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fashion-mnist-pretext.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(
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 6.8402e+00 (6.8402e+00)
Epoch: [0][ 25/117] Loss 6.9304e+00 (6.9245e+00)
Epoch: [0][ 50/117] Loss 6.9304e+00 (6.9274e+00)
Epoch: [0][ 75/117] Loss 6.9304e+00 (6.9284e+00)
Epoch: [0][100/117] Loss 6.9303e+00 (6.9289e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 53.89
Checkpoint ...
[33mEpoch 1/500[0m
[33m---------------[0m
Adjusted learning rate to 0.40000
Train ...
Epoch: [1][ 0/117] Loss 6.9303e+00 (6.9303e+00)
Epoch: [1][ 25/117] Loss 6.9302e+00 (6.9303e+00)
Epoch: [1][ 50/117] Loss 6.9301e+00 (6.9302e+00)
Epoch: [1][ 75/117] Loss 6.9298e+00 (6.9301e+00)
Epoch: [1][100/117] Loss 6.9290e+00 (6.9300e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 54.39
Checkpoint ...
[33mEpoch 2/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39998
Train ...
Epoch: [2][ 0/117] Loss 6.9272e+00 (6.9272e+00)
Epoch: [2][ 25/117] Loss 6.9170e+00 (6.9235e+00)
Epoch: [2][ 50/117] Loss 6.8024e+00 (6.9032e+00)
Epoch: [2][ 75/117] Loss 6.3993e+00 (6.7932e+00)
Epoch: [2][100/117] Loss 6.1268e+00 (6.6525e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 52.01
Checkpoint ...
[33mEpoch 3/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39996
Train ...
Epoch: [3][ 0/117] Loss 5.9811e+00 (5.9811e+00)
Epoch: [3][ 25/117] Loss 5.9648e+00 (5.9578e+00)
Epoch: [3][ 50/117] Loss 5.9044e+00 (5.9125e+00)
Epoch: [3][ 75/117] Loss 5.9258e+00 (5.9029e+00)
Epoch: [3][100/117] Loss 5.8168e+00 (5.8988e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 61.34
Checkpoint ...
[33mEpoch 4/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39994
Train ...
Epoch: [4][ 0/117] Loss 5.7187e+00 (5.7187e+00)
Epoch: [4][ 25/117] Loss 5.2221e+00 (5.4645e+00)
Epoch: [4][ 50/117] Loss 5.0185e+00 (5.2848e+00)
Epoch: [4][ 75/117] Loss 4.9014e+00 (5.1935e+00)
Epoch: [4][100/117] Loss 4.7785e+00 (5.1164e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 59.11
Checkpoint ...
[33mEpoch 5/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39990
Train ...
Epoch: [5][ 0/117] Loss 4.8056e+00 (4.8056e+00)
Epoch: [5][ 25/117] Loss 4.7536e+00 (4.7740e+00)
Epoch: [5][ 50/117] Loss 4.6248e+00 (4.7265e+00)
Epoch: [5][ 75/117] Loss 4.4905e+00 (4.6901e+00)
Epoch: [5][100/117] Loss 4.1925e+00 (4.6127e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 71.35
Checkpoint ...
[33mEpoch 6/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39986
Train ...
Epoch: [6][ 0/117] Loss 4.2432e+00 (4.2432e+00)
Epoch: [6][ 25/117] Loss 4.1281e+00 (4.1870e+00)
Epoch: [6][ 50/117] Loss 4.0904e+00 (4.1930e+00)
Epoch: [6][ 75/117] Loss 4.1079e+00 (4.1795e+00)
Epoch: [6][100/117] Loss 4.1611e+00 (4.1753e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 71.99
Checkpoint ...
[33mEpoch 7/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39981
Train ...
Epoch: [7][ 0/117] Loss 4.0812e+00 (4.0812e+00)
Epoch: [7][ 25/117] Loss 4.1519e+00 (4.0702e+00)
Epoch: [7][ 50/117] Loss 4.0132e+00 (4.0506e+00)
Epoch: [7][ 75/117] Loss 3.9715e+00 (4.0355e+00)
Epoch: [7][100/117] Loss 3.8905e+00 (4.0204e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 73.67
Checkpoint ...
