train.py 文件源码

def generate(decoder,
             prime_str='int ',
             predict_len=100,
             temperature=0.35,
             cuda=False,
             args=None,
             hidden=None):

     prime_input = Variable(char_tensor(prime_str).unsqueeze(0))

     if not hidden:
          hidden = decoder.init_hidden(1)
          prime_input = Variable(char_tensor(prime_str).unsqueeze(0))

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

     predicted = ''
     inp = prime_input[:,-1]
     p_list = []


     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]
          p_list.append(top_i)
          # Add predicted character to string and use as next input
          predicted_char = all_characters[top_i]

          predicted += predicted_char
          inp = Variable(char_tensor(predicted_char).unsqueeze(0))
          if cuda: inp = inp.cuda()

     # print (p_list)
     return predicted, hidden