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[Bug fix] 401_CNN.py

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1. fix typo
2. remove .squeeze for 1-D tensor
This commit is contained in:
keineahnung2345
2018-11-09 17:26:27 +08:00
committed by GitHub
parent 0f4219c72c
commit a9ef65ed9a
1 changed files with 3 additions and 3 deletions
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tutorial-contents/401_CNN.py
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@ -65,7 +65,7 @@ class CNN(nn.Module):
out_channels=16, # n_filters out_channels=16, # n_filters
kernel_size=5, # filter size kernel_size=5, # filter size
stride=1, # filter movement/step stride=1, # filter movement/step
padding=2, # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1 padding=2, # if want same width and length of this image after Conv2d, padding=(kernel_size-1)/2 if stride=1
), # output shape (16, 28, 28) ), # output shape (16, 28, 28)
nn.ReLU(), # activation nn.ReLU(), # activation
nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14) nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14)
@ -115,7 +115,7 @@ for epoch in range(EPOCH):
if step % 50 == 0: if step % 50 == 0:
test_output, last_layer = cnn(test_x) test_output, last_layer = cnn(test_x)
pred_y = torch.max(test_output, 1)[1].data.squeeze().numpy() pred_y = torch.max(test_output, 1)[1].data.numpy()
accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0)) accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0))
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy) print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
if HAS_SK: if HAS_SK:
@ -129,6 +129,6 @@ plt.ioff()
# print 10 predictions from test data # print 10 predictions from test data
test_output, _ = cnn(test_x[:10]) test_output, _ = cnn(test_x[:10])
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze() pred_y = torch.max(test_output, 1)[1].data.numpy()
print(pred_y, 'prediction number') print(pred_y, 'prediction number')
print(test_y[:10].numpy(), 'real number') print(test_y[:10].numpy(), 'real number')