generate.py 文件源码

def generate(decoder, prime_str='A', predict_len=100, temperature=0.8, cuda=False):
    hidden = decoder.init_hidden(1)
    prime_input = torch.autograd.Variable(char_tensor(prime_str).unsqueeze(0))

    if cuda:
        hidden = hidden.cuda()
        prime_input = prime_input.cuda()
    predicted = prime_str

    # Use priming string to "build up" hidden state
    for p in range(len(prime_str) - 1):
        _, hidden = decoder(prime_input[:,p], hidden)

    inp = prime_input[:,-1]

    for p in range(predict_len):
        output, hidden = decoder(inp, hidden)

        # Sample from the network as a multinomial distribution
        output_dist = output.data.view(-1).div(temperature).exp()
        top_i = torch.multinomial(output_dist, 1)[0]

        # Add predicted character to string and use as next input
        predicted_char = chars[top_i]
        predicted += predicted_char
        inp = torch.autograd.Variable(char_tensor(predicted_char).unsqueeze(0))
        if cuda:
            inp = inp.cuda()

    return predicted

# Run as standalone script