diff --git a/.gitattributes b/.gitattributes
index e8e378375c7164a8c58738ebaa17ee3631da0fd2..3dea02d49d7d72a2e593fb7907f626d35eadca56 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1318,3 +1318,55 @@ dpdm/cifar10_32_eps10.0trainval-2024-10-24-01-44-41/train/samples/iter_92000/sam
 dpdm/cifar10_32_eps10.0trainval-2024-10-24-01-44-41/train/samples/iter_94000/sample.png filter=lfs diff=lfs merge=lfs -text
 dpdm/cifar10_32_eps10.0trainval-2024-10-24-01-44-41/train/samples/iter_96000/sample.png filter=lfs diff=lfs merge=lfs -text
 dpdm/cifar10_32_eps10.0trainval-2024-10-24-01-44-41/train/samples/iter_98000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_10000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_100000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_102000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_104000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_12000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_14000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_16000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_18000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_2000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_20000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_22000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_24000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_26000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_28000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_30000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_32000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_34000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_36000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_38000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_4000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_40000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_42000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_44000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_46000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_48000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_50000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_52000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_54000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_56000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_58000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_6000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_60000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_62000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_64000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_66000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_68000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_70000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_72000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_74000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_76000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_78000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_8000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_80000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_82000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_84000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_86000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_88000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_90000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_92000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_94000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_96000/sample.png filter=lfs diff=lfs merge=lfs -text
+dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_98000/sample.png filter=lfs diff=lfs merge=lfs -text
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/stdout.txt b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/stdout.txt
new file mode 100644
index 0000000000000000000000000000000000000000..859e52d3a2883dfae125d57c017520a265a23029
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/stdout.txt
@@ -0,0 +1,2244 @@
+INFO - utils.py - 2024-10-23 14:01:17,814 - {'setup': {'method': 'dpsgd-diffusion', 'run_type': 'torchmp', 'n_gpus_per_node': 4, 'n_nodes': 1, 'node_rank': 0, 'master_address': '127.0.0.1', 'master_port': 6025, 'omp_n_threads': 8, 'workdir': 'exp/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14', 'local_rank': 0, 'global_rank': 0, 'global_size': 4, 'root_folder': '.'}, 'public_data': {'name': None, 'num_channels': 3, 'resolution': 32, 'n_classes': 1000, 'train_path': 'dataset/imagenet/imagenet_32', 'selective': {'ratio': 1.0}}, 'sensitive_data': {'name': 'cifar10', 'num_channels': 3, 'resolution': 32, 'n_classes': 10, 'train_path': 'dataset/cifar10/train_32.zip', 'test_path': 'dataset/cifar10/test_32.zip', 'fid_stats': 'dataset/cifar10/fid_stats_32.npz', 'train_num': 'val'}, 'model': {'ckpt': None, 'denoiser_name': 'edm', 'denoiser_network': 'song', 'ema_rate': 0.999, 'network': {'image_size': 32, 'num_in_channels': 3, 'num_out_channels': 3, 'label_dim': 10, 'attn_resolutions': [16], 'ch_mult': [2, 4]}, 'sampler': {'type': 'ddim', 'stochastic': False, 'num_steps': 50, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 0.0, 'snapshot_batch_size': 80, 'fid_batch_size': 256}, 'sampler_fid': {'type': 'ddim', 'stochastic': False, 'num_steps': 250, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 0.0}, 'sampler_acc': {'type': 'ddim', 'stochastic': False, 'num_steps': 250, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 0.0}, 'local_rank': 0, 'global_rank': 0, 'global_size': 4, 'fid_stats': 'dataset/cifar10/fid_stats_32.npz'}, 'pretrain': {'log_dir': 'exp/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/pretrain', 'seed': 0, 'batch_size': 64, 'n_epochs': 1, 'log_freq': 100, 'snapshot_freq': 2000, 'snapshot_threshold': 1, 'save_freq': 100000, 'save_threshold': 1, 'fid_freq': 2000, 'fid_samples': 5000, 'fid_threshold': 1, 'label_random': True, 'optim': {'optimizer': 'Adam', 'params': {'lr': 0.0003, 'weight_decay': 0.0}}, 'loss': {'version': 'edm', 'p_mean': -1.2, 'p_std': 1.2, 'n_noise_samples': 1, 'n_classes': 10}}, 'train': {'log_dir': 'exp/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train', 'seed': 0, 'batch_size': 4096, 'n_epochs': 150, 'partly_finetune': False, 'log_freq': 100, 'snapshot_freq': 2000, 'snapshot_threshold': 1, 'save_freq': 100000, 'save_threshold': 1, 'fid_freq': 2000, 'fid_samples': 5000, 'final_fid_samples': 60000, 'fid_threshold': 1, 'gen': False, 'gen_batch_size': 8192, 'optim': {'optimizer': 'Adam', 'params': {'lr': 0.0003, 'weight_decay': 0.0}}, 'loss': {'version': 'edm', 'p_mean': -1.2, 'p_std': 1.2, 'n_noise_samples': 32, 'n_classes': 10}, 'dp': {'sdq': None, 'max_grad_norm': 1.0, 'delta': 1e-05, 'epsilon': 1.0, 'max_physical_batch_size': 8192, 'n_splits': 64}}, 'gen': {'data_num': 60000, 'batch_size': 1000, 'log_dir': 'exp/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/gen'}, 'eval': {'batch_size': 1000}}
+INFO - dataset_loader.py - 2024-10-23 14:01:18,484 - delta is reset as 2.07404851125286e-06
+INFO - dpsgd_diffusion.py - 2024-10-23 14:01:18,686 - Number of trainable parameters in model: 0
+INFO - dpsgd_diffusion.py - 2024-10-23 14:01:18,686 - Number of total epochs: 150
+INFO - dpsgd_diffusion.py - 2024-10-23 14:01:18,686 - Starting training at step 0
+INFO - dpsgd_diffusion.py - 2024-10-23 14:02:28,394 - Loss: 0.8490, step: 100
+INFO - dpsgd_diffusion.py - 2024-10-23 14:03:34,522 - Loss: 0.8583, step: 200
+INFO - dpsgd_diffusion.py - 2024-10-23 14:04:34,714 - Loss: 0.7899, step: 300
+INFO - dpsgd_diffusion.py - 2024-10-23 14:05:33,033 - Loss: 0.8363, step: 400
+INFO - dpsgd_diffusion.py - 2024-10-23 14:06:32,370 - Loss: 0.8244, step: 500
+INFO - dpsgd_diffusion.py - 2024-10-23 14:07:31,682 - Loss: 0.7864, step: 600
+INFO - dpsgd_diffusion.py - 2024-10-23 14:08:29,611 - Loss: 0.7842, step: 700
+INFO - dpsgd_diffusion.py - 2024-10-23 14:08:31,928 - Eps-value after 1 epochs: 0.1393
+INFO - dpsgd_diffusion.py - 2024-10-23 14:09:29,749 - Loss: 0.7694, step: 800
+INFO - dpsgd_diffusion.py - 2024-10-23 14:10:27,818 - Loss: 0.7388, step: 900
+INFO - dpsgd_diffusion.py - 2024-10-23 14:11:28,217 - Loss: 0.7809, step: 1000
+INFO - dpsgd_diffusion.py - 2024-10-23 14:12:25,446 - Loss: 0.7332, step: 1100
+INFO - dpsgd_diffusion.py - 2024-10-23 14:13:24,658 - Loss: 0.7042, step: 1200
+INFO - dpsgd_diffusion.py - 2024-10-23 14:14:22,430 - Loss: 0.7499, step: 1300
+INFO - dpsgd_diffusion.py - 2024-10-23 14:15:21,266 - Loss: 0.7102, step: 1400
+INFO - dpsgd_diffusion.py - 2024-10-23 14:15:27,582 - Eps-value after 2 epochs: 0.1504
+INFO - dpsgd_diffusion.py - 2024-10-23 14:16:21,386 - Loss: 0.6950, step: 1500
+INFO - dpsgd_diffusion.py - 2024-10-23 14:17:20,002 - Loss: 0.7074, step: 1600
+INFO - dpsgd_diffusion.py - 2024-10-23 14:18:18,858 - Loss: 0.6730, step: 1700
+INFO - dpsgd_diffusion.py - 2024-10-23 14:19:16,814 - Loss: 0.6920, step: 1800
+INFO - dpsgd_diffusion.py - 2024-10-23 14:20:16,969 - Loss: 0.6444, step: 1900
+INFO - dpsgd_diffusion.py - 2024-10-23 14:21:14,375 - Loss: 0.6557, step: 2000
+INFO - dpsgd_diffusion.py - 2024-10-23 14:21:14,412 - Saving snapshot checkpoint and sampling single batch at iteration 2000.
+WARNING - image.py - 2024-10-23 14:21:15,114 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 14:21:32,098 - FID at iteration 2000: 368.001270
+INFO - dpsgd_diffusion.py - 2024-10-23 14:22:29,313 - Loss: 0.6501, step: 2100
+INFO - dpsgd_diffusion.py - 2024-10-23 14:22:36,094 - Eps-value after 3 epochs: 0.1615
+INFO - dpsgd_diffusion.py - 2024-10-23 14:23:28,502 - Loss: 0.5773, step: 2200
+INFO - dpsgd_diffusion.py - 2024-10-23 14:24:27,810 - Loss: 0.5884, step: 2300
+INFO - dpsgd_diffusion.py - 2024-10-23 14:25:26,650 - Loss: 0.6097, step: 2400
+INFO - dpsgd_diffusion.py - 2024-10-23 14:26:25,152 - Loss: 0.5745, step: 2500
+INFO - dpsgd_diffusion.py - 2024-10-23 14:27:22,707 - Loss: 0.5709, step: 2600
+INFO - dpsgd_diffusion.py - 2024-10-23 14:28:22,310 - Loss: 0.5832, step: 2700
+INFO - dpsgd_diffusion.py - 2024-10-23 14:29:21,725 - Loss: 0.5161, step: 2800
+INFO - dpsgd_diffusion.py - 2024-10-23 14:29:30,764 - Eps-value after 4 epochs: 0.1726
+INFO - dpsgd_diffusion.py - 2024-10-23 14:30:20,256 - Loss: 0.5100, step: 2900
+INFO - dpsgd_diffusion.py - 2024-10-23 14:31:18,936 - Loss: 0.5523, step: 3000
+INFO - dpsgd_diffusion.py - 2024-10-23 14:32:17,094 - Loss: 0.4744, step: 3100
+INFO - dpsgd_diffusion.py - 2024-10-23 14:33:15,371 - Loss: 0.5002, step: 3200
+INFO - dpsgd_diffusion.py - 2024-10-23 14:34:11,854 - Loss: 0.4874, step: 3300
+INFO - dpsgd_diffusion.py - 2024-10-23 14:35:08,489 - Loss: 0.5023, step: 3400
+INFO - dpsgd_diffusion.py - 2024-10-23 14:36:07,185 - Loss: 0.4801, step: 3500
+INFO - dpsgd_diffusion.py - 2024-10-23 14:36:19,157 - Eps-value after 5 epochs: 0.1836
+INFO - dpsgd_diffusion.py - 2024-10-23 14:37:05,000 - Loss: 0.4811, step: 3600
+INFO - dpsgd_diffusion.py - 2024-10-23 14:38:01,918 - Loss: 0.4612, step: 3700
+INFO - dpsgd_diffusion.py - 2024-10-23 14:38:59,289 - Loss: 0.4994, step: 3800
+INFO - dpsgd_diffusion.py - 2024-10-23 14:39:56,935 - Loss: 0.5118, step: 3900
+INFO - dpsgd_diffusion.py - 2024-10-23 14:40:54,363 - Loss: 0.4289, step: 4000
+INFO - dpsgd_diffusion.py - 2024-10-23 14:40:54,387 - Saving snapshot checkpoint and sampling single batch at iteration 4000.
+WARNING - image.py - 2024-10-23 14:40:54,885 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 14:41:10,430 - FID at iteration 4000: 398.710089
+INFO - dpsgd_diffusion.py - 2024-10-23 14:42:07,029 - Loss: 0.4673, step: 4100
+INFO - dpsgd_diffusion.py - 2024-10-23 14:43:04,510 - Loss: 0.4655, step: 4200
+INFO - dpsgd_diffusion.py - 2024-10-23 14:43:18,246 - Eps-value after 6 epochs: 0.1947
+INFO - dpsgd_diffusion.py - 2024-10-23 14:44:03,657 - Loss: 0.4529, step: 4300
+INFO - dpsgd_diffusion.py - 2024-10-23 14:45:02,622 - Loss: 0.4182, step: 4400
+INFO - dpsgd_diffusion.py - 2024-10-23 14:46:00,409 - Loss: 0.4488, step: 4500
+INFO - dpsgd_diffusion.py - 2024-10-23 14:46:58,761 - Loss: 0.4161, step: 4600
+INFO - dpsgd_diffusion.py - 2024-10-23 14:47:57,042 - Loss: 0.4511, step: 4700
+INFO - dpsgd_diffusion.py - 2024-10-23 14:48:53,711 - Loss: 0.4000, step: 4800
+INFO - dpsgd_diffusion.py - 2024-10-23 14:49:52,563 - Loss: 0.4519, step: 4900
+INFO - dpsgd_diffusion.py - 2024-10-23 14:50:08,377 - Eps-value after 7 epochs: 0.2058
+INFO - dpsgd_diffusion.py - 2024-10-23 14:50:51,806 - Loss: 0.4488, step: 5000
+INFO - dpsgd_diffusion.py - 2024-10-23 14:51:50,786 - Loss: 0.4278, step: 5100
+INFO - dpsgd_diffusion.py - 2024-10-23 14:52:49,428 - Loss: 0.3771, step: 5200
+INFO - dpsgd_diffusion.py - 2024-10-23 14:53:48,170 - Loss: 0.3970, step: 5300
+INFO - dpsgd_diffusion.py - 2024-10-23 14:54:47,763 - Loss: 0.4042, step: 5400
+INFO - dpsgd_diffusion.py - 2024-10-23 14:55:45,731 - Loss: 0.4194, step: 5500
+INFO - dpsgd_diffusion.py - 2024-10-23 14:56:44,148 - Loss: 0.4500, step: 5600
+INFO - dpsgd_diffusion.py - 2024-10-23 14:57:02,318 - Eps-value after 8 epochs: 0.2169
+INFO - dpsgd_diffusion.py - 2024-10-23 14:57:42,137 - Loss: 0.4034, step: 5700
+INFO - dpsgd_diffusion.py - 2024-10-23 14:58:41,742 - Loss: 0.3994, step: 5800
+INFO - dpsgd_diffusion.py - 2024-10-23 14:59:39,457 - Loss: 0.4081, step: 5900
+INFO - dpsgd_diffusion.py - 2024-10-23 15:00:36,950 - Loss: 0.3873, step: 6000
+INFO - dpsgd_diffusion.py - 2024-10-23 15:00:36,963 - Saving snapshot checkpoint and sampling single batch at iteration 6000.
+WARNING - image.py - 2024-10-23 15:00:37,453 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 15:00:52,938 - FID at iteration 6000: 388.584027
+INFO - dpsgd_diffusion.py - 2024-10-23 15:01:49,922 - Loss: 0.4186, step: 6100
+INFO - dpsgd_diffusion.py - 2024-10-23 15:02:47,377 - Loss: 0.3664, step: 6200
+INFO - dpsgd_diffusion.py - 2024-10-23 15:03:45,544 - Loss: 0.4183, step: 6300
+INFO - dpsgd_diffusion.py - 2024-10-23 15:04:05,806 - Eps-value after 9 epochs: 0.2279
+INFO - dpsgd_diffusion.py - 2024-10-23 15:04:42,876 - Loss: 0.3894, step: 6400
+INFO - dpsgd_diffusion.py - 2024-10-23 15:05:41,432 - Loss: 0.3922, step: 6500
+INFO - dpsgd_diffusion.py - 2024-10-23 15:06:37,901 - Loss: 0.3716, step: 6600
+INFO - dpsgd_diffusion.py - 2024-10-23 15:07:37,154 - Loss: 0.3579, step: 6700
+INFO - dpsgd_diffusion.py - 2024-10-23 15:08:36,798 - Loss: 0.3979, step: 6800
+INFO - dpsgd_diffusion.py - 2024-10-23 15:09:35,535 - Loss: 0.3841, step: 6900
+INFO - dpsgd_diffusion.py - 2024-10-23 15:10:34,368 - Loss: 0.3676, step: 7000
+INFO - dpsgd_diffusion.py - 2024-10-23 15:10:57,856 - Eps-value after 10 epochs: 0.2390
+INFO - dpsgd_diffusion.py - 2024-10-23 15:11:31,883 - Loss: 0.3605, step: 7100
+INFO - dpsgd_diffusion.py - 2024-10-23 15:12:30,496 - Loss: 0.3726, step: 7200
+INFO - dpsgd_diffusion.py - 2024-10-23 15:13:27,973 - Loss: 0.3876, step: 7300
+INFO - dpsgd_diffusion.py - 2024-10-23 15:14:25,254 - Loss: 0.3891, step: 7400
+INFO - dpsgd_diffusion.py - 2024-10-23 15:15:22,195 - Loss: 0.3954, step: 7500
+INFO - dpsgd_diffusion.py - 2024-10-23 15:16:22,181 - Loss: 0.3602, step: 7600
+INFO - dpsgd_diffusion.py - 2024-10-23 15:17:21,792 - Loss: 0.3798, step: 7700
+INFO - dpsgd_diffusion.py - 2024-10-23 15:17:47,711 - Eps-value after 11 epochs: 0.2501
+INFO - dpsgd_diffusion.py - 2024-10-23 15:18:20,963 - Loss: 0.3658, step: 7800
+INFO - dpsgd_diffusion.py - 2024-10-23 15:19:19,525 - Loss: 0.3824, step: 7900
+INFO - dpsgd_diffusion.py - 2024-10-23 15:20:17,972 - Loss: 0.3745, step: 8000
+INFO - dpsgd_diffusion.py - 2024-10-23 15:20:17,977 - Saving snapshot checkpoint and sampling single batch at iteration 8000.
+WARNING - image.py - 2024-10-23 15:20:18,479 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 15:20:34,097 - FID at iteration 8000: 338.106156
+INFO - dpsgd_diffusion.py - 2024-10-23 15:21:32,583 - Loss: 0.3784, step: 8100
+INFO - dpsgd_diffusion.py - 2024-10-23 15:22:29,681 - Loss: 0.3857, step: 8200
+INFO - dpsgd_diffusion.py - 2024-10-23 15:23:27,747 - Loss: 0.3707, step: 8300
+INFO - dpsgd_diffusion.py - 2024-10-23 15:24:25,127 - Loss: 0.3724, step: 8400
+INFO - dpsgd_diffusion.py - 2024-10-23 15:24:52,889 - Eps-value after 12 epochs: 0.2612
+INFO - dpsgd_diffusion.py - 2024-10-23 15:25:23,658 - Loss: 0.3946, step: 8500
+INFO - dpsgd_diffusion.py - 2024-10-23 15:26:21,912 - Loss: 0.3675, step: 8600
+INFO - dpsgd_diffusion.py - 2024-10-23 15:27:18,453 - Loss: 0.3672, step: 8700
+INFO - dpsgd_diffusion.py - 2024-10-23 15:28:16,800 - Loss: 0.3458, step: 8800
+INFO - dpsgd_diffusion.py - 2024-10-23 15:29:14,962 - Loss: 0.3710, step: 8900
+INFO - dpsgd_diffusion.py - 2024-10-23 15:30:13,699 - Loss: 0.3670, step: 9000
+INFO - dpsgd_diffusion.py - 2024-10-23 15:31:12,092 - Loss: 0.3902, step: 9100
+INFO - dpsgd_diffusion.py - 2024-10-23 15:31:42,110 - Eps-value after 13 epochs: 0.2723
+INFO - dpsgd_diffusion.py - 2024-10-23 15:32:10,324 - Loss: 0.3699, step: 9200
+INFO - dpsgd_diffusion.py - 2024-10-23 15:33:08,698 - Loss: 0.3616, step: 9300
+INFO - dpsgd_diffusion.py - 2024-10-23 15:34:05,894 - Loss: 0.3760, step: 9400
+INFO - dpsgd_diffusion.py - 2024-10-23 15:35:02,709 - Loss: 0.3423, step: 9500
+INFO - dpsgd_diffusion.py - 2024-10-23 15:36:00,665 - Loss: 0.3538, step: 9600
+INFO - dpsgd_diffusion.py - 2024-10-23 15:37:00,237 - Loss: 0.3469, step: 9700
+INFO - dpsgd_diffusion.py - 2024-10-23 15:37:56,632 - Loss: 0.3266, step: 9800
+INFO - dpsgd_diffusion.py - 2024-10-23 15:38:29,029 - Eps-value after 14 epochs: 0.2831
+INFO - dpsgd_diffusion.py - 2024-10-23 15:38:55,749 - Loss: 0.3508, step: 9900
+INFO - dpsgd_diffusion.py - 2024-10-23 15:39:54,255 - Loss: 0.3778, step: 10000
+INFO - dpsgd_diffusion.py - 2024-10-23 15:39:54,313 - Saving snapshot checkpoint and sampling single batch at iteration 10000.
+WARNING - image.py - 2024-10-23 15:39:54,869 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 15:40:10,321 - FID at iteration 10000: 318.931313
+INFO - dpsgd_diffusion.py - 2024-10-23 15:41:09,672 - Loss: 0.3128, step: 10100
+INFO - dpsgd_diffusion.py - 2024-10-23 15:42:07,242 - Loss: 0.3597, step: 10200
+INFO - dpsgd_diffusion.py - 2024-10-23 15:43:04,525 - Loss: 0.3904, step: 10300
+INFO - dpsgd_diffusion.py - 2024-10-23 15:44:02,719 - Loss: 0.3395, step: 10400
+INFO - dpsgd_diffusion.py - 2024-10-23 15:44:59,734 - Loss: 0.3574, step: 10500
+INFO - dpsgd_diffusion.py - 2024-10-23 15:45:34,324 - Eps-value after 15 epochs: 0.2936
+INFO - dpsgd_diffusion.py - 2024-10-23 15:45:58,563 - Loss: 0.3325, step: 10600
+INFO - dpsgd_diffusion.py - 2024-10-23 15:46:57,576 - Loss: 0.3410, step: 10700
+INFO - dpsgd_diffusion.py - 2024-10-23 15:47:57,228 - Loss: 0.3336, step: 10800
+INFO - dpsgd_diffusion.py - 2024-10-23 15:48:55,382 - Loss: 0.3386, step: 10900
+INFO - dpsgd_diffusion.py - 2024-10-23 15:49:53,290 - Loss: 0.3542, step: 11000
+INFO - dpsgd_diffusion.py - 2024-10-23 15:50:50,191 - Loss: 0.3458, step: 11100
+INFO - dpsgd_diffusion.py - 2024-10-23 15:51:48,367 - Loss: 0.3408, step: 11200
+INFO - dpsgd_diffusion.py - 2024-10-23 15:52:25,021 - Eps-value after 16 epochs: 0.3038
+INFO - dpsgd_diffusion.py - 2024-10-23 15:52:45,664 - Loss: 0.3308, step: 11300
+INFO - dpsgd_diffusion.py - 2024-10-23 15:53:42,435 - Loss: 0.3614, step: 11400
+INFO - dpsgd_diffusion.py - 2024-10-23 15:54:41,496 - Loss: 0.3369, step: 11500
+INFO - dpsgd_diffusion.py - 2024-10-23 15:55:39,432 - Loss: 0.3545, step: 11600
+INFO - dpsgd_diffusion.py - 2024-10-23 15:56:37,419 - Loss: 0.3400, step: 11700
+INFO - dpsgd_diffusion.py - 2024-10-23 15:57:34,699 - Loss: 0.3295, step: 11800
+INFO - dpsgd_diffusion.py - 2024-10-23 15:58:31,677 - Loss: 0.3384, step: 11900
+INFO - dpsgd_diffusion.py - 2024-10-23 15:59:10,725 - Eps-value after 17 epochs: 0.3137
+INFO - dpsgd_diffusion.py - 2024-10-23 15:59:30,038 - Loss: 0.3321, step: 12000
+INFO - dpsgd_diffusion.py - 2024-10-23 15:59:30,073 - Saving snapshot checkpoint and sampling single batch at iteration 12000.
+WARNING - image.py - 2024-10-23 15:59:30,584 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 15:59:46,177 - FID at iteration 12000: 319.289591
+INFO - dpsgd_diffusion.py - 2024-10-23 16:00:42,878 - Loss: 0.3389, step: 12100
+INFO - dpsgd_diffusion.py - 2024-10-23 16:01:40,781 - Loss: 0.3508, step: 12200
+INFO - dpsgd_diffusion.py - 2024-10-23 16:02:37,676 - Loss: 0.3361, step: 12300
+INFO - dpsgd_diffusion.py - 2024-10-23 16:03:34,747 - Loss: 0.3389, step: 12400
+INFO - dpsgd_diffusion.py - 2024-10-23 16:04:32,898 - Loss: 0.3641, step: 12500
+INFO - dpsgd_diffusion.py - 2024-10-23 16:05:30,751 - Loss: 0.3683, step: 12600
+INFO - dpsgd_diffusion.py - 2024-10-23 16:06:12,132 - Eps-value after 18 epochs: 0.3234
+INFO - dpsgd_diffusion.py - 2024-10-23 16:06:29,095 - Loss: 0.3442, step: 12700
+INFO - dpsgd_diffusion.py - 2024-10-23 16:07:28,576 - Loss: 0.3526, step: 12800
+INFO - dpsgd_diffusion.py - 2024-10-23 16:08:27,989 - Loss: 0.3043, step: 12900
+INFO - dpsgd_diffusion.py - 2024-10-23 16:09:25,803 - Loss: 0.3247, step: 13000
+INFO - dpsgd_diffusion.py - 2024-10-23 16:10:22,956 - Loss: 0.3309, step: 13100
+INFO - dpsgd_diffusion.py - 2024-10-23 16:11:20,089 - Loss: 0.3164, step: 13200
+INFO - dpsgd_diffusion.py - 2024-10-23 16:12:18,419 - Loss: 0.3150, step: 13300
+INFO - dpsgd_diffusion.py - 2024-10-23 16:13:02,730 - Eps-value after 19 epochs: 0.3328
+INFO - dpsgd_diffusion.py - 2024-10-23 16:13:16,346 - Loss: 0.3393, step: 13400
+INFO - dpsgd_diffusion.py - 2024-10-23 16:14:14,544 - Loss: 0.3115, step: 13500
+INFO - dpsgd_diffusion.py - 2024-10-23 16:15:13,388 - Loss: 0.3096, step: 13600
+INFO - dpsgd_diffusion.py - 2024-10-23 16:16:10,781 - Loss: 0.2940, step: 13700
+INFO - dpsgd_diffusion.py - 2024-10-23 16:17:08,394 - Loss: 0.3206, step: 13800
+INFO - dpsgd_diffusion.py - 2024-10-23 16:18:05,885 - Loss: 0.2940, step: 13900
+INFO - dpsgd_diffusion.py - 2024-10-23 16:19:05,384 - Loss: 0.3098, step: 14000
+INFO - dpsgd_diffusion.py - 2024-10-23 16:19:05,410 - Saving snapshot checkpoint and sampling single batch at iteration 14000.
+WARNING - image.py - 2024-10-23 16:19:05,921 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 16:19:21,418 - FID at iteration 14000: 317.893454
+INFO - dpsgd_diffusion.py - 2024-10-23 16:20:06,981 - Eps-value after 20 epochs: 0.3419
+INFO - dpsgd_diffusion.py - 2024-10-23 16:20:18,810 - Loss: 0.3283, step: 14100
+INFO - dpsgd_diffusion.py - 2024-10-23 16:21:17,594 - Loss: 0.3129, step: 14200
+INFO - dpsgd_diffusion.py - 2024-10-23 16:22:15,816 - Loss: 0.3109, step: 14300
+INFO - dpsgd_diffusion.py - 2024-10-23 16:23:15,003 - Loss: 0.3269, step: 14400
+INFO - dpsgd_diffusion.py - 2024-10-23 16:24:13,479 - Loss: 0.3118, step: 14500
+INFO - dpsgd_diffusion.py - 2024-10-23 16:25:10,785 - Loss: 0.3276, step: 14600
+INFO - dpsgd_diffusion.py - 2024-10-23 16:26:08,865 - Loss: 0.3082, step: 14700
+INFO - dpsgd_diffusion.py - 2024-10-23 16:26:55,887 - Eps-value after 21 epochs: 0.3509
+INFO - dpsgd_diffusion.py - 2024-10-23 16:27:05,548 - Loss: 0.3165, step: 14800
+INFO - dpsgd_diffusion.py - 2024-10-23 16:28:03,133 - Loss: 0.3205, step: 14900
+INFO - dpsgd_diffusion.py - 2024-10-23 16:29:01,658 - Loss: 0.3272, step: 15000
+INFO - dpsgd_diffusion.py - 2024-10-23 16:29:59,014 - Loss: 0.3011, step: 15100
+INFO - dpsgd_diffusion.py - 2024-10-23 16:30:57,046 - Loss: 0.3224, step: 15200
+INFO - dpsgd_diffusion.py - 2024-10-23 16:31:54,446 - Loss: 0.2992, step: 15300
+INFO - dpsgd_diffusion.py - 2024-10-23 16:32:52,572 - Loss: 0.3495, step: 15400
+INFO - dpsgd_diffusion.py - 2024-10-23 16:33:43,551 - Eps-value after 22 epochs: 0.3596
+INFO - dpsgd_diffusion.py - 2024-10-23 16:33:50,663 - Loss: 0.3131, step: 15500
+INFO - dpsgd_diffusion.py - 2024-10-23 16:34:47,289 - Loss: 0.3152, step: 15600
+INFO - dpsgd_diffusion.py - 2024-10-23 16:35:47,237 - Loss: 0.2989, step: 15700
+INFO - dpsgd_diffusion.py - 2024-10-23 16:36:46,035 - Loss: 0.3240, step: 15800
+INFO - dpsgd_diffusion.py - 2024-10-23 16:37:44,725 - Loss: 0.3209, step: 15900
+INFO - dpsgd_diffusion.py - 2024-10-23 16:38:41,882 - Loss: 0.3060, step: 16000
+INFO - dpsgd_diffusion.py - 2024-10-23 16:38:41,887 - Saving snapshot checkpoint and sampling single batch at iteration 16000.
+WARNING - image.py - 2024-10-23 16:38:42,403 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 16:38:57,928 - FID at iteration 16000: 312.640466
+INFO - dpsgd_diffusion.py - 2024-10-23 16:39:57,425 - Loss: 0.3168, step: 16100
+INFO - dpsgd_diffusion.py - 2024-10-23 16:40:52,092 - Eps-value after 23 epochs: 0.3682
+INFO - dpsgd_diffusion.py - 2024-10-23 16:40:56,791 - Loss: 0.3397, step: 16200
+INFO - dpsgd_diffusion.py - 2024-10-23 16:41:55,931 - Loss: 0.3020, step: 16300
+INFO - dpsgd_diffusion.py - 2024-10-23 16:42:53,620 - Loss: 0.3177, step: 16400
+INFO - dpsgd_diffusion.py - 2024-10-23 16:43:51,454 - Loss: 0.2948, step: 16500
+INFO - dpsgd_diffusion.py - 2024-10-23 16:44:49,532 - Loss: 0.2807, step: 16600
+INFO - dpsgd_diffusion.py - 2024-10-23 16:45:47,492 - Loss: 0.3484, step: 16700
+INFO - dpsgd_diffusion.py - 2024-10-23 16:46:44,241 - Loss: 0.3201, step: 16800
+INFO - dpsgd_diffusion.py - 2024-10-23 16:47:40,581 - Eps-value after 24 epochs: 0.3766
+INFO - dpsgd_diffusion.py - 2024-10-23 16:47:42,940 - Loss: 0.3132, step: 16900
+INFO - dpsgd_diffusion.py - 2024-10-23 16:48:42,413 - Loss: 0.3157, step: 17000
+INFO - dpsgd_diffusion.py - 2024-10-23 16:49:39,640 - Loss: 0.3021, step: 17100
+INFO - dpsgd_diffusion.py - 2024-10-23 16:50:38,106 - Loss: 0.3376, step: 17200
+INFO - dpsgd_diffusion.py - 2024-10-23 16:51:35,009 - Loss: 0.3094, step: 17300
+INFO - dpsgd_diffusion.py - 2024-10-23 16:52:32,205 - Loss: 0.3030, step: 17400
+INFO - dpsgd_diffusion.py - 2024-10-23 16:53:30,998 - Loss: 0.2960, step: 17500
+INFO - dpsgd_diffusion.py - 2024-10-23 16:54:29,220 - Loss: 0.3159, step: 17600
+INFO - dpsgd_diffusion.py - 2024-10-23 16:54:29,230 - Eps-value after 25 epochs: 0.3848
+INFO - dpsgd_diffusion.py - 2024-10-23 16:55:27,958 - Loss: 0.2907, step: 17700
+INFO - dpsgd_diffusion.py - 2024-10-23 16:56:26,179 - Loss: 0.3147, step: 17800
+INFO - dpsgd_diffusion.py - 2024-10-23 16:57:23,610 - Loss: 0.3108, step: 17900
+INFO - dpsgd_diffusion.py - 2024-10-23 16:58:21,622 - Loss: 0.3259, step: 18000
+INFO - dpsgd_diffusion.py - 2024-10-23 16:58:21,645 - Saving snapshot checkpoint and sampling single batch at iteration 18000.
+WARNING - image.py - 2024-10-23 16:58:22,129 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 16:58:37,578 - FID at iteration 18000: 306.159236
+INFO - dpsgd_diffusion.py - 2024-10-23 16:59:34,515 - Loss: 0.3054, step: 18100
+INFO - dpsgd_diffusion.py - 2024-10-23 17:00:32,141 - Loss: 0.2998, step: 18200
+INFO - dpsgd_diffusion.py - 2024-10-23 17:01:29,029 - Loss: 0.3158, step: 18300
+INFO - dpsgd_diffusion.py - 2024-10-23 17:01:31,295 - Eps-value after 26 epochs: 0.3929
+INFO - dpsgd_diffusion.py - 2024-10-23 17:02:27,991 - Loss: 0.3228, step: 18400
+INFO - dpsgd_diffusion.py - 2024-10-23 17:03:27,537 - Loss: 0.3113, step: 18500
+INFO - dpsgd_diffusion.py - 2024-10-23 17:04:26,627 - Loss: 0.2971, step: 18600
+INFO - dpsgd_diffusion.py - 2024-10-23 17:05:26,396 - Loss: 0.3310, step: 18700
+INFO - dpsgd_diffusion.py - 2024-10-23 17:06:24,077 - Loss: 0.3023, step: 18800
+INFO - dpsgd_diffusion.py - 2024-10-23 17:07:21,241 - Loss: 0.2791, step: 18900
+INFO - dpsgd_diffusion.py - 2024-10-23 17:08:18,378 - Loss: 0.3001, step: 19000
+INFO - dpsgd_diffusion.py - 2024-10-23 17:08:22,961 - Eps-value after 27 epochs: 0.4008
+INFO - dpsgd_diffusion.py - 2024-10-23 17:09:15,459 - Loss: 0.3205, step: 19100
+INFO - dpsgd_diffusion.py - 2024-10-23 17:10:13,866 - Loss: 0.3085, step: 19200
+INFO - dpsgd_diffusion.py - 2024-10-23 17:11:11,438 - Loss: 0.2813, step: 19300
+INFO - dpsgd_diffusion.py - 2024-10-23 17:12:09,518 - Loss: 0.2971, step: 19400
+INFO - dpsgd_diffusion.py - 2024-10-23 17:13:07,840 - Loss: 0.3023, step: 19500
+INFO - dpsgd_diffusion.py - 2024-10-23 17:14:06,967 - Loss: 0.3144, step: 19600
+INFO - dpsgd_diffusion.py - 2024-10-23 17:15:05,500 - Loss: 0.3163, step: 19700
+INFO - dpsgd_diffusion.py - 2024-10-23 17:15:12,344 - Eps-value after 28 epochs: 0.4087
+INFO - dpsgd_diffusion.py - 2024-10-23 17:16:03,790 - Loss: 0.3043, step: 19800
+INFO - dpsgd_diffusion.py - 2024-10-23 17:17:01,497 - Loss: 0.3342, step: 19900
+INFO - dpsgd_diffusion.py - 2024-10-23 17:17:59,661 - Loss: 0.3055, step: 20000
+INFO - dpsgd_diffusion.py - 2024-10-23 17:17:59,684 - Saving snapshot checkpoint and sampling single batch at iteration 20000.
+WARNING - image.py - 2024-10-23 17:18:00,165 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 17:18:15,593 - FID at iteration 20000: 301.319119
+INFO - dpsgd_diffusion.py - 2024-10-23 17:19:13,966 - Loss: 0.2637, step: 20100
+INFO - dpsgd_diffusion.py - 2024-10-23 17:20:13,569 - Loss: 0.3372, step: 20200
+INFO - dpsgd_diffusion.py - 2024-10-23 17:21:11,973 - Loss: 0.3297, step: 20300
+INFO - dpsgd_diffusion.py - 2024-10-23 17:22:09,599 - Loss: 0.3440, step: 20400
+INFO - dpsgd_diffusion.py - 2024-10-23 17:22:18,787 - Eps-value after 29 epochs: 0.4163
+INFO - dpsgd_diffusion.py - 2024-10-23 17:23:08,336 - Loss: 0.3278, step: 20500
+INFO - dpsgd_diffusion.py - 2024-10-23 17:24:07,423 - Loss: 0.3118, step: 20600
+INFO - dpsgd_diffusion.py - 2024-10-23 17:25:06,430 - Loss: 0.2979, step: 20700
+INFO - dpsgd_diffusion.py - 2024-10-23 17:26:06,102 - Loss: 0.3081, step: 20800
+INFO - dpsgd_diffusion.py - 2024-10-23 17:27:05,477 - Loss: 0.3106, step: 20900
+INFO - dpsgd_diffusion.py - 2024-10-23 17:28:03,580 - Loss: 0.3146, step: 21000
+INFO - dpsgd_diffusion.py - 2024-10-23 17:29:02,058 - Loss: 0.3043, step: 21100
+INFO - dpsgd_diffusion.py - 2024-10-23 17:29:13,490 - Eps-value after 30 epochs: 0.4239
+INFO - dpsgd_diffusion.py - 2024-10-23 17:29:59,750 - Loss: 0.2946, step: 21200
+INFO - dpsgd_diffusion.py - 2024-10-23 17:30:57,456 - Loss: 0.3011, step: 21300
+INFO - dpsgd_diffusion.py - 2024-10-23 17:31:57,053 - Loss: 0.2653, step: 21400
+INFO - dpsgd_diffusion.py - 2024-10-23 17:32:55,683 - Loss: 0.2844, step: 21500
+INFO - dpsgd_diffusion.py - 2024-10-23 17:33:54,374 - Loss: 0.3530, step: 21600
+INFO - dpsgd_diffusion.py - 2024-10-23 17:34:52,434 - Loss: 0.3187, step: 21700
+INFO - dpsgd_diffusion.py - 2024-10-23 17:35:50,386 - Loss: 0.3325, step: 21800
+INFO - dpsgd_diffusion.py - 2024-10-23 17:36:04,121 - Eps-value after 31 epochs: 0.4313
+INFO - dpsgd_diffusion.py - 2024-10-23 17:36:47,651 - Loss: 0.3238, step: 21900
+INFO - dpsgd_diffusion.py - 2024-10-23 17:37:45,055 - Loss: 0.3033, step: 22000
+INFO - dpsgd_diffusion.py - 2024-10-23 17:37:45,080 - Saving snapshot checkpoint and sampling single batch at iteration 22000.
+WARNING - image.py - 2024-10-23 17:37:45,559 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 17:38:00,953 - FID at iteration 22000: 296.303427
+INFO - dpsgd_diffusion.py - 2024-10-23 17:39:00,272 - Loss: 0.2926, step: 22100
+INFO - dpsgd_diffusion.py - 2024-10-23 17:39:59,406 - Loss: 0.3047, step: 22200
+INFO - dpsgd_diffusion.py - 2024-10-23 17:40:57,707 - Loss: 0.3019, step: 22300
+INFO - dpsgd_diffusion.py - 2024-10-23 17:41:55,484 - Loss: 0.3327, step: 22400
+INFO - dpsgd_diffusion.py - 2024-10-23 17:42:54,540 - Loss: 0.2869, step: 22500
+INFO - dpsgd_diffusion.py - 2024-10-23 17:43:10,293 - Eps-value after 32 epochs: 0.4387
+INFO - dpsgd_diffusion.py - 2024-10-23 17:43:51,951 - Loss: 0.3205, step: 22600
+INFO - dpsgd_diffusion.py - 2024-10-23 17:44:51,616 - Loss: 0.3072, step: 22700
+INFO - dpsgd_diffusion.py - 2024-10-23 17:45:50,123 - Loss: 0.3010, step: 22800
+INFO - dpsgd_diffusion.py - 2024-10-23 17:46:47,561 - Loss: 0.2863, step: 22900
+INFO - dpsgd_diffusion.py - 2024-10-23 17:47:46,310 - Loss: 0.2957, step: 23000
+INFO - dpsgd_diffusion.py - 2024-10-23 17:48:44,667 - Loss: 0.3073, step: 23100
+INFO - dpsgd_diffusion.py - 2024-10-23 17:49:42,227 - Loss: 0.2879, step: 23200
+INFO - dpsgd_diffusion.py - 2024-10-23 17:50:00,983 - Eps-value after 33 epochs: 0.4459
+INFO - dpsgd_diffusion.py - 2024-10-23 17:50:40,519 - Loss: 0.3140, step: 23300
+INFO - dpsgd_diffusion.py - 2024-10-23 17:51:38,172 - Loss: 0.2922, step: 23400
+INFO - dpsgd_diffusion.py - 2024-10-23 17:52:36,981 - Loss: 0.2917, step: 23500
+INFO - dpsgd_diffusion.py - 2024-10-23 17:53:36,204 - Loss: 0.3273, step: 23600
+INFO - dpsgd_diffusion.py - 2024-10-23 17:54:34,996 - Loss: 0.2908, step: 23700
+INFO - dpsgd_diffusion.py - 2024-10-23 17:55:33,858 - Loss: 0.2895, step: 23800
+INFO - dpsgd_diffusion.py - 2024-10-23 17:56:32,461 - Loss: 0.2834, step: 23900
+INFO - dpsgd_diffusion.py - 2024-10-23 17:56:52,781 - Eps-value after 34 epochs: 0.4530
+INFO - dpsgd_diffusion.py - 2024-10-23 17:57:29,552 - Loss: 0.3197, step: 24000
+INFO - dpsgd_diffusion.py - 2024-10-23 17:57:29,558 - Saving snapshot checkpoint and sampling single batch at iteration 24000.
+WARNING - image.py - 2024-10-23 17:57:30,088 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 17:57:45,471 - FID at iteration 24000: 292.663283
+INFO - dpsgd_diffusion.py - 2024-10-23 17:58:43,663 - Loss: 0.2821, step: 24100
+INFO - dpsgd_diffusion.py - 2024-10-23 17:59:41,656 - Loss: 0.3058, step: 24200
+INFO - dpsgd_diffusion.py - 2024-10-23 18:00:40,983 - Loss: 0.3101, step: 24300
+INFO - dpsgd_diffusion.py - 2024-10-23 18:01:39,552 - Loss: 0.2866, step: 24400
+INFO - dpsgd_diffusion.py - 2024-10-23 18:02:38,171 - Loss: 0.3036, step: 24500
+INFO - dpsgd_diffusion.py - 2024-10-23 18:03:37,822 - Loss: 0.3027, step: 24600
+INFO - dpsgd_diffusion.py - 2024-10-23 18:04:00,130 - Eps-value after 35 epochs: 0.4600
+INFO - dpsgd_diffusion.py - 2024-10-23 18:04:34,992 - Loss: 0.3190, step: 24700
+INFO - dpsgd_diffusion.py - 2024-10-23 18:05:33,012 - Loss: 0.2579, step: 24800
+INFO - dpsgd_diffusion.py - 2024-10-23 18:06:31,075 - Loss: 0.2759, step: 24900
+INFO - dpsgd_diffusion.py - 2024-10-23 18:07:28,919 - Loss: 0.3025, step: 25000
+INFO - dpsgd_diffusion.py - 2024-10-23 18:08:26,747 - Loss: 0.3106, step: 25100
+INFO - dpsgd_diffusion.py - 2024-10-23 18:09:24,989 - Loss: 0.2857, step: 25200
+INFO - dpsgd_diffusion.py - 2024-10-23 18:10:22,830 - Loss: 0.3121, step: 25300
+INFO - dpsgd_diffusion.py - 2024-10-23 18:10:47,673 - Eps-value after 36 epochs: 0.4670
+INFO - dpsgd_diffusion.py - 2024-10-23 18:11:20,015 - Loss: 0.2871, step: 25400
+INFO - dpsgd_diffusion.py - 2024-10-23 18:12:18,845 - Loss: 0.2899, step: 25500
+INFO - dpsgd_diffusion.py - 2024-10-23 18:13:16,045 - Loss: 0.3039, step: 25600
+INFO - dpsgd_diffusion.py - 2024-10-23 18:14:13,357 - Loss: 0.3088, step: 25700
+INFO - dpsgd_diffusion.py - 2024-10-23 18:15:10,505 - Loss: 0.2852, step: 25800
+INFO - dpsgd_diffusion.py - 2024-10-23 18:16:09,064 - Loss: 0.3184, step: 25900
+INFO - dpsgd_diffusion.py - 2024-10-23 18:17:06,990 - Loss: 0.3052, step: 26000
+INFO - dpsgd_diffusion.py - 2024-10-23 18:17:07,008 - Saving snapshot checkpoint and sampling single batch at iteration 26000.
