Saves all weights to a single file or sharded files.

By default, the weights are saved in a single .weights.h5 file. Enable sharding via max_shard_size to split weights across multiple files (in GB) and produce a .weights.json manifest that tracks shard metadata.

The saved sharded files contain:

• *.weights.json: configuration file containing metadata and weight_map entries.

• *_xxxxxx.weights.h5: weight shards limited by max_shard_size.

model <-
  keras_model_sequential(input_shape = 2) |>
  layer_dense(4)

path_h5 <- tempfile(fileext = ".weights.h5")
path_json <- tempfile(fileext = ".weights.json")

model |> save_model_weights(path_h5)
model |> save_model_weights(path_json, max_shard_size = 0.01)

model |> load_model_weights(path_h5)
model |> load_model_weights(path_json)

Usage

save_model_weights(model, filepath, overwrite = FALSE, max_shard_size = NULL)

Arguments

model

A keras Model object.

filepath

Path where the weights will be saved. Accepts .weights.h5, or when sharding is enabled, a .weights.json manifest path. If .weights.h5 is provided while sharding, the filename will be overridden to end in .weights.json.

overwrite

Whether to overwrite any existing weights at the target location, or instead ask the user via an interactive prompt.

max_shard_size

Numeric size in GB for each sharded file. Use NULL to disable sharding.

Value

This is called primarily for side effects. model is returned, invisibly, to enable usage with the pipe.

See also

• https://keras.io/api/models/model_saving_apis/weights_saving_and_loading#saveweights-method

Other saving and loading functions:
export_savedmodel.keras.src.models.model.Model()
layer_tfsm()
load_model()
load_model_weights()
register_keras_serializable()
save_model()
save_model_config()
with_custom_object_scope()