How to create weighted cross entropy loss?

I suggest in the first instance to resort to using class_weight from Keras.

class_weight

is a dictionary with {label:weight}

For example, if you have 20 times more examples in label 1 than in label 0, then you can write

# Assign 20 times more weight to label 0
model.fit(..., class_weight = {0:20, 1:0})

In this way you don't need to worry implementing weighted CCE on your own.

Additional note : in your model.compile() do not forget to use weighted_metrics=['accuracy'] in order to have a relevant reflection of your accuracy.

model.fit(..., class_weight = {0:20, 1:0}, weighted_metrics = ['accuracy'])