Pruning models from tf.keras.applications (efficientnet, resnet etc)

I am finetuning the tf.keras.applications model on my data for the problem of image classification. I was able to complete training and test results look good. In order to deploy the model, I wanted to perform some pruning and optimization. I tried following the Keras Pruning tutorial, but I could not figure out how to enable pruning on the efficientnet layer. If we could have a tutorial on this or a worked out example, that would be great, thank you!

Here is the model summary:

Model: "model_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_8 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
efficientnetb0 (Functional)  (None, 7, 7, 1280)        4049571   
_________________________________________________________________
global_average_pooling2d_3 ( (None, 1280)              0         
_________________________________________________________________
dense_6 (Dense)              (None, 8)                 10248     
_________________________________________________________________
dropout_3 (Dropout)          (None, 8)                 0         
_________________________________________________________________
dense_7 (Dense)              (None, 1)                 9         
=================================================================
Total params: 4,059,828
Trainable params: 10,257
Non-trainable params: 4,049,571

[liyunlu0618]

Thanks for reporting the error. I’m working on a change to support pruning a model recursively. Will let you know once it’s checked-in.

[liyunlu0618]

You may have to build from source at the moment: https://www.tensorflow.org/model_optimization/guide/install#installing_from_source

Will let you know once we have a new release.