I am working on a 2D RGB pixel-based image classification problem via convolution neural networks (CNN) in Keras. My full CNN model can be found here.
I do the following to train/fit the CNN model:
model = my_CNN_unet()
model_checkpoint = ModelCheckpoint('testweights_{epoch:02d}.hdf5')
model.fit(x_trn, y_trn, batch_size=50, epochs=3, verbose=1, shuffle=True,
callbacks=[model_checkpoint], validation_data=(x_val, y_val))
How can I change my code, so that I use pre-trained weights (i.e., transfer learning) from well-known CNN architectures such as VGG
and Inception