o 2h@sddlZddlZddlmZddlmZddlmZddlm Z ddlm Z ddlm Z ddlm Z dd lm Z dd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZee e e e e eeeeeeeeeeehZ dde DZ!e!"dde De!"e ee e eee dedddZ#edd%d d!Z$ed"d#d$Z%dS)&N)backend)ops) keras_export)STFT)Constant)Identity)Ones)Zeros) Initializer) GlorotNormal) GlorotUniform)HeNormal) HeUniform) LecunNormal) LecunUniform) Orthogonal) RandomNormal) RandomUniform)TruncatedNormal)VarianceScaling)serialization_lib) to_snake_casecCsi|]}|j|qS)__name__.0clsrrZ/var/www/html/chatgem/venv/lib/python3.10/site-packages/keras/src/initializers/__init__.py 0srcCsi|]}t|j|qSr)rrrrrrr2s)IdentityInitializernormaloneSTFTInitializerOrthogonalInitializeruniformzerozkeras.initializers.serializecCs t|S)z7Returns the initializer configuration as a Python dict.)rserialize_keras_object) initializerrrr serializeBs r(zkeras.initializers.deserializecCstj|t|dS)z9Returns a Keras initializer object via its configuration.)module_objectscustom_objects)rdeserialize_keras_objectALL_OBJECTS_DICT)configr*rrr deserializeHs r.zkeras.initializers.getcsdurdSttrt}n+ttr!tid}t|}nts/ttjtjfr9dfdd }|}n}t |rIt |rG|}|St d)aRetrieves a Keras initializer object via an identifier. The `identifier` may be the string name of a initializers function or class (case-sensitively). >>> identifier = 'Ones' >>> keras.initializers.get(identifier) <...keras.initializers.initializers.Ones...> You can also specify `config` of the initializer to this function by passing dict containing `class_name` and `config` as an identifier. Also note that the `class_name` must map to a `Initializer` class. >>> cfg = {'class_name': 'Ones', 'config': {}} >>> keras.initializers.get(cfg) <...keras.initializers.initializers.Ones...> In the case that the `identifier` is a class, this method will return a new instance of the class by its constructor. You may also pass a callable function with a signature that includes `shape` and `dtype=None` as an identifier. >>> fn = lambda shape, dtype=None: ops.ones(shape, dtype) >>> keras.initializers.get(fn) at ...> Alternatively, you can pass a backend tensor or numpy array as the `identifier` to define the initializer values directly. Note that when calling the initializer, the specified `shape` argument must be the same as the shape of the tensor. >>> tensor = ops.ones(shape=(5, 5)) >>> keras.initializers.get(tensor) .initialize_fn at ...> Args: identifier: A string, dict, callable function, or tensor specifying the initializer. If a string, it should be the name of an initializer. If a dict, it should contain the configuration of an initializer. Callable functions or predefined tensors are also accepted. Returns: Initializer instance base on the input identifier. N) class_namer-csBt|}t|tjkrtdjd|t|S)NzExpected `shape` to be z2 for direct tensor as initializer. Received shape=)rstandardize_dtypestandardize_shapeshape ValueErrorrcast)r2dtype identifierrr initialize_fns    zget..initialize_fnz,Could not interpret initializer identifier: N) isinstancedictr.strr is_tensornpgenericndarraycallableinspectisclassr3)r7objr-r8rr6rgetRs(0        rEr9)&rBnumpyr> keras.srcrrkeras.src.api_exportr,keras.src.initializers.constant_initializersrrrrr "keras.src.initializers.initializerr *keras.src.initializers.random_initializersr r r rrrrrrrrkeras.src.savingrkeras.src.utils.namingr ALL_OBJECTSr,updater(r.rErrrrs|