6biSrSSKrSSKrSSKrSSKrSSKrSSKrSSKJ s J r SSK J r SSK Jr SSKJr SSKJr SSKJr SSKJr SS KJr SS KJr SS KJr SS KJr SS KJr SSK!J"r" SSK!J#r# SSK!J$r$ SSK!J%r% SSK!J&r' SSK!J(r( SSK)J*r* SSK+J,r- SSK.J/r/ SSK.J0r0 SSK1J2r2 SSK1J3r3 SSK1J4r4 SSK1J5r5 SSK6J7r7 SSK8J9r9 SSK8J:r: SSK;Jr> SS"K;J?r? SS#K;J@r@ SS$K;JArA SS%K;JBrB SS&K;JCrC SS'KDJErE SSKFrF\"S(S)5"S*S+\"R\CR55rJ"S,S-5rK"S.S/\K5rLS0rMSBS1jrNS2rOS3rPS4rQS5rRS6rSS7rTS8rUS9rVS:rWS;rXS<rYS=rZS>r[S?r\S@r]SAr^g!\Ga SrFNf=f)Cz-Training-related part of the TF-Keras engine.N)distribute_utils) input_ops)context) tf_logging) keras_export) doc_controls)backend) callbacks) optimizers) dtensor_api) layout_map) base_layer)base_layer_utils) compile_utils) data_adapter) input_layer)training_utils) base_metric)loss_scale_optimizer) optimizer) optimizer_v1) pickle_utils) saving_api) saving_lib)serialization_lib) serialization) json_utils)model_serialization) generic_utils)io_utils) layer_utils)steps_per_execution_tuning) tf_inspect)tf_utils)traceback_utils) version_utils)ModeKeysz keras.Modelzkeras.models.Modelc^\rSrSrSr\"\R"S\RR55r Sr U4Sjr \ RRR \R$U4Sj55rSr\ RRR S5rU4S jrU4S jrU4S jrS r\R6U4S j5r\R$U4Sj5r\R>SdSj5r \R$SeSj5r!Sr"\ RRR S5r#\ RRR S5r$\%S5r&\%S5r'\%S5r(\%S5r)\%S5r*\*RVS5r*\%S5r,\,RVS5r,\%S5r-\-RVS5r-\%S5r.\.RVS5r.\%S 5r/\/RVS!5r/S"r0S#r1SfS$jr2S%r3S&r4S'r5S(r6SgS)jr7\R$ShS*j5r8S+r9S,r:SgS-jr;\R$SiS.j5rS1r?S2r@SgS3jrA\R$SjS4j5rBS5rCSkS6jrDSlS7jrES8rF\RSmS9j5rH\RSnS:j5rI\RSnS;j5rJ\%S<5rK\%S=5rLU4S>jrM\R$SoS?j5rN\R$SpS@j5rO\R$SqSAj5rPSBrQ\R6SC5rR\SSrSDj5rTSErUSFrVSGrW\%\RSH55rX\%SI5rY\%SJ5rZSsSKjr[\%SL5r\\\RVSM5r\SdSNjr]SOr^SPr_SQr`SRra\ RRR SdU4SSjj5rbStSTjrcSUrdSVreSWrfSXrgSYrhSZriSdS[jrj\%S\5rkSuU4S]jjrlS^rmStS_jrnS`roSarp\%Sb5rqScrrU=rs$)vModelFa A model grouping layers into an object with training/inference features. Args: inputs: The input(s) of the model: a `keras.Input` object or a combination of `keras.Input` objects in a dict, list or tuple. outputs: The output(s) of the model: a tensor that originated from `keras.Input` objects or a combination of such tensors in a dict, list or tuple. See Functional API example below. name: String, the name of the model. There are two ways to instantiate a `Model`: 1 - With the "Functional API", where you start from `Input`, you chain layer calls to specify the model's forward pass, and finally you create your model from inputs and outputs: ```python import tensorflow as tf inputs = tf.keras.Input(shape=(3,)) x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs) outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x) model = tf.keras.Model(inputs=inputs, outputs=outputs) ``` Note: Only dicts, lists, and tuples of input tensors are supported. Nested inputs are not supported (e.g. lists of list or dicts of dict). A new Functional API model can also be created by using the intermediate tensors. This enables you to quickly extract sub-components of the model. Example: ```python inputs = keras.Input(shape=(None, None, 3)) processed = keras.layers.RandomCrop(width=32, height=32)(inputs) conv = keras.layers.Conv2D(filters=2, kernel_size=3)(processed) pooling = keras.layers.GlobalAveragePooling2D()(conv) feature = keras.layers.Dense(10)(pooling) full_model = keras.Model(inputs, feature) backbone = keras.Model(processed, conv) activations = keras.Model(conv, feature) ``` Note that the `backbone` and `activations` models are not created with `keras.Input` objects, but with the tensors that are originated from `keras.Input` objects. Under the hood, the layers and weights will be shared across these models, so that user can train the `full_model`, and use `backbone` or `activations` to do feature extraction. The inputs and outputs of the model can be nested structures of tensors as well, and the created models are standard Functional API models that support all the existing APIs. 2 - By subclassing the `Model` class: in that case, you should define your layers in `__init__()` and you should implement the model's forward pass in `call()`. ```python import tensorflow as tf class MyModel(tf.keras.Model): def __init__(self): super().__init__() self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu) self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax) def call(self, inputs): x = self.dense1(inputs) return self.dense2(x) model = MyModel() ``` If you subclass `Model`, you can optionally have a `training` argument (boolean) in `call()`, which you can use to specify a different behavior in training and inference: ```python import tensorflow as tf class MyModel(tf.keras.Model): def __init__(self): super().__init__() self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu) self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax) self.dropout = tf.keras.layers.Dropout(0.5) def call(self, inputs, training=False): x = self.dense1(inputs) if training: x = self.dropout(x, training=training) return self.dense2(x) model = MyModel() ``` Once the model is created, you can config the model with losses and metrics with `model.compile()`, train the model with `model.fit()`, or use the model to do prediction with `model.predict()`. )_train_counter _test_counter_predict_counter_steps_per_execution_compiled_trainable_stateFc>[X5(a$U[:XaSSKJn UR"USS0UD6$[ [U]"U/UQ70UD6$)Nr functional skip_initT)is_functional_model_init_paramsr)tf_keras.src.enginer2 Functionalsuper__new__)clsargskwargsr2 __class__s V/home/james-whalen/.local/lib/python3.13/site-packages/tf_keras/src/engine/training.pyr8 Model.__new__sN *4 8 8SE\ 6(($I4I&I I,SB4B6B Bc>SUlSSKJn [X5(Ga.[ XR 5(Gd/SQnUVs0sHoUU;dM XRU_M nnUVs0sHoUU;dM XRU_M nn[ UR5 UR R"U/UQ70UD6 /nSn URRH;n [XR 5(aSn M!U (dM*URU 5 M= U(a UHn U R"U/UQ70UD6 M gU(a[SRU55eg[R"U1Sk5 [ T U] "S 0UD6 SUlSUlSUlSUlSUlSUlSUlSUlSUlSUlUR7SS5 UR7S S5 [8R:R=5(a$[8R:R?5Ul OSUl SUl!SUl"SUl#URI5 SUl%SUl&SUl'[8RPRS[TRV"U5S 9Ul,SUl-SUl.SUl/[`Rb"5Ul2URg5 SUl4SUl5gs snfs snf) NTrr1)inputsoutputsname trainabler3FzGThe following keyword arguments passed to `Model` aren't supported: {}.>rCdtyperAdynamicrBautocastrD _is_compiledr)root)6_is_model_for_instrumentationr5r2r4 isinstancer6inject_functional_model_classr<__init__ __bases__ issubclassappend TypeErrorformatrvalidate_kwargsr7_is_graph_networkrArB input_names output_names stop_traininghistory compiled_losscompiled_metrics _compute_output_and_mask_jointly_maybe_create_attributetf distribute has_strategy get_strategy_distribution_strategy_distribute_reduction_method_cluster_coordinator _run_eagerly_reset_compile_cache_training_state_saved_model_inputs_spec_saved_model_arg_spectrain Checkpointweakrefref _checkpointr._steps_per_execution_tuner_autotune_steps_per_executionlayout_map_libget_current_layout_map _layout_map_init_batch_counters_base_model_initialized _jit_compile) selfr:r;r2supported_kwargsk model_kwargs other_kwargs clz_to_initfound_functional_classclzr<s r=rNModel.__init__s.2* 3 *4 8 8 ''B B   '-&,5E0E !9 f '-&,9I0I !9 f  *$.. 9  ! ! * *4 G$ G, G K%* "~~//c#8#899-1*))&&s+ 0&CLL== ='  %%+VL%9  %%    "6"!&   " " $16- $$^U; $$[$7 == % % ' '*,--*D*D*FD '*.D ',0)$(!! !!# $(,%%)"88..GKK4E.F$(!*.'-2*)@@B !!#'+$ !]s K- K- K2, K2cURc0[RRn[R"USUS9$[ R RURR5SS9n[ R"USUS9$)zHelper function for counter variable creation. For the DTensor use case with layout map, since the variable are not tracked by model, they can't be visited by the layout map, and need to be properly initialized as DVariable. int64)rE aggregationr)meshrank)rElayout) rsr^VariableAggregationONLY_FIRST_REPLICAVariabler Layout replicatedget_default_mesh DVariable)rw init_valueaggrs r=_create_counter_variableModel._create_counter_variableNs    #((;;C;;zcJ J ''22%%668q3F(('& r?c[R"5(dCURS5UlURS5UlURS5UlggNr)r^inside_functionrr+r,r-rws r=rtModel._init_batch_countersbsS!!###'"?"?"BD !%!>!>q!AD $($A$A!$DD !$r?c>[USS5(d[TU] X5 g[S[R R U555(a UR [TU] X5 g![a [S5ef=f)N_self_setattr_trackingTc3# UHLn[U[R[R45=(d [ R "U5v MN g7fN)rLrLayerr^rr has_weights).0vs r= $Model.__setattr__..vsG , q:++R[[9 : /++A. /+sAAzsIt looks like you are subclassing `Model` and you forgot to call `super().__init__()`. Always start with this line.) getattrr7 __setattr__allr^nestflattenruAttributeError RuntimeError)rwrCvaluer<s r=rModel.__setattr__qst5t<< G  ,   WW__U+    ,, D(" "4 s  A55B c>UR(a'[R[R"U544$[TU]5$r)builtrdeserialize_model_from_bytecodeserialize_model_as_bytecoder7 __reduce__rwr<s r=rModel.__reduce__s> ::<<99$?A 7%' 'r?c@>UR(a9[R"[R"U55nX![ U5'U$[ TU]5tp4nU"U6nX![ U5'U(a)[R"USUS9nURU5 U$)Nr)memo) rrrridr7rcopydeepcopy __setstate__)rwrnew deserializer serializedreststater<s r= __deepcopy__Model.__deepcopy__s ::>>88>C!DN /4g.@.B +Lt +C DN d1gD9  ' r?c$UR05$r)rrs r=__copy__Model.__copy__s  $$r?c >UR(a[T U] U5 gUc [S5e[[ [ R[4n[X5(d#[SR[U555eU(GaUR(Gd[ R"5(a [ RRS5nO[ R""5nUR%5 [U[ 5(a"['SU55(a [ U5n[U[ 5(a'UVs/sHn[(R*"U5PM nnOe[U[5(a:UR-5VVs0sHupdU[(R*"U5_M nnnO[(R*"U5n0nUR.R0nUR2n [5U 5S:aPUR6(aU S[5UR65*n OU SSn U Hn U S:XaSUS'M[S 5e O[5U 5S:a [S 5eUR8"U40UD6 SSS5 [T U] U5 gs snfs snnf![ R:R<[>4an [S U S 35eSn A ff=f!,(df  Nd=f) a%Builds the model based on input shapes received. This is to be used for subclassed models, which do not know at instantiation time what their inputs look like. This method only exists for users who want to call `model.build()` in a standalone way (as a substitute for calling the model on real data to build it). It will never be called by the framework (and thus it will never throw unexpected errors in an unrelated workflow). Args: input_shape: Single tuple, `TensorShape` instance, or list/dict of shapes, where shapes are tuples, integers, or `TensorShape` instances. Raises: ValueError: 1. In case of invalid user-provided data (not of type tuple, list, `TensorShape`, or dict). 2. If the model requires call arguments that are agnostic to the input shapes (positional or keyword arg in call signature). 3. If not all layers were properly built. 4. If float type inputs are not supported within the layers. In each of these cases, the user should build their model by calling it on real tensor data. NzIInput shape must be defined when calling `build()` on a `Model` subclass.zSpecified input shape is not one of the valid types. Please specify a batch input shape of type tuple or list of input shapes. User provided input type: {}. build_graphc3V# UHoSL=(d [U[5v M! g7fr)rLint)rds r=rModel.build..s$9=HI3As!33[')trainingFaqCurrently, you cannot build your model if it has positional or keyword arguments that are not inputs to the model, but are required for its `call()` method. Instead, in order to instantiate and build your model, `call()` your model on real tensor data with all expected call arguments. The argument for `call()` can be a single list/tuple that contains multiple inputs.z[You can only call `build()` on a model if its `call()` method accepts an `inputs` argument.zYou cannot build your model by calling `build` if your layers do not support float type inputs. Instead, in order to instantiate and build your model, call your model on real tensor data (of the correct dtype). The actual error from `call` is: .) rUr7build ValueErrortuplelistr^ TensorShapedictrLrStyperAexecuting_eagerly __internal__ FuncGraphr get_graph as_defaultrr generate_placeholders_from_shapeitems _call_spec full_argspecr:lendefaultscallerrorsInvalidArgumentErrorrR) rw input_shape valid_typesgraphshapexryr;call_signature call_argsarger<s r=r Model.builds<  ! ! GM+ &   & dBNND9 +33"#)&k):";   t{{{ ##%%11-@))+!!