Book Image

Deep Learning with TensorFlow

By : Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy
Book Image

Deep Learning with TensorFlow

By: Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy

Overview of this book

Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.
Table of Contents (11 chapters)

How to save and restore a TensorFlow model

Let's suppose we want to use the results of this trained model repeatedly, but without re-training the model each time.

Saving a model

To save a model, we use the Saver() class. It saves the graph structure using checkpoints: these are binary files in a proprietary format, which map variable names to tensor values. The following code saves the model into our current working directory as two files:

  • softmax_mnist.ckpt, which contains the weights
  • softmax_mnist.ckpt.meta, which contains the graph definition

The following code must be inserted at the end of the previous model:

saver = tf.train.Saver() 
save_path = saver.save(sess, "softmax_mnist")
print("Model saved to %s" % save_path)
...