ortho_gru_cell.py 文件源码

def call(self, inputs, state):
        """Gated recurrent unit (GRU) with nunits cells."""
        with tf.variable_scope("gates"):
            input_to_gates = tf.layers.dense(
                inputs, 2 * self._num_units, name="input_proj",
                kernel_initializer=tf.glorot_normal_initializer(),
                use_bias=self.use_input_bias)

            # Nematus does the orthogonal initialization probably differently
            state_to_gates = tf.layers.dense(
                state, 2 * self._num_units,
                use_bias=self.use_state_bias,
                kernel_initializer=orthogonal_initializer(),
                name="state_proj")

            gates_input = state_to_gates + input_to_gates
            reset, update = tf.split(
                tf.sigmoid(gates_input), num_or_size_splits=2, axis=1)

        with tf.variable_scope("candidate"):
            input_to_candidate = tf.layers.dense(
                inputs, self._num_units, use_bias=self.use_input_bias,
                kernel_initializer=tf.glorot_normal_initializer(),
                name="input_proj")

            state_to_candidate = tf.layers.dense(
                state, self._num_units, use_bias=self.use_state_bias,
                kernel_initializer=orthogonal_initializer(),
                name="state_proj")

            candidate = self._activation(
                state_to_candidate * reset + input_to_candidate)

        new_state = update * state + (1 - update) * candidate
        return new_state, new_state