#k400S9=H966#( "4Kk400&1%0E)II%P%0A T22 )4(9(9(; )A%%..$-a3~7N7N3O2O$P $-abM (*,27F:.#-!< #  )&^a'$H IIa*6*u$H  k"} ` 66 B$& '(S +w$#sJ=AK& J .+K&#J%URGbUR(Gd[R"U5n[R"U5nURR X45unnnSn[ R RXe5n[ R RXc5n[ R RXd5n[R"UR5 [TU],"U/UQ70UD6 SSS5 [R"XR5 [TU],"U0UD6$!,(df  N>=f)Nc[U[R[R[ [ 45(a6[R"U5n[R"UR5$gr) rLr^Tensornpndarrayfloatrconvert_to_tensorinput_layer_moduleInputrrs r=_convert_to_graph_inputs0Model.__call__.._convert_to_graph_inputs8sKa"))RZZ!DEE,,Q/A-33AGG<<Fr?) rsrrrsplit_out_first_argr^r map_structurerqlayout_map_scoper7__call___map_subclass_model_variable)rwr:r; copied_args copied_kwargsrArr<s r=rModel.__call__(s     ' ))D/K IIf-M 33KO   = WW**+CLF''//(KGG11(M 001A1AB G+GGC  7 7>N>N Ow000 CBs -D:: Ec[S5e)aCalls the model on new inputs and returns the outputs as tensors. In this case `call()` just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs). Note: This method should not be called directly. It is only meant to be overridden when subclassing `tf.keras.Model`. To call a model on an input, always use the `__call__()` method, i.e. `model(inputs)`, which relies on the underlying `call()` method. Args: inputs: Input tensor, or dict/list/tuple of input tensors. training: Boolean or boolean scalar tensor, indicating whether to run the `Network` in training mode or inference mode. mask: A mask or list of masks. A mask can be either a boolean tensor or None (no mask). For more details, check the guide [here](https://www.tensorflow.org/guide/tf_keras/masking_and_padding). Returns: A tensor if there is a single output, or a list of tensors if there are more than one outputs. zUnimplemented `tf.keras.Model.call()`: if you intend to create a `Model` with the Functional API, please provide `inputs` and `outputs` arguments. Otherwise, subclass `Model` with an overridden `call()` method.)NotImplementedError)rwrArmasks r=r Model.callNs2" *  r?c U(a"SS9(dSn[R"UUUUUUUUS9UlUR R 5 SU ;a.[R"S5 U(dU RS5nU RSS5n UR"X40U D6 X`l URU5Ul Sn URbURR5n [!U["R$5(aX lO&["R$"UUUR(U S 9Ul["R*"UUUR(U U S 9UlUS :XaVUR.cUR1S 5 [2R4"URUR.5UlSUlOUR1U=(d S 5 UR;U 5UlUR?5 SUl U=(d 0Ul!UR(dURD(aU(a [GS 5eXl$SSS5 g!,(df  g=f)a^Configures the model for training. Example: ```python model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3), loss=tf.keras.losses.BinaryCrossentropy(), metrics=[tf.keras.metrics.BinaryAccuracy(), tf.keras.metrics.FalseNegatives()]) ``` Args: optimizer: String (name of optimizer) or optimizer instance. See `tf.keras.optimizers`. loss: Loss function. May be a string (name of loss function), or a `tf.keras.losses.Loss` instance. See `tf.keras.losses`. A loss function is any callable with the signature `loss = fn(y_true, y_pred)`, where `y_true` are the ground truth values, and `y_pred` are the model's predictions. `y_true` should have shape `(batch_size, d0, .. dN)` (except in the case of sparse loss functions such as sparse categorical crossentropy which expects integer arrays of shape `(batch_size, d0, .. dN-1)`). `y_pred` should have shape `(batch_size, d0, .. dN)`. The loss function should return a float tensor. If a custom `Loss` instance is used and reduction is set to `None`, return value has shape `(batch_size, d0, .. dN-1)` i.e. per-sample or per-timestep loss values; otherwise, it is a scalar. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss value that will be minimized by the model will then be the sum of all individual losses, unless `loss_weights` is specified. metrics: List of metrics to be evaluated by the model during training and testing. Each of this can be a string (name of a built-in function), function or a `tf.keras.metrics.Metric` instance. See `tf.keras.metrics`. Typically you will use `metrics=['accuracy']`. A function is any callable with the signature `result = fn(y_true, y_pred)`. To specify different metrics for different outputs of a multi-output model, you could also pass a dictionary, such as `metrics={'output_a':'accuracy', 'output_b':['accuracy', 'mse']}`. You can also pass a list to specify a metric or a list of metrics for each output, such as `metrics=[['accuracy'], ['accuracy', 'mse']]` or `metrics=['accuracy', ['accuracy', 'mse']]`. When you pass the strings 'accuracy' or 'acc', we convert this to one of `tf.keras.metrics.BinaryAccuracy`, `tf.keras.metrics.CategoricalAccuracy`, `tf.keras.metrics.SparseCategoricalAccuracy` based on the shapes of the targets and of the model output. We do a similar conversion for the strings 'crossentropy' and 'ce' as well. The metrics passed here are evaluated without sample weighting; if you would like sample weighting to apply, you can specify your metrics via the `weighted_metrics` argument instead. loss_weights: Optional list or dictionary specifying scalar coefficients (Python floats) to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be the *weighted sum* of all individual losses, weighted by the `loss_weights` coefficients. If a list, it is expected to have a 1:1 mapping to the model's outputs. If a dict, it is expected to map output names (strings) to scalar coefficients. weighted_metrics: List of metrics to be evaluated and weighted by `sample_weight` or `class_weight` during training and testing. run_eagerly: Bool. If `True`, this `Model`'s logic will not be wrapped in a `tf.function`. Recommended to leave this as `None` unless your `Model` cannot be run inside a `tf.function`. `run_eagerly=True` is not supported when using `tf.distribute.experimental.ParameterServerStrategy`. Defaults to `False`. steps_per_execution: Int or `'auto'`. The number of batches to run during each `tf.function` call. If set to "auto", keras will automatically tune `steps_per_execution` during runtime. Running multiple batches inside a single `tf.function` call can greatly improve performance on TPUs, when used with distributed strategies such as `ParameterServerStrategy`, or with small models with a large Python overhead. At most, one full epoch will be run each execution. If a number larger than the size of the epoch is passed, the execution will be truncated to the size of the epoch. Note that if `steps_per_execution` is set to `N`, `Callback.on_batch_begin` and `Callback.on_batch_end` methods will only be called every `N` batches (i.e. before/after each `tf.function` execution). Defaults to `1`. jit_compile: If `True`, compile the model training step with XLA. [XLA](https://www.tensorflow.org/xla) is an optimizing compiler for machine learning. `jit_compile` is not enabled for by default. Note that `jit_compile=True` may not necessarily work for all models. For more information on supported operations please refer to the [XLA documentation](https://www.tensorflow.org/xla). Also refer to [known XLA issues](https://www.tensorflow.org/xla/known_issues) for more details. pss_evaluation_shards: Integer or 'auto'. Used for `tf.distribute.ParameterServerStrategy` training only. This arg sets the number of shards to split the dataset into, to enable an exact visitation guarantee for evaluation, meaning the model will be applied to each dataset element exactly once, even if workers fail. The dataset must be sharded to ensure separate workers do not process the same data. The number of shards should be at least the number of workers for good performance. A value of 'auto' turns on exact evaluation and uses a heuristic for the number of shards based on the number of workers. 0, meaning no visitation guarantee is provided. NOTE: Custom implementations of `Model.test_step` will be ignored when doing exact evaluation. Defaults to `0`. **kwargs: Arguments supported for backwards compatibility only. TwarnF)rlossmetrics loss_weightsweighted_metrics run_eagerlysteps_per_execution jit_compile experimental_steps_per_executionzThe argument `steps_per_execution` is no longer experimental. Pass `steps_per_execution` instead of `experimental_steps_per_execution`.from_serializedN)rWr)rWr rautozCYou cannot enable `run_eagerly` and `jit_compile` at the same time.)%r$can_jit_compilerConfig_compile_configdistribute_strategyscopeloggingwarningpop_validate_compilere_get_optimizerrrsrrLrLossesContainerrZrWMetricsContainerr[r._configure_steps_per_executionr"StepsPerExecutionTunerrorp_infer_exact_eval_shards_pss_evaluation_shardsrfrHrrFrrv) rwrrrrrrr r pss_evaluation_shardsr;r rs r=compile Model.compileos*z x77TBK077%-# 3#  % % + + -1V;: +*0**:+'%jj):EBO  " "9 @ @ + !00;DND+''88:$ = =>>%)"%2%B%B !%!2!2 &" %2$B$B !.. / %D !#f,,,477:.EE(A(A/ 6:2334G4L1M*.*G*G%+D '  % % ' $D  DI!!T\\{ ( %0!M. - -s G?I&& I4cN^U4Sjn[RRX!5$)z7Wraps `optimizer` in `LossScaleOptimizer` if necessary.c>[R"U5nTRRS:Xa5[ U[ R 5(d[ R "U5nU$)N mixed_float16)r get dtype_policyrCrLlsoBaseLossScaleOptimizer)optrws r=_get_single_optimizer3Model._get_optimizer.._get_single_optimizerCsU..%C  %%8S//BB 005Jr?)r^rr)rwrr*s` r=rModel._get_optimizer@s  ww$$%:FFr?cfSUlSUlSUlSUlUR 5Ulgr)train_function test_functionpredict_functiontrain_tf_function_get_trainable_stater/rs r=rfModel._reset_compile_cacheOs7"! $ "&*.)B)B)D&r?c0URU5Ulgr)rr.)rwr s r=r$Model._configure_steps_per_execution^s$($A$A % !r?cg)NFrJrs r=_should_compute_maskModel._should_compute_maskdsr?c$/nUR(aJURbXRR- nURbXRR- nUR 5HnUR UR 5 M U$)awReturn metrics added using `compile()` or `add_metric()`. Note: Metrics passed to `compile()` are available only after a `keras.Model` has been trained/evaluated on actual data. Examples: >>> inputs = tf.keras.layers.Input(shape=(3,)) >>> outputs = tf.keras.layers.Dense(2)(inputs) >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs) >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) >>> [m.name for m in model.metrics] [] >>> x = np.random.random((2, 3)) >>> y = np.random.randint(0, 2, (2, 2)) >>> model.fit(x, y) >>> [m.name for m in model.metrics] ['loss', 'mae'] >>> inputs = tf.keras.layers.Input(shape=(3,)) >>> d = tf.keras.layers.Dense(2, name='out') >>> output_1 = d(inputs) >>> output_2 = d(inputs) >>> model = tf.keras.models.Model( ... inputs=inputs, outputs=[output_1, output_2]) >>> model.add_metric( ... tf.reduce_sum(output_2), name='mean', aggregation='mean') >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"]) >>> model.fit(x, (y, y)) >>> [m.name for m in model.metrics] ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae', 'out_1_acc', 'mean'] )rHrZrr[_flatten_layersextend_metrics)rwrls r=r Model.metricshs{J   !!