tf.keras.backend.clear_session

Resets all state generated by Keras.

tf.keras.backend.clear_session( free_memory=True )

Used in the notebooks

Used in the guide Used in the tutorials

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.

free_memory Whether to call Python garbage collection. It's usually a good practice to call it to make sure memory used by deleted objects is immediately freed. However, it may take a few seconds to execute, so when using clear_session() in a short loop, you may want to skip it.

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

for _ in range(100): # Without `clear_session()`, each iteration of this loop will # slightly increase the size of the global state managed by Keras model = keras.Sequential([ keras.layers.Dense(10) for _ in range(10)]) for _ in range(100): # With `clear_session()` called at the beginning, # Keras starts with a blank state at each iteration # and memory consumption is constant over time. keras.backend.clear_session() model = keras.Sequential([ keras.layers.Dense(10) for _ in range(10)])

Example 2: resetting the layer name generation counter

layers = [keras.layers.Dense(10) for _ in range(10)] new_layer = keras.layers.Dense(10) print(new_layer.name) dense_10 keras.backend.clear_session() new_layer = keras.layers.Dense(10) print(new_layer.name) dense