layers.py 文件源码

def _compute_states(self):
    """ Compute hidden states.

    Returns:
      A tuple, (outputs, states).
    """

    _inputs = tf.transpose(self.inputs, [1, 0, 2])
    x_ta = tf.TensorArray(tf.float32, size=self.length).unstack(_inputs)
    h_ta = tf.TensorArray(tf.float32, size=self.length)

    def cond(t, h, h_ta):
      return tf.less(t, self.length)

    def body(t, h, h_ta):

      x = x_ta.read(t)
      num_units, input_size = self.num_hidden_units, self.input_size

      with tf.variable_scope('simple_rnn'):
        h_new = self.activation(self._linear(h, x, num_units, scope='simple_rnn'))

      h_ta_new = h_ta.write(t, h_new)
      return t + 1, h_new, h_ta_new

    t = tf.constant(0)
    h = tf.squeeze(self.initial_states, [1])
    _, _, h_ta = tf.while_loop(cond, body, [t, h, h_ta])

    states = tf.transpose(h_ta.stack(), [1, 0, 2], name='states')
    outputs = tf.identity(states, name='outputs')
    return outputs, states