load_model: Loads a model saved via 'save_model()'.

In rstudio/keras: R Interface to 'Keras'

Loads a model saved via save_model().

Description

Loads a model saved via save_model().

Usage

load_model(model, custom_objects = NULL, compile = TRUE, safe_mode = TRUE)

Arguments

model	string, path to the saved model file, or a raw vector, as returned by save_model(filepath = NULL)
custom_objects	Optional named list mapping names to custom classes or functions to be considered during deserialization.
compile	Boolean, whether to compile the model after loading.
safe_mode	Boolean, whether to disallow unsafe lambda deserialization. When safe_mode=FALSE, loading an object has the potential to trigger arbitrary code execution. This argument is only applicable to the Keras v3 model format. Defaults to TRUE.

Value

A Keras model instance. If the original model was compiled, and the argument compile = TRUE is set, then the returned model will be compiled. Otherwise, the model will be left uncompiled.

Examples

model <- keras_model_sequential(input_shape = c(3)) |>
  layer_dense(5) |>
  layer_activation_softmax()

model |> save_model("model.keras")
loaded_model <- load_model("model.keras")

x <- random_uniform(c(10, 3))
stopifnot(all.equal(
  model |> predict(x),
  loaded_model |> predict(x)
))

Note that the model variables may have different name values (var$name property, e.g. "dense_1/kernel:0") after being reloaded. It is recommended that you use layer attributes to access specific variables, e.g. model |> get_layer("dense_1") |> _$kernel.

See Also

• https://keras.io/api/models/model_saving_apis/model_saving_and_loading#loadmodel-function

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

rstudio/keras documentation built on April 27, 2024, 10:11 p.m.