2025-07-13 11:18:33,402 - INFO - Using device: cpu
2025-07-13 11:18:33,403 - INFO - Hyperparameters - Latent dim: 100, Epochs: 100, Batch size: 128, LR: 0.0002
2025-07-13 11:18:33,404 - INFO - Loading MNIST dataset...
2025-07-13 11:20:17,010 - INFO - Using MPS (Metal Performance Shaders) for GPU acceleration
2025-07-13 11:20:17,011 - INFO - Device: mps
2025-07-13 11:20:17,011 - INFO - Hyperparameters - Latent dim: 100, Epochs: 100, Batch size: 128, LR: 0.0002
2025-07-13 11:20:17,011 - INFO - Loading MNIST dataset...
2025-07-13 11:20:27,813 - INFO - Dataset loaded - Total samples: 60000, Batches per epoch: 469
2025-07-13 11:20:27,814 - INFO - DataLoader optimized with 4 workers and memory pinning for faster data transfer
2025-07-13 11:20:27,815 - INFO - Creating and initializing models...
2025-07-13 11:20:27,815 - INFO - Initializing Generator architecture...
2025-07-13 11:20:27,824 - INFO - Generator architecture complete - Output shape: (batch_size, 1, 64, 64)
2025-07-13 11:20:27,866 - INFO - Initializing Discriminator architecture...
2025-07-13 11:20:27,871 - INFO - Discriminator architecture complete - Output: Real/Fake probability
2025-07-13 11:20:27,875 - INFO - Generator parameters: 3,574,656
2025-07-13 11:20:27,876 - INFO - Discriminator parameters: 2,763,520
2025-07-13 11:20:27,876 - INFO - Models moved to MPS (Apple Silicon GPU)
2025-07-13 11:20:27,876 - INFO - Note: MPS provides significant acceleration for matrix operations on M-series chips
2025-07-13 11:20:27,877 - INFO - Applying weight initialization...
2025-07-13 11:20:28,177 - INFO - Optimizers initialized with Adam (lr=0.0002, betas=(0.5, 0.999))
2025-07-13 11:20:28,228 - INFO - Fixed noise vector created for consistent progress tracking
2025-07-13 11:20:28,229 - INFO - ============================================================
2025-07-13 11:20:28,229 - INFO - STARTING TRAINING ON MPS (APPLE SILICON GPU)
2025-07-13 11:20:28,229 - INFO - ============================================================
2025-07-13 11:20:28,230 - INFO - Starting Epoch 1/100
2025-07-13 11:20:32,930 - INFO - Epoch [1/100] Batch [1/469] D_Loss: 2.2925 G_Loss: 2.4867 D_Real: 0.2779 D_Fake: 0.4786 Time: 3.036s (42.2 img/s)
2025-07-13 11:20:46,893 - INFO - Epoch [1/100] Batch [101/469] D_Loss: 0.1327 G_Loss: 5.0302 D_Real: 0.9274 D_Fake: 0.0380 Time: 0.139s (919.6 img/s)
2025-07-13 11:21:00,856 - INFO - Epoch [1/100] Batch [201/469] D_Loss: 0.0768 G_Loss: 5.0648 D_Real: 0.9507 D_Fake: 0.0206 Time: 0.140s (917.3 img/s)
2025-07-13 11:22:12,887 - INFO - Using MPS (optimized for efficiency)
2025-07-13 11:22:12,888 - INFO - LITE MODE - Latent: 64, Epochs: 50, Batch: 64, Subset: 10000
2025-07-13 11:22:12,889 - INFO - Loading MNIST subset...
2025-07-13 11:22:12,906 - INFO - Using 10000 samples, 157 batches per epoch
2025-07-13 11:22:12,908 - INFO - Simple Generator: Linear layers only (much faster)
2025-07-13 11:22:12,931 - INFO - Simple Discriminator: Linear layers only (much faster)
2025-07-13 11:22:12,934 - INFO - Generator params: 576,656 (vs 3.5M before)
2025-07-13 11:22:12,934 - INFO - Discriminator params: 533,505 (vs 2.7M before)
2025-07-13 11:22:12,938 - INFO - ==================================================