+WARNING - image.py - 2024-10-23 18:17:07,523 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 18:17:23,135 - FID at iteration 26000: 290.774176
+INFO - dpsgd_diffusion.py - 2024-10-23 18:17:51,622 - Eps-value after 37 epochs: 0.4738
+INFO - dpsgd_diffusion.py - 2024-10-23 18:18:21,999 - Loss: 0.3070, step: 26100
+INFO - dpsgd_diffusion.py - 2024-10-23 18:19:20,541 - Loss: 0.2973, step: 26200
+INFO - dpsgd_diffusion.py - 2024-10-23 18:20:18,414 - Loss: 0.3105, step: 26300
+INFO - dpsgd_diffusion.py - 2024-10-23 18:21:16,748 - Loss: 0.3162, step: 26400
+INFO - dpsgd_diffusion.py - 2024-10-23 18:22:13,059 - Loss: 0.2967, step: 26500
+INFO - dpsgd_diffusion.py - 2024-10-23 18:23:11,387 - Loss: 0.3220, step: 26600
+INFO - dpsgd_diffusion.py - 2024-10-23 18:24:10,513 - Loss: 0.2994, step: 26700
+INFO - dpsgd_diffusion.py - 2024-10-23 18:24:40,253 - Eps-value after 38 epochs: 0.4806
+INFO - dpsgd_diffusion.py - 2024-10-23 18:25:07,786 - Loss: 0.2981, step: 26800
+INFO - dpsgd_diffusion.py - 2024-10-23 18:26:05,890 - Loss: 0.3025, step: 26900
+INFO - dpsgd_diffusion.py - 2024-10-23 18:27:02,756 - Loss: 0.2966, step: 27000
+INFO - dpsgd_diffusion.py - 2024-10-23 18:28:01,226 - Loss: 0.2839, step: 27100
+INFO - dpsgd_diffusion.py - 2024-10-23 18:29:00,388 - Loss: 0.2833, step: 27200
+INFO - dpsgd_diffusion.py - 2024-10-23 18:29:56,548 - Loss: 0.2980, step: 27300
+INFO - dpsgd_diffusion.py - 2024-10-23 18:30:54,143 - Loss: 0.3058, step: 27400
+INFO - dpsgd_diffusion.py - 2024-10-23 18:31:26,690 - Eps-value after 39 epochs: 0.4872
+INFO - dpsgd_diffusion.py - 2024-10-23 18:31:52,088 - Loss: 0.3195, step: 27500
+INFO - dpsgd_diffusion.py - 2024-10-23 18:32:48,634 - Loss: 0.2849, step: 27600
+INFO - dpsgd_diffusion.py - 2024-10-23 18:33:48,889 - Loss: 0.2957, step: 27700
+INFO - dpsgd_diffusion.py - 2024-10-23 18:34:47,609 - Loss: 0.2997, step: 27800
+INFO - dpsgd_diffusion.py - 2024-10-23 18:35:44,246 - Loss: 0.3164, step: 27900
+INFO - dpsgd_diffusion.py - 2024-10-23 18:36:40,891 - Loss: 0.2913, step: 28000
+INFO - dpsgd_diffusion.py - 2024-10-23 18:36:40,894 - Saving snapshot checkpoint and sampling single batch at iteration 28000.
+WARNING - image.py - 2024-10-23 18:36:41,413 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 18:36:56,802 - FID at iteration 28000: 286.895132
+INFO - dpsgd_diffusion.py - 2024-10-23 18:37:55,588 - Loss: 0.3214, step: 28100
+INFO - dpsgd_diffusion.py - 2024-10-23 18:38:31,212 - Eps-value after 40 epochs: 0.4938
+INFO - dpsgd_diffusion.py - 2024-10-23 18:38:54,595 - Loss: 0.2917, step: 28200
+INFO - dpsgd_diffusion.py - 2024-10-23 18:39:53,238 - Loss: 0.2617, step: 28300
+INFO - dpsgd_diffusion.py - 2024-10-23 18:40:50,346 - Loss: 0.2762, step: 28400
+INFO - dpsgd_diffusion.py - 2024-10-23 18:41:48,395 - Loss: 0.2682, step: 28500
+INFO - dpsgd_diffusion.py - 2024-10-23 18:42:45,804 - Loss: 0.2968, step: 28600
+INFO - dpsgd_diffusion.py - 2024-10-23 18:43:43,180 - Loss: 0.2838, step: 28700
+INFO - dpsgd_diffusion.py - 2024-10-23 18:44:40,840 - Loss: 0.2620, step: 28800
+INFO - dpsgd_diffusion.py - 2024-10-23 18:45:18,994 - Eps-value after 41 epochs: 0.5003
+INFO - dpsgd_diffusion.py - 2024-10-23 18:45:40,707 - Loss: 0.2871, step: 28900
+INFO - dpsgd_diffusion.py - 2024-10-23 18:46:39,515 - Loss: 0.2960, step: 29000
+INFO - dpsgd_diffusion.py - 2024-10-23 18:47:38,494 - Loss: 0.2901, step: 29100
+INFO - dpsgd_diffusion.py - 2024-10-23 18:48:36,346 - Loss: 0.2977, step: 29200
+INFO - dpsgd_diffusion.py - 2024-10-23 18:49:34,190 - Loss: 0.2776, step: 29300
+INFO - dpsgd_diffusion.py - 2024-10-23 18:50:32,605 - Loss: 0.3107, step: 29400
+INFO - dpsgd_diffusion.py - 2024-10-23 18:51:29,576 - Loss: 0.2820, step: 29500
+INFO - dpsgd_diffusion.py - 2024-10-23 18:52:09,810 - Eps-value after 42 epochs: 0.5068
+INFO - dpsgd_diffusion.py - 2024-10-23 18:52:28,482 - Loss: 0.2966, step: 29600
+INFO - dpsgd_diffusion.py - 2024-10-23 18:53:25,253 - Loss: 0.2802, step: 29700
+INFO - dpsgd_diffusion.py - 2024-10-23 18:54:22,726 - Loss: 0.3068, step: 29800
+INFO - dpsgd_diffusion.py - 2024-10-23 18:55:20,813 - Loss: 0.2900, step: 29900
+INFO - dpsgd_diffusion.py - 2024-10-23 18:56:18,970 - Loss: 0.2902, step: 30000
+INFO - dpsgd_diffusion.py - 2024-10-23 18:56:19,030 - Saving snapshot checkpoint and sampling single batch at iteration 30000.
+WARNING - image.py - 2024-10-23 18:56:19,588 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 18:56:35,110 - FID at iteration 30000: 285.244061
+INFO - dpsgd_diffusion.py - 2024-10-23 18:57:33,615 - Loss: 0.3212, step: 30100
+INFO - dpsgd_diffusion.py - 2024-10-23 18:58:32,804 - Loss: 0.3274, step: 30200
+INFO - dpsgd_diffusion.py - 2024-10-23 18:59:14,584 - Eps-value after 43 epochs: 0.5132
+INFO - dpsgd_diffusion.py - 2024-10-23 18:59:30,640 - Loss: 0.3004, step: 30300
+INFO - dpsgd_diffusion.py - 2024-10-23 19:00:27,763 - Loss: 0.2926, step: 30400
+INFO - dpsgd_diffusion.py - 2024-10-23 19:01:25,176 - Loss: 0.2820, step: 30500
+INFO - dpsgd_diffusion.py - 2024-10-23 19:02:24,734 - Loss: 0.2754, step: 30600
+INFO - dpsgd_diffusion.py - 2024-10-23 19:03:23,545 - Loss: 0.2880, step: 30700
+INFO - dpsgd_diffusion.py - 2024-10-23 19:04:21,326 - Loss: 0.2909, step: 30800
+INFO - dpsgd_diffusion.py - 2024-10-23 19:05:18,125 - Loss: 0.2852, step: 30900
+INFO - dpsgd_diffusion.py - 2024-10-23 19:06:02,743 - Eps-value after 44 epochs: 0.5195
+INFO - dpsgd_diffusion.py - 2024-10-23 19:06:17,191 - Loss: 0.2712, step: 31000
+INFO - dpsgd_diffusion.py - 2024-10-23 19:07:16,732 - Loss: 0.2968, step: 31100
+INFO - dpsgd_diffusion.py - 2024-10-23 19:08:16,322 - Loss: 0.3036, step: 31200
+INFO - dpsgd_diffusion.py - 2024-10-23 19:09:12,691 - Loss: 0.3144, step: 31300
+INFO - dpsgd_diffusion.py - 2024-10-23 19:10:10,497 - Loss: 0.3056, step: 31400
+INFO - dpsgd_diffusion.py - 2024-10-23 19:11:08,225 - Loss: 0.3186, step: 31500
+INFO - dpsgd_diffusion.py - 2024-10-23 19:12:05,293 - Loss: 0.2696, step: 31600
+INFO - dpsgd_diffusion.py - 2024-10-23 19:12:52,190 - Eps-value after 45 epochs: 0.5257
+INFO - dpsgd_diffusion.py - 2024-10-23 19:13:03,770 - Loss: 0.2914, step: 31700
+INFO - dpsgd_diffusion.py - 2024-10-23 19:14:01,643 - Loss: 0.2640, step: 31800
+INFO - dpsgd_diffusion.py - 2024-10-23 19:14:58,662 - Loss: 0.2736, step: 31900
+INFO - dpsgd_diffusion.py - 2024-10-23 19:15:55,679 - Loss: 0.2867, step: 32000
+INFO - dpsgd_diffusion.py - 2024-10-23 19:15:55,684 - Saving snapshot checkpoint and sampling single batch at iteration 32000.
+WARNING - image.py - 2024-10-23 19:15:56,203 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 19:16:11,419 - FID at iteration 32000: 279.241494
+INFO - dpsgd_diffusion.py - 2024-10-23 19:17:11,331 - Loss: 0.2852, step: 32100
+INFO - dpsgd_diffusion.py - 2024-10-23 19:18:08,787 - Loss: 0.2778, step: 32200
+INFO - dpsgd_diffusion.py - 2024-10-23 19:19:06,848 - Loss: 0.2990, step: 32300
+INFO - dpsgd_diffusion.py - 2024-10-23 19:19:55,899 - Eps-value after 46 epochs: 0.5319
+INFO - dpsgd_diffusion.py - 2024-10-23 19:20:05,503 - Loss: 0.2807, step: 32400
+INFO - dpsgd_diffusion.py - 2024-10-23 19:21:04,043 - Loss: 0.2903, step: 32500
+INFO - dpsgd_diffusion.py - 2024-10-23 19:22:01,678 - Loss: 0.2846, step: 32600
+INFO - dpsgd_diffusion.py - 2024-10-23 19:22:59,618 - Loss: 0.3208, step: 32700
+INFO - dpsgd_diffusion.py - 2024-10-23 19:23:57,275 - Loss: 0.3047, step: 32800
+INFO - dpsgd_diffusion.py - 2024-10-23 19:24:55,060 - Loss: 0.3091, step: 32900
+INFO - dpsgd_diffusion.py - 2024-10-23 19:25:54,518 - Loss: 0.2973, step: 33000
+INFO - dpsgd_diffusion.py - 2024-10-23 19:26:47,064 - Eps-value after 47 epochs: 0.5380
+INFO - dpsgd_diffusion.py - 2024-10-23 19:26:54,547 - Loss: 0.3045, step: 33100
+INFO - dpsgd_diffusion.py - 2024-10-23 19:27:54,200 - Loss: 0.2888, step: 33200
+INFO - dpsgd_diffusion.py - 2024-10-23 19:28:50,288 - Loss: 0.2937, step: 33300
+INFO - dpsgd_diffusion.py - 2024-10-23 19:29:48,328 - Loss: 0.2619, step: 33400
+INFO - dpsgd_diffusion.py - 2024-10-23 19:30:46,730 - Loss: 0.2718, step: 33500
+INFO - dpsgd_diffusion.py - 2024-10-23 19:31:45,212 - Loss: 0.2708, step: 33600
+INFO - dpsgd_diffusion.py - 2024-10-23 19:32:42,599 - Loss: 0.2802, step: 33700
+INFO - dpsgd_diffusion.py - 2024-10-23 19:33:37,042 - Eps-value after 48 epochs: 0.5441
+INFO - dpsgd_diffusion.py - 2024-10-23 19:33:41,801 - Loss: 0.2715, step: 33800
+INFO - dpsgd_diffusion.py - 2024-10-23 19:34:40,578 - Loss: 0.2950, step: 33900
+INFO - dpsgd_diffusion.py - 2024-10-23 19:35:38,430 - Loss: 0.3020, step: 34000
+INFO - dpsgd_diffusion.py - 2024-10-23 19:35:38,444 - Saving snapshot checkpoint and sampling single batch at iteration 34000.
+WARNING - image.py - 2024-10-23 19:35:38,946 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 19:35:54,397 - FID at iteration 34000: 277.995465
+INFO - dpsgd_diffusion.py - 2024-10-23 19:36:51,367 - Loss: 0.2897, step: 34100
+INFO - dpsgd_diffusion.py - 2024-10-23 19:37:47,711 - Loss: 0.2946, step: 34200
+INFO - dpsgd_diffusion.py - 2024-10-23 19:38:44,171 - Loss: 0.2735, step: 34300
+INFO - dpsgd_diffusion.py - 2024-10-23 19:39:41,846 - Loss: 0.3083, step: 34400
+INFO - dpsgd_diffusion.py - 2024-10-23 19:40:39,154 - Eps-value after 49 epochs: 0.5501
+INFO - dpsgd_diffusion.py - 2024-10-23 19:40:41,469 - Loss: 0.3124, step: 34500
+INFO - dpsgd_diffusion.py - 2024-10-23 19:41:40,019 - Loss: 0.2858, step: 34600
+INFO - dpsgd_diffusion.py - 2024-10-23 19:42:38,400 - Loss: 0.2763, step: 34700
+INFO - dpsgd_diffusion.py - 2024-10-23 19:43:37,225 - Loss: 0.2763, step: 34800
+INFO - dpsgd_diffusion.py - 2024-10-23 19:44:35,325 - Loss: 0.3013, step: 34900
+INFO - dpsgd_diffusion.py - 2024-10-23 19:45:34,139 - Loss: 0.2740, step: 35000
+INFO - dpsgd_diffusion.py - 2024-10-23 19:46:31,732 - Loss: 0.2859, step: 35100
+INFO - dpsgd_diffusion.py - 2024-10-23 19:47:30,533 - Loss: 0.2974, step: 35200
+INFO - dpsgd_diffusion.py - 2024-10-23 19:47:30,543 - Eps-value after 50 epochs: 0.5560
+INFO - dpsgd_diffusion.py - 2024-10-23 19:48:28,715 - Loss: 0.2652, step: 35300
+INFO - dpsgd_diffusion.py - 2024-10-23 19:49:27,477 - Loss: 0.2681, step: 35400
+INFO - dpsgd_diffusion.py - 2024-10-23 19:50:24,921 - Loss: 0.2942, step: 35500
+INFO - dpsgd_diffusion.py - 2024-10-23 19:51:24,278 - Loss: 0.2929, step: 35600
+INFO - dpsgd_diffusion.py - 2024-10-23 19:52:23,084 - Loss: 0.2742, step: 35700
+INFO - dpsgd_diffusion.py - 2024-10-23 19:53:22,709 - Loss: 0.2469, step: 35800
+INFO - dpsgd_diffusion.py - 2024-10-23 19:54:19,771 - Loss: 0.2917, step: 35900
+INFO - dpsgd_diffusion.py - 2024-10-23 19:54:22,020 - Eps-value after 51 epochs: 0.5619
+INFO - dpsgd_diffusion.py - 2024-10-23 19:55:18,197 - Loss: 0.2931, step: 36000
+INFO - dpsgd_diffusion.py - 2024-10-23 19:55:18,220 - Saving snapshot checkpoint and sampling single batch at iteration 36000.
+WARNING - image.py - 2024-10-23 19:55:18,702 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 19:55:34,176 - FID at iteration 36000: 275.804959
+INFO - dpsgd_diffusion.py - 2024-10-23 19:56:30,676 - Loss: 0.2941, step: 36100
+INFO - dpsgd_diffusion.py - 2024-10-23 19:57:29,539 - Loss: 0.2724, step: 36200
+INFO - dpsgd_diffusion.py - 2024-10-23 19:58:28,465 - Loss: 0.2870, step: 36300
+INFO - dpsgd_diffusion.py - 2024-10-23 19:59:25,849 - Loss: 0.2978, step: 36400
+INFO - dpsgd_diffusion.py - 2024-10-23 20:00:22,963 - Loss: 0.2715, step: 36500
+INFO - dpsgd_diffusion.py - 2024-10-23 20:01:21,736 - Loss: 0.2556, step: 36600
+INFO - dpsgd_diffusion.py - 2024-10-23 20:01:26,553 - Eps-value after 52 epochs: 0.5678
+INFO - dpsgd_diffusion.py - 2024-10-23 20:02:20,630 - Loss: 0.3194, step: 36700
+INFO - dpsgd_diffusion.py - 2024-10-23 20:03:18,286 - Loss: 0.2729, step: 36800
+INFO - dpsgd_diffusion.py - 2024-10-23 20:04:16,631 - Loss: 0.2728, step: 36900
+INFO - dpsgd_diffusion.py - 2024-10-23 20:05:14,575 - Loss: 0.2993, step: 37000
+INFO - dpsgd_diffusion.py - 2024-10-23 20:06:12,795 - Loss: 0.2953, step: 37100
+INFO - dpsgd_diffusion.py - 2024-10-23 20:07:11,065 - Loss: 0.2817, step: 37200
+INFO - dpsgd_diffusion.py - 2024-10-23 20:08:08,491 - Loss: 0.2994, step: 37300
+INFO - dpsgd_diffusion.py - 2024-10-23 20:08:15,159 - Eps-value after 53 epochs: 0.5735
+INFO - dpsgd_diffusion.py - 2024-10-23 20:09:05,696 - Loss: 0.2818, step: 37400
+INFO - dpsgd_diffusion.py - 2024-10-23 20:10:04,345 - Loss: 0.2909, step: 37500
+INFO - dpsgd_diffusion.py - 2024-10-23 20:11:02,502 - Loss: 0.2647, step: 37600
+INFO - dpsgd_diffusion.py - 2024-10-23 20:12:01,271 - Loss: 0.2772, step: 37700
+INFO - dpsgd_diffusion.py - 2024-10-23 20:12:59,012 - Loss: 0.2897, step: 37800
+INFO - dpsgd_diffusion.py - 2024-10-23 20:13:57,019 - Loss: 0.2670, step: 37900
+INFO - dpsgd_diffusion.py - 2024-10-23 20:14:55,524 - Loss: 0.2870, step: 38000
+INFO - dpsgd_diffusion.py - 2024-10-23 20:14:55,547 - Saving snapshot checkpoint and sampling single batch at iteration 38000.
+WARNING - image.py - 2024-10-23 20:14:56,019 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 20:15:11,427 - FID at iteration 38000: 273.749051
+INFO - dpsgd_diffusion.py - 2024-10-23 20:15:20,452 - Eps-value after 54 epochs: 0.5793
+INFO - dpsgd_diffusion.py - 2024-10-23 20:16:09,072 - Loss: 0.2800, step: 38100
+INFO - dpsgd_diffusion.py - 2024-10-23 20:17:07,873 - Loss: 0.2739, step: 38200
+INFO - dpsgd_diffusion.py - 2024-10-23 20:18:05,431 - Loss: 0.2709, step: 38300
+INFO - dpsgd_diffusion.py - 2024-10-23 20:19:01,409 - Loss: 0.2928, step: 38400
+INFO - dpsgd_diffusion.py - 2024-10-23 20:20:00,014 - Loss: 0.2817, step: 38500
+INFO - dpsgd_diffusion.py - 2024-10-23 20:20:57,666 - Loss: 0.2871, step: 38600
+INFO - dpsgd_diffusion.py - 2024-10-23 20:21:56,084 - Loss: 0.2743, step: 38700
+INFO - dpsgd_diffusion.py - 2024-10-23 20:22:07,144 - Eps-value after 55 epochs: 0.5850
+INFO - dpsgd_diffusion.py - 2024-10-23 20:22:53,993 - Loss: 0.3023, step: 38800
+INFO - dpsgd_diffusion.py - 2024-10-23 20:23:51,850 - Loss: 0.2726, step: 38900
+INFO - dpsgd_diffusion.py - 2024-10-23 20:24:49,585 - Loss: 0.2852, step: 39000
+INFO - dpsgd_diffusion.py - 2024-10-23 20:25:47,534 - Loss: 0.2891, step: 39100
+INFO - dpsgd_diffusion.py - 2024-10-23 20:26:45,638 - Loss: 0.3009, step: 39200
+INFO - dpsgd_diffusion.py - 2024-10-23 20:27:44,832 - Loss: 0.2720, step: 39300
+INFO - dpsgd_diffusion.py - 2024-10-23 20:28:43,917 - Loss: 0.2901, step: 39400
+INFO - dpsgd_diffusion.py - 2024-10-23 20:28:58,046 - Eps-value after 56 epochs: 0.5906
+INFO - dpsgd_diffusion.py - 2024-10-23 20:29:41,357 - Loss: 0.2822, step: 39500
+INFO - dpsgd_diffusion.py - 2024-10-23 20:30:39,662 - Loss: 0.2940, step: 39600
+INFO - dpsgd_diffusion.py - 2024-10-23 20:31:39,027 - Loss: 0.2702, step: 39700
+INFO - dpsgd_diffusion.py - 2024-10-23 20:32:37,468 - Loss: 0.3036, step: 39800
+INFO - dpsgd_diffusion.py - 2024-10-23 20:33:34,748 - Loss: 0.2712, step: 39900
+INFO - dpsgd_diffusion.py - 2024-10-23 20:34:32,679 - Loss: 0.2649, step: 40000
+INFO - dpsgd_diffusion.py - 2024-10-23 20:34:32,684 - Saving snapshot checkpoint and sampling single batch at iteration 40000.
+WARNING - image.py - 2024-10-23 20:34:33,168 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 20:34:48,285 - FID at iteration 40000: 270.556984
+INFO - dpsgd_diffusion.py - 2024-10-23 20:35:46,508 - Loss: 0.2839, step: 40100
+INFO - dpsgd_diffusion.py - 2024-10-23 20:36:02,500 - Eps-value after 57 epochs: 0.5961
+INFO - dpsgd_diffusion.py - 2024-10-23 20:36:43,260 - Loss: 0.2830, step: 40200
+INFO - dpsgd_diffusion.py - 2024-10-23 20:37:40,209 - Loss: 0.2813, step: 40300
+INFO - dpsgd_diffusion.py - 2024-10-23 20:38:38,159 - Loss: 0.3005, step: 40400
+INFO - dpsgd_diffusion.py - 2024-10-23 20:39:36,036 - Loss: 0.3018, step: 40500
+INFO - dpsgd_diffusion.py - 2024-10-23 20:40:33,599 - Loss: 0.2884, step: 40600
+INFO - dpsgd_diffusion.py - 2024-10-23 20:41:30,771 - Loss: 0.2684, step: 40700
+INFO - dpsgd_diffusion.py - 2024-10-23 20:42:28,867 - Loss: 0.2735, step: 40800
+INFO - dpsgd_diffusion.py - 2024-10-23 20:42:47,061 - Eps-value after 58 epochs: 0.6017
+INFO - dpsgd_diffusion.py - 2024-10-23 20:43:25,503 - Loss: 0.2945, step: 40900
+INFO - dpsgd_diffusion.py - 2024-10-23 20:44:23,513 - Loss: 0.2766, step: 41000
+INFO - dpsgd_diffusion.py - 2024-10-23 20:45:22,814 - Loss: 0.3093, step: 41100
+INFO - dpsgd_diffusion.py - 2024-10-23 20:46:21,531 - Loss: 0.2901, step: 41200
+INFO - dpsgd_diffusion.py - 2024-10-23 20:47:19,117 - Loss: 0.2798, step: 41300
+INFO - dpsgd_diffusion.py - 2024-10-23 20:48:17,658 - Loss: 0.2836, step: 41400
+INFO - dpsgd_diffusion.py - 2024-10-23 20:49:17,068 - Loss: 0.2599, step: 41500
+INFO - dpsgd_diffusion.py - 2024-10-23 20:49:38,140 - Eps-value after 59 epochs: 0.6073
+INFO - dpsgd_diffusion.py - 2024-10-23 20:50:16,711 - Loss: 0.2800, step: 41600
+INFO - dpsgd_diffusion.py - 2024-10-23 20:51:15,900 - Loss: 0.2678, step: 41700
+INFO - dpsgd_diffusion.py - 2024-10-23 20:52:12,710 - Loss: 0.2419, step: 41800
+INFO - dpsgd_diffusion.py - 2024-10-23 20:53:09,779 - Loss: 0.2884, step: 41900
+INFO - dpsgd_diffusion.py - 2024-10-23 20:54:08,733 - Loss: 0.2780, step: 42000
+INFO - dpsgd_diffusion.py - 2024-10-23 20:54:08,755 - Saving snapshot checkpoint and sampling single batch at iteration 42000.
+WARNING - image.py - 2024-10-23 20:54:09,241 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 20:54:24,333 - FID at iteration 42000: 268.175904
+INFO - dpsgd_diffusion.py - 2024-10-23 20:55:22,114 - Loss: 0.2707, step: 42100
+INFO - dpsgd_diffusion.py - 2024-10-23 20:56:20,695 - Loss: 0.2709, step: 42200
+INFO - dpsgd_diffusion.py - 2024-10-23 20:56:44,033 - Eps-value after 60 epochs: 0.6127
+INFO - dpsgd_diffusion.py - 2024-10-23 20:57:18,759 - Loss: 0.2846, step: 42300
+INFO - dpsgd_diffusion.py - 2024-10-23 20:58:17,244 - Loss: 0.2922, step: 42400
+INFO - dpsgd_diffusion.py - 2024-10-23 20:59:15,492 - Loss: 0.2757, step: 42500
+INFO - dpsgd_diffusion.py - 2024-10-23 21:00:11,890 - Loss: 0.3109, step: 42600
+INFO - dpsgd_diffusion.py - 2024-10-23 21:01:08,688 - Loss: 0.2968, step: 42700
+INFO - dpsgd_diffusion.py - 2024-10-23 21:02:06,153 - Loss: 0.2650, step: 42800
+INFO - dpsgd_diffusion.py - 2024-10-23 21:03:03,859 - Loss: 0.2899, step: 42900
+INFO - dpsgd_diffusion.py - 2024-10-23 21:03:28,856 - Eps-value after 61 epochs: 0.6181
+INFO - dpsgd_diffusion.py - 2024-10-23 21:04:00,804 - Loss: 0.2615, step: 43000
+INFO - dpsgd_diffusion.py - 2024-10-23 21:04:58,416 - Loss: 0.2863, step: 43100
+INFO - dpsgd_diffusion.py - 2024-10-23 21:05:56,040 - Loss: 0.2943, step: 43200
+INFO - dpsgd_diffusion.py - 2024-10-23 21:06:53,569 - Loss: 0.3020, step: 43300
+INFO - dpsgd_diffusion.py - 2024-10-23 21:07:50,480 - Loss: 0.2788, step: 43400
+INFO - dpsgd_diffusion.py - 2024-10-23 21:08:49,085 - Loss: 0.2829, step: 43500
+INFO - dpsgd_diffusion.py - 2024-10-23 21:09:46,766 - Loss: 0.2806, step: 43600
+INFO - dpsgd_diffusion.py - 2024-10-23 21:10:14,630 - Eps-value after 62 epochs: 0.6235
+INFO - dpsgd_diffusion.py - 2024-10-23 21:10:44,214 - Loss: 0.2667, step: 43700
+INFO - dpsgd_diffusion.py - 2024-10-23 21:11:40,336 - Loss: 0.2708, step: 43800
+INFO - dpsgd_diffusion.py - 2024-10-23 21:12:37,349 - Loss: 0.2868, step: 43900
+INFO - dpsgd_diffusion.py - 2024-10-23 21:13:35,597 - Loss: 0.2794, step: 44000
+INFO - dpsgd_diffusion.py - 2024-10-23 21:13:35,656 - Saving snapshot checkpoint and sampling single batch at iteration 44000.
+WARNING - image.py - 2024-10-23 21:13:36,137 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 21:13:51,538 - FID at iteration 44000: 265.659097
+INFO - dpsgd_diffusion.py - 2024-10-23 21:14:49,409 - Loss: 0.2930, step: 44100
+INFO - dpsgd_diffusion.py - 2024-10-23 21:15:46,949 - Loss: 0.2799, step: 44200
+INFO - dpsgd_diffusion.py - 2024-10-23 21:16:44,336 - Loss: 0.3064, step: 44300
+INFO - dpsgd_diffusion.py - 2024-10-23 21:17:15,301 - Eps-value after 63 epochs: 0.6289
+INFO - dpsgd_diffusion.py - 2024-10-23 21:17:43,415 - Loss: 0.2918, step: 44400
+INFO - dpsgd_diffusion.py - 2024-10-23 21:18:41,779 - Loss: 0.2870, step: 44500
+INFO - dpsgd_diffusion.py - 2024-10-23 21:19:41,050 - Loss: 0.2907, step: 44600
+INFO - dpsgd_diffusion.py - 2024-10-23 21:20:39,196 - Loss: 0.2898, step: 44700
+INFO - dpsgd_diffusion.py - 2024-10-23 21:21:37,293 - Loss: 0.2762, step: 44800
+INFO - dpsgd_diffusion.py - 2024-10-23 21:22:35,245 - Loss: 0.2539, step: 44900
+INFO - dpsgd_diffusion.py - 2024-10-23 21:23:32,868 - Loss: 0.2706, step: 45000
+INFO - dpsgd_diffusion.py - 2024-10-23 21:24:04,649 - Eps-value after 64 epochs: 0.6341
+INFO - dpsgd_diffusion.py - 2024-10-23 21:24:29,825 - Loss: 0.2797, step: 45100
+INFO - dpsgd_diffusion.py - 2024-10-23 21:25:27,424 - Loss: 0.2936, step: 45200
+INFO - dpsgd_diffusion.py - 2024-10-23 21:26:26,571 - Loss: 0.3051, step: 45300
+INFO - dpsgd_diffusion.py - 2024-10-23 21:27:25,115 - Loss: 0.2690, step: 45400
+INFO - dpsgd_diffusion.py - 2024-10-23 21:28:23,777 - Loss: 0.3019, step: 45500
+INFO - dpsgd_diffusion.py - 2024-10-23 21:29:22,884 - Loss: 0.2625, step: 45600
+INFO - dpsgd_diffusion.py - 2024-10-23 21:30:22,700 - Loss: 0.2947, step: 45700
+INFO - dpsgd_diffusion.py - 2024-10-23 21:30:58,706 - Eps-value after 65 epochs: 0.6393
+INFO - dpsgd_diffusion.py - 2024-10-23 21:31:21,794 - Loss: 0.2962, step: 45800
+INFO - dpsgd_diffusion.py - 2024-10-23 21:32:19,157 - Loss: 0.2768, step: 45900
+INFO - dpsgd_diffusion.py - 2024-10-23 21:33:19,021 - Loss: 0.2640, step: 46000
+INFO - dpsgd_diffusion.py - 2024-10-23 21:33:19,080 - Saving snapshot checkpoint and sampling single batch at iteration 46000.
+WARNING - image.py - 2024-10-23 21:33:19,565 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 21:33:35,012 - FID at iteration 46000: 263.732657
+INFO - dpsgd_diffusion.py - 2024-10-23 21:34:33,772 - Loss: 0.2836, step: 46100
+INFO - dpsgd_diffusion.py - 2024-10-23 21:35:31,614 - Loss: 0.2718, step: 46200
+INFO - dpsgd_diffusion.py - 2024-10-23 21:36:30,113 - Loss: 0.2999, step: 46300
+INFO - dpsgd_diffusion.py - 2024-10-23 21:37:29,237 - Loss: 0.2832, step: 46400
+INFO - dpsgd_diffusion.py - 2024-10-23 21:38:05,785 - Eps-value after 66 epochs: 0.6446
+INFO - dpsgd_diffusion.py - 2024-10-23 21:38:26,492 - Loss: 0.2511, step: 46500
+INFO - dpsgd_diffusion.py - 2024-10-23 21:39:24,003 - Loss: 0.2725, step: 46600
+INFO - dpsgd_diffusion.py - 2024-10-23 21:40:23,152 - Loss: 0.2623, step: 46700
+INFO - dpsgd_diffusion.py - 2024-10-23 21:41:21,673 - Loss: 0.2609, step: 46800
+INFO - dpsgd_diffusion.py - 2024-10-23 21:42:19,114 - Loss: 0.2941, step: 46900
+INFO - dpsgd_diffusion.py - 2024-10-23 21:43:16,478 - Loss: 0.2984, step: 47000
+INFO - dpsgd_diffusion.py - 2024-10-23 21:44:14,119 - Loss: 0.2974, step: 47100
+INFO - dpsgd_diffusion.py - 2024-10-23 21:44:53,899 - Eps-value after 67 epochs: 0.6498
+INFO - dpsgd_diffusion.py - 2024-10-23 21:45:12,835 - Loss: 0.2796, step: 47200
+INFO - dpsgd_diffusion.py - 2024-10-23 21:46:10,403 - Loss: 0.2692, step: 47300
+INFO - dpsgd_diffusion.py - 2024-10-23 21:47:07,443 - Loss: 0.2768, step: 47400
+INFO - dpsgd_diffusion.py - 2024-10-23 21:48:04,973 - Loss: 0.2781, step: 47500
+INFO - dpsgd_diffusion.py - 2024-10-23 21:49:01,979 - Loss: 0.2645, step: 47600
+INFO - dpsgd_diffusion.py - 2024-10-23 21:50:00,318 - Loss: 0.2861, step: 47700
+INFO - dpsgd_diffusion.py - 2024-10-23 21:50:57,966 - Loss: 0.2696, step: 47800
+INFO - dpsgd_diffusion.py - 2024-10-23 21:51:40,766 - Eps-value after 68 epochs: 0.6550
+INFO - dpsgd_diffusion.py - 2024-10-23 21:51:58,261 - Loss: 0.2595, step: 47900
+INFO - dpsgd_diffusion.py - 2024-10-23 21:52:56,853 - Loss: 0.2755, step: 48000
+INFO - dpsgd_diffusion.py - 2024-10-23 21:52:56,859 - Saving snapshot checkpoint and sampling single batch at iteration 48000.
+WARNING - image.py - 2024-10-23 21:52:57,369 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 21:53:12,800 - FID at iteration 48000: 260.765010
+INFO - dpsgd_diffusion.py - 2024-10-23 21:54:11,955 - Loss: 0.2677, step: 48100
+INFO - dpsgd_diffusion.py - 2024-10-23 21:55:10,897 - Loss: 0.2934, step: 48200
+INFO - dpsgd_diffusion.py - 2024-10-23 21:56:09,157 - Loss: 0.3029, step: 48300
+INFO - dpsgd_diffusion.py - 2024-10-23 21:57:06,599 - Loss: 0.2932, step: 48400
+INFO - dpsgd_diffusion.py - 2024-10-23 21:58:05,373 - Loss: 0.2926, step: 48500
+INFO - dpsgd_diffusion.py - 2024-10-23 21:58:50,001 - Eps-value after 69 epochs: 0.6601
+INFO - dpsgd_diffusion.py - 2024-10-23 21:59:03,639 - Loss: 0.2823, step: 48600
+INFO - dpsgd_diffusion.py - 2024-10-23 22:00:01,547 - Loss: 0.2692, step: 48700
+INFO - dpsgd_diffusion.py - 2024-10-23 22:01:00,308 - Loss: 0.2591, step: 48800
+INFO - dpsgd_diffusion.py - 2024-10-23 22:01:57,131 - Loss: 0.2450, step: 48900
+INFO - dpsgd_diffusion.py - 2024-10-23 22:02:55,089 - Loss: 0.2990, step: 49000
+INFO - dpsgd_diffusion.py - 2024-10-23 22:03:54,780 - Loss: 0.2980, step: 49100
+INFO - dpsgd_diffusion.py - 2024-10-23 22:04:53,666 - Loss: 0.2680, step: 49200
+INFO - dpsgd_diffusion.py - 2024-10-23 22:05:40,207 - Eps-value after 70 epochs: 0.6651
+INFO - dpsgd_diffusion.py - 2024-10-23 22:05:51,766 - Loss: 0.2928, step: 49300
+INFO - dpsgd_diffusion.py - 2024-10-23 22:06:48,907 - Loss: 0.2701, step: 49400
+INFO - dpsgd_diffusion.py - 2024-10-23 22:07:46,831 - Loss: 0.2603, step: 49500
+INFO - dpsgd_diffusion.py - 2024-10-23 22:08:44,022 - Loss: 0.2707, step: 49600
+INFO - dpsgd_diffusion.py - 2024-10-23 22:09:40,466 - Loss: 0.2791, step: 49700
+INFO - dpsgd_diffusion.py - 2024-10-23 22:10:37,368 - Loss: 0.2809, step: 49800
+INFO - dpsgd_diffusion.py - 2024-10-23 22:11:34,237 - Loss: 0.2777, step: 49900
+INFO - dpsgd_diffusion.py - 2024-10-23 22:12:22,033 - Eps-value after 71 epochs: 0.6701
+INFO - dpsgd_diffusion.py - 2024-10-23 22:12:31,303 - Loss: 0.2723, step: 50000
+INFO - dpsgd_diffusion.py - 2024-10-23 22:12:31,337 - Saving snapshot checkpoint and sampling single batch at iteration 50000.
+WARNING - image.py - 2024-10-23 22:12:31,861 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 22:12:47,340 - FID at iteration 50000: 259.136053
+INFO - dpsgd_diffusion.py - 2024-10-23 22:13:43,436 - Loss: 0.2898, step: 50100
+INFO - dpsgd_diffusion.py - 2024-10-23 22:14:42,172 - Loss: 0.2672, step: 50200
+INFO - dpsgd_diffusion.py - 2024-10-23 22:15:39,942 - Loss: 0.3058, step: 50300
+INFO - dpsgd_diffusion.py - 2024-10-23 22:16:38,583 - Loss: 0.3041, step: 50400
+INFO - dpsgd_diffusion.py - 2024-10-23 22:17:35,535 - Loss: 0.2814, step: 50500
+INFO - dpsgd_diffusion.py - 2024-10-23 22:18:34,272 - Loss: 0.2814, step: 50600
+INFO - dpsgd_diffusion.py - 2024-10-23 22:19:26,337 - Eps-value after 72 epochs: 0.6752
+INFO - dpsgd_diffusion.py - 2024-10-23 22:19:33,291 - Loss: 0.2740, step: 50700
+INFO - dpsgd_diffusion.py - 2024-10-23 22:20:30,612 - Loss: 0.2537, step: 50800
+INFO - dpsgd_diffusion.py - 2024-10-23 22:21:28,506 - Loss: 0.2905, step: 50900
+INFO - dpsgd_diffusion.py - 2024-10-23 22:22:25,380 - Loss: 0.3001, step: 51000
+INFO - dpsgd_diffusion.py - 2024-10-23 22:23:22,347 - Loss: 0.2909, step: 51100
+INFO - dpsgd_diffusion.py - 2024-10-23 22:24:20,721 - Loss: 0.2587, step: 51200
+INFO - dpsgd_diffusion.py - 2024-10-23 22:25:18,716 - Loss: 0.2773, step: 51300
+INFO - dpsgd_diffusion.py - 2024-10-23 22:26:12,398 - Eps-value after 73 epochs: 0.6802
+INFO - dpsgd_diffusion.py - 2024-10-23 22:26:17,130 - Loss: 0.2508, step: 51400
+INFO - dpsgd_diffusion.py - 2024-10-23 22:27:16,011 - Loss: 0.2980, step: 51500
+INFO - dpsgd_diffusion.py - 2024-10-23 22:28:14,632 - Loss: 0.3057, step: 51600
+INFO - dpsgd_diffusion.py - 2024-10-23 22:29:10,562 - Loss: 0.3224, step: 51700
+INFO - dpsgd_diffusion.py - 2024-10-23 22:30:09,849 - Loss: 0.2857, step: 51800
+INFO - dpsgd_diffusion.py - 2024-10-23 22:31:08,110 - Loss: 0.2595, step: 51900
+INFO - dpsgd_diffusion.py - 2024-10-23 22:32:06,075 - Loss: 0.2789, step: 52000
+INFO - dpsgd_diffusion.py - 2024-10-23 22:32:06,099 - Saving snapshot checkpoint and sampling single batch at iteration 52000.
+WARNING - image.py - 2024-10-23 22:32:06,608 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 22:32:22,144 - FID at iteration 52000: 257.749511
+INFO - dpsgd_diffusion.py - 2024-10-23 22:33:17,474 - Eps-value after 74 epochs: 0.6852
+INFO - dpsgd_diffusion.py - 2024-10-23 22:33:19,852 - Loss: 0.2485, step: 52100
+INFO - dpsgd_diffusion.py - 2024-10-23 22:34:17,660 - Loss: 0.2697, step: 52200
+INFO - dpsgd_diffusion.py - 2024-10-23 22:35:14,370 - Loss: 0.2694, step: 52300
+INFO - dpsgd_diffusion.py - 2024-10-23 22:36:12,673 - Loss: 0.2730, step: 52400
+INFO - dpsgd_diffusion.py - 2024-10-23 22:37:10,002 - Loss: 0.2809, step: 52500
+INFO - dpsgd_diffusion.py - 2024-10-23 22:38:08,451 - Loss: 0.2668, step: 52600
+INFO - dpsgd_diffusion.py - 2024-10-23 22:39:07,180 - Loss: 0.2773, step: 52700
+INFO - dpsgd_diffusion.py - 2024-10-23 22:40:04,885 - Loss: 0.2892, step: 52800
+INFO - dpsgd_diffusion.py - 2024-10-23 22:40:04,895 - Eps-value after 75 epochs: 0.6900
+INFO - dpsgd_diffusion.py - 2024-10-23 22:41:02,572 - Loss: 0.2556, step: 52900
+INFO - dpsgd_diffusion.py - 2024-10-23 22:41:59,767 - Loss: 0.2829, step: 53000
+INFO - dpsgd_diffusion.py - 2024-10-23 22:42:56,950 - Loss: 0.2783, step: 53100
+INFO - dpsgd_diffusion.py - 2024-10-23 22:43:55,560 - Loss: 0.2453, step: 53200
+INFO - dpsgd_diffusion.py - 2024-10-23 22:44:54,502 - Loss: 0.2936, step: 53300
+INFO - dpsgd_diffusion.py - 2024-10-23 22:45:53,513 - Loss: 0.2794, step: 53400
+INFO - dpsgd_diffusion.py - 2024-10-23 22:46:52,591 - Loss: 0.2848, step: 53500
+INFO - dpsgd_diffusion.py - 2024-10-23 22:46:54,907 - Eps-value after 76 epochs: 0.6949
+INFO - dpsgd_diffusion.py - 2024-10-23 22:47:49,359 - Loss: 0.2712, step: 53600
+INFO - dpsgd_diffusion.py - 2024-10-23 22:48:46,168 - Loss: 0.2564, step: 53700
+INFO - dpsgd_diffusion.py - 2024-10-23 22:49:44,365 - Loss: 0.2646, step: 53800
+INFO - dpsgd_diffusion.py - 2024-10-23 22:50:43,359 - Loss: 0.2577, step: 53900
+INFO - dpsgd_diffusion.py - 2024-10-23 22:51:42,385 - Loss: 0.2925, step: 54000
+INFO - dpsgd_diffusion.py - 2024-10-23 22:51:42,435 - Saving snapshot checkpoint and sampling single batch at iteration 54000.
+WARNING - image.py - 2024-10-23 22:51:42,959 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 22:51:58,429 - FID at iteration 54000: 256.664311
+INFO - dpsgd_diffusion.py - 2024-10-23 22:52:57,132 - Loss: 0.2660, step: 54100
+INFO - dpsgd_diffusion.py - 2024-10-23 22:53:56,379 - Loss: 0.2972, step: 54200
+INFO - dpsgd_diffusion.py - 2024-10-23 22:54:01,103 - Eps-value after 77 epochs: 0.6998
+INFO - dpsgd_diffusion.py - 2024-10-23 22:54:55,516 - Loss: 0.2983, step: 54300
+INFO - dpsgd_diffusion.py - 2024-10-23 22:55:52,937 - Loss: 0.2512, step: 54400
+INFO - dpsgd_diffusion.py - 2024-10-23 22:56:50,938 - Loss: 0.2661, step: 54500
+INFO - dpsgd_diffusion.py - 2024-10-23 22:57:49,397 - Loss: 0.2819, step: 54600
+INFO - dpsgd_diffusion.py - 2024-10-23 22:58:47,884 - Loss: 0.2717, step: 54700
+INFO - dpsgd_diffusion.py - 2024-10-23 22:59:45,969 - Loss: 0.2775, step: 54800
+INFO - dpsgd_diffusion.py - 2024-10-23 23:00:44,687 - Loss: 0.2699, step: 54900
+INFO - dpsgd_diffusion.py - 2024-10-23 23:00:51,577 - Eps-value after 78 epochs: 0.7046
+INFO - dpsgd_diffusion.py - 2024-10-23 23:01:42,698 - Loss: 0.2917, step: 55000
+INFO - dpsgd_diffusion.py - 2024-10-23 23:02:40,451 - Loss: 0.2886, step: 55100
+INFO - dpsgd_diffusion.py - 2024-10-23 23:03:39,116 - Loss: 0.2642, step: 55200
+INFO - dpsgd_diffusion.py - 2024-10-23 23:04:37,908 - Loss: 0.2866, step: 55300
+INFO - dpsgd_diffusion.py - 2024-10-23 23:05:36,178 - Loss: 0.2438, step: 55400
+INFO - dpsgd_diffusion.py - 2024-10-23 23:06:35,034 - Loss: 0.2837, step: 55500
+INFO - dpsgd_diffusion.py - 2024-10-23 23:07:32,614 - Loss: 0.2777, step: 55600
+INFO - dpsgd_diffusion.py - 2024-10-23 23:07:41,693 - Eps-value after 79 epochs: 0.7095
+INFO - dpsgd_diffusion.py - 2024-10-23 23:08:31,598 - Loss: 0.2856, step: 55700
+INFO - dpsgd_diffusion.py - 2024-10-23 23:09:28,413 - Loss: 0.2631, step: 55800
+INFO - dpsgd_diffusion.py - 2024-10-23 23:10:25,619 - Loss: 0.2751, step: 55900
+INFO - dpsgd_diffusion.py - 2024-10-23 23:11:23,948 - Loss: 0.2746, step: 56000
+INFO - dpsgd_diffusion.py - 2024-10-23 23:11:23,954 - Saving snapshot checkpoint and sampling single batch at iteration 56000.
