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fashion-mnist-perspective.out
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[31m{'setup': 'simclr', 'backbone': 'resnet18', 'model_kwargs': {'head': 'mlp', 'features_dim': 128}, 'train_db_name': 'fashion-mnist', 'val_db_name': 'fashion-mnist', 'num_classes': 10, 'criterion': 'simclr', 'criterion_kwargs': {'temperature': 0.1}, 'epochs': 500, 'optimizer': 'sgd', 'optimizer_kwargs': {'nesterov': False, 'weight_decay': 0.0001, 'momentum': 0.9, 'lr': 0.4}, 'scheduler': 'cosine', 'scheduler_kwargs': {'lr_decay_rate': 0.1}, 'batch_size': 512, 'num_workers': 8, 'augmentation_strategy': 'simclr', 'augmentation_kwargs': {'random_resized_crop': {'size': 28, 'scale': [0.2, 1.0]}, 'color_jitter_random_apply': {'p': 0.8}, 'color_jitter': {'brightness': 0.4, 'contrast': 0.4, 'saturation': 0.4, 'hue': 0.1}, 'random_grayscale': {'p': 0.2}, 'normalize': {'mean': [0.2861, 0.2861, 0.2861], 'std': [0.2747, 0.2747, 0.2747]}}, 'transformation_kwargs': {'crop_size': 28, 'normalize': {'mean': [0.2861, 0.2861, 0.2861], 'std': [0.2747, 0.2747, 0.2747]}}, 'pretext_dir': 'output/fashion-mnist/pretext', 'pretext_checkpoint': 'output/fashion-mnist/pretext/checkpoint.pth.tar', 'pretext_model': 'output/fashion-mnist/pretext/model.pth.tar', 'topk_neighbors_train_path': 'output/fashion-mnist/pretext/topk-train-neighbors.npy', 'topk_neighbors_val_path': 'output/fashion-mnist/pretext/topk-val-neighbors.npy'}[0m
[34mRetrieve model[0m
Model is ContrastiveModel
Model parameters: 11.50M
ContrastiveModel(
(backbone): ResNet(
(conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
)
(contrastive_head): Sequential(
(0): Linear(in_features=512, out_features=512, bias=True)
(1): ReLU()
(2): Linear(in_features=512, out_features=128, bias=True)
)
)
[34mSet CuDNN benchmark[0m
[34mRetrieve dataset[0m
Train transforms: Compose(
RandomPerspective(p=0.5)
RandomResizedCrop(size=(28, 28), scale=(0.2, 1.0), ratio=(0.75, 1.3333), interpolation=PIL.Image.BILINEAR)
RandomHorizontalFlip(p=0.5)
RandomApply(
p=0.8
ColorJitter(brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[-0.1, 0.1])
)
RandomGrayscale(p=0.2)
ToTensor()
Normalize(mean=[0.2861, 0.2861, 0.2861], std=[0.2747, 0.2747, 0.2747])
)
Validation transforms: Compose(
CenterCrop(size=(28, 28))
ToTensor()
Normalize(mean=[0.2861, 0.2861, 0.2861], std=[0.2747, 0.2747, 0.2747])
)
Dataset contains 60000/10000 train/val samples
[34mBuild MemoryBank[0m
[34mRetrieve criterion[0m
Criterion is SimCLRLoss
[34mRetrieve optimizer[0m
SGD (
Parameter Group 0
dampening: 0
lr: 0.4
momentum: 0.9
nesterov: False
weight_decay: 0.0001
)
[34mNo checkpoint file at output/fashion-mnist/pretext/checkpoint.pth.tar[0m
[34mStarting main loop[0m
[33mEpoch 0/500[0m
[33m---------------[0m
Adjusted learning rate to 0.40000
Train ...
Epoch: [0][ 0/117] Loss 6.8846e+00 (6.8846e+00)
Epoch: [0][ 25/117] Loss 6.9119e+00 (6.9312e+00)
Epoch: [0][ 50/117] Loss 6.7015e+00 (6.8800e+00)
Epoch: [0][ 75/117] Loss 6.4128e+00 (6.7735e+00)
Epoch: [0][100/117] Loss 6.3169e+00 (6.6634e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 45.30
Checkpoint ...
[33mEpoch 1/500[0m
[33m---------------[0m
Adjusted learning rate to 0.40000
Train ...
Epoch: [1][ 0/117] Loss 6.1997e+00 (6.1997e+00)
Epoch: [1][ 25/117] Loss 6.0770e+00 (6.1408e+00)
Epoch: [1][ 50/117] Loss 6.0716e+00 (6.0985e+00)
Epoch: [1][ 75/117] Loss 6.0601e+00 (6.0824e+00)
Epoch: [1][100/117] Loss 5.9464e+00 (6.0602e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 54.46
Checkpoint ...
