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Easy methods to Save and Load Your Keras Deep Studying Mannequin


Final Up to date on June 20, 2022

Keras is a straightforward and highly effective Python library for deep studying.

On condition that deep studying fashions can take hours, days and even weeks to coach, it is very important know learn how to save and cargo them from disk.

On this submit, you’ll uncover how one can save your Keras fashions to file and cargo them up once more to make predictions.

After studying this tutorial you’ll know:

  • Easy methods to save mannequin weights and mannequin structure in separate recordsdata.
  • Easy methods to save mannequin structure in each YAML and JSON format.
  • Easy methods to save mannequin weights and structure right into a single file for later use.

Kick-start your mission with my new e book Deep Studying With Python, together with step-by-step tutorials and the Python supply code recordsdata for all examples.

Let’s get began.

  • Replace Mar 2017: Added directions to put in h5py first.
  • Replace Mar/2017: Up to date examples for adjustments to the Keras API.
  • Replace Mar/2018: Added alternate hyperlink to obtain the dataset.
  • Replace Could/2019: Added part on saving and loading the mannequin to a single file.
  • Replace Sep/2019: Added word about utilizing PyYAML model 5.
  • Replace Jun/2022: Added word about deprecated YAML format and added part about protocol buffer.

Easy methods to Save and Load Your Keras Deep Studying Fashions
Photograph by art_inthecity, some rights reserved.

Tutorial Overview

In case you are new to Keras or deep studying, see this step-by-step Keras tutorial.

Keras separates the issues of saving your mannequin structure and saving your mannequin weights.

Mannequin weights are saved to HDF5 format. This can be a grid format that’s very best for storing multi-dimensional arrays of numbers.

The mannequin construction could be described and saved utilizing two totally different codecs: JSON and YAML.

On this submit we’re going to have a look at three examples of saving and loading your mannequin to file:

  • Save Mannequin to JSON.
  • Save Mannequin to YAML.
  • Save Mannequin to HDF5.

The primary two examples save the mannequin structure and weights individually. The mannequin weights are saved right into a HDF5 format file in all circumstances.

The examples will use the identical easy community skilled on the Pima Indians onset of diabetes binary classification dataset. This can be a small dataset that comprises all numerical knowledge and is straightforward to work with. You possibly can obtain this dataset and place it in your working listing with the filename “pima-indians-diabetes.csv” (replace: obtain from right here).

Affirm that you’ve got TensorFlow v2.x put in (e.g. v2.9 as of June 2022).

Notice: Saving fashions requires that you’ve got the h5py library put in. It’s often put in as a dependency with TensorFlow. You can too set up it simply as follows:


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Save Your Neural Community Mannequin to JSON

JSON is a straightforward file format for describing knowledge hierarchically.

Keras gives the flexibility to explain any mannequin utilizing JSON format with a to_json() operate. This may be saved to file and later loaded by way of the model_from_json() operate that can create a brand new mannequin from the JSON specification.

The weights are saved immediately from the mannequin utilizing the save_weights() operate and later loaded utilizing the symmetrical load_weights() operate.

The instance under trains and evaluates a easy mannequin on the Pima Indians dataset. The mannequin is then transformed to JSON format and written to mannequin.json within the native listing. The community weights are written to mannequin.h5 within the native listing.

The mannequin and weight knowledge is loaded from the saved recordsdata and a brand new mannequin is created. It is very important compile the loaded mannequin earlier than it’s used. That is in order that predictions made utilizing the mannequin can use the suitable environment friendly computation from the Keras backend.

The mannequin is evaluated in the identical approach printing the identical analysis rating.

Notice: Your outcomes could range given the stochastic nature of the algorithm or analysis process, or variations in numerical precision. Think about working the instance a number of occasions and examine the typical final result.

Working this instance gives the output under.

The JSON format of the mannequin seems to be like the next:

Save Your Neural Community Mannequin to YAML

Notice: This technique solely applies to TensorFlow 2.5 or earlier. If you happen to run it in later variations of TensorFlow, you will note a RuntimeError with the message “Methodology mannequin.to_yaml() has been eliminated attributable to safety danger of arbitrary code execution. Please use mannequin.to_json() as a substitute.”

This instance is way the identical because the above JSON instance, besides the YAML format is used for the mannequin specification.

Notice, this instance assumes that you’ve got PyYAML 5 put in, for instance:

On this instance, the mannequin is described utilizing YAML, saved to file mannequin.yaml and later loaded into a brand new mannequin by way of the model_from_yaml() operate.

Weights are dealt with in the identical approach as above in HDF5 format as mannequin.h5.

Notice: Your outcomes could range given the stochastic nature of the algorithm or analysis process, or variations in numerical precision. Think about working the instance a number of occasions and examine the typical final result.

Working the instance shows the next output.

The mannequin described in YAML format seems to be like the next:

Save Mannequin Weights and Structure Collectively

Keras additionally helps an easier interface to save lots of each the mannequin weights and mannequin structure collectively right into a single H5 file.

Saving the mannequin on this approach contains every little thing we have to know in regards to the mannequin, together with:

  • Mannequin weights.
  • Mannequin structure.
  • Mannequin compilation particulars (loss and metrics).
  • Mannequin optimizer state.

Because of this we are able to load and use the mannequin immediately, with out having to re-compile it as we did within the examples above.

Notice: that is the popular approach for saving and loading your Keras mannequin.

Easy methods to Save a Keras Mannequin

It can save you your mannequin by calling the save() operate on the mannequin and specifying the filename.

The instance under demonstrates this by first becoming a mannequin, evaluating it and saving it to the file mannequin.h5.

Notice: Your outcomes could range given the stochastic nature of the algorithm or analysis process, or variations in numerical precision. Think about working the instance a number of occasions and examine the typical final result.

Working the instance matches the mannequin, summarizes the fashions efficiency on the coaching dataset and saves the mannequin to file.

We are able to later load this mannequin from file and use it.

Notice that in Keras library, there’s one other operate doing the identical, as follows:

Easy methods to Load a Keras Mannequin

Your saved mannequin can then be loaded later by calling the load_model() operate and passing the filename. The operate returns the mannequin with the identical structure and weights.

On this case, we load the mannequin, summarize the structure and consider it on the identical dataset to verify the weights and structure are the identical.

Working the instance first hundreds the mannequin, prints a abstract of the mannequin structure then evaluates the loaded mannequin on the identical dataset.

Notice: Your outcomes could range given the stochastic nature of the algorithm or analysis process, or variations in numerical precision. Think about working the instance a number of occasions and examine the typical final result.

The mannequin achieves the identical accuracy rating which on this case is 77%.

Protocol Buffer Format

Whereas saving and loading a Keras mannequin utilizing HDF5 format is the really helpful approach, TensorFlow helps one more format, the protocol buffer. It’s thought-about quicker to save lots of and cargo a protocol buffer format however doing so will produce a number of recordsdata. The syntax is identical, besides that we don’t want to supply the .h5 extension to the filename:

These will create a listing “mannequin” with the next recordsdata:

That is additionally the format we used to save lots of a mannequin in TensorFlow v1.x. You could encounter this if you obtain a pretrained mannequin from TensorFlow Hub.

Additional Studying

Abstract

On this submit, you found learn how to serialize your Keras deep studying fashions.

You discovered how one can save your skilled fashions to recordsdata and later load them up and use them to make predictions.

You additionally discovered that mannequin weights are simply saved utilizing  HDF5 format and that the community construction could be saved in both JSON or YAML format.

Do you’ve any questions on saving your deep studying fashions or about this submit?
Ask your questions within the feedback and I’ll do my finest to reply them.

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