o ·2úhT ã@sdddlZddlZddlmZddlmZe ¡ae ¡add„Z ddd„Z ed d gƒdd d „ƒZ dS)éN)Úbackend)Ú keras_exportcCstt||ƒdS©N)ÚsetattrÚGLOBAL_STATE_TRACKER)ÚnameÚvalue©r ú`/var/www/html/chatgem/venv/lib/python3.10/site-packages/keras/src/backend/common/global_state.pyÚset_global_attribute sr FcCs2tt|dƒ}|dur|dur|}|rt||ƒ|Sr)Úgetattrrr )rÚdefaultÚset_to_defaultÚattrr r r Úget_global_attributes  rzkeras.utils.clear_sessionzkeras.backend.clear_sessionTcCsŠt ¡at ¡at ¡dkr+ddlm}|jj  ¡|  ¡r*ddl m }|  ¡  ¡nt ¡dkr;ddlm}| ¡|rCt ¡dSdS)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 Ú tensorflowr)r)ÚcontextÚtorchN)Ú threadingÚlocalrÚGLOBAL_SETTINGS_TRACKERrÚkeras.src.utils.module_utilsrÚcompatÚv1Úreset_default_graphÚexecuting_eagerlyÚtensorflow.python.eagerrÚclear_kernel_cacheÚ torch._dynamoÚ_dynamoÚresetÚgcÚcollect)Ú free_memoryÚtfrÚdynamor r r Ú clear_sessions4     €   þr&)NF)T) r!rÚ keras.srcrÚkeras.src.api_exportrrrrr rr&r r r r Ús