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mnist-noflip.out
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[31m{'setup': 'simclr', 'backbone': 'resnet18', 'model_kwargs': {'head': 'mlp', 'features_dim': 128}, 'train_db_name': 'mnist', 'val_db_name': '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': [0.1307, 0.1307, 0.1307], 'std': [0.3081, 0.3081, 0.3081]}}, 'transformation_kwargs': {'crop_size': 28, 'normalize': {'mean': [0.1307, 0.1307, 0.1307], 'std': [0.3081, 0.3081, 0.3081]}}, 'pretext_dir': 'output/mnist/pretext', 'pretext_checkpoint': 'output/mnist/pretext/checkpoint.pth.tar', 'pretext_model': 'output/mnist/pretext/model.pth.tar', 'topk_neighbors_train_path': 'output/mnist/pretext/topk-train-neighbors.npy', 'topk_neighbors_val_path': 'output/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)
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=[0.1307, 0.1307, 0.1307], std=[0.3081, 0.3081, 0.3081])
)
Validation transforms: Compose(
CenterCrop(size=(28, 28))
ToTensor()
Normalize(mean=[0.1307, 0.1307, 0.1307], std=[0.3081, 0.3081, 0.3081])
)
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/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 6.9006e+00 (6.9006e+00)
Epoch: [0][ 25/117] Loss 6.9279e+00 (6.9159e+00)
Epoch: [0][ 50/117] Loss 6.7099e+00 (6.8978e+00)
Epoch: [0][ 75/117] Loss 6.3435e+00 (6.7741e+00)
Epoch: [0][100/117] Loss 6.1098e+00 (6.6303e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 57.11
Checkpoint ...
[33mEpoch 1/500[0m
[33m---------------[0m
Adjusted learning rate to 0.40000
Train ...
Epoch: [1][ 0/117] Loss 5.9355e+00 (5.9355e+00)
Epoch: [1][ 25/117] Loss 5.9060e+00 (5.9934e+00)
Epoch: [1][ 50/117] Loss 5.7552e+00 (5.9393e+00)
Epoch: [1][ 75/117] Loss 5.3939e+00 (5.8100e+00)
Epoch: [1][100/117] Loss 4.9925e+00 (5.6417e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 73.22
Checkpoint ...
[33mEpoch 2/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39998
Train ...
Epoch: [2][ 0/117] Loss 4.8075e+00 (4.8075e+00)
Epoch: [2][ 25/117] Loss 4.5195e+00 (4.5390e+00)
Epoch: [2][ 50/117] Loss 4.4508e+00 (4.4811e+00)
Epoch: [2][ 75/117] Loss 4.2317e+00 (4.4286e+00)
Epoch: [2][100/117] Loss 4.2716e+00 (4.3812e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 74.58
Checkpoint ...
[33mEpoch 3/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39996
Train ...
Epoch: [3][ 0/117] Loss 4.1360e+00 (4.1360e+00)
Epoch: [3][ 25/117] Loss 4.1915e+00 (4.1041e+00)
Epoch: [3][ 50/117] Loss 3.8199e+00 (3.9936e+00)
Epoch: [3][ 75/117] Loss 3.6682e+00 (3.9079e+00)
Epoch: [3][100/117] Loss 3.5083e+00 (3.8431e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 84.81
Checkpoint ...
[33mEpoch 4/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39994
Train ...
Epoch: [4][ 0/117] Loss 3.7722e+00 (3.7722e+00)
Epoch: [4][ 25/117] Loss 3.4387e+00 (3.5840e+00)
Epoch: [4][ 50/117] Loss 3.5774e+00 (3.5650e+00)
Epoch: [4][ 75/117] Loss 3.5582e+00 (3.5497e+00)
Epoch: [4][100/117] Loss 3.5220e+00 (3.5327e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 86.22
Checkpoint ...
[33mEpoch 5/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39990
Train ...
Epoch: [5][ 0/117] Loss 3.5128e+00 (3.5128e+00)
Epoch: [5][ 25/117] Loss 3.5264e+00 (3.4716e+00)
Epoch: [5][ 50/117] Loss 3.3643e+00 (3.4513e+00)
Epoch: [5][ 75/117] Loss 3.1375e+00 (3.4070e+00)
Epoch: [5][100/117] Loss 3.2267e+00 (3.3625e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 89.56
Checkpoint ...
[33mEpoch 6/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39986
Train ...
