How freeze training of particular layer after particular epoches - tensorflow

Question

I want to freeze training of first two layers of following code after 3rd epoch. Total epoch is set to 10.
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

Answer 1

How can I "freeze" Keras layers?
To "freeze" a layer means to exclude it from training, i.e. its weights will never be updated. This is useful in the context of fine-tuning a model or using fixed embeddings for a text input.
You can change the trainable attribute of a layer.
for layer in model.layers[:2]:
layer.trainable = False
For this to take effect, you will need to call compile() on your model after modifying the trainable property. If you don't you will receive a warning "Discrepancy between trainable weights and collected trainable weights" and all your layers will be still trainable. So:
Build and compile the model
Train it for 3 epochs
Freeze layers you want
compile the model again
Train the rest epochs

Answer 2

This should work:
for epoch in range(3):
model.fit(.., epochs=1)
# save the weights of this model
model.save_weights("weight_file.h5")
# freeze the layers you want
for layer in model.layers[:2]:
layer.trainable = False
In order to train further with these weights but first two layers frozen, you need to compile the model again.
model.compile(..)
# train further
for epoch in range(3, 10):
model.fit(..., epochs=1)