tf.keras.backend.clear_session resets all state generated by Keras.
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)])
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)
#Output
dense_10
tf.keras.backend.set_learning_phase(1)
print(tf.keras.backend.learning_phase())
tf.keras.backend.clear_session()
new_layer = tf.keras.layers.Dense(10)
print(new_layer.name)
# Output
dense
