Book Image

Python: Advanced Guide to Artificial Intelligence

By : Giuseppe Bonaccorso, Rajalingappaa Shanmugamani
Book Image

Python: Advanced Guide to Artificial Intelligence

By: Giuseppe Bonaccorso, Rajalingappaa Shanmugamani

Overview of this book

This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: • Mastering Machine Learning Algorithms by Giuseppe Bonaccorso • Mastering TensorFlow 1.x by Armando Fandango • Deep Learning for Computer Vision by Rajalingappaa Shanmugamani
Table of Contents (31 chapters)
Title Page
About Packt
Tensor Processing Units

Saving and restoring Keras models

In Keras, saving and restoring models is very simple. Keras provides three options:

  • Save the complete model with its network architecture, weights (parameters), training configuration, and optimizer state.
  • Save only the architecture.
  • Save only the weights.

For saving the complete model, use the function. This will save the complete model in an HDF5 file. The saved model can be loaded back using the keras.models.load_model(filepath) function. This function loads everything back, and then also compiles the model.

For saving the architecture of a model, use either the model.to_json() or model.to_yaml() function. These functions return a string that can be written to the disk file. While restoring the architecture, the string can be read back and the model architecture restored using the keras.models.model_from_json(json_string) or the  keras.models.model_from_yaml(yaml_string) function. Both these functions return a model instance.

For saving...