+WARNING - image.py - 2024-10-23 23:11:24,469 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 23:11:39,981 - FID at iteration 56000: 251.991705
+INFO - dpsgd_diffusion.py - 2024-10-23 23:12:38,951 - Loss: 0.2605, step: 56100
+INFO - dpsgd_diffusion.py - 2024-10-23 23:13:35,897 - Loss: 0.2509, step: 56200
+INFO - dpsgd_diffusion.py - 2024-10-23 23:14:33,834 - Loss: 0.2671, step: 56300
+INFO - dpsgd_diffusion.py - 2024-10-23 23:14:45,295 - Eps-value after 80 epochs: 0.7143
+INFO - dpsgd_diffusion.py - 2024-10-23 23:15:31,569 - Loss: 0.2573, step: 56400
+INFO - dpsgd_diffusion.py - 2024-10-23 23:16:29,327 - Loss: 0.3018, step: 56500
+INFO - dpsgd_diffusion.py - 2024-10-23 23:17:27,276 - Loss: 0.2627, step: 56600
+INFO - dpsgd_diffusion.py - 2024-10-23 23:18:25,738 - Loss: 0.3056, step: 56700
+INFO - dpsgd_diffusion.py - 2024-10-23 23:19:23,018 - Loss: 0.2897, step: 56800
+INFO - dpsgd_diffusion.py - 2024-10-23 23:20:21,235 - Loss: 0.2846, step: 56900
+INFO - dpsgd_diffusion.py - 2024-10-23 23:21:18,968 - Loss: 0.2613, step: 57000
+INFO - dpsgd_diffusion.py - 2024-10-23 23:21:32,602 - Eps-value after 81 epochs: 0.7190
+INFO - dpsgd_diffusion.py - 2024-10-23 23:22:15,988 - Loss: 0.2546, step: 57100
+INFO - dpsgd_diffusion.py - 2024-10-23 23:23:15,272 - Loss: 0.2656, step: 57200
+INFO - dpsgd_diffusion.py - 2024-10-23 23:24:14,399 - Loss: 0.2790, step: 57300
+INFO - dpsgd_diffusion.py - 2024-10-23 23:25:12,974 - Loss: 0.2710, step: 57400
+INFO - dpsgd_diffusion.py - 2024-10-23 23:26:10,495 - Loss: 0.2500, step: 57500
+INFO - dpsgd_diffusion.py - 2024-10-23 23:27:10,161 - Loss: 0.2702, step: 57600
+INFO - dpsgd_diffusion.py - 2024-10-23 23:28:07,629 - Loss: 0.2607, step: 57700
+INFO - dpsgd_diffusion.py - 2024-10-23 23:28:22,772 - Eps-value after 82 epochs: 0.7237
+INFO - dpsgd_diffusion.py - 2024-10-23 23:29:04,386 - Loss: 0.2726, step: 57800
+INFO - dpsgd_diffusion.py - 2024-10-23 23:30:02,780 - Loss: 0.2481, step: 57900
+INFO - dpsgd_diffusion.py - 2024-10-23 23:31:00,816 - Loss: 0.2760, step: 58000
+INFO - dpsgd_diffusion.py - 2024-10-23 23:31:00,842 - Saving snapshot checkpoint and sampling single batch at iteration 58000.
+WARNING - image.py - 2024-10-23 23:31:01,324 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 23:31:16,845 - FID at iteration 58000: 248.822971
+INFO - dpsgd_diffusion.py - 2024-10-23 23:32:14,506 - Loss: 0.2760, step: 58100
+INFO - dpsgd_diffusion.py - 2024-10-23 23:33:14,254 - Loss: 0.2572, step: 58200
+INFO - dpsgd_diffusion.py - 2024-10-23 23:34:11,756 - Loss: 0.2893, step: 58300
+INFO - dpsgd_diffusion.py - 2024-10-23 23:35:10,405 - Loss: 0.2688, step: 58400
+INFO - dpsgd_diffusion.py - 2024-10-23 23:35:29,639 - Eps-value after 83 epochs: 0.7283
+INFO - dpsgd_diffusion.py - 2024-10-23 23:36:09,593 - Loss: 0.2744, step: 58500
+INFO - dpsgd_diffusion.py - 2024-10-23 23:37:07,833 - Loss: 0.2780, step: 58600
+INFO - dpsgd_diffusion.py - 2024-10-23 23:38:07,209 - Loss: 0.2906, step: 58700
+INFO - dpsgd_diffusion.py - 2024-10-23 23:39:07,445 - Loss: 0.2786, step: 58800
+INFO - dpsgd_diffusion.py - 2024-10-23 23:40:05,158 - Loss: 0.2725, step: 58900
+INFO - dpsgd_diffusion.py - 2024-10-23 23:41:03,517 - Loss: 0.2809, step: 59000
+INFO - dpsgd_diffusion.py - 2024-10-23 23:42:01,394 - Loss: 0.2748, step: 59100
+INFO - dpsgd_diffusion.py - 2024-10-23 23:42:22,599 - Eps-value after 84 epochs: 0.7330
+INFO - dpsgd_diffusion.py - 2024-10-23 23:42:58,961 - Loss: 0.2868, step: 59200
+INFO - dpsgd_diffusion.py - 2024-10-23 23:43:57,842 - Loss: 0.2453, step: 59300
+INFO - dpsgd_diffusion.py - 2024-10-23 23:44:56,341 - Loss: 0.2875, step: 59400
+INFO - dpsgd_diffusion.py - 2024-10-23 23:45:54,554 - Loss: 0.2820, step: 59500
+INFO - dpsgd_diffusion.py - 2024-10-23 23:46:55,436 - Loss: 0.2921, step: 59600
+INFO - dpsgd_diffusion.py - 2024-10-23 23:47:53,218 - Loss: 0.3023, step: 59700
+INFO - dpsgd_diffusion.py - 2024-10-23 23:48:51,638 - Loss: 0.2469, step: 59800
+INFO - dpsgd_diffusion.py - 2024-10-23 23:49:14,023 - Eps-value after 85 epochs: 0.7377
+INFO - dpsgd_diffusion.py - 2024-10-23 23:49:49,950 - Loss: 0.2481, step: 59900
+INFO - dpsgd_diffusion.py - 2024-10-23 23:50:48,455 - Loss: 0.2543, step: 60000
+INFO - dpsgd_diffusion.py - 2024-10-23 23:50:48,512 - Saving snapshot checkpoint and sampling single batch at iteration 60000.
+WARNING - image.py - 2024-10-23 23:50:49,044 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-23 23:51:04,467 - FID at iteration 60000: 248.128957
+INFO - dpsgd_diffusion.py - 2024-10-23 23:52:01,921 - Loss: 0.2620, step: 60100
+INFO - dpsgd_diffusion.py - 2024-10-23 23:53:01,027 - Loss: 0.2850, step: 60200
+INFO - dpsgd_diffusion.py - 2024-10-23 23:53:57,706 - Loss: 0.2780, step: 60300
+INFO - dpsgd_diffusion.py - 2024-10-23 23:54:56,786 - Loss: 0.2629, step: 60400
+INFO - dpsgd_diffusion.py - 2024-10-23 23:55:56,021 - Loss: 0.2527, step: 60500
+INFO - dpsgd_diffusion.py - 2024-10-23 23:56:20,709 - Eps-value after 86 epochs: 0.7424
+INFO - dpsgd_diffusion.py - 2024-10-23 23:56:52,866 - Loss: 0.2801, step: 60600
+INFO - dpsgd_diffusion.py - 2024-10-23 23:57:50,492 - Loss: 0.2521, step: 60700
+INFO - dpsgd_diffusion.py - 2024-10-23 23:58:50,797 - Loss: 0.2473, step: 60800
+INFO - dpsgd_diffusion.py - 2024-10-23 23:59:49,101 - Loss: 0.2677, step: 60900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:00:46,275 - Loss: 0.2996, step: 61000
+INFO - dpsgd_diffusion.py - 2024-10-24 00:01:43,292 - Loss: 0.2965, step: 61100
+INFO - dpsgd_diffusion.py - 2024-10-24 00:02:39,507 - Loss: 0.2868, step: 61200
+INFO - dpsgd_diffusion.py - 2024-10-24 00:03:06,926 - Eps-value after 87 epochs: 0.7470
+INFO - dpsgd_diffusion.py - 2024-10-24 00:03:37,327 - Loss: 0.2721, step: 61300
+INFO - dpsgd_diffusion.py - 2024-10-24 00:04:34,874 - Loss: 0.2743, step: 61400
+INFO - dpsgd_diffusion.py - 2024-10-24 00:05:31,296 - Loss: 0.2629, step: 61500
+INFO - dpsgd_diffusion.py - 2024-10-24 00:06:29,256 - Loss: 0.3158, step: 61600
+INFO - dpsgd_diffusion.py - 2024-10-24 00:07:27,034 - Loss: 0.2672, step: 61700
+INFO - dpsgd_diffusion.py - 2024-10-24 00:08:24,825 - Loss: 0.2949, step: 61800
+INFO - dpsgd_diffusion.py - 2024-10-24 00:09:22,049 - Loss: 0.2897, step: 61900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:09:52,371 - Eps-value after 88 epochs: 0.7515
+INFO - dpsgd_diffusion.py - 2024-10-24 00:10:21,300 - Loss: 0.3176, step: 62000
+INFO - dpsgd_diffusion.py - 2024-10-24 00:10:21,351 - Saving snapshot checkpoint and sampling single batch at iteration 62000.
+WARNING - image.py - 2024-10-24 00:10:21,881 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 00:10:37,327 - FID at iteration 62000: 246.448579
+INFO - dpsgd_diffusion.py - 2024-10-24 00:11:35,891 - Loss: 0.2735, step: 62100
+INFO - dpsgd_diffusion.py - 2024-10-24 00:12:33,648 - Loss: 0.2866, step: 62200
+INFO - dpsgd_diffusion.py - 2024-10-24 00:13:32,628 - Loss: 0.2646, step: 62300
+INFO - dpsgd_diffusion.py - 2024-10-24 00:14:29,511 - Loss: 0.2758, step: 62400
+INFO - dpsgd_diffusion.py - 2024-10-24 00:15:27,635 - Loss: 0.2532, step: 62500
+INFO - dpsgd_diffusion.py - 2024-10-24 00:16:26,511 - Loss: 0.2665, step: 62600
+INFO - dpsgd_diffusion.py - 2024-10-24 00:16:59,405 - Eps-value after 89 epochs: 0.7560
+INFO - dpsgd_diffusion.py - 2024-10-24 00:17:24,518 - Loss: 0.2968, step: 62700
+INFO - dpsgd_diffusion.py - 2024-10-24 00:18:22,334 - Loss: 0.2624, step: 62800
+INFO - dpsgd_diffusion.py - 2024-10-24 00:19:20,365 - Loss: 0.2928, step: 62900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:20:18,360 - Loss: 0.2615, step: 63000
+INFO - dpsgd_diffusion.py - 2024-10-24 00:21:17,362 - Loss: 0.2906, step: 63100
+INFO - dpsgd_diffusion.py - 2024-10-24 00:22:17,020 - Loss: 0.2678, step: 63200
+INFO - dpsgd_diffusion.py - 2024-10-24 00:23:14,394 - Loss: 0.2754, step: 63300
+INFO - dpsgd_diffusion.py - 2024-10-24 00:23:48,890 - Eps-value after 90 epochs: 0.7605
+INFO - dpsgd_diffusion.py - 2024-10-24 00:24:13,110 - Loss: 0.2807, step: 63400
+INFO - dpsgd_diffusion.py - 2024-10-24 00:25:12,741 - Loss: 0.2784, step: 63500
+INFO - dpsgd_diffusion.py - 2024-10-24 00:26:09,888 - Loss: 0.2902, step: 63600
+INFO - dpsgd_diffusion.py - 2024-10-24 00:27:08,554 - Loss: 0.2997, step: 63700
+INFO - dpsgd_diffusion.py - 2024-10-24 00:28:05,386 - Loss: 0.2804, step: 63800
+INFO - dpsgd_diffusion.py - 2024-10-24 00:29:02,663 - Loss: 0.2694, step: 63900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:29:59,658 - Loss: 0.2660, step: 64000
+INFO - dpsgd_diffusion.py - 2024-10-24 00:29:59,664 - Saving snapshot checkpoint and sampling single batch at iteration 64000.
+WARNING - image.py - 2024-10-24 00:30:00,180 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 00:30:15,524 - FID at iteration 64000: 244.212165
+INFO - dpsgd_diffusion.py - 2024-10-24 00:30:52,608 - Eps-value after 91 epochs: 0.7650
+INFO - dpsgd_diffusion.py - 2024-10-24 00:31:13,418 - Loss: 0.2690, step: 64100
+INFO - dpsgd_diffusion.py - 2024-10-24 00:32:11,390 - Loss: 0.2482, step: 64200
+INFO - dpsgd_diffusion.py - 2024-10-24 00:33:09,686 - Loss: 0.2755, step: 64300
+INFO - dpsgd_diffusion.py - 2024-10-24 00:34:07,932 - Loss: 0.2515, step: 64400
+INFO - dpsgd_diffusion.py - 2024-10-24 00:35:05,566 - Loss: 0.2851, step: 64500
+INFO - dpsgd_diffusion.py - 2024-10-24 00:36:03,971 - Loss: 0.2626, step: 64600
+INFO - dpsgd_diffusion.py - 2024-10-24 00:37:01,923 - Loss: 0.2802, step: 64700
+INFO - dpsgd_diffusion.py - 2024-10-24 00:37:41,225 - Eps-value after 92 epochs: 0.7696
+INFO - dpsgd_diffusion.py - 2024-10-24 00:38:00,363 - Loss: 0.2627, step: 64800
+INFO - dpsgd_diffusion.py - 2024-10-24 00:38:58,844 - Loss: 0.2744, step: 64900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:39:58,345 - Loss: 0.2928, step: 65000
+INFO - dpsgd_diffusion.py - 2024-10-24 00:40:55,573 - Loss: 0.2717, step: 65100
+INFO - dpsgd_diffusion.py - 2024-10-24 00:41:52,568 - Loss: 0.2853, step: 65200
+INFO - dpsgd_diffusion.py - 2024-10-24 00:42:51,766 - Loss: 0.2720, step: 65300
+INFO - dpsgd_diffusion.py - 2024-10-24 00:43:49,099 - Loss: 0.2659, step: 65400
+INFO - dpsgd_diffusion.py - 2024-10-24 00:44:30,806 - Eps-value after 93 epochs: 0.7741
+INFO - dpsgd_diffusion.py - 2024-10-24 00:44:47,513 - Loss: 0.2749, step: 65500
+INFO - dpsgd_diffusion.py - 2024-10-24 00:45:44,036 - Loss: 0.2422, step: 65600
+INFO - dpsgd_diffusion.py - 2024-10-24 00:46:41,320 - Loss: 0.3035, step: 65700
+INFO - dpsgd_diffusion.py - 2024-10-24 00:47:39,581 - Loss: 0.2504, step: 65800
+INFO - dpsgd_diffusion.py - 2024-10-24 00:48:37,881 - Loss: 0.2774, step: 65900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:49:35,605 - Loss: 0.2557, step: 66000
+INFO - dpsgd_diffusion.py - 2024-10-24 00:49:35,644 - Saving snapshot checkpoint and sampling single batch at iteration 66000.
+WARNING - image.py - 2024-10-24 00:49:36,154 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 00:49:51,670 - FID at iteration 66000: 239.774039
+INFO - dpsgd_diffusion.py - 2024-10-24 00:50:49,142 - Loss: 0.2924, step: 66100
+INFO - dpsgd_diffusion.py - 2024-10-24 00:51:34,244 - Eps-value after 94 epochs: 0.7785
+INFO - dpsgd_diffusion.py - 2024-10-24 00:51:48,579 - Loss: 0.2739, step: 66200
+INFO - dpsgd_diffusion.py - 2024-10-24 00:52:46,493 - Loss: 0.2842, step: 66300
+INFO - dpsgd_diffusion.py - 2024-10-24 00:53:43,610 - Loss: 0.2779, step: 66400
+INFO - dpsgd_diffusion.py - 2024-10-24 00:54:41,294 - Loss: 0.2564, step: 66500
+INFO - dpsgd_diffusion.py - 2024-10-24 00:55:40,194 - Loss: 0.2769, step: 66600
+INFO - dpsgd_diffusion.py - 2024-10-24 00:56:38,900 - Loss: 0.2760, step: 66700
+INFO - dpsgd_diffusion.py - 2024-10-24 00:57:35,442 - Loss: 0.2490, step: 66800
+INFO - dpsgd_diffusion.py - 2024-10-24 00:58:22,723 - Eps-value after 95 epochs: 0.7829
+INFO - dpsgd_diffusion.py - 2024-10-24 00:58:34,860 - Loss: 0.2523, step: 66900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:59:32,084 - Loss: 0.2708, step: 67000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:00:29,949 - Loss: 0.2642, step: 67100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:01:26,892 - Loss: 0.2560, step: 67200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:02:23,654 - Loss: 0.2725, step: 67300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:03:22,474 - Loss: 0.2703, step: 67400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:04:19,863 - Loss: 0.2556, step: 67500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:05:09,283 - Eps-value after 96 epochs: 0.7872
+INFO - dpsgd_diffusion.py - 2024-10-24 01:05:19,535 - Loss: 0.2877, step: 67600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:06:19,046 - Loss: 0.3076, step: 67700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:07:17,239 - Loss: 0.2814, step: 67800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:08:14,299 - Loss: 0.2638, step: 67900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:09:11,656 - Loss: 0.2676, step: 68000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:09:11,677 - Saving snapshot checkpoint and sampling single batch at iteration 68000.
+WARNING - image.py - 2024-10-24 01:09:12,204 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 01:09:27,696 - FID at iteration 68000: 236.915911
+INFO - dpsgd_diffusion.py - 2024-10-24 01:10:25,688 - Loss: 0.2642, step: 68100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:11:24,936 - Loss: 0.2658, step: 68200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:12:15,185 - Eps-value after 97 epochs: 0.7916
+INFO - dpsgd_diffusion.py - 2024-10-24 01:12:22,418 - Loss: 0.2757, step: 68300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:13:20,347 - Loss: 0.2902, step: 68400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:14:18,921 - Loss: 0.2695, step: 68500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:15:18,371 - Loss: 0.2818, step: 68600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:16:18,235 - Loss: 0.2724, step: 68700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:17:15,858 - Loss: 0.2618, step: 68800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:18:14,720 - Loss: 0.2538, step: 68900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:19:09,347 - Eps-value after 98 epochs: 0.7959
+INFO - dpsgd_diffusion.py - 2024-10-24 01:19:14,115 - Loss: 0.2555, step: 69000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:20:15,065 - Loss: 0.2623, step: 69100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:21:14,433 - Loss: 0.2567, step: 69200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:22:11,717 - Loss: 0.2845, step: 69300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:23:08,884 - Loss: 0.2733, step: 69400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:24:06,594 - Loss: 0.2780, step: 69500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:25:05,442 - Loss: 0.2595, step: 69600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:25:59,981 - Eps-value after 99 epochs: 0.8003
+INFO - dpsgd_diffusion.py - 2024-10-24 01:26:02,393 - Loss: 0.2892, step: 69700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:27:00,728 - Loss: 0.2642, step: 69800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:27:59,624 - Loss: 0.2659, step: 69900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:28:59,729 - Loss: 0.2675, step: 70000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:28:59,821 - Saving snapshot checkpoint and sampling single batch at iteration 70000.
+WARNING - image.py - 2024-10-24 01:29:00,327 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 01:29:15,832 - FID at iteration 70000: 233.189566
+INFO - dpsgd_diffusion.py - 2024-10-24 01:30:15,445 - Loss: 0.2734, step: 70100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:31:13,315 - Loss: 0.2850, step: 70200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:32:11,259 - Loss: 0.2892, step: 70300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:33:10,011 - Loss: 0.2891, step: 70400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:33:10,021 - Eps-value after 100 epochs: 0.8046
+INFO - dpsgd_diffusion.py - 2024-10-24 01:34:08,102 - Loss: 0.2651, step: 70500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:35:06,379 - Loss: 0.2622, step: 70600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:36:04,252 - Loss: 0.2737, step: 70700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:37:02,014 - Loss: 0.2691, step: 70800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:38:00,388 - Loss: 0.3051, step: 70900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:38:58,383 - Loss: 0.2514, step: 71000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:39:55,481 - Loss: 0.2966, step: 71100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:39:57,707 - Eps-value after 101 epochs: 0.8089
+INFO - dpsgd_diffusion.py - 2024-10-24 01:40:55,436 - Loss: 0.2556, step: 71200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:41:53,811 - Loss: 0.2747, step: 71300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:42:50,521 - Loss: 0.2666, step: 71400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:43:47,746 - Loss: 0.2752, step: 71500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:44:45,690 - Loss: 0.2570, step: 71600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:45:43,849 - Loss: 0.2395, step: 71700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:46:40,747 - Loss: 0.2548, step: 71800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:46:45,284 - Eps-value after 102 epochs: 0.8133
+INFO - dpsgd_diffusion.py - 2024-10-24 01:47:39,320 - Loss: 0.2546, step: 71900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:48:36,383 - Loss: 0.2437, step: 72000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:48:36,388 - Saving snapshot checkpoint and sampling single batch at iteration 72000.
+WARNING - image.py - 2024-10-24 01:48:36,897 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 01:48:52,297 - FID at iteration 72000: 230.779667
+INFO - dpsgd_diffusion.py - 2024-10-24 01:49:49,394 - Loss: 0.2516, step: 72100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:50:48,257 - Loss: 0.2793, step: 72200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:51:46,365 - Loss: 0.2555, step: 72300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:52:44,749 - Loss: 0.2577, step: 72400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:53:43,798 - Loss: 0.2884, step: 72500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:53:50,872 - Eps-value after 103 epochs: 0.8175
+INFO - dpsgd_diffusion.py - 2024-10-24 01:54:42,286 - Loss: 0.2786, step: 72600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:55:39,051 - Loss: 0.2756, step: 72700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:56:37,861 - Loss: 0.2841, step: 72800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:57:35,544 - Loss: 0.2551, step: 72900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:58:33,705 - Loss: 0.2928, step: 73000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:59:31,008 - Loss: 0.2829, step: 73100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:00:29,046 - Loss: 0.2727, step: 73200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:00:37,901 - Eps-value after 104 epochs: 0.8217
+INFO - dpsgd_diffusion.py - 2024-10-24 02:01:26,135 - Loss: 0.2810, step: 73300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:02:23,684 - Loss: 0.2976, step: 73400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:03:23,070 - Loss: 0.2828, step: 73500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:04:20,632 - Loss: 0.2697, step: 73600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:05:19,114 - Loss: 0.2463, step: 73700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:06:16,900 - Loss: 0.2561, step: 73800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:07:15,778 - Loss: 0.2518, step: 73900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:07:27,296 - Eps-value after 105 epochs: 0.8258
+INFO - dpsgd_diffusion.py - 2024-10-24 02:08:14,254 - Loss: 0.2729, step: 74000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:08:14,309 - Saving snapshot checkpoint and sampling single batch at iteration 74000.
+WARNING - image.py - 2024-10-24 02:08:14,808 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 02:08:30,470 - FID at iteration 74000: 228.707611
+INFO - dpsgd_diffusion.py - 2024-10-24 02:09:28,269 - Loss: 0.2664, step: 74100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:10:25,665 - Loss: 0.2884, step: 74200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:11:23,530 - Loss: 0.2771, step: 74300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:12:21,341 - Loss: 0.2598, step: 74400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:13:18,925 - Loss: 0.2942, step: 74500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:14:16,927 - Loss: 0.2635, step: 74600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:14:30,036 - Eps-value after 106 epochs: 0.8300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:15:14,058 - Loss: 0.2642, step: 74700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:16:12,438 - Loss: 0.2561, step: 74800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:17:11,481 - Loss: 0.2466, step: 74900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:18:10,833 - Loss: 0.2790, step: 75000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:19:08,827 - Loss: 0.2717, step: 75100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:20:06,817 - Loss: 0.2645, step: 75200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:21:05,859 - Loss: 0.2587, step: 75300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:21:22,038 - Eps-value after 107 epochs: 0.8342
+INFO - dpsgd_diffusion.py - 2024-10-24 02:22:04,276 - Loss: 0.2572, step: 75400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:23:03,983 - Loss: 0.2775, step: 75500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:24:01,054 - Loss: 0.2597, step: 75600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:24:57,417 - Loss: 0.2752, step: 75700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:25:58,397 - Loss: 0.2672, step: 75800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:26:55,000 - Loss: 0.2840, step: 75900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:27:53,157 - Loss: 0.2912, step: 76000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:27:53,160 - Saving snapshot checkpoint and sampling single batch at iteration 76000.
+WARNING - image.py - 2024-10-24 02:27:53,666 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 02:28:09,061 - FID at iteration 76000: 228.460948
+INFO - dpsgd_diffusion.py - 2024-10-24 02:28:27,075 - Eps-value after 108 epochs: 0.8383
+INFO - dpsgd_diffusion.py - 2024-10-24 02:29:07,471 - Loss: 0.2706, step: 76100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:30:06,098 - Loss: 0.2692, step: 76200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:31:04,955 - Loss: 0.2577, step: 76300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:32:03,861 - Loss: 0.2562, step: 76400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:33:03,785 - Loss: 0.2838, step: 76500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:34:00,947 - Loss: 0.2767, step: 76600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:35:00,349 - Loss: 0.2673, step: 76700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:35:20,210 - Eps-value after 109 epochs: 0.8425
+INFO - dpsgd_diffusion.py - 2024-10-24 02:35:57,058 - Loss: 0.2854, step: 76800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:36:55,408 - Loss: 0.2541, step: 76900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:37:52,633 - Loss: 0.2864, step: 77000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:38:51,750 - Loss: 0.2732, step: 77100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:39:50,243 - Loss: 0.2692, step: 77200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:40:48,740 - Loss: 0.2575, step: 77300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:41:47,009 - Loss: 0.2553, step: 77400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:42:10,555 - Eps-value after 110 epochs: 0.8467
+INFO - dpsgd_diffusion.py - 2024-10-24 02:42:44,496 - Loss: 0.3096, step: 77500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:43:42,081 - Loss: 0.2661, step: 77600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:44:40,231 - Loss: 0.2789, step: 77700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:45:38,817 - Loss: 0.2522, step: 77800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:46:37,574 - Loss: 0.2443, step: 77900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:47:37,010 - Loss: 0.2603, step: 78000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:47:37,072 - Saving snapshot checkpoint and sampling single batch at iteration 78000.
+WARNING - image.py - 2024-10-24 02:47:37,589 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 02:47:53,140 - FID at iteration 78000: 227.740443
+INFO - dpsgd_diffusion.py - 2024-10-24 02:48:51,250 - Loss: 0.2755, step: 78100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:49:17,234 - Eps-value after 111 epochs: 0.8509
+INFO - dpsgd_diffusion.py - 2024-10-24 02:49:50,103 - Loss: 0.2573, step: 78200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:50:48,656 - Loss: 0.2508, step: 78300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:51:46,821 - Loss: 0.2362, step: 78400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:52:45,930 - Loss: 0.2649, step: 78500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:53:43,854 - Loss: 0.2643, step: 78600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:54:43,731 - Loss: 0.2613, step: 78700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:55:41,362 - Loss: 0.2794, step: 78800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:56:08,987 - Eps-value after 112 epochs: 0.8550
+INFO - dpsgd_diffusion.py - 2024-10-24 02:56:40,693 - Loss: 0.2814, step: 78900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:57:39,269 - Loss: 0.2520, step: 79000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:58:37,646 - Loss: 0.2773, step: 79100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:59:34,570 - Loss: 0.2818, step: 79200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:00:33,792 - Loss: 0.2389, step: 79300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:01:31,533 - Loss: 0.2955, step: 79400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:02:30,287 - Loss: 0.2613, step: 79500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:03:00,090 - Eps-value after 113 epochs: 0.8590
+INFO - dpsgd_diffusion.py - 2024-10-24 03:03:27,797 - Loss: 0.2772, step: 79600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:04:25,790 - Loss: 0.2726, step: 79700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:05:23,500 - Loss: 0.2540, step: 79800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:06:21,746 - Loss: 0.2631, step: 79900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:07:19,273 - Loss: 0.2420, step: 80000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:07:19,278 - Saving snapshot checkpoint and sampling single batch at iteration 80000.
+WARNING - image.py - 2024-10-24 03:07:19,799 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 03:07:35,208 - FID at iteration 80000: 226.080981
+INFO - dpsgd_diffusion.py - 2024-10-24 03:08:34,191 - Loss: 0.2709, step: 80100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:09:33,348 - Loss: 0.2807, step: 80200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:10:04,582 - Eps-value after 114 epochs: 0.8630
+INFO - dpsgd_diffusion.py - 2024-10-24 03:10:29,978 - Loss: 0.2481, step: 80300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:11:27,931 - Loss: 0.2673, step: 80400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:12:25,774 - Loss: 0.2664, step: 80500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:13:23,951 - Loss: 0.2665, step: 80600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:14:20,552 - Loss: 0.2682, step: 80700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:15:18,896 - Loss: 0.2541, step: 80800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:16:16,805 - Loss: 0.2419, step: 80900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:16:51,205 - Eps-value after 115 epochs: 0.8670
+INFO - dpsgd_diffusion.py - 2024-10-24 03:17:14,525 - Loss: 0.2651, step: 81000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:18:12,479 - Loss: 0.2706, step: 81100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:19:11,249 - Loss: 0.2664, step: 81200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:20:10,725 - Loss: 0.2451, step: 81300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:21:08,096 - Loss: 0.2704, step: 81400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:22:07,090 - Loss: 0.2687, step: 81500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:23:03,655 - Loss: 0.2890, step: 81600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:23:40,845 - Eps-value after 116 epochs: 0.8710
+INFO - dpsgd_diffusion.py - 2024-10-24 03:24:02,407 - Loss: 0.2677, step: 81700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:24:59,863 - Loss: 0.2738, step: 81800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:25:58,630 - Loss: 0.2625, step: 81900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:26:56,654 - Loss: 0.2664, step: 82000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:26:56,690 - Saving snapshot checkpoint and sampling single batch at iteration 82000.
+WARNING - image.py - 2024-10-24 03:26:57,243 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 03:27:12,608 - FID at iteration 82000: 225.366614
+INFO - dpsgd_diffusion.py - 2024-10-24 03:28:09,179 - Loss: 0.2744, step: 82100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:29:07,865 - Loss: 0.2668, step: 82200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:30:05,781 - Loss: 0.2920, step: 82300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:30:45,239 - Eps-value after 117 epochs: 0.8750
+INFO - dpsgd_diffusion.py - 2024-10-24 03:31:03,796 - Loss: 0.2750, step: 82400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:32:01,995 - Loss: 0.2621, step: 82500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:33:01,258 - Loss: 0.2821, step: 82600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:33:58,326 - Loss: 0.2815, step: 82700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:34:58,382 - Loss: 0.2690, step: 82800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:35:56,912 - Loss: 0.2658, step: 82900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:36:54,614 - Loss: 0.2795, step: 83000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:37:36,302 - Eps-value after 118 epochs: 0.8790
+INFO - dpsgd_diffusion.py - 2024-10-24 03:37:52,106 - Loss: 0.2579, step: 83100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:38:49,091 - Loss: 0.2417, step: 83200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:39:48,037 - Loss: 0.2326, step: 83300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:40:45,709 - Loss: 0.2611, step: 83400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:41:43,489 - Loss: 0.2751, step: 83500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:42:41,105 - Loss: 0.2552, step: 83600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:43:37,796 - Loss: 0.2709, step: 83700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:44:21,202 - Eps-value after 119 epochs: 0.8830
+INFO - dpsgd_diffusion.py - 2024-10-24 03:44:35,549 - Loss: 0.2786, step: 83800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:45:32,455 - Loss: 0.2715, step: 83900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:46:31,166 - Loss: 0.2403, step: 84000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:46:31,200 - Saving snapshot checkpoint and sampling single batch at iteration 84000.
+WARNING - image.py - 2024-10-24 03:46:31,724 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 03:46:47,067 - FID at iteration 84000: 222.840388
+INFO - dpsgd_diffusion.py - 2024-10-24 03:47:44,694 - Loss: 0.2856, step: 84100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:48:42,314 - Loss: 0.2755, step: 84200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:49:38,894 - Loss: 0.2722, step: 84300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:50:37,256 - Loss: 0.2442, step: 84400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:51:23,470 - Eps-value after 120 epochs: 0.8870
+INFO - dpsgd_diffusion.py - 2024-10-24 03:51:35,001 - Loss: 0.2721, step: 84500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:52:32,145 - Loss: 0.2425, step: 84600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:53:31,067 - Loss: 0.2533, step: 84700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:54:29,215 - Loss: 0.2826, step: 84800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:55:28,235 - Loss: 0.2763, step: 84900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:56:26,277 - Loss: 0.2649, step: 85000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:57:24,335 - Loss: 0.2834, step: 85100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:58:13,904 - Eps-value after 121 epochs: 0.8910
+INFO - dpsgd_diffusion.py - 2024-10-24 03:58:23,201 - Loss: 0.2588, step: 85200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:59:20,713 - Loss: 0.2529, step: 85300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:00:19,281 - Loss: 0.2537, step: 85400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:01:17,536 - Loss: 0.2708, step: 85500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:02:16,151 - Loss: 0.2469, step: 85600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:03:12,820 - Loss: 0.2552, step: 85700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:04:09,369 - Loss: 0.2818, step: 85800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:04:59,623 - Eps-value after 122 epochs: 0.8950
+INFO - dpsgd_diffusion.py - 2024-10-24 04:05:06,819 - Loss: 0.2360, step: 85900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:06:04,642 - Loss: 0.2546, step: 86000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:06:04,694 - Saving snapshot checkpoint and sampling single batch at iteration 86000.
+WARNING - image.py - 2024-10-24 04:06:05,197 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 04:06:20,575 - FID at iteration 86000: 220.908431
+INFO - dpsgd_diffusion.py - 2024-10-24 04:07:16,380 - Loss: 0.2623, step: 86100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:08:14,196 - Loss: 0.2626, step: 86200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:09:11,256 - Loss: 0.2519, step: 86300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:10:08,322 - Loss: 0.2777, step: 86400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:11:07,281 - Loss: 0.2682, step: 86500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:11:59,827 - Eps-value after 123 epochs: 0.8990
+INFO - dpsgd_diffusion.py - 2024-10-24 04:12:04,631 - Loss: 0.2467, step: 86600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:13:02,832 - Loss: 0.2450, step: 86700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:14:01,054 - Loss: 0.2582, step: 86800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:14:59,351 - Loss: 0.2697, step: 86900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:15:57,026 - Loss: 0.2435, step: 87000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:16:53,959 - Loss: 0.2924, step: 87100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:17:50,813 - Loss: 0.2436, step: 87200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:18:46,414 - Eps-value after 124 epochs: 0.9029
+INFO - dpsgd_diffusion.py - 2024-10-24 04:18:48,759 - Loss: 0.2766, step: 87300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:19:46,139 - Loss: 0.2900, step: 87400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:20:43,250 - Loss: 0.2805, step: 87500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:21:41,718 - Loss: 0.2561, step: 87600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:22:39,515 - Loss: 0.2816, step: 87700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:23:37,768 - Loss: 0.2854, step: 87800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:24:36,622 - Loss: 0.2726, step: 87900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:25:34,402 - Loss: 0.2535, step: 88000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:25:34,412 - Eps-value after 125 epochs: 0.9067
+INFO - dpsgd_diffusion.py - 2024-10-24 04:25:34,415 - Saving snapshot checkpoint and sampling single batch at iteration 88000.
+WARNING - image.py - 2024-10-24 04:25:34,928 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 04:25:50,294 - FID at iteration 88000: 220.982139
+INFO - dpsgd_diffusion.py - 2024-10-24 04:26:47,551 - Loss: 0.2592, step: 88100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:27:47,621 - Loss: 0.2752, step: 88200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:28:45,971 - Loss: 0.2537, step: 88300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:29:44,831 - Loss: 0.2663, step: 88400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:30:43,905 - Loss: 0.2572, step: 88500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:31:42,152 - Loss: 0.2490, step: 88600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:32:39,620 - Loss: 0.2673, step: 88700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:32:41,920 - Eps-value after 126 epochs: 0.9105
+INFO - dpsgd_diffusion.py - 2024-10-24 04:33:36,943 - Loss: 0.2549, step: 88800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:34:34,750 - Loss: 0.2986, step: 88900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:35:33,055 - Loss: 0.2931, step: 89000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:36:32,285 - Loss: 0.2474, step: 89100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:37:32,132 - Loss: 0.2561, step: 89200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:38:29,973 - Loss: 0.2844, step: 89300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:39:27,596 - Loss: 0.2572, step: 89400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:39:32,206 - Eps-value after 127 epochs: 0.9143
+INFO - dpsgd_diffusion.py - 2024-10-24 04:40:26,186 - Loss: 0.2426, step: 89500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:41:23,107 - Loss: 0.2511, step: 89600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:42:21,665 - Loss: 0.2725, step: 89700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:43:19,369 - Loss: 0.2478, step: 89800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:44:18,988 - Loss: 0.2935, step: 89900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:45:17,892 - Loss: 0.2472, step: 90000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:45:17,907 - Saving snapshot checkpoint and sampling single batch at iteration 90000.
+WARNING - image.py - 2024-10-24 04:45:18,446 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 04:45:33,925 - FID at iteration 90000: 221.250613
+INFO - dpsgd_diffusion.py - 2024-10-24 04:46:33,471 - Loss: 0.2757, step: 90100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:46:40,691 - Eps-value after 128 epochs: 0.9181
+INFO - dpsgd_diffusion.py - 2024-10-24 04:47:32,202 - Loss: 0.2817, step: 90200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:48:30,886 - Loss: 0.2842, step: 90300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:49:28,991 - Loss: 0.2711, step: 90400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:50:28,104 - Loss: 0.2344, step: 90500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:51:26,363 - Loss: 0.2714, step: 90600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:52:25,610 - Loss: 0.2664, step: 90700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:53:23,974 - Loss: 0.2543, step: 90800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:53:33,188 - Eps-value after 129 epochs: 0.9220
+INFO - dpsgd_diffusion.py - 2024-10-24 04:54:22,002 - Loss: 0.2479, step: 90900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:55:21,278 - Loss: 0.2528, step: 91000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:56:20,208 - Loss: 0.2676, step: 91100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:57:18,576 - Loss: 0.2717, step: 91200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:58:17,366 - Loss: 0.2583, step: 91300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:59:13,734 - Loss: 0.2775, step: 91400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:00:12,086 - Loss: 0.2538, step: 91500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:00:23,207 - Eps-value after 130 epochs: 0.9258
+INFO - dpsgd_diffusion.py - 2024-10-24 05:01:09,297 - Loss: 0.2660, step: 91600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:02:06,236 - Loss: 0.2555, step: 91700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:03:04,842 - Loss: 0.2665, step: 91800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:04:02,596 - Loss: 0.2578, step: 91900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:04:59,802 - Loss: 0.2482, step: 92000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:04:59,824 - Saving snapshot checkpoint and sampling single batch at iteration 92000.
+WARNING - image.py - 2024-10-24 05:05:00,330 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 05:05:15,720 - FID at iteration 92000: 216.821479
+INFO - dpsgd_diffusion.py - 2024-10-24 05:06:12,442 - Loss: 0.2650, step: 92100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:07:10,653 - Loss: 0.2611, step: 92200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:07:24,368 - Eps-value after 131 epochs: 0.9296
+INFO - dpsgd_diffusion.py - 2024-10-24 05:08:07,484 - Loss: 0.2545, step: 92300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:09:04,532 - Loss: 0.2451, step: 92400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:10:02,312 - Loss: 0.2675, step: 92500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:11:00,192 - Loss: 0.2554, step: 92600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:11:57,791 - Loss: 0.2714, step: 92700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:12:56,365 - Loss: 0.2664, step: 92800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:13:56,893 - Loss: 0.2474, step: 92900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:14:14,866 - Eps-value after 132 epochs: 0.9334
+INFO - dpsgd_diffusion.py - 2024-10-24 05:14:55,653 - Loss: 0.2728, step: 93000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:15:54,370 - Loss: 0.2590, step: 93100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:16:52,199 - Loss: 0.2647, step: 93200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:17:50,261 - Loss: 0.2567, step: 93300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:18:48,826 - Loss: 0.2707, step: 93400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:19:46,545 - Loss: 0.2653, step: 93500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:20:45,163 - Loss: 0.2551, step: 93600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:21:03,670 - Eps-value after 133 epochs: 0.9372
+INFO - dpsgd_diffusion.py - 2024-10-24 05:21:44,351 - Loss: 0.2694, step: 93700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:22:42,081 - Loss: 0.2413, step: 93800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:23:40,133 - Loss: 0.2615, step: 93900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:24:37,764 - Loss: 0.2552, step: 94000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:24:37,779 - Saving snapshot checkpoint and sampling single batch at iteration 94000.
+WARNING - image.py - 2024-10-24 05:24:38,286 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 05:24:53,721 - FID at iteration 94000: 217.748279
+INFO - dpsgd_diffusion.py - 2024-10-24 05:25:51,755 - Loss: 0.2942, step: 94100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:26:49,327 - Loss: 0.2450, step: 94200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:27:47,442 - Loss: 0.2780, step: 94300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:28:07,510 - Eps-value after 134 epochs: 0.9411
+INFO - dpsgd_diffusion.py - 2024-10-24 05:28:44,768 - Loss: 0.2626, step: 94400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:29:44,569 - Loss: 0.2571, step: 94500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:30:43,106 - Loss: 0.2553, step: 94600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:31:41,123 - Loss: 0.2869, step: 94700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:32:38,272 - Loss: 0.2420, step: 94800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:33:36,570 - Loss: 0.2526, step: 94900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:34:34,886 - Loss: 0.2594, step: 95000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:34:58,396 - Eps-value after 135 epochs: 0.9449
+INFO - dpsgd_diffusion.py - 2024-10-24 05:35:32,606 - Loss: 0.2733, step: 95100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:36:30,203 - Loss: 0.2802, step: 95200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:37:28,164 - Loss: 0.2788, step: 95300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:38:27,628 - Loss: 0.2471, step: 95400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:39:25,789 - Loss: 0.2778, step: 95500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:40:23,805 - Loss: 0.2419, step: 95600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:41:20,112 - Loss: 0.2350, step: 95700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:41:44,983 - Eps-value after 136 epochs: 0.9487
+INFO - dpsgd_diffusion.py - 2024-10-24 05:42:16,989 - Loss: 0.2501, step: 95800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:43:15,098 - Loss: 0.2568, step: 95900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:44:12,992 - Loss: 0.2484, step: 96000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:44:12,998 - Saving snapshot checkpoint and sampling single batch at iteration 96000.
+WARNING - image.py - 2024-10-24 05:44:13,512 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 05:44:28,953 - FID at iteration 96000: 214.819546
+INFO - dpsgd_diffusion.py - 2024-10-24 05:45:26,611 - Loss: 0.2454, step: 96100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:46:24,451 - Loss: 0.2508, step: 96200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:47:23,473 - Loss: 0.2677, step: 96300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:48:22,743 - Loss: 0.2386, step: 96400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:48:52,320 - Eps-value after 137 epochs: 0.9524
+INFO - dpsgd_diffusion.py - 2024-10-24 05:49:24,338 - Loss: 0.2809, step: 96500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:50:23,019 - Loss: 0.2247, step: 96600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:51:21,277 - Loss: 0.2586, step: 96700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:52:18,721 - Loss: 0.2522, step: 96800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:53:16,083 - Loss: 0.2668, step: 96900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:54:13,381 - Loss: 0.2746, step: 97000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:55:11,104 - Loss: 0.2559, step: 97100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:55:41,019 - Eps-value after 138 epochs: 0.9561
+INFO - dpsgd_diffusion.py - 2024-10-24 05:56:09,073 - Loss: 0.2760, step: 97200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:57:06,851 - Loss: 0.2572, step: 97300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:58:04,214 - Loss: 0.2884, step: 97400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:59:01,652 - Loss: 0.2578, step: 97500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:00:00,994 - Loss: 0.2520, step: 97600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:00:59,840 - Loss: 0.2464, step: 97700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:01:58,388 - Loss: 0.2436, step: 97800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:02:30,839 - Eps-value after 139 epochs: 0.9597
+INFO - dpsgd_diffusion.py - 2024-10-24 06:02:56,917 - Loss: 0.2785, step: 97900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:03:55,396 - Loss: 0.2624, step: 98000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:03:55,411 - Saving snapshot checkpoint and sampling single batch at iteration 98000.
+WARNING - image.py - 2024-10-24 06:03:55,925 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 06:04:11,466 - FID at iteration 98000: 212.968513
+INFO - dpsgd_diffusion.py - 2024-10-24 06:05:10,907 - Loss: 0.2602, step: 98100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:06:09,326 - Loss: 0.2604, step: 98200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:07:07,373 - Loss: 0.2825, step: 98300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:08:06,854 - Loss: 0.2870, step: 98400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:09:04,086 - Loss: 0.2585, step: 98500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:09:37,848 - Eps-value after 140 epochs: 0.9634
+INFO - dpsgd_diffusion.py - 2024-10-24 06:10:00,238 - Loss: 0.2820, step: 98600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:10:57,884 - Loss: 0.2453, step: 98700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:11:55,780 - Loss: 0.2389, step: 98800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:12:55,155 - Loss: 0.2721, step: 98900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:13:53,797 - Loss: 0.2481, step: 99000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:14:50,396 - Loss: 0.2491, step: 99100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:15:47,924 - Loss: 0.2590, step: 99200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:16:24,954 - Eps-value after 141 epochs: 0.9670
+INFO - dpsgd_diffusion.py - 2024-10-24 06:16:45,831 - Loss: 0.2605, step: 99300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:17:44,754 - Loss: 0.2503, step: 99400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:18:43,172 - Loss: 0.2706, step: 99500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:19:41,211 - Loss: 0.2542, step: 99600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:20:39,610 - Loss: 0.2893, step: 99700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:21:36,066 - Loss: 0.2449, step: 99800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:22:32,884 - Loss: 0.2772, step: 99900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:23:13,697 - Eps-value after 142 epochs: 0.9706
+INFO - dpsgd_diffusion.py - 2024-10-24 06:23:32,115 - Loss: 0.2617, step: 100000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:23:32,139 - Saving snapshot checkpoint and sampling single batch at iteration 100000.
