rnn_cell.py 文件源码

def __call__(self, inputs, state, scope=None):
        with tf.variable_scope(scope, default_name="gru_cell",
                               values=[inputs, state]):
            if not isinstance(inputs, (list, tuple)):
                inputs = [inputs]

            all_inputs = list(inputs) + [state]
            r = tf.nn.sigmoid(linear(all_inputs, self._num_units, False, False,
                                     scope="reset_gate"))
            u = tf.nn.sigmoid(linear(all_inputs, self._num_units, False, False,
                                     scope="update_gate"))
            all_inputs = list(inputs) + [r * state]
            c = linear(all_inputs, self._num_units, True, False,
                       scope="candidate")

            new_state = (1.0 - u) * state + u * tf.tanh(c)

        return new_state, new_state