[33mEpoch 2/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39998
Train ...
Epoch: [2][ 0/117] Loss 5.6671e+00 (5.6671e+00)
Epoch: [2][ 25/117] Loss 5.5750e+00 (5.6484e+00)
Epoch: [2][ 50/117] Loss 5.2230e+00 (5.5148e+00)
Epoch: [2][ 75/117] Loss 5.1618e+00 (5.4105e+00)
Epoch: [2][100/117] Loss 5.7306e+00 (5.3787e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 59.24
Checkpoint ...
[33mEpoch 3/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39996
Train ...
Epoch: [3][ 0/117] Loss 5.7650e+00 (5.7650e+00)
Epoch: [3][ 25/117] Loss 5.3668e+00 (5.5234e+00)
Epoch: [3][ 50/117] Loss 4.9912e+00 (5.3312e+00)
Epoch: [3][ 75/117] Loss 5.0420e+00 (5.2357e+00)
Epoch: [3][100/117] Loss 5.1252e+00 (5.1881e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 57.87
Checkpoint ...
[33mEpoch 4/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39994
Train ...
Epoch: [4][ 0/117] Loss 5.0028e+00 (5.0028e+00)
Epoch: [4][ 25/117] Loss 4.8771e+00 (4.9267e+00)
Epoch: [4][ 50/117] Loss 4.9454e+00 (4.9118e+00)
Epoch: [4][ 75/117] Loss 4.8669e+00 (4.8873e+00)
Epoch: [4][100/117] Loss 4.8911e+00 (4.8722e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 61.45
Checkpoint ...
[33mEpoch 5/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39990
Train ...
Epoch: [5][ 0/117] Loss 4.8523e+00 (4.8523e+00)
Epoch: [5][ 25/117] Loss 4.8627e+00 (4.7751e+00)
Epoch: [5][ 50/117] Loss 4.9086e+00 (4.7735e+00)
Epoch: [5][ 75/117] Loss 4.7917e+00 (4.7962e+00)
Epoch: [5][100/117] Loss 4.7823e+00 (4.7972e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 59.82
Checkpoint ...
[33mEpoch 6/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39986
Train ...
Epoch: [6][ 0/117] Loss 4.7780e+00 (4.7780e+00)
Epoch: [6][ 25/117] Loss 4.6742e+00 (4.7447e+00)
Epoch: [6][ 50/117] Loss 4.8317e+00 (4.7508e+00)
Epoch: [6][ 75/117] Loss 4.6915e+00 (4.7394e+00)
Epoch: [6][100/117] Loss 4.7004e+00 (4.7233e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 62.55
Checkpoint ...
[33mEpoch 7/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39981
Train ...
Epoch: [7][ 0/117] Loss 4.6404e+00 (4.6404e+00)
Epoch: [7][ 25/117] Loss 4.5959e+00 (4.6782e+00)
Epoch: [7][ 50/117] Loss 4.6244e+00 (4.6761e+00)
Epoch: [7][ 75/117] Loss 4.6566e+00 (4.6717e+00)
Epoch: [7][100/117] Loss 4.5869e+00 (4.6637e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 63.03
Checkpoint ...
[33mEpoch 8/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39975
Train ...
Epoch: [8][ 0/117] Loss 4.6368e+00 (4.6368e+00)
Epoch: [8][ 25/117] Loss 4.6232e+00 (4.6475e+00)
Epoch: [8][ 50/117] Loss 4.6940e+00 (4.6356e+00)
Epoch: [8][ 75/117] Loss 4.5972e+00 (4.6291e+00)
Epoch: [8][100/117] Loss 4.5782e+00 (4.6240e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 63.61
Checkpoint ...
[33mEpoch 9/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39968
Train ...
Epoch: [9][ 0/117] Loss 4.5996e+00 (4.5996e+00)
Epoch: [9][ 25/117] Loss 4.5715e+00 (4.5961e+00)
Epoch: [9][ 50/117] Loss 4.7224e+00 (4.6092e+00)
Epoch: [9][ 75/117] Loss 4.6501e+00 (4.6338e+00)
Epoch: [9][100/117] Loss 4.5544e+00 (4.6286e+00)
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Evaluate ...
Result of kNN evaluation is 64.35
Checkpoint ...
[33mEpoch 10/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39961
Train ...
