layer_norm_layers.py 文件源码

def step(self, x, states):
        h_tm1 = states[0]
        c_tm1 = states[1]
        B_U = states[2]
        B_W = states[3]

        z = LN(K.dot(x * B_W[0], self.kernel), self.gamma_1, self.beta_1) +  \
            LN(K.dot(h_tm1 * B_U[0], self.recurrent_kernel), self.gamma_2, self.beta_2)
        if self.use_bias:
            z = K.bias_add(z, self.bias)

        z0 = z[:, :self.units]
        z1 = z[:, self.units: 2 * self.units]
        z2 = z[:, 2 * self.units: 3 * self.units]
        z3 = z[:, 3 * self.units:]

        i = self.recurrent_activation(z0)
        f = self.recurrent_activation(z1)
        c = f * c_tm1 + i * self.activation(z2)
        o = self.recurrent_activation(z3)

        h = o * self.activation(LN(c, self.gamma_3, self.beta_3))
        return h, [h, c]