[33mEpoch 8/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39975
Train ...
Epoch: [8][ 0/117] Loss 3.8646e+00 (3.8646e+00)
Epoch: [8][ 25/117] Loss 3.9484e+00 (3.9776e+00)
Epoch: [8][ 50/117] Loss 3.8716e+00 (3.9557e+00)
Epoch: [8][ 75/117] Loss 3.9765e+00 (3.9460e+00)
Epoch: [8][100/117] Loss 3.9347e+00 (3.9384e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 74.10
Checkpoint ...
[33mEpoch 9/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39968
Train ...
Epoch: [9][ 0/117] Loss 3.8167e+00 (3.8167e+00)
Epoch: [9][ 25/117] Loss 3.9225e+00 (3.9034e+00)
Epoch: [9][ 50/117] Loss 3.8492e+00 (3.8894e+00)
Epoch: [9][ 75/117] Loss 3.8171e+00 (3.8796e+00)
Epoch: [9][100/117] Loss 3.8331e+00 (3.8696e+00)
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Evaluate ...
Result of kNN evaluation is 74.93
Checkpoint ...
[33mEpoch 10/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39961
Train ...
Epoch: [10][ 0/117] Loss 3.8706e+00 (3.8706e+00)
Epoch: [10][ 25/117] Loss 3.8271e+00 (3.8231e+00)
Epoch: [10][ 50/117] Loss 3.7596e+00 (3.8205e+00)
Epoch: [10][ 75/117] Loss 3.8447e+00 (3.8227e+00)
Epoch: [10][100/117] Loss 3.8372e+00 (3.8219e+00)
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Evaluate ...
Result of kNN evaluation is 75.71
Checkpoint ...
[33mEpoch 11/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39952
Train ...
Epoch: [11][ 0/117] Loss 3.8770e+00 (3.8770e+00)
Epoch: [11][ 25/117] Loss 3.8304e+00 (3.7969e+00)
Epoch: [11][ 50/117] Loss 3.7708e+00 (3.8098e+00)
Epoch: [11][ 75/117] Loss 3.7373e+00 (3.8047e+00)
Epoch: [11][100/117] Loss 3.8303e+00 (3.8008e+00)
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Evaluate ...
Result of kNN evaluation is 75.64
Checkpoint ...
[33mEpoch 12/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39943
Train ...
Epoch: [12][ 0/117] Loss 3.7198e+00 (3.7198e+00)
Epoch: [12][ 25/117] Loss 3.7408e+00 (3.7662e+00)
Epoch: [12][ 50/117] Loss 3.7584e+00 (3.7737e+00)
Epoch: [12][ 75/117] Loss 3.7738e+00 (3.7696e+00)
Epoch: [12][100/117] Loss 3.7591e+00 (3.7693e+00)
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Evaluate ...
Result of kNN evaluation is 75.72
Checkpoint ...
[33mEpoch 13/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39933
Train ...
Epoch: [13][ 0/117] Loss 3.7450e+00 (3.7450e+00)
Epoch: [13][ 25/117] Loss 3.6952e+00 (3.7448e+00)
Epoch: [13][ 50/117] Loss 3.7801e+00 (3.7415e+00)
Epoch: [13][ 75/117] Loss 3.6764e+00 (3.7419e+00)
Epoch: [13][100/117] Loss 3.7468e+00 (3.7427e+00)
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Evaluate ...
Result of kNN evaluation is 75.79
Checkpoint ...
[33mEpoch 14/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39923
Train ...
Epoch: [14][ 0/117] Loss 3.7751e+00 (3.7751e+00)
Epoch: [14][ 25/117] Loss 3.7069e+00 (3.7199e+00)
Epoch: [14][ 50/117] Loss 3.7321e+00 (3.7270e+00)
Epoch: [14][ 75/117] Loss 3.7399e+00 (3.7319e+00)
Epoch: [14][100/117] Loss 3.7847e+00 (3.7284e+00)
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Evaluate ...
Result of kNN evaluation is 76.50
Checkpoint ...
[33mEpoch 15/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39911
Train ...
