shape [(1, 32, 60, 80), (1, 32, 60, 80)] opt {}
186: op Conv shape [(1, 32, 60, 80), (32, 32, 3, 3), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
187: op Sigmoid shape [(1, 32, 60, 80)] opt {}
188: op Mul shape [(1, 32, 60, 80), (1, 32, 60, 80)] opt {}
189: op Conv shape [(1, 32, 60, 80), (32, 32, 1, 1), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
190: op Constant shape [] opt {'value': <Tensor <LB HIP (3,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
191: op Constant shape [] opt {'value': <Tensor <LB HIP (3,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
192: op Constant shape [] opt {'value': <Tensor <LB HIP (3,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
193: op Reshape shape [(1, 32, 60, 80), (3,)] opt {'allowzero': 0}
194: op Conv shape [(1, 128, 30, 40), (32, 128, 3, 3), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
195: op Sigmoid shape [(1, 32, 30, 40)] opt {}
196: op Mul shape [(1, 32, 30, 40), (1, 32, 30, 40)] opt {}
197: op Conv shape [(1, 32, 30, 40), (32, 32, 3, 3), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
198: op Sigmoid shape [(1, 32, 30, 40)] opt {}
199: op Mul shape [(1, 32, 30, 40), (1, 32, 30, 40)] opt {}
200: op Conv shape [(1, 32, 30, 40), (32, 32, 1, 1), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
201: op Reshape shape [(1, 32, 30, 40), (3,)] opt {'allowzero': 0}
202: op Conv shape [(1, 256, 15, 20), (32, 256, 3, 3), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
203: op Sigmoid shape [(1, 32, 15, 20)] opt {}
204: op Mul shape [(1, 32, 15, 20), (1, 32, 15, 20)] opt {}
205: op Conv shape [(1, 32, 15, 20), (32, 32, 3, 3), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
206: op Sigmoid shape [(1, 32, 15, 20)] opt {}
207: op Mul shape [(1, 32, 15, 20), (1, 32, 15, 20)] opt {}
208: op Conv shape [(1, 32, 15, 20), (32, 32, 1, 1), (32,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
209: op Reshape shape [(1, 32, 15, 20), (3,)] opt {'allowzero': 0}
210: op Concat shape [(1, 32, 4800), (1, 32, 1200), (1, 32, 300)] opt {'axis': 2}
211: op Conv shape [(1, 64, 60, 80), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
212: op Sigmoid shape [(1, 64, 60, 80)] opt {}
213: op Mul shape [(1, 64, 60, 80), (1, 64, 60, 80)] opt {}
214: op Conv shape [(1, 64, 60, 80), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
215: op Sigmoid shape [(1, 64, 60, 80)] opt {}
216: op Mul shape [(1, 64, 60, 80), (1, 64, 60, 80)] opt {}
217: op Conv shape [(1, 64, 60, 80), (64, 64, 1, 1), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
218: op Conv shape [(1, 64, 60, 80), (80, 64, 3, 3), (80,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
219: op Sigmoid shape [(1, 80, 60, 80)] opt {}
220: op Mul shape [(1, 80, 60, 80), (1, 80, 60, 80)] opt {}
221: op Conv shape [(1, 80, 60, 80), (80, 80, 3, 3), (80,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
222: op Sigmoid shape [(1, 80, 60, 80)] opt {}
223: op Mul shape [(1, 80, 60, 80), (1, 80, 60, 80)] opt {}
224: op Conv shape [(1, 80, 60, 80), (80, 80, 1, 1), (80,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
225: op Concat shape [(1, 64, 60, 80), (1, 80, 60, 80)] opt {'axis': 1}
226: op Conv shape [(1, 128, 30, 40), (64, 128, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
227: op Sigmoid shape [(1, 64, 30, 40)] opt {}
228: op Mul shape [(1, 64, 30, 40), (1, 64, 30, 40)] opt {}
229: op Conv shape [(1, 64, 30, 40), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
230: op Sigmoid shape [(1, 64, 30, 40)] opt {}
231: op Mul shape [(1, 64, 30, 40), (1, 64, 30, 40)] opt {}
