Problem with GRU simulation

[kokhazade]

Hello,

I am going to implement the GRU computations in my code and compare the results with the output of a GRU layer. To this end, I am using a same input for both cases and get the weights of GRU and pass it to my_GRU_simulation function. By using the following code for inner gates calculations, I get two different outputs for these two cases.
Is there any extra calculations (rather than following) that I have missed?
How can I create the exactly same output as a GRU layer?

        import tensorflow as tf
        from keras import backend

        GRU_layer_input = tf.squeeze(GRU_layer_input, axis=1)
        matrix_x = backend.dot(tf.convert_to_tensor(GRU_layer_input), tf.convert_to_tensor(gru_layer_weights[0]))
        matrix_x = backend.bias_add(matrix_x, gru_layer_weights[2][0])
        x_z, x_r, x_h = tf.split(matrix_x, 3, axis=1)

        matrix_y = backend.dot(tf.convert_to_tensor(previous_gru_output), tf.convert_to_tensor(gru_layer_weights[1]))
        matrix_y = backend.bias_add(matrix_y, gru_layer_weights[2][1])
        recurrent_z, recurrent_r, recurrent_h = tf.split(matrix_y, 3, axis=1)

        z = tf.sigmoid(x_z + recurrent_z)
        r = tf.sigmoid(x_r + recurrent_r)
        hh = tf.tanh(x_h + r * recurrent_h)

        h = z * h_tm1 + (1 - z) * hh
        new_state = [h] if tf.nest.is_nested(previous_gru_output) else h

previous_gru_output is all zero with GRU units size.
I am using tensorflow and keras version 2.11.0