+WARNING - image.py - 2024-10-24 06:23:32,647 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 06:23:48,099 - FID at iteration 100000: 213.311497
+INFO - dpsgd_diffusion.py - 2024-10-24 06:23:48,777 - Saving  checkpoint at iteration 100000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:24:46,245 - Loss: 0.2486, step: 100100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:25:44,478 - Loss: 0.2662, step: 100200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:26:43,486 - Loss: 0.2712, step: 100300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:27:42,061 - Loss: 0.2547, step: 100400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:28:38,740 - Loss: 0.2548, step: 100500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:29:37,284 - Loss: 0.2680, step: 100600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:30:19,338 - Eps-value after 143 epochs: 0.9743
+INFO - dpsgd_diffusion.py - 2024-10-24 06:30:35,328 - Loss: 0.2607, step: 100700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:31:31,103 - Loss: 0.2659, step: 100800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:32:28,223 - Loss: 0.2588, step: 100900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:33:25,784 - Loss: 0.2345, step: 101000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:34:24,497 - Loss: 0.2638, step: 101100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:35:23,558 - Loss: 0.2482, step: 101200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:36:23,052 - Loss: 0.2744, step: 101300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:37:06,521 - Eps-value after 144 epochs: 0.9779
+INFO - dpsgd_diffusion.py - 2024-10-24 06:37:20,711 - Loss: 0.2483, step: 101400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:38:18,833 - Loss: 0.2392, step: 101500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:39:16,808 - Loss: 0.2526, step: 101600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:40:14,785 - Loss: 0.2275, step: 101700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:41:13,039 - Loss: 0.2530, step: 101800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:42:10,596 - Loss: 0.2715, step: 101900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:43:09,604 - Loss: 0.2485, step: 102000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:43:09,643 - Saving snapshot checkpoint and sampling single batch at iteration 102000.
+WARNING - image.py - 2024-10-24 06:43:10,169 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 06:43:25,587 - FID at iteration 102000: 211.957541
+INFO - dpsgd_diffusion.py - 2024-10-24 06:44:12,564 - Eps-value after 145 epochs: 0.9816
+INFO - dpsgd_diffusion.py - 2024-10-24 06:44:24,502 - Loss: 0.2624, step: 102100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:45:21,108 - Loss: 0.2576, step: 102200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:46:18,738 - Loss: 0.2963, step: 102300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:47:14,953 - Loss: 0.2676, step: 102400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:48:15,624 - Loss: 0.2631, step: 102500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:49:13,554 - Loss: 0.2220, step: 102600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:50:10,164 - Loss: 0.2742, step: 102700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:51:00,881 - Eps-value after 146 epochs: 0.9852
+INFO - dpsgd_diffusion.py - 2024-10-24 06:51:10,540 - Loss: 0.2795, step: 102800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:52:07,567 - Loss: 0.2492, step: 102900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:53:04,852 - Loss: 0.2890, step: 103000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:54:04,176 - Loss: 0.2661, step: 103100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:55:03,704 - Loss: 0.2617, step: 103200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:56:01,088 - Loss: 0.2490, step: 103300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:56:59,642 - Loss: 0.2774, step: 103400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:57:49,413 - Eps-value after 147 epochs: 0.9889
+INFO - dpsgd_diffusion.py - 2024-10-24 06:57:56,449 - Loss: 0.2415, step: 103500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:58:54,377 - Loss: 0.2902, step: 103600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:59:53,193 - Loss: 0.2413, step: 103700
+INFO - dpsgd_diffusion.py - 2024-10-24 07:00:50,512 - Loss: 0.2721, step: 103800
+INFO - dpsgd_diffusion.py - 2024-10-24 07:01:48,415 - Loss: 0.2680, step: 103900
+INFO - dpsgd_diffusion.py - 2024-10-24 07:02:46,596 - Loss: 0.2704, step: 104000
+INFO - dpsgd_diffusion.py - 2024-10-24 07:02:46,602 - Saving snapshot checkpoint and sampling single batch at iteration 104000.
+WARNING - image.py - 2024-10-24 07:02:47,122 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 07:03:02,468 - FID at iteration 104000: 213.775498
+INFO - dpsgd_diffusion.py - 2024-10-24 07:04:00,572 - Loss: 0.2684, step: 104100
+INFO - dpsgd_diffusion.py - 2024-10-24 07:04:55,227 - Eps-value after 148 epochs: 0.9925
+INFO - dpsgd_diffusion.py - 2024-10-24 07:04:59,988 - Loss: 0.2768, step: 104200
+INFO - dpsgd_diffusion.py - 2024-10-24 07:05:57,824 - Loss: 0.2738, step: 104300
+INFO - dpsgd_diffusion.py - 2024-10-24 07:06:54,967 - Loss: 0.2749, step: 104400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:07:52,384 - Loss: 0.2597, step: 104500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:08:51,521 - Loss: 0.2755, step: 104600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:09:49,683 - Loss: 0.2778, step: 104700
+INFO - dpsgd_diffusion.py - 2024-10-24 07:10:47,510 - Loss: 0.2878, step: 104800
+INFO - dpsgd_diffusion.py - 2024-10-24 07:11:42,475 - Eps-value after 149 epochs: 0.9962
+INFO - dpsgd_diffusion.py - 2024-10-24 07:11:44,812 - Loss: 0.2647, step: 104900
+INFO - dpsgd_diffusion.py - 2024-10-24 07:12:42,331 - Loss: 0.2470, step: 105000
+INFO - dpsgd_diffusion.py - 2024-10-24 07:13:39,553 - Loss: 0.2629, step: 105100
+INFO - dpsgd_diffusion.py - 2024-10-24 07:14:37,129 - Loss: 0.3023, step: 105200
+INFO - dpsgd_diffusion.py - 2024-10-24 07:15:35,434 - Loss: 0.2522, step: 105300
+INFO - dpsgd_diffusion.py - 2024-10-24 07:16:34,407 - Loss: 0.2497, step: 105400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:17:33,251 - Loss: 0.2780, step: 105500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:18:32,157 - Loss: 0.2401, step: 105600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:18:32,167 - Eps-value after 150 epochs: 0.9998
+INFO - dpsgd_diffusion.py - 2024-10-24 07:18:32,826 - Saving final checkpoint.
+INFO - dpsgd_diffusion.py - 2024-10-24 07:18:32,829 - start to generate 60000 samples
+INFO - dpsgd_diffusion.py - 2024-10-24 07:30:53,277 - Generation Finished!
+INFO - dataset_loader.py - 2024-10-24 22:50:38,929 - delta is reset as 2.07404851125286e-06
+INFO - evaluator.py - 2024-10-24 22:51:42,797 - Epoch: 0 Train acc: 12.345454545454546 Val acc: 16.04 Test acc16.64; Train loss: 0.019152536686983974 Val loss: 0.0038591532230377195
+INFO - evaluator.py - 2024-10-24 22:52:26,189 - Epoch: 1 Train acc: 20.94181818181818 Val acc: 14.32 Test acc14.69; Train loss: 0.015907720203833145 Val loss: 0.03522734222412109
+INFO - evaluator.py - 2024-10-24 22:53:10,932 - Epoch: 2 Train acc: 28.50909090909091 Val acc: 10.34 Test acc10.96; Train loss: 0.014460172889449379 Val loss: 0.8980123168945312
+INFO - evaluator.py - 2024-10-24 22:53:56,661 - Epoch: 3 Train acc: 36.28545454545454 Val acc: 9.700000000000001 Test acc10.42; Train loss: 0.012659973456642845 Val loss: 8.82157890625
+INFO - evaluator.py - 2024-10-24 22:54:40,452 - Epoch: 4 Train acc: 40.96181818181818 Val acc: 10.100000000000001 Test acc10.6; Train loss: 0.011724157058108937 Val loss: 47.9229640625
+INFO - evaluator.py - 2024-10-24 22:55:25,414 - Epoch: 5 Train acc: 47.18727272727273 Val acc: 9.36 Test acc10.05; Train loss: 0.010747470133954828 Val loss: 52.17366171875
+INFO - evaluator.py - 2024-10-24 22:56:10,797 - Epoch: 6 Train acc: 57.470909090909096 Val acc: 9.34 Test acc10.01; Train loss: 0.008714992159063167 Val loss: 436.346525
+INFO - evaluator.py - 2024-10-24 22:56:56,512 - Epoch: 7 Train acc: 80.83272727272727 Val acc: 10.16 Test acc10.0; Train loss: 0.0042654643741520965 Val loss: 1540.8262
+INFO - evaluator.py - 2024-10-24 22:57:43,789 - Epoch: 8 Train acc: 88.79272727272726 Val acc: 10.16 Test acc10.01; Train loss: 0.002548944828997959 Val loss: 196.770165625
+INFO - evaluator.py - 2024-10-24 22:58:27,613 - Epoch: 9 Train acc: 92.00363636363636 Val acc: 10.16 Test acc10.0; Train loss: 0.0018753687024116517 Val loss: 7574.6347
+INFO - evaluator.py - 2024-10-24 22:59:12,252 - Epoch: 10 Train acc: 92.75454545454545 Val acc: 10.16 Test acc10.01; Train loss: 0.001709711799567396 Val loss: 0.03131639633178711
+INFO - evaluator.py - 2024-10-24 22:59:58,221 - Epoch: 11 Train acc: 94.26909090909092 Val acc: 11.559999999999999 Test acc11.469999999999999; Train loss: 0.0013734356074847959 Val loss: 0.1985406707763672
+INFO - evaluator.py - 2024-10-24 23:00:44,018 - Epoch: 12 Train acc: 95.18727272727273 Val acc: 10.92 Test acc10.6; Train loss: 0.0011267945203591477 Val loss: 41.0890765625
+INFO - evaluator.py - 2024-10-24 23:01:30,094 - Epoch: 13 Train acc: 95.61454545454545 Val acc: 11.14 Test acc10.56; Train loss: 0.001068953408842737 Val loss: 0.4911522277832031
+INFO - evaluator.py - 2024-10-24 23:02:15,102 - Epoch: 14 Train acc: 96.11090909090909 Val acc: 10.620000000000001 Test acc10.0; Train loss: 0.0009392685561017556 Val loss: 53.8553890625
+INFO - evaluator.py - 2024-10-24 23:03:00,797 - Epoch: 15 Train acc: 96.52545454545455 Val acc: 10.14 Test acc10.0; Train loss: 0.0008304334660484032 Val loss: 2003.87525
+INFO - evaluator.py - 2024-10-24 23:03:48,384 - Epoch: 16 Train acc: 96.78181818181818 Val acc: 10.26 Test acc10.13; Train loss: 0.000763607754219662 Val loss: 2144.76665
+INFO - evaluator.py - 2024-10-24 23:04:35,088 - Epoch: 17 Train acc: 97.25272727272727 Val acc: 10.100000000000001 Test acc10.0; Train loss: 0.0006595437628301707 Val loss: 1.2164449462890625
+INFO - evaluator.py - 2024-10-24 23:05:21,935 - Epoch: 18 Train acc: 97.02727272727273 Val acc: 14.42 Test acc14.11; Train loss: 0.0007183363248170777 Val loss: 0.02424395751953125
+INFO - evaluator.py - 2024-10-24 23:06:08,804 - Epoch: 19 Train acc: 97.53272727272727 Val acc: 9.180000000000001 Test acc8.76; Train loss: 0.0006003291206095706 Val loss: 0.0332125503540039
+INFO - evaluator.py - 2024-10-24 23:06:57,010 - Epoch: 20 Train acc: 97.8 Val acc: 10.059999999999999 Test acc9.21; Train loss: 0.0005324538295919245 Val loss: 0.016061045837402345
+INFO - evaluator.py - 2024-10-24 23:07:44,774 - Epoch: 21 Train acc: 97.77090909090909 Val acc: 12.1 Test acc12.13; Train loss: 0.0005444987746124918 Val loss: 0.014419026947021484
+INFO - evaluator.py - 2024-10-24 23:08:32,236 - Epoch: 22 Train acc: 98.40545454545455 Val acc: 16.5 Test acc16.830000000000002; Train loss: 0.000419300721145489 Val loss: 0.018283279418945312
+INFO - evaluator.py - 2024-10-24 23:09:19,985 - Epoch: 23 Train acc: 97.97090909090909 Val acc: 12.479999999999999 Test acc12.41; Train loss: 0.0004958677164194258 Val loss: 0.01949915657043457
+INFO - evaluator.py - 2024-10-24 23:10:06,194 - Epoch: 24 Train acc: 98.01272727272728 Val acc: 15.18 Test acc15.03; Train loss: 0.0004901174025779421 Val loss: 0.028570676040649414
+INFO - evaluator.py - 2024-10-24 23:10:53,479 - Epoch: 25 Train acc: 98.10363636363635 Val acc: 13.04 Test acc12.889999999999999; Train loss: 0.00047054960750551384 Val loss: 0.019289554595947264
+INFO - evaluator.py - 2024-10-24 23:11:39,360 - Epoch: 26 Train acc: 98.47818181818182 Val acc: 14.92 Test acc15.27; Train loss: 0.00039430822857062923 Val loss: 0.02361544723510742
+INFO - evaluator.py - 2024-10-24 23:12:26,038 - Epoch: 27 Train acc: 98.31454545454545 Val acc: 12.82 Test acc13.84; Train loss: 0.000438536366532472 Val loss: 0.02222787437438965
+INFO - evaluator.py - 2024-10-24 23:13:10,448 - Epoch: 28 Train acc: 98.36363636363636 Val acc: 17.72 Test acc17.66; Train loss: 0.000406392795228484 Val loss: 0.022220295333862303
+INFO - evaluator.py - 2024-10-24 23:13:56,505 - Epoch: 29 Train acc: 98.12727272727273 Val acc: 14.14 Test acc14.299999999999999; Train loss: 0.00045841123283925383 Val loss: 0.029562771606445312
+INFO - evaluator.py - 2024-10-24 23:14:41,262 - Epoch: 30 Train acc: 98.57272727272726 Val acc: 9.82 Test acc10.58; Train loss: 0.00036812453128566797 Val loss: 0.03703552169799805
+INFO - evaluator.py - 2024-10-24 23:15:27,256 - Epoch: 31 Train acc: 98.33818181818181 Val acc: 15.72 Test acc15.8; Train loss: 0.00040771088161590423 Val loss: 0.01649148406982422
+INFO - evaluator.py - 2024-10-24 23:16:13,682 - Epoch: 32 Train acc: 98.49818181818182 Val acc: 9.36 Test acc10.02; Train loss: 0.0003856401377696205 Val loss: 0.0387443977355957
+INFO - evaluator.py - 2024-10-24 23:17:00,593 - Epoch: 33 Train acc: 98.2890909090909 Val acc: 18.0 Test acc18.56; Train loss: 0.00043588869885795496 Val loss: 0.012655327033996583
+INFO - evaluator.py - 2024-10-24 23:17:46,448 - Epoch: 34 Train acc: 98.58363636363636 Val acc: 13.08 Test acc13.239999999999998; Train loss: 0.000355906406536021 Val loss: 0.023391894149780275
+INFO - evaluator.py - 2024-10-24 23:18:32,614 - Epoch: 35 Train acc: 98.39272727272727 Val acc: 10.92 Test acc10.620000000000001; Train loss: 0.0003896183706608347 Val loss: 0.024792383193969727
+INFO - evaluator.py - 2024-10-24 23:19:16,884 - Epoch: 36 Train acc: 98.54363636363637 Val acc: 15.659999999999998 Test acc15.85; Train loss: 0.00036279911047101696 Val loss: 0.015946743965148925
+INFO - evaluator.py - 2024-10-24 23:20:03,112 - Epoch: 37 Train acc: 98.38545454545454 Val acc: 10.86 Test acc11.89; Train loss: 0.0004113260635428808 Val loss: 0.020145153427124023
+INFO - evaluator.py - 2024-10-24 23:20:48,443 - Epoch: 38 Train acc: 98.43636363636364 Val acc: 23.74 Test acc22.81; Train loss: 0.0003870007736862383 Val loss: 0.008553846549987794
+INFO - evaluator.py - 2024-10-24 23:21:33,333 - Epoch: 39 Train acc: 98.81818181818181 Val acc: 20.96 Test acc20.53; Train loss: 0.0003000048402696848 Val loss: 0.010484438133239746
+INFO - evaluator.py - 2024-10-24 23:22:19,165 - Epoch: 40 Train acc: 98.33272727272727 Val acc: 14.06 Test acc14.27; Train loss: 0.00040445495523851025 Val loss: 0.019780115509033202
+INFO - evaluator.py - 2024-10-24 23:23:05,083 - Epoch: 41 Train acc: 98.45636363636365 Val acc: 12.08 Test acc12.590000000000002; Train loss: 0.00038192390945570714 Val loss: 0.01784603500366211
+INFO - evaluator.py - 2024-10-24 23:23:49,900 - Epoch: 42 Train acc: 98.7 Val acc: 12.120000000000001 Test acc11.899999999999999; Train loss: 0.00033094826478680426 Val loss: 0.022558807373046875
+INFO - evaluator.py - 2024-10-24 23:24:35,260 - Epoch: 43 Train acc: 98.6509090909091 Val acc: 13.76 Test acc13.120000000000001; Train loss: 0.000335766617085954 Val loss: 0.02389903984069824
+INFO - evaluator.py - 2024-10-24 23:25:20,246 - Epoch: 44 Train acc: 98.5109090909091 Val acc: 10.2 Test acc10.07; Train loss: 0.0003820509042760188 Val loss: 0.040024459075927735
+INFO - evaluator.py - 2024-10-24 23:26:06,440 - Epoch: 45 Train acc: 98.61818181818181 Val acc: 10.14 Test acc10.63; Train loss: 0.00034813228927298704 Val loss: 0.023047268676757812
+INFO - evaluator.py - 2024-10-24 23:26:53,182 - Epoch: 46 Train acc: 98.72909090909091 Val acc: 21.98 Test acc21.63; Train loss: 0.00032053173077716067 Val loss: 0.012382217597961426
+INFO - evaluator.py - 2024-10-24 23:27:38,917 - Epoch: 47 Train acc: 98.29272727272728 Val acc: 13.059999999999999 Test acc13.48; Train loss: 0.00042631830553608864 Val loss: 0.01784613800048828
+INFO - evaluator.py - 2024-10-24 23:28:25,969 - Epoch: 48 Train acc: 98.79636363636364 Val acc: 14.979999999999999 Test acc14.610000000000001; Train loss: 0.0003121919367462397 Val loss: 0.014208633613586426
+INFO - evaluator.py - 2024-10-24 23:29:11,037 - Epoch: 49 Train acc: 98.34727272727272 Val acc: 10.22 Test acc10.16; Train loss: 0.0004084266836162318 Val loss: 0.031061592483520507
+INFO - evaluator.py - 2024-10-24 23:29:57,152 - Epoch: 50 Train acc: 98.81636363636363 Val acc: 12.139999999999999 Test acc12.64; Train loss: 0.00030684025876901366 Val loss: 0.036046534729003904
+INFO - evaluator.py - 2024-10-24 23:30:43,013 - Epoch: 51 Train acc: 98.52909090909091 Val acc: 11.66 Test acc11.08; Train loss: 0.0003700472453016449 Val loss: 0.026629590606689454
+INFO - evaluator.py - 2024-10-24 23:31:28,214 - Epoch: 52 Train acc: 98.65818181818182 Val acc: 10.24 Test acc10.16; Train loss: 0.0003275566268161955 Val loss: 0.02762926025390625
+INFO - evaluator.py - 2024-10-24 23:32:14,804 - Epoch: 53 Train acc: 98.63272727272727 Val acc: 14.42 Test acc14.84; Train loss: 0.0003512313801117919 Val loss: 0.022521040344238283
+INFO - evaluator.py - 2024-10-24 23:33:01,146 - Epoch: 54 Train acc: 98.75636363636363 Val acc: 14.06 Test acc14.21; Train loss: 0.00031919210421514106 Val loss: 0.019195743560791016
+INFO - evaluator.py - 2024-10-24 23:33:46,648 - Epoch: 55 Train acc: 98.66545454545455 Val acc: 14.26 Test acc14.580000000000002; Train loss: 0.00034670285322618757 Val loss: 0.03489190444946289
+INFO - evaluator.py - 2024-10-24 23:34:33,530 - Epoch: 56 Train acc: 98.36363636363636 Val acc: 10.299999999999999 Test acc10.16; Train loss: 0.00041143924403427677 Val loss: 0.02575349998474121
+INFO - evaluator.py - 2024-10-24 23:35:17,851 - Epoch: 57 Train acc: 98.9509090909091 Val acc: 10.66 Test acc10.51; Train loss: 0.00027648768432607704 Val loss: 0.04067393798828125
+INFO - evaluator.py - 2024-10-24 23:36:04,228 - Epoch: 58 Train acc: 98.28545454545454 Val acc: 10.2 Test acc10.66; Train loss: 0.00043761366852982476 Val loss: 0.021410371780395506
+INFO - evaluator.py - 2024-10-24 23:36:49,939 - Epoch: 59 Train acc: 98.91090909090909 Val acc: 10.68 Test acc10.59; Train loss: 0.00029056454766965045 Val loss: 0.038975506591796875
+INFO - evaluator.py - 2024-10-24 23:37:34,406 - Epoch: 60 Train acc: 99.92 Val acc: 17.78 Test acc17.76; Train loss: 3.593214916393415e-05 Val loss: 0.024207668685913085
+INFO - evaluator.py - 2024-10-24 23:38:19,280 - Epoch: 61 Train acc: 99.98181818181818 Val acc: 20.24 Test acc19.41; Train loss: 1.638864789115773e-05 Val loss: 0.021297907638549805
+INFO - evaluator.py - 2024-10-24 23:39:05,137 - Epoch: 62 Train acc: 99.9890909090909 Val acc: 13.96 Test acc13.56; Train loss: 1.3237206608226354e-05 Val loss: 0.038071781921386716
+INFO - evaluator.py - 2024-10-24 23:39:47,430 - Epoch: 63 Train acc: 99.99272727272728 Val acc: 13.320000000000002 Test acc12.4; Train loss: 1.3852597490503368e-05 Val loss: 0.051543180084228514
+INFO - evaluator.py - 2024-10-24 23:40:32,361 - Epoch: 64 Train acc: 99.99818181818182 Val acc: 11.940000000000001 Test acc11.37; Train loss: 1.3272563606204295e-05 Val loss: 0.07867670135498046
+INFO - evaluator.py - 2024-10-24 23:41:17,276 - Epoch: 65 Train acc: 99.99636363636364 Val acc: 11.68 Test acc10.89; Train loss: 1.3482892910145563e-05 Val loss: 0.11482203979492188
+INFO - evaluator.py - 2024-10-24 23:42:01,989 - Epoch: 66 Train acc: 99.98545454545454 Val acc: 11.76 Test acc11.42; Train loss: 1.5183300295823508e-05 Val loss: 0.1321637191772461
+INFO - evaluator.py - 2024-10-24 23:42:46,793 - Epoch: 67 Train acc: 99.99818181818182 Val acc: 11.899999999999999 Test acc11.39; Train loss: 1.3440318558026444e-05 Val loss: 0.16353974609375
+INFO - evaluator.py - 2024-10-24 23:43:32,188 - Epoch: 68 Train acc: 99.99454545454546 Val acc: 11.540000000000001 Test acc11.65; Train loss: 1.3325117928483948e-05 Val loss: 0.1912262176513672
+INFO - evaluator.py - 2024-10-24 23:44:16,908 - Epoch: 69 Train acc: 99.99272727272728 Val acc: 11.42 Test acc11.559999999999999; Train loss: 1.3825618219561875e-05 Val loss: 0.23842035217285157
+INFO - evaluator.py - 2024-10-24 23:45:01,017 - Epoch: 70 Train acc: 100.0 Val acc: 9.700000000000001 Test acc10.440000000000001; Train loss: 1.1788712118074976e-05 Val loss: 0.31373099365234375
+INFO - evaluator.py - 2024-10-24 23:45:44,790 - Epoch: 71 Train acc: 100.0 Val acc: 9.64 Test acc10.26; Train loss: 1.270814581763592e-05 Val loss: 0.3673673095703125
+INFO - evaluator.py - 2024-10-24 23:46:28,438 - Epoch: 72 Train acc: 99.99818181818182 Val acc: 10.4 Test acc11.03; Train loss: 1.1317736702568998e-05 Val loss: 0.4310925598144531
+INFO - evaluator.py - 2024-10-24 23:47:12,704 - Epoch: 73 Train acc: 99.98 Val acc: 12.36 Test acc13.26; Train loss: 1.7436753315004435e-05 Val loss: 0.5485327270507813
+INFO - evaluator.py - 2024-10-24 23:47:56,711 - Epoch: 74 Train acc: 99.97454545454545 Val acc: 12.24 Test acc12.889999999999999; Train loss: 2.073408683539707e-05 Val loss: 0.6238599243164062
+INFO - evaluator.py - 2024-10-24 23:48:40,932 - Epoch: 75 Train acc: 99.98 Val acc: 10.38 Test acc10.780000000000001; Train loss: 2.2724257109009408e-05 Val loss: 1.0383649291992187
+INFO - evaluator.py - 2024-10-24 23:49:26,014 - Epoch: 76 Train acc: 99.91818181818182 Val acc: 10.08 Test acc10.0; Train loss: 3.864384298530323e-05 Val loss: 1.737174365234375
+INFO - evaluator.py - 2024-10-24 23:50:11,726 - Epoch: 77 Train acc: 99.88909090909091 Val acc: 10.100000000000001 Test acc10.03; Train loss: 4.9621377853592014e-05 Val loss: 1.13999521484375
+INFO - evaluator.py - 2024-10-24 23:50:56,083 - Epoch: 78 Train acc: 99.75454545454545 Val acc: 9.379999999999999 Test acc10.209999999999999; Train loss: 8.572240736631846e-05 Val loss: 0.4744546936035156
+INFO - evaluator.py - 2024-10-24 23:51:41,661 - Epoch: 79 Train acc: 99.70909090909092 Val acc: 10.440000000000001 Test acc10.040000000000001; Train loss: 9.09784634671682e-05 Val loss: 0.33435894165039065
+INFO - evaluator.py - 2024-10-24 23:52:25,911 - Epoch: 80 Train acc: 99.64727272727274 Val acc: 10.94 Test acc11.450000000000001; Train loss: 0.00010630712215629914 Val loss: 0.09163599090576172
+INFO - evaluator.py - 2024-10-24 23:53:07,174 - Epoch: 81 Train acc: 99.57090909090908 Val acc: 10.280000000000001 Test acc11.01; Train loss: 0.00012320133750538595 Val loss: 0.10216260681152343
+INFO - evaluator.py - 2024-10-24 23:53:48,602 - Epoch: 82 Train acc: 99.71454545454546 Val acc: 13.26 Test acc13.08; Train loss: 9.413825958247551e-05 Val loss: 0.0703307846069336
+INFO - evaluator.py - 2024-10-24 23:54:31,749 - Epoch: 83 Train acc: 99.72 Val acc: 11.200000000000001 Test acc10.69; Train loss: 8.279834357301959e-05 Val loss: 0.05664826126098633
+INFO - evaluator.py - 2024-10-24 23:55:14,351 - Epoch: 84 Train acc: 99.5890909090909 Val acc: 10.9 Test acc10.43; Train loss: 0.00011378462011079219 Val loss: 0.0426196907043457
+INFO - evaluator.py - 2024-10-24 23:55:58,189 - Epoch: 85 Train acc: 99.88181818181818 Val acc: 10.18 Test acc10.0; Train loss: 4.4384817588037217e-05 Val loss: 0.07943617401123047
+INFO - evaluator.py - 2024-10-24 23:56:42,451 - Epoch: 86 Train acc: 99.75818181818182 Val acc: 11.600000000000001 Test acc11.459999999999999; Train loss: 7.173063834832811e-05 Val loss: 0.05752346115112305
+INFO - evaluator.py - 2024-10-24 23:57:25,545 - Epoch: 87 Train acc: 99.81272727272727 Val acc: 10.280000000000001 Test acc10.07; Train loss: 6.337463140212508e-05 Val loss: 0.06510349578857422
+INFO - evaluator.py - 2024-10-24 23:58:09,292 - Epoch: 88 Train acc: 99.6509090909091 Val acc: 11.459999999999999 Test acc12.479999999999999; Train loss: 9.840036571999504e-05 Val loss: 0.04392695159912109
+INFO - evaluator.py - 2024-10-24 23:58:52,614 - Epoch: 89 Train acc: 99.70727272727272 Val acc: 16.400000000000002 Test acc15.909999999999998; Train loss: 8.697542800747958e-05 Val loss: 0.018358598709106444
+INFO - evaluator.py - 2024-10-24 23:59:36,337 - Epoch: 90 Train acc: 99.86545454545454 Val acc: 13.5 Test acc12.959999999999999; Train loss: 4.2668510335815055e-05 Val loss: 0.02564464302062988
+INFO - evaluator.py - 2024-10-25 00:00:20,237 - Epoch: 91 Train acc: 99.66909090909091 Val acc: 15.68 Test acc15.85; Train loss: 9.857913137053733e-05 Val loss: 0.020575054931640625
+INFO - evaluator.py - 2024-10-25 00:01:06,049 - Epoch: 92 Train acc: 99.60909090909091 Val acc: 17.96 Test acc17.0; Train loss: 0.00011361135021381249 Val loss: 0.01459389705657959
+INFO - evaluator.py - 2024-10-25 00:01:48,605 - Epoch: 93 Train acc: 99.62363636363636 Val acc: 11.219999999999999 Test acc10.79; Train loss: 0.00011054630772718651 Val loss: 0.016073784637451172
+INFO - evaluator.py - 2024-10-25 00:02:35,181 - Epoch: 94 Train acc: 99.59272727272727 Val acc: 13.320000000000002 Test acc14.04; Train loss: 0.00011650625968732955 Val loss: 0.01199496784210205
+INFO - evaluator.py - 2024-10-25 00:03:21,769 - Epoch: 95 Train acc: 99.87454545454545 Val acc: 10.26 Test acc10.02; Train loss: 4.444331870340234e-05 Val loss: 0.028780784225463867
+INFO - evaluator.py - 2024-10-25 00:04:07,161 - Epoch: 96 Train acc: 99.83999999999999 Val acc: 15.4 Test acc15.340000000000002; Train loss: 5.4679429457544094e-05 Val loss: 0.022483678817749025
+INFO - evaluator.py - 2024-10-25 00:04:51,605 - Epoch: 97 Train acc: 99.87636363636364 Val acc: 13.819999999999999 Test acc13.239999999999998; Train loss: 4.47555714122824e-05 Val loss: 0.0197381649017334
+INFO - evaluator.py - 2024-10-25 00:05:35,257 - Epoch: 98 Train acc: 99.77454545454545 Val acc: 13.639999999999999 Test acc14.399999999999999; Train loss: 7.051538763229143e-05 Val loss: 0.01912631072998047
+INFO - evaluator.py - 2024-10-25 00:06:20,419 - Epoch: 99 Train acc: 99.55636363636363 Val acc: 9.68 Test acc10.27; Train loss: 0.00012939039865529842 Val loss: 0.027315230178833007
+INFO - evaluator.py - 2024-10-25 00:07:04,486 - Epoch: 100 Train acc: 99.58545454545454 Val acc: 10.24 Test acc10.03; Train loss: 0.00012227243270767344 Val loss: 0.01740374755859375
+INFO - evaluator.py - 2024-10-25 00:07:50,017 - Epoch: 101 Train acc: 99.76545454545455 Val acc: 16.580000000000002 Test acc16.36; Train loss: 7.304372681008483e-05 Val loss: 0.008400512504577637
+INFO - evaluator.py - 2024-10-25 00:08:35,816 - Epoch: 102 Train acc: 99.87818181818182 Val acc: 13.819999999999999 Test acc13.600000000000001; Train loss: 3.861285385871518e-05 Val loss: 0.017791930389404298
+INFO - evaluator.py - 2024-10-25 00:09:20,026 - Epoch: 103 Train acc: 99.83272727272727 Val acc: 17.2 Test acc16.31; Train loss: 5.472413556946611e-05 Val loss: 0.017078869247436523
+INFO - evaluator.py - 2024-10-25 00:10:02,681 - Epoch: 104 Train acc: 99.52909090909091 Val acc: 15.459999999999999 Test acc14.12; Train loss: 0.0001238181859805164 Val loss: 0.009855231475830077
+INFO - evaluator.py - 2024-10-25 00:10:46,305 - Epoch: 105 Train acc: 99.76181818181819 Val acc: 14.719999999999999 Test acc14.430000000000001; Train loss: 7.182131995849143e-05 Val loss: 0.01542904109954834
+INFO - evaluator.py - 2024-10-25 00:11:29,792 - Epoch: 106 Train acc: 99.75454545454545 Val acc: 12.94 Test acc12.55; Train loss: 7.158945834539322e-05 Val loss: 0.016448634719848632
+INFO - evaluator.py - 2024-10-25 00:12:13,827 - Epoch: 107 Train acc: 99.77818181818182 Val acc: 13.239999999999998 Test acc14.069999999999999; Train loss: 6.69756844158242e-05 Val loss: 0.020008247375488283
+INFO - evaluator.py - 2024-10-25 00:12:56,299 - Epoch: 108 Train acc: 99.81454545454545 Val acc: 15.58 Test acc15.540000000000001; Train loss: 5.680460513527082e-05 Val loss: 0.010261703491210937
+INFO - evaluator.py - 2024-10-25 00:13:37,423 - Epoch: 109 Train acc: 99.98727272727272 Val acc: 10.08 Test acc10.67; Train loss: 1.387242011974608e-05 Val loss: 0.03606086654663086
+INFO - evaluator.py - 2024-10-25 00:14:22,611 - Epoch: 110 Train acc: 99.41272727272728 Val acc: 14.66 Test acc15.129999999999999; Train loss: 0.00015479103818996173 Val loss: 0.011653146171569824
+INFO - evaluator.py - 2024-10-25 00:15:06,189 - Epoch: 111 Train acc: 99.7309090909091 Val acc: 12.46 Test acc12.42; Train loss: 8.657632826361805e-05 Val loss: 0.011077325439453126
+INFO - evaluator.py - 2024-10-25 00:15:50,130 - Epoch: 112 Train acc: 99.88 Val acc: 14.24 Test acc13.88; Train loss: 4.45717014168622e-05 Val loss: 0.014549190521240235
+INFO - evaluator.py - 2024-10-25 00:16:34,429 - Epoch: 113 Train acc: 99.53818181818181 Val acc: 10.08 Test acc9.99; Train loss: 0.00013174572766927833 Val loss: 0.022886433792114257
+INFO - evaluator.py - 2024-10-25 00:17:18,427 - Epoch: 114 Train acc: 99.61454545454545 Val acc: 13.639999999999999 Test acc13.48; Train loss: 0.00010333534380929036 Val loss: 0.015777749252319336
+INFO - evaluator.py - 2024-10-25 00:18:03,108 - Epoch: 115 Train acc: 99.65636363636364 Val acc: 13.459999999999999 Test acc13.120000000000001; Train loss: 9.78160472988913e-05 Val loss: 0.01423818187713623
+INFO - evaluator.py - 2024-10-25 00:18:46,688 - Epoch: 116 Train acc: 99.69090909090909 Val acc: 23.64 Test acc22.74; Train loss: 9.055650135533968e-05 Val loss: 0.006287332057952881
+INFO - evaluator.py - 2024-10-25 00:19:30,768 - Epoch: 117 Train acc: 99.87272727272727 Val acc: 13.100000000000001 Test acc13.209999999999999; Train loss: 4.074988928253085e-05 Val loss: 0.014079090690612793
+INFO - evaluator.py - 2024-10-25 00:20:15,425 - Epoch: 118 Train acc: 99.97636363636364 Val acc: 13.639999999999999 Test acc13.81; Train loss: 1.5653984446544202e-05 Val loss: 0.01922363739013672
+INFO - evaluator.py - 2024-10-25 00:20:59,753 - Epoch: 119 Train acc: 99.99818181818182 Val acc: 16.76 Test acc15.22; Train loss: 6.573567641581493e-06 Val loss: 0.021077651977539063
+INFO - evaluator.py - 2024-10-25 00:21:42,568 - Epoch: 120 Train acc: 100.0 Val acc: 15.58 Test acc14.430000000000001; Train loss: 4.973408794400959e-06 Val loss: 0.02368329658508301
+INFO - evaluator.py - 2024-10-25 00:22:26,107 - Epoch: 121 Train acc: 99.99636363636364 Val acc: 15.840000000000002 Test acc14.57; Train loss: 5.600223770703782e-06 Val loss: 0.023443709564208985
+INFO - evaluator.py - 2024-10-25 00:23:10,141 - Epoch: 122 Train acc: 99.99818181818182 Val acc: 14.78 Test acc13.850000000000001; Train loss: 5.3805337337755856e-06 Val loss: 0.02541568946838379
+INFO - evaluator.py - 2024-10-25 00:23:54,868 - Epoch: 123 Train acc: 99.99818181818182 Val acc: 14.64 Test acc13.59; Train loss: 5.439578171618367e-06 Val loss: 0.025647470092773437
+INFO - evaluator.py - 2024-10-25 00:24:39,232 - Epoch: 124 Train acc: 100.0 Val acc: 13.819999999999999 Test acc12.889999999999999; Train loss: 5.182551228525964e-06 Val loss: 0.0279505672454834
+INFO - evaluator.py - 2024-10-25 00:25:23,740 - Epoch: 125 Train acc: 100.0 Val acc: 12.78 Test acc12.139999999999999; Train loss: 5.2150607479482215e-06 Val loss: 0.03078906364440918
+INFO - evaluator.py - 2024-10-25 00:26:08,378 - Epoch: 126 Train acc: 100.0 Val acc: 12.78 Test acc12.27; Train loss: 5.248448563824323e-06 Val loss: 0.03050376853942871
+INFO - evaluator.py - 2024-10-25 00:26:52,443 - Epoch: 127 Train acc: 100.0 Val acc: 12.34 Test acc11.97; Train loss: 4.913998300220224e-06 Val loss: 0.0322932991027832
+INFO - evaluator.py - 2024-10-25 00:27:35,797 - Epoch: 128 Train acc: 100.0 Val acc: 12.379999999999999 Test acc11.84; Train loss: 5.483891610691154e-06 Val loss: 0.0330935661315918
+INFO - evaluator.py - 2024-10-25 00:28:21,084 - Epoch: 129 Train acc: 99.99818181818182 Val acc: 12.0 Test acc11.63; Train loss: 6.252703378612006e-06 Val loss: 0.034558382415771485
+INFO - evaluator.py - 2024-10-25 00:29:03,582 - Epoch: 130 Train acc: 100.0 Val acc: 11.72 Test acc11.24; Train loss: 5.96609986122613e-06 Val loss: 0.03709003448486328
+INFO - evaluator.py - 2024-10-25 00:29:48,143 - Epoch: 131 Train acc: 100.0 Val acc: 11.799999999999999 Test acc11.35; Train loss: 5.924843565761959e-06 Val loss: 0.03752219009399414
+INFO - evaluator.py - 2024-10-25 00:30:29,766 - Epoch: 132 Train acc: 100.0 Val acc: 11.72 Test acc11.35; Train loss: 6.084204667290165e-06 Val loss: 0.03726447906494141
+INFO - evaluator.py - 2024-10-25 00:31:15,555 - Epoch: 133 Train acc: 100.0 Val acc: 11.540000000000001 Test acc11.24; Train loss: 5.907123441945508e-06 Val loss: 0.03899700698852539
+INFO - evaluator.py - 2024-10-25 00:31:59,472 - Epoch: 134 Train acc: 100.0 Val acc: 11.44 Test acc11.12; Train loss: 6.1411763536108825e-06 Val loss: 0.04111646270751953
+INFO - evaluator.py - 2024-10-25 00:32:44,496 - Epoch: 135 Train acc: 100.0 Val acc: 11.1 Test acc10.82; Train loss: 6.052378490461375e-06 Val loss: 0.045041267395019534
+INFO - evaluator.py - 2024-10-25 00:33:29,409 - Epoch: 136 Train acc: 99.99636363636364 Val acc: 10.96 Test acc10.72; Train loss: 7.346780356337232e-06 Val loss: 0.048010198974609376
+INFO - evaluator.py - 2024-10-25 00:34:13,142 - Epoch: 137 Train acc: 99.99818181818182 Val acc: 10.74 Test acc10.38; Train loss: 7.269765151960944e-06 Val loss: 0.04809732208251953
+INFO - evaluator.py - 2024-10-25 00:34:56,537 - Epoch: 138 Train acc: 100.0 Val acc: 10.879999999999999 Test acc10.530000000000001; Train loss: 6.4932159693192016e-06 Val loss: 0.04510926513671875
+INFO - evaluator.py - 2024-10-25 00:35:40,942 - Epoch: 139 Train acc: 100.0 Val acc: 10.94 Test acc10.51; Train loss: 6.634374140412547e-06 Val loss: 0.046895947265625
+INFO - evaluator.py - 2024-10-25 00:36:25,401 - Epoch: 140 Train acc: 100.0 Val acc: 10.68 Test acc10.37; Train loss: 6.745988509448414e-06 Val loss: 0.05117699584960937
+INFO - evaluator.py - 2024-10-25 00:37:11,103 - Epoch: 141 Train acc: 100.0 Val acc: 10.620000000000001 Test acc10.280000000000001; Train loss: 6.744601922533051e-06 Val loss: 0.0514101921081543
+INFO - evaluator.py - 2024-10-25 00:37:56,365 - Epoch: 142 Train acc: 100.0 Val acc: 10.7 Test acc10.35; Train loss: 6.816498408178714e-06 Val loss: 0.04893369293212891
+INFO - evaluator.py - 2024-10-25 00:38:41,364 - Epoch: 143 Train acc: 100.0 Val acc: 10.54 Test acc10.27; Train loss: 6.66275066958571e-06 Val loss: 0.05356490325927735
+INFO - evaluator.py - 2024-10-25 00:39:24,428 - Epoch: 144 Train acc: 100.0 Val acc: 10.7 Test acc10.36; Train loss: 6.870164969322187e-06 Val loss: 0.05022898635864258
+INFO - evaluator.py - 2024-10-25 00:40:08,168 - Epoch: 145 Train acc: 100.0 Val acc: 10.72 Test acc10.37; Train loss: 6.829083152644506e-06 Val loss: 0.051102369689941404
+INFO - evaluator.py - 2024-10-25 00:40:50,100 - Epoch: 146 Train acc: 100.0 Val acc: 10.84 Test acc10.48; Train loss: 6.661457844099707e-06 Val loss: 0.04853194580078125
+INFO - evaluator.py - 2024-10-25 00:41:34,195 - Epoch: 147 Train acc: 100.0 Val acc: 10.620000000000001 Test acc10.299999999999999; Train loss: 7.136815779482607e-06 Val loss: 0.054110665130615236
+INFO - evaluator.py - 2024-10-25 00:42:17,697 - Epoch: 148 Train acc: 100.0 Val acc: 10.56 Test acc10.36; Train loss: 7.020424709613012e-06 Val loss: 0.05047670669555664
+INFO - evaluator.py - 2024-10-25 00:43:01,148 - Epoch: 149 Train acc: 100.0 Val acc: 10.72 Test acc10.4; Train loss: 7.259834505913948e-06 Val loss: 0.05153408203125
+INFO - evaluator.py - 2024-10-25 00:43:44,011 - Epoch: 150 Train acc: 99.99636363636364 Val acc: 10.48 Test acc10.25; Train loss: 8.285156142135913e-06 Val loss: 0.054744341278076175
+INFO - evaluator.py - 2024-10-25 00:44:28,213 - Epoch: 151 Train acc: 100.0 Val acc: 10.56 Test acc10.280000000000001; Train loss: 7.3977217665577135e-06 Val loss: 0.055819489288330075
+INFO - evaluator.py - 2024-10-25 00:45:12,173 - Epoch: 152 Train acc: 100.0 Val acc: 10.6 Test acc10.36; Train loss: 6.8329550055998634e-06 Val loss: 0.05474453811645508
+INFO - evaluator.py - 2024-10-25 00:45:56,882 - Epoch: 153 Train acc: 100.0 Val acc: 10.42 Test acc10.209999999999999; Train loss: 7.126459840219468e-06 Val loss: 0.05840193634033203
+INFO - evaluator.py - 2024-10-25 00:46:40,975 - Epoch: 154 Train acc: 100.0 Val acc: 10.58 Test acc10.34; Train loss: 7.427080802153796e-06 Val loss: 0.055208422088623046
+INFO - evaluator.py - 2024-10-25 00:47:24,435 - Epoch: 155 Train acc: 100.0 Val acc: 10.639999999999999 Test acc10.37; Train loss: 7.580637088341808e-06 Val loss: 0.05641526031494141
+INFO - evaluator.py - 2024-10-25 00:48:08,964 - Epoch: 156 Train acc: 99.99818181818182 Val acc: 10.6 Test acc10.39; Train loss: 7.490932677385651e-06 Val loss: 0.05787023468017578
+INFO - evaluator.py - 2024-10-25 00:48:52,236 - Epoch: 157 Train acc: 100.0 Val acc: 10.54 Test acc10.31; Train loss: 7.612921431956982e-06 Val loss: 0.06539807281494141
+INFO - evaluator.py - 2024-10-25 00:49:36,387 - Epoch: 158 Train acc: 100.0 Val acc: 10.56 Test acc10.38; Train loss: 7.289972453674471e-06 Val loss: 0.06133686294555664
+INFO - evaluator.py - 2024-10-25 00:50:21,238 - Epoch: 159 Train acc: 100.0 Val acc: 10.74 Test acc10.36; Train loss: 7.486758900763975e-06 Val loss: 0.058562511444091796
+INFO - evaluator.py - 2024-10-25 00:51:05,680 - Epoch: 160 Train acc: 99.99818181818182 Val acc: 10.7 Test acc10.299999999999999; Train loss: 8.30899123525755e-06 Val loss: 0.06285366821289062
+INFO - evaluator.py - 2024-10-25 00:51:50,256 - Epoch: 161 Train acc: 100.0 Val acc: 10.32 Test acc10.17; Train loss: 7.912832471563227e-06 Val loss: 0.06998545989990235
+INFO - evaluator.py - 2024-10-25 00:52:33,881 - Epoch: 162 Train acc: 100.0 Val acc: 10.38 Test acc10.16; Train loss: 7.230076067869297e-06 Val loss: 0.06424804382324219
+INFO - evaluator.py - 2024-10-25 00:53:19,649 - Epoch: 163 Train acc: 99.99818181818182 Val acc: 10.48 Test acc10.290000000000001; Train loss: 7.164526103720577e-06 Val loss: 0.06899149169921875
+INFO - evaluator.py - 2024-10-25 00:54:03,285 - Epoch: 164 Train acc: 99.99818181818182 Val acc: 10.58 Test acc10.22; Train loss: 7.356038549914956e-06 Val loss: 0.0673689453125
+INFO - evaluator.py - 2024-10-25 00:54:45,663 - Epoch: 165 Train acc: 100.0 Val acc: 10.68 Test acc10.36; Train loss: 7.31909479945898e-06 Val loss: 0.0641198989868164
+INFO - evaluator.py - 2024-10-25 00:55:30,420 - Epoch: 166 Train acc: 100.0 Val acc: 10.780000000000001 Test acc10.37; Train loss: 7.088962183016437e-06 Val loss: 0.06227085418701172
+INFO - evaluator.py - 2024-10-25 00:56:13,853 - Epoch: 167 Train acc: 99.99818181818182 Val acc: 10.54 Test acc10.22; Train loss: 7.744934936900708e-06 Val loss: 0.07838672637939453
+INFO - evaluator.py - 2024-10-25 00:56:58,272 - Epoch: 168 Train acc: 100.0 Val acc: 10.54 Test acc10.209999999999999; Train loss: 7.28702787309885e-06 Val loss: 0.07307826232910156
+INFO - evaluator.py - 2024-10-25 00:57:43,344 - Epoch: 169 Train acc: 100.0 Val acc: 10.4 Test acc10.18; Train loss: 7.248020098565824e-06 Val loss: 0.07878407287597657
+INFO - evaluator.py - 2024-10-25 00:58:26,881 - Epoch: 170 Train acc: 100.0 Val acc: 10.979999999999999 Test acc10.38; Train loss: 7.368196843361313e-06 Val loss: 0.06165690383911133
+INFO - evaluator.py - 2024-10-25 00:59:11,330 - Epoch: 171 Train acc: 99.99454545454546 Val acc: 10.24 Test acc10.05; Train loss: 9.95130062484267e-06 Val loss: 0.0836934326171875
+INFO - evaluator.py - 2024-10-25 00:59:56,077 - Epoch: 172 Train acc: 100.0 Val acc: 10.440000000000001 Test acc10.16; Train loss: 7.719712355174125e-06 Val loss: 0.07311139678955078
+INFO - evaluator.py - 2024-10-25 01:00:39,484 - Epoch: 173 Train acc: 100.0 Val acc: 10.440000000000001 Test acc10.190000000000001; Train loss: 7.1035025334409014e-06 Val loss: 0.07190213012695312
+INFO - evaluator.py - 2024-10-25 01:01:24,121 - Epoch: 174 Train acc: 99.99818181818182 Val acc: 10.280000000000001 Test acc10.11; Train loss: 7.374645609260452e-06 Val loss: 0.07406606903076172
+INFO - evaluator.py - 2024-10-25 01:02:06,552 - Epoch: 175 Train acc: 99.99818181818182 Val acc: 11.32 Test acc10.549999999999999; Train loss: 7.4026050363582646e-06 Val loss: 0.058519945526123045
+INFO - evaluator.py - 2024-10-25 01:02:47,952 - Epoch: 176 Train acc: 99.99818181818182 Val acc: 10.68 Test acc10.38; Train loss: 8.914955046599393e-06 Val loss: 0.06954411315917969
+INFO - evaluator.py - 2024-10-25 01:03:32,394 - Epoch: 177 Train acc: 100.0 Val acc: 11.0 Test acc10.280000000000001; Train loss: 6.976034198040989e-06 Val loss: 0.06646866302490234
+INFO - evaluator.py - 2024-10-25 01:04:16,163 - Epoch: 178 Train acc: 100.0 Val acc: 10.620000000000001 Test acc10.39; Train loss: 6.529483886499127e-06 Val loss: 0.06510645904541015
+INFO - evaluator.py - 2024-10-25 01:04:59,733 - Epoch: 179 Train acc: 99.99818181818182 Val acc: 10.38 Test acc10.13; Train loss: 7.387557563329623e-06 Val loss: 0.07005768280029297
+INFO - evaluator.py - 2024-10-25 01:05:44,037 - Epoch: 180 Train acc: 100.0 Val acc: 10.36 Test acc10.27; Train loss: 7.160511198030277e-06 Val loss: 0.06229532012939453
+INFO - evaluator.py - 2024-10-25 01:06:27,135 - Epoch: 181 Train acc: 100.0 Val acc: 10.459999999999999 Test acc10.22; Train loss: 6.599310374903408e-06 Val loss: 0.058071485900878905
+INFO - evaluator.py - 2024-10-25 01:07:10,856 - Epoch: 182 Train acc: 100.0 Val acc: 10.6 Test acc10.25; Train loss: 6.379676337184554e-06 Val loss: 0.055555615997314456
