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Official documents state that "It is not recommended to use pickle or cPickle to save a Keras model."
However, my need for pickling Keras model stems from hyperparameter optimization using sklearn's RandomizedSearchCV (or any other hyperparameter optimizers). It's essential to save the results to a file, since then the script can be executed remotely in a detached session etc.
Essentially, I want to:
trial_search = RandomizedSearchCV( estimator=keras_model, ... )
pickle.dump( trial_search, open( "trial_search.pickle", "wb" ) )
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As of now, Keras models are pickle-able. But we still recommend using model. save() to save model to disk.
To save the ML model using Pickle all we need to do is pass the model object into the dump() function of Pickle. This will serialize the object and convert it into a “byte stream” that we can save as a file called model. pkl .
Save the entire modelCall tf.keras.Model.save to save a model's architecture, weights, and training configuration in a single file/folder . This allows you to export a model so it can be used without access to the original Python code*.
We can use the Keras callback keras. callbacks. ModelCheckpoint() to save the model at its best performing epoch.
As of now, Keras models are pickle-able. But we still recommend using model.save() to save model to disk.
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