&VfT ddlZddlZddlmZddlmZejaejadZ d dZ eddgd d Z dS) N)backend) keras_exportc2tt||dSN)setattrGLOBAL_STATE_TRACKER)namevalues b/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/backend/common/global_state.pyset_global_attributer s $.....Fcbtt|d}|||}|rt|||Sr)getattrrr )r defaultset_to_defaultattrs r get_global_attributers@ 't 4 4D |+  - t , , , Kr zkeras.utils.clear_sessionzkeras.backend.clear_sessionTctjatjat jdkreddlm}|jj | r,ddl m }| n1t jdkrddlm}||rt#jdSdS)a4Resets 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. Args: 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 ```python 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 tensorflowr)r)contexttorchN) threadinglocalrGLOBAL_SETTINGS_TRACKERrkeras.src.utils.module_utilsrcompatv1reset_default_graphexecuting_eagerlytensorflow.python.eagerrclear_kernel_cache torch._dynamo_dynamoresetgccollect) free_memorytfrdynamos r clear_sessionr*sh%?,,'o//L((AAAAAA ((***   ! ! 3 8 7 7 7 7 7 OO   0 0 2 2 2   g % %&&&&&&   r )NF)T) r%r keras.srcrkeras.src.api_exportrrrrr rr*r r r.s ------&y(())/++///*,IJKKIIILKIIIr