Epoch: [15][ 0/117] Loss 3.7473e+00 (3.7473e+00)
Epoch: [15][ 25/117] Loss 3.6700e+00 (3.7198e+00)
Epoch: [15][ 50/117] Loss 3.7247e+00 (3.7148e+00)
Epoch: [15][ 75/117] Loss 3.6884e+00 (3.7125e+00)
Epoch: [15][100/117] Loss 3.7184e+00 (3.7143e+00)
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Evaluate ...
Result of kNN evaluation is 76.21
Checkpoint ...
[33mEpoch 16/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39899
Train ...
Epoch: [16][ 0/117] Loss 3.7433e+00 (3.7433e+00)
Epoch: [16][ 25/117] Loss 3.7244e+00 (3.7162e+00)
Epoch: [16][ 50/117] Loss 3.7308e+00 (3.7189e+00)
Epoch: [16][ 75/117] Loss 3.7175e+00 (3.7155e+00)
Epoch: [16][100/117] Loss 3.7156e+00 (3.7089e+00)
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Evaluate ...
Result of kNN evaluation is 77.70
Checkpoint ...
[33mEpoch 17/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39886
Train ...
Epoch: [17][ 0/117] Loss 3.5602e+00 (3.5602e+00)
Epoch: [17][ 25/117] Loss 3.4101e+00 (3.4680e+00)
Epoch: [17][ 50/117] Loss 3.3097e+00 (3.4495e+00)
Epoch: [17][ 75/117] Loss 3.2989e+00 (3.4260e+00)
Epoch: [17][100/117] Loss 3.3757e+00 (3.4067e+00)
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Evaluate ...
Result of kNN evaluation is 79.12
Checkpoint ...
[33mEpoch 18/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39872
Train ...
Epoch: [18][ 0/117] Loss 3.3163e+00 (3.3163e+00)
Epoch: [18][ 25/117] Loss 3.2595e+00 (3.3246e+00)
Epoch: [18][ 50/117] Loss 3.2810e+00 (3.2800e+00)
Epoch: [18][ 75/117] Loss 3.1008e+00 (3.2285e+00)
Epoch: [18][100/117] Loss 3.1603e+00 (3.1898e+00)
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Evaluate ...
Result of kNN evaluation is 79.91
Checkpoint ...
[33mEpoch 19/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39858
Train ...
Epoch: [19][ 0/117] Loss 3.0367e+00 (3.0367e+00)
Epoch: [19][ 25/117] Loss 3.0221e+00 (3.0174e+00)
Epoch: [19][ 50/117] Loss 3.0132e+00 (3.0088e+00)
Epoch: [19][ 75/117] Loss 2.9560e+00 (2.9950e+00)
Epoch: [19][100/117] Loss 2.9314e+00 (2.9891e+00)
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Evaluate ...
Result of kNN evaluation is 81.07
Checkpoint ...
[33mEpoch 20/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39842
Train ...
Epoch: [20][ 0/117] Loss 2.8532e+00 (2.8532e+00)
Epoch: [20][ 25/117] Loss 2.8871e+00 (2.8546e+00)
Epoch: [20][ 50/117] Loss 2.8056e+00 (2.8277e+00)
Epoch: [20][ 75/117] Loss 2.8065e+00 (2.8196e+00)
Epoch: [20][100/117] Loss 2.6775e+00 (2.7965e+00)
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Evaluate ...
Result of kNN evaluation is 82.50
Checkpoint ...
[33mEpoch 21/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39826
Train ...
Epoch: [21][ 0/117] Loss 2.7295e+00 (2.7295e+00)
Epoch: [21][ 25/117] Loss 2.5808e+00 (2.6497e+00)
Epoch: [21][ 50/117] Loss 2.5355e+00 (2.6186e+00)
Epoch: [21][ 75/117] Loss 2.5545e+00 (2.5922e+00)
Epoch: [21][100/117] Loss 2.3869e+00 (2.5613e+00)
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Evaluate ...
Result of kNN evaluation is 82.81
Checkpoint ...
[33mEpoch 22/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39809
Train ...
Epoch: [22][ 0/117] Loss 2.4675e+00 (2.4675e+00)
Epoch: [22][ 25/117] Loss 2.4278e+00 (2.4326e+00)
Epoch: [22][ 50/117] Loss 2.3152e+00 (2.4172e+00)
Epoch: [22][ 75/117] Loss 2.3713e+00 (2.3993e+00)
Epoch: [22][100/117] Loss 2.3468e+00 (2.3907e+00)
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Evaluate ...