2025-07-13 11:22:12,938 - INFO - STARTING LITE TRAINING (HEAT OPTIMIZED)
2025-07-13 11:22:12,938 - INFO - ==================================================
2025-07-13 11:22:15,868 - INFO - Epoch [1/50] Batch [1/157] D_Loss: 1.414 G_Loss: 0.727
2025-07-13 11:22:16,196 - INFO - Epoch [1/50] Batch [51/157] D_Loss: 0.487 G_Loss: 1.930
2025-07-13 11:22:16,509 - INFO - Epoch [1/50] Batch [101/157] D_Loss: 0.087 G_Loss: 5.107
2025-07-13 11:22:16,822 - INFO - Epoch [1/50] Batch [151/157] D_Loss: 0.188 G_Loss: 9.829
2025-07-13 11:22:17,650 - INFO - Epoch 1 - D_Loss: 0.499, G_Loss: 3.914, Time: 4.7s
2025-07-13 11:22:18,681 - INFO - Epoch [2/50] Batch [1/157] D_Loss: 0.198 G_Loss: 10.054
2025-07-13 11:22:19,003 - INFO - Epoch [2/50] Batch [51/157] D_Loss: 0.197 G_Loss: 10.293
2025-07-13 11:22:19,325 - INFO - Epoch [2/50] Batch [101/157] D_Loss: 0.107 G_Loss: 3.773
2025-07-13 11:22:19,643 - INFO - Epoch [2/50] Batch [151/157] D_Loss: 0.120 G_Loss: 4.523
2025-07-13 11:22:19,939 - INFO - Epoch 2 - D_Loss: 0.149, G_Loss: 8.121, Time: 2.2s
2025-07-13 11:22:20,903 - INFO - Epoch [3/50] Batch [1/157] D_Loss: 0.090 G_Loss: 4.251
2025-07-13 11:22:21,220 - INFO - Epoch [3/50] Batch [51/157] D_Loss: 0.055 G_Loss: 5.018
2025-07-13 11:22:21,535 - INFO - Epoch [3/50] Batch [101/157] D_Loss: 0.037 G_Loss: 6.808
2025-07-13 11:22:21,853 - INFO - Epoch [3/50] Batch [151/157] D_Loss: 0.025 G_Loss: 6.606
2025-07-13 11:22:22,134 - INFO - Epoch 3 - D_Loss: 0.050, G_Loss: 5.622, Time: 2.2s
2025-07-13 11:22:23,103 - INFO - Epoch [4/50] Batch [1/157] D_Loss: 0.020 G_Loss: 6.747
2025-07-13 11:22:23,420 - INFO - Epoch [4/50] Batch [51/157] D_Loss: 0.016 G_Loss: 6.293
2025-07-13 11:22:23,732 - INFO - Epoch [4/50] Batch [101/157] D_Loss: 0.016 G_Loss: 6.285
2025-07-13 11:22:24,054 - INFO - Epoch [4/50] Batch [151/157] D_Loss: 0.011 G_Loss: 6.005
2025-07-13 11:22:24,333 - INFO - Epoch 4 - D_Loss: 0.019, G_Loss: 6.681, Time: 2.2s
2025-07-13 11:22:25,286 - INFO - Epoch [5/50] Batch [1/157] D_Loss: 0.013 G_Loss: 7.839
2025-07-13 11:22:25,602 - INFO - Epoch [5/50] Batch [51/157] D_Loss: 0.055 G_Loss: 5.665
2025-07-13 11:22:25,914 - INFO - Epoch [5/50] Batch [101/157] D_Loss: 0.131 G_Loss: 7.865
2025-07-13 11:22:26,231 - INFO - Epoch [5/50] Batch [151/157] D_Loss: 0.208 G_Loss: 4.971
2025-07-13 11:22:26,514 - INFO - Epoch 5 - D_Loss: 0.080, G_Loss: 6.832, Time: 2.2s
2025-07-13 11:22:27,479 - INFO - Epoch [6/50] Batch [1/157] D_Loss: 0.059 G_Loss: 5.910
2025-07-13 11:22:27,797 - INFO - Epoch [6/50] Batch [51/157] D_Loss: 0.327 G_Loss: 5.019
2025-07-13 11:22:28,110 - INFO - Epoch [6/50] Batch [101/157] D_Loss: 0.069 G_Loss: 7.908
2025-07-13 11:22:28,422 - INFO - Epoch [6/50] Batch [151/157] D_Loss: 0.041 G_Loss: 7.707
2025-07-13 11:22:28,705 - INFO - Epoch 6 - D_Loss: 0.125, G_Loss: 7.011, Time: 2.2s
2025-07-13 11:22:29,668 - INFO - Epoch [7/50] Batch [1/157] D_Loss: 0.094 G_Loss: 9.911
2025-07-13 11:22:29,985 - INFO - Epoch [7/50] Batch [51/157] D_Loss: 0.099 G_Loss: 7.200
2025-07-13 11:22:30,294 - INFO - Epoch [7/50] Batch [101/157] D_Loss: 0.106 G_Loss: 6.806
2025-07-13 11:22:30,604 - INFO - Epoch [7/50] Batch [151/157] D_Loss: 0.072 G_Loss: 8.220
2025-07-13 11:22:30,884 - INFO - Epoch 7 - D_Loss: 0.160, G_Loss: 7.298, Time: 2.2s