Epoch: [10][ 0/117] Loss 4.6310e+00 (4.6310e+00)
Epoch: [10][ 25/117] Loss 4.5515e+00 (4.5745e+00)
Epoch: [10][ 50/117] Loss 4.5817e+00 (4.5719e+00)
Epoch: [10][ 75/117] Loss 4.6183e+00 (4.5688e+00)
Epoch: [10][100/117] Loss 4.5834e+00 (4.5670e+00)
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Evaluate ...
Result of kNN evaluation is 65.95
Checkpoint ...
[33mEpoch 11/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39952
Train ...
Epoch: [11][ 0/117] Loss 4.5077e+00 (4.5077e+00)
Epoch: [11][ 25/117] Loss 4.4778e+00 (4.5485e+00)
Epoch: [11][ 50/117] Loss 4.4861e+00 (4.5464e+00)
Epoch: [11][ 75/117] Loss 4.5255e+00 (4.5484e+00)
Epoch: [11][100/117] Loss 4.5881e+00 (4.5571e+00)
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Evaluate ...
Result of kNN evaluation is 63.82
Checkpoint ...
[33mEpoch 12/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39943
Train ...
Epoch: [12][ 0/117] Loss 4.4590e+00 (4.4590e+00)
Epoch: [12][ 25/117] Loss 4.6548e+00 (4.5481e+00)
Epoch: [12][ 50/117] Loss 4.5529e+00 (4.5439e+00)
Epoch: [12][ 75/117] Loss 4.4852e+00 (4.5418e+00)
Epoch: [12][100/117] Loss 4.4714e+00 (4.5424e+00)
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Evaluate ...
Result of kNN evaluation is 65.61
Checkpoint ...
[33mEpoch 13/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39933
Train ...
Epoch: [13][ 0/117] Loss 4.5535e+00 (4.5535e+00)
Epoch: [13][ 25/117] Loss 4.5242e+00 (4.5314e+00)
Epoch: [13][ 50/117] Loss 4.5521e+00 (4.5270e+00)
Epoch: [13][ 75/117] Loss 4.5163e+00 (4.5187e+00)
Epoch: [13][100/117] Loss 4.5915e+00 (4.5185e+00)
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Evaluate ...
Result of kNN evaluation is 66.02
Checkpoint ...
[33mEpoch 14/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39923
Train ...
Epoch: [14][ 0/117] Loss 4.5679e+00 (4.5679e+00)
Epoch: [14][ 25/117] Loss 4.5208e+00 (4.4970e+00)
Epoch: [14][ 50/117] Loss 4.3820e+00 (4.4879e+00)
Epoch: [14][ 75/117] Loss 4.5088e+00 (4.4896e+00)
Epoch: [14][100/117] Loss 4.4873e+00 (4.4958e+00)
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Evaluate ...
Result of kNN evaluation is 67.04
Checkpoint ...
[33mEpoch 15/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39911
Train ...
Epoch: [15][ 0/117] Loss 4.4581e+00 (4.4581e+00)
Epoch: [15][ 25/117] Loss 4.4756e+00 (4.5010e+00)
Epoch: [15][ 50/117] Loss 4.3633e+00 (4.4763e+00)
Epoch: [15][ 75/117] Loss 4.0565e+00 (4.3944e+00)
Epoch: [15][100/117] Loss 4.0568e+00 (4.3255e+00)
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Evaluate ...
Result of kNN evaluation is 72.47
Checkpoint ...
[33mEpoch 16/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39899
Train ...
Epoch: [16][ 0/117] Loss 4.0434e+00 (4.0434e+00)
Epoch: [16][ 25/117] Loss 4.0438e+00 (4.0365e+00)
Epoch: [16][ 50/117] Loss 3.9980e+00 (4.0396e+00)
Epoch: [16][ 75/117] Loss 4.0408e+00 (4.0304e+00)
Epoch: [16][100/117] Loss 3.9300e+00 (4.0219e+00)
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Evaluate ...
Result of kNN evaluation is 73.41
Checkpoint ...
[33mEpoch 17/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39886
Train ...
Epoch: [17][ 0/117] Loss 3.9405e+00 (3.9405e+00)
Epoch: [17][ 25/117] Loss 3.9877e+00 (3.9786e+00)
Epoch: [17][ 50/117] Loss 3.9776e+00 (3.9582e+00)
Epoch: [17][ 75/117] Loss 3.9599e+00 (3.9291e+00)
Epoch: [17][100/117] Loss 3.7871e+00 (3.8957e+00)
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Evaluate ...
Result of kNN evaluation is 78.26
Checkpoint ...
[33mEpoch 18/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39872
Train ...
