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)

I would rather use my own rmse this one does some weighting and it just does not make sense to me.

I dont care about the loss, i care about the rmse metric. When i calculate it for each output it just does not add out to what the training method says. 🫤

oh, this looks promising, i'll give it a try and see what comes up.

I see, i was not sure about that since you wrote it twice... and also you phrased the reply into two paragraphs.

But what am i looking for tho. i've been looking at loadings matrix for couple minutes but cant really figure out the connections. Lets say i want to select 7 feature out of 38, so i performa pca for 7 components and im looking at loading matrix (correlation between 38 feature's and 7 pca's . do i just look at the component with best correlation with the input features and the 7 highest correlation with that pca component ?

whats PDA ?

I dont think PCA will help me, i need to reduce the number of feature in order to simplify the system im working with. those removed feature will no longer be aquired and thus i cant retrain the model in the future. i need to somehow pick 2-10 features out of 38 for which i can finetune the model and deploy it. only those selected features will be logged for future.