train.py 文件源码

def generate(self,
                  prime_str='int ',
                  predict_len=100,
                  temperature=0.1,
                  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