232: op Conv shape [(1, 64, 30, 40), (64, 64, 1, 1), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
233: op Conv shape [(1, 128, 30, 40), (80, 128, 3, 3), (80,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
234: op Sigmoid shape [(1, 80, 30, 40)] opt {}
235: op Mul shape [(1, 80, 30, 40), (1, 80, 30, 40)] opt {}
236: op Conv shape [(1, 80, 30, 40), (80, 80, 3, 3), (80,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
237: op Sigmoid shape [(1, 80, 30, 40)] opt {}
238: op Mul shape [(1, 80, 30, 40), (1, 80, 30, 40)] opt {}
239: op Conv shape [(1, 80, 30, 40), (80, 80, 1, 1), (80,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
240: op Concat shape [(1, 64, 30, 40), (1, 80, 30, 40)] opt {'axis': 1}
241: op Conv shape [(1, 256, 15, 20), (64, 256, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
242: op Sigmoid shape [(1, 64, 15, 20)] opt {}
243: op Mul shape [(1, 64, 15, 20), (1, 64, 15, 20)] opt {}
244: op Conv shape [(1, 64, 15, 20), (64, 64, 3, 3), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
245: op Sigmoid shape [(1, 64, 15, 20)] opt {}
246: op Mul shape [(1, 64, 15, 20), (1, 64, 15, 20)] opt {}
247: op Conv shape [(1, 64, 15, 20), (64, 64, 1, 1), (64,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
248: op Conv shape [(1, 256, 15, 20), (80, 256, 3, 3), (80,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
249: op Sigmoid shape [(1, 80, 15, 20)] opt {}
250: op Mul shape [(1, 80, 15, 20), (1, 80, 15, 20)] opt {}
251: op Conv shape [(1, 80, 15, 20), (80, 80, 3, 3), (80,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (3, 3), 'pads': (1, 1, 1, 1), 'strides': (1, 1)}
252: op Sigmoid shape [(1, 80, 15, 20)] opt {}
253: op Mul shape [(1, 80, 15, 20), (1, 80, 15, 20)] opt {}
254: op Conv shape [(1, 80, 15, 20), (80, 80, 1, 1), (80,)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
255: op Concat shape [(1, 64, 15, 20), (1, 80, 15, 20)] opt {'axis': 1}
256: op Constant shape [] opt {'value': <Tensor <LB HIP (3,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
257: op Constant shape [] opt {'value': <Tensor <LB HIP (3,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
258: op Constant shape [] opt {'value': <Tensor <LB HIP (3,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
259: op Reshape shape [(1, 144, 60, 80), (3,)] opt {'allowzero': 0}
260: op Reshape shape [(1, 144, 30, 40), (3,)] opt {'allowzero': 0}
261: op Reshape shape [(1, 144, 15, 20), (3,)] opt {'allowzero': 0}
262: op Concat shape [(1, 144, 4800), (1, 144, 1200), (1, 144, 300)] opt {'axis': 2}
263: op Constant shape [] opt {'value': <Tensor <LB HIP (2,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
264: op Split shape [(1, 144, 6300), (2,)] opt {'axis': 1}
265: op Constant shape [] opt {'value': <Tensor <LB HIP (4,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
266: op Reshape shape [(1, 64, 6300), (4,)] opt {'allowzero': 0}
267: op Transpose shape [(1, 4, 16, 6300)] opt {'perm': (0, 2, 1, 3)}
268: op Softmax shape [(1, 16, 4, 6300)] opt {'axis': 1}
269: op Conv shape [(1, 16, 4, 6300), (1, 16, 1, 1)] opt {'dilations': (1, 1), 'group': 1, 'kernel_shape': (1, 1), 'pads': (0, 0, 0, 0), 'strides': (1, 1)}
270: op Constant shape [] opt {'value': <Tensor <LB HIP (3,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
271: op Reshape shape [(1, 1, 4, 6300), (3,)] opt {'allowzero': 0}
272: op Shape shape [(1, 4, 6300)] opt {}
273: op Constant shape [] opt {'value': <Tensor <LB HIP (1,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
274: op Gather shape [(3,), (1,)] opt {'axis': 0}