Epoch: [6][ 0/117] Loss 3.1478e+00 (3.1478e+00)
Epoch: [6][ 25/117] Loss 3.1505e+00 (3.1124e+00)
Epoch: [6][ 50/117] Loss 2.9990e+00 (3.0918e+00)
Epoch: [6][ 75/117] Loss 3.0381e+00 (3.0799e+00)
Epoch: [6][100/117] Loss 2.9502e+00 (3.0671e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 89.20
Checkpoint ...
[33mEpoch 7/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39981
Train ...
Epoch: [7][ 0/117] Loss 3.0325e+00 (3.0325e+00)
Epoch: [7][ 25/117] Loss 3.0122e+00 (3.0134e+00)
Epoch: [7][ 50/117] Loss 2.9034e+00 (2.9885e+00)
Epoch: [7][ 75/117] Loss 2.8745e+00 (2.9693e+00)
Epoch: [7][100/117] Loss 2.7915e+00 (2.9413e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 91.53
Checkpoint ...
[33mEpoch 8/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39975
Train ...
Epoch: [8][ 0/117] Loss 2.8437e+00 (2.8437e+00)
Epoch: [8][ 25/117] Loss 2.6270e+00 (2.7190e+00)
Epoch: [8][ 50/117] Loss 2.4887e+00 (2.6817e+00)
Epoch: [8][ 75/117] Loss 2.4893e+00 (2.6384e+00)
Epoch: [8][100/117] Loss 2.4467e+00 (2.5989e+00)
Fill memory bank for kNN...
Fill Memory Bank [0/118]
Fill Memory Bank [100/118]
Evaluate ...
Result of kNN evaluation is 90.95
Checkpoint ...
[33mEpoch 9/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39968
Train ...
Epoch: [9][ 0/117] Loss 2.3563e+00 (2.3563e+00)
Epoch: [9][ 25/117] Loss 2.1831e+00 (2.2847e+00)
Epoch: [9][ 50/117] Loss 2.1908e+00 (2.2518e+00)
Epoch: [9][ 75/117] Loss 2.0188e+00 (2.2019e+00)
Epoch: [9][100/117] Loss 1.9432e+00 (2.1559e+00)
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Evaluate ...
Result of kNN evaluation is 92.25
Checkpoint ...
[33mEpoch 10/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39961
Train ...
Epoch: [10][ 0/117] Loss 2.0347e+00 (2.0347e+00)
Epoch: [10][ 25/117] Loss 1.7437e+00 (1.8771e+00)
Epoch: [10][ 50/117] Loss 1.6547e+00 (1.8472e+00)
Epoch: [10][ 75/117] Loss 1.7380e+00 (1.8272e+00)
Epoch: [10][100/117] Loss 1.7089e+00 (1.7995e+00)
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Evaluate ...
Result of kNN evaluation is 93.85
Checkpoint ...
[33mEpoch 11/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39952
Train ...
Epoch: [11][ 0/117] Loss 1.5564e+00 (1.5564e+00)
Epoch: [11][ 25/117] Loss 1.6307e+00 (1.6097e+00)
Epoch: [11][ 50/117] Loss 1.5199e+00 (1.5950e+00)
Epoch: [11][ 75/117] Loss 1.4110e+00 (1.5844e+00)
Epoch: [11][100/117] Loss 1.4485e+00 (1.5717e+00)
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Evaluate ...
Result of kNN evaluation is 94.43
Checkpoint ...
[33mEpoch 12/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39943
Train ...
Epoch: [12][ 0/117] Loss 1.5057e+00 (1.5057e+00)
Epoch: [12][ 25/117] Loss 1.4122e+00 (1.4666e+00)
Epoch: [12][ 50/117] Loss 1.4365e+00 (1.4686e+00)
Epoch: [12][ 75/117] Loss 1.4523e+00 (1.4537e+00)
Epoch: [12][100/117] Loss 1.3629e+00 (1.4501e+00)
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Evaluate ...
Result of kNN evaluation is 94.16
Checkpoint ...
[33mEpoch 13/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39933
Train ...
Epoch: [13][ 0/117] Loss 1.3563e+00 (1.3563e+00)
Epoch: [13][ 25/117] Loss 1.3544e+00 (1.3552e+00)
Epoch: [13][ 50/117] Loss 1.2552e+00 (1.3539e+00)
Epoch: [13][ 75/117] Loss 1.2636e+00 (1.3449e+00)
Epoch: [13][100/117] Loss 1.3827e+00 (1.3387e+00)
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Evaluate ...
