56 lines
1.4 KiB
Plaintext
56 lines
1.4 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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MLP(
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(flatten): Flatten(start_dim=1, end_dim=-1)
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(dnn_model): Sequential(
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(0): Linear(in_features=784, out_features=256, bias=True)
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(1): ReLU()
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(2): 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: 2.308738 [ 0/60000]
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loss: 0.643267 [25600/60000]
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loss: 0.384817 [51200/60000]
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Test Error:
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Accuracy: 90.0%, Avg loss: 0.368403
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Epoch 2
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-------------------------------
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loss: 0.323617 [ 0/60000]
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loss: 0.300336 [25600/60000]
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loss: 0.268706 [51200/60000]
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Test Error:
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Accuracy: 91.6%, Avg loss: 0.301624
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Epoch 3
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-------------------------------
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loss: 0.314411 [ 0/60000]
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loss: 0.337723 [25600/60000]
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loss: 0.234745 [51200/60000]
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Test Error:
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Accuracy: 92.4%, Avg loss: 0.267629
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Epoch 4
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-------------------------------
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loss: 0.258774 [ 0/60000]
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loss: 0.222343 [25600/60000]
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loss: 0.363658 [51200/60000]
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Test Error:
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Accuracy: 92.9%, Avg loss: 0.245184
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Epoch 5
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-------------------------------
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loss: 0.365407 [ 0/60000]
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loss: 0.286917 [25600/60000]
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loss: 0.213272 [51200/60000]
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Test Error:
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Accuracy: 93.9%, Avg loss: 0.222085
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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: 93.87
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loaded image shape: torch.Size([1, 1, 28, 28])
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Predicted: "3", Actual: "6" |