rnn_cell.py 文件源码

def __call__(self, inputs, state, scope=None):
    """LSTM cell with layer normalization and recurrent dropout."""

    with vs.variable_scope(scope or type(self).__name__) as scope:  # LayerNormBasicLSTMCell  # pylint: disable=unused-variables
      c, h = state
      args = array_ops.concat(1, [inputs, h])
      concat = self._linear(args)

      i, j, f, o = array_ops.split(1, 4, concat)
      if self._layer_norm:
        i = self._norm(i, "input")
        j = self._norm(j, "transform")
        f = self._norm(f, "forget")
        o = self._norm(o, "output")

      g = self._activation(j)
      if (not isinstance(self._keep_prob, float)) or self._keep_prob < 1:
        g = nn_ops.dropout(g, self._keep_prob, seed=self._seed)

      new_c = (c * math_ops.sigmoid(f + self._forget_bias)
               + math_ops.sigmoid(i) * g)
      if self._layer_norm:
        new_c = self._norm(new_c, "state")
      new_h = self._activation(new_c) * math_ops.sigmoid(o)

      new_state = rnn_cell.LSTMStateTuple(new_c, new_h)
      return new_h, new_state