Result of kNN evaluation is 95.06
Checkpoint ...
[33mEpoch 14/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39923
Train ...
Epoch: [14][ 0/117] Loss 1.3256e+00 (1.3256e+00)
Epoch: [14][ 25/117] Loss 1.2442e+00 (1.2800e+00)
Epoch: [14][ 50/117] Loss 1.2231e+00 (1.2782e+00)
Epoch: [14][ 75/117] Loss 1.3025e+00 (1.2673e+00)
Epoch: [14][100/117] Loss 1.2023e+00 (1.2666e+00)
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Evaluate ...
Result of kNN evaluation is 95.53
Checkpoint ...
[33mEpoch 15/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39911
Train ...
Epoch: [15][ 0/117] Loss 1.0640e+00 (1.0640e+00)
Epoch: [15][ 25/117] Loss 1.2833e+00 (1.1800e+00)
Epoch: [15][ 50/117] Loss 1.3135e+00 (1.1793e+00)
Epoch: [15][ 75/117] Loss 1.1800e+00 (1.1847e+00)
Epoch: [15][100/117] Loss 1.1644e+00 (1.1795e+00)
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Evaluate ...
Result of kNN evaluation is 95.14
Checkpoint ...
[33mEpoch 16/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39899
Train ...
Epoch: [16][ 0/117] Loss 1.2014e+00 (1.2014e+00)
Epoch: [16][ 25/117] Loss 1.1783e+00 (1.1399e+00)
Epoch: [16][ 50/117] Loss 1.0953e+00 (1.1322e+00)
Epoch: [16][ 75/117] Loss 1.1601e+00 (1.1299e+00)
Epoch: [16][100/117] Loss 1.1690e+00 (1.1257e+00)
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Evaluate ...
Result of kNN evaluation is 95.33
Checkpoint ...
[33mEpoch 17/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39886
Train ...
Epoch: [17][ 0/117] Loss 1.1618e+00 (1.1618e+00)
Epoch: [17][ 25/117] Loss 1.1181e+00 (1.0949e+00)
Epoch: [17][ 50/117] Loss 1.1558e+00 (1.0947e+00)
Epoch: [17][ 75/117] Loss 1.1195e+00 (1.1025e+00)
Epoch: [17][100/117] Loss 1.0988e+00 (1.0919e+00)
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Evaluate ...
Result of kNN evaluation is 95.94
Checkpoint ...
[33mEpoch 18/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39872
Train ...
Epoch: [18][ 0/117] Loss 9.9947e-01 (9.9947e-01)
Epoch: [18][ 25/117] Loss 1.0421e+00 (1.0624e+00)
Epoch: [18][ 50/117] Loss 1.0284e+00 (1.0600e+00)
Epoch: [18][ 75/117] Loss 1.1715e+00 (1.0636e+00)
Epoch: [18][100/117] Loss 1.1549e+00 (1.0624e+00)
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Evaluate ...
Result of kNN evaluation is 95.67
Checkpoint ...
[33mEpoch 19/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39858
Train ...
Epoch: [19][ 0/117] Loss 1.1328e+00 (1.1328e+00)
Epoch: [19][ 25/117] Loss 1.1017e+00 (1.0314e+00)
Epoch: [19][ 50/117] Loss 9.3516e-01 (1.0379e+00)
Epoch: [19][ 75/117] Loss 9.7611e-01 (1.0320e+00)
Epoch: [19][100/117] Loss 9.9282e-01 (1.0248e+00)
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Evaluate ...
Result of kNN evaluation is 95.86
Checkpoint ...
[33mEpoch 20/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39842
Train ...
Epoch: [20][ 0/117] Loss 9.2901e-01 (9.2901e-01)
Epoch: [20][ 25/117] Loss 1.0883e+00 (1.0020e+00)
Epoch: [20][ 50/117] Loss 1.0745e+00 (1.0041e+00)
Epoch: [20][ 75/117] Loss 9.4426e-01 (9.9602e-01)
Epoch: [20][100/117] Loss 9.3827e-01 (9.9221e-01)
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Evaluate ...
Result of kNN evaluation is 95.94
Checkpoint ...
[33mEpoch 21/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39826
Train ...