---555$$000888%%'A NN1:: &(r?cXURVs/sHoRPM sn$s snf)aReturns the model's display labels for all outputs. Note: `metrics_names` are available only after a `keras.Model` has been trained/evaluated on actual data. Examples: >>> inputs = tf.keras.layers.Input(shape=(3,)) >>> outputs = tf.keras.layers.Dense(2)(inputs) >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs) >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) >>> model.metrics_names [] >>> x = np.random.random((2, 3)) >>> y = np.random.randint(0, 2, (2, 2)) >>> model.fit(x, y) >>> model.metrics_names ['loss', 'mae'] >>> inputs = tf.keras.layers.Input(shape=(3,)) >>> d = tf.keras.layers.Dense(2, name='out') >>> output_1 = d(inputs) >>> output_2 = d(inputs) >>> model = tf.keras.models.Model( ... inputs=inputs, outputs=[output_1, output_2]) >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"]) >>> model.fit(x, (y, y)) >>> model.metrics_names ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae', 'out_1_acc'] )rrCrwms r= metrics_namesModel.metrics_namess#L!% - 1 ---s'cdUR=(d [RR5$)z:The `tf.distribute.Strategy` this model was created under.)rbr^r_rars r=rModel.distribute_strategys"**Jbmm.H.H.JJr?cfUR(aURS:Xa [S5eUR(aUR(a [S5eUR=(dF UR=(d3 [R R 5=(a URSL$)aSettable attribute indicating whether the model should run eagerly. Running eagerly means that your model will be run step by step, like Python code. Your model might run slower, but it should become easier for you to debug it by stepping into individual layer calls. By default, we will attempt to compile your model to a static graph to deliver the best execution performance. Returns: Boolean, whether the model should run eagerly. FzYour model contains layers that can only be successfully run in eager execution (layers constructed with `dynamic=True`). You cannot set `run_eagerly=False`.zRWhen using `Model` with `ParameterServerStrategy`, `run_eagerly` is not supported.N)rFrerrdr^configfunctions_run_eagerlyrs r=rModel.run_eagerlys <, and therefore expects target data to be provided in `fit()`.z]No loss found. You may have forgotten to provide a `loss` argument in the `compile()` method.)rr)rwyrs r=_validate_target_and_lossModel._validate_target_and_loss=sT" 99 {#OO \6 r?cV[R"U5up#n[R"5nU"USS9nUR X#Xd5nSSS5 UR UW5 UR RXpRWS9 URX#WU5$!,(df  NW=f)aThe logic for one training step. This method can be overridden to support custom training logic. For concrete examples of how to override this method see [Customizing what happens in fit]( https://www.tensorflow.org/guide/tf_keras/customizing_what_happens_in_fit). This method is called by `Model.make_train_function`. This method should contain the mathematical logic for one step of training. This typically includes the forward pass, loss calculation, backpropagation, and metric updates. Configuration details for *how* this logic is run (e.g. `tf.function` and `tf.distribute.Strategy` settings), should be left to `Model.make_train_function`, which can also be overridden. Args: data: A nested structure of `Tensor`s. Returns: A `dict` containing values that will be passed to `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the values of the `Model`'s metrics are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`. TrN)tape) runpack_x_y_sample_weightr^ GradientTape compute_lossr_rminimizetrainable_variablescompute_metrics)rwdatarr^ sample_weightrcy_predrs r= train_stepModel.train_step^s4+CCDIm __ $!d+F$$Q6AD &&q$/ &>&>TJ##A&-@@  s B B(c8AURX#X@RS9$)aCompute the total loss, validate it, and return it. Subclasses can optionally override this method to provide custom loss computation logic. Example: ```python class MyModel(tf.keras.Model): def __init__(self, *args, **kwargs): super(MyModel, self).__init__(*args, **kwargs) self.loss_tracker = tf.keras.metrics.Mean(name='loss') def compute_loss(self, x, y, y_pred, sample_weight): loss = tf.reduce_mean(tf.math.squared_difference(y_pred, y)) loss += tf.add_n(self.losses) self.loss_tracker.update_state(loss) return loss def reset_metrics(self): self.loss_tracker.reset_states() @property def metrics(self): return [self.loss_tracker] tensors = tf.random.uniform((10, 10)), tf.random.uniform((10,)) dataset = tf.data.Dataset.from_tensor_slices(tensors).repeat().batch(1) inputs = tf.keras.layers.Input(shape=(10,), name='my_input') outputs = tf.keras.layers.Dense(10)(inputs) model = MyModel(inputs, outputs) model.add_loss(tf.reduce_sum(outputs)) optimizer = tf.keras.optimizers.SGD() model.compile(optimizer, loss='mse', steps_per_execution=10) model.fit(dataset, epochs=2, steps_per_epoch=10) print('My custom loss: ', model.loss_tracker.result().numpy()) ``` Args: x: Input data. y: Target data. y_pred: Predictions returned by the model (output of `model(x)`) sample_weight: Sample weights for weighting the loss function. Returns: The total loss as a `tf.Tensor`, or `None` if no loss results (which is the case when called by `Model.test_step`). )regularization_losses)rZlossesrwrr^rlrks r=rfModel.compute_losss)f !! }KK"  r?c\AURRX#U5 UR5$)aUpdate metric states and collect all metrics to be returned. Subclasses can optionally override this method to provide custom metric updating and collection logic. Example: ```python class MyModel(tf.keras.Sequential): def compute_metrics(self, x, y, y_pred, sample_weight): # This super call updates `self.compiled_metrics` and returns # results for all metrics listed in `self.metrics`. metric_results = super(MyModel, self).compute_metrics( x, y, y_pred, sample_weight) # Note that `self.custom_metric` is not listed in `self.metrics`. self.custom_metric.update_state(x, y, y_pred, sample_weight) metric_results['custom_metric_name'] = self.custom_metric.result() return metric_results ``` Args: x: Input data. y: Target data. y_pred: Predictions returned by the model (output of `model.call(x)`) sample_weight: Sample weights for weighting the loss function. Returns: A `dict` containing values that will be passed to `tf.keras.callbacks.CallbackList.on_train_batch_end()`. Typically, the values of the metrics listed in `self.metrics` are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`. )r[ update_stateget_metrics_resultrrs r=riModel.compute_metricss-F  **1mD&&((r?c0nURHInUR5n[U[5(aUR U5 M;X1UR 'MK U$)a:Returns the model's metrics values as a dict. If any of the metric result is a dict (containing multiple metrics), each of them gets added to the top level returned dict of this method. Returns: A `dict` containing values of the metrics listed in `self.metrics`. Example: `{'loss': 0.2, 'accuracy': 0.7}`. )rresultrLrupdaterC)rwreturn_metricsmetricrys r=rvModel.get_metrics_resultsQllF]]_F&$''%%f-.4v{{+ # r?cSnUR5n[U[5(aL[UR 55[UR 55:Xa[ R "U5nU$UR(a[R"U5 U$![a+ UR(a[R"U5 U$f=f)aReturns model metrics as a dict if the keys match with input logs. When the training / evalution is performed with asynchronous steps, such as the case with `tf.distribute.ParameterServerStrategy`, the last scheduled `train / test_step` may not give the latest metrics because it is not guaranteed to be executed the last. This method gets metrics from the model directly instead of relying on the return from last step function. It logs a warning if the metric results could not be overridden when used with `tf.distribute.ParameterServerStrategy`. When the user has custom train / test step functions, the metrics returned may be different from `Model.metrics`. In those instances, this function will be no-op and return the logs. Args: logs: A `dict` of metrics returned by train / test step function. Returns: A `dict` containing values of the metrics listed in `self.metrics` when logs and model metrics keys match. Otherwise it returns input `logs`. zCould not get Model metric results. Using the results of last step function could lead to incorrect results when used with ParameterServerStrategy) rvrLrsetkeysr$sync_to_numpy_or_python_typerdrrrR)rwlogs PSS_WARN_MSG metric_logss r= _validate_and_get_metrics_result&Model._validate_and_get_metrics_results28  .113K$%%#diik*:c  "?+ <<[I ** -  .(( -  .sB1CCc <UGHnURHnURUR5;a6[R"[R SURS3S5 MWX#Rn[ U5[ UR5:wa;[S[ UR5SURS[ U5S35e[URU5HupVURU5 M M GM UR5$)Nz$No matching result found for metric z1. This metric's computed result may be incorrect.z Expected z variables in result for metric z , but found r) rrCrr log_first_nWARNrweightsrzip assign_addrv)rwr shard_resultr| metric_resultweightvals r=_aggregate_exact_metricsModel._aggregate_exact_metrics"s!L,,;;l&7&7&99'' >v{{mLJJ   ,[[ 9 }%V^^)<<$#C$7#89&&,kk],}-.a1 $'v~~}#EKF%%c*$F!'!&&&((r?c^^^TRbU(d TR$U4SjmTRb=TRR5R5S:XaTR(dqUU4SjmTR (d[ R"TSS9mTTlTR(aUU4SjTlTR$TTlTR$TR(aLUU4SjmTR (d[ R"TSS9mTTlUU4SjTlTR$UU4S jmTR (d[ R"TSS9mTTlTTlTR$) aCreates a function that executes one step of training. This method can be overridden to support custom training logic. This method is called by `Model.fit` and `Model.train_on_batch`. Typically, this method directly controls `tf.function` and `tf.distribute.Strategy` settings, and delegates the actual training logic to `Model.train_step`. This function is cached the first time `Model.fit` or `Model.train_on_batch` is called. The cache is cleared whenever `Model.compile` is called. You can skip the cache and generate again the function with `force=True`. Args: force: Whether to regenerate the train function and skip the cached function if available. Returns: Function. The function created by this method should accept a `tf.data.Iterator`, and return a `dict` containing values that will be passed to `tf.keras.Callbacks.on_train_batch_end`, such as `{'loss': 0.2, 'accuracy': 0.7}`. c>^U4SjnTR(a[R"USSS9n[U5nTRR X#4S9n[ UTRTRS9nU$)zRuns a single training step.c>TRU5n[R"[U55 TRR S5 SSS5 U$!,(df  U$=frP)rmr^control_dependencies_minimum_control_depsr+rrjrBmodels r=run_stepBModel.make_train_function..step_function..run_stepYsU**40,,-B7-KL((33A6MML A A'Tr reduce_retracingr: reductionr r^functionnextrrunreduce_per_replicarZriteratorrrjrBrws` r= step_function0Model.make_train_function..step_functionVst ;;$>D//33H73KG(((::G Nr?rc>T"TU5$)z-Runs a training execution with a single step.rJrrwrs r=r.1Model.make_train_function..train_functiont$T844r?Trc:>TRRTU4S9$Nrrdscheduleitrwr.s r=+Model.make_train_function..s#t88AA&bU B r?cT>[R"U5H nT"TU5nM W$z.Runs a training execution with multiple steps.r^rangerr _rBrwrs r=r.r("56A+D(;G7r?cl>TRRTUTRR54S9$rrdrr.rrs r=rrs5T-F-F-O-Ob$*C*C*I*I*K%L.P.r?ch>[R"TR5H nT"TU5nM W$rr^rr.rrrBrwrs r=r.r-$";";<#>#> % ;;(- +*D#/#@#@#Bx""$((/!668 , 2 2 4[[55;;#&+%)'1 <&::4@'+':':8'DH+77 ' 2 2 4#+D'+l.I.I'IH%884H#11 %! 