+INFO - evaluator.py - 2024-10-25 01:07:55,172 - Epoch: 183 Train acc: 100.0 Val acc: 10.58 Test acc10.27; Train loss: 6.7723642807157545e-06 Val loss: 0.05021322021484375
+INFO - evaluator.py - 2024-10-25 01:08:39,443 - Epoch: 184 Train acc: 100.0 Val acc: 10.459999999999999 Test acc10.25; Train loss: 6.598180368944834e-06 Val loss: 0.04932939300537109
+INFO - evaluator.py - 2024-10-25 01:09:25,162 - Epoch: 185 Train acc: 100.0 Val acc: 10.7 Test acc10.37; Train loss: 6.735882767349143e-06 Val loss: 0.043701815795898435
+INFO - evaluator.py - 2024-10-25 01:10:10,059 - Epoch: 186 Train acc: 100.0 Val acc: 10.92 Test acc10.43; Train loss: 6.672183124200356e-06 Val loss: 0.039761345672607425
+INFO - evaluator.py - 2024-10-25 01:10:55,735 - Epoch: 187 Train acc: 100.0 Val acc: 10.780000000000001 Test acc10.45; Train loss: 6.488577031996101e-06 Val loss: 0.03694210510253906
+INFO - evaluator.py - 2024-10-25 01:11:39,663 - Epoch: 188 Train acc: 100.0 Val acc: 10.9 Test acc10.51; Train loss: 6.844694168963046e-06 Val loss: 0.034773983764648436
+INFO - evaluator.py - 2024-10-25 01:12:23,880 - Epoch: 189 Train acc: 100.0 Val acc: 11.04 Test acc10.67; Train loss: 6.913963826479051e-06 Val loss: 0.03257621688842773
+INFO - evaluator.py - 2024-10-25 01:13:08,444 - Epoch: 190 Train acc: 100.0 Val acc: 10.9 Test acc10.620000000000001; Train loss: 6.48409001137638e-06 Val loss: 0.03237699508666992
+INFO - evaluator.py - 2024-10-25 01:13:51,701 - Epoch: 191 Train acc: 100.0 Val acc: 10.84 Test acc10.5; Train loss: 6.392095452809537e-06 Val loss: 0.0315965446472168
+INFO - evaluator.py - 2024-10-25 01:14:34,198 - Epoch: 192 Train acc: 100.0 Val acc: 11.3 Test acc10.83; Train loss: 6.646409578917717e-06 Val loss: 0.02736565132141113
+INFO - evaluator.py - 2024-10-25 01:15:17,503 - Epoch: 193 Train acc: 100.0 Val acc: 11.3 Test acc10.85; Train loss: 6.5336460221177815e-06 Val loss: 0.027962417221069336
+INFO - evaluator.py - 2024-10-25 01:16:00,252 - Epoch: 194 Train acc: 100.0 Val acc: 11.28 Test acc10.8; Train loss: 6.7565600837538525e-06 Val loss: 0.026872494506835938
+INFO - evaluator.py - 2024-10-25 01:16:41,568 - Epoch: 195 Train acc: 100.0 Val acc: 11.42 Test acc10.95; Train loss: 6.525416453686458e-06 Val loss: 0.024965807723999024
+INFO - evaluator.py - 2024-10-25 01:17:24,172 - Epoch: 196 Train acc: 100.0 Val acc: 11.24 Test acc10.84; Train loss: 6.561410856771876e-06 Val loss: 0.024612431716918945
+INFO - evaluator.py - 2024-10-25 01:18:06,835 - Epoch: 197 Train acc: 100.0 Val acc: 11.42 Test acc10.879999999999999; Train loss: 6.505143044474111e-06 Val loss: 0.022740457153320313
+INFO - evaluator.py - 2024-10-25 01:18:49,590 - Epoch: 198 Train acc: 100.0 Val acc: 11.52 Test acc11.129999999999999; Train loss: 6.972809469814159e-06 Val loss: 0.02141835136413574
+INFO - evaluator.py - 2024-10-25 01:19:32,266 - Epoch: 199 Train acc: 100.0 Val acc: 11.92 Test acc11.18; Train loss: 6.4589509724597025e-06 Val loss: 0.02008635673522949
+INFO - evaluator.py - 2024-10-25 01:19:32,282 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnet is 23.74 and 22.81
+INFO - evaluator.py - 2024-10-25 01:19:32,282 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnet is 23.74 and 22.81
+INFO - evaluator.py - 2024-10-25 01:19:32,282 - The best acc test dataset from resnet is 22.81
+INFO - evaluator.py - 2024-10-25 01:21:01,262 - Epoch: 0 Train acc: 39.334545454545456 Val acc: 28.360000000000003 Test acc27.96; Train loss: 0.012464823967760259 Val loss: 0.004926170825958252
+INFO - evaluator.py - 2024-10-25 01:22:29,172 - Epoch: 1 Train acc: 67.34181818181818 Val acc: 19.24 Test acc19.84; Train loss: 0.006893907073411074 Val loss: 0.010896759223937989
+INFO - evaluator.py - 2024-10-25 01:23:57,075 - Epoch: 2 Train acc: 92.45636363636363 Val acc: 16.76 Test acc17.34; Train loss: 0.0017379596118222583 Val loss: 0.016738204383850097
+INFO - evaluator.py - 2024-10-25 01:25:25,202 - Epoch: 3 Train acc: 96.48909090909092 Val acc: 15.18 Test acc15.1; Train loss: 0.0008410543234849518 Val loss: 0.023432204437255858
+INFO - evaluator.py - 2024-10-25 01:26:52,957 - Epoch: 4 Train acc: 97.64727272727272 Val acc: 16.12 Test acc15.61; Train loss: 0.0005985041664574634 Val loss: 0.023565948486328123
+INFO - evaluator.py - 2024-10-25 01:28:20,836 - Epoch: 5 Train acc: 97.76727272727273 Val acc: 20.599999999999998 Test acc20.45; Train loss: 0.000546664658815346 Val loss: 0.02174954948425293
+INFO - evaluator.py - 2024-10-25 01:29:48,718 - Epoch: 6 Train acc: 97.85454545454544 Val acc: 12.540000000000001 Test acc12.04; Train loss: 0.0005213981658558954 Val loss: 0.035319002532958985
+INFO - evaluator.py - 2024-10-25 01:31:16,647 - Epoch: 7 Train acc: 98.18363636363637 Val acc: 13.200000000000001 Test acc13.08; Train loss: 0.0004516414656049826 Val loss: 0.02934621696472168
+INFO - evaluator.py - 2024-10-25 01:32:44,651 - Epoch: 8 Train acc: 98.48181818181818 Val acc: 14.16 Test acc13.62; Train loss: 0.0003898320121351968 Val loss: 0.03444983978271484
+INFO - evaluator.py - 2024-10-25 01:34:12,773 - Epoch: 9 Train acc: 98.32545454545455 Val acc: 25.979999999999997 Test acc24.69; Train loss: 0.0004222877369600941 Val loss: 0.016244538879394532
+INFO - evaluator.py - 2024-10-25 01:35:40,614 - Epoch: 10 Train acc: 98.54727272727273 Val acc: 16.400000000000002 Test acc15.629999999999999; Train loss: 0.00038059498244388535 Val loss: 0.02855215530395508
+INFO - evaluator.py - 2024-10-25 01:37:08,510 - Epoch: 11 Train acc: 98.7109090909091 Val acc: 22.24 Test acc21.64; Train loss: 0.0003232448070733385 Val loss: 0.01688373146057129
+INFO - evaluator.py - 2024-10-25 01:38:36,315 - Epoch: 12 Train acc: 98.55454545454545 Val acc: 23.86 Test acc23.01; Train loss: 0.0003846062615598467 Val loss: 0.009829733657836914
+INFO - evaluator.py - 2024-10-25 01:40:04,199 - Epoch: 13 Train acc: 98.78181818181818 Val acc: 24.3 Test acc24.6; Train loss: 0.0003219434049996463 Val loss: 0.00848046169281006
+INFO - evaluator.py - 2024-10-25 01:41:31,997 - Epoch: 14 Train acc: 98.77090909090909 Val acc: 12.9 Test acc13.36; Train loss: 0.0003192929180762307 Val loss: 0.025139524841308592
+INFO - evaluator.py - 2024-10-25 01:42:59,868 - Epoch: 15 Train acc: 98.4909090909091 Val acc: 15.76 Test acc16.78; Train loss: 0.00039576046262783084 Val loss: 0.011613710403442383
+INFO - evaluator.py - 2024-10-25 01:44:27,726 - Epoch: 16 Train acc: 99.02909090909091 Val acc: 22.2 Test acc22.650000000000002; Train loss: 0.0002734065892725167 Val loss: 0.010137113571166992
+INFO - evaluator.py - 2024-10-25 01:45:55,565 - Epoch: 17 Train acc: 98.7309090909091 Val acc: 20.979999999999997 Test acc21.54; Train loss: 0.0003455030986107886 Val loss: 0.00767019624710083
+INFO - evaluator.py - 2024-10-25 01:47:23,434 - Epoch: 18 Train acc: 98.89636363636363 Val acc: 20.9 Test acc21.790000000000003; Train loss: 0.0003017092470164326 Val loss: 0.009516537857055665
+INFO - evaluator.py - 2024-10-25 01:48:51,314 - Epoch: 19 Train acc: 98.96909090909091 Val acc: 11.0 Test acc11.459999999999999; Train loss: 0.00027729614151811055 Val loss: 0.02350151176452637
+INFO - evaluator.py - 2024-10-25 01:50:19,137 - Epoch: 20 Train acc: 98.91636363636364 Val acc: 22.46 Test acc21.8; Train loss: 0.00029975029545920815 Val loss: 0.01058114356994629
+INFO - evaluator.py - 2024-10-25 01:51:47,210 - Epoch: 21 Train acc: 98.91272727272728 Val acc: 12.24 Test acc13.07; Train loss: 0.00029308662710034037 Val loss: 0.01355833339691162
+INFO - evaluator.py - 2024-10-25 01:53:14,928 - Epoch: 22 Train acc: 98.90545454545455 Val acc: 14.360000000000001 Test acc14.760000000000002; Train loss: 0.000293768156548454 Val loss: 0.012741995811462401
+INFO - evaluator.py - 2024-10-25 01:54:42,728 - Epoch: 23 Train acc: 99.04363636363637 Val acc: 21.58 Test acc21.66; Train loss: 0.00026097206215120177 Val loss: 0.006727146053314209
+INFO - evaluator.py - 2024-10-25 01:56:10,647 - Epoch: 24 Train acc: 99.02727272727273 Val acc: 21.22 Test acc20.87; Train loss: 0.00027229924519038334 Val loss: 0.007877613925933839
+INFO - evaluator.py - 2024-10-25 01:57:38,568 - Epoch: 25 Train acc: 99.02 Val acc: 21.58 Test acc22.06; Train loss: 0.0002598600998478518 Val loss: 0.008601756858825683
+INFO - evaluator.py - 2024-10-25 01:59:06,360 - Epoch: 26 Train acc: 99.00363636363636 Val acc: 26.840000000000003 Test acc25.45; Train loss: 0.00026981065634384076 Val loss: 0.007928859996795655
+INFO - evaluator.py - 2024-10-25 02:00:34,078 - Epoch: 27 Train acc: 98.95454545454545 Val acc: 20.34 Test acc20.47; Train loss: 0.00029119769181548195 Val loss: 0.008615230560302735
+INFO - evaluator.py - 2024-10-25 02:02:01,790 - Epoch: 28 Train acc: 99.00181818181818 Val acc: 12.2 Test acc12.26; Train loss: 0.0002881151453422552 Val loss: 0.01666111373901367
+INFO - evaluator.py - 2024-10-25 02:03:29,646 - Epoch: 29 Train acc: 99.07272727272726 Val acc: 21.7 Test acc21.04; Train loss: 0.0002537754377096214 Val loss: 0.009144564247131347
+INFO - evaluator.py - 2024-10-25 02:04:57,584 - Epoch: 30 Train acc: 98.91090909090909 Val acc: 13.88 Test acc13.66; Train loss: 0.00028975195701403376 Val loss: 0.01091526927947998
+INFO - evaluator.py - 2024-10-25 02:06:25,454 - Epoch: 31 Train acc: 99.00909090909092 Val acc: 18.48 Test acc18.709999999999997; Train loss: 0.0002799146804873916 Val loss: 0.013709602737426758
+INFO - evaluator.py - 2024-10-25 02:07:53,374 - Epoch: 32 Train acc: 99.18181818181819 Val acc: 25.2 Test acc24.54; Train loss: 0.0002229574159676717 Val loss: 0.005816537094116211
+INFO - evaluator.py - 2024-10-25 02:09:21,304 - Epoch: 33 Train acc: 98.77636363636364 Val acc: 13.0 Test acc13.059999999999999; Train loss: 0.0003299153504596854 Val loss: 0.010677084732055664
+INFO - evaluator.py - 2024-10-25 02:10:49,214 - Epoch: 34 Train acc: 99.22181818181818 Val acc: 20.4 Test acc20.349999999999998; Train loss: 0.0002212726773059165 Val loss: 0.009970218658447265
+INFO - evaluator.py - 2024-10-25 02:12:17,080 - Epoch: 35 Train acc: 98.9509090909091 Val acc: 24.84 Test acc24.22; Train loss: 0.00029075585040052167 Val loss: 0.00618305196762085
+INFO - evaluator.py - 2024-10-25 02:13:44,999 - Epoch: 36 Train acc: 99.06181818181818 Val acc: 16.580000000000002 Test acc16.54; Train loss: 0.0002580180578611114 Val loss: 0.010390157508850098
+INFO - evaluator.py - 2024-10-25 02:15:12,855 - Epoch: 37 Train acc: 99.14909090909092 Val acc: 16.28 Test acc16.470000000000002; Train loss: 0.0002459404711061242 Val loss: 0.010836030197143555
+INFO - evaluator.py - 2024-10-25 02:16:40,643 - Epoch: 38 Train acc: 99.16727272727273 Val acc: 15.7 Test acc16.08; Train loss: 0.0002437506817027249 Val loss: 0.01396047420501709
+INFO - evaluator.py - 2024-10-25 02:18:08,373 - Epoch: 39 Train acc: 99.10909090909091 Val acc: 17.44 Test acc16.86; Train loss: 0.00024666530202481555 Val loss: 0.012456459426879883
+INFO - evaluator.py - 2024-10-25 02:19:36,032 - Epoch: 40 Train acc: 99.03454545454545 Val acc: 15.24 Test acc14.940000000000001; Train loss: 0.0002760219583584165 Val loss: 0.011117095375061035
+INFO - evaluator.py - 2024-10-25 02:21:03,725 - Epoch: 41 Train acc: 98.95272727272727 Val acc: 23.14 Test acc23.419999999999998; Train loss: 0.0002842396185225384 Val loss: 0.007328984451293945
+INFO - evaluator.py - 2024-10-25 02:22:31,611 - Epoch: 42 Train acc: 99.12727272727273 Val acc: 22.400000000000002 Test acc21.759999999999998; Train loss: 0.00024257837984372269 Val loss: 0.00801837100982666
+INFO - evaluator.py - 2024-10-25 02:23:59,426 - Epoch: 43 Train acc: 98.94727272727273 Val acc: 15.040000000000001 Test acc15.4; Train loss: 0.00027951886278301986 Val loss: 0.007790700817108154
+INFO - evaluator.py - 2024-10-25 02:25:27,274 - Epoch: 44 Train acc: 99.2 Val acc: 11.559999999999999 Test acc11.29; Train loss: 0.0002155079871670089 Val loss: 0.022071310424804688
+INFO - evaluator.py - 2024-10-25 02:26:55,125 - Epoch: 45 Train acc: 99.11272727272727 Val acc: 18.26 Test acc18.21; Train loss: 0.0002606276760639792 Val loss: 0.009762262153625488
+INFO - evaluator.py - 2024-10-25 02:28:22,953 - Epoch: 46 Train acc: 99.25818181818182 Val acc: 11.76 Test acc11.700000000000001; Train loss: 0.00021693873141722923 Val loss: 0.019726690673828123
+INFO - evaluator.py - 2024-10-25 02:29:50,887 - Epoch: 47 Train acc: 99.03454545454545 Val acc: 21.3 Test acc21.0; Train loss: 0.0002683080732822418 Val loss: 0.006675291061401367
+INFO - evaluator.py - 2024-10-25 02:31:18,826 - Epoch: 48 Train acc: 99.14363636363636 Val acc: 23.04 Test acc21.9; Train loss: 0.00024977039832367815 Val loss: 0.008346449089050294
+INFO - evaluator.py - 2024-10-25 02:32:46,698 - Epoch: 49 Train acc: 98.86363636363636 Val acc: 13.88 Test acc14.2; Train loss: 0.0003100218432955444 Val loss: 0.015073316192626952
+INFO - evaluator.py - 2024-10-25 02:34:14,630 - Epoch: 50 Train acc: 99.10727272727273 Val acc: 19.68 Test acc19.93; Train loss: 0.00024503597610799424 Val loss: 0.006654109573364258
+INFO - evaluator.py - 2024-10-25 02:35:42,561 - Epoch: 51 Train acc: 99.22 Val acc: 19.88 Test acc19.3; Train loss: 0.000221677774165503 Val loss: 0.015014164924621582
+INFO - evaluator.py - 2024-10-25 02:37:10,426 - Epoch: 52 Train acc: 98.99636363636364 Val acc: 12.94 Test acc12.540000000000001; Train loss: 0.0002809049141677943 Val loss: 0.015430970001220703
+INFO - evaluator.py - 2024-10-25 02:38:38,332 - Epoch: 53 Train acc: 99.00363636363636 Val acc: 21.12 Test acc21.02; Train loss: 0.0002782109491696412 Val loss: 0.005195327854156494
+INFO - evaluator.py - 2024-10-25 02:40:06,145 - Epoch: 54 Train acc: 99.22545454545455 Val acc: 12.18 Test acc12.98; Train loss: 0.0002291193134744059 Val loss: 0.013064877891540528
+INFO - evaluator.py - 2024-10-25 02:41:33,996 - Epoch: 55 Train acc: 98.89454545454545 Val acc: 18.04 Test acc17.98; Train loss: 0.0002921393324367025 Val loss: 0.008855979537963868
+INFO - evaluator.py - 2024-10-25 02:43:01,903 - Epoch: 56 Train acc: 99.00545454545454 Val acc: 14.02 Test acc13.66; Train loss: 0.00027552480654875663 Val loss: 0.01452449722290039
+INFO - evaluator.py - 2024-10-25 02:44:29,844 - Epoch: 57 Train acc: 99.00727272727273 Val acc: 19.919999999999998 Test acc20.41; Train loss: 0.0002760625448628244 Val loss: 0.006361677074432373
+INFO - evaluator.py - 2024-10-25 02:45:57,910 - Epoch: 58 Train acc: 99.0890909090909 Val acc: 10.34 Test acc10.17; Train loss: 0.0002553909742400389 Val loss: 0.018671939849853516
+INFO - evaluator.py - 2024-10-25 02:47:25,791 - Epoch: 59 Train acc: 99.38363636363636 Val acc: 17.299999999999997 Test acc16.6; Train loss: 0.00019081023481081833 Val loss: 0.010156320190429688
+INFO - evaluator.py - 2024-10-25 02:48:53,691 - Epoch: 60 Train acc: 99.91636363636364 Val acc: 17.28 Test acc16.76; Train loss: 3.690097181719135e-05 Val loss: 0.012193162536621093
+INFO - evaluator.py - 2024-10-25 02:50:21,591 - Epoch: 61 Train acc: 99.9890909090909 Val acc: 16.16 Test acc15.43; Train loss: 1.863009910289706e-05 Val loss: 0.01768895721435547
+INFO - evaluator.py - 2024-10-25 02:51:49,480 - Epoch: 62 Train acc: 99.99272727272728 Val acc: 14.799999999999999 Test acc14.24; Train loss: 1.672652665187012e-05 Val loss: 0.026162258529663085
+INFO - evaluator.py - 2024-10-25 02:53:17,293 - Epoch: 63 Train acc: 100.0 Val acc: 13.0 Test acc13.100000000000001; Train loss: 1.5652052774517373e-05 Val loss: 0.04020077438354492
+INFO - evaluator.py - 2024-10-25 02:54:45,072 - Epoch: 64 Train acc: 99.99454545454546 Val acc: 13.04 Test acc12.740000000000002; Train loss: 1.762012952451848e-05 Val loss: 0.055375160217285155
+INFO - evaluator.py - 2024-10-25 02:56:12,853 - Epoch: 65 Train acc: 99.99272727272728 Val acc: 12.0 Test acc11.700000000000001; Train loss: 1.8628203207415276e-05 Val loss: 0.08102151489257813
+INFO - evaluator.py - 2024-10-25 02:57:40,635 - Epoch: 66 Train acc: 99.99454545454546 Val acc: 12.659999999999998 Test acc12.65; Train loss: 1.9568271446041764e-05 Val loss: 0.09060285339355469
+INFO - evaluator.py - 2024-10-25 02:59:08,398 - Epoch: 67 Train acc: 99.99818181818182 Val acc: 12.740000000000002 Test acc12.770000000000001; Train loss: 2.053840454794805e-05 Val loss: 0.11538282012939453
+INFO - evaluator.py - 2024-10-25 03:00:36,236 - Epoch: 68 Train acc: 99.99818181818182 Val acc: 12.879999999999999 Test acc12.68; Train loss: 1.970802954165265e-05 Val loss: 0.14843160705566405
+INFO - evaluator.py - 2024-10-25 03:02:04,001 - Epoch: 69 Train acc: 99.99636363636364 Val acc: 13.139999999999999 Test acc13.370000000000001; Train loss: 2.1131191461939705e-05 Val loss: 0.1785182373046875
+INFO - evaluator.py - 2024-10-25 03:03:31,802 - Epoch: 70 Train acc: 99.99090909090908 Val acc: 12.78 Test acc12.879999999999999; Train loss: 2.212750146652318e-05 Val loss: 0.22952270812988282
+INFO - evaluator.py - 2024-10-25 03:04:59,543 - Epoch: 71 Train acc: 99.99636363636364 Val acc: 13.320000000000002 Test acc13.28; Train loss: 2.020902756008912e-05 Val loss: 0.2793785400390625
+INFO - evaluator.py - 2024-10-25 03:06:27,268 - Epoch: 72 Train acc: 99.98727272727272 Val acc: 15.14 Test acc15.079999999999998; Train loss: 2.431069132236933e-05 Val loss: 0.25362254943847656
+INFO - evaluator.py - 2024-10-25 03:07:55,025 - Epoch: 73 Train acc: 99.98181818181818 Val acc: 14.899999999999999 Test acc14.649999999999999; Train loss: 2.8236327853731135e-05 Val loss: 0.2931911437988281
+INFO - evaluator.py - 2024-10-25 03:09:22,908 - Epoch: 74 Train acc: 99.92 Val acc: 13.459999999999999 Test acc13.62; Train loss: 4.757732512344691e-05 Val loss: 0.3241334716796875
+INFO - evaluator.py - 2024-10-25 03:10:50,745 - Epoch: 75 Train acc: 99.97454545454545 Val acc: 11.82 Test acc11.95; Train loss: 3.145337284182791e-05 Val loss: 0.4280216064453125
+INFO - evaluator.py - 2024-10-25 03:12:18,601 - Epoch: 76 Train acc: 99.97454545454545 Val acc: 13.68 Test acc13.780000000000001; Train loss: 2.4880453676450996e-05 Val loss: 0.4369194152832031
+INFO - evaluator.py - 2024-10-25 03:13:46,388 - Epoch: 77 Train acc: 99.97272727272727 Val acc: 12.839999999999998 Test acc12.540000000000001; Train loss: 3.4568645067470656e-05 Val loss: 0.3866658142089844
+INFO - evaluator.py - 2024-10-25 03:15:14,202 - Epoch: 78 Train acc: 99.9309090909091 Val acc: 11.379999999999999 Test acc11.200000000000001; Train loss: 4.430096303006973e-05 Val loss: 0.31674390258789065
+INFO - evaluator.py - 2024-10-25 03:16:42,074 - Epoch: 79 Train acc: 99.75818181818182 Val acc: 10.6 Test acc10.71; Train loss: 0.00010773582980036736 Val loss: 0.12100752410888672
+INFO - evaluator.py - 2024-10-25 03:18:09,919 - Epoch: 80 Train acc: 99.61818181818181 Val acc: 10.26 Test acc10.12; Train loss: 0.0001325593083821745 Val loss: 0.0733609375
+INFO - evaluator.py - 2024-10-25 03:19:37,749 - Epoch: 81 Train acc: 99.77454545454545 Val acc: 10.459999999999999 Test acc10.38; Train loss: 7.985532340508971e-05 Val loss: 0.03886889724731445
+INFO - evaluator.py - 2024-10-25 03:21:05,426 - Epoch: 82 Train acc: 99.98181818181818 Val acc: 10.48 Test acc10.35; Train loss: 2.2312475363088942e-05 Val loss: 0.05986393203735352
+INFO - evaluator.py - 2024-10-25 03:22:33,144 - Epoch: 83 Train acc: 99.61818181818181 Val acc: 12.42 Test acc12.08; Train loss: 0.00013145300314380704 Val loss: 0.025386722946166992
+INFO - evaluator.py - 2024-10-25 03:24:00,859 - Epoch: 84 Train acc: 99.81636363636363 Val acc: 10.58 Test acc10.32; Train loss: 7.790486002552578e-05 Val loss: 0.028939968490600584
+INFO - evaluator.py - 2024-10-25 03:25:28,572 - Epoch: 85 Train acc: 99.9309090909091 Val acc: 10.48 Test acc10.17; Train loss: 3.9976951367729765e-05 Val loss: 0.03737169418334961
+INFO - evaluator.py - 2024-10-25 03:26:56,275 - Epoch: 86 Train acc: 99.93454545454546 Val acc: 11.020000000000001 Test acc10.870000000000001; Train loss: 3.4034425086892125e-05 Val loss: 0.03809637069702149
+INFO - evaluator.py - 2024-10-25 03:28:23,947 - Epoch: 87 Train acc: 99.72181818181818 Val acc: 12.9 Test acc12.43; Train loss: 9.93938503310677e-05 Val loss: 0.015635749435424805
+INFO - evaluator.py - 2024-10-25 03:29:51,679 - Epoch: 88 Train acc: 99.78363636363636 Val acc: 12.5 Test acc12.790000000000001; Train loss: 8.280723811952736e-05 Val loss: 0.013797908592224122
+INFO - evaluator.py - 2024-10-25 03:31:19,670 - Epoch: 89 Train acc: 99.63090909090909 Val acc: 14.940000000000001 Test acc15.17; Train loss: 0.00012568276246416974 Val loss: 0.009962193679809571
+INFO - evaluator.py - 2024-10-25 03:32:47,427 - Epoch: 90 Train acc: 99.88 Val acc: 19.72 Test acc20.1; Train loss: 5.883329861522229e-05 Val loss: 0.006894399261474609
+INFO - evaluator.py - 2024-10-25 03:34:15,201 - Epoch: 91 Train acc: 99.79272727272728 Val acc: 12.64 Test acc12.770000000000001; Train loss: 7.410731522408738e-05 Val loss: 0.01062674388885498
+INFO - evaluator.py - 2024-10-25 03:35:42,903 - Epoch: 92 Train acc: 99.9109090909091 Val acc: 13.88 Test acc13.43; Train loss: 4.448491970331154e-05 Val loss: 0.010394498634338379
+INFO - evaluator.py - 2024-10-25 03:37:10,595 - Epoch: 93 Train acc: 99.98545454545454 Val acc: 20.119999999999997 Test acc19.8; Train loss: 1.6368762611686677e-05 Val loss: 0.009302042007446288
+INFO - evaluator.py - 2024-10-25 03:38:38,329 - Epoch: 94 Train acc: 99.86727272727272 Val acc: 12.3 Test acc12.86; Train loss: 5.959795214743777e-05 Val loss: 0.01671034240722656
+INFO - evaluator.py - 2024-10-25 03:40:06,301 - Epoch: 95 Train acc: 99.76181818181819 Val acc: 21.4 Test acc20.580000000000002; Train loss: 9.514861854682253e-05 Val loss: 0.0066237748146057126
+INFO - evaluator.py - 2024-10-25 03:41:34,144 - Epoch: 96 Train acc: 99.71818181818182 Val acc: 15.06 Test acc14.44; Train loss: 0.00010313211027956144 Val loss: 0.008355263900756835
+INFO - evaluator.py - 2024-10-25 03:43:01,979 - Epoch: 97 Train acc: 99.7890909090909 Val acc: 18.62 Test acc19.11; Train loss: 7.968115486119959e-05 Val loss: 0.007704733943939209
+INFO - evaluator.py - 2024-10-25 03:44:29,758 - Epoch: 98 Train acc: 99.92545454545456 Val acc: 26.279999999999998 Test acc25.4; Train loss: 3.943549141721715e-05 Val loss: 0.004766456031799316
+INFO - evaluator.py - 2024-10-25 03:45:57,512 - Epoch: 99 Train acc: 99.57090909090908 Val acc: 15.7 Test acc15.329999999999998; Train loss: 0.0001323501426197419 Val loss: 0.006634070205688476
+INFO - evaluator.py - 2024-10-25 03:47:25,173 - Epoch: 100 Train acc: 99.87818181818182 Val acc: 25.019999999999996 Test acc24.55; Train loss: 5.4818992858583274e-05 Val loss: 0.004179737758636475
+INFO - evaluator.py - 2024-10-25 03:48:52,805 - Epoch: 101 Train acc: 99.86181818181818 Val acc: 18.54 Test acc18.65; Train loss: 5.9690464290112934e-05 Val loss: 0.007206170177459717
+INFO - evaluator.py - 2024-10-25 03:50:20,445 - Epoch: 102 Train acc: 99.78 Val acc: 20.28 Test acc19.919999999999998; Train loss: 8.038866370493039e-05 Val loss: 0.005436101531982422
+INFO - evaluator.py - 2024-10-25 03:51:48,074 - Epoch: 103 Train acc: 99.8490909090909 Val acc: 19.06 Test acc18.57; Train loss: 6.236039578956976e-05 Val loss: 0.0073630789756774905
+INFO - evaluator.py - 2024-10-25 03:53:15,793 - Epoch: 104 Train acc: 99.81818181818181 Val acc: 15.079999999999998 Test acc15.299999999999999; Train loss: 7.340845615624196e-05 Val loss: 0.00737320146560669
+INFO - evaluator.py - 2024-10-25 03:54:43,701 - Epoch: 105 Train acc: 99.82727272727273 Val acc: 21.42 Test acc21.75; Train loss: 6.25467973729511e-05 Val loss: 0.005908790302276611
+INFO - evaluator.py - 2024-10-25 03:56:11,381 - Epoch: 106 Train acc: 99.9 Val acc: 20.119999999999997 Test acc19.689999999999998; Train loss: 4.701253496195105e-05 Val loss: 0.008379681968688964
+INFO - evaluator.py - 2024-10-25 03:57:38,907 - Epoch: 107 Train acc: 99.80909090909091 Val acc: 22.1 Test acc21.740000000000002; Train loss: 7.711601725419644e-05 Val loss: 0.005545624256134033
+INFO - evaluator.py - 2024-10-25 03:59:06,525 - Epoch: 108 Train acc: 99.83818181818181 Val acc: 28.88 Test acc26.950000000000003; Train loss: 7.104708749525757e-05 Val loss: 0.0037603322982788087
+INFO - evaluator.py - 2024-10-25 04:00:34,194 - Epoch: 109 Train acc: 99.69636363636364 Val acc: 15.42 Test acc15.58; Train loss: 0.00010701682314446026 Val loss: 0.009341845703125
+INFO - evaluator.py - 2024-10-25 04:02:01,813 - Epoch: 110 Train acc: 99.77636363636364 Val acc: 22.96 Test acc21.82; Train loss: 7.720221389741214e-05 Val loss: 0.005311676597595215
+INFO - evaluator.py - 2024-10-25 04:03:29,529 - Epoch: 111 Train acc: 99.87636363636364 Val acc: 18.56 Test acc17.97; Train loss: 5.1136705275116994e-05 Val loss: 0.006392905330657959
+INFO - evaluator.py - 2024-10-25 04:04:57,115 - Epoch: 112 Train acc: 99.97454545454545 Val acc: 22.82 Test acc21.93; Train loss: 1.869101057897999e-05 Val loss: 0.00721624231338501
+INFO - evaluator.py - 2024-10-25 04:06:24,825 - Epoch: 113 Train acc: 99.68181818181819 Val acc: 21.04 Test acc21.91; Train loss: 0.00011166000261860477 Val loss: 0.005361733341217041
+INFO - evaluator.py - 2024-10-25 04:07:52,514 - Epoch: 114 Train acc: 99.75454545454545 Val acc: 23.06 Test acc22.79; Train loss: 8.563075486824593e-05 Val loss: 0.004780505466461181
+INFO - evaluator.py - 2024-10-25 04:09:20,148 - Epoch: 115 Train acc: 99.81090909090909 Val acc: 15.58 Test acc15.42; Train loss: 6.983860228528183e-05 Val loss: 0.009077244758605958
+INFO - evaluator.py - 2024-10-25 04:10:47,791 - Epoch: 116 Train acc: 99.82727272727273 Val acc: 19.54 Test acc20.119999999999997; Train loss: 6.493742309129712e-05 Val loss: 0.005380006313323975
+INFO - evaluator.py - 2024-10-25 04:12:15,474 - Epoch: 117 Train acc: 99.75636363636363 Val acc: 22.6 Test acc21.61; Train loss: 8.670324263141744e-05 Val loss: 0.003881881046295166
+INFO - evaluator.py - 2024-10-25 04:13:43,120 - Epoch: 118 Train acc: 99.79818181818182 Val acc: 21.52 Test acc21.3; Train loss: 7.33905770572495e-05 Val loss: 0.004395703506469726
+INFO - evaluator.py - 2024-10-25 04:15:10,678 - Epoch: 119 Train acc: 99.84181818181818 Val acc: 22.3 Test acc22.08; Train loss: 6.224279518091035e-05 Val loss: 0.005245346832275391
+INFO - evaluator.py - 2024-10-25 04:16:38,231 - Epoch: 120 Train acc: 99.97272727272727 Val acc: 23.16 Test acc22.84; Train loss: 1.9978873828023843e-05 Val loss: 0.005204992580413818
+INFO - evaluator.py - 2024-10-25 04:18:05,828 - Epoch: 121 Train acc: 99.99090909090908 Val acc: 23.44 Test acc22.79; Train loss: 1.2986877789212899e-05 Val loss: 0.005397137832641601
+INFO - evaluator.py - 2024-10-25 04:19:33,409 - Epoch: 122 Train acc: 99.99818181818182 Val acc: 23.7 Test acc23.14; Train loss: 9.812456829240545e-06 Val loss: 0.005642071533203125
+INFO - evaluator.py - 2024-10-25 04:21:01,008 - Epoch: 123 Train acc: 99.99636363636364 Val acc: 23.36 Test acc23.02; Train loss: 1.0541573697066103e-05 Val loss: 0.006198830699920654
+INFO - evaluator.py - 2024-10-25 04:22:28,604 - Epoch: 124 Train acc: 100.0 Val acc: 22.06 Test acc21.68; Train loss: 9.4973998922135e-06 Val loss: 0.006786678504943848
+INFO - evaluator.py - 2024-10-25 04:23:56,213 - Epoch: 125 Train acc: 100.0 Val acc: 20.46 Test acc20.04; Train loss: 9.968933956803415e-06 Val loss: 0.008024850177764893
+INFO - evaluator.py - 2024-10-25 04:25:23,803 - Epoch: 126 Train acc: 99.99454545454546 Val acc: 19.16 Test acc18.360000000000003; Train loss: 1.0124757198553363e-05 Val loss: 0.008789614868164063
+INFO - evaluator.py - 2024-10-25 04:26:51,453 - Epoch: 127 Train acc: 99.99818181818182 Val acc: 19.220000000000002 Test acc18.25; Train loss: 9.118543774291704e-06 Val loss: 0.009174049186706543
+INFO - evaluator.py - 2024-10-25 04:28:19,009 - Epoch: 128 Train acc: 99.99636363636364 Val acc: 18.26 Test acc17.1; Train loss: 9.996840358309617e-06 Val loss: 0.009820748329162597
+INFO - evaluator.py - 2024-10-25 04:29:46,533 - Epoch: 129 Train acc: 99.99636363636364 Val acc: 18.02 Test acc17.299999999999997; Train loss: 1.1043420855209908e-05 Val loss: 0.009836386680603028
+INFO - evaluator.py - 2024-10-25 04:31:13,965 - Epoch: 130 Train acc: 100.0 Val acc: 17.740000000000002 Test acc16.77; Train loss: 1.0407833514777436e-05 Val loss: 0.009818707847595216
+INFO - evaluator.py - 2024-10-25 04:32:41,661 - Epoch: 131 Train acc: 100.0 Val acc: 16.98 Test acc15.8; Train loss: 1.0129413489167663e-05 Val loss: 0.011177260208129883
+INFO - evaluator.py - 2024-10-25 04:34:09,257 - Epoch: 132 Train acc: 99.99636363636364 Val acc: 16.68 Test acc15.55; Train loss: 1.0173971313898537e-05 Val loss: 0.010585068321228028
+INFO - evaluator.py - 2024-10-25 04:35:36,788 - Epoch: 133 Train acc: 99.99272727272728 Val acc: 17.18 Test acc16.16; Train loss: 1.1444660570387814e-05 Val loss: 0.01044460620880127
+INFO - evaluator.py - 2024-10-25 04:37:04,334 - Epoch: 134 Train acc: 99.99272727272728 Val acc: 17.52 Test acc16.46; Train loss: 1.2544045572973449e-05 Val loss: 0.009851381492614747
+INFO - evaluator.py - 2024-10-25 04:38:31,963 - Epoch: 135 Train acc: 99.99818181818182 Val acc: 16.96 Test acc16.21; Train loss: 1.1556544710501013e-05 Val loss: 0.009666147041320801
+INFO - evaluator.py - 2024-10-25 04:39:59,635 - Epoch: 136 Train acc: 99.99818181818182 Val acc: 17.02 Test acc16.3; Train loss: 1.2030194091229615e-05 Val loss: 0.010087225341796876
+INFO - evaluator.py - 2024-10-25 04:41:27,274 - Epoch: 137 Train acc: 99.98363636363636 Val acc: 15.379999999999999 Test acc14.67; Train loss: 1.4805356969802894e-05 Val loss: 0.011503936576843262
+INFO - evaluator.py - 2024-10-25 04:42:54,796 - Epoch: 138 Train acc: 100.0 Val acc: 15.040000000000001 Test acc14.42; Train loss: 1.2139840461102061e-05 Val loss: 0.011517874717712403
+INFO - evaluator.py - 2024-10-25 04:44:22,258 - Epoch: 139 Train acc: 99.99454545454546 Val acc: 15.14 Test acc14.799999999999999; Train loss: 1.3163677362767472e-05 Val loss: 0.011193948554992676
+INFO - evaluator.py - 2024-10-25 04:45:49,819 - Epoch: 140 Train acc: 100.0 Val acc: 15.079999999999998 Test acc14.44; Train loss: 1.201315001605756e-05 Val loss: 0.011828023719787598
+INFO - evaluator.py - 2024-10-25 04:47:17,459 - Epoch: 141 Train acc: 100.0 Val acc: 15.24 Test acc14.92; Train loss: 1.3108411621810361e-05 Val loss: 0.010858572578430176
+INFO - evaluator.py - 2024-10-25 04:48:45,065 - Epoch: 142 Train acc: 99.99636363636364 Val acc: 14.879999999999999 Test acc14.280000000000001; Train loss: 1.2263454690533268e-05 Val loss: 0.011719361686706543
+INFO - evaluator.py - 2024-10-25 04:50:12,716 - Epoch: 143 Train acc: 100.0 Val acc: 15.260000000000002 Test acc15.22; Train loss: 1.2437125479548492e-05 Val loss: 0.01049326686859131
+INFO - evaluator.py - 2024-10-25 04:51:40,340 - Epoch: 144 Train acc: 100.0 Val acc: 14.760000000000002 Test acc14.78; Train loss: 1.2200875213073396e-05 Val loss: 0.011849232292175292
+INFO - evaluator.py - 2024-10-25 04:53:07,973 - Epoch: 145 Train acc: 100.0 Val acc: 14.62 Test acc14.78; Train loss: 1.3209892832674086e-05 Val loss: 0.011505970001220703
+INFO - evaluator.py - 2024-10-25 04:54:35,536 - Epoch: 146 Train acc: 100.0 Val acc: 16.02 Test acc15.629999999999999; Train loss: 1.239603107625788e-05 Val loss: 0.010067285537719727
+INFO - evaluator.py - 2024-10-25 04:56:03,124 - Epoch: 147 Train acc: 100.0 Val acc: 14.580000000000002 Test acc14.860000000000001; Train loss: 1.3449864337136123e-05 Val loss: 0.011728307914733886
+INFO - evaluator.py - 2024-10-25 04:57:30,782 - Epoch: 148 Train acc: 99.99818181818182 Val acc: 14.760000000000002 Test acc15.09; Train loss: 1.3672827169383792e-05 Val loss: 0.010641062927246094
+INFO - evaluator.py - 2024-10-25 04:58:58,574 - Epoch: 149 Train acc: 99.99272727272728 Val acc: 15.82 Test acc15.57; Train loss: 1.5356097722807053e-05 Val loss: 0.010241378402709961
+INFO - evaluator.py - 2024-10-25 05:00:26,142 - Epoch: 150 Train acc: 99.99454545454546 Val acc: 15.1 Test acc15.0; Train loss: 1.5487723230299626e-05 Val loss: 0.010839285469055175
+INFO - evaluator.py - 2024-10-25 05:01:53,772 - Epoch: 151 Train acc: 99.99272727272728 Val acc: 14.96 Test acc14.95; Train loss: 1.7365037341898478e-05 Val loss: 0.010737288284301759
+INFO - evaluator.py - 2024-10-25 05:03:21,423 - Epoch: 152 Train acc: 99.99272727272728 Val acc: 15.24 Test acc14.85; Train loss: 1.5879018446007235e-05 Val loss: 0.011087526893615723
+INFO - evaluator.py - 2024-10-25 05:04:49,010 - Epoch: 153 Train acc: 99.99636363636364 Val acc: 15.06 Test acc14.82; Train loss: 1.483115276681598e-05 Val loss: 0.010772154808044434
+INFO - evaluator.py - 2024-10-25 05:06:16,648 - Epoch: 154 Train acc: 99.99636363636364 Val acc: 15.14 Test acc15.040000000000001; Train loss: 1.6511976791926744e-05 Val loss: 0.010445282936096192
+INFO - evaluator.py - 2024-10-25 05:07:44,271 - Epoch: 155 Train acc: 99.99272727272728 Val acc: 13.68 Test acc13.87; Train loss: 1.5858696484726598e-05 Val loss: 0.013707036399841308
+INFO - evaluator.py - 2024-10-25 05:09:11,896 - Epoch: 156 Train acc: 100.0 Val acc: 12.879999999999999 Test acc13.58; Train loss: 1.4214230206033045e-05 Val loss: 0.014236460876464845
+INFO - evaluator.py - 2024-10-25 05:10:39,478 - Epoch: 157 Train acc: 99.99090909090908 Val acc: 12.920000000000002 Test acc13.62; Train loss: 1.5483967385212467e-05 Val loss: 0.013167216300964355
+INFO - evaluator.py - 2024-10-25 05:12:07,027 - Epoch: 158 Train acc: 99.97636363636364 Val acc: 13.4 Test acc13.18; Train loss: 2.1213917223609644e-05 Val loss: 0.01603821849822998
+INFO - evaluator.py - 2024-10-25 05:13:34,699 - Epoch: 159 Train acc: 99.99818181818182 Val acc: 13.4 Test acc13.320000000000002; Train loss: 1.5770393547559666e-05 Val loss: 0.01604364814758301
+INFO - evaluator.py - 2024-10-25 05:15:02,316 - Epoch: 160 Train acc: 99.99272727272728 Val acc: 13.52 Test acc13.48; Train loss: 1.627506767293777e-05 Val loss: 0.015793188667297363
+INFO - evaluator.py - 2024-10-25 05:16:29,937 - Epoch: 161 Train acc: 99.99090909090908 Val acc: 13.94 Test acc14.219999999999999; Train loss: 1.838257762666961e-05 Val loss: 0.013457451248168946
+INFO - evaluator.py - 2024-10-25 05:17:57,745 - Epoch: 162 Train acc: 99.99272727272728 Val acc: 13.4 Test acc13.66; Train loss: 1.6347223476887767e-05 Val loss: 0.012834445381164551
+INFO - evaluator.py - 2024-10-25 05:19:25,289 - Epoch: 163 Train acc: 99.99818181818182 Val acc: 13.76 Test acc13.88; Train loss: 1.432022205765613e-05 Val loss: 0.012810430908203125
+INFO - evaluator.py - 2024-10-25 05:20:52,871 - Epoch: 164 Train acc: 100.0 Val acc: 12.2 Test acc12.61; Train loss: 1.477969596691599e-05 Val loss: 0.015080214118957519
+INFO - evaluator.py - 2024-10-25 05:22:20,387 - Epoch: 165 Train acc: 100.0 Val acc: 12.839999999999998 Test acc13.28; Train loss: 1.4023481064941733e-05 Val loss: 0.012564905166625976
+INFO - evaluator.py - 2024-10-25 05:23:47,964 - Epoch: 166 Train acc: 99.99818181818182 Val acc: 13.5 Test acc13.63; Train loss: 1.4486805943306536e-05 Val loss: 0.012968133926391602
+INFO - evaluator.py - 2024-10-25 05:25:15,517 - Epoch: 167 Train acc: 100.0 Val acc: 12.879999999999999 Test acc13.22; Train loss: 1.3945805154402148e-05 Val loss: 0.013541365242004395
+INFO - evaluator.py - 2024-10-25 05:26:43,076 - Epoch: 168 Train acc: 99.99272727272728 Val acc: 12.76 Test acc13.38; Train loss: 1.625238315159963e-05 Val loss: 0.012132881546020508
+INFO - evaluator.py - 2024-10-25 05:28:10,693 - Epoch: 169 Train acc: 99.99818181818182 Val acc: 12.02 Test acc12.049999999999999; Train loss: 1.4892288638194176e-05 Val loss: 0.014594845390319824
+INFO - evaluator.py - 2024-10-25 05:29:38,227 - Epoch: 170 Train acc: 99.96909090909091 Val acc: 12.08 Test acc12.0; Train loss: 2.2628939568742434e-05 Val loss: 0.016947417449951173
+INFO - evaluator.py - 2024-10-25 05:31:05,682 - Epoch: 171 Train acc: 99.96727272727273 Val acc: 12.06 Test acc12.049999999999999; Train loss: 2.536479495042427e-05 Val loss: 0.014644697761535645
+INFO - evaluator.py - 2024-10-25 05:32:33,103 - Epoch: 172 Train acc: 99.97818181818182 Val acc: 11.379999999999999 Test acc10.95; Train loss: 2.3115853636144574e-05 Val loss: 0.01819050064086914
+INFO - evaluator.py - 2024-10-25 05:34:00,645 - Epoch: 173 Train acc: 99.9890909090909 Val acc: 12.4 Test acc12.46; Train loss: 1.8846035715912214e-05 Val loss: 0.014117844200134278
+INFO - evaluator.py - 2024-10-25 05:35:28,202 - Epoch: 174 Train acc: 99.98363636363636 Val acc: 12.78 Test acc12.73; Train loss: 1.990339595676315e-05 Val loss: 0.01398458366394043
+INFO - evaluator.py - 2024-10-25 05:36:55,750 - Epoch: 175 Train acc: 99.99454545454546 Val acc: 13.18 Test acc13.450000000000001; Train loss: 1.7762518756684253e-05 Val loss: 0.012323491287231446
+INFO - evaluator.py - 2024-10-25 05:38:23,282 - Epoch: 176 Train acc: 99.99818181818182 Val acc: 12.740000000000002 Test acc12.76; Train loss: 1.4947419786106117e-05 Val loss: 0.013715618705749512
+INFO - evaluator.py - 2024-10-25 05:39:50,862 - Epoch: 177 Train acc: 99.96000000000001 Val acc: 12.76 Test acc13.01; Train loss: 3.094567080790346e-05 Val loss: 0.010643343162536621
+INFO - evaluator.py - 2024-10-25 05:41:18,406 - Epoch: 178 Train acc: 99.97454545454545 Val acc: 12.620000000000001 Test acc12.989999999999998; Train loss: 2.2830188191834498e-05 Val loss: 0.010686805534362792
+INFO - evaluator.py - 2024-10-25 05:42:46,011 - Epoch: 179 Train acc: 99.99818181818182 Val acc: 12.559999999999999 Test acc12.83; Train loss: 1.4940360017036173e-05 Val loss: 0.01068285026550293
+INFO - evaluator.py - 2024-10-25 05:44:13,549 - Epoch: 180 Train acc: 99.99818181818182 Val acc: 12.6 Test acc12.959999999999999; Train loss: 1.5042897695887156e-05 Val loss: 0.01115400218963623
+INFO - evaluator.py - 2024-10-25 05:45:41,131 - Epoch: 181 Train acc: 100.0 Val acc: 13.120000000000001 Test acc13.23; Train loss: 1.3868527837224643e-05 Val loss: 0.009779086112976074
+INFO - evaluator.py - 2024-10-25 05:47:08,715 - Epoch: 182 Train acc: 100.0 Val acc: 12.94 Test acc12.950000000000001; Train loss: 1.3378780053674498e-05 Val loss: 0.009721649742126465
+INFO - evaluator.py - 2024-10-25 05:48:36,218 - Epoch: 183 Train acc: 100.0 Val acc: 12.86 Test acc12.959999999999999; Train loss: 1.329367222708904e-05 Val loss: 0.00926866397857666
+INFO - evaluator.py - 2024-10-25 05:50:03,731 - Epoch: 184 Train acc: 100.0 Val acc: 12.520000000000001 Test acc12.67; Train loss: 1.3640141643753106e-05 Val loss: 0.009871022987365722
+INFO - evaluator.py - 2024-10-25 05:51:31,246 - Epoch: 185 Train acc: 99.99818181818182 Val acc: 13.100000000000001 Test acc13.0; Train loss: 1.3473239959090609e-05 Val loss: 0.00910334243774414
+INFO - evaluator.py - 2024-10-25 05:52:58,686 - Epoch: 186 Train acc: 100.0 Val acc: 13.139999999999999 Test acc13.03; Train loss: 1.3733288529329002e-05 Val loss: 0.008684687423706055
+INFO - evaluator.py - 2024-10-25 05:54:26,168 - Epoch: 187 Train acc: 99.99818181818182 Val acc: 13.5 Test acc13.370000000000001; Train loss: 1.3493010206994685e-05 Val loss: 0.007748383808135986
+INFO - evaluator.py - 2024-10-25 05:55:53,659 - Epoch: 188 Train acc: 99.99818181818182 Val acc: 12.959999999999999 Test acc12.91; Train loss: 1.3407668686175549e-05 Val loss: 0.008618726921081543
+INFO - evaluator.py - 2024-10-25 05:57:21,176 - Epoch: 189 Train acc: 99.99818181818182 Val acc: 13.52 Test acc13.309999999999999; Train loss: 1.3518423566975715e-05 Val loss: 0.007831643486022949
+INFO - evaluator.py - 2024-10-25 05:58:48,681 - Epoch: 190 Train acc: 100.0 Val acc: 13.320000000000002 Test acc13.120000000000001; Train loss: 1.3386415219230747e-05 Val loss: 0.007979614639282227
+INFO - evaluator.py - 2024-10-25 06:00:16,157 - Epoch: 191 Train acc: 99.99818181818182 Val acc: 13.600000000000001 Test acc13.489999999999998; Train loss: 1.3805215013086456e-05 Val loss: 0.007522994995117187
+INFO - evaluator.py - 2024-10-25 06:01:43,688 - Epoch: 192 Train acc: 100.0 Val acc: 13.74 Test acc13.569999999999999; Train loss: 1.3441931042523885e-05 Val loss: 0.007473759746551513
+INFO - evaluator.py - 2024-10-25 06:03:11,317 - Epoch: 193 Train acc: 100.0 Val acc: 13.48 Test acc13.569999999999999; Train loss: 1.355546598047526e-05 Val loss: 0.0075633127212524414
+INFO - evaluator.py - 2024-10-25 06:04:38,946 - Epoch: 194 Train acc: 100.0 Val acc: 13.76 Test acc13.569999999999999; Train loss: 1.3080383635083721e-05 Val loss: 0.007583335018157959
+INFO - evaluator.py - 2024-10-25 06:06:06,589 - Epoch: 195 Train acc: 100.0 Val acc: 13.700000000000001 Test acc13.66; Train loss: 1.3500135060696101e-05 Val loss: 0.0077752668380737305
+INFO - evaluator.py - 2024-10-25 06:07:34,168 - Epoch: 196 Train acc: 100.0 Val acc: 13.900000000000002 Test acc13.66; Train loss: 1.3261987385340035e-05 Val loss: 0.007466300487518311
+INFO - evaluator.py - 2024-10-25 06:09:01,748 - Epoch: 197 Train acc: 99.99818181818182 Val acc: 14.299999999999999 Test acc14.12; Train loss: 1.3350872153585608e-05 Val loss: 0.006670548820495606
+INFO - evaluator.py - 2024-10-25 06:10:29,323 - Epoch: 198 Train acc: 100.0 Val acc: 13.8 Test acc13.56; Train loss: 1.3372124436269091e-05 Val loss: 0.007581486415863037