Result of kNN evaluation is 83.03
Checkpoint ...
[33mEpoch 23/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39792
Train ...
Epoch: [23][ 0/117] Loss 2.3220e+00 (2.3220e+00)
Epoch: [23][ 25/117] Loss 2.3983e+00 (2.3370e+00)
Epoch: [23][ 50/117] Loss 2.3997e+00 (2.3237e+00)
Epoch: [23][ 75/117] Loss 2.2639e+00 (2.3056e+00)
Epoch: [23][100/117] Loss 2.2676e+00 (2.2925e+00)
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Evaluate ...
Result of kNN evaluation is 83.73
Checkpoint ...
[33mEpoch 24/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39773
Train ...
Epoch: [24][ 0/117] Loss 2.2284e+00 (2.2284e+00)
Epoch: [24][ 25/117] Loss 2.1211e+00 (2.2040e+00)
Epoch: [24][ 50/117] Loss 2.2267e+00 (2.1931e+00)
Epoch: [24][ 75/117] Loss 2.1134e+00 (2.1873e+00)
Epoch: [24][100/117] Loss 2.2259e+00 (2.1667e+00)
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Evaluate ...
Result of kNN evaluation is 84.05
Checkpoint ...
[33mEpoch 25/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39754
Train ...
Epoch: [25][ 0/117] Loss 2.1526e+00 (2.1526e+00)
Epoch: [25][ 25/117] Loss 2.0301e+00 (2.0959e+00)
Epoch: [25][ 50/117] Loss 2.0475e+00 (2.0806e+00)
Epoch: [25][ 75/117] Loss 2.0015e+00 (2.0667e+00)
Epoch: [25][100/117] Loss 1.9785e+00 (2.0571e+00)
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Evaluate ...
Result of kNN evaluation is 84.24
Checkpoint ...
[33mEpoch 26/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39734
Train ...
Epoch: [26][ 0/117] Loss 2.0320e+00 (2.0320e+00)
Epoch: [26][ 25/117] Loss 1.9398e+00 (1.9721e+00)
Epoch: [26][ 50/117] Loss 1.9776e+00 (1.9536e+00)
Epoch: [26][ 75/117] Loss 2.0206e+00 (1.9515e+00)
Epoch: [26][100/117] Loss 1.9783e+00 (1.9409e+00)
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Evaluate ...
Result of kNN evaluation is 84.54
Checkpoint ...
[33mEpoch 27/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39713
Train ...
Epoch: [27][ 0/117] Loss 1.9099e+00 (1.9099e+00)
Epoch: [27][ 25/117] Loss 1.8641e+00 (1.8725e+00)
Epoch: [27][ 50/117] Loss 1.7841e+00 (1.8461e+00)
Epoch: [27][ 75/117] Loss 1.7245e+00 (1.8285e+00)
Epoch: [27][100/117] Loss 1.8213e+00 (1.8142e+00)
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Evaluate ...
Result of kNN evaluation is 85.03
Checkpoint ...
[33mEpoch 28/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39692
Train ...
Epoch: [28][ 0/117] Loss 1.7990e+00 (1.7990e+00)
Epoch: [28][ 25/117] Loss 1.7265e+00 (1.7196e+00)
Epoch: [28][ 50/117] Loss 1.6797e+00 (1.7181e+00)
Epoch: [28][ 75/117] Loss 1.6686e+00 (1.7149e+00)
Epoch: [28][100/117] Loss 1.6762e+00 (1.7163e+00)
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Evaluate ...
Result of kNN evaluation is 84.46
Checkpoint ...
[33mEpoch 29/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39669
Train ...
Epoch: [29][ 0/117] Loss 1.7171e+00 (1.7171e+00)
Epoch: [29][ 25/117] Loss 1.6483e+00 (1.6526e+00)
Epoch: [29][ 50/117] Loss 1.5745e+00 (1.6262e+00)
Epoch: [29][ 75/117] Loss 1.5642e+00 (1.6224e+00)
Epoch: [29][100/117] Loss 1.6521e+00 (1.6169e+00)
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Evaluate ...
