Load the weights from a single file or sharded files.

Weights are loaded based on the network's topology. This means the architecture should be the same as when the weights were saved. Note that layers that don't have weights are not taken into account in the topological ordering, so adding or removing layers is fine as long as they don't have weights.

Sharding

When loading sharded weights, specify a filepath ending in ".weights.json" (the configuration file), with the corresponding shard files (*_xxxxx.weights.h5) located alongside it.

Partial weight loading

If you have modified your model, for instance by adding a new layer (with weights) or by changing the shape of the weights of a layer, you can choose to ignore errors and continue loading by setting skip_mismatch=TRUE. In this case any layer with mismatching weights will be skipped. A warning will be displayed for each skipped layer.

Usage

load_model_weights(model, filepath, skip_mismatch = FALSE, ...)

Arguments

model

A keras model.

filepath

Path or path-like object to the weights. Accepts .weights.h5, legacy .h5, or sharded weights through a .weights.json manifest sitting alongside the shard files (*_xxxxx.weights.h5).

skip_mismatch

Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weights.

...

For forward/backward compatability.

Value

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

Examples

model |> load_model_weights("model.weights.h5")
model |> load_model_weights("model.weights.json")

See also

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

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