+INFO - evaluator.py - 2024-10-25 06:11:56,894 - Epoch: 199 Train acc: 100.0 Val acc: 13.74 Test acc13.4; Train loss: 1.315822517816824e-05 Val loss: 0.00765489912033081
+INFO - evaluator.py - 2024-10-25 06:11:56,899 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from wrn is 28.88 and 26.950000000000003
+INFO - evaluator.py - 2024-10-25 06:11:56,899 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from wrn is 28.88 and 26.950000000000003
+INFO - evaluator.py - 2024-10-25 06:11:56,899 - The best acc test dataset from wrn is 27.96
+INFO - evaluator.py - 2024-10-25 06:14:08,036 - Epoch: 0 Train acc: 22.521818181818183 Val acc: 18.12 Test acc18.47; Train loss: 0.02232551528974013 Val loss: 0.011275789833068848
+INFO - evaluator.py - 2024-10-25 06:16:18,471 - Epoch: 1 Train acc: 36.62909090909091 Val acc: 21.04 Test acc20.9; Train loss: 0.012860274895754727 Val loss: 0.020923736572265626
+INFO - evaluator.py - 2024-10-25 06:18:28,868 - Epoch: 2 Train acc: 47.910909090909094 Val acc: 16.76 Test acc17.43; Train loss: 0.010650290536880493 Val loss: 0.033148858642578126
+INFO - evaluator.py - 2024-10-25 06:20:39,306 - Epoch: 3 Train acc: 57.1490909090909 Val acc: 13.28 Test acc14.08; Train loss: 0.008885887004028667 Val loss: 0.06999432601928711
+INFO - evaluator.py - 2024-10-25 06:22:49,827 - Epoch: 4 Train acc: 71.61999999999999 Val acc: 12.479999999999999 Test acc13.370000000000001; Train loss: 0.005997643518989736 Val loss: 0.1603109893798828
+INFO - evaluator.py - 2024-10-25 06:25:00,432 - Epoch: 5 Train acc: 89.5690909090909 Val acc: 9.34 Test acc10.0; Train loss: 0.002382486525584351 Val loss: 0.5951335327148437
+INFO - evaluator.py - 2024-10-25 06:27:10,805 - Epoch: 6 Train acc: 95.52545454545455 Val acc: 9.92 Test acc9.78; Train loss: 0.0010860252449458295 Val loss: 2.12308525390625
+INFO - evaluator.py - 2024-10-25 06:29:21,238 - Epoch: 7 Train acc: 97.14181818181818 Val acc: 10.12 Test acc10.0; Train loss: 0.0006951003701510754 Val loss: 10.4425861328125
+INFO - evaluator.py - 2024-10-25 06:31:31,808 - Epoch: 8 Train acc: 97.74363636363637 Val acc: 10.12 Test acc10.0; Train loss: 0.0005647331540049477 Val loss: 25.0925640625
+INFO - evaluator.py - 2024-10-25 06:33:42,334 - Epoch: 9 Train acc: 97.83090909090909 Val acc: 10.12 Test acc10.0; Train loss: 0.0005304603263397109 Val loss: 27.72124375
+INFO - dataset_loader.py - 2024-10-26 01:54:18,661 - delta is reset as 2.07404851125286e-06
+INFO - evaluator.py - 2024-10-26 01:55:21,991 - Epoch: 0 Train acc: 10.629090909090909 Val acc: 10.7 Test acc10.79; Train loss: 0.01925799826708707 Val loss: 0.012695633697509765
+INFO - evaluator.py - 2024-10-26 01:56:09,946 - Epoch: 1 Train acc: 12.703636363636365 Val acc: 11.86 Test acc12.540000000000001; Train loss: 0.018042018578269266 Val loss: 0.20173982543945312
+INFO - evaluator.py - 2024-10-26 01:56:56,436 - Epoch: 2 Train acc: 21.623636363636365 Val acc: 15.18 Test acc15.17; Train loss: 0.015748655956441707 Val loss: 0.09776797943115234
+INFO - evaluator.py - 2024-10-26 01:57:43,456 - Epoch: 3 Train acc: 31.04 Val acc: 9.98 Test acc10.18; Train loss: 0.013833769770102068 Val loss: 7.21908056640625
+INFO - evaluator.py - 2024-10-26 01:58:27,173 - Epoch: 4 Train acc: 40.04 Val acc: 9.959999999999999 Test acc10.0; Train loss: 0.011916462987119502 Val loss: 2646.40065
+INFO - evaluator.py - 2024-10-26 01:59:14,925 - Epoch: 5 Train acc: 52.11272727272728 Val acc: 10.38 Test acc10.22; Train loss: 0.00965673565972935 Val loss: 1885.276325
+INFO - evaluator.py - 2024-10-26 02:00:03,460 - Epoch: 6 Train acc: 67.35636363636364 Val acc: 10.16 Test acc10.0; Train loss: 0.006884072709083557 Val loss: 7500.9254
+INFO - evaluator.py - 2024-10-26 02:00:51,449 - Epoch: 7 Train acc: 83.24545454545455 Val acc: 10.100000000000001 Test acc9.92; Train loss: 0.003795535172115673 Val loss: 5.27256669921875
+INFO - evaluator.py - 2024-10-26 02:01:38,076 - Epoch: 8 Train acc: 90.03454545454545 Val acc: 10.059999999999999 Test acc9.93; Train loss: 0.0022880444916811857 Val loss: 70.4002421875
+INFO - evaluator.py - 2024-10-26 02:02:24,355 - Epoch: 9 Train acc: 93.04727272727273 Val acc: 9.3 Test acc9.41; Train loss: 0.0016415822736241602 Val loss: 0.2875272399902344
+INFO - evaluator.py - 2024-10-26 02:03:11,949 - Epoch: 10 Train acc: 93.80909090909091 Val acc: 7.24 Test acc7.26; Train loss: 0.001437604039365595 Val loss: 30.626555859375
+INFO - evaluator.py - 2024-10-26 02:03:58,539 - Epoch: 11 Train acc: 93.28909090909092 Val acc: 10.7 Test acc10.94; Train loss: 0.0015679286485368556 Val loss: 0.024683978652954103
+INFO - evaluator.py - 2024-10-26 02:04:43,565 - Epoch: 12 Train acc: 95.96545454545455 Val acc: 10.16 Test acc10.01; Train loss: 0.0009617108001627705 Val loss: 78.98505
+INFO - evaluator.py - 2024-10-26 02:05:30,412 - Epoch: 13 Train acc: 96.28363636363636 Val acc: 10.16 Test acc10.0; Train loss: 0.0008897671285000714 Val loss: 6015.7311
+INFO - evaluator.py - 2024-10-26 02:06:17,938 - Epoch: 14 Train acc: 95.76363636363637 Val acc: 11.04 Test acc10.73; Train loss: 0.0010040932195769115 Val loss: 0.17575700073242187
+INFO - evaluator.py - 2024-10-26 02:07:06,472 - Epoch: 15 Train acc: 96.52727272727273 Val acc: 10.299999999999999 Test acc10.22; Train loss: 0.000821594911813736 Val loss: 54.2147515625
+INFO - evaluator.py - 2024-10-26 02:07:51,315 - Epoch: 16 Train acc: 95.77636363636364 Val acc: 11.42 Test acc11.799999999999999; Train loss: 0.0010064894469962878 Val loss: 0.01305277500152588
+INFO - evaluator.py - 2024-10-26 02:08:34,620 - Epoch: 17 Train acc: 97.34 Val acc: 11.540000000000001 Test acc11.68; Train loss: 0.0006514016244391149 Val loss: 0.018012440872192382
+INFO - evaluator.py - 2024-10-26 02:09:28,071 - Epoch: 18 Train acc: 97.53636363636363 Val acc: 11.78 Test acc11.63; Train loss: 0.0005909262866967104 Val loss: 0.03270531997680664
+INFO - evaluator.py - 2024-10-26 02:10:17,632 - Epoch: 19 Train acc: 97.27454545454546 Val acc: 11.1 Test acc11.790000000000001; Train loss: 0.000656241522509266 Val loss: 0.026358308029174805
+INFO - evaluator.py - 2024-10-26 02:11:06,918 - Epoch: 20 Train acc: 97.52727272727273 Val acc: 10.8 Test acc11.469999999999999; Train loss: 0.0005976187108761885 Val loss: 0.02849169235229492
+INFO - evaluator.py - 2024-10-26 02:11:53,784 - Epoch: 21 Train acc: 97.85636363636364 Val acc: 17.28 Test acc17.11; Train loss: 0.0005251176264475692 Val loss: 0.016401841735839844
+INFO - evaluator.py - 2024-10-26 02:12:38,345 - Epoch: 22 Train acc: 97.81090909090909 Val acc: 13.54 Test acc14.32; Train loss: 0.0005309584506702694 Val loss: 0.024905322265625
+INFO - evaluator.py - 2024-10-26 02:13:21,704 - Epoch: 23 Train acc: 97.7309090909091 Val acc: 12.520000000000001 Test acc12.91; Train loss: 0.0005415527998893099 Val loss: 0.027510935211181642
+INFO - evaluator.py - 2024-10-26 02:14:06,388 - Epoch: 24 Train acc: 97.61636363636363 Val acc: 15.02 Test acc14.67; Train loss: 0.0005610392557541755 Val loss: 0.020559469985961913
+INFO - evaluator.py - 2024-10-26 02:14:53,209 - Epoch: 25 Train acc: 98.22727272727273 Val acc: 14.44 Test acc13.600000000000001; Train loss: 0.00044587992450899696 Val loss: 0.020378067779541015
+INFO - evaluator.py - 2024-10-26 02:15:42,204 - Epoch: 26 Train acc: 97.76545454545455 Val acc: 9.76 Test acc10.51; Train loss: 0.000555423815175891 Val loss: 0.02474435577392578
+INFO - evaluator.py - 2024-10-26 02:16:32,433 - Epoch: 27 Train acc: 98.11090909090909 Val acc: 10.74 Test acc11.57; Train loss: 0.00047239384427243337 Val loss: 0.021348362731933593
+INFO - evaluator.py - 2024-10-26 02:17:19,386 - Epoch: 28 Train acc: 98.16 Val acc: 14.799999999999999 Test acc14.530000000000001; Train loss: 0.0004551111403852701 Val loss: 0.023333713150024415
+INFO - evaluator.py - 2024-10-26 02:18:03,818 - Epoch: 29 Train acc: 98.12181818181817 Val acc: 23.84 Test acc22.36; Train loss: 0.00047718487307429316 Val loss: 0.008334244537353515
+INFO - evaluator.py - 2024-10-26 02:18:51,825 - Epoch: 30 Train acc: 98.12545454545455 Val acc: 11.899999999999999 Test acc12.379999999999999; Train loss: 0.0004648481744172221 Val loss: 0.019747254943847655
+INFO - evaluator.py - 2024-10-26 02:19:39,383 - Epoch: 31 Train acc: 98.10727272727273 Val acc: 16.66 Test acc16.5; Train loss: 0.0004603518645194444 Val loss: 0.02085089988708496
+INFO - evaluator.py - 2024-10-26 02:20:27,040 - Epoch: 32 Train acc: 97.93636363636364 Val acc: 11.64 Test acc11.16; Train loss: 0.000502309608954767 Val loss: 0.023009899139404297
+INFO - evaluator.py - 2024-10-26 02:21:13,735 - Epoch: 33 Train acc: 98.29818181818182 Val acc: 11.44 Test acc11.57; Train loss: 0.0004231049708260054 Val loss: 0.029554375076293944
+INFO - evaluator.py - 2024-10-26 02:21:58,263 - Epoch: 34 Train acc: 98.30181818181818 Val acc: 12.44 Test acc12.24; Train loss: 0.0004177156035957689 Val loss: 0.028080062484741212
+INFO - evaluator.py - 2024-10-26 02:22:44,880 - Epoch: 35 Train acc: 97.93272727272728 Val acc: 11.68 Test acc11.799999999999999; Train loss: 0.0004998428212648088 Val loss: 0.014525216484069825
+INFO - evaluator.py - 2024-10-26 02:23:31,626 - Epoch: 36 Train acc: 98.38727272727272 Val acc: 11.52 Test acc11.72; Train loss: 0.0004013828297103332 Val loss: 0.027697956466674806
+INFO - evaluator.py - 2024-10-26 02:24:18,018 - Epoch: 37 Train acc: 97.88363636363636 Val acc: 17.86 Test acc17.560000000000002; Train loss: 0.0005008460439233617 Val loss: 0.014217123222351075
+INFO - evaluator.py - 2024-10-26 02:25:03,161 - Epoch: 38 Train acc: 98.54363636363637 Val acc: 13.04 Test acc12.889999999999999; Train loss: 0.00036348077722571115 Val loss: 0.02087658882141113
+INFO - evaluator.py - 2024-10-26 02:25:47,905 - Epoch: 39 Train acc: 98.28181818181818 Val acc: 16.24 Test acc16.439999999999998; Train loss: 0.00041697168809954417 Val loss: 0.018150221252441406
+INFO - evaluator.py - 2024-10-26 02:26:36,068 - Epoch: 40 Train acc: 98.06727272727272 Val acc: 10.100000000000001 Test acc10.100000000000001; Train loss: 0.00045880490368316795 Val loss: 0.030562567520141602
+INFO - evaluator.py - 2024-10-26 02:27:20,943 - Epoch: 41 Train acc: 98.32181818181817 Val acc: 15.4 Test acc15.370000000000001; Train loss: 0.0004203877529722046 Val loss: 0.012287746047973632
+INFO - evaluator.py - 2024-10-26 02:28:05,042 - Epoch: 42 Train acc: 98.47818181818182 Val acc: 16.76 Test acc16.42; Train loss: 0.0003743234263860028 Val loss: 0.019465096282958984
+INFO - evaluator.py - 2024-10-26 02:28:49,360 - Epoch: 43 Train acc: 98.2 Val acc: 14.219999999999999 Test acc14.23; Train loss: 0.00044932311889292167 Val loss: 0.017940229415893554
+INFO - evaluator.py - 2024-10-26 02:29:34,740 - Epoch: 44 Train acc: 98.31818181818181 Val acc: 10.040000000000001 Test acc10.0; Train loss: 0.00039382684366269543 Val loss: 0.04524444198608398
+INFO - evaluator.py - 2024-10-26 02:30:19,173 - Epoch: 45 Train acc: 98.31818181818181 Val acc: 14.940000000000001 Test acc14.66; Train loss: 0.00040941191450269383 Val loss: 0.027904569625854494
+INFO - evaluator.py - 2024-10-26 02:31:04,984 - Epoch: 46 Train acc: 98.27090909090909 Val acc: 9.32 Test acc10.0; Train loss: 0.0004277358719994399 Val loss: 0.048519478607177736
+INFO - evaluator.py - 2024-10-26 02:31:50,280 - Epoch: 47 Train acc: 98.29818181818182 Val acc: 14.940000000000001 Test acc15.129999999999999; Train loss: 0.0004306714398659427 Val loss: 0.02652575149536133
+INFO - evaluator.py - 2024-10-26 02:32:36,264 - Epoch: 48 Train acc: 97.91090909090909 Val acc: 17.66 Test acc17.740000000000002; Train loss: 0.0005150971463373439 Val loss: 0.011282966423034668
+INFO - evaluator.py - 2024-10-26 02:33:20,930 - Epoch: 49 Train acc: 98.55818181818182 Val acc: 10.48 Test acc10.58; Train loss: 0.00036169068103825505 Val loss: 0.019381586456298828
+INFO - evaluator.py - 2024-10-26 02:34:04,862 - Epoch: 50 Train acc: 98.23818181818181 Val acc: 11.799999999999999 Test acc11.48; Train loss: 0.00042250579470683907 Val loss: 0.024291653060913087
+INFO - evaluator.py - 2024-10-26 02:34:51,099 - Epoch: 51 Train acc: 98.39636363636363 Val acc: 10.14 Test acc10.02; Train loss: 0.00039654651425609534 Val loss: 0.0486560302734375
+INFO - evaluator.py - 2024-10-26 02:35:36,742 - Epoch: 52 Train acc: 98.43818181818182 Val acc: 15.22 Test acc14.580000000000002; Train loss: 0.0003798971407157792 Val loss: 0.02014687271118164
+INFO - evaluator.py - 2024-10-26 02:36:20,635 - Epoch: 53 Train acc: 98.42181818181818 Val acc: 16.56 Test acc16.009999999999998; Train loss: 0.00041093000245111234 Val loss: 0.018778543853759766
+INFO - evaluator.py - 2024-10-26 02:37:05,388 - Epoch: 54 Train acc: 98.45272727272727 Val acc: 19.38 Test acc19.45; Train loss: 0.0003731809036975557 Val loss: 0.011871026802062989
+INFO - evaluator.py - 2024-10-26 02:37:49,800 - Epoch: 55 Train acc: 98.23818181818181 Val acc: 18.54 Test acc18.81; Train loss: 0.0004266604969066314 Val loss: 0.010401795959472656
+INFO - evaluator.py - 2024-10-26 02:38:34,118 - Epoch: 56 Train acc: 98.4890909090909 Val acc: 9.700000000000001 Test acc10.040000000000001; Train loss: 0.0003703477438073605 Val loss: 0.0343824089050293
+INFO - evaluator.py - 2024-10-26 02:39:21,012 - Epoch: 57 Train acc: 98.54545454545455 Val acc: 11.12 Test acc11.12; Train loss: 0.00036033349391072987 Val loss: 0.029556945037841797
+INFO - evaluator.py - 2024-10-26 02:40:06,795 - Epoch: 58 Train acc: 98.5690909090909 Val acc: 10.16 Test acc10.0; Train loss: 0.00036687625662677666 Val loss: 0.03370416870117188
+INFO - evaluator.py - 2024-10-26 02:40:53,190 - Epoch: 59 Train acc: 98.32909090909091 Val acc: 9.4 Test acc10.01; Train loss: 0.0004165180168537931 Val loss: 0.05370528564453125
+INFO - evaluator.py - 2024-10-26 02:41:35,983 - Epoch: 60 Train acc: 99.84727272727272 Val acc: 14.84 Test acc15.27; Train loss: 5.4487164808564225e-05 Val loss: 0.028041060638427734
+INFO - evaluator.py - 2024-10-26 02:42:23,431 - Epoch: 61 Train acc: 99.95454545454545 Val acc: 13.719999999999999 Test acc13.77; Train loss: 2.4577727937668733e-05 Val loss: 0.04319585189819336
+INFO - evaluator.py - 2024-10-26 02:43:09,339 - Epoch: 62 Train acc: 99.98363636363636 Val acc: 14.14 Test acc13.19; Train loss: 1.9101981063034724e-05 Val loss: 0.062253244018554685
+INFO - evaluator.py - 2024-10-26 02:43:52,625 - Epoch: 63 Train acc: 99.98363636363636 Val acc: 12.58 Test acc11.55; Train loss: 1.8687085912097246e-05 Val loss: 0.09371834106445312
+INFO - evaluator.py - 2024-10-26 02:44:37,016 - Epoch: 64 Train acc: 99.99454545454546 Val acc: 10.56 Test acc10.25; Train loss: 1.673686048015952e-05 Val loss: 0.1534382293701172
+INFO - evaluator.py - 2024-10-26 02:45:23,591 - Epoch: 65 Train acc: 99.99636363636364 Val acc: 10.26 Test acc10.040000000000001; Train loss: 1.674250000188212e-05 Val loss: 0.22172544860839843
+INFO - evaluator.py - 2024-10-26 02:46:08,561 - Epoch: 66 Train acc: 99.99636363636364 Val acc: 10.22 Test acc10.03; Train loss: 1.8157588595270433e-05 Val loss: 0.27096552734375
+INFO - evaluator.py - 2024-10-26 02:46:54,722 - Epoch: 67 Train acc: 99.99090909090908 Val acc: 10.16 Test acc10.01; Train loss: 1.8034390634751285e-05 Val loss: 0.3804460021972656
+INFO - evaluator.py - 2024-10-26 02:47:40,754 - Epoch: 68 Train acc: 99.99272727272728 Val acc: 10.16 Test acc10.01; Train loss: 1.7282950363799252e-05 Val loss: 0.3844195190429687
+INFO - evaluator.py - 2024-10-26 02:48:27,137 - Epoch: 69 Train acc: 99.99090909090908 Val acc: 10.16 Test acc10.02; Train loss: 1.7489245941396803e-05 Val loss: 0.3912135498046875
+INFO - evaluator.py - 2024-10-26 02:49:11,026 - Epoch: 70 Train acc: 99.99818181818182 Val acc: 10.12 Test acc9.959999999999999; Train loss: 1.586829330348833e-05 Val loss: 0.3988230651855469
+INFO - evaluator.py - 2024-10-26 02:49:54,615 - Epoch: 71 Train acc: 99.99272727272728 Val acc: 10.02 Test acc9.68; Train loss: 1.818937949137762e-05 Val loss: 0.4567967529296875
+INFO - evaluator.py - 2024-10-26 02:50:39,557 - Epoch: 72 Train acc: 99.99818181818182 Val acc: 8.32 Test acc8.68; Train loss: 1.4855785024437038e-05 Val loss: 0.4448603820800781
+INFO - evaluator.py - 2024-10-26 02:51:25,646 - Epoch: 73 Train acc: 99.99090909090908 Val acc: 8.92 Test acc9.5; Train loss: 1.919874591456557e-05 Val loss: 0.39268468017578123
+INFO - evaluator.py - 2024-10-26 02:52:11,446 - Epoch: 74 Train acc: 99.98363636363636 Val acc: 9.879999999999999 Test acc9.71; Train loss: 2.0972320801493797e-05 Val loss: 0.691459228515625
+INFO - evaluator.py - 2024-10-26 02:52:57,107 - Epoch: 75 Train acc: 99.99454545454546 Val acc: 10.52 Test acc10.43; Train loss: 1.5778458227445795e-05 Val loss: 0.8409037475585938
+INFO - evaluator.py - 2024-10-26 02:53:42,942 - Epoch: 76 Train acc: 99.96909090909091 Val acc: 11.799999999999999 Test acc11.18; Train loss: 2.2371654785026543e-05 Val loss: 1.0088495483398439
+INFO - evaluator.py - 2024-10-26 02:54:28,754 - Epoch: 77 Train acc: 99.92 Val acc: 9.9 Test acc10.16; Train loss: 4.490937352265147e-05 Val loss: 0.662556298828125
+INFO - evaluator.py - 2024-10-26 02:55:13,291 - Epoch: 78 Train acc: 99.81818181818181 Val acc: 10.26 Test acc10.05; Train loss: 7.303519461579113e-05 Val loss: 0.9013625610351562
+INFO - evaluator.py - 2024-10-26 02:55:59,606 - Epoch: 79 Train acc: 99.85636363636362 Val acc: 10.94 Test acc11.03; Train loss: 6.246309996773066e-05 Val loss: 0.4400322204589844
+INFO - evaluator.py - 2024-10-26 02:56:45,569 - Epoch: 80 Train acc: 99.64 Val acc: 10.92 Test acc11.32; Train loss: 0.00011546794026243415 Val loss: 0.113058837890625
+INFO - evaluator.py - 2024-10-26 02:57:29,629 - Epoch: 81 Train acc: 99.68181818181819 Val acc: 10.56 Test acc10.190000000000001; Train loss: 9.830131778091361e-05 Val loss: 0.10984285278320312
+INFO - evaluator.py - 2024-10-26 02:58:16,356 - Epoch: 82 Train acc: 99.71818181818182 Val acc: 12.24 Test acc12.55; Train loss: 8.465144693195312e-05 Val loss: 0.09112464294433593
+INFO - evaluator.py - 2024-10-26 02:59:00,911 - Epoch: 83 Train acc: 99.76727272727273 Val acc: 10.24 Test acc10.05; Train loss: 8.013038742567667e-05 Val loss: 0.12011952056884766
+INFO - evaluator.py - 2024-10-26 02:59:46,939 - Epoch: 84 Train acc: 99.50363636363636 Val acc: 10.16 Test acc10.0; Train loss: 0.0001444741157836027 Val loss: 0.0874213363647461
+INFO - evaluator.py - 2024-10-26 03:00:34,945 - Epoch: 85 Train acc: 99.80545454545454 Val acc: 14.280000000000001 Test acc13.700000000000001; Train loss: 6.839013961884616e-05 Val loss: 0.023284589767456055
+INFO - evaluator.py - 2024-10-26 03:01:20,500 - Epoch: 86 Train acc: 99.86181818181818 Val acc: 13.420000000000002 Test acc13.38; Train loss: 5.202490354475396e-05 Val loss: 0.027891870880126953
+INFO - evaluator.py - 2024-10-26 03:02:07,559 - Epoch: 87 Train acc: 99.71272727272728 Val acc: 10.5 Test acc10.870000000000001; Train loss: 9.173640320077538e-05 Val loss: 0.04302162857055664
+INFO - evaluator.py - 2024-10-26 03:02:54,990 - Epoch: 88 Train acc: 99.87818181818182 Val acc: 10.9 Test acc11.01; Train loss: 5.177985517022369e-05 Val loss: 0.0273489501953125
+INFO - evaluator.py - 2024-10-26 03:03:41,926 - Epoch: 89 Train acc: 99.48727272727272 Val acc: 10.26 Test acc10.07; Train loss: 0.00014867144075687974 Val loss: 0.042131631469726566
+INFO - evaluator.py - 2024-10-26 03:04:31,676 - Epoch: 90 Train acc: 99.74909090909091 Val acc: 14.7 Test acc14.78; Train loss: 7.703513803002848e-05 Val loss: 0.018683171463012696
+INFO - evaluator.py - 2024-10-26 03:05:14,540 - Epoch: 91 Train acc: 99.84363636363636 Val acc: 17.06 Test acc17.94; Train loss: 5.480630870654502e-05 Val loss: 0.0281590274810791
+INFO - evaluator.py - 2024-10-26 03:05:58,809 - Epoch: 92 Train acc: 99.88363636363637 Val acc: 11.3 Test acc11.85; Train loss: 4.278032963100651e-05 Val loss: 0.03303360786437988
+INFO - evaluator.py - 2024-10-26 03:06:44,499 - Epoch: 93 Train acc: 99.56727272727272 Val acc: 9.94 Test acc10.5; Train loss: 0.00012187819430096583 Val loss: 0.03843674697875977
+INFO - evaluator.py - 2024-10-26 03:07:28,234 - Epoch: 94 Train acc: 99.74545454545455 Val acc: 12.94 Test acc13.11; Train loss: 8.355168744191443e-05 Val loss: 0.027249835586547852
+INFO - evaluator.py - 2024-10-26 03:08:14,307 - Epoch: 95 Train acc: 99.76363636363637 Val acc: 11.459999999999999 Test acc11.360000000000001; Train loss: 8.1207636335272e-05 Val loss: 0.02092707405090332
+INFO - evaluator.py - 2024-10-26 03:09:00,380 - Epoch: 96 Train acc: 99.60545454545453 Val acc: 10.72 Test acc10.52; Train loss: 0.00011494513901204548 Val loss: 0.019687679290771483
+INFO - evaluator.py - 2024-10-26 03:09:49,094 - Epoch: 97 Train acc: 99.85636363636362 Val acc: 12.879999999999999 Test acc13.059999999999999; Train loss: 4.977483863106252e-05 Val loss: 0.026140225982666014
+INFO - evaluator.py - 2024-10-26 03:10:33,831 - Epoch: 98 Train acc: 99.56727272727272 Val acc: 16.400000000000002 Test acc16.37; Train loss: 0.00012463332889072428 Val loss: 0.01172156867980957
+INFO - evaluator.py - 2024-10-26 03:11:17,990 - Epoch: 99 Train acc: 99.66000000000001 Val acc: 12.82 Test acc12.94; Train loss: 9.87562683834271e-05 Val loss: 0.014405255126953126
+INFO - evaluator.py - 2024-10-26 03:12:03,981 - Epoch: 100 Train acc: 99.61454545454545 Val acc: 19.900000000000002 Test acc20.31; Train loss: 0.00010937704285395078 Val loss: 0.0066759524345397945
+INFO - evaluator.py - 2024-10-26 03:12:49,257 - Epoch: 101 Train acc: 99.90545454545455 Val acc: 16.7 Test acc16.23; Train loss: 4.3030773606998e-05 Val loss: 0.00981240119934082
+INFO - evaluator.py - 2024-10-26 03:13:35,016 - Epoch: 102 Train acc: 99.89272727272727 Val acc: 12.920000000000002 Test acc12.629999999999999; Train loss: 4.080691814676604e-05 Val loss: 0.018011404418945314
+INFO - evaluator.py - 2024-10-26 03:14:21,976 - Epoch: 103 Train acc: 99.58181818181818 Val acc: 13.98 Test acc13.76; Train loss: 0.00011882566397173584 Val loss: 0.015759152793884276
+INFO - evaluator.py - 2024-10-26 03:15:06,859 - Epoch: 104 Train acc: 99.75090909090909 Val acc: 14.82 Test acc15.09; Train loss: 8.002958412134004e-05 Val loss: 0.013895664405822754
+INFO - evaluator.py - 2024-10-26 03:15:51,601 - Epoch: 105 Train acc: 99.83818181818181 Val acc: 14.92 Test acc14.680000000000001; Train loss: 5.623052492684854e-05 Val loss: 0.019340599822998048
+INFO - evaluator.py - 2024-10-26 03:16:36,843 - Epoch: 106 Train acc: 99.91818181818182 Val acc: 14.56 Test acc14.510000000000002; Train loss: 3.302143542334141e-05 Val loss: 0.026974763870239258
+INFO - evaluator.py - 2024-10-26 03:17:22,180 - Epoch: 107 Train acc: 99.74545454545455 Val acc: 20.34 Test acc20.43; Train loss: 8.778748091056265e-05 Val loss: 0.01624936294555664
+INFO - evaluator.py - 2024-10-26 03:18:08,439 - Epoch: 108 Train acc: 99.53090909090909 Val acc: 14.299999999999999 Test acc14.21; Train loss: 0.00013464900822569191 Val loss: 0.019327939605712892
+INFO - evaluator.py - 2024-10-26 03:18:57,046 - Epoch: 109 Train acc: 99.52363636363636 Val acc: 11.78 Test acc11.42; Train loss: 0.00013404923900750212 Val loss: 0.01573744888305664
+INFO - evaluator.py - 2024-10-26 03:19:42,750 - Epoch: 110 Train acc: 99.77636363636364 Val acc: 20.560000000000002 Test acc20.54; Train loss: 7.068460329160602e-05 Val loss: 0.007009502410888672
+INFO - evaluator.py - 2024-10-26 03:20:28,230 - Epoch: 111 Train acc: 99.92 Val acc: 12.8 Test acc12.629999999999999; Train loss: 3.0271712834523483e-05 Val loss: 0.016841996383666992
+INFO - evaluator.py - 2024-10-26 03:21:14,020 - Epoch: 112 Train acc: 99.93818181818182 Val acc: 20.880000000000003 Test acc19.97; Train loss: 3.074996081281411e-05 Val loss: 0.013470761108398437
+INFO - evaluator.py - 2024-10-26 03:21:59,098 - Epoch: 113 Train acc: 99.61454545454545 Val acc: 20.080000000000002 Test acc19.29; Train loss: 0.00011582412413448434 Val loss: 0.007087872791290284
+INFO - evaluator.py - 2024-10-26 03:22:42,608 - Epoch: 114 Train acc: 99.54363636363637 Val acc: 19.16 Test acc19.57; Train loss: 0.0001315874312051826 Val loss: 0.006931615352630615
+INFO - evaluator.py - 2024-10-26 03:23:25,665 - Epoch: 115 Train acc: 99.67818181818183 Val acc: 16.46 Test acc16.189999999999998; Train loss: 9.647386571688747e-05 Val loss: 0.008228447914123535
+INFO - evaluator.py - 2024-10-26 03:24:09,862 - Epoch: 116 Train acc: 99.63818181818182 Val acc: 13.100000000000001 Test acc13.08; Train loss: 0.00011333477621982721 Val loss: 0.011578029441833495
+INFO - evaluator.py - 2024-10-26 03:24:54,486 - Epoch: 117 Train acc: 99.70727272727272 Val acc: 10.56 Test acc10.280000000000001; Train loss: 9.27148766244169e-05 Val loss: 0.019399931716918947
+INFO - evaluator.py - 2024-10-26 03:25:38,707 - Epoch: 118 Train acc: 99.96181818181819 Val acc: 12.78 Test acc12.520000000000001; Train loss: 2.13093079441354e-05 Val loss: 0.020294671249389648
+INFO - evaluator.py - 2024-10-26 03:26:22,162 - Epoch: 119 Train acc: 99.92909090909092 Val acc: 14.12 Test acc14.11; Train loss: 2.7451128118396313e-05 Val loss: 0.019624518966674803
+INFO - evaluator.py - 2024-10-26 03:27:06,241 - Epoch: 120 Train acc: 99.99454545454546 Val acc: 19.28 Test acc18.6; Train loss: 9.743434979728507e-06 Val loss: 0.011954263877868652
+INFO - evaluator.py - 2024-10-26 03:27:51,833 - Epoch: 121 Train acc: 99.99636363636364 Val acc: 17.14 Test acc16.93; Train loss: 7.38732793490106e-06 Val loss: 0.012425445747375488
+INFO - evaluator.py - 2024-10-26 03:28:38,882 - Epoch: 122 Train acc: 99.99818181818182 Val acc: 16.56 Test acc16.2; Train loss: 6.351070720649494e-06 Val loss: 0.01288986873626709
+INFO - evaluator.py - 2024-10-26 03:29:23,213 - Epoch: 123 Train acc: 100.0 Val acc: 15.0 Test acc14.580000000000002; Train loss: 5.961319625070742e-06 Val loss: 0.014909064483642579
+INFO - evaluator.py - 2024-10-26 03:30:07,702 - Epoch: 124 Train acc: 99.99636363636364 Val acc: 13.700000000000001 Test acc13.13; Train loss: 6.16895372778262e-06 Val loss: 0.01811315383911133
+INFO - evaluator.py - 2024-10-26 03:30:51,062 - Epoch: 125 Train acc: 100.0 Val acc: 13.26 Test acc12.709999999999999; Train loss: 5.994731347362342e-06 Val loss: 0.01882580795288086
+INFO - evaluator.py - 2024-10-26 03:31:38,008 - Epoch: 126 Train acc: 100.0 Val acc: 13.38 Test acc12.690000000000001; Train loss: 6.124838022779758e-06 Val loss: 0.018655714416503905
+INFO - evaluator.py - 2024-10-26 03:32:24,599 - Epoch: 127 Train acc: 100.0 Val acc: 12.659999999999998 Test acc12.07; Train loss: 5.69807794791731e-06 Val loss: 0.021315585708618166
+INFO - evaluator.py - 2024-10-26 03:33:09,713 - Epoch: 128 Train acc: 99.99818181818182 Val acc: 12.46 Test acc12.11; Train loss: 6.5231761408292435e-06 Val loss: 0.022558869171142578
+INFO - evaluator.py - 2024-10-26 03:33:55,232 - Epoch: 129 Train acc: 99.99818181818182 Val acc: 12.0 Test acc11.65; Train loss: 6.795909776701592e-06 Val loss: 0.023464069747924806
+INFO - evaluator.py - 2024-10-26 03:34:42,118 - Epoch: 130 Train acc: 100.0 Val acc: 11.559999999999999 Test acc11.129999999999999; Train loss: 6.42306868262081e-06 Val loss: 0.026993580627441408
+INFO - evaluator.py - 2024-10-26 03:35:26,971 - Epoch: 131 Train acc: 100.0 Val acc: 11.4 Test acc10.96; Train loss: 6.655603895937516e-06 Val loss: 0.028083466720581055
+INFO - evaluator.py - 2024-10-26 03:36:12,299 - Epoch: 132 Train acc: 100.0 Val acc: 11.1 Test acc10.65; Train loss: 6.952021281044422e-06 Val loss: 0.03102166748046875
+INFO - evaluator.py - 2024-10-26 03:36:57,074 - Epoch: 133 Train acc: 100.0 Val acc: 11.0 Test acc10.61; Train loss: 6.85641063821756e-06 Val loss: 0.03273886337280273
+INFO - evaluator.py - 2024-10-26 03:37:40,248 - Epoch: 134 Train acc: 100.0 Val acc: 10.879999999999999 Test acc10.52; Train loss: 7.17236882456663e-06 Val loss: 0.0351756217956543
+INFO - evaluator.py - 2024-10-26 03:38:26,393 - Epoch: 135 Train acc: 100.0 Val acc: 10.72 Test acc10.440000000000001; Train loss: 6.888501204296269e-06 Val loss: 0.03643789672851563
+INFO - evaluator.py - 2024-10-26 03:39:14,419 - Epoch: 136 Train acc: 100.0 Val acc: 10.66 Test acc10.33; Train loss: 7.753894992426716e-06 Val loss: 0.03987898330688477
+INFO - evaluator.py - 2024-10-26 03:40:00,004 - Epoch: 137 Train acc: 100.0 Val acc: 10.66 Test acc10.42; Train loss: 7.5488563701087105e-06 Val loss: 0.038290409851074216
+INFO - evaluator.py - 2024-10-26 03:40:45,169 - Epoch: 138 Train acc: 100.0 Val acc: 10.620000000000001 Test acc10.280000000000001; Train loss: 7.584743236657232e-06 Val loss: 0.041804833984375
+INFO - evaluator.py - 2024-10-26 03:41:33,421 - Epoch: 139 Train acc: 100.0 Val acc: 10.6 Test acc10.299999999999999; Train loss: 7.901154429948126e-06 Val loss: 0.04195718994140625
+INFO - evaluator.py - 2024-10-26 03:42:19,934 - Epoch: 140 Train acc: 100.0 Val acc: 10.48 Test acc10.2; Train loss: 7.71476966276003e-06 Val loss: 0.04483129653930664
+INFO - evaluator.py - 2024-10-26 03:43:03,925 - Epoch: 141 Train acc: 100.0 Val acc: 10.38 Test acc10.2; Train loss: 7.813424862582576e-06 Val loss: 0.046375970458984374
+INFO - evaluator.py - 2024-10-26 03:43:47,877 - Epoch: 142 Train acc: 100.0 Val acc: 10.32 Test acc10.12; Train loss: 8.27950509159233e-06 Val loss: 0.05104452438354492
+INFO - evaluator.py - 2024-10-26 03:44:31,687 - Epoch: 143 Train acc: 100.0 Val acc: 10.280000000000001 Test acc10.09; Train loss: 7.832259767350148e-06 Val loss: 0.05495095977783203
+INFO - evaluator.py - 2024-10-26 03:45:15,509 - Epoch: 144 Train acc: 100.0 Val acc: 10.299999999999999 Test acc10.12; Train loss: 8.020259557418864e-06 Val loss: 0.05222318420410156
+INFO - evaluator.py - 2024-10-26 03:46:00,443 - Epoch: 145 Train acc: 100.0 Val acc: 10.32 Test acc10.12; Train loss: 8.146560336039825e-06 Val loss: 0.05463007049560547
+INFO - evaluator.py - 2024-10-26 03:46:47,060 - Epoch: 146 Train acc: 99.99818181818182 Val acc: 10.32 Test acc10.12; Train loss: 8.367066364206204e-06 Val loss: 0.0534688591003418
+INFO - evaluator.py - 2024-10-26 03:47:33,828 - Epoch: 147 Train acc: 100.0 Val acc: 10.24 Test acc10.100000000000001; Train loss: 8.371013445271687e-06 Val loss: 0.05937597427368164
+INFO - evaluator.py - 2024-10-26 03:48:19,008 - Epoch: 148 Train acc: 100.0 Val acc: 10.24 Test acc10.09; Train loss: 8.278769023970448e-06 Val loss: 0.05885826644897461
+INFO - evaluator.py - 2024-10-26 03:49:03,477 - Epoch: 149 Train acc: 100.0 Val acc: 10.24 Test acc10.07; Train loss: 8.564068600323728e-06 Val loss: 0.06039247741699219
+INFO - evaluator.py - 2024-10-26 03:49:47,637 - Epoch: 150 Train acc: 99.99818181818182 Val acc: 10.22 Test acc10.05; Train loss: 8.464102040108463e-06 Val loss: 0.06537135009765625
+INFO - evaluator.py - 2024-10-26 03:50:33,210 - Epoch: 151 Train acc: 99.99818181818182 Val acc: 10.22 Test acc10.07; Train loss: 9.296958594561809e-06 Val loss: 0.06338825988769531
+INFO - evaluator.py - 2024-10-26 03:51:21,475 - Epoch: 152 Train acc: 100.0 Val acc: 10.22 Test acc10.07; Train loss: 8.194125653773715e-06 Val loss: 0.06316796188354493
+INFO - evaluator.py - 2024-10-26 03:52:05,068 - Epoch: 153 Train acc: 100.0 Val acc: 10.22 Test acc10.08; Train loss: 8.435802610950884e-06 Val loss: 0.06463222579956054
+INFO - evaluator.py - 2024-10-26 03:52:51,490 - Epoch: 154 Train acc: 100.0 Val acc: 10.22 Test acc10.08; Train loss: 8.740311599632894e-06 Val loss: 0.06328580474853515
+INFO - evaluator.py - 2024-10-26 03:53:37,504 - Epoch: 155 Train acc: 100.0 Val acc: 10.22 Test acc10.07; Train loss: 8.617680239364166e-06 Val loss: 0.06826424407958985
+INFO - evaluator.py - 2024-10-26 03:54:25,624 - Epoch: 156 Train acc: 99.99818181818182 Val acc: 10.22 Test acc10.03; Train loss: 8.939013599989597e-06 Val loss: 0.07176650543212891
+INFO - evaluator.py - 2024-10-26 03:55:10,696 - Epoch: 157 Train acc: 100.0 Val acc: 10.2 Test acc10.02; Train loss: 8.590674432079222e-06 Val loss: 0.07440789031982421
+INFO - evaluator.py - 2024-10-26 03:55:58,548 - Epoch: 158 Train acc: 100.0 Val acc: 10.22 Test acc10.01; Train loss: 8.326658870580353e-06 Val loss: 0.07356615142822266
+INFO - evaluator.py - 2024-10-26 03:56:45,249 - Epoch: 159 Train acc: 100.0 Val acc: 10.2 Test acc10.01; Train loss: 8.858242097564718e-06 Val loss: 0.07199778594970703
+INFO - evaluator.py - 2024-10-26 03:57:28,485 - Epoch: 160 Train acc: 100.0 Val acc: 10.18 Test acc10.01; Train loss: 8.560773146084764e-06 Val loss: 0.07430461730957032
+INFO - evaluator.py - 2024-10-26 03:58:10,730 - Epoch: 161 Train acc: 99.99818181818182 Val acc: 10.16 Test acc10.0; Train loss: 9.634362958604469e-06 Val loss: 0.08175045623779296
+INFO - evaluator.py - 2024-10-26 03:58:56,210 - Epoch: 162 Train acc: 100.0 Val acc: 10.18 Test acc10.01; Train loss: 8.347227328605103e-06 Val loss: 0.0780935302734375
+INFO - evaluator.py - 2024-10-26 03:59:42,509 - Epoch: 163 Train acc: 100.0 Val acc: 10.18 Test acc10.0; Train loss: 8.245138975855133e-06 Val loss: 0.08234890441894531
+INFO - evaluator.py - 2024-10-26 04:00:28,073 - Epoch: 164 Train acc: 100.0 Val acc: 10.18 Test acc10.02; Train loss: 8.463728328404779e-06 Val loss: 0.07931236419677734
+INFO - evaluator.py - 2024-10-26 04:01:14,764 - Epoch: 165 Train acc: 100.0 Val acc: 10.18 Test acc10.02; Train loss: 8.727376166180791e-06 Val loss: 0.07838601989746094
+INFO - evaluator.py - 2024-10-26 04:02:02,989 - Epoch: 166 Train acc: 100.0 Val acc: 10.18 Test acc10.03; Train loss: 8.042406110325829e-06 Val loss: 0.07813091278076172
+INFO - evaluator.py - 2024-10-26 04:02:48,821 - Epoch: 167 Train acc: 99.99636363636364 Val acc: 10.16 Test acc10.0; Train loss: 9.355671841397205e-06 Val loss: 0.08475986938476562
+INFO - evaluator.py - 2024-10-26 04:03:36,309 - Epoch: 168 Train acc: 100.0 Val acc: 10.16 Test acc10.0; Train loss: 8.995376299770379e-06 Val loss: 0.07492205505371094
+INFO - evaluator.py - 2024-10-26 04:04:19,721 - Epoch: 169 Train acc: 100.0 Val acc: 10.18 Test acc10.0; Train loss: 8.484305783746425e-06 Val loss: 0.07990805206298827
+INFO - evaluator.py - 2024-10-26 04:05:06,446 - Epoch: 170 Train acc: 100.0 Val acc: 10.16 Test acc10.01; Train loss: 9.231026244180447e-06 Val loss: 0.07178651885986329
+INFO - evaluator.py - 2024-10-26 04:05:48,707 - Epoch: 171 Train acc: 100.0 Val acc: 10.16 Test acc10.01; Train loss: 8.734909251374616e-06 Val loss: 0.07690797882080078
+INFO - evaluator.py - 2024-10-26 04:06:37,942 - Epoch: 172 Train acc: 99.99818181818182 Val acc: 10.16 Test acc10.01; Train loss: 8.772893892389469e-06 Val loss: 0.07305140838623046
+INFO - evaluator.py - 2024-10-26 04:07:22,116 - Epoch: 173 Train acc: 100.0 Val acc: 10.18 Test acc10.040000000000001; Train loss: 9.031782380770891e-06 Val loss: 0.08110297241210937
+INFO - evaluator.py - 2024-10-26 04:08:06,499 - Epoch: 174 Train acc: 99.96727272727273 Val acc: 10.42 Test acc10.23; Train loss: 2.0355919418348507e-05 Val loss: 0.03603578109741211
+INFO - evaluator.py - 2024-10-26 04:08:50,980 - Epoch: 175 Train acc: 99.99818181818182 Val acc: 10.52 Test acc10.36; Train loss: 1.1430802401578562e-05 Val loss: 0.03968711166381836
+INFO - evaluator.py - 2024-10-26 04:09:36,523 - Epoch: 176 Train acc: 99.99818181818182 Val acc: 10.72 Test acc10.549999999999999; Train loss: 1.0024707872335883e-05 Val loss: 0.04155505981445313
+INFO - evaluator.py - 2024-10-26 04:10:21,047 - Epoch: 177 Train acc: 100.0 Val acc: 10.440000000000001 Test acc10.37; Train loss: 8.542084954255684e-06 Val loss: 0.044777346801757816
+INFO - evaluator.py - 2024-10-26 04:11:07,194 - Epoch: 178 Train acc: 100.0 Val acc: 10.36 Test acc10.290000000000001; Train loss: 7.851455581840128e-06 Val loss: 0.0470234489440918
+INFO - evaluator.py - 2024-10-26 04:11:54,162 - Epoch: 179 Train acc: 99.99818181818182 Val acc: 10.74 Test acc10.52; Train loss: 9.42163352156058e-06 Val loss: 0.047286112213134765
+INFO - evaluator.py - 2024-10-26 04:12:39,019 - Epoch: 180 Train acc: 99.99818181818182 Val acc: 10.620000000000001 Test acc10.56; Train loss: 9.70877865541049e-06 Val loss: 0.040329364013671876
+INFO - evaluator.py - 2024-10-26 04:13:24,636 - Epoch: 181 Train acc: 100.0 Val acc: 10.84 Test acc10.7; Train loss: 8.114755145718597e-06 Val loss: 0.03611096420288086
+INFO - evaluator.py - 2024-10-26 04:14:11,464 - Epoch: 182 Train acc: 100.0 Val acc: 10.620000000000001 Test acc10.54; Train loss: 7.630697888618504e-06 Val loss: 0.03334098205566406
+INFO - evaluator.py - 2024-10-26 04:14:55,716 - Epoch: 183 Train acc: 99.99818181818182 Val acc: 10.879999999999999 Test acc10.68; Train loss: 8.194219965000892e-06 Val loss: 0.030345605087280274
+INFO - evaluator.py - 2024-10-26 04:15:38,449 - Epoch: 184 Train acc: 100.0 Val acc: 11.04 Test acc10.870000000000001; Train loss: 7.902930808698081e-06 Val loss: 0.02800265655517578
+INFO - evaluator.py - 2024-10-26 04:16:22,373 - Epoch: 185 Train acc: 100.0 Val acc: 11.14 Test acc10.85; Train loss: 7.806521457810462e-06 Val loss: 0.026052961349487303
+INFO - evaluator.py - 2024-10-26 04:17:05,679 - Epoch: 186 Train acc: 100.0 Val acc: 11.18 Test acc10.89; Train loss: 7.958921140313825e-06 Val loss: 0.024737034606933595
+INFO - evaluator.py - 2024-10-26 04:17:51,372 - Epoch: 187 Train acc: 100.0 Val acc: 11.360000000000001 Test acc11.07; Train loss: 7.739782409573143e-06 Val loss: 0.02245941047668457
+INFO - evaluator.py - 2024-10-26 04:18:41,045 - Epoch: 188 Train acc: 100.0 Val acc: 11.64 Test acc11.25; Train loss: 7.962762540079314e-06 Val loss: 0.020574520492553712
+INFO - evaluator.py - 2024-10-26 04:19:24,658 - Epoch: 189 Train acc: 99.99818181818182 Val acc: 11.540000000000001 Test acc11.200000000000001; Train loss: 8.881624762117016e-06 Val loss: 0.019900898742675782
+INFO - evaluator.py - 2024-10-26 04:20:09,240 - Epoch: 190 Train acc: 99.99818181818182 Val acc: 11.44 Test acc11.15; Train loss: 8.542271950070492e-06 Val loss: 0.02039972724914551
+INFO - evaluator.py - 2024-10-26 04:20:53,473 - Epoch: 191 Train acc: 100.0 Val acc: 11.64 Test acc11.3; Train loss: 7.71427130040882e-06 Val loss: 0.0186724853515625
+INFO - evaluator.py - 2024-10-26 04:21:36,920 - Epoch: 192 Train acc: 100.0 Val acc: 12.08 Test acc11.64; Train loss: 7.6913782146717e-06 Val loss: 0.017235228729248046
+INFO - evaluator.py - 2024-10-26 04:22:22,929 - Epoch: 193 Train acc: 99.99818181818182 Val acc: 11.34 Test acc10.95; Train loss: 7.772804732138122e-06 Val loss: 0.018354781723022462
+INFO - evaluator.py - 2024-10-26 04:23:05,913 - Epoch: 194 Train acc: 99.99636363636364 Val acc: 11.24 Test acc10.93; Train loss: 8.212976005796174e-06 Val loss: 0.018313469696044922
+INFO - evaluator.py - 2024-10-26 04:23:47,516 - Epoch: 195 Train acc: 100.0 Val acc: 11.74 Test acc11.37; Train loss: 7.465571245517243e-06 Val loss: 0.01661026039123535
+INFO - evaluator.py - 2024-10-26 04:24:32,534 - Epoch: 196 Train acc: 100.0 Val acc: 11.32 Test acc10.95; Train loss: 7.522851234005595e-06 Val loss: 0.01691241912841797
+INFO - evaluator.py - 2024-10-26 04:25:14,915 - Epoch: 197 Train acc: 100.0 Val acc: 11.52 Test acc11.15; Train loss: 7.458537078822371e-06 Val loss: 0.015389593124389648
+INFO - evaluator.py - 2024-10-26 04:26:04,621 - Epoch: 198 Train acc: 100.0 Val acc: 11.78 Test acc11.42; Train loss: 8.091069823554293e-06 Val loss: 0.014381836891174317
+INFO - evaluator.py - 2024-10-26 04:26:47,771 - Epoch: 199 Train acc: 100.0 Val acc: 12.24 Test acc11.76; Train loss: 7.402704684169624e-06 Val loss: 0.013374807357788086
+INFO - evaluator.py - 2024-10-26 04:26:47,807 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnet is 23.84 and 22.36
+INFO - evaluator.py - 2024-10-26 04:26:47,807 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnet is 23.84 and 22.36
+INFO - evaluator.py - 2024-10-26 04:26:47,807 - The best acc test dataset from resnet is 22.36
+INFO - evaluator.py - 2024-10-26 04:28:17,172 - Epoch: 0 Train acc: 40.14909090909091 Val acc: 28.12 Test acc28.410000000000004; Train loss: 0.012397019756924022 Val loss: 0.004843869686126709
+INFO - evaluator.py - 2024-10-26 04:29:45,654 - Epoch: 1 Train acc: 67.43272727272728 Val acc: 21.84 Test acc21.39; Train loss: 0.006864267989180305 Val loss: 0.009686502647399902
+INFO - evaluator.py - 2024-10-26 04:31:14,342 - Epoch: 2 Train acc: 90.47090909090909 Val acc: 19.54 Test acc19.3; Train loss: 0.0021936307775703343 Val loss: 0.012408186531066895
+INFO - evaluator.py - 2024-10-26 04:32:42,898 - Epoch: 3 Train acc: 95.47818181818182 Val acc: 16.46 Test acc16.73; Train loss: 0.0010580781553956595 Val loss: 0.020611013412475586
+INFO - evaluator.py - 2024-10-26 04:34:11,610 - Epoch: 4 Train acc: 96.99818181818182 Val acc: 15.78 Test acc15.17; Train loss: 0.0007105207325044003 Val loss: 0.027552807235717774
+INFO - evaluator.py - 2024-10-26 04:35:40,006 - Epoch: 5 Train acc: 97.58727272727272 Val acc: 14.2 Test acc13.950000000000001; Train loss: 0.0005878626459024169 Val loss: 0.029613556671142578
+INFO - evaluator.py - 2024-10-26 04:37:08,438 - Epoch: 6 Train acc: 97.78363636363636 Val acc: 21.5 Test acc21.08; Train loss: 0.000540253850038756 Val loss: 0.016980283164978027
