B d#@sNdZddlZddlmmZddlmZddlmZddlm Z ddl m Z ddl m Z ddl m Z dd l mZdd l mZdd l mZdd l mZdd l mZddl mZddl mZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlm Z ddlm!Z!ddlm"Z#ddlm$Z$ddlm%Z%ddl&m'Z'ddl&m(Z(dd l&m)Z)dd!l*m+Z+dd"l,m-Z-dd#l.m/Z/dd$l.m0Z1dd%l2m3Z3eee e e eeeeeeeee eeeeeeee!e#e$e%fZ4eee'e(e)fZ5e6a7d&d'Z8e3d(d)d*Z9e3d+d2d,d-Z:d.d/Z;d3d0d1Zzz1populate_deserializable_objects..)Z obj_filtercst|ot|S)N)r&r'r()r))r*r+r,r-r.ZBatchNormalizationV1ZBatchNormalizationV2r)models)SequenceFeatures) LinearModel) WideDeepModelInput InputSpec FunctionalModelr0 Sequentialr1r2) DenseFeaturesr8addsubtractmultiplyaveragemaximumminimum concatenatedot),hasattrLOCALr%ZGENERATED_WITH_V2tfZ __internal__Ztf2enabledrZLayerr"Z!populate_dict_with_module_objects ALL_MODULESALL_V2_MODULESrZBatchNormalizationrZkerasr/Z,keras.feature_column.sequence_feature_columnr0Zkeras.premade_models.linearr1Zkeras.premade_models.wide_deepr2rr3rr4r5r6r7Z&keras.feature_column.dense_features_v2r8Z#keras.feature_column.dense_featuresr r9r:r;r<r=r>r?r@)r/r0r1r2r8r+)r*r,populate_deserializable_objectsdsZ                           rGzkeras.layers.serializecCs t|S)aSerializes a `Layer` object into a JSON-compatible representation. Args: layer: The `Layer` object to serialize. Returns: A JSON-serializable dict representing the object's config. Example: ```python from pprint import pprint model = tf.keras.models.Sequential() model.add(tf.keras.Input(shape=(16,))) model.add(tf.keras.layers.Dense(32, activation='relu')) pprint(tf.keras.layers.serialize(model)) # prints the configuration of the model, as a dict. )r Zserialize_keras_object)layerr+r+r, serializesrIzkeras.layers.deserializecCsttj|tj|ddS)a8Instantiates a layer from a config dictionary. Args: config: dict of the form {'class_name': str, 'config': dict} custom_objects: dict mapping class names (or function names) of custom (non-Keras) objects to class/functions Returns: Layer instance (may be Model, Sequential, Network, Layer...) Example: ```python # Configuration of Dense(32, activation='relu') config = { 'class_name': 'Dense', 'config': { 'activation': 'relu', 'activity_regularizer': None, 'bias_constraint': None, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'bias_regularizer': None, 'dtype': 'float32', 'kernel_constraint': None, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'kernel_regularizer': None, 'name': 'dense', 'trainable': True, 'units': 32, 'use_bias': True } } dense_layer = tf.keras.layers.deserialize(config) ``` rH)module_objectscustom_objectsZprintable_module_name)rGr Zdeserialize_keras_objectrBr%)configrKr+r+r, deserializes &rMcCsttdsttj|S)z?Returns class if `class_name` is registered, else returns None.r%)rArBrGr%get) class_namer+r+r,get_builtin_layers rPcCs"ttj|tj|d}t||S)z(Instantiates a layer from a JSON string.)rJrK)rGr!Zdecode_and_deserializerBr%rM)Z json_stringrKrLr+r+r,deserialize_from_json s rQ)N)N)=__doc__ threadingZtensorflow.compat.v2compatZv2rCZ keras.enginerrrZ keras.layersrrrrr r r r r rZkeras.layers.normalizationrrrrrZkeras.layers.preprocessingrrrrrrrZpreprocessing_normalizationrrZkeras.layers.rnnrrrZkeras.saving.legacyr Zkeras.saving.legacy.saved_modelr!Z keras.utilsr"r#r&Z tensorflow.python.util.tf_exportr$rErFlocalrBrGrIrMrPrQr+r+r+r,s                                   Y .