2025-07-13 11:22:31,835 - INFO - Epoch [8/50] Batch [1/157] D_Loss: 0.127 G_Loss: 7.432
2025-07-13 11:22:32,158 - INFO - Epoch [8/50] Batch [51/157] D_Loss: 0.182 G_Loss: 7.185
2025-07-13 11:22:32,503 - INFO - Epoch [8/50] Batch [101/157] D_Loss: 0.324 G_Loss: 4.899
2025-07-13 11:22:32,816 - INFO - Epoch [8/50] Batch [151/157] D_Loss: 0.448 G_Loss: 5.160
2025-07-13 11:22:33,117 - INFO - Epoch 8 - D_Loss: 0.254, G_Loss: 6.831, Time: 2.2s
2025-07-13 11:22:34,086 - INFO - Epoch [9/50] Batch [1/157] D_Loss: 0.496 G_Loss: 4.999
2025-07-13 11:22:34,405 - INFO - Epoch [9/50] Batch [51/157] D_Loss: 0.343 G_Loss: 5.772
2025-07-13 11:22:34,719 - INFO - Epoch [9/50] Batch [101/157] D_Loss: 0.256 G_Loss: 4.703
2025-07-13 11:22:35,030 - INFO - Epoch [9/50] Batch [151/157] D_Loss: 0.150 G_Loss: 6.448
2025-07-13 11:22:35,309 - INFO - Epoch 9 - D_Loss: 0.330, G_Loss: 5.221, Time: 2.2s
2025-07-13 11:22:36,308 - INFO - Epoch [10/50] Batch [1/157] D_Loss: 0.299 G_Loss: 8.258
2025-07-13 11:22:36,653 - INFO - Epoch [10/50] Batch [51/157] D_Loss: 0.249 G_Loss: 5.360
2025-07-13 11:22:36,972 - INFO - Epoch [10/50] Batch [101/157] D_Loss: 0.340 G_Loss: 4.701
2025-07-13 11:22:37,305 - INFO - Epoch [10/50] Batch [151/157] D_Loss: 0.095 G_Loss: 6.552
2025-07-13 11:22:37,599 - INFO - Epoch 10 - D_Loss: 0.267, G_Loss: 5.786, Time: 2.3s
2025-07-13 11:22:38,595 - INFO - Epoch [11/50] Batch [1/157] D_Loss: 0.123 G_Loss: 8.758
2025-07-13 11:22:38,928 - INFO - Epoch [11/50] Batch [51/157] D_Loss: 0.231 G_Loss: 4.707
2025-07-13 11:22:39,252 - INFO - Epoch [11/50] Batch [101/157] D_Loss: 0.213 G_Loss: 6.000
2025-07-13 11:22:39,577 - INFO - Epoch [11/50] Batch [151/157] D_Loss: 0.315 G_Loss: 5.685
2025-07-13 11:22:39,862 - INFO - Epoch 11 - D_Loss: 0.252, G_Loss: 5.776, Time: 2.3s
2025-07-13 11:22:40,887 - INFO - Epoch [12/50] Batch [1/157] D_Loss: 0.267 G_Loss: 4.521
2025-07-13 11:22:41,220 - INFO - Epoch [12/50] Batch [51/157] D_Loss: 0.164 G_Loss: 6.560
2025-07-13 11:22:41,538 - INFO - Epoch [12/50] Batch [101/157] D_Loss: 0.360 G_Loss: 4.705
2025-07-13 11:22:41,864 - INFO - Epoch [12/50] Batch [151/157] D_Loss: 0.204 G_Loss: 5.475
2025-07-13 11:22:42,163 - INFO - Epoch 12 - D_Loss: 0.240, G_Loss: 5.424, Time: 2.2s
2025-07-13 11:22:43,122 - INFO - Epoch [13/50] Batch [1/157] D_Loss: 0.163 G_Loss: 6.708
2025-07-13 11:22:43,441 - INFO - Epoch [13/50] Batch [51/157] D_Loss: 0.314 G_Loss: 4.626
2025-07-13 11:22:43,773 - INFO - Epoch [13/50] Batch [101/157] D_Loss: 0.132 G_Loss: 6.173
2025-07-13 11:22:44,085 - INFO - Epoch [13/50] Batch [151/157] D_Loss: 0.378 G_Loss: 4.652
2025-07-13 11:22:44,373 - INFO - Epoch 13 - D_Loss: 0.230, G_Loss: 5.482, Time: 2.2s
2025-07-13 11:22:45,336 - INFO - Epoch [14/50] Batch [1/157] D_Loss: 0.259 G_Loss: 5.078
2025-07-13 11:22:45,655 - INFO - Epoch [14/50] Batch [51/157] D_Loss: 0.147 G_Loss: 5.611
2025-07-13 11:22:45,968 - INFO - Epoch [14/50] Batch [101/157] D_Loss: 0.212 G_Loss: 4.202
2025-07-13 11:22:46,279 - INFO - Epoch [14/50] Batch [151/157] D_Loss: 0.119 G_Loss: 7.232
2025-07-13 11:22:46,561 - INFO - Epoch 14 - D_Loss: 0.238, G_Loss: 5.009, Time: 2.2s
2025-07-13 11:22:47,526 - INFO - Epoch [15/50] Batch [1/157] D_Loss: 0.522 G_Loss: 7.273