Epoch: [18][ 0/117] Loss 3.6853e+00 (3.6853e+00)
Epoch: [18][ 25/117] Loss 3.6733e+00 (3.6686e+00)
Epoch: [18][ 50/117] Loss 3.4425e+00 (3.6184e+00)
Epoch: [18][ 75/117] Loss 3.3845e+00 (3.5676e+00)
Epoch: [18][100/117] Loss 3.3277e+00 (3.5303e+00)
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Evaluate ...
Result of kNN evaluation is 78.85
Checkpoint ...
[33mEpoch 19/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39858
Train ...
Epoch: [19][ 0/117] Loss 3.3065e+00 (3.3065e+00)
Epoch: [19][ 25/117] Loss 3.3375e+00 (3.3733e+00)
Epoch: [19][ 50/117] Loss 3.3487e+00 (3.3534e+00)
Epoch: [19][ 75/117] Loss 3.3864e+00 (3.3431e+00)
Epoch: [19][100/117] Loss 3.2911e+00 (3.3344e+00)
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Evaluate ...
Result of kNN evaluation is 79.91
Checkpoint ...
[33mEpoch 20/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39842
Train ...
Epoch: [20][ 0/117] Loss 3.2184e+00 (3.2184e+00)
Epoch: [20][ 25/117] Loss 3.1135e+00 (3.1835e+00)
Epoch: [20][ 50/117] Loss 3.0936e+00 (3.1737e+00)
Epoch: [20][ 75/117] Loss 3.1103e+00 (3.1630e+00)
Epoch: [20][100/117] Loss 3.0246e+00 (3.1449e+00)
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Evaluate ...
Result of kNN evaluation is 81.34
Checkpoint ...
[33mEpoch 21/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39826
Train ...
Epoch: [21][ 0/117] Loss 3.0406e+00 (3.0406e+00)
Epoch: [21][ 25/117] Loss 2.9456e+00 (3.0383e+00)
Epoch: [21][ 50/117] Loss 3.0136e+00 (3.0063e+00)
Epoch: [21][ 75/117] Loss 2.8437e+00 (2.9795e+00)
Epoch: [21][100/117] Loss 2.9589e+00 (2.9590e+00)
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Evaluate ...
Result of kNN evaluation is 82.19
Checkpoint ...
[33mEpoch 22/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39809
Train ...
Epoch: [22][ 0/117] Loss 2.7666e+00 (2.7666e+00)
Epoch: [22][ 25/117] Loss 2.7745e+00 (2.8177e+00)
Epoch: [22][ 50/117] Loss 2.9063e+00 (2.8099e+00)
Epoch: [22][ 75/117] Loss 2.8011e+00 (2.7931e+00)
Epoch: [22][100/117] Loss 2.6448e+00 (2.7808e+00)
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Evaluate ...
Result of kNN evaluation is 82.77
Checkpoint ...
[33mEpoch 23/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39792
Train ...
Epoch: [23][ 0/117] Loss 2.6124e+00 (2.6124e+00)
Epoch: [23][ 25/117] Loss 2.6996e+00 (2.6881e+00)
Epoch: [23][ 50/117] Loss 2.6038e+00 (2.6633e+00)
Epoch: [23][ 75/117] Loss 2.4162e+00 (2.6398e+00)
Epoch: [23][100/117] Loss 2.4980e+00 (2.6147e+00)
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Evaluate ...
Result of kNN evaluation is 83.40
Checkpoint ...
[33mEpoch 24/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39773
Train ...
Epoch: [24][ 0/117] Loss 2.3952e+00 (2.3952e+00)
Epoch: [24][ 25/117] Loss 2.4288e+00 (2.4665e+00)
Epoch: [24][ 50/117] Loss 2.4164e+00 (2.4631e+00)
Epoch: [24][ 75/117] Loss 2.4695e+00 (2.4522e+00)
Epoch: [24][100/117] Loss 2.4460e+00 (2.4451e+00)
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Evaluate ...
Result of kNN evaluation is 83.93
Checkpoint ...
[33mEpoch 25/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39754
Train ...
Epoch: [25][ 0/117] Loss 2.4907e+00 (2.4907e+00)
Epoch: [25][ 25/117] Loss 2.3078e+00 (2.3961e+00)
Epoch: [25][ 50/117] Loss 2.3396e+00 (2.3888e+00)
Epoch: [25][ 75/117] Loss 2.3195e+00 (2.3827e+00)
Epoch: [25][100/117] Loss 2.4178e+00 (2.3752e+00)
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Evaluate ...
Result of kNN evaluation is 83.59
Checkpoint ...
[33mEpoch 26/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39734
Train ...
Epoch: [26][ 0/117] Loss 2.3997e+00 (2.3997e+00)
Epoch: [26][ 25/117] Loss 2.3210e+00 (2.3068e+00)
Epoch: [26][ 50/117] Loss 2.3745e+00 (2.3024e+00)
Epoch: [26][ 75/117] Loss 2.2638e+00 (2.2978e+00)
Epoch: [26][100/117] Loss 2.3452e+00 (2.2987e+00)
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Evaluate ...
