i did write my own metric based on examples from keras. But since i have to do it using callbacks and their backend, it works only on one output at a time meaning both predictions and true values are vector.
What i meant by that is that when i call model.fit(....) i tells me at each epoch something like this:

Epoch 1/500

63/63 [==============================] - 1s 6ms/step - loss: 4171.9570 - root_mean_squared_error: 42.4592 - val_loss: 2544.3647 - val_root_mean_squared_error: 44.4907

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where root_mean_squared_error is a custom metric as follow.

def root_mean_squared_error(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true)))

which when called directly wants data in a for of vector, meaning this function has to be called for each output separatly.

In order to better optimize my model i need to understand how the losses/metrics are calculated so it results in one number (as shown above during training)