49 lines
2.0 KiB
Plaintext
49 lines
2.0 KiB
Plaintext
device: cuda
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batch_size: 64
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optimizer: Adam (
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Parameter Group 0
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amsgrad: False
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betas: (0.9, 0.999)
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capturable: False
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differentiable: False
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eps: 1e-08
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foreach: None
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fused: True
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lr: 0.0001
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maximize: False
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weight_decay: 0
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)
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loss_function: CrossEntropyLoss()
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augment_data: True
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model: Sequential(
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(0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)
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(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(2): ReLU()
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(3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)
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(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(5): ReLU()
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(6): MaxPool2d(kernel_size=3, stride=3, padding=1, dilation=1, ceil_mode=False)
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(7): Dropout2d(p=0.1, inplace=False)
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(8): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)
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(9): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(10): ReLU()
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(11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)
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(12): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(13): ReLU()
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(14): MaxPool2d(kernel_size=3, stride=3, padding=1, dilation=1, ceil_mode=False)
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(15): Dropout2d(p=0.1, inplace=False)
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(16): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)
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(17): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(18): ReLU()
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(19): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, bias=False)
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(20): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(21): ReLU()
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(22): MaxPool2d(kernel_size=3, stride=3, padding=1, dilation=1, ceil_mode=False)
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(23): Flatten(start_dim=1, end_dim=-1)
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(24): Dropout(p=0.25, inplace=False)
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(25): Linear(in_features=2048, out_features=1024, bias=True)
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(26): ReLU()
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(27): Linear(in_features=1024, out_features=512, bias=True)
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(28): ReLU()
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(29): Linear(in_features=512, out_features=20, bias=False)
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) |