2025-07-13 11:22:47,841 - INFO - Epoch [15/50] Batch [51/157] D_Loss: 0.183 G_Loss: 4.704
2025-07-13 11:22:48,154 - INFO - Epoch [15/50] Batch [101/157] D_Loss: 0.415 G_Loss: 3.363
2025-07-13 11:22:48,467 - INFO - Epoch [15/50] Batch [151/157] D_Loss: 0.218 G_Loss: 4.362
2025-07-13 11:22:48,746 - INFO - Epoch 15 - D_Loss: 0.244, G_Loss: 4.437, Time: 2.2s
2025-07-13 11:22:49,726 - INFO - Epoch [16/50] Batch [1/157] D_Loss: 0.514 G_Loss: 3.931
2025-07-13 11:22:50,044 - INFO - Epoch [16/50] Batch [51/157] D_Loss: 0.189 G_Loss: 5.084
2025-07-13 11:22:50,360 - INFO - Epoch [16/50] Batch [101/157] D_Loss: 0.212 G_Loss: 4.539
2025-07-13 11:22:50,677 - INFO - Epoch [16/50] Batch [151/157] D_Loss: 0.151 G_Loss: 4.327
2025-07-13 11:22:50,966 - INFO - Epoch 16 - D_Loss: 0.242, G_Loss: 4.478, Time: 2.2s
2025-07-13 11:22:51,921 - INFO - Epoch [17/50] Batch [1/157] D_Loss: 0.183 G_Loss: 5.415
2025-07-13 11:22:52,240 - INFO - Epoch [17/50] Batch [51/157] D_Loss: 0.397 G_Loss: 7.084
2025-07-13 11:22:52,554 - INFO - Epoch [17/50] Batch [101/157] D_Loss: 0.333 G_Loss: 3.645
2025-07-13 11:22:52,868 - INFO - Epoch [17/50] Batch [151/157] D_Loss: 0.181 G_Loss: 5.427
2025-07-13 11:22:53,153 - INFO - Epoch 17 - D_Loss: 0.220, G_Loss: 4.776, Time: 2.2s
2025-07-13 11:22:54,127 - INFO - Epoch [18/50] Batch [1/157] D_Loss: 0.107 G_Loss: 5.884
2025-07-13 11:22:54,444 - INFO - Epoch [18/50] Batch [51/157] D_Loss: 0.216 G_Loss: 4.467
2025-07-13 11:22:54,755 - INFO - Epoch [18/50] Batch [101/157] D_Loss: 0.212 G_Loss: 5.575
2025-07-13 11:22:55,075 - INFO - Epoch [18/50] Batch [151/157] D_Loss: 0.205 G_Loss: 3.849
2025-07-13 11:22:55,378 - INFO - Epoch 18 - D_Loss: 0.229, G_Loss: 4.560, Time: 2.2s
2025-07-13 11:22:56,348 - INFO - Epoch [19/50] Batch [1/157] D_Loss: 0.166 G_Loss: 4.697
2025-07-13 11:22:56,670 - INFO - Epoch [19/50] Batch [51/157] D_Loss: 0.530 G_Loss: 3.344
2025-07-13 11:22:56,992 - INFO - Epoch [19/50] Batch [101/157] D_Loss: 0.307 G_Loss: 5.135
2025-07-13 11:22:57,320 - INFO - Epoch [19/50] Batch [151/157] D_Loss: 0.153 G_Loss: 3.771
2025-07-13 11:22:57,602 - INFO - Epoch 19 - D_Loss: 0.249, G_Loss: 4.461, Time: 2.2s
2025-07-13 11:22:58,569 - INFO - Epoch [20/50] Batch [1/157] D_Loss: 0.382 G_Loss: 4.253
2025-07-13 11:22:58,886 - INFO - Epoch [20/50] Batch [51/157] D_Loss: 0.380 G_Loss: 3.176
2025-07-13 11:22:59,201 - INFO - Epoch [20/50] Batch [101/157] D_Loss: 0.239 G_Loss: 4.258
2025-07-13 11:22:59,529 - INFO - Epoch [20/50] Batch [151/157] D_Loss: 0.216 G_Loss: 5.879
2025-07-13 11:22:59,808 - INFO - Epoch 20 - D_Loss: 0.272, G_Loss: 4.471, Time: 2.2s
2025-07-13 11:23:00,782 - INFO - Epoch [21/50] Batch [1/157] D_Loss: 0.237 G_Loss: 6.417
2025-07-13 11:23:01,100 - INFO - Epoch [21/50] Batch [51/157] D_Loss: 0.330 G_Loss: 4.075
2025-07-13 11:23:01,422 - INFO - Epoch [21/50] Batch [101/157] D_Loss: 0.220 G_Loss: 5.957
2025-07-13 11:23:01,735 - INFO - Epoch [21/50] Batch [151/157] D_Loss: 0.061 G_Loss: 5.366
2025-07-13 11:23:02,014 - INFO - Epoch 21 - D_Loss: 0.209, G_Loss: 4.974, Time: 2.2s
2025-07-13 11:23:03,016 - INFO - Epoch [22/50] Batch [1/157] D_Loss: 0.125 G_Loss: 5.063
2025-07-13 11:23:03,330 - INFO - Epoch [22/50] Batch [51/157] D_Loss: 0.137 G_Loss: 5.944
