Pruning Machine Learning Models in TensorFlow

Pruning Machine Learning Models in TensorFlow

Read this overview to learn how to make your models smaller via pruning. Pruning Machine Learning Models in TensorFlow

In a previous article, we reviewed some of the pre-eminent literature on pruning neural networks. We learned that pruning is a model optimization technique that involves eliminating unnecessary values in the weight tensor. This results in smaller models with accuracy very close to the baseline model.

In this article, we’ll work through an example as we apply pruning and view the effect on the final model size and prediction errors.

Import the Usual Suspects

Our first step is to get a couple of imports out of the way:

  • Os and Zipfile will help us in assessing the size of the models.
  • tensorflow_model_optimization for model pruning.
  • load_model for loading a saved model.
  • and of course tensorflow and keras.

Finally, we initialize TensorBoard so that we’ll able to visualize the models:

import os
import zipfile
import tensorflow as tf
import tensorflow_model_optimization as tfmot
from tensorflow.keras.models import load_model
from tensorflow import keras
%load_ext tensorboard

Dataset Generation

For this experiment, we’ll generate a regression dataset using scikit-learn. Thereafter, we split the dataset into a training and test set:

from sklearn.datasets import make_friedman1
X, y = make_friedman1(n_samples=10000, n_features=10, random_state=0)from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

machine learning python tensorflow

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