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Keras manages a global state, which it uses to implement the Functional model-building API and to uniquify autogenerated layer names.

If you are creating many models in a loop, this global state will consume an increasing amount of memory over time, and you may want to clear it. Calling clear_session() releases the global state: this helps avoid clutter from old models and layers, especially when memory is limited.

Example 1: calling clear_session() when creating models in a loop

for (i in 1:100) {
  # Without `clear_session()`, each iteration of this loop will
  # slightly increase the size of the global state managed by Keras
  model <- keras_model_sequential()
  for (j in 1:10) {
    model <- model |> layer_dense(units = 10)
  }
}

for (i in 1:100) {
  # With `clear_session()` called at the beginning,
  # Keras starts with a blank state at each iteration
  # and memory consumption is constant over time.
  clear_session()
  model <- keras_model_sequential()
  for (j in 1:10) {
    model <- model |> layer_dense(units = 10)
  }
}

Example 2: resetting the layer name generation counter

layers <- lapply(1:10, \(i) layer_dense(units = 10))

new_layer <- layer_dense(units = 10)
print(new_layer$name)

## [1] "dense_10"

clear_session()
new_layer <- layer_dense(units = 10)
print(new_layer$name)

## [1] "dense"

Usage

clear_session()

Value

NULL, invisibly, called for side effects.