loss function design to incorporate different weight for false positive and false negative

Question

I am trying to solve a semantic segmentation problem. In accordance with the real constraints, the criteria for false positive and the criteria for false negative is different. For instance, if a pixel is miscorrected as foreground is less desirable than a pixel is miscorrected as background. How to handle this kind of constraint in setting up the loss function.

Answer 1

You can use the class_weight parameter of model.fit to weight your classes and, as such, punish misclassifications differently depending on the class.

class_weight: optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class.

For example:

out = Dense(2, activation='softmax')
model = Model(input=..., output=out)
model.fit(X, Y, class_weight={0: 1, 1: 0.5})

This would punish the second class less than the first.