Result of kNN evaluation is 85.06
Checkpoint ...
[33mEpoch 30/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39646
Train ...
Epoch: [30][ 0/117] Loss 1.5596e+00 (1.5596e+00)
Epoch: [30][ 25/117] Loss 1.4927e+00 (1.5369e+00)
Epoch: [30][ 50/117] Loss 1.5047e+00 (1.5407e+00)
Epoch: [30][ 75/117] Loss 1.4700e+00 (1.5387e+00)
Epoch: [30][100/117] Loss 1.4895e+00 (1.5319e+00)
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Evaluate ...
Result of kNN evaluation is 85.55
Checkpoint ...
[33mEpoch 31/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39622
Train ...
Epoch: [31][ 0/117] Loss 1.4420e+00 (1.4420e+00)
Epoch: [31][ 25/117] Loss 1.4483e+00 (1.4668e+00)
Epoch: [31][ 50/117] Loss 1.4755e+00 (1.4672e+00)
Epoch: [31][ 75/117] Loss 1.4663e+00 (1.4715e+00)
Epoch: [31][100/117] Loss 1.4244e+00 (1.4678e+00)
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Evaluate ...
Result of kNN evaluation is 86.01
Checkpoint ...
[33mEpoch 32/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39598
Train ...
Epoch: [32][ 0/117] Loss 1.3992e+00 (1.3992e+00)
Epoch: [32][ 25/117] Loss 1.4046e+00 (1.4436e+00)
Epoch: [32][ 50/117] Loss 1.3357e+00 (1.4413e+00)
Epoch: [32][ 75/117] Loss 1.3731e+00 (1.4243e+00)
Epoch: [32][100/117] Loss 1.3338e+00 (1.4145e+00)
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Evaluate ...
Result of kNN evaluation is 86.00
Checkpoint ...
[33mEpoch 33/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39572
Train ...
Epoch: [33][ 0/117] Loss 1.4451e+00 (1.4451e+00)
Epoch: [33][ 25/117] Loss 1.3870e+00 (1.3837e+00)
Epoch: [33][ 50/117] Loss 1.5205e+00 (1.3848e+00)
Epoch: [33][ 75/117] Loss 1.2532e+00 (1.3725e+00)
Epoch: [33][100/117] Loss 1.3841e+00 (1.3666e+00)
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Evaluate ...
Result of kNN evaluation is 86.22
Checkpoint ...
[33mEpoch 34/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39546
Train ...
Epoch: [34][ 0/117] Loss 1.3436e+00 (1.3436e+00)
Epoch: [34][ 25/117] Loss 1.2726e+00 (1.3255e+00)
Epoch: [34][ 50/117] Loss 1.2496e+00 (1.3177e+00)
Epoch: [34][ 75/117] Loss 1.3696e+00 (1.3152e+00)
Epoch: [34][100/117] Loss 1.3283e+00 (1.3165e+00)
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Evaluate ...
Result of kNN evaluation is 86.73
Checkpoint ...
[33mEpoch 35/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39519
Train ...
Epoch: [35][ 0/117] Loss 1.2577e+00 (1.2577e+00)
Epoch: [35][ 25/117] Loss 1.1985e+00 (1.2762e+00)
Epoch: [35][ 50/117] Loss 1.2923e+00 (1.2790e+00)
Epoch: [35][ 75/117] Loss 1.3199e+00 (1.2778e+00)
Epoch: [35][100/117] Loss 1.3317e+00 (1.2735e+00)
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Evaluate ...
Result of kNN evaluation is 86.17
Checkpoint ...
[33mEpoch 36/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39491
Train ...
Epoch: [36][ 0/117] Loss 1.1823e+00 (1.1823e+00)
Epoch: [36][ 25/117] Loss 1.2009e+00 (1.2468e+00)
Epoch: [36][ 50/117] Loss 1.1711e+00 (1.2381e+00)
Epoch: [36][ 75/117] Loss 1.1737e+00 (1.2393e+00)
Epoch: [36][100/117] Loss 1.2858e+00 (1.2360e+00)
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Evaluate ...
Result of kNN evaluation is 85.91
Checkpoint ...
