Extract encoder and decoder from trained autoencoder

Question

I want to divide the autoencoder learning and applying into two parts following https://blog.keras.io/building-autoencoders-in-keras.html and using the fashion-mnist data fo

Answer 1

Since you are using functional API for creating the autoencoder, the best way to reconstruct the encoder and decoder is to use the functional API and the Model class again:

autoencoder= K.models.load_model('fashion-autoencoder.hdf5')

encoder = Model(autoencoder.input, autoencoder.layers[-2].output)

decoder_input = Input(shape=(encoding_dim,))
decoder = Model(decoder_input, autoencoder.layers[-1](decoder_input))

encoder.summary()
decoder.summary()

The models summary:

Layer (type)                 Output Shape              Param #   
=================================================================
input_4 (InputLayer)         (None, 784)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 32)                25120     
=================================================================
Total params: 25,120
Trainable params: 25,120
Non-trainable params: 0
_________________________________________________________________


Layer (type)                 Output Shape              Param #   
=================================================================
input_6 (InputLayer)         (None, 32)                0         
_________________________________________________________________
dense_4 (Dense)              (None, 784)               25872     
=================================================================
Total params: 25,872
Trainable params: 25,872
Non-trainable params: 0
_________________________________________________________________

The solution involving pop() on layers attribute does not work since you need to update some of the internal attributes of the model. Although, for sequential models a built-in pop() method has been implemented.