save_model_hdf5: Save/Load models using HDF5 files

In dfalbel/keras: R Interface to 'Keras'

Description

Save/Load models using HDF5 files

Usage

save_model_hdf5(object, filepath, overwrite = TRUE, include_optimizer = TRUE)

load_model_hdf5(filepath, custom_objects = NULL, compile = TRUE)

Arguments

object	Model object to save
filepath	File path
overwrite	Overwrite existing file if necessary
include_optimizer	If TRUE, save optimizer's state.
custom_objects	Mapping class names (or function names) of custom (non-Keras) objects to class/functions (for example, custom metrics or custom loss functions).
compile	Whether to compile the model after loading.

Details

The following components of the model are saved:

• The model architecture, allowing to re-instantiate the model.

• The model weights.

• The state of the optimizer, allowing to resume training exactly where you left off. This allows you to save the entirety of the state of a model in a single file.

Saved models can be reinstantiated via load_model_hdf5(). The model returned by load_model_hdf5() is a compiled model ready to be used (unless the saved model was never compiled in the first place or compile = FALSE is specified).

As an alternative to providing the custom_objects argument, you can execute the definition and persistence of your model using the with_custom_object_scope() function.

Note

The serialize_model() function enables saving Keras models to R objects that can be persisted across R sessions.

See Also

Other model persistence: get_weights(), model_to_json(), model_to_yaml(), save_model_tf(), save_model_weights_hdf5(), serialize_model()

dfalbel/keras documentation built on Nov. 27, 2019, 8:16 p.m.