[33mEpoch 37/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39463
Train ...
Epoch: [37][ 0/117] Loss 1.2649e+00 (1.2649e+00)
Epoch: [37][ 25/117] Loss 1.2506e+00 (1.2261e+00)
Epoch: [37][ 50/117] Loss 1.1914e+00 (1.2177e+00)
Epoch: [37][ 75/117] Loss 1.1810e+00 (1.2179e+00)
Epoch: [37][100/117] Loss 1.2505e+00 (1.2143e+00)
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Evaluate ...
Result of kNN evaluation is 86.36
Checkpoint ...
[33mEpoch 38/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39433
Train ...
Epoch: [38][ 0/117] Loss 1.2479e+00 (1.2479e+00)
Epoch: [38][ 25/117] Loss 1.2006e+00 (1.1752e+00)
Epoch: [38][ 50/117] Loss 1.1062e+00 (1.1691e+00)
Epoch: [38][ 75/117] Loss 1.1810e+00 (1.1708e+00)
Epoch: [38][100/117] Loss 1.1499e+00 (1.1709e+00)
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Evaluate ...
Result of kNN evaluation is 86.65
Checkpoint ...
[33mEpoch 39/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39403
Train ...
Epoch: [39][ 0/117] Loss 1.1472e+00 (1.1472e+00)
Epoch: [39][ 25/117] Loss 1.2796e+00 (1.1869e+00)
Epoch: [39][ 50/117] Loss 1.1496e+00 (1.1721e+00)
Epoch: [39][ 75/117] Loss 1.2012e+00 (1.1695e+00)
Epoch: [39][100/117] Loss 1.0598e+00 (1.1622e+00)
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Evaluate ...
Result of kNN evaluation is 86.88
Checkpoint ...
[33mEpoch 40/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39372
Train ...
Epoch: [40][ 0/117] Loss 1.1883e+00 (1.1883e+00)
Epoch: [40][ 25/117] Loss 1.0786e+00 (1.1465e+00)
Epoch: [40][ 50/117] Loss 1.1430e+00 (1.1482e+00)
Epoch: [40][ 75/117] Loss 1.1656e+00 (1.1453e+00)
Epoch: [40][100/117] Loss 1.2915e+00 (1.1444e+00)
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Evaluate ...
Result of kNN evaluation is 86.89
Checkpoint ...
[33mEpoch 41/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39341
Train ...
Epoch: [41][ 0/117] Loss 1.1106e+00 (1.1106e+00)
Epoch: [41][ 25/117] Loss 1.0748e+00 (1.1085e+00)
Epoch: [41][ 50/117] Loss 1.1028e+00 (1.1088e+00)
Epoch: [41][ 75/117] Loss 1.0965e+00 (1.1110e+00)
Epoch: [41][100/117] Loss 1.1221e+00 (1.1118e+00)
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Evaluate ...
Result of kNN evaluation is 86.97
Checkpoint ...
[33mEpoch 42/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39308
Train ...
Epoch: [42][ 0/117] Loss 1.1552e+00 (1.1552e+00)
Epoch: [42][ 25/117] Loss 1.1082e+00 (1.0961e+00)
Epoch: [42][ 50/117] Loss 1.1216e+00 (1.0899e+00)
Epoch: [42][ 75/117] Loss 1.1408e+00 (1.0885e+00)
Epoch: [42][100/117] Loss 1.1621e+00 (1.0902e+00)
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Evaluate ...
Result of kNN evaluation is 86.88
Checkpoint ...
[33mEpoch 43/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39275
Train ...
Epoch: [43][ 0/117] Loss 1.1094e+00 (1.1094e+00)
Epoch: [43][ 25/117] Loss 1.0142e+00 (1.0681e+00)
Epoch: [43][ 50/117] Loss 1.0768e+00 (1.0626e+00)
Epoch: [43][ 75/117] Loss 1.0937e+00 (1.0640e+00)
Epoch: [43][100/117] Loss 1.0846e+00 (1.0648e+00)
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Evaluate ...
Result of kNN evaluation is 87.15
Checkpoint ...
[33mEpoch 44/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39241
Train ...