Result of kNN evaluation is 83.59
Checkpoint ...
[33mEpoch 27/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39713
Train ...
Epoch: [27][ 0/117] Loss 2.1612e+00 (2.1612e+00)
Epoch: [27][ 25/117] Loss 2.2155e+00 (2.2485e+00)
Epoch: [27][ 50/117] Loss 2.1650e+00 (2.2375e+00)
Epoch: [27][ 75/117] Loss 2.1697e+00 (2.2264e+00)
Epoch: [27][100/117] Loss 2.1574e+00 (2.2215e+00)
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Evaluate ...
Result of kNN evaluation is 84.06
Checkpoint ...
[33mEpoch 28/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39692
Train ...
Epoch: [28][ 0/117] Loss 2.2354e+00 (2.2354e+00)
Epoch: [28][ 25/117] Loss 2.1347e+00 (2.1815e+00)
Epoch: [28][ 50/117] Loss 2.0523e+00 (2.1613e+00)
Epoch: [28][ 75/117] Loss 2.0048e+00 (2.1501e+00)
Epoch: [28][100/117] Loss 2.2587e+00 (2.1411e+00)
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Evaluate ...
Result of kNN evaluation is 84.91
Checkpoint ...
[33mEpoch 29/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39669
Train ...
Epoch: [29][ 0/117] Loss 2.0944e+00 (2.0944e+00)
Epoch: [29][ 25/117] Loss 2.1046e+00 (2.0950e+00)
Epoch: [29][ 50/117] Loss 2.0307e+00 (2.0917e+00)
Epoch: [29][ 75/117] Loss 2.1154e+00 (2.0804e+00)
Epoch: [29][100/117] Loss 1.9772e+00 (2.0710e+00)
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Evaluate ...
Result of kNN evaluation is 85.22
Checkpoint ...
[33mEpoch 30/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39646
Train ...
Epoch: [30][ 0/117] Loss 2.1213e+00 (2.1213e+00)
Epoch: [30][ 25/117] Loss 2.0038e+00 (2.0122e+00)
Epoch: [30][ 50/117] Loss 2.0529e+00 (2.0260e+00)
Epoch: [30][ 75/117] Loss 2.0159e+00 (2.0255e+00)
Epoch: [30][100/117] Loss 1.9998e+00 (2.0269e+00)
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Evaluate ...
Result of kNN evaluation is 85.00
Checkpoint ...
[33mEpoch 31/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39622
Train ...
Epoch: [31][ 0/117] Loss 1.9229e+00 (1.9229e+00)
Epoch: [31][ 25/117] Loss 2.0353e+00 (1.9883e+00)
Epoch: [31][ 50/117] Loss 1.9642e+00 (1.9950e+00)
Epoch: [31][ 75/117] Loss 1.9235e+00 (1.9772e+00)
Epoch: [31][100/117] Loss 1.9209e+00 (1.9671e+00)
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Evaluate ...
Result of kNN evaluation is 84.79
Checkpoint ...
[33mEpoch 32/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39598
Train ...
Epoch: [32][ 0/117] Loss 1.9385e+00 (1.9385e+00)
Epoch: [32][ 25/117] Loss 1.9467e+00 (1.9098e+00)
Epoch: [32][ 50/117] Loss 1.8873e+00 (1.9117e+00)
Epoch: [32][ 75/117] Loss 1.9814e+00 (1.9055e+00)
Epoch: [32][100/117] Loss 1.8733e+00 (1.8955e+00)
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Evaluate ...
Result of kNN evaluation is 85.43
Checkpoint ...
[33mEpoch 33/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39572
Train ...
Epoch: [33][ 0/117] Loss 1.9664e+00 (1.9664e+00)
Epoch: [33][ 25/117] Loss 1.8909e+00 (1.8765e+00)
Epoch: [33][ 50/117] Loss 1.7792e+00 (1.8794e+00)
Epoch: [33][ 75/117] Loss 1.7614e+00 (1.8726e+00)
Epoch: [33][100/117] Loss 1.7335e+00 (1.8665e+00)
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Evaluate ...
Result of kNN evaluation is 85.57
Checkpoint ...
[33mEpoch 34/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39546
Train ...
Epoch: [34][ 0/117] Loss 1.8084e+00 (1.8084e+00)
Epoch: [34][ 25/117] Loss 1.8497e+00 (1.8087e+00)
Epoch: [34][ 50/117] Loss 1.8894e+00 (1.8270e+00)
Epoch: [34][ 75/117] Loss 1.8007e+00 (1.8256e+00)
Epoch: [34][100/117] Loss 1.7704e+00 (1.8229e+00)
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Evaluate ...
