how to pass in 'training' argument to tf,keras.Model when using model.fit
As far as i know, there is no argument  for this. Model.fit simply trains the model on whatever training data provided, and at the end of each epoch evaluates the training on either provided validation data, OR by the use of validation_split.
So I have this model written by the subclassing API, the call signature looks like call(x, training), where training argument is needed to differentiate between training and non-training when doing batchnorm and dropout. How do I make the model forward pass know I am in training mode or eval mode when I use model.fit?
Thanks!