# How do I use intensity levels in a neural network?

I'm building a neural network classifier, lets say for cats and dogs.

I have a dataset that include a measure of intensity for the training examples. Dog example A may thus have an intensity of 200, while Dog Training B may have an intensity of 300. The intensity indicates how much they fit into the classes, so Dog Example B is 100 better than Dog Training Example A of containing the features of a Dog.

How can I include these intensity levels into my neural network, so that the neural network knows which training example's features to give more impact?

Would it make sense to just repeat the examples for their intensity levels, i.e. train the network 300 times on Dog Training example B per epoch, or would that lead to overfitting?

• "I have a dataset that include a measure of intensity for the training examples" - so just to be clear, this intensity will not be available during inference time? Sep 17 '19 at 2:29

E.g., say $$intensity$$ is a normalized value ($$0 < intensity \leq 1$$), and $$L$$ is the loss function. I would train with a new loss function
$$L' = L * intensity$$