Is it possible to use pre-trained model features from VGG-16 and pass to GlobalAveragePooling2D() layer of other model in Keras?
Sample code for storing offline features of VGG-16 network:
model = applications.VGG16(include_top=False, weights='imagenet')
bottleneck_features_train = model.predict(input)
Sample code for top model:
model = Sequential()
model.add(GlobalAveragePooling2D()) # Here I want to use pre-trained feature from VGG-16 net as input.
I can not use Flatten() layer as I want to predict multi-labels with multi-classes.