+INFO - evaluator.py - 2024-10-26 04:38:36,569 - Epoch: 7 Train acc: 98.25272727272727 Val acc: 14.6 Test acc13.84; Train loss: 0.000435202690992843 Val loss: 0.039404601287841796
+INFO - evaluator.py - 2024-10-26 04:40:04,894 - Epoch: 8 Train acc: 98.08545454545454 Val acc: 17.740000000000002 Test acc16.78; Train loss: 0.00048336167052726855 Val loss: 0.022583145904541014
+INFO - evaluator.py - 2024-10-26 04:41:33,305 - Epoch: 9 Train acc: 98.28 Val acc: 15.8 Test acc15.52; Train loss: 0.00043770434138449757 Val loss: 0.028475571060180664
+INFO - evaluator.py - 2024-10-26 04:43:01,645 - Epoch: 10 Train acc: 98.38 Val acc: 13.36 Test acc13.26; Train loss: 0.0004316636416891759 Val loss: 0.028506919860839842
+INFO - evaluator.py - 2024-10-26 04:44:30,716 - Epoch: 11 Train acc: 98.45636363636365 Val acc: 18.62 Test acc18.65; Train loss: 0.00040945332477038555 Val loss: 0.014587672996520995
+INFO - evaluator.py - 2024-10-26 04:45:59,571 - Epoch: 12 Train acc: 98.57454545454546 Val acc: 22.08 Test acc21.990000000000002; Train loss: 0.00037071670474992556 Val loss: 0.012076818466186523
+INFO - evaluator.py - 2024-10-26 04:47:27,774 - Epoch: 13 Train acc: 98.51818181818182 Val acc: 16.5 Test acc16.71; Train loss: 0.00038653421740640295 Val loss: 0.011010262489318847
+INFO - evaluator.py - 2024-10-26 04:48:56,176 - Epoch: 14 Train acc: 98.48727272727272 Val acc: 15.32 Test acc15.620000000000001; Train loss: 0.00038634390336546033 Val loss: 0.011813413429260254
+INFO - evaluator.py - 2024-10-26 04:50:24,329 - Epoch: 15 Train acc: 98.87090909090908 Val acc: 15.559999999999999 Test acc15.49; Train loss: 0.00031733755689452995 Val loss: 0.015305371475219726
+INFO - evaluator.py - 2024-10-26 04:51:53,109 - Epoch: 16 Train acc: 98.65454545454546 Val acc: 19.1 Test acc18.72; Train loss: 0.00035829889525405384 Val loss: 0.01277172088623047
+INFO - evaluator.py - 2024-10-26 04:53:21,500 - Epoch: 17 Train acc: 98.98181818181818 Val acc: 15.299999999999999 Test acc15.740000000000002; Train loss: 0.00028550753781402655 Val loss: 0.01461376075744629
+INFO - evaluator.py - 2024-10-26 04:54:50,148 - Epoch: 18 Train acc: 98.66909090909091 Val acc: 18.08 Test acc17.740000000000002; Train loss: 0.0003575726000761444 Val loss: 0.011331073760986328
+INFO - evaluator.py - 2024-10-26 04:56:18,772 - Epoch: 19 Train acc: 98.92545454545456 Val acc: 17.22 Test acc16.78; Train loss: 0.0002840087101371451 Val loss: 0.010891446113586426
+INFO - evaluator.py - 2024-10-26 04:57:47,416 - Epoch: 20 Train acc: 99.03090909090909 Val acc: 13.36 Test acc13.16; Train loss: 0.0002713787435003641 Val loss: 0.019857360458374024
+INFO - evaluator.py - 2024-10-26 04:59:16,070 - Epoch: 21 Train acc: 99.03454545454545 Val acc: 20.48 Test acc20.89; Train loss: 0.000275569771992212 Val loss: 0.0099783634185791
+INFO - evaluator.py - 2024-10-26 05:00:44,680 - Epoch: 22 Train acc: 98.92545454545456 Val acc: 18.42 Test acc18.69; Train loss: 0.00029076593918725847 Val loss: 0.007436170101165772
+INFO - evaluator.py - 2024-10-26 05:02:14,041 - Epoch: 23 Train acc: 99.08 Val acc: 15.440000000000001 Test acc15.35; Train loss: 0.00025923980309373954 Val loss: 0.016093370819091797
+INFO - evaluator.py - 2024-10-26 05:03:43,067 - Epoch: 24 Train acc: 98.92545454545456 Val acc: 20.76 Test acc20.11; Train loss: 0.0002929323295596987 Val loss: 0.006964762020111084
+INFO - evaluator.py - 2024-10-26 05:05:12,171 - Epoch: 25 Train acc: 98.9309090909091 Val acc: 18.88 Test acc18.8; Train loss: 0.00028055075307122687 Val loss: 0.008353558349609375
+INFO - evaluator.py - 2024-10-26 05:06:41,077 - Epoch: 26 Train acc: 99.30727272727272 Val acc: 20.8 Test acc21.529999999999998; Train loss: 0.00019907915087374436 Val loss: 0.008706013107299805
+INFO - evaluator.py - 2024-10-26 05:08:09,746 - Epoch: 27 Train acc: 98.84727272727272 Val acc: 22.06 Test acc22.3; Train loss: 0.0003163526906420223 Val loss: 0.006678035640716552
+INFO - evaluator.py - 2024-10-26 05:09:38,354 - Epoch: 28 Train acc: 99.18363636363637 Val acc: 19.7 Test acc19.56; Train loss: 0.0002415322332219644 Val loss: 0.014360575866699218
+INFO - evaluator.py - 2024-10-26 05:11:06,984 - Epoch: 29 Train acc: 98.75272727272727 Val acc: 12.2 Test acc12.26; Train loss: 0.0003351765849669887 Val loss: 0.013118914794921875
+INFO - evaluator.py - 2024-10-26 05:12:35,726 - Epoch: 30 Train acc: 99.5109090909091 Val acc: 19.34 Test acc19.439999999999998; Train loss: 0.00015682397483509372 Val loss: 0.012533761978149415
+INFO - evaluator.py - 2024-10-26 05:14:04,689 - Epoch: 31 Train acc: 98.96000000000001 Val acc: 19.64 Test acc19.06; Train loss: 0.00027364362788555974 Val loss: 0.012061150550842285
+INFO - evaluator.py - 2024-10-26 05:15:33,545 - Epoch: 32 Train acc: 98.89818181818183 Val acc: 18.04 Test acc18.11; Train loss: 0.00029628581440245566 Val loss: 0.008303765487670898
+INFO - evaluator.py - 2024-10-26 05:17:02,407 - Epoch: 33 Train acc: 98.98363636363636 Val acc: 14.04 Test acc14.49; Train loss: 0.000271482666996731 Val loss: 0.016226923179626464
+INFO - evaluator.py - 2024-10-26 05:18:31,523 - Epoch: 34 Train acc: 98.98 Val acc: 12.959999999999999 Test acc12.790000000000001; Train loss: 0.000270206134412861 Val loss: 0.011738982391357422
+INFO - evaluator.py - 2024-10-26 05:20:00,467 - Epoch: 35 Train acc: 99.31272727272727 Val acc: 21.12 Test acc20.8; Train loss: 0.0002026154680008238 Val loss: 0.009353866767883301
+INFO - evaluator.py - 2024-10-26 05:21:29,901 - Epoch: 36 Train acc: 99.00545454545454 Val acc: 19.1 Test acc18.52; Train loss: 0.0002719531781590459 Val loss: 0.010004718017578126
+INFO - evaluator.py - 2024-10-26 05:22:58,794 - Epoch: 37 Train acc: 99.35272727272726 Val acc: 15.7 Test acc15.86; Train loss: 0.00018116576047825882 Val loss: 0.011521209526062011
+INFO - evaluator.py - 2024-10-26 05:24:28,012 - Epoch: 38 Train acc: 99.04181818181819 Val acc: 24.060000000000002 Test acc24.08; Train loss: 0.0002640457731163637 Val loss: 0.0061462797164916995
+INFO - evaluator.py - 2024-10-26 05:25:56,725 - Epoch: 39 Train acc: 99.15636363636364 Val acc: 21.26 Test acc20.46; Train loss: 0.0002461410958827897 Val loss: 0.007940395641326904
+INFO - evaluator.py - 2024-10-26 05:27:25,534 - Epoch: 40 Train acc: 99.00727272727273 Val acc: 12.139999999999999 Test acc12.53; Train loss: 0.0002770852674645456 Val loss: 0.012670742416381836
+INFO - evaluator.py - 2024-10-26 05:28:54,543 - Epoch: 41 Train acc: 99.18363636363637 Val acc: 16.72 Test acc16.509999999999998; Train loss: 0.00023528300717854026 Val loss: 0.010180828857421874
+INFO - evaluator.py - 2024-10-26 05:30:23,246 - Epoch: 42 Train acc: 98.92181818181818 Val acc: 17.54 Test acc17.76; Train loss: 0.0002894283216679469 Val loss: 0.006154496955871582
+INFO - evaluator.py - 2024-10-26 05:31:51,768 - Epoch: 43 Train acc: 99.22181818181818 Val acc: 19.900000000000002 Test acc20.09; Train loss: 0.0002243006957843053 Val loss: 0.007042449283599854
+INFO - evaluator.py - 2024-10-26 05:33:20,526 - Epoch: 44 Train acc: 99.16181818181819 Val acc: 15.0 Test acc14.66; Train loss: 0.00024335639821788805 Val loss: 0.014383745002746582
+INFO - evaluator.py - 2024-10-26 05:34:49,091 - Epoch: 45 Train acc: 99.1290909090909 Val acc: 18.72 Test acc18.93; Train loss: 0.0002526381238279018 Val loss: 0.008269670295715332
+INFO - evaluator.py - 2024-10-26 05:36:17,405 - Epoch: 46 Train acc: 98.97818181818182 Val acc: 17.24 Test acc17.119999999999997; Train loss: 0.00028569597041403703 Val loss: 0.009851044845581055
+INFO - evaluator.py - 2024-10-26 05:37:45,884 - Epoch: 47 Train acc: 99.18181818181819 Val acc: 17.98 Test acc17.330000000000002; Train loss: 0.00021464130579366942 Val loss: 0.01160760612487793
+INFO - evaluator.py - 2024-10-26 05:39:14,234 - Epoch: 48 Train acc: 99.13636363636364 Val acc: 16.439999999999998 Test acc15.85; Train loss: 0.00024159963422200898 Val loss: 0.012127892303466796
+INFO - evaluator.py - 2024-10-26 05:40:42,540 - Epoch: 49 Train acc: 98.7690909090909 Val acc: 18.88 Test acc18.790000000000003; Train loss: 0.00031728861778127876 Val loss: 0.007044771385192871
+INFO - evaluator.py - 2024-10-26 05:42:10,890 - Epoch: 50 Train acc: 99.2109090909091 Val acc: 19.66 Test acc19.89; Train loss: 0.0002240663263862106 Val loss: 0.007345924091339111
+INFO - evaluator.py - 2024-10-26 05:43:39,243 - Epoch: 51 Train acc: 98.92363636363638 Val acc: 22.18 Test acc21.85; Train loss: 0.00029654322693717074 Val loss: 0.009023225402832031
+INFO - evaluator.py - 2024-10-26 05:45:07,702 - Epoch: 52 Train acc: 99.24 Val acc: 15.7 Test acc15.260000000000002; Train loss: 0.00021178677435541016 Val loss: 0.010648463439941407
+INFO - evaluator.py - 2024-10-26 05:46:36,059 - Epoch: 53 Train acc: 99.03454545454545 Val acc: 17.0 Test acc17.14; Train loss: 0.0002682143183171072 Val loss: 0.009833136749267578
+INFO - evaluator.py - 2024-10-26 05:48:04,440 - Epoch: 54 Train acc: 99.00363636363636 Val acc: 15.76 Test acc15.27; Train loss: 0.00028209696519188585 Val loss: 0.008636637306213378
+INFO - evaluator.py - 2024-10-26 05:49:32,794 - Epoch: 55 Train acc: 98.98545454545454 Val acc: 19.18 Test acc18.68; Train loss: 0.0002758572318887507 Val loss: 0.010504107093811036
+INFO - evaluator.py - 2024-10-26 05:51:01,426 - Epoch: 56 Train acc: 99.42727272727274 Val acc: 21.18 Test acc20.7; Train loss: 0.00017486122619064356 Val loss: 0.009315977096557617
+INFO - evaluator.py - 2024-10-26 05:52:29,773 - Epoch: 57 Train acc: 99.15272727272728 Val acc: 10.26 Test acc10.41; Train loss: 0.00024197168417528948 Val loss: 0.02264856948852539
+INFO - evaluator.py - 2024-10-26 05:53:58,364 - Epoch: 58 Train acc: 99.08363636363636 Val acc: 21.36 Test acc20.9; Train loss: 0.000259541094167666 Val loss: 0.0070974559783935545
+INFO - evaluator.py - 2024-10-26 05:55:26,562 - Epoch: 59 Train acc: 98.94 Val acc: 14.7 Test acc14.979999999999999; Train loss: 0.000288450536462055 Val loss: 0.0065808529853820805
+INFO - evaluator.py - 2024-10-26 05:56:54,901 - Epoch: 60 Train acc: 99.93454545454546 Val acc: 24.64 Test acc24.25; Train loss: 3.440686370648274e-05 Val loss: 0.006261393356323242
+INFO - evaluator.py - 2024-10-26 05:58:23,263 - Epoch: 61 Train acc: 99.98 Val acc: 23.44 Test acc22.23; Train loss: 2.0441857578275218e-05 Val loss: 0.00883709888458252
+INFO - evaluator.py - 2024-10-26 05:59:51,488 - Epoch: 62 Train acc: 99.99090909090908 Val acc: 18.14 Test acc17.080000000000002; Train loss: 1.664961666154506e-05 Val loss: 0.015627835083007812
+INFO - evaluator.py - 2024-10-26 06:01:19,700 - Epoch: 63 Train acc: 99.99454545454546 Val acc: 15.24 Test acc14.74; Train loss: 1.705007659461857e-05 Val loss: 0.023448649215698242
+INFO - evaluator.py - 2024-10-26 06:02:47,914 - Epoch: 64 Train acc: 99.99454545454546 Val acc: 12.8 Test acc12.5; Train loss: 1.7236903949048033e-05 Val loss: 0.041543310546875
+INFO - evaluator.py - 2024-10-26 06:04:16,173 - Epoch: 65 Train acc: 99.99636363636364 Val acc: 12.379999999999999 Test acc12.34; Train loss: 1.918011954879727e-05 Val loss: 0.05383150482177734
+INFO - evaluator.py - 2024-10-26 06:05:44,504 - Epoch: 66 Train acc: 100.0 Val acc: 11.84 Test acc12.01; Train loss: 1.8159991818141532e-05 Val loss: 0.07339104614257813
+INFO - evaluator.py - 2024-10-26 06:07:12,795 - Epoch: 67 Train acc: 99.99272727272728 Val acc: 11.06 Test acc11.4; Train loss: 1.9736675914927302e-05 Val loss: 0.10169738311767579
+INFO - evaluator.py - 2024-10-26 06:08:41,043 - Epoch: 68 Train acc: 99.99818181818182 Val acc: 10.620000000000001 Test acc10.91; Train loss: 1.8134227800394663e-05 Val loss: 0.14751055603027344
+INFO - evaluator.py - 2024-10-26 06:10:09,483 - Epoch: 69 Train acc: 99.99818181818182 Val acc: 10.639999999999999 Test acc10.89; Train loss: 1.9964802589013496e-05 Val loss: 0.18535189208984376
+INFO - evaluator.py - 2024-10-26 06:11:37,931 - Epoch: 70 Train acc: 99.99272727272728 Val acc: 10.299999999999999 Test acc10.879999999999999; Train loss: 2.0852255187293683e-05 Val loss: 0.23420345153808594
+INFO - evaluator.py - 2024-10-26 06:13:06,358 - Epoch: 71 Train acc: 99.98545454545454 Val acc: 10.5 Test acc10.61; Train loss: 2.228415422247384e-05 Val loss: 0.3440568359375
+INFO - evaluator.py - 2024-10-26 06:14:34,655 - Epoch: 72 Train acc: 99.9890909090909 Val acc: 11.06 Test acc11.39; Train loss: 2.4110362081873146e-05 Val loss: 0.3528788146972656
+INFO - evaluator.py - 2024-10-26 06:16:03,140 - Epoch: 73 Train acc: 99.99636363636364 Val acc: 10.459999999999999 Test acc10.56; Train loss: 1.8843364864799448e-05 Val loss: 0.4743940979003906
+INFO - evaluator.py - 2024-10-26 06:17:31,181 - Epoch: 74 Train acc: 99.99454545454546 Val acc: 10.6 Test acc10.280000000000001; Train loss: 2.0135003309273584e-05 Val loss: 0.65177890625
+INFO - evaluator.py - 2024-10-26 06:19:00,260 - Epoch: 75 Train acc: 99.91454545454546 Val acc: 11.3 Test acc10.56; Train loss: 5.41589560516348e-05 Val loss: 0.35875343017578126
+INFO - evaluator.py - 2024-10-26 06:20:28,498 - Epoch: 76 Train acc: 99.94727272727273 Val acc: 12.26 Test acc11.940000000000001; Train loss: 3.9021438711576844e-05 Val loss: 0.38093717041015623
+INFO - evaluator.py - 2024-10-26 06:21:56,488 - Epoch: 77 Train acc: 99.86909090909091 Val acc: 10.52 Test acc10.59; Train loss: 6.260781554843891e-05 Val loss: 0.28136503295898435
+INFO - evaluator.py - 2024-10-26 06:23:24,401 - Epoch: 78 Train acc: 99.89272727272727 Val acc: 10.32 Test acc10.52; Train loss: 5.7271866508844225e-05 Val loss: 0.22047614440917968
+INFO - evaluator.py - 2024-10-26 06:24:52,603 - Epoch: 79 Train acc: 99.88181818181818 Val acc: 10.38 Test acc9.9; Train loss: 6.336627953093161e-05 Val loss: 0.20476065368652344
+INFO - evaluator.py - 2024-10-26 06:26:21,383 - Epoch: 80 Train acc: 99.83272727272727 Val acc: 10.18 Test acc10.03; Train loss: 7.17716070103713e-05 Val loss: 0.20200647277832032
+INFO - evaluator.py - 2024-10-26 06:27:49,466 - Epoch: 81 Train acc: 99.89818181818183 Val acc: 10.32 Test acc10.190000000000001; Train loss: 5.113559505711733e-05 Val loss: 0.09722713165283203
+INFO - evaluator.py - 2024-10-26 06:29:18,568 - Epoch: 82 Train acc: 99.8 Val acc: 10.620000000000001 Test acc11.57; Train loss: 7.447267208904536e-05 Val loss: 0.056973661804199216
+INFO - evaluator.py - 2024-10-26 06:30:46,789 - Epoch: 83 Train acc: 99.77636363636364 Val acc: 10.9 Test acc10.83; Train loss: 9.303340506104921e-05 Val loss: 0.034414559173583985
+INFO - evaluator.py - 2024-10-26 06:32:15,390 - Epoch: 84 Train acc: 99.78363636363636 Val acc: 10.16 Test acc10.16; Train loss: 8.250318324849517e-05 Val loss: 0.03711073608398437
+INFO - evaluator.py - 2024-10-26 06:33:44,049 - Epoch: 85 Train acc: 99.84727272727272 Val acc: 11.52 Test acc12.25; Train loss: 6.929391126825728e-05 Val loss: 0.019218500137329102
+INFO - evaluator.py - 2024-10-26 06:35:12,874 - Epoch: 86 Train acc: 99.85090909090908 Val acc: 16.74 Test acc17.04; Train loss: 6.455059492833573e-05 Val loss: 0.016569657135009765
+INFO - evaluator.py - 2024-10-26 06:36:41,267 - Epoch: 87 Train acc: 99.74181818181819 Val acc: 13.04 Test acc12.64; Train loss: 9.537997794845565e-05 Val loss: 0.01259221248626709
+INFO - evaluator.py - 2024-10-26 06:38:09,707 - Epoch: 88 Train acc: 99.85454545454544 Val acc: 15.120000000000001 Test acc15.120000000000001; Train loss: 6.443761578570543e-05 Val loss: 0.009663685607910156
+INFO - evaluator.py - 2024-10-26 06:39:38,669 - Epoch: 89 Train acc: 99.92181818181818 Val acc: 12.16 Test acc12.24; Train loss: 3.9301125643859536e-05 Val loss: 0.013676240348815918
+INFO - evaluator.py - 2024-10-26 06:41:07,747 - Epoch: 90 Train acc: 99.87272727272727 Val acc: 12.879999999999999 Test acc12.790000000000001; Train loss: 5.665624947075478e-05 Val loss: 0.015006339645385742
+INFO - evaluator.py - 2024-10-26 06:42:36,622 - Epoch: 91 Train acc: 99.69272727272728 Val acc: 11.799999999999999 Test acc12.27; Train loss: 0.00010569607602559368 Val loss: 0.011307307052612304
+INFO - evaluator.py - 2024-10-26 06:44:05,355 - Epoch: 92 Train acc: 99.74545454545455 Val acc: 22.88 Test acc21.47; Train loss: 9.267064037902111e-05 Val loss: 0.005029709243774414
+INFO - evaluator.py - 2024-10-26 06:45:33,947 - Epoch: 93 Train acc: 99.86545454545454 Val acc: 19.32 Test acc19.0; Train loss: 5.311940097546374e-05 Val loss: 0.006444672679901123
+INFO - evaluator.py - 2024-10-26 06:47:02,427 - Epoch: 94 Train acc: 99.9109090909091 Val acc: 13.020000000000001 Test acc13.200000000000001; Train loss: 4.428675300018354e-05 Val loss: 0.010114598083496094
+INFO - evaluator.py - 2024-10-26 06:48:31,208 - Epoch: 95 Train acc: 99.89272727272727 Val acc: 17.84 Test acc17.59; Train loss: 4.2722480492243035e-05 Val loss: 0.00942756633758545
+INFO - evaluator.py - 2024-10-26 06:49:59,605 - Epoch: 96 Train acc: 99.76181818181819 Val acc: 19.54 Test acc19.62; Train loss: 9.218193951634351e-05 Val loss: 0.0062382390022277835
+INFO - evaluator.py - 2024-10-26 06:51:28,067 - Epoch: 97 Train acc: 99.68727272727273 Val acc: 23.580000000000002 Test acc22.759999999999998; Train loss: 0.00010940393901811066 Val loss: 0.005617140007019043
+INFO - evaluator.py - 2024-10-26 06:52:56,476 - Epoch: 98 Train acc: 99.81272727272727 Val acc: 15.82 Test acc15.58; Train loss: 7.6048388897272e-05 Val loss: 0.007254564952850342
+INFO - evaluator.py - 2024-10-26 06:54:24,777 - Epoch: 99 Train acc: 99.95272727272727 Val acc: 18.92 Test acc18.18; Train loss: 3.437307231572711e-05 Val loss: 0.006135500621795654
+INFO - evaluator.py - 2024-10-26 06:55:53,109 - Epoch: 100 Train acc: 99.83454545454545 Val acc: 11.92 Test acc11.26; Train loss: 6.567924763869748e-05 Val loss: 0.015405876541137696
+INFO - evaluator.py - 2024-10-26 06:57:21,399 - Epoch: 101 Train acc: 99.86 Val acc: 13.900000000000002 Test acc13.950000000000001; Train loss: 5.86650037238459e-05 Val loss: 0.0077606026649475095
+INFO - evaluator.py - 2024-10-26 06:58:49,709 - Epoch: 102 Train acc: 99.82181818181817 Val acc: 16.78 Test acc15.49; Train loss: 7.387243176133118e-05 Val loss: 0.007734785556793213
+INFO - evaluator.py - 2024-10-26 07:00:18,000 - Epoch: 103 Train acc: 99.84181818181818 Val acc: 19.759999999999998 Test acc19.73; Train loss: 6.261336033401842e-05 Val loss: 0.006891121578216553
+INFO - evaluator.py - 2024-10-26 07:01:46,412 - Epoch: 104 Train acc: 99.66363636363637 Val acc: 25.480000000000004 Test acc24.14; Train loss: 0.00010625008747235618 Val loss: 0.004043725156784058
+INFO - evaluator.py - 2024-10-26 07:03:14,733 - Epoch: 105 Train acc: 99.82727272727273 Val acc: 18.9 Test acc18.529999999999998; Train loss: 7.288988658163527e-05 Val loss: 0.006428286552429199
+INFO - evaluator.py - 2024-10-26 07:04:43,232 - Epoch: 106 Train acc: 99.79636363636364 Val acc: 18.459999999999997 Test acc17.77; Train loss: 7.366208196617663e-05 Val loss: 0.008595393562316894
+INFO - evaluator.py - 2024-10-26 07:06:11,527 - Epoch: 107 Train acc: 99.88363636363637 Val acc: 23.78 Test acc23.97; Train loss: 4.853372755460441e-05 Val loss: 0.005409040641784668
+INFO - evaluator.py - 2024-10-26 07:07:39,975 - Epoch: 108 Train acc: 99.81090909090909 Val acc: 15.42 Test acc14.89; Train loss: 7.077174854401329e-05 Val loss: 0.00786880989074707
+INFO - evaluator.py - 2024-10-26 07:09:08,325 - Epoch: 109 Train acc: 99.82909090909091 Val acc: 15.440000000000001 Test acc15.790000000000001; Train loss: 7.20859475010498e-05 Val loss: 0.00991148567199707
+INFO - evaluator.py - 2024-10-26 07:10:36,744 - Epoch: 110 Train acc: 99.9109090909091 Val acc: 20.24 Test acc19.72; Train loss: 4.4086343034128234e-05 Val loss: 0.007187497711181641
+INFO - evaluator.py - 2024-10-26 07:12:05,139 - Epoch: 111 Train acc: 99.75090909090909 Val acc: 20.32 Test acc20.22; Train loss: 8.745595975673164e-05 Val loss: 0.006741143798828125
+INFO - evaluator.py - 2024-10-26 07:13:33,266 - Epoch: 112 Train acc: 99.79090909090908 Val acc: 16.18 Test acc16.35; Train loss: 7.833318141056224e-05 Val loss: 0.006924671840667725
+INFO - evaluator.py - 2024-10-26 07:15:01,545 - Epoch: 113 Train acc: 99.89454545454547 Val acc: 15.8 Test acc15.93; Train loss: 4.620906745337627e-05 Val loss: 0.008741345024108886
+INFO - evaluator.py - 2024-10-26 07:16:29,829 - Epoch: 114 Train acc: 99.82545454545455 Val acc: 22.38 Test acc22.23; Train loss: 6.611900330648164e-05 Val loss: 0.006376466464996338
+INFO - evaluator.py - 2024-10-26 07:17:59,144 - Epoch: 115 Train acc: 99.7 Val acc: 23.84 Test acc23.46; Train loss: 0.00010025842375206676 Val loss: 0.0046869564056396485
+INFO - evaluator.py - 2024-10-26 07:19:27,668 - Epoch: 116 Train acc: 99.81818181818181 Val acc: 18.82 Test acc19.96; Train loss: 7.024186120834202e-05 Val loss: 0.005536202812194824
+INFO - evaluator.py - 2024-10-26 07:20:56,047 - Epoch: 117 Train acc: 99.79818181818182 Val acc: 23.74 Test acc22.56; Train loss: 7.368043002735993e-05 Val loss: 0.00424683952331543
+INFO - evaluator.py - 2024-10-26 07:22:24,225 - Epoch: 118 Train acc: 99.81454545454545 Val acc: 14.180000000000001 Test acc14.680000000000001; Train loss: 7.108751427466897e-05 Val loss: 0.008761121749877929
+INFO - evaluator.py - 2024-10-26 07:23:52,627 - Epoch: 119 Train acc: 99.91636363636364 Val acc: 23.22 Test acc21.84; Train loss: 4.326950296950103e-05 Val loss: 0.004727137279510498
+INFO - evaluator.py - 2024-10-26 07:25:20,859 - Epoch: 120 Train acc: 99.98727272727272 Val acc: 23.26 Test acc22.66; Train loss: 1.366963231332854e-05 Val loss: 0.004739517593383789
+INFO - evaluator.py - 2024-10-26 07:26:49,138 - Epoch: 121 Train acc: 99.98545454545454 Val acc: 23.66 Test acc23.62; Train loss: 1.31232718561395e-05 Val loss: 0.004952514743804932
+INFO - evaluator.py - 2024-10-26 07:28:17,578 - Epoch: 122 Train acc: 99.9890909090909 Val acc: 22.919999999999998 Test acc22.59; Train loss: 1.1050172548592938e-05 Val loss: 0.005297688007354737
+INFO - evaluator.py - 2024-10-26 07:29:45,757 - Epoch: 123 Train acc: 99.99272727272728 Val acc: 22.34 Test acc21.95; Train loss: 9.99975710556927e-06 Val loss: 0.005704235935211181
+INFO - evaluator.py - 2024-10-26 07:31:13,975 - Epoch: 124 Train acc: 100.0 Val acc: 19.900000000000002 Test acc19.89; Train loss: 9.515957901550626e-06 Val loss: 0.0067594956398010254
+INFO - evaluator.py - 2024-10-26 07:32:42,591 - Epoch: 125 Train acc: 99.99636363636364 Val acc: 19.78 Test acc19.79; Train loss: 1.0112158094257624e-05 Val loss: 0.006901019287109375
+INFO - evaluator.py - 2024-10-26 07:34:11,031 - Epoch: 126 Train acc: 99.99636363636364 Val acc: 19.64 Test acc19.0; Train loss: 9.922337292862886e-06 Val loss: 0.007245163726806641
+INFO - evaluator.py - 2024-10-26 07:35:39,377 - Epoch: 127 Train acc: 99.99818181818182 Val acc: 19.52 Test acc19.07; Train loss: 9.09677756135352e-06 Val loss: 0.0072086224555969235
+INFO - evaluator.py - 2024-10-26 07:37:07,681 - Epoch: 128 Train acc: 99.99636363636364 Val acc: 17.52 Test acc17.21; Train loss: 9.868449899791317e-06 Val loss: 0.008137715053558349
+INFO - evaluator.py - 2024-10-26 07:38:35,826 - Epoch: 129 Train acc: 100.0 Val acc: 16.56 Test acc16.12; Train loss: 1.006924279605631e-05 Val loss: 0.008950455856323243
+INFO - evaluator.py - 2024-10-26 07:40:04,056 - Epoch: 130 Train acc: 99.99818181818182 Val acc: 16.42 Test acc16.45; Train loss: 1.0468302538025785e-05 Val loss: 0.009231241226196289
+INFO - evaluator.py - 2024-10-26 07:41:32,605 - Epoch: 131 Train acc: 99.99818181818182 Val acc: 17.0 Test acc16.35; Train loss: 1.0627671507906846e-05 Val loss: 0.009509036827087402
+INFO - evaluator.py - 2024-10-26 07:43:00,939 - Epoch: 132 Train acc: 99.99818181818182 Val acc: 15.24 Test acc14.91; Train loss: 1.0355968347919936e-05 Val loss: 0.010923477363586426
+INFO - evaluator.py - 2024-10-26 07:44:28,715 - Epoch: 133 Train acc: 99.99818181818182 Val acc: 14.66 Test acc14.499999999999998; Train loss: 1.0698220422703095e-05 Val loss: 0.01135333137512207
+INFO - evaluator.py - 2024-10-26 07:45:56,816 - Epoch: 134 Train acc: 99.99636363636364 Val acc: 14.96 Test acc14.330000000000002; Train loss: 1.1768725372596898e-05 Val loss: 0.011467341041564941
+INFO - evaluator.py - 2024-10-26 07:47:25,208 - Epoch: 135 Train acc: 99.99818181818182 Val acc: 14.760000000000002 Test acc14.37; Train loss: 1.2100974767765199e-05 Val loss: 0.01165324649810791
+INFO - evaluator.py - 2024-10-26 07:48:53,826 - Epoch: 136 Train acc: 99.99636363636364 Val acc: 13.36 Test acc13.07; Train loss: 1.2155986852435903e-05 Val loss: 0.015220841598510742
+INFO - evaluator.py - 2024-10-26 07:50:22,067 - Epoch: 137 Train acc: 99.9890909090909 Val acc: 12.479999999999999 Test acc12.17; Train loss: 1.3721507863903587e-05 Val loss: 0.015709734535217287
+INFO - evaluator.py - 2024-10-26 07:51:50,567 - Epoch: 138 Train acc: 99.99818181818182 Val acc: 12.08 Test acc12.02; Train loss: 1.225290876622735e-05 Val loss: 0.01619430389404297
+INFO - evaluator.py - 2024-10-26 07:53:19,249 - Epoch: 139 Train acc: 99.99454545454546 Val acc: 12.18 Test acc12.049999999999999; Train loss: 1.3591514360582964e-05 Val loss: 0.017773268508911134
+INFO - evaluator.py - 2024-10-26 07:54:47,956 - Epoch: 140 Train acc: 99.99454545454546 Val acc: 11.62 Test acc11.459999999999999; Train loss: 1.3137082702649587e-05 Val loss: 0.01884413185119629
+INFO - evaluator.py - 2024-10-26 07:56:16,552 - Epoch: 141 Train acc: 99.98727272727272 Val acc: 11.940000000000001 Test acc11.76; Train loss: 1.6818604194982484e-05 Val loss: 0.016556720733642578
+INFO - evaluator.py - 2024-10-26 07:57:45,178 - Epoch: 142 Train acc: 99.99818181818182 Val acc: 11.940000000000001 Test acc11.76; Train loss: 1.2658121472817253e-05 Val loss: 0.016040857315063477
+INFO - evaluator.py - 2024-10-26 07:59:13,411 - Epoch: 143 Train acc: 99.99454545454546 Val acc: 12.1 Test acc11.89; Train loss: 1.3351412615950474e-05 Val loss: 0.016801404190063478
+INFO - evaluator.py - 2024-10-26 08:00:41,709 - Epoch: 144 Train acc: 100.0 Val acc: 12.120000000000001 Test acc11.959999999999999; Train loss: 1.2424842556091872e-05 Val loss: 0.01758685836791992
+INFO - evaluator.py - 2024-10-26 08:02:09,951 - Epoch: 145 Train acc: 99.99818181818182 Val acc: 11.64 Test acc11.67; Train loss: 1.341207964049483e-05 Val loss: 0.01881371879577637
+INFO - evaluator.py - 2024-10-26 08:03:38,583 - Epoch: 146 Train acc: 99.99818181818182 Val acc: 11.84 Test acc11.76; Train loss: 1.3145213753027333e-05 Val loss: 0.015625886154174803
+INFO - evaluator.py - 2024-10-26 08:05:06,605 - Epoch: 147 Train acc: 99.99636363636364 Val acc: 12.0 Test acc11.95; Train loss: 1.4321327718525108e-05 Val loss: 0.017048805618286134
+INFO - evaluator.py - 2024-10-26 08:06:34,702 - Epoch: 148 Train acc: 99.99090909090908 Val acc: 12.740000000000002 Test acc12.43; Train loss: 1.5702900362455032e-05 Val loss: 0.015018731117248536
+INFO - evaluator.py - 2024-10-26 08:08:03,235 - Epoch: 149 Train acc: 99.99272727272728 Val acc: 12.06 Test acc11.98; Train loss: 1.5053561773278157e-05 Val loss: 0.018238385391235352
+INFO - evaluator.py - 2024-10-26 08:09:31,553 - Epoch: 150 Train acc: 99.99818181818182 Val acc: 11.940000000000001 Test acc11.87; Train loss: 1.4183850640388714e-05 Val loss: 0.016843105697631835
+INFO - evaluator.py - 2024-10-26 08:11:00,012 - Epoch: 151 Train acc: 99.98727272727272 Val acc: 12.139999999999999 Test acc12.13; Train loss: 1.718195782000707e-05 Val loss: 0.019136532974243163
+INFO - evaluator.py - 2024-10-26 08:12:28,337 - Epoch: 152 Train acc: 99.99454545454546 Val acc: 11.379999999999999 Test acc11.58; Train loss: 1.5822754852177406e-05 Val loss: 0.019049473571777345
+INFO - evaluator.py - 2024-10-26 08:13:56,719 - Epoch: 153 Train acc: 99.99818181818182 Val acc: 11.4 Test acc11.44; Train loss: 1.546713129130446e-05 Val loss: 0.023160863876342773
+INFO - evaluator.py - 2024-10-26 08:15:24,810 - Epoch: 154 Train acc: 99.99818181818182 Val acc: 11.48 Test acc11.53; Train loss: 1.3608026242052967e-05 Val loss: 0.020895802307128907
+INFO - evaluator.py - 2024-10-26 08:16:53,944 - Epoch: 155 Train acc: 99.99818181818182 Val acc: 11.14 Test acc11.03; Train loss: 1.570653720250861e-05 Val loss: 0.02515575714111328
+INFO - evaluator.py - 2024-10-26 08:18:22,305 - Epoch: 156 Train acc: 99.99818181818182 Val acc: 11.44 Test acc11.27; Train loss: 1.3907910850618712e-05 Val loss: 0.021850944900512696
+INFO - evaluator.py - 2024-10-26 08:19:50,931 - Epoch: 157 Train acc: 99.99272727272728 Val acc: 11.74 Test acc11.72; Train loss: 1.679917278868908e-05 Val loss: 0.01918262062072754
+INFO - evaluator.py - 2024-10-26 08:21:19,661 - Epoch: 158 Train acc: 100.0 Val acc: 11.44 Test acc11.34; Train loss: 1.4211814625146375e-05 Val loss: 0.021108173751831053
+INFO - evaluator.py - 2024-10-26 08:22:48,218 - Epoch: 159 Train acc: 100.0 Val acc: 11.18 Test acc10.99; Train loss: 1.4505613741295581e-05 Val loss: 0.020966564559936524
+INFO - evaluator.py - 2024-10-26 08:24:17,244 - Epoch: 160 Train acc: 99.99818181818182 Val acc: 11.379999999999999 Test acc11.07; Train loss: 1.5614896898411893e-05 Val loss: 0.020482415008544922
+INFO - dataset_loader.py - 2024-10-29 02:35:26,383 - delta is reset as 1.8484667129285888e-06
+INFO - evaluator.py - 2024-10-29 03:27:02,083 - The FID of synthetic images is 219.54836766661845
+INFO - evaluator.py - 2024-10-29 03:27:02,086 - The Inception Score of synthetic images is 1.7984269857406616
+INFO - evaluator.py - 2024-10-29 03:27:02,086 - The Precision and Recall of synthetic images is 0.6327812671661377 and 0.0003600000054575503
+INFO - evaluator.py - 2024-10-29 03:27:02,086 - The FLD of synthetic images is 27.920222282409668
+INFO - evaluator.py - 2024-10-29 03:27:02,086 - The ImageReward of synthetic images is -2.2739594591539354
+INFO - dataset_loader.py - 2024-10-29 03:27:02,840 - delta is reset as 1.8484667129285888e-06
+INFO - evaluator.py - 2024-10-29 04:18:40,193 - The FID of synthetic images is 201.61100378723972
+INFO - evaluator.py - 2024-10-29 04:18:40,221 - The Inception Score of synthetic images is 1.9011380672454834
+INFO - evaluator.py - 2024-10-29 04:18:40,221 - The Precision and Recall of synthetic images is 0.6231746077537537 and 0.0005200000014156103
+INFO - evaluator.py - 2024-10-29 04:18:40,221 - The FLD of synthetic images is 27.509820461273193
+INFO - evaluator.py - 2024-10-29 04:18:40,221 - The ImageReward of synthetic images is -2.2687564725081124
+INFO - dataset_loader.py - 2024-10-29 04:18:42,924 - delta is reset as 2.6201132658294697e-07
+INFO - evaluator.py - 2024-10-29 05:19:05,639 - The FID of synthetic images is 22.085653876120773
+INFO - evaluator.py - 2024-10-29 05:19:05,642 - The Inception Score of synthetic images is 1.7683465480804443
+INFO - evaluator.py - 2024-10-29 05:19:05,642 - The Precision and Recall of synthetic images is 0.7516825795173645 and 0.3393147587776184
+INFO - evaluator.py - 2024-10-29 05:19:05,642 - The FLD of synthetic images is -6.095540523529053
+INFO - evaluator.py - 2024-10-29 05:19:05,642 - The ImageReward of synthetic images is -1.793093251128755
+INFO - dataset_loader.py - 2024-10-29 05:19:06,164 - delta is reset as 1.5148623360286113e-06
+INFO - evaluator.py - 2024-10-29 06:19:12,182 - The FID of synthetic images is 36.55868340206811
+INFO - evaluator.py - 2024-10-29 06:19:12,189 - The Inception Score of synthetic images is 1.9821124076843262
+INFO - evaluator.py - 2024-10-29 06:19:12,189 - The Precision and Recall of synthetic images is 0.19395314157009125 and 0.42188334465026855
+INFO - evaluator.py - 2024-10-29 06:19:12,189 - The FLD of synthetic images is 16.948330402374268
+INFO - evaluator.py - 2024-10-29 06:19:12,189 - The ImageReward of synthetic images is -2.130454104746692
+INFO - dataset_loader.py - 2024-10-29 06:19:12,548 - delta is reset as 1.8484667129285888e-06
+INFO - evaluator.py - 2024-10-29 07:17:08,735 - The FID of synthetic images is 103.17130225064335
+INFO - evaluator.py - 2024-10-29 07:17:08,757 - The Inception Score of synthetic images is 3.3248043060302734
+INFO - evaluator.py - 2024-10-29 07:17:08,757 - The Precision and Recall of synthetic images is 0.5925872921943665 and 0.05226000025868416
+INFO - evaluator.py - 2024-10-29 07:17:08,757 - The FLD of synthetic images is 18.80326271057129
+INFO - evaluator.py - 2024-10-29 07:17:08,757 - The ImageReward of synthetic images is -2.2046342791773026
+INFO - dataset_loader.py - 2024-10-29 07:17:09,384 - delta is reset as 1.5148623360286113e-06
+INFO - evaluator.py - 2024-10-29 08:12:19,400 - The FID of synthetic images is 36.16871462142183
+INFO - evaluator.py - 2024-10-29 08:12:19,442 - The Inception Score of synthetic images is 1.9785420894622803
+INFO - evaluator.py - 2024-10-29 08:12:19,442 - The Precision and Recall of synthetic images is 0.2151111215353012 and 0.38750001788139343
+INFO - evaluator.py - 2024-10-29 08:12:19,442 - The FLD of synthetic images is 16.76713228225708
+INFO - evaluator.py - 2024-10-29 08:12:19,442 - The ImageReward of synthetic images is -2.120978381105832
+INFO - dataset_loader.py - 2024-10-29 08:12:21,784 - delta is reset as 2.6201132658294697e-07
+INFO - evaluator.py - 2024-10-29 09:12:37,637 - The FID of synthetic images is 57.79238987775261
+INFO - evaluator.py - 2024-10-29 09:12:37,646 - The Inception Score of synthetic images is 1.4734785556793213
+INFO - evaluator.py - 2024-10-29 09:12:37,647 - The Precision and Recall of synthetic images is 0.6569206714630127 and 0.13006387650966644
+INFO - evaluator.py - 2024-10-29 09:12:37,647 - The FLD of synthetic images is -1.181638240814209
+INFO - evaluator.py - 2024-10-29 09:12:37,647 - The ImageReward of synthetic images is -2.0049813007896855
+INFO - dataset_loader.py - 2024-10-29 09:12:39,948 - delta is reset as 2.6201132658294697e-07
+INFO - evaluator.py - 2024-10-29 10:14:57,218 - The FID of synthetic images is 29.3289837710725
+INFO - evaluator.py - 2024-10-29 10:14:57,224 - The Inception Score of synthetic images is 1.652750849723816
+INFO - evaluator.py - 2024-10-29 10:14:57,224 - The Precision and Recall of synthetic images is 0.7476875185966492 and 0.285733163356781
+INFO - evaluator.py - 2024-10-29 10:14:57,224 - The FLD of synthetic images is -4.951715469360352
+INFO - evaluator.py - 2024-10-29 10:14:57,224 - The ImageReward of synthetic images is -1.871033944573719
+INFO - dataset_loader.py - 2024-10-29 10:14:57,630 - delta is reset as 1.5148623360286113e-06
+INFO - evaluator.py - 2024-10-29 11:06:17,358 - The FID of synthetic images is 5.286480673016143
+INFO - evaluator.py - 2024-10-29 11:06:17,363 - The Inception Score of synthetic images is 2.071159601211548
+INFO - evaluator.py - 2024-10-29 11:06:17,363 - The Precision and Recall of synthetic images is 0.6194375157356262 and 0.7204999923706055
+INFO - evaluator.py - 2024-10-29 11:06:17,364 - The FLD of synthetic images is 3.7299275398254395
+INFO - evaluator.py - 2024-10-29 11:06:17,364 - The ImageReward of synthetic images is -2.0137405606759713
+INFO - dataset_loader.py - 2024-10-29 11:06:17,775 - delta is reset as 4.329102935418938e-06
+INFO - evaluator.py - 2024-10-29 11:39:57,591 - The FID of synthetic images is 168.59217462930627
+INFO - evaluator.py - 2024-10-29 11:39:57,596 - The Inception Score of synthetic images is 1.6652277708053589
+INFO - evaluator.py - 2024-10-29 11:39:57,596 - The Precision and Recall of synthetic images is 0.6194375157356262 and 0.00952173862606287
+INFO - evaluator.py - 2024-10-29 11:39:57,596 - The FLD of synthetic images is 20.778346061706543
+INFO - evaluator.py - 2024-10-29 11:39:57,596 - The ImageReward of synthetic images is -1.5740511079076678
+INFO - dataset_loader.py - 2024-10-29 11:39:58,114 - delta is reset as 1.8484667129285888e-06
+INFO - evaluator.py - 2024-10-29 12:12:19,140 - The FID of synthetic images is 231.37436795903784
+INFO - evaluator.py - 2024-10-29 12:12:19,145 - The Inception Score of synthetic images is 1.7306236028671265
+INFO - evaluator.py - 2024-10-29 12:12:19,145 - The Precision and Recall of synthetic images is 0.7602222561836243 and 0.00043999997433274984
+INFO - evaluator.py - 2024-10-29 12:12:19,145 - The FLD of synthetic images is 23.48649501800537
+INFO - evaluator.py - 2024-10-29 12:12:19,146 - The ImageReward of synthetic images is -2.2615407764571054
+INFO - dataset_loader.py - 2024-10-29 12:12:19,367 - delta is reset as 4.329102935418938e-06
+INFO - evaluator.py - 2024-10-29 12:45:16,662 - The FID of synthetic images is 237.36948091544997
+INFO - evaluator.py - 2024-10-29 12:45:16,666 - The Inception Score of synthetic images is 1.2807329893112183
+INFO - evaluator.py - 2024-10-29 12:45:16,667 - The Precision and Recall of synthetic images is 0.5577656626701355 and 4.347825961303897e-05
+INFO - evaluator.py - 2024-10-29 12:45:16,667 - The FLD of synthetic images is 30.49933910369873
+INFO - evaluator.py - 2024-10-29 12:45:16,667 - The ImageReward of synthetic images is -1.8509714604625478
+INFO - dataset_loader.py - 2024-10-29 12:45:17,305 - delta is reset as 1.5148623360286113e-06
+INFO - evaluator.py - 2024-10-29 13:18:11,048 - The FID of synthetic images is 53.50594213505724
+INFO - evaluator.py - 2024-10-29 13:18:11,054 - The Inception Score of synthetic images is 3.4386539459228516
+INFO - evaluator.py - 2024-10-29 13:18:11,055 - The Precision and Recall of synthetic images is 0.26606249809265137 and 0.12056666612625122
+INFO - evaluator.py - 2024-10-29 13:18:11,055 - The FLD of synthetic images is 20.395588874816895
+INFO - evaluator.py - 2024-10-29 13:18:11,055 - The ImageReward of synthetic images is -1.8617446345779531
+INFO - dataset_loader.py - 2024-10-29 13:18:11,648 - delta is reset as 1.5148623360286113e-06
+INFO - evaluator.py - 2024-10-29 13:50:35,681 - The FID of synthetic images is 4.446889068230405
+INFO - evaluator.py - 2024-10-29 13:50:35,832 - The Inception Score of synthetic images is 2.0761497020721436
+INFO - evaluator.py - 2024-10-29 13:50:35,832 - The Precision and Recall of synthetic images is 0.6322698593139648 and 0.7390333414077759
+INFO - evaluator.py - 2024-10-29 13:50:35,833 - The FLD of synthetic images is 3.283095359802246
+INFO - evaluator.py - 2024-10-29 13:50:35,833 - The ImageReward of synthetic images is -2.0057078354216755
+INFO - dataset_loader.py - 2024-10-29 13:50:37,371 - delta is reset as 5.11965868690912e-07
+INFO - evaluator.py - 2024-10-29 14:31:49,267 - The FID of synthetic images is 28.848900967099837
+INFO - evaluator.py - 2024-10-29 14:31:49,353 - The Inception Score of synthetic images is 2.238304853439331
+INFO - evaluator.py - 2024-10-29 14:31:49,353 - The Precision and Recall of synthetic images is 0.6088594198226929 and 0.1520366072654724
+INFO - evaluator.py - 2024-10-29 14:31:49,354 - The FLD of synthetic images is nan
+INFO - evaluator.py - 2024-10-29 14:31:49,354 - The ImageReward of synthetic images is -1.3833920579410202
+INFO - dataset_loader.py - 2024-10-29 14:31:49,986 - delta is reset as 1.8484667129285888e-06
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/checkpoints/checkpoint_100000.pth b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/checkpoints/checkpoint_100000.pth
new file mode 100644
index 0000000000000000000000000000000000000000..3d7e138c5e0f0ae549c0db2a32bd8eb97f7495ca
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/checkpoints/checkpoint_100000.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:80d6a09bab87dd8832a646c1ec88b5c7c3d84a61a245e189dbc5cb8e3b12c809
+size 61695625
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/checkpoints/final_checkpoint.pth b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/checkpoints/final_checkpoint.pth
new file mode 100644
index 0000000000000000000000000000000000000000..a19d26b54f67aeea59b5c6ad535f6c14f480a06c
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/checkpoints/final_checkpoint.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f74a14fd8fbd6fb0ad77c161dc52639cb8178f5023a033b29d8d419275557790
+size 61694566
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/checkpoints/snapshot_checkpoint.pth b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/checkpoints/snapshot_checkpoint.pth
new file mode 100644
index 0000000000000000000000000000000000000000..d7f631ca9994b4cf8fc1099e79c940c8755e441a
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/checkpoints/snapshot_checkpoint.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09c7c9084460a5d47467487f9bad37df595cef746ea5def0d5d7625a3d9efc01
+size 61697743
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_10000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_10000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..8f64a8852d69ba3c53a262fe9510e8d5d75a04cd
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_10000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dfe4a15ec1ec9e09b48e904cfe266dbfe573b404362c2941f4ea04a3a3efc6c1
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_10000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_10000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..eedefa774f47dd0f83b16449bfa8d922f2126c2e
