55b880469ced115de9949d5d25ff835dabbb2caa,12_2_hello_rnn.py,,,#,65

Before Change

    loss.backward()
    optimizer.step()
    _, idx = outputs.max(1)
    idx = idx.data.numpy()
    result_str = [idx2char[c] for c in idx.squeeze()]
    print("epoch: %d, loss: %1.3f" % (epoch + 1, loss.data[0]))
    print("Predicted string: ", "".join(result_str))

After Change

    sys.stdout.write("predicted string: ")
    for input, label in zip(inputs, labels):
        // print(input.size(), label.size())
        hidden, output = model(hidden, input)
        val, idx = output.max(1)
        sys.stdout.write(idx2char[idx.data[0]])
        loss += criterion(output, label)

Instances