使用model.fit时如何将'training'参数传递给tf，keras.Model

Question

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!

Answer 1

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.

Answer 2

Actually, in the documentation https://www.tensorflow.org/beta/guide/keras/custom_layers_and_models , it says "Some layers, in particular the BatchNormalization layer and the Dropout layer, have different behaviors during training and inference. For such layers, it is standard practice to expose a training (boolean) argument in the call method.

By exposing this argument in call, you enable the built-in training and evaluation loops (eg fit) to correctly use the layer in training and inference." So I think the training argument is passed in automatically by keras. I tried to remove the default value for training argument and no errors were thrown, so it is very likely keras built-in loop did the thing.