sentiment.py 文件源码

def __call__(self, inputs, state, scope=None):
    with tf.variable_scope(scope or type(self).__name__):
      lhs, rhs = state
      c0, h0 = lhs
      c1, h1 = rhs
      concat = tf.contrib.layers.linear(
          tf.concat([inputs, h0, h1], 1), 5 * self._num_units)

      # i = input_gate, j = new_input, f = forget_gate, o = output_gate
      i, j, f0, f1, o = tf.split(value=concat, num_or_size_splits=5, axis=1)

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

      new_c = (c0 * tf.sigmoid(f0 + self._forget_bias) +
               c1 * tf.sigmoid(f1 + self._forget_bias) +
               tf.sigmoid(i) * j)
      new_h = self._activation(new_c) * tf.sigmoid(o)

      new_state = tf.contrib.rnn.LSTMStateTuple(new_c, new_h)

      return new_h, new_state