Result of kNN evaluation is 85.69
Checkpoint ...
[33mEpoch 35/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39519
Train ...
Epoch: [35][ 0/117] Loss 1.8809e+00 (1.8809e+00)
Epoch: [35][ 25/117] Loss 1.8507e+00 (1.7954e+00)
Epoch: [35][ 50/117] Loss 1.8666e+00 (1.7935e+00)
Epoch: [35][ 75/117] Loss 1.8032e+00 (1.7923e+00)
Epoch: [35][100/117] Loss 1.7115e+00 (1.7897e+00)
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Evaluate ...
Result of kNN evaluation is 85.17
Checkpoint ...
[33mEpoch 36/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39491
Train ...
Epoch: [36][ 0/117] Loss 1.7880e+00 (1.7880e+00)
Epoch: [36][ 25/117] Loss 1.7119e+00 (1.7929e+00)
Epoch: [36][ 50/117] Loss 1.7984e+00 (1.7785e+00)
Epoch: [36][ 75/117] Loss 1.7982e+00 (1.7766e+00)
Epoch: [36][100/117] Loss 1.7318e+00 (1.7695e+00)
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Evaluate ...
Result of kNN evaluation is 85.07
Checkpoint ...
[33mEpoch 37/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39463
Train ...
Epoch: [37][ 0/117] Loss 1.6702e+00 (1.6702e+00)
Epoch: [37][ 25/117] Loss 1.7478e+00 (1.7217e+00)
Epoch: [37][ 50/117] Loss 1.8107e+00 (1.7289e+00)
Epoch: [37][ 75/117] Loss 1.6820e+00 (1.7213e+00)
Epoch: [37][100/117] Loss 1.7987e+00 (1.7180e+00)
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Evaluate ...
Result of kNN evaluation is 85.47
Checkpoint ...
[33mEpoch 38/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39433
Train ...
Epoch: [38][ 0/117] Loss 1.6828e+00 (1.6828e+00)
Epoch: [38][ 25/117] Loss 1.7645e+00 (1.7228e+00)
Epoch: [38][ 50/117] Loss 1.7207e+00 (1.7091e+00)
Epoch: [38][ 75/117] Loss 1.5894e+00 (1.7029e+00)
Epoch: [38][100/117] Loss 1.6695e+00 (1.7026e+00)
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Evaluate ...
Result of kNN evaluation is 86.61
Checkpoint ...
[33mEpoch 39/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39403
Train ...
Epoch: [39][ 0/117] Loss 1.7648e+00 (1.7648e+00)
Epoch: [39][ 25/117] Loss 1.7254e+00 (1.6812e+00)
Epoch: [39][ 50/117] Loss 1.6943e+00 (1.6793e+00)
Epoch: [39][ 75/117] Loss 1.6403e+00 (1.6763e+00)
Epoch: [39][100/117] Loss 1.7604e+00 (1.6740e+00)
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Evaluate ...
Result of kNN evaluation is 85.66
Checkpoint ...
[33mEpoch 40/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39372
Train ...
Epoch: [40][ 0/117] Loss 1.5909e+00 (1.5909e+00)
Epoch: [40][ 25/117] Loss 1.6454e+00 (1.6460e+00)
Epoch: [40][ 50/117] Loss 1.6059e+00 (1.6324e+00)
Epoch: [40][ 75/117] Loss 1.6293e+00 (1.6416e+00)
Epoch: [40][100/117] Loss 1.6377e+00 (1.6396e+00)
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Evaluate ...
Result of kNN evaluation is 85.87
Checkpoint ...
[33mEpoch 41/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39341
Train ...
Epoch: [41][ 0/117] Loss 1.6739e+00 (1.6739e+00)
Epoch: [41][ 25/117] Loss 1.7655e+00 (1.6446e+00)
Epoch: [41][ 50/117] Loss 1.6297e+00 (1.6329e+00)
Epoch: [41][ 75/117] Loss 1.6383e+00 (1.6279e+00)
Epoch: [41][100/117] Loss 1.6859e+00 (1.6203e+00)
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Evaluate ...
Result of kNN evaluation is 86.65
Checkpoint ...
[33mEpoch 42/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39308
Train ...
Epoch: [42][ 0/117] Loss 1.7944e+00 (1.7944e+00)
Epoch: [42][ 25/117] Loss 1.6223e+00 (1.6074e+00)
Epoch: [42][ 50/117] Loss 1.5866e+00 (1.5968e+00)
Epoch: [42][ 75/117] Loss 1.6590e+00 (1.6014e+00)
Epoch: [42][100/117] Loss 1.6016e+00 (1.5931e+00)
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Evaluate ...
