|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v2.15.0/keras/backend.py#L226-L297) | Resets all state generated by Keras. tf.keras.backend.clear_session() 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 _ in range(100): # Without `clear_session()`, each iteration of this loop will # slightly increase the size of the global state managed by Keras model = tf.keras.Sequential([ tf.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. tf.keras.backend.clear_session() model = tf.keras.Sequential([ tf.keras.layers.Dense(10) for _ in range(10)]) Example 2: resetting the layer name generation counter import tensorflow as tf layers = [tf.keras.layers.Dense(10) for _ in range(10)] new_layer = tf.keras.layers.Dense(10) print(new_layer.name) dense_10 tf.keras.backend.set_learning_phase(1) print(tf.keras.backend.learning_phase()) 1 tf.keras.backend.clear_session() new_layer = tf.keras.layers.Dense(10) print(new_layer.name) dense