Epoch: [21][ 0/117] Loss 9.7225e-01 (9.7225e-01)
Epoch: [21][ 25/117] Loss 9.8958e-01 (9.7423e-01)
Epoch: [21][ 50/117] Loss 9.0995e-01 (9.7277e-01)
Epoch: [21][ 75/117] Loss 9.8195e-01 (9.7398e-01)
Epoch: [21][100/117] Loss 9.6360e-01 (9.7272e-01)
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Evaluate ...
Result of kNN evaluation is 95.51
Checkpoint ...
[33mEpoch 22/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39809
Train ...
Epoch: [22][ 0/117] Loss 1.0231e+00 (1.0231e+00)
Epoch: [22][ 25/117] Loss 9.7114e-01 (9.8756e-01)
Epoch: [22][ 50/117] Loss 9.8904e-01 (9.7553e-01)
Epoch: [22][ 75/117] Loss 9.4803e-01 (9.6913e-01)
Epoch: [22][100/117] Loss 9.7154e-01 (9.6840e-01)
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Evaluate ...
Result of kNN evaluation is 96.40
Checkpoint ...
[33mEpoch 23/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39792
Train ...
Epoch: [23][ 0/117] Loss 9.4684e-01 (9.4684e-01)
Epoch: [23][ 25/117] Loss 9.6726e-01 (9.2899e-01)
Epoch: [23][ 50/117] Loss 9.6947e-01 (9.3696e-01)
Epoch: [23][ 75/117] Loss 9.5741e-01 (9.3893e-01)
Epoch: [23][100/117] Loss 1.0166e+00 (9.4077e-01)
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Evaluate ...
Result of kNN evaluation is 96.32
Checkpoint ...
[33mEpoch 24/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39773
Train ...
Epoch: [24][ 0/117] Loss 8.9679e-01 (8.9679e-01)
Epoch: [24][ 25/117] Loss 9.6779e-01 (9.4795e-01)
Epoch: [24][ 50/117] Loss 9.5631e-01 (9.4167e-01)
Epoch: [24][ 75/117] Loss 9.7638e-01 (9.4271e-01)
Epoch: [24][100/117] Loss 9.4813e-01 (9.3864e-01)
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Evaluate ...
Result of kNN evaluation is 96.29
Checkpoint ...
[33mEpoch 25/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39754
Train ...
Epoch: [25][ 0/117] Loss 8.9551e-01 (8.9551e-01)
Epoch: [25][ 25/117] Loss 8.7882e-01 (9.2925e-01)
Epoch: [25][ 50/117] Loss 8.8892e-01 (9.1966e-01)
Epoch: [25][ 75/117] Loss 9.4354e-01 (9.1014e-01)
Epoch: [25][100/117] Loss 9.6906e-01 (9.1087e-01)
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Evaluate ...
Result of kNN evaluation is 95.96
Checkpoint ...
[33mEpoch 26/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39734
Train ...
Epoch: [26][ 0/117] Loss 8.8461e-01 (8.8461e-01)
Epoch: [26][ 25/117] Loss 1.0512e+00 (8.8111e-01)
Epoch: [26][ 50/117] Loss 9.5897e-01 (8.8481e-01)
Epoch: [26][ 75/117] Loss 8.5334e-01 (8.9038e-01)
Epoch: [26][100/117] Loss 9.2690e-01 (8.9093e-01)
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Evaluate ...
Result of kNN evaluation is 96.15
Checkpoint ...
[33mEpoch 27/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39713
Train ...
Epoch: [27][ 0/117] Loss 9.2777e-01 (9.2777e-01)
Epoch: [27][ 25/117] Loss 9.5401e-01 (9.0511e-01)
Epoch: [27][ 50/117] Loss 8.4910e-01 (8.9939e-01)
Epoch: [27][ 75/117] Loss 8.5492e-01 (8.9797e-01)
Epoch: [27][100/117] Loss 8.6122e-01 (8.9449e-01)
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Evaluate ...
Result of kNN evaluation is 96.79
Checkpoint ...
[33mEpoch 28/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39692
Train ...
Epoch: [28][ 0/117] Loss 7.9714e-01 (7.9714e-01)
Epoch: [28][ 25/117] Loss 7.7126e-01 (8.6540e-01)
Epoch: [28][ 50/117] Loss 1.0427e+00 (8.7294e-01)
Epoch: [28][ 75/117] Loss 8.4560e-01 (8.7222e-01)
Epoch: [28][100/117] Loss 9.1870e-01 (8.7121e-01)
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Evaluate ...