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_10000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c955787f468980e94e92624e147398b34491e09e1ac3364d2cb2b475b9f38ac2
+size 379533
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_100000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_100000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..d6559b64da6d7866be4b6ac58a354e1d7ec0d72d
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_100000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a37c753d16a9fcba57be0dfd7451199ffdd0b2286580ddb76188fedd7962daf1
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_100000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_100000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..987d3d0d6d073ffb2de2a00ad93bb38235e6b610
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_100000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3538d1e0d38c3afbbb3530ef50ff05e97cf383c6da0a26dec6d5903903645c7a
+size 355858
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_102000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_102000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..e3844d883eeeb7a690a3d1e48e1b5edc38abc8da
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_102000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:41b7aa2db582b05d753a641a5243c047f11428fe2887818bd19882df862de508
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_102000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_102000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..6bb2844c2971044beef3fde73d139535ee442912
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_102000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:99a352cb7799d53bfc41dd8142b19a63df82466705dc63becfc74ee1e5da4304
+size 356835
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_104000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_104000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..ca01450107825f0118a493cbeb8dae3bc1dd6d46
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_104000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3092f2cdccba5bfea9921f98ad4a0f40ca364714e50bd6cf553186cd3af6a453
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_104000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_104000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..aaa0ac243feef7b47b614df31cbcf719533c2bb5
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_104000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af8e61323f322243c24995ddb5919ecc3907098e62659d5d907e630994b23df8
+size 356933
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_12000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_12000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..dd47a193f9395a0f9ea665dbdd5cefb378b2f893
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_12000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:088bcebe4a72f16e666e7c219b6d489a668f847b3cff50a11691b47c0288f383
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_12000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_12000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..0d0d45fd97331d992bcd8d4b84eaa6378a42ef25
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_12000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:074754bb7b2b2c6b08760dcfa776f03874dc59d824b7cf680d9a1195362b26e8
+size 380504
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_14000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_14000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..c2f3f5232aa5a3042dd20166884d14c5dfb8ba03
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_14000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9af0cbb077c47da672cb50a3ec699fb36bafc97985a64b4821d3b05b0067a8c
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_14000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_14000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..52282a331bc64e003bbdb2ca1b8f5c50d0321783
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_14000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:998413dccea4692cfe8781d28b76c091f58f0563e0704da7efe503c9382b901b
+size 379789
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_16000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_16000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..fb6ab4b17d85d3c8880814345424b66314fad2ce
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_16000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:15eefdbcc4db666588bac1c33212b3de709a3bbf76b1efcf7dcb2153b98ccdbd
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_16000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_16000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..5aaa1ba46790780841a775d4e1b4e9a79e56fffd
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_16000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0acd435a9ecff2c869b4a473c7d3a890648b92d7e4eebdaf067ea27b1a28ec71
+size 377622
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_18000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_18000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..12207664f5a43a695575636b672bce8792ba2d91
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_18000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f70f2e4160d20391c3349d8cc2f1ff4dc285cf4f3ecf248731d3331652b2efb
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_18000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_18000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..35e495939a0ef978382c8172c606a1d2c0a9e432
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_18000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5265b078ad84f0f614b141fc7979b14052f6cbf955876b19d9f7dca423463a7
+size 376288
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_2000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_2000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..5468647c28e03bfb899a533272748aff18801240
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_2000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9be7b316dc3d7bfa652103db10080db9302fe5c63c0959250c0c91aa4e4991f
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_2000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_2000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..cb5de04de78ea878b0795f9135633188beaf6f7c
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_2000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:01fa20968ff1b906c866fd12933066d2b5543eca1e031bf09e0b8bacbddf1a00
+size 482670
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_20000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_20000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..1b602794396e0eb14e3c26a147b00371077398bb
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_20000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7590ff0f1a4edc7995278aa10b3ebf0abefc6ea97bd2c9a1d6d11c380a08b023
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_20000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_20000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..8b0709238366dadd79c75280e018a464bba844c9
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_20000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:618ab8798952638bc5e55e39b9d982d5e343f3dee9baf6cf18b3cbf32527ac8d
+size 373046
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_22000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_22000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..078e2ef56ba5ec7f402f1d7a917c09b77853f2da
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_22000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6ff05d1617ba5ada67fdf8ec14f01a917b66cd74ed8945a485b86f790e785f4
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_22000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_22000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..afe7169c6e472f81ba0a9f23fb42acd25b13c29a
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_22000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8164ba412c1f255511800f9b764133fcc42576d71fdd54300e7163678536c524
+size 372711
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_24000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_24000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..3f89a00d479511c322d033bd63f273f0718f90ba
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_24000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bc19ae35ccb1dc5ee0caa32c415450f44bbd812a3504e7e4d7f5dfd4be135226
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_24000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_24000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..459cc3d2622c2615e888c3f3d07c104358beeca7
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_24000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:887ca2d92ad8e1d2b2ff452289eb8afe32289586fd2d9204b35311b0f6adb19e
+size 369942
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_26000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_26000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..0006bfb77d0971d69ea525a128ec1a6209a6eed5
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_26000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e98d61265798fad6a8937bca69d7b93804ca4bc08deef320e0f0173853e41e45
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_26000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_26000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..9d9ee28502faadec8b7fc429b46df98126466ff0
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_26000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0020a955019098f924cab703fcb91213100d94e3de8186631275734c49200b8b
+size 369527
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_28000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_28000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..22d801d3b2d7446c8e94a8dd4dc4390f0cfaf2ef
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_28000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:274b2b698af6ea4152c7ccb090f1b5233d7e7437f8d81e658b2f002292f374a2
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_28000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_28000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..303c7de7b4ab07bec0eafacc044ed610ff2ccfd4
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_28000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6e02dc36440328f1f206605b72df89c971b731d72560245184b8f968374b738
+size 369550
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_30000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_30000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..69b42c4b7160d386a5c740a7990d843a8db0dabf
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_30000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8e353339781b6a75e3dfa0b7d77f0b05c4d6638a0c986f53e21ecdb26203ed0
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_30000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_30000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..552264abac91bd7c7a74ff0da9d532dd1d863ade
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_30000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3b7da6d24a18e3f0e60f45ab421e4f45c4b70eb9d09fe9769142c609f98ec02
+size 369177
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_32000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_32000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..73a395a091bfaf2a20891bea23d55dbc03c20517
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_32000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b96796a96cd38fe3f450a4885edf694fd566d9d54fc64095b261218f9cc83af4
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_32000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_32000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..30eb2ed2c1e183be494da3194929a04d8d8d3bfe
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_32000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08f248af62b9975d3df4d935dd1df97290c3beb929e2e01484d7a52d7178a0a7
+size 367467
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_34000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_34000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..596547bcd3357b9879d656ccb123e585d0a7d90f
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_34000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:92fd8192a9d6b598f5512c78861fca4f1a7b12872e69ab41ce5b427ae2026492
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_34000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_34000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..4a14c38d82ae7cddfb77c1c5249f7ceefda02cff
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_34000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0e4d35d03fde7215fd71be9676af07088fd222a15d3a0f7624475573553b122
+size 366042
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_36000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_36000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..cd3da63d92cf19e3d0fc8ecdd1ebf3d9496c854d
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_36000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:849a270c56c321305325cf816d7d6899049ff5c7b96da0d6ef0093832141960f
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_36000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_36000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..0074dc02f27614f681a2b13d6a16bbaa409e78a2
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_36000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff2f4c1ef0d17965a1c6c051fa67559162422b77093accd4cfaa117a8b6daab3
+size 366653
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_38000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_38000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..064bdb32fc69a9564d09a63dc69f1427a5e218b6
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_38000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d5d0251ea9cfd1e0ad11004508273bdf61898e0548148d157e101399b6bf64e
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_38000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_38000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..3708bf147c04b726d26c041ab7470f6dd94b155b
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_38000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ccc30363c9f516a61a3fb9c3ad6533fa0bdff02402802e24355856a9ecf3be58
+size 365458
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_4000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_4000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..5dc518bc6d7cee208c9a97713dd79e8e38d64ca2
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_4000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9127ea3ff80e3c957329830e4ba957ad76687901988a91fb4bcff3120aeec9bc
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_4000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_4000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..d768ef50a3b745d0a0ff2f26b408540d154640f0
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_4000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d24c5c2d1f742fa691728e486b3eae4809f85c9a04ddae352dafbbe2668e0d6
+size 427745
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_40000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_40000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..991967e20455c8035a3cf3b8074c112b179e06cb
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_40000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e8bdcbe25f47fd31a3a4ecabece8b333c5673012f4f1e42bc69ed02cc2ba860e
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_40000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_40000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..094085e58a418605566af66187367289b97060a2
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_40000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e8f44e9bdb6eeaea8b4eb0447ca542cc0bf70f2b88d8782e153135c2b0e79b4d
+size 366057
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_42000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_42000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..6e40a79d9105cb6da00e8ad45636abf6551a253f
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_42000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:013df443f7c6f266d404e717b1b00dc8d740afdd59e546c6a320cfbaa7fa3e0b
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_42000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_42000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..b410f19520825d8dfdc1e503d925168fe7bf54cc
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_42000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25b269e5d3d27b9d165e17d60ab4b3d87378d574c91b59ced8efabcd4c9a505c
+size 365795
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_44000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_44000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..1e180efd87d2d1f02d1d0e76404a38ee119d4962
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_44000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d2a196231f1d56adea122947b1cd154b4bf28cc23334000e30b95bec64100559
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_44000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_44000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..f96d906a95a1f38582a616f517bd89b3c403ef0d
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_44000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f64ebc2fc1f05212e117a007d902f42372fffda3376bc7373a9704c5ebef8eb
+size 364591
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_46000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_46000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..11be3f8ff8ddbfdbaf01498e332a3fd7977f4489
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_46000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7559449c9bf873e0ed796ebe09d63b4f4c7e6c3760980127631459ee2335b8e7
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_46000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_46000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..58167896d271b34a09ec6297cebfdccc3ef9f6bf
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_46000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:88d9520238141add138f2bdbca7ddbae9e7a4b491ab49c743c4c3c27de314bcf
+size 363852
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_48000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_48000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..0b2e6c651add18854d1663845e5b389353e8ea1b
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_48000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c903ade492a8710fd21e85beeed21b58dd75d437eec103a9152fcdeac392b1fe
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_48000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_48000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..3a49cea4e9352f83ec4e12b314b64e517fef9da9
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_48000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:24ba0bd3ecfa24bc65cc6441c42e62bab2c654373fbad87a7e6623f152825341
+size 363458
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_50000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_50000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..5f69c3709b41dc2ba908c76bb698088fd5b36492
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_50000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3defb17db3a5eb572069cfe49b0205aaf54f04c7eb7c8a33dba2d02177ee33ed
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_50000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_50000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..2e6c5d953d966606b6d939d2b7a8d3a94a550e6d
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_50000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d43ce2a3322eb2291ad315f9050606aac96cdd126cb029e6e9663a7bda4ee88
+size 362358
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_52000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_52000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..4d950848ce90a8df090519aa1e465ea65f1cbc8f
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_52000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05f890577b9a0dcc72e69216b48f691543be2eb915b19f75e669fdcb7d615157
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_52000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_52000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..3845f695ff585057d63b35d80bfd4ecd60d6ee72
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_52000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac09c4008fe3881499c5fcffedea4ae665fef801f30a77150a74c44cdca72e81
+size 363150
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_54000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_54000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..29a53ef83d945decabbc04de0045a6c0d9350505
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_54000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cecf47bffc7d97da7d51de86a88fe9d76951b8cbbeb9cc10caa4ce7e7e503daf
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_54000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_54000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..7b972e1852f6e5bc54127e41e99938aceb16221f
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_54000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bfea5175269fe11371896859e1126dc901ad97bdbb9c0f570ee8dcfd8ba2046f
+size 363692
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_56000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_56000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..62f43bea31dd4a39de2c19e2209a6422f072dec9
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_56000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a75a297e9b5cdb263cad5e55a466ba6d2aa65fff3aa4dec9dc5afa164e43a227
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_56000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_56000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..6f50fa5c8df713ebb4a0b2f316504f7b8dbd6b47
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_56000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:404ed17b59ee8861fe6c8861d20864dfe7459c29c3e375c1bb1f9d11dd115fdd
+size 361269
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_58000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_58000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..6214117a95236aec1762169eb8f05b7cd3b54f8a
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_58000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:738b44cc6ed274c970e3385aa2fc95352ddc82e4164bf844fec09e457a853635
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_58000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_58000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..b44b6f47f4468e4c501bf9489c6c65be8223e3f2
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_58000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf20f30465bd0ba9ba186f392305b4ea3164beee9068f90562121b69244b1794
+size 362223
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_6000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_6000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..3bb32b7fd88d1ae2ea1a8dc71dfb276fe4685301
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_6000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ebcffed373135282cc2eb897a6b9d407cd2f633b8d9d07b218d4b70b5dc0c6b3
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_6000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_6000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..55a4183b30f3abb65492aef30de2d5b408a0c104
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_6000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc22fac623ee4a95ea8ae65a49d760d761d3fe891980e51b84203f95d3e5df7d
+size 395574
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_60000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_60000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..ef4d8d7cad164dbbae23655e8bdde80f22583117
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_60000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f437a18965c7eb90c86dc7ddd225449b719b1f08072e204aa5a98895f2194778
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_60000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_60000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..7895768f40d2a564662ebe1c2b34a06ef56a2f65
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_60000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba70f56273f26dc5ac311cdba83f0141dc0fb2fddf7016b3ac7f00f743efce40
+size 361593
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_62000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_62000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..9d08a706ebc11bd3ee03c40f41cd02950fccb0bc
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_62000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:edd3efbb8653a868f5a9d8a3833a2ca05e545293f85b9e02359e7b8b2c1f3805
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_62000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_62000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..220bc0dde70b83c9997f521511cef00ea6157d1f
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_62000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce12958f9f4f9facddaa61a4843f825963e5b706c44e74eda1b002f70d706545
+size 361613
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_64000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_64000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..be37b914c84b1f13347954ef430d55e7a37fdef6
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_64000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9bc0204b16f96d3728efb9866ea06f82f9640909da23a7c4fcbbb008bf68f914
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_64000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_64000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..81754c10a5d089697c6cfbec9b163c7622b336bc
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_64000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7393a42a1ca4f0b743ea33dd035db12a4f0525a5d908afdc7dab718bfd5cf0e6
+size 361477
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_66000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_66000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..724db173dcf97cf96a7f608ea9f3f5eaf95dcaf6
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_66000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:acea6af2a9a1528e0c16735a9fb7713ab387e441f3ea4d10e89353d93557bb0b
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_66000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_66000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..7ab9a6d537471b3c97b03744b7a0786b9ee3ee2a
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_66000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c02200d1a14d0fe22d350deb9ea111e2bed2807e37234aaca50a20dd201ed6c7
+size 360585
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_68000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_68000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..b36b40460c8c031767bcd0f74637a53e6ebeae33
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_68000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87ec6475ca15247ee23adc4af9af1488bc34e0cd4af10e259496954eaefb2d90
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_68000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_68000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..f02fe19d25a51799b748f33bb99434a2c81fdca8
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_68000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:53df531d9d8872e0eb4e527db15aecd84b5720d21118557997834ed67465c39c
+size 359763
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_70000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_70000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..9cd4d6cf8983728ab49b9fec2a40ec84c8960558
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_70000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c816c8ec4d9b2161e58b5c3d1d26105b2a21cd6405c468e44b7aa115538006d8
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_70000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_70000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..c45ba960ed1b2e005850274bf80b71df8d885821
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_70000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a9234f9fab55f7c29f0d8e0fb9c3d7d9876d9b1fb71c75b736a0b02d689d67a4
+size 359863
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_72000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_72000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..7785bc499e14389712843f40612d7374a3393348
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_72000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7dc3951962b777e2909d0abd903e442d611077e099e065c76612fa9fa7ef1b83
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_72000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_72000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..ae7bc533e1d240f1f809b1653f97a6f1515e5cff
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_72000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f239f391567ebd7dcb078b2eae6d672c5f63c4a9d9ab19d0d91539657c2ae85f
+size 359401
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_74000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_74000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..4a0596cc71f8631bc81f83c6f56917295cbb1913
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_74000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a94bf43b0777cdf28d2398c21fb3ae0b137b83cb52ff0dfb9647d88141716dbd
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_74000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_74000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..4be6e93546a37a54506607a4b83d74c1b1897601
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_74000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:073ab5df46310526cef2d484520657ec683d860f2517b50aa721b2f20bb5dea3
+size 358011
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_76000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_76000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..862d605a5597ac68c96db7bf858e7b2f8dd0c0cc
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_76000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6ce6ba66a8c2b71efc6244ecea3e70d5cd803eb8ba8532db581308ac2d09bb11
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_76000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_76000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..23f34b32de53d7444cb41d91180902333db03d15
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_76000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a39f7e6de1b0fb8b57cf3f968ec0907537e2df5a2274eafa3a390d5401fc8019
+size 358981
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_78000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_78000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..8d8113c47a249334df37698853b09df213a62155
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_78000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aedc6514e17d6c61f38a5f4476e58d32a5ef20a7c2165457698fbd783240e52c
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_78000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_78000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..bf240ec0ada33c47dcd8f63a8c4c984f83b77fdd
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_78000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:931d8a52d7545589907428ecd67fbec954ca80c9781fe61abd611c8551349411
+size 359018
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_8000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_8000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..6490365558615b8a1d593a6cf3825c66fbdd8b04
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_8000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d7604985b051cf4c71884598501e33fd2aa7b50355ab9bdbb1c36d6b94b4556d
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_8000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_8000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..43bd925b21a84ade3a820cfaf48860d0056a1ec3
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_8000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:75b853e0886e970a712235a7eb18775a82b9ef0b691e823edee4bfb6b0f9be31
+size 381678
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_80000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_80000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..912f9f2e69c329878cdd6e867e7821d547a3cad6
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_80000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42521fb1b00dc7e2e70622052c43741ede797c26c8574d5637c4fe1ed24cce2b
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_80000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_80000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..511fcd67faef0ad05b10d8ecd9961f08e77aae19
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_80000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:15efdfd69bbe73c6e8d59cc6211895ad9092c1c57df2dc8eda5da6754a483f27
+size 358789
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_82000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_82000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..9163d70988463020794140c02453ed4bb44066d9
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_82000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8353ef3d5c9e6557771ae3ea2d7e42e41f41d1da4b54183e676ac60d504eea8
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_82000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_82000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..7de50afde7af9327ab2a7a1d07a5acdfa579e4a3
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_82000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a8872d258cfdfa234b92decf7b952f3da5497f9238a6faebf3b0f2a2cca0f4a
+size 358391
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_84000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_84000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..986ddf15890112cccd976651c21f7fb54fdf8a3a
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_84000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c4615908b946385e9f4e977662655e28b24b6d703af163ab4eeac2fb437e253
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_84000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_84000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..ae02f0ddc7c288d97869b6fb971009c18f83cca7
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_84000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be0ca3938616a3676a8ec8c8b95427e2c4aabf3e9fb1f50911c3008e2ff6a66f
+size 358927
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_86000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_86000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..3b97418dc2c164f213f5c3b644d482252bb18dba
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_86000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ab338eea49025060bb908e12f3e12776db6ca38fe9af2d3fb9f3285eadc6c90
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_86000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_86000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..f069e248a9a76ff998404b18ea73b93cd89f0544
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_86000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55d20d8157fa08b1f7ef6963bf244e0b8d25a796127b3a2f7d0bdec961655183
+size 358108
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_88000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_88000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..4f1233bafc186687fec53caa25a72d3fab954843
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_88000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:58994c8aebe4b48a19ebc8b90500d1a5bb3b30517d6598c03cef4095639bc3ae
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_88000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_88000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..7f0f0f4edcf224e64b60ca0044a392f86120f680
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_88000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a2286a369af666c2f3878eb3864877806ed8352130627028b96a5a101d7df21
+size 357775
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_90000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_90000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..7e7c6d6b9430cebce59b9dc17b06c0d6b3c5d3fa
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_90000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a75d0d8ed269e9f5c8a0561976e9a3e9274a80377fcd20ac903262060fd480d4
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_90000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_90000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..e5faf55809067c97b26c45bd159a2863f1a4514b
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_90000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f764e8051f9c25c00f07f270aef25e3338245f475cede0ec9d65ff560d739241
+size 357911
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_92000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_92000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..04d29030955a06d1728f226e2e0a8c509baece1a
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_92000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:498c14c1b18a3fc82da6b9b7c1b42040335fcd2653786a14c9295c62190f564e
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_92000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_92000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..d4b7742a486e11300eb302988f1e9b6b1c735c71
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_92000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:92ae3f43c3f21a9318a90b9ea1576b2a1a84909515bb7ce43091f8269c49ebdb
+size 357505
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_94000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_94000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..ff94b48b1eec2cab58786f7cbb15b20d90fcfe34
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_94000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f1dd7463b376a4641363634894a25c7430ae9cd7c2b761987db1d809ff07237a
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_94000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_94000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..dfc63d689b95481848e707c54c159155380ac844
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_94000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71f631c1471585c816ce81e34f810e4b7bd999a7052d8240b72ea3ebfb075daf
+size 357019
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_96000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_96000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..2ee5fbd6666ba8e17964d4624d328cf6c2b78b93
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_96000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08eb054d8488a953b2461297f532afdac5d73c6128d73593558a4b5d3eb79396
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_96000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_96000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..e6a9af068450c2202adcdfb2aa5a581aacf2d220
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_96000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:04f55280718f81fe44e7cef974cda218523fb1cd2470bc315ac35dc62d984d9b
+size 357004
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_98000/sample.npy b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_98000/sample.npy
new file mode 100644
index 0000000000000000000000000000000000000000..1998fd02a7de120cf68b48768ecb6697d2551dcc
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_98000/sample.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:27bfcbcd18a39185f0533d79e9202c2893a521dd4549d70e941b1e9833ee3f69
+size 983168
diff --git a/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_98000/sample.png b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_98000/sample.png
new file mode 100644
index 0000000000000000000000000000000000000000..226003f6db0ddc6eafdd8bd3a31b3bf0ae8874b1
--- /dev/null
+++ b/dpdm/cifar10_32_eps1.0trainval-2024-10-23-14-01-14/train/samples/iter_98000/sample.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:72a5669e9ef75e7f264f47c4da386e68bec509f441e391b7d58ebf62f64c1e30
+size 356029