2!59( <2O2O##*;'<'J ,<*+#$+9$+0C"&040I0I262M2M3/ $}}&7#8#FJ."+'5 ',?$(15 - H=ENN>6A:77,, t148D+00//446  " " " 6<V  V!  V! (DV9?V3 7V9B-V9; W V V! ! V0 +V99 W W W!c[R"U5up#nU"USS9nURX#XT5 URX#XT5$)aThe logic for one evaluation step. This method can be overridden to support custom evaluation logic. This method is called by `Model.make_test_function`. This function should contain the mathematical logic for one step of evaluation. This typically includes the forward pass, loss calculation, and metrics updates. Configuration details for *how* this logic is run (e.g. `tf.function` and `tf.distribute.Strategy` settings), should be left to `Model.make_test_function`, which can also be overridden. Args: data: A nested structure of `Tensor`s. Returns: A `dict` containing values that will be passed to `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the values of the `Model`'s metrics are returned. Frb)rrdrfri)rwrjrr^rkrls r= test_stepModel.test_stepasH.+CCDIma%( !6##A&@@r?c^^^[TSS5(a TR$U4SjmUU4SjmTR(d[R"TSS9mUU4SjTlTR$)N_shard_test_functionc>U4SjnTR(a[R"USSS9nTRR X4S9n[ UTRTR S9nU$)NcL>[R"U5upnT"USS9nXXC4$)NFrbrrd)rjrr^rkrlrws r=rHModel._make_test_function_exact..step_function..run_steps4&2&K&K'#ma%0V22r?Trrr)rvr^rrrrrZ)batchrrBrws r=r6Model._make_test_function_exact..step_functionsk 3  ;;$..228(2KG(((::G Nr?cb>//pC[R"5 TRRH-nUcMUR [ R "U55 M/ TRRH-nUcMUR [ R "U55 M/ [RRTRR55nSSS5 [R"XU5n[U5n[ R""5 UHLnT"U5uppUHn U R%XU 5 M UHnUR%X5 M W"XU 5 MN SSS5 UU-WR&-nUVs0sHoUR(UR*_M nn[,R."[1U55 TR2R5S5 SSS5 U$!,(df  GN=f!,(df  N=fs snf!,(df  U$=frP)r$with_metric_local_vars_scoper[unweighted_metricsrQr clone_metricrrr from_configrZ get_configrauto_shard_datasetiterrcache_variable_readsrurrCrr^rrr,r)dataset total_shards shard_idxlocal_unweighted_metricslocal_weighted_metricsr| local_lossrr&rr^rlrkweighted_metricunweighted_metric local_metricsrBrwrs r=shard_test_function.shard_test_functions @B2&<668#33FFF)077'44V<G #33DDF).55'44V<E +::FF&&113 9" 22yGG}H!668%E2?2F/A&+A'44Q N,B-E))66qA.Fq-8 &9)()$$%  BOOv{{FNN2GO(()>w)GH""--a0INK98*98PHHNs7G7AG7A G7AH  HH7 H H H.Trc8>TRRTUS9$rr)r:rwr:s r=r1Model._make_test_function_exact..s!$33<<#=r?)rr!rr^r)rwr:rs`@@r=_make_test_function_exactModel._make_test_function_exactsd 4/ 6 6,, , ,+ Z"$++#d#    ! (((r?c^^^TRbU(d TR$U4SjmTRb=TRR5R5S:Xa{TR(djUU4SjmTR (d[ R"TSS9mTR(aUU4SjTlTR$TTlTR$TR(aEUU4SjmTR (d[ R"TSS9mUU4SjTlTR$UU4S jmTR (d[ R"TSS9mTTlTR$) anCreates a function that executes one step of evaluation. This method can be overridden to support custom evaluation logic. This method is called by `Model.evaluate` and `Model.test_on_batch`. Typically, this method directly controls `tf.function` and `tf.distribute.Strategy` settings, and delegates the actual evaluation logic to `Model.test_step`. This function is cached the first time `Model.evaluate` or `Model.test_on_batch` is called. The cache is cleared whenever `Model.compile` is called. You can skip the cache and generate again the function with `force=True`. Args: force: Whether to regenerate the test function and skip the cached function if available. Returns: Function. The function created by this method should accept a `tf.data.Iterator`, and return a `dict` containing values that will be passed to `tf.keras.Callbacks.on_test_batch_end`. c>^U4SjnTR(a[R"USSS9n[U5nTRR X#4S9n[ UTRTRS9nU$)Runs a single evaluation step.c>TRU5n[R"[U55 TRR S5 SSS5 U$!,(df  U$=frP)rr^rrr,rrs r=rAModel.make_test_function..step_function..run_stepsS//$/,,-B7-KL''2215MMLrTrrrrrs` r=r/Model.make_test_function..step_functionst ;;$>D//33H73KG(((::G Nr?rc>T"TU5$)z)Runs a test execution with a single step.rJrs r=r//Model.make_test_function..test_function rr?Trc:>TRRTU4S9$rrrrwr/s r=r*Model.make_test_function..s#t88AA%RE B r?cT>[R"U5H nT"TU5nM W$z*Runs a test execution with multiple steps.rrs r=r/rG$rr?cl>TRRTUTRR54S9$rrrIs r=rrJ/s5D,E,E,N,NR)B)B)H)H)J$K-O-r?ch>[R"TR5H nT"TU5nM W$rLrrs r=r/rG4rr?) r/r.rrrMrr^rrd)rwrrr/s` @@r=make_test_functionModel.make_test_functions>0    )%%% % 4  % % -((..05571<55 5## " !D! (("R!!!G&3"F!!!= & &  ## " !D! "D "!!!  ## " !D! "/D !!!r?c  [R"SS5 UR5 URS5 UR X5 [ S5 U R SS5n U (a%[S[U R5535eURR(aB[RRRR!UR5Ul[%X@R5nUR&(aUR)5 URR+5 U (a[-USS5b UR.nO4[0R2"UUUUUSS UU U UUR4UR&S 9 n[7U[8R:5(d'[8R:"US US:gUUS UR<S 9n0nUR?U5nUR@RCS5 URE5 URF(aURHRK5 URM5HunnURO5 URQ5 URS5Hdn[RTRRWS US S9 URYU5 UR[UUUUR&5nSSS5 Mf SSS5 M [\R^"U5nUR&(aURaU5nOURcU5nURF(aURHRe5 URgUS9 U (a UsSSS5 $[iXRj5sSSS5 $!,(df  GM8=f!,(df  GM=f!,(df  g=f)aReturns the loss value & metrics values for the model in test mode. Computation is done in batches (see the `batch_size` arg.) Args: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. - A `tf.data` dataset. Should return a tuple of either `(inputs, targets)` or `(inputs, targets, sample_weights)`. - A generator or `keras.utils.Sequence` returning `(inputs, targets)` or `(inputs, targets, sample_weights)`. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the `Unpacking behavior for iterator-like inputs` section of `Model.fit`. y: Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely). If `x` is a dataset, generator or `keras.utils.Sequence` instance, `y` should not be specified (since targets will be obtained from the iterator/dataset). batch_size: Integer or `None`. Number of samples per batch of computation. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` if your data is in the form of a dataset, generators, or `keras.utils.Sequence` instances (since they generate batches). verbose: `"auto"`, 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. `"auto"` becomes 1 for most cases, and to 2 when used with `ParameterServerStrategy`. Note that the progress bar is not particularly useful when logged to a file, so `verbose=2` is recommended when not running interactively (e.g. in a production environment). Defaults to 'auto'. sample_weight: Optional Numpy array of weights for the test samples, used for weighting the loss function. You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape `(samples, sequence_length)`, to apply a different weight to every timestep of every sample. This argument is not supported when `x` is a dataset, instead pass sample weights as the third element of `x`. steps: Integer or `None`. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of `None`. If x is a `tf.data` dataset and `steps` is None, 'evaluate' will run until the dataset is exhausted. This argument is not supported with array inputs. callbacks: List of `keras.callbacks.Callback` instances. List of callbacks to apply during evaluation. See [callbacks](https://www.tensorflow.org/api_docs/python/tf/tf_keras/callbacks). max_queue_size: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum size for the generator queue. If unspecified, `max_queue_size` will default to 10. workers: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. use_multiprocessing: Boolean. Used for generator or `keras.utils.Sequence` input only. If `True`, use process-based threading. If unspecified, `use_multiprocessing` will default to `False`. Note that because this implementation relies on multiprocessing, you should not pass non-pickleable arguments to the generator as they can't be passed easily to children processes. return_dict: If `True`, loss and metric results are returned as a dict, with each key being the name of the metric. If `False`, they are returned as a list. **kwargs: Unused at this time. See the discussion of `Unpacking behavior for iterator-like inputs` for `Model.fit`. Returns: Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. Raises: RuntimeError: If `model.evaluate` is wrapped in a `tf.function`. r)rrFzInvalid keyword arguments: rNrrrTrtest)rrr)6r&rrr_check_sample_weight_warningrrrRrrrrr^r_rrrrdrrrrrrrrr.rLrrr_get_test_function_runnerr,rT on_test_beginrMrorrrrrrron_test_batch_beginrr$rrrr  on_test_endflatten_metrics_in_orderrB)rwrr^rrrkrr rrrrr;use_cached_eval_datasetrrtest_function_runnerrdataset_or_iteratorrs r=rModel.evaluateBsML ++GZ@ '') j) ))!;$Z0"(**-G"O 9$v{{}:M9NOP P  # # @ @ **66II,,  % !*B*BC  & &  6 6 8  % % + + -(D"6=I#66  ,<<"/)$)"##1#(;(,(A(A*.*E*E  "i)9)F)FGG,99 $ '1 #&55 D#'#A#A)#L    % %a (  # # %00//557..0#""$!668 , 2 2 4[[55;;"Ta<&99$?#7#@#@ 3 , $ $ ; ; $D !598188>D**44T:<r/_ExactTestFunctionrO _TestFunction)rwr rZs r=rTModel._get_test_function_runner& su  ' '((EE!%!?!?!AD #5""I$ $#"&!8!8!:D #01C1CY#O ##r?cD[R"U5un nU"USS9$)ahThe logic for one inference step. This method can be overridden to support custom inference logic. This method is called by `Model.make_predict_function`. This method should contain the mathematical logic for one step of inference. This typically includes the forward pass. Configuration details for *how* this logic is run (e.g. `tf.function` and `tf.distribute.Strategy` settings), should be left to `Model.make_predict_function`, which can also be overridden. Args: data: A nested structure of `Tensor`s. Returns: The result of one inference step, typically the output of calling the `Model` on data. Frbr$)rwrjrrs r= predict_stepModel.predict_step4 s'(77=1aA&&r?cv^^TRbU(d TR$U4SjmTRb=TRR5R5S:XaTR(dUU4SjnOUU4SjnTR (d[ R"USS9nUTlTR$)a6Creates a function that executes one step of inference. This method can be overridden to support custom inference logic. This method is called by `Model.predict` and `Model.predict_on_batch`. Typically, this method directly controls `tf.function` and `tf.distribute.Strategy` settings, and delegates the actual evaluation logic to `Model.predict_step`. This function is cached the first time `Model.predict` or `Model.predict_on_batch` is called. The cache is cleared whenever `Model.compile` is called. You can skip the cache and generate again the function with `force=True`. Args: force: Whether to regenerate the predict function and skip the cached function if available. Returns: Function. The function created by this method should accept a `tf.data.Iterator`, and return the outputs of the `Model`. c>^U4SjnTR(a[R"USSS9n[U5nTRR X#4S9n[ UTRSS9nU$)rBc>TRU5n[R"[U55 TRR S5 SSS5 U$!,(df  U$=frP)rkr^rrr-rrs r=rDModel.make_predict_function..step_function..run_steph sU,,T2,,-B7-KL**55a8MMLrTrrconcatr)r r^rrrrrrs` r=r2Model.make_predict_function..step_functione sl ;;$>D//33H73KG(11XGNr?rc>T"TU5$)z0Runs an evaluation execution with a single step.rJrs r=r05Model.make_predict_function..predict_function rr?cT>T"TU5n[R"TRS- 5Hvn[RRR U[R RSU54/S9 T"TU5n[R RSX5nMx U$)z1Runs an evaluation execution with multiple steps.rc@[R"USS9R$)NT) dynamic_batch)r$get_tensor_specr)ts r=rGModel.make_predict_function..predict_function.. sh.F.F()/&&+e/,r?)shape_invariantsc[X/5$r)rq)t1t2s r=rrz s vrh'7r?)r^rr. autographrset_loop_optionsrr)rrBr step_outputsrwrs r=r0rt s'h7$";";a"?