Result of kNN evaluation is 96.15
Checkpoint ...
[33mEpoch 29/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39669
Train ...
Epoch: [29][ 0/117] Loss 7.8617e-01 (7.8617e-01)
Epoch: [29][ 25/117] Loss 8.1269e-01 (8.6294e-01)
Epoch: [29][ 50/117] Loss 8.3540e-01 (8.7408e-01)
Epoch: [29][ 75/117] Loss 8.2476e-01 (8.7533e-01)
Epoch: [29][100/117] Loss 8.4987e-01 (8.7882e-01)
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Evaluate ...
Result of kNN evaluation is 96.08
Checkpoint ...
[33mEpoch 30/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39646
Train ...
Epoch: [30][ 0/117] Loss 8.6023e-01 (8.6023e-01)
Epoch: [30][ 25/117] Loss 8.5920e-01 (8.7126e-01)
Epoch: [30][ 50/117] Loss 9.0477e-01 (8.6501e-01)
Epoch: [30][ 75/117] Loss 8.9214e-01 (8.6359e-01)
Epoch: [30][100/117] Loss 8.5462e-01 (8.5620e-01)
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Evaluate ...
Result of kNN evaluation is 96.33
Checkpoint ...
[33mEpoch 31/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39622
Train ...
Epoch: [31][ 0/117] Loss 8.1399e-01 (8.1399e-01)
Epoch: [31][ 25/117] Loss 8.3112e-01 (8.6200e-01)
Epoch: [31][ 50/117] Loss 7.7604e-01 (8.5011e-01)
Epoch: [31][ 75/117] Loss 7.5734e-01 (8.4564e-01)
Epoch: [31][100/117] Loss 8.0066e-01 (8.4436e-01)
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Evaluate ...
Result of kNN evaluation is 96.57
Checkpoint ...
[33mEpoch 32/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39598
Train ...
Epoch: [32][ 0/117] Loss 9.3122e-01 (9.3122e-01)
Epoch: [32][ 25/117] Loss 8.3759e-01 (8.3952e-01)
Epoch: [32][ 50/117] Loss 9.4368e-01 (8.5115e-01)
Epoch: [32][ 75/117] Loss 8.5400e-01 (8.4978e-01)
Epoch: [32][100/117] Loss 8.2690e-01 (8.5233e-01)
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Evaluate ...
Result of kNN evaluation is 96.56
Checkpoint ...
[33mEpoch 33/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39572
Train ...
Epoch: [33][ 0/117] Loss 7.6655e-01 (7.6655e-01)
Epoch: [33][ 25/117] Loss 8.2902e-01 (8.4793e-01)
Epoch: [33][ 50/117] Loss 8.5009e-01 (8.4922e-01)
Epoch: [33][ 75/117] Loss 8.2938e-01 (8.4800e-01)
Epoch: [33][100/117] Loss 8.0760e-01 (8.4923e-01)
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Evaluate ...
Result of kNN evaluation is 96.37
Checkpoint ...
[33mEpoch 34/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39546
Train ...
Epoch: [34][ 0/117] Loss 9.1653e-01 (9.1653e-01)
Epoch: [34][ 25/117] Loss 8.2351e-01 (8.1799e-01)
Epoch: [34][ 50/117] Loss 8.6919e-01 (8.1752e-01)
Epoch: [34][ 75/117] Loss 8.1297e-01 (8.1945e-01)
Epoch: [34][100/117] Loss 8.0417e-01 (8.1790e-01)
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Evaluate ...
Result of kNN evaluation is 96.19
Checkpoint ...
[33mEpoch 35/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39519
Train ...
Epoch: [35][ 0/117] Loss 7.3632e-01 (7.3632e-01)
Epoch: [35][ 25/117] Loss 8.8220e-01 (8.2039e-01)
Epoch: [35][ 50/117] Loss 7.6505e-01 (8.0703e-01)
Epoch: [35][ 75/117] Loss 7.8639e-01 (8.1314e-01)
Epoch: [35][100/117] Loss 8.0632e-01 (8.1508e-01)
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Evaluate ...
Result of kNN evaluation is 96.72
Checkpoint ...
[33mEpoch 36/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39491
Train ...