2025-07-13 11:23:03,642 - INFO - Epoch [22/50] Batch [101/157] D_Loss: 0.156 G_Loss: 5.161
2025-07-13 11:23:03,953 - INFO - Epoch [22/50] Batch [151/157] D_Loss: 0.131 G_Loss: 6.399
2025-07-13 11:23:04,263 - INFO - Epoch 22 - D_Loss: 0.170, G_Loss: 5.673, Time: 2.2s
2025-07-13 11:23:05,263 - INFO - Epoch [23/50] Batch [1/157] D_Loss: 0.085 G_Loss: 6.612
2025-07-13 11:23:05,618 - INFO - Epoch [23/50] Batch [51/157] D_Loss: 0.198 G_Loss: 3.426
2025-07-13 11:23:05,956 - INFO - Epoch [23/50] Batch [101/157] D_Loss: 0.142 G_Loss: 5.337
2025-07-13 11:23:06,307 - INFO - Epoch [23/50] Batch [151/157] D_Loss: 0.283 G_Loss: 4.375
2025-07-13 11:23:06,595 - INFO - Epoch 23 - D_Loss: 0.243, G_Loss: 5.066, Time: 2.3s
2025-07-13 11:23:07,564 - INFO - Epoch [24/50] Batch [1/157] D_Loss: 0.246 G_Loss: 4.877
2025-07-13 11:23:07,879 - INFO - Epoch [24/50] Batch [51/157] D_Loss: 0.196 G_Loss: 3.591
2025-07-13 11:23:08,193 - INFO - Epoch [24/50] Batch [101/157] D_Loss: 0.164 G_Loss: 5.076
2025-07-13 11:23:08,506 - INFO - Epoch [24/50] Batch [151/157] D_Loss: 0.220 G_Loss: 5.017
2025-07-13 11:23:08,786 - INFO - Epoch 24 - D_Loss: 0.212, G_Loss: 5.454, Time: 2.2s
2025-07-13 11:23:09,735 - INFO - Epoch [25/50] Batch [1/157] D_Loss: 0.376 G_Loss: 4.019
2025-07-13 11:23:10,050 - INFO - Epoch [25/50] Batch [51/157] D_Loss: 0.268 G_Loss: 5.462
2025-07-13 11:23:10,361 - INFO - Epoch [25/50] Batch [101/157] D_Loss: 0.176 G_Loss: 7.060
2025-07-13 11:23:10,676 - INFO - Epoch [25/50] Batch [151/157] D_Loss: 0.318 G_Loss: 3.175
2025-07-13 11:23:10,955 - INFO - Epoch 25 - D_Loss: 0.269, G_Loss: 4.973, Time: 2.2s
2025-07-13 11:23:11,911 - INFO - Epoch [26/50] Batch [1/157] D_Loss: 0.259 G_Loss: 5.458
2025-07-13 11:23:12,224 - INFO - Epoch [26/50] Batch [51/157] D_Loss: 0.416 G_Loss: 4.406
2025-07-13 11:23:12,550 - INFO - Epoch [26/50] Batch [101/157] D_Loss: 0.185 G_Loss: 4.671
2025-07-13 11:23:12,919 - INFO - Epoch [26/50] Batch [151/157] D_Loss: 0.239 G_Loss: 5.871
2025-07-13 11:23:13,231 - INFO - Epoch 26 - D_Loss: 0.290, G_Loss: 4.670, Time: 2.3s
2025-07-13 11:23:14,247 - INFO - Epoch [27/50] Batch [1/157] D_Loss: 0.355 G_Loss: 5.252
2025-07-13 11:23:14,611 - INFO - Epoch [27/50] Batch [51/157] D_Loss: 0.354 G_Loss: 4.411
2025-07-13 11:23:15,011 - INFO - Epoch [27/50] Batch [101/157] D_Loss: 0.322 G_Loss: 5.771
2025-07-13 11:23:15,413 - INFO - Epoch [27/50] Batch [151/157] D_Loss: 0.497 G_Loss: 3.684
2025-07-13 11:23:15,724 - INFO - Epoch 27 - D_Loss: 0.342, G_Loss: 3.951, Time: 2.5s
2025-07-13 11:23:16,720 - INFO - Epoch [28/50] Batch [1/157] D_Loss: 0.169 G_Loss: 5.322
2025-07-13 11:23:17,095 - INFO - Epoch [28/50] Batch [51/157] D_Loss: 0.243 G_Loss: 5.145
2025-07-13 11:23:17,418 - INFO - Epoch [28/50] Batch [101/157] D_Loss: 0.291 G_Loss: 4.104
2025-07-13 11:23:17,731 - INFO - Epoch [28/50] Batch [151/157] D_Loss: 0.321 G_Loss: 4.954
2025-07-13 11:23:18,013 - INFO - Epoch 28 - D_Loss: 0.355, G_Loss: 3.937, Time: 2.3s
2025-07-13 11:23:18,976 - INFO - Epoch [29/50] Batch [1/157] D_Loss: 0.367 G_Loss: 3.584
2025-07-13 11:23:19,302 - INFO - Epoch [29/50] Batch [51/157] D_Loss: 0.571 G_Loss: 2.301
2025-07-13 11:23:19,625 - INFO - Epoch [29/50] Batch [101/157] D_Loss: 0.377 G_Loss: 3.155
