fed7cc257e960a3849978088dccd500b0fbfc753,keras/layers/recurrent.py,LSTM,step,#LSTM#Any#Any#,563

Before Change

        i = self.inner_activation(x_i + K.dot(h_tm1 * B_U[0], self.U_i))
        f = self.inner_activation(x_f + K.dot(h_tm1 * B_U[1], self.U_f))
        c = f * c_tm1 + i * self.activation(x_c + K.dot(h_tm1 * B_U[2], self.U_c))
        o = self.inner_activation(x_o + K.dot(h_tm1 * B_U[3], self.U_o))
        h = o * self.activation(c)
        return h, [h, c]

After Change

        x_o = K.dot(x * K.gather(B_W, 3), self.W_o) + self.b_o

        i = self.inner_activation(x_i + K.dot(h_tm1 * K.gather(B_U, 0), self.U_i))
        f = self.inner_activation(x_f + K.dot(h_tm1 * K.gather(B_U, 1), self.U_f))
        c = f * c_tm1 + i * self.activation(x_c + K.dot(h_tm1 * K.gather(B_U, 2), self.U_c))
        o = self.inner_activation(x_o + K.dot(h_tm1 * K.gather(B_U, 3), self.U_o))
        h = o * self.activation(c)
        return h, [h, c]

Instances