275: op Constant shape [] opt {'value': <Tensor <LB HIP (1,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
276: op Constant shape [] opt {'value': <Tensor <LB HIP (1,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
277: op Add shape [(1,), (1,)] opt {}
278: op Constant shape [] opt {'value': <Tensor <LB HIP (1,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
279: op Div shape [(1,), (1,)] opt {}
280: op Constant shape [] opt {'value': <Tensor <LB HIP (1,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
281: op Mul shape [(1,), (1,)] opt {}
282: op Slice shape [(1, 4, 6300), (1,), (1,), (1,)] opt {}
283: op Constant shape [] opt {'value': <Tensor <LB HIP (1,) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
284: op Mul shape [(1,), (1,)] opt {}
285: op Slice shape [(1, 4, 6300), (1,), (1,), (1,)] opt {}
286: op Constant shape [] opt {'value': <Tensor <LB HIP (1, 2, 6300) contig:True (<LoadOps.COPY: 3>, None)> on HIP with grad None>}
287: op Sub shape [(1, 2, 6300), (1, 3, 6300)] opt {}
Traceback (most recent call last):
File "/home/jebba/devel/tinygrad/tinygrad/examples/yolov8-onnx.py", line 18, in <module>
run_onnx({"images": Tensor.zeros(1,3,480,640)}, debug=True)
File "/home/jebba/devel/tinygrad/tinygrad/extra/onnx.py", line 211, in run_onnx
ret = real_fxn(*inp, **opt)
^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/extra/onnx_ops.py", line 18, in Sub
def Sub(x: Union[Tensor, Any], other: Tensor): return x - other # some test has input as int
~~^~~~~~~
File "/home/jebba/devel/tinygrad/tinygrad/tinygrad/tensor.py", line 858, in __sub__
def __sub__(self, x) -> Tensor: return self.sub(x)
^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/tinygrad/tensor.py", line 812, in sub
return mlops.Sub.apply(*self._broadcasted(x, reverse)) if x.__class__ is Tensor or x else (-self if reverse else self)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/tinygrad/tensor.py", line 800, in _broadcasted
return x.expand(broadcasted_shape), y.expand(broadcasted_shape)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/tinygrad/tensor.py", line 309, in expand
return mlops.Expand.apply(self, shape=new_shape) if new_shape != self.shape else self
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/tinygrad/tensor.py", line 34, in apply
ret.lazydata, ret.requires_grad, ret.grad = ctx.forward(*[t.lazydata for t in x], **kwargs), ctx.requires_grad, None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/tinygrad/mlops.py", line 168, in forward
return x.expand(shape)
^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/tinygrad/lazy.py", line 147, in expand
def expand(self, arg:Tuple[sint, ...]): return self._view(self.st.expand(arg))
^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/tinygrad/shape/shapetracker.py", line 180, in expand
def expand(self, new_shape: Tuple[sint, ...]) -> ShapeTracker: return ShapeTracker(self.views[0:-1] + (self.views[-1].expand(new_shape), ))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jebba/devel/tinygrad/tinygrad/tinygrad/shape/view.py", line 156, in expand
assert all((s == x or (s == 1 and st == 0)) for s,x,st in zip(self.shape, new_shape, self.strides)), f"can't expand {self.shape} into {new_shape}"
AssertionError: can't expand (1, 2, 6300) into (1, 3, 6300)
yolov8.py
Error: Image URL or path not provided.
mlperf/model_spec.py
testing resnet
8.32 GOPS, 0.00 ms
testing retinanet
23.98 GOPS, 0.00 ms
testing unet3d
1068.83 GOPS, 0.00 ms
testing rnnt
47.32 GOPS, 0.00 ms
testing bert
273.53 GOPS, 0.00 ms
testing mrcnn
89.61 GOPS, 2046.28 ms