2025-07-13 11:23:19,957 - INFO - Epoch [29/50] Batch [151/157] D_Loss: 0.408 G_Loss: 4.367
2025-07-13 11:23:20,239 - INFO - Epoch 29 - D_Loss: 0.388, G_Loss: 3.713, Time: 2.2s
2025-07-13 11:23:21,206 - INFO - Epoch [30/50] Batch [1/157] D_Loss: 0.328 G_Loss: 5.198
2025-07-13 11:23:21,527 - INFO - Epoch [30/50] Batch [51/157] D_Loss: 0.388 G_Loss: 4.180
2025-07-13 11:23:21,840 - INFO - Epoch [30/50] Batch [101/157] D_Loss: 0.284 G_Loss: 3.585
2025-07-13 11:23:22,155 - INFO - Epoch [30/50] Batch [151/157] D_Loss: 0.301 G_Loss: 4.791
2025-07-13 11:23:22,437 - INFO - Epoch 30 - D_Loss: 0.333, G_Loss: 4.205, Time: 2.2s
2025-07-13 11:23:23,391 - INFO - Epoch [31/50] Batch [1/157] D_Loss: 0.571 G_Loss: 5.715
2025-07-13 11:23:23,711 - INFO - Epoch [31/50] Batch [51/157] D_Loss: 0.334 G_Loss: 2.926
2025-07-13 11:23:24,036 - INFO - Epoch [31/50] Batch [101/157] D_Loss: 0.267 G_Loss: 3.822
2025-07-13 11:23:24,349 - INFO - Epoch [31/50] Batch [151/157] D_Loss: 0.307 G_Loss: 4.891
2025-07-13 11:23:24,633 - INFO - Epoch 31 - D_Loss: 0.351, G_Loss: 4.128, Time: 2.2s
2025-07-13 11:23:25,636 - INFO - Epoch [32/50] Batch [1/157] D_Loss: 0.423 G_Loss: 3.483
2025-07-13 11:23:25,953 - INFO - Epoch [32/50] Batch [51/157] D_Loss: 0.502 G_Loss: 4.788
2025-07-13 11:23:26,272 - INFO - Epoch [32/50] Batch [101/157] D_Loss: 0.248 G_Loss: 3.862
2025-07-13 11:23:26,587 - INFO - Epoch [32/50] Batch [151/157] D_Loss: 0.297 G_Loss: 3.970
2025-07-13 11:23:26,868 - INFO - Epoch 32 - D_Loss: 0.328, G_Loss: 4.403, Time: 2.2s
2025-07-13 11:23:27,829 - INFO - Epoch [33/50] Batch [1/157] D_Loss: 0.348 G_Loss: 5.138
2025-07-13 11:23:28,144 - INFO - Epoch [33/50] Batch [51/157] D_Loss: 0.211 G_Loss: 4.601
2025-07-13 11:23:28,457 - INFO - Epoch [33/50] Batch [101/157] D_Loss: 0.236 G_Loss: 5.468
2025-07-13 11:23:28,771 - INFO - Epoch [33/50] Batch [151/157] D_Loss: 0.325 G_Loss: 4.500
2025-07-13 11:23:29,050 - INFO - Epoch 33 - D_Loss: 0.311, G_Loss: 4.536, Time: 2.2s
2025-07-13 11:23:30,015 - INFO - Epoch [34/50] Batch [1/157] D_Loss: 0.383 G_Loss: 4.953
2025-07-13 11:23:30,329 - INFO - Epoch [34/50] Batch [51/157] D_Loss: 0.253 G_Loss: 4.417
2025-07-13 11:23:30,641 - INFO - Epoch [34/50] Batch [101/157] D_Loss: 0.426 G_Loss: 3.786
2025-07-13 11:23:30,954 - INFO - Epoch [34/50] Batch [151/157] D_Loss: 0.419 G_Loss: 3.220
2025-07-13 11:23:31,232 - INFO - Epoch 34 - D_Loss: 0.341, G_Loss: 4.485, Time: 2.2s
2025-07-13 11:23:32,203 - INFO - Epoch [35/50] Batch [1/157] D_Loss: 0.517 G_Loss: 2.477
2025-07-13 11:23:32,519 - INFO - Epoch [35/50] Batch [51/157] D_Loss: 0.407 G_Loss: 2.256
2025-07-13 11:23:32,833 - INFO - Epoch [35/50] Batch [101/157] D_Loss: 0.437 G_Loss: 3.213
2025-07-13 11:23:33,145 - INFO - Epoch [35/50] Batch [151/157] D_Loss: 0.509 G_Loss: 2.451
2025-07-13 11:23:33,422 - INFO - Epoch 35 - D_Loss: 0.442, G_Loss: 3.479, Time: 2.2s
2025-07-13 11:23:34,375 - INFO - Epoch [36/50] Batch [1/157] D_Loss: 0.686 G_Loss: 2.102
2025-07-13 11:23:34,692 - INFO - Epoch [36/50] Batch [51/157] D_Loss: 0.358 G_Loss: 4.190
2025-07-13 11:23:35,005 - INFO - Epoch [36/50] Batch [101/157] D_Loss: 0.343 G_Loss: 3.795
2025-07-13 11:23:35,317 - INFO - Epoch [36/50] Batch [151/157] D_Loss: 0.439 G_Loss: 2.760