Epoch: [36][ 0/117] Loss 8.2013e-01 (8.2013e-01)
Epoch: [36][ 25/117] Loss 8.4173e-01 (8.1664e-01)
Epoch: [36][ 50/117] Loss 8.6299e-01 (8.1428e-01)
Epoch: [36][ 75/117] Loss 7.9409e-01 (8.1404e-01)
Epoch: [36][100/117] Loss 8.8037e-01 (8.1961e-01)
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Evaluate ...
Result of kNN evaluation is 96.47
Checkpoint ...
[33mEpoch 37/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39463
Train ...
Epoch: [37][ 0/117] Loss 8.4398e-01 (8.4398e-01)
Epoch: [37][ 25/117] Loss 7.7773e-01 (8.2687e-01)
Epoch: [37][ 50/117] Loss 7.8770e-01 (8.2537e-01)
Epoch: [37][ 75/117] Loss 8.1165e-01 (8.2163e-01)
Epoch: [37][100/117] Loss 8.1668e-01 (8.1855e-01)
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Evaluate ...
Result of kNN evaluation is 96.21
Checkpoint ...
[33mEpoch 38/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39433
Train ...
Epoch: [38][ 0/117] Loss 9.1408e-01 (9.1408e-01)
Epoch: [38][ 25/117] Loss 7.5298e-01 (8.0255e-01)
Epoch: [38][ 50/117] Loss 8.6948e-01 (7.9849e-01)
Epoch: [38][ 75/117] Loss 7.7539e-01 (7.9723e-01)
Epoch: [38][100/117] Loss 7.6757e-01 (7.9751e-01)
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Evaluate ...
Result of kNN evaluation is 96.61
Checkpoint ...
[33mEpoch 39/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39403
Train ...
Epoch: [39][ 0/117] Loss 8.4928e-01 (8.4928e-01)
Epoch: [39][ 25/117] Loss 8.1205e-01 (8.1542e-01)
Epoch: [39][ 50/117] Loss 8.5199e-01 (7.9711e-01)
Epoch: [39][ 75/117] Loss 8.4492e-01 (7.9163e-01)
Epoch: [39][100/117] Loss 7.1859e-01 (7.8629e-01)
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Evaluate ...
Result of kNN evaluation is 96.63
Checkpoint ...
[33mEpoch 40/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39372
Train ...
Epoch: [40][ 0/117] Loss 8.2542e-01 (8.2542e-01)
Epoch: [40][ 25/117] Loss 8.0536e-01 (7.9645e-01)
Epoch: [40][ 50/117] Loss 7.7571e-01 (8.0536e-01)
Epoch: [40][ 75/117] Loss 8.6780e-01 (8.0029e-01)
Epoch: [40][100/117] Loss 7.7153e-01 (8.0281e-01)
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Evaluate ...
Result of kNN evaluation is 96.60
Checkpoint ...
[33mEpoch 41/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39341
Train ...
Epoch: [41][ 0/117] Loss 7.6340e-01 (7.6340e-01)
Epoch: [41][ 25/117] Loss 7.7205e-01 (7.8503e-01)
Epoch: [41][ 50/117] Loss 6.9444e-01 (7.8319e-01)
Epoch: [41][ 75/117] Loss 7.2327e-01 (7.8493e-01)
Epoch: [41][100/117] Loss 7.8820e-01 (7.8795e-01)
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Evaluate ...
Result of kNN evaluation is 96.92
Checkpoint ...
[33mEpoch 42/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39308
Train ...
Epoch: [42][ 0/117] Loss 7.7878e-01 (7.7878e-01)
Epoch: [42][ 25/117] Loss 7.5665e-01 (7.8230e-01)
Epoch: [42][ 50/117] Loss 7.7822e-01 (7.7801e-01)
Epoch: [42][ 75/117] Loss 8.1117e-01 (7.7794e-01)
Epoch: [42][100/117] Loss 7.9221e-01 (7.8065e-01)
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Evaluate ...
Result of kNN evaluation is 96.80
Checkpoint ...
[33mEpoch 43/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39275
Train ...
Epoch: [43][ 0/117] Loss 7.6613e-01 (7.6613e-01)
Epoch: [43][ 25/117] Loss 7.5392e-01 (7.7337e-01)
Epoch: [43][ 50/117] Loss 7.6036e-01 (7.8222e-01)
Epoch: [43][ 75/117] Loss 7.1262e-01 (7.8716e-01)
Epoch: [43][100/117] Loss 7.9778e-01 (7.8107e-01)
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Evaluate ...
Result of kNN evaluation is 96.34
Checkpoint ...