Result of kNN evaluation is 86.83
Checkpoint ...
[33mEpoch 43/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39275
Train ...
Epoch: [43][ 0/117] Loss 1.5864e+00 (1.5864e+00)
Epoch: [43][ 25/117] Loss 1.6288e+00 (1.5538e+00)
Epoch: [43][ 50/117] Loss 1.5763e+00 (1.5594e+00)
Epoch: [43][ 75/117] Loss 1.5070e+00 (1.5560e+00)
Epoch: [43][100/117] Loss 1.4957e+00 (1.5543e+00)
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Evaluate ...
Result of kNN evaluation is 86.19
Checkpoint ...
[33mEpoch 44/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39241
Train ...
Epoch: [44][ 0/117] Loss 1.5921e+00 (1.5921e+00)
Epoch: [44][ 25/117] Loss 1.4813e+00 (1.5288e+00)
Epoch: [44][ 50/117] Loss 1.5770e+00 (1.5401e+00)
Epoch: [44][ 75/117] Loss 1.5692e+00 (1.5441e+00)
Epoch: [44][100/117] Loss 1.4498e+00 (1.5385e+00)
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Evaluate ...
Result of kNN evaluation is 86.13
Checkpoint ...
[33mEpoch 45/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39207
Train ...
Epoch: [45][ 0/117] Loss 1.4717e+00 (1.4717e+00)
Epoch: [45][ 25/117] Loss 1.5592e+00 (1.5210e+00)
Epoch: [45][ 50/117] Loss 1.6261e+00 (1.5348e+00)
Epoch: [45][ 75/117] Loss 1.3972e+00 (1.5265e+00)
Epoch: [45][100/117] Loss 1.5733e+00 (1.5280e+00)
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Evaluate ...
Result of kNN evaluation is 86.70
Checkpoint ...
[33mEpoch 46/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39171
Train ...
Epoch: [46][ 0/117] Loss 1.5509e+00 (1.5509e+00)
Epoch: [46][ 25/117] Loss 1.5559e+00 (1.5242e+00)
Epoch: [46][ 50/117] Loss 1.4567e+00 (1.5214e+00)
Epoch: [46][ 75/117] Loss 1.4666e+00 (1.5173e+00)
Epoch: [46][100/117] Loss 1.4540e+00 (1.5191e+00)
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Evaluate ...
Result of kNN evaluation is 87.40
Checkpoint ...
[33mEpoch 47/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39135
Train ...
Epoch: [47][ 0/117] Loss 1.5347e+00 (1.5347e+00)
Epoch: [47][ 25/117] Loss 1.4543e+00 (1.4975e+00)
Epoch: [47][ 50/117] Loss 1.4553e+00 (1.4987e+00)
Epoch: [47][ 75/117] Loss 1.5353e+00 (1.4969e+00)
Epoch: [47][100/117] Loss 1.4400e+00 (1.4894e+00)
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Evaluate ...
Result of kNN evaluation is 86.26
Checkpoint ...
[33mEpoch 48/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39098
Train ...
Epoch: [48][ 0/117] Loss 1.4659e+00 (1.4659e+00)
Epoch: [48][ 25/117] Loss 1.4975e+00 (1.5040e+00)
Epoch: [48][ 50/117] Loss 1.4441e+00 (1.4819e+00)
Epoch: [48][ 75/117] Loss 1.3584e+00 (1.4831e+00)
Epoch: [48][100/117] Loss 1.4336e+00 (1.4877e+00)
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Evaluate ...
Result of kNN evaluation is 86.53
Checkpoint ...
[33mEpoch 49/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39061
Train ...
Epoch: [49][ 0/117] Loss 1.4771e+00 (1.4771e+00)
Epoch: [49][ 25/117] Loss 1.5155e+00 (1.4679e+00)
Epoch: [49][ 50/117] Loss 1.4914e+00 (1.4755e+00)
Epoch: [49][ 75/117] Loss 1.3568e+00 (1.4808e+00)
Epoch: [49][100/117] Loss 1.3878e+00 (1.4788e+00)
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Evaluate ...
Result of kNN evaluation is 87.22
Checkpoint ...
[33mEpoch 50/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39022
Train ...
Epoch: [50][ 0/117] Loss 1.5487e+00 (1.5487e+00)
Epoch: [50][ 25/117] Loss 1.4789e+00 (1.4634e+00)
Epoch: [50][ 50/117] Loss 1.5019e+00 (1.4583e+00)
Epoch: [50][ 75/117] Loss 1.4955e+00 (1.4488e+00)
Epoch: [50][100/117] Loss 1.4910e+00 (1.4496e+00)
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Evaluate ...
