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Merge pull request #60 from keineahnung2345/402-squeeze

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402 - remove squeeze for 1-D array
This commit is contained in:
Morvan
2018-11-13 10:32:26 +08:00
committed by GitHub
parent 903820ef35 7411f602fb
commit 0b3aa238ba
1 changed files with 3 additions and 3 deletions
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tutorial-contents/402_RNN_classifier.py
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@ -47,7 +47,7 @@ train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_
# convert test data into Variable, pick 2000 samples to speed up testing # convert test data into Variable, pick 2000 samples to speed up testing
test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor()) test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor())
test_x = test_data.test_data.type(torch.FloatTensor)[:2000]/255. # shape (2000, 28, 28) value in range(0,1) test_x = test_data.test_data.type(torch.FloatTensor)[:2000]/255. # shape (2000, 28, 28) value in range(0,1)
test_y = test_data.test_labels.numpy().squeeze()[:2000] # covert to numpy array test_y = test_data.test_labels.numpy()[:2000] # covert to numpy array
class RNN(nn.Module): class RNN(nn.Module):
@ -94,13 +94,13 @@ for epoch in range(EPOCH):
if step % 50 == 0: if step % 50 == 0:
test_output = rnn(test_x) # (samples, time_step, input_size) test_output = rnn(test_x) # (samples, time_step, input_size)
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze() pred_y = torch.max(test_output, 1)[1].data.numpy()
accuracy = float((pred_y == test_y).astype(int).sum()) / float(test_y.size) accuracy = float((pred_y == test_y).astype(int).sum()) / float(test_y.size)
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)
# print 10 predictions from test data # print 10 predictions from test data
test_output = rnn(test_x[:10].view(-1, 28, 28)) test_output = rnn(test_x[:10].view(-1, 28, 28))
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], 'real number') print(test_y[:10], 'real number')