@ALL-->>!( " 5 5%,%, !" *? $1x#@L gg337GA$r?Tr)r0r.rrrMrr^r)rwrr0rs` @r=make_predict_functionModel.make_predict_functionK s.  ,U(( ( 0  % % -((..05571<55 5  .!{{ 4  !1$$$r?c [R"SS5 URS5 [S5 Sn URR (aURn SUlUR(aSUl[X0R5nSn URR5 [RRRR[RR4n UR5(d[!UR5(a[#X5(ao[RR%5n [RR&R(R*n U U R,lUR1U 5n[8R:"UUUSSUUUUUR<S 9 n[#U[>R@5(d'[>R@"US US:gUUSURBS 9nURE5Ul#URHRKS5 URM5 URN(aURPRS5 SnURU5HunnURW5 URY5HnUR[U5 URGU5nUR\(a[^R`"5 UnU c"[RbReS U5n O-[RfRbRiUS U U5 UURj-nURmUSU05 M SSS5 M Uc [3S5eURN(aURPRo5 URq5 SSS5 [RfRbRiW[rU 5nU bXl[tRv"U5$![2a [4R6"SSS9 GNf=f!,(df  GM=f!,(df  N=f)aGenerates output predictions for the input samples. Computation is done in batches. This method is designed for batch processing of large numbers of inputs. It is not intended for use inside of loops that iterate over your data and process small numbers of inputs at a time. For small numbers of inputs that fit in one batch, directly use `__call__()` for faster execution, e.g., `model(x)`, or `model(x, training=False)` if you have layers such as `tf.keras.layers.BatchNormalization` that behave differently during inference. You may pair the individual model call with a `tf.function` for additional performance inside your inner loop. If you need access to numpy array values instead of tensors after your model call, you can use `tensor.numpy()` to get the numpy array value of an eager tensor. Also, note the fact that test loss is not affected by regularization layers like noise and dropout. Note: See [this FAQ entry]( https://keras.io/getting_started/faq/#whats-the-difference-between-model-methods-predict-and-call) for more details about the difference between `Model` methods `predict()` and `__call__()`. Args: x: Input samples. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A `tf.data` dataset. - A generator or `keras.utils.Sequence` instance. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the `Unpacking behavior for iterator-like inputs` section of `Model.fit`. batch_size: Integer or `None`. Number of samples per batch. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` if your data is in the form of dataset, generators, or `keras.utils.Sequence` instances (since they generate batches). verbose: `"auto"`, 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. `"auto"` becomes 1 for most cases, and to 2 when used with `ParameterServerStrategy`. Note that the progress bar is not particularly useful when logged to a file, so `verbose=2` is recommended when not running interactively (e.g. in a production environment). Defaults to 'auto'. steps: Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of `None`. If x is a `tf.data` dataset and `steps` is None, `predict()` will run until the input dataset is exhausted. callbacks: List of `keras.callbacks.Callback` instances. List of callbacks to apply during prediction. See [callbacks]( https://www.tensorflow.org/api_docs/python/tf/tf_keras/callbacks). max_queue_size: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum size for the generator queue. If unspecified, `max_queue_size` will default to 10. workers: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. use_multiprocessing: Boolean. Used for generator or `keras.utils.Sequence` input only. If `True`, use process-based threading. If unspecified, `use_multiprocessing` will default to `False`. Note that because this implementation relies on multiprocessing, you should not pass non-pickleable arguments to the generator as they can't be passed easily to children processes. See the discussion of `Unpacking behavior for iterator-like inputs` for `Model.fit`. Note that Model.predict uses the same interpretation rules as `Model.fit` and `Model.evaluate`, so inputs must be unambiguous for all three methods. Returns: Numpy array(s) of predictions. Raises: RuntimeError: If `model.predict` is wrapped in a `tf.function`. ValueError: In case of mismatch between the provided input data and the model's expectations, or in case a stateful model receives a number of samples that is not a multiple of the batch size. r)predictNzUsing Model.predict with MultiWorkerMirroredStrategy or TPUStrategy and AutoShardPolicy.FILE might lead to out-of-order result. Consider setting it to AutoShardPolicy.DATA.r stacklevelrr) rrrrrrrrrr TrcU/$rrJ) batch_outputs r=rModel.predict..b sl^r?c$URU5$r)rQ)outputrs r=rrh sV]]$0>"r?rBzUnexpected result of `predict_function` (Empty batch_outputs). Please use `Model.compile(..., run_eagerly=True)`, or `tf.config.run_functions_eagerly(True)` for more information of where went wrong, or file a issue/bug to `tf.keras`.)&>??Q.. ggoo/G"$''"6"6"F"F"K"KK#33Ew/A(88% %-$7$($=$= Li)9)F)FGG,99 $ '1 #&55 %)$>$>$@D !  ! ! ( ( +  & & (00//557 M+<<> 8!668 , 2 2 4!88>,0,A,A(,K)'33#..0-&#?&(gg&;&; C -'G OO00DD -!"!( - $(,*E*E#E!66$y-&@/!598 ?8$ /00//446  $ $ &{.|oo**>> 4g  ! ,*? '44[AAu"MM0$% R98o. -sM-BQ6A.P&$DQ$CQ ;AQ&Q QQ  Q Q  Q Q-cJURHnUR5 M g)aResets the state of all the metrics in the model. Examples: >>> inputs = tf.keras.layers.Input(shape=(3,)) >>> outputs = tf.keras.layers.Dense(2)(inputs) >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs) >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) >>> x = np.random.random((2, 3)) >>> y = np.random.randint(0, 2, (2, 2)) >>> _ = model.fit(x, y, verbose=0) >>> assert all(float(m.result()) for m in model.metrics) >>> model.reset_metrics() >>> assert all(float(m.result()) == 0 for m in model.metrics) N)r reset_stater@s r=rModel.reset_metrics s&A MMOr?c HUR5 URS5 [S5 U(aUR5 URR 5 [ R"U5 [R"URXX45nUR5Ul URU5nSSS5 SSS5 [R"W5nU(aU$[XR5$!,(df  NJ=f!,(df  NS=f)aRuns a single gradient update on a single batch of data. Args: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. y: Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). sample_weight: Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. class_weight: Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class. When `class_weight` is specified and targets have a rank of 2 or greater, either `y` must be one-hot encoded, or an explicit final dimension of `1` must be included for sparse class labels. reset_metrics: If `True`, the metrics returned will be only for this batch. If `False`, the metrics will be statefully accumulated across batches. return_dict: If `True`, loss and metric results are returned as a dict, with each key being the name of the metric. If `False`, they are returned as a list. Returns: Scalar training loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. Raises: RuntimeError: If `model.train_on_batch` is wrapped in a `tf.function`. train_on_batchN)rrrrrrrrrsingle_batch_iteratorrr.r$rrXrB) rwrr^rkrrrrrs r=rModel.train_on_batch sd '') ./$%56      % % + + -~/[/[ 0 $99((! H#'":":"D D D D!cUR5 URS5 [S5 U(aUR5 URR 5 [ R"URXU5nUR5Ul URU5nSSS5 [R"W5nU(aU$[XpR5$!,(df  NB=f)aGTest the model on a single batch of samples. Args: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. y: Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely). sample_weight: Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. reset_metrics: If `True`, the metrics returned will be only for this batch. If `False`, the metrics will be statefully accumulated across batches. return_dict: If `True`, loss and metric results are returned as a dict, with each key being the name of the metric. If `False`, they are returned as a list. Returns: Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. Raises: RuntimeError: If `model.test_on_batch` is wrapped in a `tf.function`. test_on_batchN)rrrrrrrrrOr/r$rrXrB)rwrr^rkrrrrs r=rModel.test_on_batch sV '') o.$_5      % % + + -#99((! H"&!8!8!:D %%h/D .44T: K+D2D2DE E. -s A C## C1c\URS5 [S5 URR5 [R "URU5nUR 5UlURU5nSSS5 [R"W5$!,(df  N$=f)aReturns predictions for a single batch of samples. Args: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). Returns: Numpy array(s) of predictions. Raises: RuntimeError: If `model.predict_on_batch` is wrapped in a `tf.function`. predict_on_batchN) rrrrrrrr0r$r)rwrrrBs r=rModel.predict_on_batch# s" 01$%78  % % + + -#99((!H%)$>$>$@D !++H5G . 44W== . -s AB B+cd[R"SSS9 URUUUUUUUUU U U U U US9$)zFits the model on data yielded batch-by-batch by a Python generator. DEPRECATED: `Model.fit` now supports generators, so there is no longer any need to use this endpoint. z`Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.rr) rrrr r r rrrrrrr)rrr)rw generatorrrrr r r rrrrrrrs r= fit_generatorModel.fit_generator> s]0   A  xx ++-+%) 3'  r?c x[R"SSS9 URS5 URUUUUUUUS9$)zEvaluates the model on a data generator. DEPRECATED: `Model.evaluate` now supports generators, so there is no longer any need to use this endpoint. z`Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.rrevaluate_generatorrrrrrr )rrrrrwrrr rrrrs r=rModel.evaluate_generatorm sW"   F  23}} ) 3  r?c V[R"SSS9 URUUUUUUUS9$)zGenerates predictions for the input samples from a data generator. DEPRECATED: `Model.predict` now supports generators, so there is no longer any need to use this endpoint. z`Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.rrr)rrrrs r=predict_generatorModel.predict_generator sH"   E  || ) 3  r?cUR5 UR(d/$/nURHnXR- nM XR- nUR U5$r)_assert_weights_created _trainable_self_tracked_trackablesrh_trainable_weights_dedup_weights)rwrh trackable_objs r=trainable_weightsModel.trainable_weights s` $$&I !::M #D#D D ;666""#677r?cNUR5 /nURHnXR- nM UR(dC/nURHnX2R- nM UUR -U-UR -nOXR -nURU5$r)rrnon_trainable_variablesrrhr_non_trainable_weightsr)rwrrrhs r=non_trainable_weightsModel.non_trainable_weights s $$&"$!::M #'L'L L #;"$ !%!>!> #'H'HH#"?$))*)*--. $(*E*EE $""#:;;r?c>URR5 [TU] 5sSSS5 $!,(df  g=f)zORetrieves the weights of the model. Returns: A flat list of Numpy arrays. N)rrr7 get_weightsrs r=rModel.get_weights s.  % % + + -7&(. - -s 4 Ac :[R"U4UUUS.UD6 g)a9Saves a model as a TensorFlow SavedModel or HDF5 file. See the [Serialization and Saving guide]( https://keras.io/guides/serialization_and_saving/) for details. Args: model: TF-Keras model instance to be saved. filepath: `str` or `pathlib.Path` object. Path where to save the model. overwrite: Whether we should overwrite any existing model at the target location, or instead ask the user via an interactive prompt. save_format: Either `"keras"`, `"tf"`, `"h5"`, indicating whether to save the model in the native TF-Keras format (`.keras`), in the TensorFlow SavedModel format (referred to as "SavedModel" below), or in the legacy HDF5 format (`.h5`). Defaults to `"tf"` in TF 2.X, and `"h5"` in TF 1.X. SavedModel format arguments: include_optimizer: Only applied to SavedModel and legacy HDF5 formats. If False, do not save the optimizer state. Defaults to `True`. signatures: Only applies to SavedModel format. Signatures to save with the SavedModel. See the `signatures` argument in `tf.saved_model.save` for details. options: Only applies to SavedModel format. `tf.saved_model.SaveOptions` object that specifies SavedModel saving options. save_traces: Only applies to SavedModel format. When enabled, the SavedModel will store the function traces for each layer. This can be disabled, so that only the configs of each layer are stored. Defaults to `True`. Disabling this will decrease serialization time and reduce file size, but it requires that all custom layers/models implement a `get_config()` method. Example: ```python model = tf.keras.Sequential([ tf.keras.layers.Dense(5, input_shape=(3,)), tf.keras.layers.Softmax()]) model.save("model.keras") loaded_model = tf.keras.models.load_model("model.keras") x = tf.random.uniform((10, 3)) assert np.allclose(model.predict(x), loaded_model.predict(x)) ``` Note that `model.save()` is an alias for `tf.keras.models.save_model()`. )filepath overwrite save_formatN)r save_model)rwrrrr;s r=save Model.save s.l   #    r?c4[R"UUUUUS9 g)a Saves all layer weights. Either saves in HDF5 or in TensorFlow format based on the `save_format` argument. When saving in HDF5 format, the weight file has: - `layer_names` (attribute), a list of strings (ordered names of model layers). - For every layer, a `group` named `layer.name` - For every such layer group, a group attribute `weight_names`, a list of strings (ordered names of weights tensor of the layer). - For every weight in the layer, a dataset storing the weight value, named after the weight tensor. When saving in TensorFlow format, all objects referenced by the network are saved in the same format as `tf.train.Checkpoint`, including any `Layer` instances or `Optimizer` instances assigned to object attributes. For networks constructed from inputs and outputs using `tf.keras.Model(inputs, outputs)`, `Layer` instances used by the network are tracked/saved automatically. For user-defined classes which inherit from `tf.keras.Model`, `Layer` instances must be assigned to object attributes, typically in the constructor. See the documentation of `tf.train.Checkpoint` and `tf.keras.Model` for details. While the formats are the same, do not mix `save_weights` and `tf.train.Checkpoint`. Checkpoints saved by `Model.save_weights` should be loaded using `Model.load_weights`. Checkpoints saved using `tf.train.Checkpoint.save` should be restored using the corresponding `tf.train.Checkpoint.restore`. Prefer `tf.train.Checkpoint` over `save_weights` for training checkpoints. The TensorFlow format matches objects and variables by starting at a root object, `self` for `save_weights`, and greedily matching attribute names. For `Model.save` this is the `Model`, and for `Checkpoint.save` this is the `Checkpoint` even if the `Checkpoint` has a model attached. This means saving a `tf.keras.Model` using `save_weights` and loading into a `tf.train.Checkpoint` with a `Model` attached (or vice versa) will not match the `Model`'s variables. See the [guide to training checkpoints]( https://www.tensorflow.org/guide/checkpoint) for details on the TensorFlow format. Args: filepath: String or PathLike, path to the file to save the weights to. When saving in TensorFlow format, this is the prefix used for checkpoint files (multiple files are generated). Note that the '.h5' suffix causes weights to be saved in HDF5 format. overwrite: Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt. save_format: Either 'tf' or 'h5'. A `filepath` ending in '.h5' or '.keras' will default to HDF5 if `save_format` is `None`. Otherwise, `None` becomes 'tf'. Defaults to `None`. options: Optional `tf.train.CheckpointOptions` object that specifies options for saving weights. Raises: ImportError: If `h5py` is not available when attempting to save in HDF5 format. )rrrrN)r save_weights)rwrrrrs r=rModel.save_weights" s"@  #  r?c2[R"UUUUUS9$)aLoads all layer weights from a saved files. The saved file could be a SavedModel file, a `.keras` file (v3 saving format), or a file created via `model.save_weights()`. By default, weights are loaded based on the network's topology. This means the architecture should be the same as when the weights were saved. Note that layers that don't have weights are not taken into account in the topological ordering, so adding or removing layers is fine as long as they don't have weights. **Partial weight loading** If you have modified your model, for instance by adding a new layer (with weights) or by changing the shape of the weights of a layer, you can choose to ignore errors and continue loading by setting `skip_mismatch=True`. In this case any layer with mismatching weights will be skipped. A warning will be displayed for each skipped layer. **Weight loading by name** If your weights are saved as a `.h5` file created via `model.save_weights()`, you can use the argument `by_name=True`. In this case, weights are loaded into layers only if they share the same name. This is useful for fine-tuning or transfer-learning models where some of the layers have changed. Note that only topological loading (`by_name=False`) is supported when loading weights from the `.keras` v3 format or from the TensorFlow SavedModel format. Args: filepath: String, path to the weights file to load. For weight files in TensorFlow format, this is the file prefix (the same as was passed to `save_weights()`). This can also be a path to a SavedModel or a `.keras` file (v3 saving format) saved via `model.save()`. skip_mismatch: Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weights. by_name: Boolean, whether to load weights by name or by topological order. Only topological loading is supported for weight files in the `.keras` v3 format or in the TensorFlow SavedModel format. options: Optional `tf.train.CheckpointOptions` object that specifies options for loading weights (only valid for a SavedModel file). )rby_name skip_mismatchr)r load_weights)rwrrrrs r=rModel.load_weightsj s'h&& '   r?cSSKJn UR5nURRUU[ R "5S.nU$)zyUtil shared between different serialization methods. Returns: Model config with TF-Keras version information added. r) __version__) class_namerG keras_versionr ) tf_keras.srcrr-r<__name__r )rwrrG model_configs r=_updated_configModel._updated_config s? >"..11*(  r?c[R"UR5(a"[RRU5nU$0nU$![ a 0n[ R"S5 U$f=f)aReturns the config of the `Model`. Config is a Python dictionary (serializable) containing the configuration of an object, which in this case is a `Model`. This allows the `Model` to be be reinstantiated later (without its trained weights) from this configuration. Note that `get_config()` does not guarantee to return a fresh copy of dict every time it is called. The callers should make a copy of the returned dict if they want to modify it. Developers of subclassed `Model` are advised to override this method, and continue to update the dict from `super(MyModel, self).get_config()` to provide the proper configuration of this `Model`. The default config will return config dict for init parameters if they are basic types. Raises `NotImplementedError` when in cases where a custom `get_config()` implementation is required for the subclassed model. Returns: Python dictionary containing the configuration of this `Model`. zModel's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.)r is_defaultr-rrrrr)rwrGs r=r-Model.get_config su2  # #DOO 4 4 #))44T: F ' 2  sA "A21A2c ^SSKJn [R"5 /SQn[ U4SjU55n[ R "UR5n[ R "URR5RSSnXR[1;=(d> URSSU:H=(d% URS:H=(a URS:HnU(aDU(a=URTU5upn U"XTRS5S 9n URX5 O U"S0TD6n U sSSS5 $![ a&n [!S US UR"S TS U 35eSn A ff=f!,(df  g=f)Nrr1)rClayers input_layers output_layersc3,># UH oT;v M g7frrJ)rkeyrGs r=r$Model.from_config.. s')?#v )?srr:r;rC)rArBrCzUnable to revive model from config. When overriding the `get_config()` method, make sure that the returned config contains all items used as arguments in the constructor to z, which is the default behavior. You can override this default behavior by defining a `from_config(cls, config)` class method to specify how to create an instance of z# from its config. Received config=z, Error encountered during deserialization: rJ)r5r2rSharedObjectLoadingScoperr#getfullargspecrNr6r:r)varargsvarkwreconstruct_from_configr%connect_ancillary_layersrRr)r9rGcustom_objectsr2functional_config_keysis_functional_configargspecfunctional_init_argsrevivable_as_functionalrArBrrrs ` r=r,Model.from_config s 3  3 3 5& " $'')?'$ !// =G#-#<#<%%..$d12$ --u55M<<#';;MOOv-K'--82K $ $(?+5*L*LN+'!F9K33EBM&MEm6 5P! #2367' (+||n5++1(3EEFC I   Q6 5s0DE93E;E9 E6!E11E66E99 Fc pUR5n[R"U4S[R0UD6$)a"Returns a JSON string containing the network configuration. To load a network from a JSON save file, use `keras.models.model_from_json(json_string, custom_objects={})`. Args: **kwargs: Additional keyword arguments to be passed to *`json.dumps()`. Returns: A JSON string. default)rjsondumpsr get_json_type)rwr;rs r=to_json Model.to_json s<++- zz  ",":": >D  r?c [S5e)aGReturns a yaml string containing the network configuration. Note: Since TF 2.6, this method is no longer supported and will raise a RuntimeError. To load a network from a yaml save file, use `keras.models.model_from_yaml(yaml_string, custom_objects={})`. `custom_objects` should be a dictionary mapping the names of custom losses / layers / etc to the corresponding functions / classes. Args: **kwargs: Additional keyword arguments to be passed to `yaml.dump()`. Returns: A YAML string. Raises: RuntimeError: announces that the method poses a security risk zMethod `model.to_yaml()` has been removed due to security risk of arbitrary code execution. Please use `model.to_json()` instead.)r)rwr;s r=to_yaml Model.to_yaml1 s. N  r?cURH:n[US5(dM[USS5(dM*UR5 M< g)N reset_statesstatefulF)rhasattrrr )rwr_s r=r Model.reset_statesM s>[[Eun--'z533""$ !r?c[R"SSS9 /nURH8n[USS5(dM[ US5(dM*XR - nM: U$)aDeprecated, do NOT use! Returns the `updates` from all layers that are stateful. This is useful for separating training updates and state updates, e.g. when we need to update a layer's internal state during prediction. Returns: A list of update ops. z`Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.rrr Fupdates)rrrrr r)rw state_updatesr_s r=rModel.state_updatesT s_   6   [[Euj%005),,!]]2M!r?c8URUR5$)zReturns the list of all layer variables/weights. Note: This will not track the weights of nested `tf.Modules` that are not themselves TF-Keras layers. Returns: A list of variables. )r_undeduplicated_weightsrs r=r Model.weightso s""4#?