Epoch: [44][ 0/117] Loss 1.0785e+00 (1.0785e+00)
Epoch: [44][ 25/117] Loss 1.0096e+00 (1.0455e+00)
Epoch: [44][ 50/117] Loss 1.0175e+00 (1.0420e+00)
Epoch: [44][ 75/117] Loss 1.0320e+00 (1.0500e+00)
Epoch: [44][100/117] Loss 1.0339e+00 (1.0527e+00)
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Evaluate ...
Result of kNN evaluation is 87.12
Checkpoint ...
[33mEpoch 45/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39207
Train ...
Epoch: [45][ 0/117] Loss 1.0283e+00 (1.0283e+00)
Epoch: [45][ 25/117] Loss 1.0263e+00 (1.0437e+00)
Epoch: [45][ 50/117] Loss 9.7087e-01 (1.0364e+00)
Epoch: [45][ 75/117] Loss 9.9879e-01 (1.0351e+00)
Epoch: [45][100/117] Loss 1.0681e+00 (1.0381e+00)
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Evaluate ...
Result of kNN evaluation is 87.31
Checkpoint ...
[33mEpoch 46/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39171
Train ...
Epoch: [46][ 0/117] Loss 1.0282e+00 (1.0282e+00)
Epoch: [46][ 25/117] Loss 9.6236e-01 (1.0090e+00)
Epoch: [46][ 50/117] Loss 1.0101e+00 (1.0188e+00)
Epoch: [46][ 75/117] Loss 1.0071e+00 (1.0186e+00)
Epoch: [46][100/117] Loss 1.0804e+00 (1.0223e+00)
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Evaluate ...
Result of kNN evaluation is 87.41
Checkpoint ...
[33mEpoch 47/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39135
Train ...
Epoch: [47][ 0/117] Loss 1.0797e+00 (1.0797e+00)
Epoch: [47][ 25/117] Loss 1.0923e+00 (1.0356e+00)
Epoch: [47][ 50/117] Loss 1.1094e+00 (1.0261e+00)
Epoch: [47][ 75/117] Loss 1.0853e+00 (1.0223e+00)
Epoch: [47][100/117] Loss 9.9459e-01 (1.0213e+00)
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Evaluate ...
Result of kNN evaluation is 87.74
Checkpoint ...
[33mEpoch 48/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39098
Train ...
Epoch: [48][ 0/117] Loss 1.0064e+00 (1.0064e+00)
Epoch: [48][ 25/117] Loss 9.8996e-01 (1.0113e+00)
Epoch: [48][ 50/117] Loss 9.6551e-01 (1.0033e+00)
Epoch: [48][ 75/117] Loss 1.0043e+00 (1.0023e+00)
Epoch: [48][100/117] Loss 9.9839e-01 (1.0063e+00)
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Evaluate ...
Result of kNN evaluation is 87.75
Checkpoint ...
[33mEpoch 49/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39061
Train ...
Epoch: [49][ 0/117] Loss 9.7325e-01 (9.7325e-01)
Epoch: [49][ 25/117] Loss 1.0334e+00 (9.8334e-01)
Epoch: [49][ 50/117] Loss 1.0119e+00 (9.9702e-01)
Epoch: [49][ 75/117] Loss 9.9950e-01 (9.9532e-01)
Epoch: [49][100/117] Loss 1.0769e+00 (9.9031e-01)
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Evaluate ...
Result of kNN evaluation is 87.81
Checkpoint ...
[33mEpoch 50/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39022
Train ...
Epoch: [50][ 0/117] Loss 9.7597e-01 (9.7597e-01)
Epoch: [50][ 25/117] Loss 9.8901e-01 (9.7567e-01)
Epoch: [50][ 50/117] Loss 9.0322e-01 (9.7316e-01)
Epoch: [50][ 75/117] Loss 9.7094e-01 (9.8071e-01)
Epoch: [50][100/117] Loss 1.0125e+00 (9.8160e-01)
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Evaluate ...
Result of kNN evaluation is 87.68
Checkpoint ...
[33mEpoch 51/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38983
Train ...
