64 lines
1.8 KiB
Plaintext
64 lines
1.8 KiB
Plaintext
Train #: 60000; Test #: 10000
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Labels: tensor([5, 0, 4, 1, 9, 2, 1, 3]); Batch shape: torch.Size([8, 1, 28, 28])
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LeNet(
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(dnn_model): Sequential(
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(0): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1))
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(1): ReLU()
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(2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1))
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(3): ReLU()
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(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
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(5): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
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(6): ReLU()
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(7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
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(8): Flatten(start_dim=1, end_dim=-1)
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(9): Linear(in_features=576, out_features=256, bias=True)
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(10): ReLU()
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(11): Linear(in_features=256, out_features=10, bias=True)
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)
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)
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Epoch 1
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-------------------------------
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loss: 2.299683 [ 0/60000]
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loss: 0.348185 [25600/60000]
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loss: 0.239826 [51200/60000]
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Test Error:
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Accuracy: 95.7%, Avg loss: 0.133152
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Epoch 2
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-------------------------------
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loss: 0.220472 [ 0/60000]
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loss: 0.056995 [25600/60000]
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loss: 0.078447 [51200/60000]
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Test Error:
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Accuracy: 90.3%, Avg loss: 0.284392
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Epoch 3
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-------------------------------
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loss: 0.255085 [ 0/60000]
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loss: 0.071809 [25600/60000]
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loss: 0.080565 [51200/60000]
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Test Error:
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Accuracy: 98.2%, Avg loss: 0.056539
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Epoch 4
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-------------------------------
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loss: 0.074963 [ 0/60000]
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loss: 0.053749 [25600/60000]
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loss: 0.042180 [51200/60000]
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Test Error:
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Accuracy: 98.3%, Avg loss: 0.048151
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Epoch 5
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-------------------------------
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loss: 0.018707 [ 0/60000]
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loss: 0.047416 [25600/60000]
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loss: 0.022978 [51200/60000]
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Test Error:
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Accuracy: 98.6%, Avg loss: 0.041758
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Done!
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Labels : tensor([5, 0, 4, 1, 9, 2, 1, 3])
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Prediction: tensor([5, 0, 4, 1, 9, 2, 1, 3])
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Accuracy: 98.60
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loaded image shape: torch.Size([1, 1, 28, 28])
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Predicted: "0", Actual: "6" |