Can we prune pre-trained model like VGG16 etc... using this optimization library

I tried to create a model like:

`def Vgg16():
    vgg16 = VGG16(include_top=False, 
                                           weights='imagenet',
                                           input_shape = (32, 32, 3))
    top_model = Sequential()
    top_model.add(Flatten(input_shape=vgg16.output_shape[1:]))
    top_model.add(Dense(512, activation='relu'))
    top_model.add(Dropout(0.5))
    top_model.add(Dense(256, activation='relu'))
    top_model.add(Dropout(0.5))
    top_model.add(Dense(10, activation='sigmoid'))
    model = Model(vgg16.input,top_model(vgg16.output))
    return model`

and when I call

`new_pruning_params = {
      'pruning_schedule': sparsity.PolynomialDecay(initial_sparsity=0.5,
                                                   final_sparsity=0.80,
                                                   begin_step=0,
                                                   end_step=end_step,
                                                   frequency=100)
}

**pruned_model = sparsity.prune_low_magnitude(loaded_model, **new_pruning_params)`**

it generates error as: Please initialize Prune with a supported layer. Layers should either be a PrunableLayer instance, or should be supported by the PruneRegistry. You passed: <class ‘tensorflow.python.keras.engine.sequential.Sequential’>

[alanchiao]

In general, yes you can.

There are some caveats (e.g. lack of subclassed model support / nesting of models within models like in both examples (tejalal@ and Cospel@). Created https://github.com/tensorflow/model-optimization/issues/155 in light of this for making subclassed support better.

[gnhearx]

Hi everyone 😃 I have a similar issue with pruning nested models, even if I apply the pruning wrappers per layer inside all the nested Functional API models, they don’t prune.

Is this expected behaviour at all for nested models? Because I would think that if any layer in a model has that wrapper, then it will be pruned when the pruning callback is called in the training phase. Unfortunately, this does not happen. Instead everything not nested (that have pruning wrappers) do prune, and anything inside a nested model does not.

I can also confirm that if I create a model with no nested models at all, then everything I set to prune does in fact prune the way it should.

Side note: My nested model is a pretrained VGG16 from keras and I apply pruning wrappers to each layer within the nested model.

If anyone perhaps have a solution to this or workaround that would seriously be very helpful, thank you.