Epoch: [51][ 0/117] Loss 1.0591e+00 (1.0591e+00)
Epoch: [51][ 25/117] Loss 8.9949e-01 (9.7589e-01)
Epoch: [51][ 50/117] Loss 9.5427e-01 (9.7344e-01)
Epoch: [51][ 75/117] Loss 1.0596e+00 (9.7671e-01)
Epoch: [51][100/117] Loss 9.2051e-01 (9.7309e-01)
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Evaluate ...
Result of kNN evaluation is 87.38
Checkpoint ...
[33mEpoch 52/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38943
Train ...
Epoch: [52][ 0/117] Loss 9.8881e-01 (9.8881e-01)
Epoch: [52][ 25/117] Loss 8.7250e-01 (9.5333e-01)
Epoch: [52][ 50/117] Loss 9.8094e-01 (9.4717e-01)
Epoch: [52][ 75/117] Loss 9.7607e-01 (9.5610e-01)
Epoch: [52][100/117] Loss 1.1185e+00 (9.5239e-01)
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Evaluate ...
Result of kNN evaluation is 87.65
Checkpoint ...
[33mEpoch 53/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38902
Train ...
Epoch: [53][ 0/117] Loss 9.9468e-01 (9.9468e-01)
Epoch: [53][ 25/117] Loss 9.9318e-01 (9.4018e-01)
Epoch: [53][ 50/117] Loss 9.0339e-01 (9.3888e-01)
Epoch: [53][ 75/117] Loss 1.1429e+00 (9.5002e-01)
Epoch: [53][100/117] Loss 1.0361e+00 (9.7978e-01)
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Evaluate ...
Result of kNN evaluation is 87.70
Checkpoint ...
[33mEpoch 54/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38861
Train ...
Epoch: [54][ 0/117] Loss 9.8342e-01 (9.8342e-01)
Epoch: [54][ 25/117] Loss 9.5742e-01 (9.6137e-01)
Epoch: [54][ 50/117] Loss 9.1217e-01 (9.7737e-01)
Epoch: [54][ 75/117] Loss 9.9156e-01 (9.7916e-01)
Epoch: [54][100/117] Loss 8.6520e-01 (9.7218e-01)
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Evaluate ...
Result of kNN evaluation is 87.95
Checkpoint ...
[33mEpoch 55/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38819
Train ...
Epoch: [55][ 0/117] Loss 9.5629e-01 (9.5629e-01)
Epoch: [55][ 25/117] Loss 8.6949e-01 (9.5252e-01)
Epoch: [55][ 50/117] Loss 8.8838e-01 (9.4159e-01)
Epoch: [55][ 75/117] Loss 9.8350e-01 (9.3018e-01)
Epoch: [55][100/117] Loss 8.8759e-01 (9.2840e-01)
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Evaluate ...
Result of kNN evaluation is 88.44
Checkpoint ...
[33mEpoch 56/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38776
Train ...
Epoch: [56][ 0/117] Loss 8.6989e-01 (8.6989e-01)
Epoch: [56][ 25/117] Loss 8.9841e-01 (9.0521e-01)
Epoch: [56][ 50/117] Loss 9.6137e-01 (9.0918e-01)
Epoch: [56][ 75/117] Loss 9.2987e-01 (9.2312e-01)
Epoch: [56][100/117] Loss 8.9141e-01 (9.2811e-01)
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Evaluate ...
Result of kNN evaluation is 87.81
Checkpoint ...
[33mEpoch 57/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38732
Train ...
Epoch: [57][ 0/117] Loss 9.7176e-01 (9.7176e-01)
Epoch: [57][ 25/117] Loss 9.2658e-01 (9.3644e-01)
Epoch: [57][ 50/117] Loss 8.0200e-01 (9.2306e-01)
Epoch: [57][ 75/117] Loss 9.0938e-01 (9.2023e-01)
Epoch: [57][100/117] Loss 8.8351e-01 (9.1529e-01)
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Evaluate ...
Result of kNN evaluation is 88.19
Checkpoint ...
[33mEpoch 58/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38688
Train ...
Epoch: [58][ 0/117] Loss 9.2384e-01 (9.2384e-01)
Epoch: [58][ 25/117] Loss 8.7559e-01 (9.1529e-01)
Epoch: [58][ 50/117] Loss 8.9451e-01 (9.1193e-01)
Epoch: [58][ 75/117] Loss 8.4602e-01 (9.0846e-01)