How to do predict when trained with Normalization or dropout layer in Keras?

Question

I trained a model with Normalization layer. The code is as this:

In training phase：

model=Sequential()
model.add()

...

k.set_learning_phase(1)
ModelCheckpoint(weights_file)
model.fit()

In inference time:

k.set_learning_phase(0)
model.load_weights(weights_file)
model.predict_classes()

...

The version of Keras：2.0.8. Is that right，or need some special codes to compute the BN after training like using SegNet in Caffe?

Answer 1

No, you don't need to do anything special when using BatchNormalization or Dropout layers. Keras already tracks the learning/testing phases, so when using predict or predict_classes , it does the right thing.

You do not even need to set the learning phase manually, Keras already does it.