2025-07-13 11:23:35,595 - INFO - Epoch 36 - D_Loss: 0.439, G_Loss: 3.209, Time: 2.2s
2025-07-13 11:23:36,555 - INFO - Epoch [37/50] Batch [1/157] D_Loss: 0.443 G_Loss: 2.539
2025-07-13 11:23:36,871 - INFO - Epoch [37/50] Batch [51/157] D_Loss: 0.595 G_Loss: 2.278
2025-07-13 11:23:37,183 - INFO - Epoch [37/50] Batch [101/157] D_Loss: 0.492 G_Loss: 3.511
2025-07-13 11:23:37,495 - INFO - Epoch [37/50] Batch [151/157] D_Loss: 0.374 G_Loss: 2.574
2025-07-13 11:23:37,775 - INFO - Epoch 37 - D_Loss: 0.443, G_Loss: 3.252, Time: 2.2s
2025-07-13 11:23:38,745 - INFO - Epoch [38/50] Batch [1/157] D_Loss: 0.327 G_Loss: 2.378
2025-07-13 11:23:39,061 - INFO - Epoch [38/50] Batch [51/157] D_Loss: 0.474 G_Loss: 2.944
2025-07-13 11:23:39,374 - INFO - Epoch [38/50] Batch [101/157] D_Loss: 0.268 G_Loss: 3.171
2025-07-13 11:23:39,685 - INFO - Epoch [38/50] Batch [151/157] D_Loss: 0.497 G_Loss: 2.537
2025-07-13 11:23:39,964 - INFO - Epoch 38 - D_Loss: 0.447, G_Loss: 3.178, Time: 2.2s
2025-07-13 11:23:40,915 - INFO - Epoch [39/50] Batch [1/157] D_Loss: 0.484 G_Loss: 2.305
2025-07-13 11:23:41,243 - INFO - Epoch [39/50] Batch [51/157] D_Loss: 0.554 G_Loss: 2.446
2025-07-13 11:23:41,556 - INFO - Epoch [39/50] Batch [101/157] D_Loss: 0.564 G_Loss: 3.132
2025-07-13 11:23:41,868 - INFO - Epoch [39/50] Batch [151/157] D_Loss: 0.445 G_Loss: 3.345
2025-07-13 11:23:42,147 - INFO - Epoch 39 - D_Loss: 0.446, G_Loss: 3.142, Time: 2.2s
2025-07-13 11:23:43,110 - INFO - Epoch [40/50] Batch [1/157] D_Loss: 0.472 G_Loss: 2.334
2025-07-13 11:23:43,426 - INFO - Epoch [40/50] Batch [51/157] D_Loss: 0.500 G_Loss: 2.421
2025-07-13 11:23:43,739 - INFO - Epoch [40/50] Batch [101/157] D_Loss: 0.437 G_Loss: 3.359
2025-07-13 11:23:44,051 - INFO - Epoch [40/50] Batch [151/157] D_Loss: 0.531 G_Loss: 3.274
2025-07-13 11:23:44,331 - INFO - Epoch 40 - D_Loss: 0.428, G_Loss: 3.358, Time: 2.2s
2025-07-13 11:23:45,288 - INFO - Epoch [41/50] Batch [1/157] D_Loss: 0.536 G_Loss: 3.828
2025-07-13 11:23:45,604 - INFO - Epoch [41/50] Batch [51/157] D_Loss: 0.369 G_Loss: 3.995
2025-07-13 11:23:45,917 - INFO - Epoch [41/50] Batch [101/157] D_Loss: 0.412 G_Loss: 2.651
2025-07-13 11:23:46,230 - INFO - Epoch [41/50] Batch [151/157] D_Loss: 0.381 G_Loss: 4.241
2025-07-13 11:23:46,509 - INFO - Epoch 41 - D_Loss: 0.474, G_Loss: 3.121, Time: 2.2s
2025-07-13 11:23:47,491 - INFO - Epoch [42/50] Batch [1/157] D_Loss: 0.312 G_Loss: 3.891
2025-07-13 11:23:47,806 - INFO - Epoch [42/50] Batch [51/157] D_Loss: 0.189 G_Loss: 4.416
2025-07-13 11:23:48,119 - INFO - Epoch [42/50] Batch [101/157] D_Loss: 0.594 G_Loss: 2.705
2025-07-13 11:23:48,432 - INFO - Epoch [42/50] Batch [151/157] D_Loss: 0.336 G_Loss: 3.526
2025-07-13 11:23:48,710 - INFO - Epoch 42 - D_Loss: 0.449, G_Loss: 3.213, Time: 2.1s
2025-07-13 11:23:49,673 - INFO - Epoch [43/50] Batch [1/157] D_Loss: 0.465 G_Loss: 4.197
2025-07-13 11:23:49,990 - INFO - Epoch [43/50] Batch [51/157] D_Loss: 0.288 G_Loss: 3.148
2025-07-13 11:23:50,301 - INFO - Epoch [43/50] Batch [101/157] D_Loss: 0.571 G_Loss: 1.964
2025-07-13 11:23:50,612 - INFO - Epoch [43/50] Batch [151/157] D_Loss: 0.386 G_Loss: 4.316
2025-07-13 11:23:50,892 - INFO - Epoch 43 - D_Loss: 0.452, G_Loss: 3.185, Time: 2.2s
