deep_gru_lstm_call_gru.py

def _call_gru(self,
                level: int,
                x: Union[np.ndarray, tf.Tensor]) -> tf.Tensor:
    
    n = x.shape[0]
    
    self.a[level] = self.a[level][0:n]
    
    concat_matrix = tf.concat([self.a[level], x], axis=1)
    
    relevance_gate = tf.math.sigmoid(
                            tf.linalg.matmul(concat_matrix, self.wr[level])
                            + self.br[level])
    update_gate = tf.math.sigmoid(
                            tf.linalg.matmul(concat_matrix, self.wu[level])
                            + self.bu[level])
    
    a_candidate = tf.math.tanh(
                        tf.linalg.matmul(
                            tf.concat([tf.math.multiply(relevance_gate, self.a[level]), x], axis=1),
                            self.wa[level])
                        + self.ba[level])
    
    self.a[level] = (tf.math.multiply(update_gate, a_candidate) +
                        tf.math.multiply((1-update_gate), self.a[level]))
    
    return self.a[level]