deep_gru_lstm__call__.py

def __call__(self,
                x: Union[np.ndarray, tf.Tensor],
                y: Union[np.ndarray, tf.Tensor, None] = None) -> tf.Tensor:
    
    for i in range(self.depth):
        x = self._call_level(i, x)
    
    y_logits = tf.linalg.matmul(x, self.wy)+self.by
    if y is None:
        # during prediction return softmax probabilities
        return tf.nn.softmax(y_logits)
    else:
        # during training return loss
        return tf.math.reduce_mean(
                    tf.nn.softmax_cross_entropy_with_logits(y, y_logits))