Extract encoder and decoder from saved autoencoder

Question

I have saved models for a large number of autoencoders that I am using for my project. They were saved using the autoencoder.save(outdir + "autoencoder_"+params) function.

Is there any way for me to extract the encoder and decoder components of each of these saved models, or would I need to rerun the script and add in the encoder = Model(input, bottleneck) and decoder = Model(bottleneck, output) lines and save those models?

Here is the autoencoder structure I am attempting to retrieve:

autoencoder.summary()

Model: "model_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 3593, 4)]         0         
_________________________________________________________________
flatten (Flatten)            (None, 14372)             0         
_________________________________________________________________
dense (Dense)                (None, 1797)              25828281  
_________________________________________________________________
dense_1 (Dense)              (None, 719)               1292762   
_________________________________________________________________
dense_2 (Dense)              (None, 180)               129600    
_________________________________________________________________
dense_3 (Dense)              (None, 719)               130139    
_________________________________________________________________
dense_4 (Dense)              (None, 1797)              1293840   
_________________________________________________________________
dense_5 (Dense)              (None, 14372)             25840856  
_________________________________________________________________
reshape (Reshape)            multiple                  0         
=================================================================
Total params: 54,515,478
Trainable params: 54,515,478
Non-trainable params: 0
_________________________________________________________________

Answer 1

You can transfer the weights to two different neural network models. All you need is to identify the index of the bottleneck layer which you can easily know by running model.summary()

Here is a snippet that can help you copy the model

bottleneck_index = # this you need to identify
encoder_model = tf.keras.Sequential()
for layer in ae_model.layers[:bottleneck_index]:
    layer_config = layer.get_config()  # to get all layer's parameters (units, activation, etc...)
    copied_layer = type(layer).from_config(layer_config) # to initialize the same layer class with same parameters
    copied_layer.build(layer.input_shape)  # build the layer to initialize the weights.
    copied_layer.set_weights(layer.get_weights())  # transfer the trainable parameters
    encoder_model.add(copied_layer)  # add it to the encoder's model

Do the same for decoder, where ae_model.layers[bottleneck_index:]

Of course, you can even identify the bottleneck index by checking the units of current layers if it is smaller than the consecutive layer.