Result of kNN evaluation is 86.72
Checkpoint ...
[33mEpoch 51/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38983
Train ...
Epoch: [51][ 0/117] Loss 1.4283e+00 (1.4283e+00)
Epoch: [51][ 25/117] Loss 1.4747e+00 (1.4321e+00)
Epoch: [51][ 50/117] Loss 1.3901e+00 (1.4370e+00)
Epoch: [51][ 75/117] Loss 1.4967e+00 (1.4389e+00)
Epoch: [51][100/117] Loss 1.3445e+00 (1.4393e+00)
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Evaluate ...
Result of kNN evaluation is 86.93
Checkpoint ...
[33mEpoch 52/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38943
Train ...
Epoch: [52][ 0/117] Loss 1.4093e+00 (1.4093e+00)
Epoch: [52][ 25/117] Loss 1.4135e+00 (1.4159e+00)
Epoch: [52][ 50/117] Loss 1.4538e+00 (1.4217e+00)
Epoch: [52][ 75/117] Loss 1.4706e+00 (1.4248e+00)
Epoch: [52][100/117] Loss 1.3553e+00 (1.4302e+00)
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Evaluate ...
Result of kNN evaluation is 87.49
Checkpoint ...
[33mEpoch 53/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38902
Train ...
Epoch: [53][ 0/117] Loss 1.4837e+00 (1.4837e+00)
Epoch: [53][ 25/117] Loss 1.3969e+00 (1.4235e+00)
Epoch: [53][ 50/117] Loss 1.5061e+00 (1.4127e+00)
Epoch: [53][ 75/117] Loss 1.3546e+00 (1.4202e+00)
Epoch: [53][100/117] Loss 1.3769e+00 (1.4237e+00)
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Evaluate ...
Result of kNN evaluation is 86.70
Checkpoint ...
[33mEpoch 54/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38861
Train ...
Epoch: [54][ 0/117] Loss 1.3791e+00 (1.3791e+00)
Epoch: [54][ 25/117] Loss 1.3048e+00 (1.4476e+00)
Epoch: [54][ 50/117] Loss 1.4454e+00 (1.4234e+00)
Epoch: [54][ 75/117] Loss 1.3644e+00 (1.4097e+00)
Epoch: [54][100/117] Loss 1.4864e+00 (1.4133e+00)
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Evaluate ...
Result of kNN evaluation is 87.30
Checkpoint ...
[33mEpoch 55/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38819
Train ...
Epoch: [55][ 0/117] Loss 1.3080e+00 (1.3080e+00)
Epoch: [55][ 25/117] Loss 1.4401e+00 (1.3853e+00)
Epoch: [55][ 50/117] Loss 1.4643e+00 (1.3969e+00)
Epoch: [55][ 75/117] Loss 1.5055e+00 (1.3929e+00)
Epoch: [55][100/117] Loss 1.5096e+00 (1.4007e+00)
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Evaluate ...
Result of kNN evaluation is 87.35
Checkpoint ...
[33mEpoch 56/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38776
Train ...
Epoch: [56][ 0/117] Loss 1.2889e+00 (1.2889e+00)
Epoch: [56][ 25/117] Loss 1.3850e+00 (1.3907e+00)
Epoch: [56][ 50/117] Loss 1.3837e+00 (1.3870e+00)
Epoch: [56][ 75/117] Loss 1.4193e+00 (1.3847e+00)
Epoch: [56][100/117] Loss 1.4513e+00 (1.3890e+00)
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Evaluate ...
Result of kNN evaluation is 87.92
Checkpoint ...
[33mEpoch 57/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38732
Train ...
Epoch: [57][ 0/117] Loss 1.3385e+00 (1.3385e+00)
Epoch: [57][ 25/117] Loss 1.2980e+00 (1.3548e+00)
Epoch: [57][ 50/117] Loss 1.3739e+00 (1.3726e+00)
Epoch: [57][ 75/117] Loss 1.3621e+00 (1.3666e+00)
Epoch: [57][100/117] Loss 1.3434e+00 (1.3640e+00)
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Evaluate ...
Result of kNN evaluation is 87.15
Checkpoint ...
[33mEpoch 58/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38688
Train ...
Epoch: [58][ 0/117] Loss 1.3859e+00 (1.3859e+00)
Epoch: [58][ 25/117] Loss 1.3380e+00 (1.3833e+00)
Epoch: [58][ 50/117] Loss 1.2585e+00 (1.3790e+00)