[33mEpoch 44/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39241
Train ...
Epoch: [44][ 0/117] Loss 7.5970e-01 (7.5970e-01)
Epoch: [44][ 25/117] Loss 7.1174e-01 (7.6421e-01)
Epoch: [44][ 50/117] Loss 6.5534e-01 (7.6907e-01)
Epoch: [44][ 75/117] Loss 7.5262e-01 (7.7120e-01)
Epoch: [44][100/117] Loss 7.7244e-01 (7.7166e-01)
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Evaluate ...
Result of kNN evaluation is 96.47
Checkpoint ...
[33mEpoch 45/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39207
Train ...
Epoch: [45][ 0/117] Loss 7.2165e-01 (7.2165e-01)
Epoch: [45][ 25/117] Loss 7.2833e-01 (7.5161e-01)
Epoch: [45][ 50/117] Loss 8.1750e-01 (7.6327e-01)
Epoch: [45][ 75/117] Loss 7.8508e-01 (7.6276e-01)
Epoch: [45][100/117] Loss 8.1756e-01 (7.6426e-01)
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Evaluate ...
Result of kNN evaluation is 96.88
Checkpoint ...
[33mEpoch 46/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39171
Train ...
Epoch: [46][ 0/117] Loss 7.8876e-01 (7.8876e-01)
Epoch: [46][ 25/117] Loss 7.4481e-01 (7.6198e-01)
Epoch: [46][ 50/117] Loss 7.0070e-01 (7.5768e-01)
Epoch: [46][ 75/117] Loss 7.6006e-01 (7.5750e-01)
Epoch: [46][100/117] Loss 7.9628e-01 (7.6182e-01)
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Evaluate ...
Result of kNN evaluation is 96.72
Checkpoint ...
[33mEpoch 47/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39135
Train ...
Epoch: [47][ 0/117] Loss 7.7789e-01 (7.7789e-01)
Epoch: [47][ 25/117] Loss 7.9632e-01 (7.6092e-01)
Epoch: [47][ 50/117] Loss 7.8120e-01 (7.6426e-01)
Epoch: [47][ 75/117] Loss 7.2157e-01 (7.5986e-01)
Epoch: [47][100/117] Loss 7.2637e-01 (7.5910e-01)
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Evaluate ...
Result of kNN evaluation is 96.78
Checkpoint ...
[33mEpoch 48/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39098
Train ...
Epoch: [48][ 0/117] Loss 6.9282e-01 (6.9282e-01)
Epoch: [48][ 25/117] Loss 7.5410e-01 (7.5140e-01)
Epoch: [48][ 50/117] Loss 7.4090e-01 (7.5185e-01)
Epoch: [48][ 75/117] Loss 7.3242e-01 (7.4758e-01)
Epoch: [48][100/117] Loss 8.4008e-01 (7.4864e-01)
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Evaluate ...
Result of kNN evaluation is 96.71
Checkpoint ...
[33mEpoch 49/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39061
Train ...
Epoch: [49][ 0/117] Loss 8.1096e-01 (8.1096e-01)
Epoch: [49][ 25/117] Loss 7.3586e-01 (7.6407e-01)
Epoch: [49][ 50/117] Loss 7.6235e-01 (7.5835e-01)
Epoch: [49][ 75/117] Loss 7.4253e-01 (7.5453e-01)
Epoch: [49][100/117] Loss 7.2304e-01 (7.5172e-01)
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Evaluate ...
Result of kNN evaluation is 96.55
Checkpoint ...
[33mEpoch 50/500[0m
[33m---------------[0m
Adjusted learning rate to 0.39022
Train ...
Epoch: [50][ 0/117] Loss 7.7565e-01 (7.7565e-01)
Epoch: [50][ 25/117] Loss 7.3961e-01 (7.6104e-01)
Epoch: [50][ 50/117] Loss 6.9143e-01 (7.4969e-01)
Epoch: [50][ 75/117] Loss 7.4518e-01 (7.5071e-01)
Epoch: [50][100/117] Loss 8.2254e-01 (7.5110e-01)
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Evaluate ...
Result of kNN evaluation is 96.40
Checkpoint ...
[33mEpoch 51/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38983
Train ...
