alanchiao
alanchiao
Currently the pruning API will throw an error when a subclassed model is passed to it. Users can get around this by diving into the subclassed models and applying pruning...
As suggested [here](https://www.tensorflow.org/model_optimization/guide/pruning), model pruning currently only provides benefits in model compression/size reduction. Further framework support is necessary to provide latency improvements in TF/TFLite.
The recommended path for pruning with a custom training loop is not as simple as it could be. ``` pruned_model = setup_pruned_model() loss = tf.keras.losses.categorical_crossentropy optimizer = keras.optimizers.Adam() log_dir =...