2025-07-13 11:23:51,855 - INFO - Epoch [44/50] Batch [1/157] D_Loss: 0.575 G_Loss: 4.070
2025-07-13 11:23:52,171 - INFO - Epoch [44/50] Batch [51/157] D_Loss: 0.508 G_Loss: 3.639
2025-07-13 11:23:52,484 - INFO - Epoch [44/50] Batch [101/157] D_Loss: 0.321 G_Loss: 4.893
2025-07-13 11:23:52,795 - INFO - Epoch [44/50] Batch [151/157] D_Loss: 0.369 G_Loss: 2.931
2025-07-13 11:23:53,075 - INFO - Epoch 44 - D_Loss: 0.446, G_Loss: 3.338, Time: 2.2s
2025-07-13 11:23:54,018 - INFO - Epoch [45/50] Batch [1/157] D_Loss: 0.476 G_Loss: 3.702
2025-07-13 11:23:54,334 - INFO - Epoch [45/50] Batch [51/157] D_Loss: 0.416 G_Loss: 3.484
2025-07-13 11:23:54,646 - INFO - Epoch [45/50] Batch [101/157] D_Loss: 0.500 G_Loss: 3.784
2025-07-13 11:23:54,957 - INFO - Epoch [45/50] Batch [151/157] D_Loss: 0.517 G_Loss: 3.381
2025-07-13 11:23:55,235 - INFO - Epoch 45 - D_Loss: 0.458, G_Loss: 3.254, Time: 2.2s
2025-07-13 11:23:56,193 - INFO - Epoch [46/50] Batch [1/157] D_Loss: 0.452 G_Loss: 2.595
2025-07-13 11:23:56,523 - INFO - Epoch [46/50] Batch [51/157] D_Loss: 0.549 G_Loss: 2.774
2025-07-13 11:23:56,838 - INFO - Epoch [46/50] Batch [101/157] D_Loss: 0.455 G_Loss: 2.451
2025-07-13 11:23:57,152 - INFO - Epoch [46/50] Batch [151/157] D_Loss: 0.312 G_Loss: 3.342
2025-07-13 11:23:57,430 - INFO - Epoch 46 - D_Loss: 0.480, G_Loss: 3.068, Time: 2.2s
2025-07-13 11:23:58,387 - INFO - Epoch [47/50] Batch [1/157] D_Loss: 0.429 G_Loss: 3.185
2025-07-13 11:23:58,722 - INFO - Epoch [47/50] Batch [51/157] D_Loss: 0.573 G_Loss: 3.059
2025-07-13 11:23:59,038 - INFO - Epoch [47/50] Batch [101/157] D_Loss: 0.666 G_Loss: 2.839
2025-07-13 11:23:59,348 - INFO - Epoch [47/50] Batch [151/157] D_Loss: 0.418 G_Loss: 3.074
2025-07-13 11:23:59,643 - INFO - Epoch 47 - D_Loss: 0.516, G_Loss: 3.013, Time: 2.2s
2025-07-13 11:24:00,604 - INFO - Epoch [48/50] Batch [1/157] D_Loss: 0.447 G_Loss: 2.961
2025-07-13 11:24:00,924 - INFO - Epoch [48/50] Batch [51/157] D_Loss: 0.595 G_Loss: 3.247
2025-07-13 11:24:01,238 - INFO - Epoch [48/50] Batch [101/157] D_Loss: 0.449 G_Loss: 2.994
2025-07-13 11:24:01,545 - INFO - Epoch [48/50] Batch [151/157] D_Loss: 0.427 G_Loss: 3.454
2025-07-13 11:24:01,825 - INFO - Epoch 48 - D_Loss: 0.471, G_Loss: 3.184, Time: 2.2s
2025-07-13 11:24:02,833 - INFO - Epoch [49/50] Batch [1/157] D_Loss: 0.600 G_Loss: 3.144
2025-07-13 11:24:03,148 - INFO - Epoch [49/50] Batch [51/157] D_Loss: 0.230 G_Loss: 3.468
2025-07-13 11:24:03,460 - INFO - Epoch [49/50] Batch [101/157] D_Loss: 0.541 G_Loss: 3.467
2025-07-13 11:24:03,771 - INFO - Epoch [49/50] Batch [151/157] D_Loss: 0.451 G_Loss: 3.256
2025-07-13 11:24:04,048 - INFO - Epoch 49 - D_Loss: 0.445, G_Loss: 3.259, Time: 2.2s
2025-07-13 11:24:05,005 - INFO - Epoch [50/50] Batch [1/157] D_Loss: 0.424 G_Loss: 2.920
2025-07-13 11:24:05,322 - INFO - Epoch [50/50] Batch [51/157] D_Loss: 0.326 G_Loss: 4.424
2025-07-13 11:24:05,639 - INFO - Epoch [50/50] Batch [101/157] D_Loss: 0.404 G_Loss: 2.636
2025-07-13 11:24:05,956 - INFO - Epoch [50/50] Batch [151/157] D_Loss: 0.300 G_Loss: 4.418
2025-07-13 11:24:06,238 - INFO - Epoch 50 - D_Loss: 0.394, G_Loss: 3.880, Time: 2.2s
2025-07-13 11:24:06,238 - INFO - Total time: 113.3s (1.9 min)
2025-07-13 11:24:06,306 - INFO - Lite model saved!