Epoch: [51][ 0/117] Loss 7.6269e-01 (7.6269e-01)
Epoch: [51][ 25/117] Loss 7.9121e-01 (7.3957e-01)
Epoch: [51][ 50/117] Loss 7.5980e-01 (7.4273e-01)
Epoch: [51][ 75/117] Loss 7.8359e-01 (7.4622e-01)
Epoch: [51][100/117] Loss 8.3567e-01 (7.4920e-01)
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Evaluate ...
Result of kNN evaluation is 96.38
Checkpoint ...
[33mEpoch 52/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38943
Train ...
Epoch: [52][ 0/117] Loss 6.8212e-01 (6.8212e-01)
Epoch: [52][ 25/117] Loss 7.7179e-01 (7.4861e-01)
Epoch: [52][ 50/117] Loss 6.7476e-01 (7.4266e-01)
Epoch: [52][ 75/117] Loss 7.6370e-01 (7.4536e-01)
Epoch: [52][100/117] Loss 7.8362e-01 (7.5132e-01)
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Evaluate ...
Result of kNN evaluation is 96.62
Checkpoint ...
[33mEpoch 53/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38902
Train ...
Epoch: [53][ 0/117] Loss 7.1548e-01 (7.1548e-01)
Epoch: [53][ 25/117] Loss 7.2567e-01 (7.2465e-01)
Epoch: [53][ 50/117] Loss 7.1510e-01 (7.3180e-01)
Epoch: [53][ 75/117] Loss 7.9681e-01 (7.3555e-01)
Epoch: [53][100/117] Loss 8.0871e-01 (7.3622e-01)
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Evaluate ...
Result of kNN evaluation is 96.49
Checkpoint ...
[33mEpoch 54/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38861
Train ...
Epoch: [54][ 0/117] Loss 8.3415e-01 (8.3415e-01)
Epoch: [54][ 25/117] Loss 6.4377e-01 (7.4384e-01)
Epoch: [54][ 50/117] Loss 7.9844e-01 (7.4406e-01)
Epoch: [54][ 75/117] Loss 7.9305e-01 (7.4180e-01)
Epoch: [54][100/117] Loss 6.8592e-01 (7.4384e-01)
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Evaluate ...
Result of kNN evaluation is 96.65
Checkpoint ...
[33mEpoch 55/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38819
Train ...
Epoch: [55][ 0/117] Loss 8.3049e-01 (8.3049e-01)
Epoch: [55][ 25/117] Loss 6.6244e-01 (7.1474e-01)
Epoch: [55][ 50/117] Loss 8.2808e-01 (7.1954e-01)
Epoch: [55][ 75/117] Loss 6.6636e-01 (7.2479e-01)
Epoch: [55][100/117] Loss 7.7296e-01 (7.2908e-01)
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Evaluate ...
Result of kNN evaluation is 96.85
Checkpoint ...
[33mEpoch 56/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38776
Train ...
Epoch: [56][ 0/117] Loss 6.9498e-01 (6.9498e-01)
Epoch: [56][ 25/117] Loss 7.1568e-01 (7.2442e-01)
Epoch: [56][ 50/117] Loss 6.6175e-01 (7.3492e-01)
Epoch: [56][ 75/117] Loss 6.7727e-01 (7.3234e-01)
Epoch: [56][100/117] Loss 7.1493e-01 (7.3635e-01)
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Evaluate ...
Result of kNN evaluation is 97.04
Checkpoint ...
[33mEpoch 57/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38732
Train ...
Epoch: [57][ 0/117] Loss 7.7207e-01 (7.7207e-01)
Epoch: [57][ 25/117] Loss 6.5431e-01 (7.0371e-01)
Epoch: [57][ 50/117] Loss 7.5779e-01 (7.1241e-01)
Epoch: [57][ 75/117] Loss 7.0316e-01 (7.1687e-01)
Epoch: [57][100/117] Loss 5.9777e-01 (7.1236e-01)
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Evaluate ...
Result of kNN evaluation is 96.99
Checkpoint ...
[33mEpoch 58/500[0m
[33m---------------[0m
Adjusted learning rate to 0.38688
Train ...
Epoch: [58][ 0/117] Loss 7.5874e-01 (7.5874e-01)
Epoch: [58][ 25/117] Loss 7.2626e-01 (7.1294e-01)
Epoch: [58][ 50/117] Loss 7.5743e-01 (7.1599e-01)
Epoch: [58][ 75/117] Loss 6.7786e-01 (7.2106e-01)
Epoch: [58][100/117] Loss 7.6421e-01 (7.2625e-01)