如何使用现有CNN模型中的预训练权重在Keras中进行迁移学习？

Question

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 (ie, transfer learning) from well-known CNN architectures such as VGG and Inception

Answer 1

As people have mentioned in the comments, keras.applications provides a way for you to access pretrained models. As an example:

import keras
from keras.models import Model

model_base = keras.applications.vgg16.VGG16(include_top=False, input_shape=(*IMG_SIZE, 3), weights='imagenet')
output = model_base.output
# Add any other layers you want to `output` here...
output = Dense(len(categories), activation='softmax')(output)
model = Model(model_base.input, output)
for layer in model_base.layers:
   layer.trainable = False
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
return model

You can train this model in the same way you trained your previous CNN. Keras applications provides access to many models such as Inception, VGG16, VGG19, ResNet, and more-- you can access them all in a similar way. I wrote a blog post walking through how to use transfer learning in Keras to build an image classifier here: http://innolitics.com/10x/pretrained-models-with-keras/ . It's got a working code example that you can look at as well.