abca83373d6d7df2747c5cdcc2bb7234395604e6,keras/layers/recurrent.py,GRU,step,#GRU#Any#Any#,408

Before Change

        B_U = states[2]  // dropout matrix for recurrent units

        x_z = K.dot(x * B_W[0], self.W_z) + self.b_z
        x_r = K.dot(x * B_W[1], self.W_r) + self.b_r
        x_h = K.dot(x * B_W[2], self.W_h) + self.b_h

        z = self.inner_activation(x_z + K.dot(h_tm1 * B_U[0], self.U_z))
        r = self.inner_activation(x_r + K.dot(h_tm1 * B_U[1], self.U_r))

After Change

    def step(self, x, states):
        h_tm1 = states[0]  // previous memory
        if len(states) == 2:
            B_U = states[1]  // dropout matrices for recurrent units
        else:
            B_U = [1., 1., 1.]

        x_z = x[:, :self.output_dim]
        x_r = x[:, self.output_dim: 2 * self.output_dim]
        x_h = x[:, 2 * self.output_dim:]

Instances