a Sic"@s4dZddlZddlmmZddlmZddlmZddlm 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$Z$ddl%m&Z&ddl%m'Z'ddl%m(Z(dd l)m*Z*dd!l+m,Z,dd"l+m-Z.dd#l/m0Z0eee e e eeeeeeeeeeeeeeee e"e#e$fZ1eee&e'e(fZ2e3a4d$d%Z5e0d&d'd(Z6e0d)d0d*d+Z7d,d-Z8d1d.d/Z9dS)2z.Layer serialization/deserialization functions.N) base_layer) input_layer) input_spec) activation) attention) convolutional)core)locally_connected)merging)pooling)regularization) reshaping)rnn)batch_normalization)batch_normalization_v1)layer_normalization)unit_normalization)category_encoding)discretization)hashed_crossing)hashing)image_preprocessing)integer_lookup) normalization) string_lookup)text_vectorization) cell_wrappers)gru)lstm) json_utils) generic_utils) tf_inspect) keras_exportcsttdsit_dt_tjr2tjtjjkr2dSit_tjjt_tj t j tjt fdddtjjrt j tjt fdddtjtjd<tjtjd<d d lm}d d lm}d d lm}d d lm}tjtjd<tjtjd<|jtjd<|jtjd<|tjd<|jtjd<|tjd<|tjd<tjjrTd dl m!}|tjd<nd dl"m!}|tjd<t#j$tjd<t#j%tjd<t#j&tjd<t#j'tjd<t#j(tjd<t#j)tjd<t#j*tjd<t#j+tjd<dS) z5Populates dict ALL_OBJECTS with every built-in layer. ALL_OBJECTSNcst|ot|SNinspectisclass issubclassxbase_clsV/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/layers/serialization.pywz1populate_deserializable_objects..) obj_filtercst|ot|Sr$r%r)r+r-r.r/r0BatchNormalizationV1BatchNormalizationV2r)models)SequenceFeatures) LinearModel) WideDeepModelInput InputSpec FunctionalModelr5 Sequentialr6r7) DenseFeaturesr=addsubtractmultiplyaveragemaximumminimum concatenatedot),hasattrLOCALr#GENERATED_WITH_V2tf __internal__tf2enabledrLayerr !populate_dict_with_module_objects ALL_MODULESALL_V2_MODULESrBatchNormalizationrkerasr4Z,keras.feature_column.sequence_feature_columnr5Zkeras.premade_models.linearr6Zkeras.premade_models.wide_deepr7rr8rr9r:r;r<Z&keras.feature_column.dense_features_v2r=Z#keras.feature_column.dense_featuresr r>r?r@rArBrCrDrE)r4r5r6r7r=r-r+r.populate_deserializable_objectsasn                            rSzkeras.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 serialize_keras_object)layerr-r-r. serializesrVzkeras.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) ``` rU)module_objectscustom_objectsprintable_module_name)rSr deserialize_keras_objectrGr#)configrXr-r-r. deserializes&r\cCsttdsttj|S)z?Returns class if `class_name` is registered, else returns None.r#)rFrGrSr#get) class_namer-r-r.get_builtin_layers r_cCs"ttj|tj|d}t||S)z(Instantiates a layer from a JSON string.)rWrX)rSrdecode_and_deserializerGr#r\) json_stringrXr[r-r-r.deserialize_from_jsonsrb)N)N):__doc__ threadingtensorflow.compat.v2compatv2rI keras.enginerrr keras.layersrrrrr r r r r rZkeras.layers.normalizationrrrrZkeras.layers.preprocessingrrrrrrrZpreprocessing_normalizationrrkeras.layers.rnnrrrkeras.saving.saved_modelr keras.utilsr r!r& tensorflow.python.util.tf_exportr"rOrPlocalrGrSrVr\r_rbr-r-r-r.s                                  Y  .