attentional_lstm_decoder.py 文件源码

def __call__(self):
    mem_optimize = nmtrain.optimization.chainer_mem_optimize
    # Calculate Attention vector
    a = self.attention(self.S, self.h)
    # Calculate context vector
    c = F.squeeze(F.batch_matmul(self.S, a, transa=True), axis=2)
    # Calculate hidden vector + context
    self.ht = self.context_project(F.concat((self.h, c), axis=1))
    # Calculate Word probability distribution
    y = mem_optimize(self.affine_vocab, F.tanh(self.ht), level=1)
    if self.use_lexicon:
      y = self.lexicon_model(y, a, self.ht, self.lexicon_matrix)

    if nmtrain.environment.is_train():
      return nmtrain.models.decoders.Output(y=y)
    else:
      # Return the vocabulary size output projection
      return nmtrain.models.decoders.Output(y=y, a=a)