Save and Load Your Keras Deep Learning Models

Keras is a simple and powerful Python library for deep learning.

Given that deep learning models can take hours, days and even weeks to train, it is important to know how to save and load them from disk.

In this post you will discover how you can save your Keras models to file and load them up again to make predictions.

Let’s get started.

Save and Load Your Keras Deep Learning Models
Photo by art_inthecity, some rights reserved.

Tutorial Overview

Keras separates the concerns of saving your model architecture and saving your model weights.

Model weights are saved to HDF5 format. This is a grid format that is ideal for storing multi-dimensional arrays of numbers.

The model structure can be described and saved using two different formats: JSON and YAML.

In this post we are going to look at two examples of saving and loading your model to file:

  • Save Model to JSON.
  • Save Model to YAML.

Each example will also demonstrate saving and loading your model weights to HDF5 formatted files.

The examples will use the same simple network trained on the Pima Indians onset of diabetes binary classification dataset. This is a small dataset that contains all numerical data and is easy to work with. You can download this dataset and place it in your working directory.

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Save Your Neural Network Model to JSON

JSON is a simple file format for describing data hierarchically.

Keras provides the ability to describe any model using JSON format with a to_json() function. This can be saved to file and later loaded via the model_from_json() function that will create a new model from the JSON specification.

The weights are saved directly from the model using the save_weights() function and later loaded using the symmetrical load_weights() function.

The example below trains and evaluates a simple model on the Pima Indians dataset. The model is then converted to JSON format and written to model.json in the local directory. The network weights are written to model.h5 in the local directory.

The model and weight data is loaded from the saved files and a new model is created. It is important to compile the loaded model before it is used. This is so that predictions made using the model can use the appropriate efficient computation from the Keras backend.

The model is evaluated in the same way printing the same evaluation score.



Running this example provides the output below.



The JSON format of the model looks like the following:



Save Your Neural Network Model to YAML

This example is much the same as the above JSON example, except the YAML format is used for the model specification.

The model is described using YAML, saved to file model.yaml and later loaded into a new model via the model_from_yaml() function. Weights are handled in the same way as above in HDF5 format as model.h5.



Running the example displays the following output:



The model described in YAML format looks like the following:



Summary

In this post you discovered how to serialize your Keras deep learning models.

You learned how you can save your trained models to files and later load them up and use them to make predictions.

You also learned that model weights are easily stored using  HDF5 format and that the network structure can be saved in either JSON or YAML format.

Do you have any questions about saving your deep learning models or about this post? Ask your questions in the comments and I will do my best to answer them.

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