#?@@r?cUR5 /nURHnXR- nM XRUR-- nU$)z?Returns the undeduplicated list of all layer variables/weights.)rr variablesrr)rwrr_s r=rModel._undeduplicated_weights{ sO $$&22E  &G3**T-H-HHHr?c pUR(d [S5e[R"UUUUUUUS9 g)apPrints a string summary of the network. Args: line_length: Total length of printed lines (e.g. set this to adapt the display to different terminal window sizes). positions: Relative or absolute positions of log elements in each line. If not provided, becomes `[0.3, 0.6, 0.70, 1.]`. Defaults to `None`. print_fn: Print function to use. By default, prints to `stdout`. If `stdout` doesn't work in your environment, change to `print`. It will be called on each line of the summary. You can set it to a custom function in order to capture the string summary. expand_nested: Whether to expand the nested models. Defaults to `False`. show_trainable: Whether to show if a layer is trainable. Defaults to `False`. layer_range: a list or tuple of 2 strings, which is the starting layer name and ending layer name (both inclusive) indicating the range of layers to be printed in summary. It also accepts regex patterns instead of exact name. In such case, start predicate will be the first element it matches to `layer_range[0]` and the end predicate will be the last element it matches to `layer_range[1]`. By default `None` which considers all layers of model. Raises: ValueError: if `summary()` is called before the model is built. zyThis model has not yet been built. Build the model first by calling `build()` or by calling the model on a batch of data.) line_length positionsprint_fn expand_nestedshow_trainable layer_rangeN)rrr! print_summary)rwrrrrrrs r=summary Model.summary sCNzz0  !! #')# r?c4[URSSS95$)NF) include_self recursive)rr:rs r=r Model.layers sD((eu(MNNr?c[S5e)NzU`Model.layers` attribute is reserved and should not be used. Please use another name.)r)rwrs r=rr& s '  r?c UbUb[SUSUS35eUbM[UR5U::a%[SUS[UR5S35eURU$UbSURHnURU:XdMUs $ [SUS[ S UR55S35e[S 5e) a8Retrieves a layer based on either its name (unique) or index. If `name` and `index` are both provided, `index` will take precedence. Indices are based on order of horizontal graph traversal (bottom-up). Args: name: String, name of layer. index: Integer, index of layer. Returns: A layer instance. z. s< u z:Provide either a layer name or layer index at `get_layer`.)rrrrCr)rwrCindexr_s r= get_layerModel.get_layer s  !1wtfA/   4;;5( ;E7*3t{{+;*< {{5))  ::% L%!$'>< <<=Q@  H  r?cB0n[RRRU5R 5unnUHWn[ U[R 5(dM$X4nSRUVs/sHofRPM sn5nXAU'MY U$s snf)a Retrieve all the variables and their paths for the model. The variable path (string) is a stable key to identify a `tf.Variable` instance owned by the model. It can be used to specify variable-specific configurations (e.g. DTensor, quantization) from a global view. This method returns a dict with weight object paths as keys and the corresponding `tf.Variable` instances as values. Note that if the model is a subclassed model and the weights haven't been initialized, an empty dict will be returned. Returns: A dict where keys are variable paths and values are `tf.Variable` instances. Example: ```python class SubclassModel(tf.keras.Model): def __init__(self, name=None): super().__init__(name=name) self.d1 = tf.keras.layers.Dense(10) self.d2 = tf.keras.layers.Dense(20) def call(self, inputs): x = self.d1(inputs) return self.d2(x) model = SubclassModel() model(tf.zeros((10, 10))) weight_paths = model.get_weight_paths() # weight_paths: # { # 'd1.kernel': model.d1.kernel, # 'd1.bias': model.d1.bias, # 'd2.kernel': model.d2.kernel, # 'd2.bias': model.d2.bias, # } # Functional model inputs = tf.keras.Input((10,), batch_size=10) x = tf.keras.layers.Dense(20, name='d1')(inputs) output = tf.keras.layers.Dense(30, name='d2')(x) model = tf.keras.Model(inputs, output) d1 = model.layers[1] d2 = model.layers[2] weight_paths = model.get_weight_paths() # weight_paths: # { # 'd1.kernel': d1.kernel, # 'd1.bias': d1.bias, # 'd2.kernel': d2.kernel, # 'd2.bias': d2.bias, # } ``` r) r^rtrackingObjectGraphViewbreadth_first_traversalrLrjoinrC)rwry descendantsobject_paths_dict descendanttrackable_referencesry object_paths r=get_weight_pathsModel.get_weight_paths sv OO $ $ 4 4  ! ! #   &J*bkk22'8'D$!hh8L'M8L18L'MN &0{# &  (Ns6B c~UR(a,[US5(aURR5$gg)aReturns a serialized config with information for compiling the model. This method returns a config dictionary containing all the information (optimizer, loss, metrics, etc.) with which the model was compiled. Returns: A dict containing information for compiling the model. rN)rHr r serializers r=get_compile_configModel.get_compile_config;s8   /@!A!A''113 3"B r?cURR[R:gnU(a[R"S5 g[ R "U5nUR"S0UD6 [US5(ab[UR[R5(a8UR(a&URRUR5 gggg)zCompiles the model with the information given in config. This method uses the information in the config (optimizer, loss, metrics, etc.) to compile the model. Args: config: Dict containing information for compiling the model. a+`compile()` was not called as part of model loading because the model's `compile()` method is custom. All subclassed Models that have `compile()` overridden should also override `get_compile_config()` and `compile_from_config(config)`. Alternatively, you can call `compile()` manually after loading.NrrJ)r<r r)rrrdeserialize_keras_objectr rLrrrrrh)rwrGhas_overridden_compiles r=compile_from_configModel.compile_from_configGs"&!7!75==!H ! OO;  44V< v D+ & &4>>9+>+>??  NN !9!9 :@ 'r?c2SSKJn URX5 g)aCreate a SavedModel artifact for inference (e.g. via TF-Serving). This method lets you export a model to a lightweight SavedModel artifact that contains the model's forward pass only (its `call()` method) and can be served via e.g. TF-Serving. The forward pass is registered under the name `serve()` (see example below). The original code of the model (including any custom layers you may have used) is *no longer* necessary to reload the artifact -- it is entirely standalone. Args: filepath: `str` or `pathlib.Path` object. Path where to save the artifact. Example: ```python # Create the artifact model.export("path/to/location") # Later, in a different process / environment... reloaded_artifact = tf.saved_model.load("path/to/location") predictions = reloaded_artifact.serve(input_data) ``` If you would like to customize your serving endpoints, you can use the lower-level `keras.export.ExportArchive` class. The `export()` method relies on `ExportArchive` internally. r) export_libN)tf_keras.src.exportrE export_model)rwrrEs r=export Model.exportgs> 3/r?c P>URbgU=(d /nU=(d 0nURnU(d[R"U5n[R R U5n/n[XE5H*upxUR[R"USUS95 M, [R RX5n[T U]5XbU5 URRS:Xa5UR c'[R R#SU5Ulggg)aDefines the save spec so that serialization can trace `call()`. The TensorSpecs of the call function `inputs`, `args`, and `kwargs` are saved into a tuple of `([inputs] + args, kwargs)`. The input `TensorSpec` names are updated to match the built `input_names`. The specs can be retrieved with the `save_spec` property. Args: inputs: possibly nested inputs passed into the call function. args: a list of positional arguments passed into call. kwargs: a dictionary of keyword arguments passed into call. NF)rwrC Sequentialc$UcS$UR$r)rrs r=r&Model._set_save_spec..s!)$88r?)rhrVrcreate_pseudo_input_namesr^rrrrQr$rxpack_sequence_asr7_set_save_specr<r_build_input_shaper) rwrAr:r;rV flat_inputs inputs_specrCtensorr<s r=rPModel._set_save_specs  ( ( 4 zr2&& 'AA&IKggoof-   9LD   ((u4P :gg..vC  {&9 NN # #| 3''/&(gg&;&;8+'D #0 4r?c"URUSS9$)aLReturns the `tf.TensorSpec` of call args as a tuple `(args, kwargs)`. This value is automatically defined after calling the model for the first time. Afterwards, you can use it when exporting the model for serving: ```python model = tf.keras.Model(...) @tf.function def serve(*args, **kwargs): outputs = model(*args, **kwargs) # Apply postprocessing steps, or add additional outputs. ... return outputs # arg_specs is `[tf.TensorSpec(...), ...]`. kwarg_specs, in this # example, is an empty dict since functional models do not use keyword # arguments. arg_specs, kwarg_specs = model.save_spec() model.save(path, signatures={ 'serving_default': serve.get_concrete_function(*arg_specs, **kwarg_specs) }) ``` Args: dynamic_batch: Whether to set the batch sizes of all the returned `tf.TensorSpec` to `None`. (Note that when defining functional or Sequential models with `tf.keras.Input([...], batch_size=X)`, the batch size will always be preserved). Defaults to `True`. Returns: If the model inputs are defined, returns a tuple `(args, kwargs)`. All elements in `args` and `kwargs` are `tf.TensorSpec`. If the model inputs are not defined, returns `None`. The model inputs are automatically set when calling the model, `model.fit`, `model.evaluate` or `model.predict`. F) inputs_only)_get_save_spec)rwrws r= save_specModel.save_specsP""=e"DDr?cUR(agSURR;a@UR[:wa+UR(d[ SUR S35eggg)a>Asserts that all the weights for the model have been created. For a non-dynamic model, the weights must already be created after the layer has been called. For a dynamic model, the exact list of weights can never be known for certain since it may change at any time during execution. We run this check right before accessing weights or getting the Numpy value for the current weights. Otherwise, if the layer has never been called, the user would just get an empty list, which is misleading. Raises: ValueError: if the weights of the network have not yet been created. NrzWeights for model 'z' have not yet been created. Weights are created when the model is first called on inputs or `build()` is called with an `input_shape`.)rFr<__dict__r)rrrCrs r=rModel._assert_weights_createdsj <<  t~~.. .%'JJ %dii[1GG  ( /r?c,URRnUR(a$URS[ UR5*nO URnSU;aUR S5 [ U5S:aUSSn[ SUSUS35eg)z0Check that `call()` has only one positional arg.NrrzModels passed to `z^` can only have `training` and the first argument in `call()` as positional arguments, found: r)rrrr:rremover)rw method_name fullargspecpositional_args extra_argss r=rModel._check_call_argssoo22   )../K#k6J6J2K1KLO)..O  (  " ": .  ! #(,J$[M2$Q(  $r?c [S[RRU555(a[ SUS35eUR SS5 UR SS5 UR SS5nUb[ SUS 35eUR S S5nUb[ S US 35e[ U5S 1- nU(a[S U4S35eUR(a[RR5(ac[RR5nURH5nURRU5(aM%[ SUSUS35e URn[RRU5HIn [!U S/5H5nURRU5(aM%[ SU SUS35e MK [RRU5HIn [!U S/5H5nURRU5(aM%[ SUSUS35e MK g)z7Performs validation checks for the default `compile()`.c3V# UHn[U[R5v M! g7fr)rLrr)rr)s r=r*Model._validate_compile..s' 1 sL22 3 31rz `tf.compat.v1.keras` Optimizer (zs) is not supported when eager execution is enabled. Use a `tf.keras` Optimizer instead, or disable eager execution.cloningNexperimental_run_tf_functionr_z}`distribute` argument in compile is not available in TF 2.0. Please create the model under the `strategy.scope()`. Received: rtarget_tensorszM`target_tensors` argument is not supported when executing eagerly. Received: sample_weight_modez,Invalid keyword argument(s) in `compile()`: z. Valid keyword arguments include "cloning", "experimental_run_tf_function", "distribute", "target_tensors", or "sample_weight_mode".z Variable (z9) was not created in the distribution strategy scope of (z). It is most likely because some layers, model, or optimizer was being created outside the distribution strategy scope. Try to make sure your code looks similar to the following. with strategy.scope(): model=_create_model() model.compile(...)rzMetric (z) passed to `model.compile` was created inside a different distribution strategy scope than the model. All metrics must be created in the same distribution strategy scope as the model (in this case z). If you pass in a string identifier for a metric to compile, the metric will automatically be created in the correct distribution strategy scope._weightsz Optimizer (z) passed to `model.compile` was created inside a different distribution strategy scope than the model. All optimizers must be created in the same distribution strategy scope as the model (in this case z). If you pass in a string identifier for an optimizer to compile, the optimizer will automatically be created in the correct distribution strategy scope.)r^r^rrrrrrRrr_r`rarextendedvariable_created_in_scoperr) rwrrr;distribute_argtarget_tensor_arginvalid_kwargsstrategyrr|r)s r=rModel._validate_compiless  wwy1   29+>   9d# 148L$7  %+,A/  #JJ'7>  (&&7%8; V(<'== >"$%&>>  ::"--4466}}113H^^((BB1EE$$QC(..6Z8//  $++ggoog.FV["5((BB1EE$"6(+<=E:FF F  6/ 77??9-CS*b1((BB1EE$%i[1)*2 37 7  2.r?cvSnURb(URRX[RS9$X#4$)a=Maybe load initial epoch from ckpt, considering worker recovery. Refer to tensorflow/python/tf_keras/distribute/worker_training_state.py for more information. Args: steps_per_epoch: The number of step per epoch. initial_epoch: The original initial_epoch user passes in `fit()`. mode: The mode for running `model.fit()`. Returns: If the training is recovering from previous failure under multi-worker training setting, return the (epoch, step) the training is supposed to continue at. Otherwise, return the `initial_epoch, initial_step` the user passes in. r)mode)rg%maybe_load_initial_counters_from_ckptr'TRAIN)rwrr initial_steps r=r,Model._maybe_load_initial_counters_from_ckptjsH&    +''MMX^^N ,,r?c<UR(d [S5eg)NzZYou must compile your model before training/testing. Use `model.compile(optimizer, loss)`.)rHrrs r=r Model._assert_compile_was_calleds$   8 !r?cNUSL=(dd [U[RR5=(a9 [UR[ 5=(a [ UR5S:HnU(a/URRc[R"S5 ggg)Nra `evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided. Did you mean to pass metrics to `weighted_metrics` in `compile()`? If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.) rLr^rjr element_specrrr[_user_weighted_metricsrr)rwrrksample_weight_presents r=rS"Model._check_sample_weight_warnings!.T 9! q"''// * )1>>51 )ANN#q(  "%%<<D OO4 E "r?c&URU5 g)zDThis method is for compat with Modelv1. Only inputs are needed here.N)rP)rwrArBrs r= _set_inputsModel._set_inputss F#r?c.[R"U5$r)rModelSavedModelSaverrs r=_trackable_saved_model_saver"Model._trackable_saved_model_savers"77==r?c >US:XaLURnURnURnURnSUlSUlSUlSUl[TU]"U40UD6nUS:XaWUlWUlWUlWUlU$)N savedmodel)r.r/r0r1r7_trackable_children) rw save_typer;r.r/r0r1childrenr<s r=rModel._trackable_childrens  $!00N ..M#44  $ 6 6 "&D !%D $(D !%)D "7.yCFC  $"0D !.D $4D !%6D "r?cUS-n[U[5(aX-S:H$[U[5(aX;$[SUS[ U5S35e)NrrzJExpected `validation_freq` to be a list or int. Received: validation_freq=z of the type r)rLrrrr)rwrrs r=rModel._should_evalsk  os + +*a/ /  . .+ +--<,=>_-.a1 r?cdUR5 URRnURRnU(d2UbURRnUbURR nUR URRUUURRS.nU$)a Used for saving or cloning a Model. Args: user_metrics: Whether to return user-supplied metrics or `Metric` objects. If True, returns the user-supplied metrics. Defaults to `True`. Returns: Dictionary of arguments that were used when compiling the model. )rrrrr) rr[ _user_metricsr~r<_weighted_metricsrrZ _user_losses_user_loss_weights)rw user_metrics saved_metricssaved_weighted_metrics compile_argss r=_get_compile_argsModel._get_compile_argss '')--;; !%!6!6!M!M( $ 5 5 > > %1)-)>)>)P)P&&&33$ 6 ..AA  r?cU$rrJrs r=_get_callback_modelModel._get_callback_models r?cJURRR5$r)rrmrrs r=rModel._in_multi_worker_modes''00FFHHr?cUR$r)rHrs r=_compile_was_calledModel._compile_was_calleds   r?))rprurQrnrdrr/r\rcrbrrHrUrKrvrsr-rrerirhr!r.ror,r+rgrZr[rYrVrArrrWrBr0rXr/r.r1)NN) rmspropNNNNNNNr)NNNN)F)NNNrr NgNTNNrNNNr rF) NNNr NNNrrFF)Nr NNrrF)NNNTF)NNTF) NrrNNNrNrrFTr)NNrrFr)TN)TNN)FFNr)NNNFFN)T) checkpoint)tr __module__ __qualname____firstlineno____doc__ frozenset itertoolschainrr_TF_MODULE_IGNORED_PROPERTIES_SCALAR_UPRANKING_ONr8r^rr0 no_automatic_dependency_trackingr%filter_tracebackrNrrtrrrrrrrrrdoc_in_current_and_subclassesrr rrfrpropertyr7rrBrrsetterrMr r rZr_rmrfrirvrrrrrr>rOrrrrTrkrrrrrrdo_not_generate_docsrrrrrrrrrrr- classmethodr,rrr rrrr!rr-r9r=rBrHrPrYrrrrrrSrrrrrrrr__static_attributes__ __classcell__r<s@r=r)r)FsgR%.     : :  %!!C__>>%%A!&?A!F(__>> E? E)*(  %|#|#|%%#1&#1J// 0 @%%  N0&N0` G__>> E? E__>> ? --^%.%.NKK% % N""22"(( ) ))4 4  ! ! $ $ ; ;!''2(2B"AH6 p%)N**X)0p#d%%  "!)r &r h A6&&!, ', \&&!  '  D&&! ' J88<<2)%%; &; z%%BFE &E N%%DH9 &9 v"%%N==~ $ 8%&&'2 A A5 nOO ]]  ) VGR 4;@!0F__>>'?'R(ET D(Tl-4 *$ >>, "<I!!r?r)c \rSrSrSrSrSrg)rhicXlX lgr) _function _callbacks)rwrr s r=rN_TestFunction.__init__s !#r?cURU5nUR(a[R"5 UnX2R-nUR R Xv5 U$r)rrrrrron_test_batch_end)rwr[rr unused_shardsrrrs r=r_TestFunction.run_stepsP>>"56  # #    555 ))(9 r?)rrN)rrrrrNrrrJr?r=rhrhs $r?rhc.^\rSrSrU4SjrSrSrU=r$)rgi c2>[TU]X5 /Ulgr)r7rN_logs)rwrr r<s r=rN_ExactTestFunction.__init__s - r?c FURU[R"U[RS9[R"U[RS95nUR(a[ R "5 URRU5 UR$)N)rE) rr^constantrrrrrrQ)rwr[rrshardsrs r=r_ExactTestFunction.run_stepsj>>  KKbhh / KKBHH -   # #     (#zzr?)r)rrrrrNrrrrs@r=rgrg s  r?rgc^^TS:Xa[R"T5(aSOSmUU4Sjn[RR X05$)a Attempt to reduce the structure `values` to single values. Given `values` (a `tf.Tensor` or a `PerReplica` structure), which represents the values across all the replicas, `reduce_per_replica` attempts to "reduce" those values and returns the corresponding structure that represents only single values. Currently, `reduce_per_replica` is only used for reducing the metric results from `tf.distribute.Strategy.run()`. Depending on the underlying `Strategy` implementation, `values` may be a `PerReplica` object, which can be thought of as a collection of values across the replicas, or a `tf.Tensor`, if the strategy has already conducted the reduction for the downstream library. There are five possible outcomes of reduction: 1) if the `values` is a structure of simple `tf.Tensor`s, meaning that reduction is not actually needed, `reduce_per_replica` returns the structure as-is. 2) else, if `reduction="auto"`, then the best reduction strategy is chosen based on the current environment. This should only be used for training cases (`fit()`). 3) else, if `reduction="first"`, then `reduce_per_replica` returns the values of the first replica. This is used in the case of training and evaluation, where `values` is expected to hold the same value across the replicas as a result of `Strategy`'s synchronization across the replicas. `reduce_per_replica` does not synchronize the values. 4) else, if `reduction="sum"`, then `reduce_per_replica` returns the sum of values for all replicas. This may be used in the custom training loop case, where each replica contain different values which are not synchronized. 5) else, if `reduction="concat"`, then `reduce_per_replica` returns the concatenation of the values across the replicas, along the axis of dimension 0. This is used in the inference case (`predict()`). Args: values: Structure of `PerReplica` objects or `tf.Tensor`s. `tf.Tensor`s are returned as-is. strategy: `tf.distribute.Strategy` object. reduction: One of `"auto"`, `"first"`, `"concat"`, or `"sum"`. `"auto"` will select `"first"` when used under a TPUStrategy, or `"sum"` otherwise. Returns: Structure of `Tensor`s, representing the result of reduction. Raises: ValueError: if the reduction method is not supported. r firstsumc>[T5(a)TS:Xa [UT5$TS:XaTRSUSS9$[U5(a [ UTT5$[ U5(dU$TS:XaTR U5S$TS:Xa6[T5(a [UT5$[TR U55$TS:Xa%[R"TR U55$[STS 35e) z$Reduce a single `PerReplica` object.rqrSUMNaxisrrzM`reduction` must be "first", "concat", "sum", or "auto". Received: reduction=r) #_collective_all_reduce_multi_worker_multi_worker_concatreduce _is_dtensor_per_replica_instance_reduce_dtensor_per_replica_is_per_replica_instanceexperimental_local_resultsr_tpu_multi_host_concatrqr^ reduce_sumr)rrrrs r=_reduce#reduce_per_replica.._reduceUs .x 8 8H$+Ax88e#uad;; +A . ..q(IF F)!,,H ' !66q9!< < ( "!(++-a::hAA!DEE % ==!D!DQ!GH H''0k4 r?)r is_tpu_strategyr^rr)valuesrrrrs `` r=rrsAhF&66x@@Ge 6 77  11r?c[US[R5(a[RR XS9$[ US5(a[R "XS9$[R"XS9$)zConcats `tensor`s along `axis`.rr sp_inputsr)rLr^ SparseTensorsparserq _is_scalarstack)tensorsrs r=rqrqss\'!*boo..yyT== GAJ  xx++yy,,r?c[U5S:XaUS$[US[R5(a[RR SUS9$[US[R 5(a[R "USS9$[RRR5(d[R "USS9$[R"UVs/sHn[R"U5SSPM sn5n[RRX"SS:HSS9n[RRU5R5R5(a=[!US5(a[R"USS9$[R "USS9$UR5R#5SSS2R%S5nUS:XaSnOUSRU*Sn[R&R)UVs/sHoR5PM snUS9R+SS5$s snfs snf) zConcats `Tensor`s along their first dimension. Args: tensors: List of `Tensor`s. Returns: Concatenation of the inputs along the first dimension -- of type `Tensor` if all input shapes are compatible, or `RaggedTensor` if not. rrrrNF) inner_shape)rrLr^rrrq RaggedTensorrtf2enabledrrmath reduce_allrrrtolistr,raggedr merge_dims)rrTnon_batch_shapes constant_dimsconstant_inner_dimensionsconstant_inner_shapes r=rr}s 7|qqz'!*boo..yyQ':: GAJ 0 0yyq)) __ ( ( * *yyq))xxG LG&&!1!"!5G LMGG&&Ra00q'M ww-(..05577 gaj ! !88G!, ,99W1- - $$&tt,2259!A%#&qz//1J0J0KL 99  &-.gFg. 2% xx%  ) )H !("3"3"B"BC34 Z%H1H%HII5 " ##- s6D.c[U[R[R45=(a URR S:H$r)rLr^rrrrrs r=rrs. a"))R[[1 2 Hqww||q7HHr?c[R"5(a/$[RRUSS9nUH'n[ U[R 5(aM$U/s $ /$)zBReturns the minimum control dependencies to ensure step succeeded.T)expand_composites)r^rrrrLr)rBouts r=rrsU  ggoogo>G#r{{++5L Ir?cl[R"5(aSRUS9n[U5eg)NaIDetected a call to `Model.{method_name}` inside a `tf.function`. `Model.{method_name} is a high-level endpoint that manages its own `tf.function`. 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