GORU.py 文件源码

def __call__(self, inputs, state, scope=None):
        with vs.variable_scope(scope or "goru_cell"):

            U_init = init_ops.random_uniform_initializer(-0.01, 0.01)
            b_init = init_ops.constant_initializer(2.)
            mod_b_init = init_ops.constant_initializer(0.01)

            U = vs.get_variable("U", [inputs.get_shape()[-1], self._hidden_size * 3], dtype=tf.float32, initializer = U_init)
            Ux = math_ops.matmul(inputs, U)
            U_cx, U_rx, U_gx = array_ops.split(Ux, 3, axis=1)

            W_r = vs.get_variable("W_r", [self._hidden_size, self._hidden_size], dtype=tf.float32, initializer = U_init)
            W_g = vs.get_variable("W_g", [self._hidden_size, self._hidden_size], dtype=tf.float32, initializer = U_init)
            W_rh = math_ops.matmul(state, W_r)
            W_gh = math_ops.matmul(state, W_g)

            bias_r = vs.get_variable("bias_r", [self._hidden_size], dtype=tf.float32, initializer = b_init)
            bias_g = vs.get_variable("bias_g", [self._hidden_size], dtype=tf.float32)
            bias_c = vs.get_variable("bias_c", [self._hidden_size], dtype=tf.float32, initializer = mod_b_init)


            r_tmp = U_rx + W_rh + bias_r
            g_tmp = U_gx + W_gh + bias_g
            r = math_ops.sigmoid(r_tmp)

            g = math_ops.sigmoid(g_tmp)

            Unitaryh = _eunn_loop(state, self._capacity, self.diag_vec, self.off_vec, self.diag, self._fft)
            c = modrelu(math_ops.multiply(r, Unitaryh) + U_cx, bias_c, False)
            new_state = math_ops.multiply(g, state) +  math_ops.multiply(1 - g, c)

        return new_state, new_state