recurrent_layers.py 文件源码

def __call__(self, inputs, state, scope=None):
        with tf.variable_scope(self.scope):
            c, h = state
            h = dropout(h, self.keep_recurrent_probs, self.is_train)

            mat = _compute_gates(inputs, h, self.num_units, self.forget_bias,
                                        self.kernel_initializer, self.recurrent_initializer, True)

            i, j, f, o = tf.split(value=mat, num_or_size_splits=4, axis=1)

            new_c = (c * self.recurrent_activation(f) + self.recurrent_activation(i) *
                     self.activation(j))
            new_h = self.activation(new_c) * self.recurrent_activation(o)

            new_state = LSTMStateTuple(new_c, new_h)

        return new_h, new_state