a Sic@sdZddlZddlZddlZddlZddlZddlZddlZddl m m Z ddl mZddl mZddl mZddlmZddlmZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddl m!Z!ddl"m#Z$ddl%m&Z&ddl%m'Z'ddl%m(Z(ddl%m)Z)ddl*m+Z+ddl,m-Z-ddl,m.Z.ddl/m0Z0ddl/m1Z1ddl/m2Z2ddl/m3Z3ddl/m4Z4ddl/m5Z5ddl6m7Z7ddl8m9Z9ddl:m;Z<dd l=m>Z>dd!l?m@Z@z ddlAZAWneBydZAYn0e>d"d#Gd$d%d%ejCe5jDZEdKd'd(ZFdLd)d*ZGd+d,ZHd-d.ZId/d0ZJd1d2ZKd3d4ZLd5d6ZMd7d8ZNd9d:ZOd;d<ZPd=d>ZQd?d@ZRdAdBZSdCdDZTdEdFZUdGdHZVdIdJZWdS)Mz*Training-related part of the Keras engine.N)backend) callbacks) optimizers) layout_map) base_layer)base_layer_utils) compile_utils) data_adapter) input_layer)training_utils)loss_scale_optimizer) optimizer_v1) optimizer) hdf5_format) pickle_utils)save) saving_utils) saving_lib) json_utils)model_serialization) generic_utils)io_utils) layer_utils)tf_utils)traceback_utils) version_utils)ModeKeys)context) tf_logging) keras_export) doc_controlsz keras.Modelzkeras.models.ModelcseZdZdZeedejj Z dZ fddZ e j jjejfddZe j jjdd Zfd d Zfd d ZfddZddZejfddZejfddZejdddZejdddZddZ e j jjddZ!e j jjd d!Z"e#d"d#Z$e#d$d%Z%e#d&d'Z&e#d(d)Z'e#d*d+Z(e(j)d,d+Z(d-d.Z*d/d0Z+dd1d2Z,d3d4Z-dd5d6Z.ejdd=d>Z/d?d@Z0ddAdBZ1ejddCdDZ2dEdFZ3ddGdHZ4ejddIdJZ5dKdLZ6ddMdNZ7ddOdPZ8dQdRZ9ej:ddSdTZ;ej:ddUdVZe#d[d\Z?fd]d^Z@ejdd_d`ZAejddadbZBejddcddZCdedfZDdgdhZEeFddidjZGdkdlZHdmdnZIdodpZJe#ej:dqdrZKe#dsdtZLe#dudvZMddwdxZNe#dydzZOeOj)d{dzZOdd|d}ZPd~dZQe j jjdfdd ZRdddZSddZTddZUddZVddZWddZXddZYddZZdddZ[e#ddZ\dfdd Z]ddZ^dddZ_ddZ`ddZae#ddZbddZcZdS)ModelaY`Model` groups layers into an object with training and inference features. Args: inputs: The input(s) of the model: a `keras.Input` object or list of `keras.Input` objects. outputs: The output(s) of the model. 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_executionFcsTt||r2|tkr2ddlm}|j|ddi|Stt|j|g|Ri|SdS)Nr functional skip_initT)is_functional_model_init_paramsr! keras.enginer' Functionalsuper__new__)clsargskwargsr' __class__Q/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/engine/training.pyr-s z Model.__new__c sd|_tjddddlm}t|rt||j sgdfddD}fddD}t |j |j j |g|Ri|g}d }|j j D]$}t||j rd}q|r||q|r|D]}|j |g|Ri|qn|rtd |dStjd dthd tj fid |_d|_d|_d|_d|_d |_d|_d|_d|_d |_|d d |ddt j!"rt j!#|_$nd|_$d|_%d|_&|'d|_(d|_)d|_*t j+j,t-.|d|_/d|_0|1d|_2d|_3t45|_6dS)NTmodelrr&)inputsoutputsname trainabler(csi|]}|vr||qSr3r3.0kr0supported_kwargsr3r4 sz"Model.__init__..csi|]}|vr||qSr3r3r:r=r3r4r?sFzGThe following keyword arguments passed to `Model` aren't supported: {}.zModel subclass>autocastr6r7dtyper9r8dynamic _is_compiledr)root)7_is_model_for_instrumentationrkeras_api_gaugeget_cellsetr*r'r) isinstancer+inject_functional_model_classr2__init__ __bases__ issubclassappend TypeErrorformatrvalidate_kwargsr,_is_graph_networkr6r7 input_names output_names stop_traininghistory compiled_losscompiled_metrics _compute_output_and_mask_jointly_maybe_create_attributetf distribute has_strategy get_strategy_distribution_strategy_cluster_coordinator _run_eagerly_reset_compile_cache_training_state_saved_model_inputs_spec_saved_model_arg_spectrain Checkpointweakrefref _checkpointr%_init_batch_counters_base_model_initialized _jit_compilelayout_map_libget_current_layout_map _layout_map) selfr/r0r' model_kwargs other_kwargs clz_to_initfound_functional_classclzr1r=r4rKs           zModel.__init__cCsBtjj}tjdd|d|_tjdd|d|_tjdd|d|_dS)Nrint64rA aggregation)r[VariableAggregationONLY_FIRST_REPLICAVariabler"r#r$)rqaggr3r3r4rkIszModel._init_batch_counterscspt|ddst||dStddtj|Dr^z |jWnty\t dYn0t||dS)N_self_setattr_trackingTcss*|]"}t|tjtjfp t|VqdSN)rIrLayerr[r|r has_weights)r;vr3r3r4 Wsz$Model.__setattr__..zsIt looks like you are subclassing `Model` and you forgot to call `super().__init__()`. Always start with this line.) getattrr, __setattr__allr[nestflattenrlAttributeError RuntimeError)rqr8valuer1r3r4rRs     zModel.__setattr__cs$|jrtjt|fStSdSr)builtrdeserialize_model_from_bytecodeserialize_model_as_bytecoder, __reduce__rqr1r3r4rgs  zModel.__reduce__csl|jr$tjt|}||t|<nDt^}}}||}||t|<|rhtj|d|d}| ||S)Nr)memo) rrrridr,rcopydeepcopy __setstate__)rqrnew deserializer serializedreststater1r3r4 __deepcopy__ws  zModel.__deepcopy__cCs |iSr)rrr3r3r4__copy__szModel.__copy__c s|jrt|dS|dur&tdtttjtf}t ||sPtd t ||r|j st rttjd}nt}|Vt |trtdd|Drt|}t |trdd|D}n(t |trd d |D}n t|}i}|jj}|j}t|d krb|jr,|d t|j }n |d d}|D]"}|d krTd |d <ntdqtjjtfy} ztd| dWYd} ~ n d} ~ 00Wdn1s0Yt|dS)aBuilds 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_graphcss |]}|dupt|tVqdSr)rIint)r;dr3r3r4rszModel.build..cSsg|]}t|qSr3r generate_placeholders_from_shape)r;shaper3r3r4 szModel.build..cSsi|]\}}|t|qSr3r)r;r<rr3r3r4r?szModel.build..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: .) rRr,build ValueErrortuplelistr[ TensorShapedictrIrPtyper6executing_eagerly __internal__ FuncGraphr get_graph as_defaultritemsrr _call_spec full_argspecr/lendefaultscallerrorsInvalidArgumentErrorrO) rq input_shape valid_typesgraphxr0call_signature call_argsarger1r3r4rsx         :z Model.buildcs|jdur|jst|}t|}|j||\}}}dd}tj||}tj||}tj||}t |j*t j |g|Ri|Wdn1s0Yt ||jt j |i|S)NcSs0t|tjtjttfr,t|}t |j SdSr) rIr[Tensornpndarrayfloatrconvert_to_tensorinput_layer_moduleInputrrr3r3r4_convert_to_graph_inputss z0Model.__call__.._convert_to_graph_inputs) rprrrsplit_out_first_argr[r map_structurernlayout_map_scoper,__call___map_subclass_model_variable)rqr/r0Z copied_argsZ copied_kwargsr6rr1r3r4r s(   8zModel.__call__NcCs tddS)aeCalls 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/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.N)NotImplementedError)rqr6rmaskr3r3r4r/sz Model.callrmspropc Kstjdd|jd| vr>td|s>| d}| dd} |j ||fi| ||_ | ||_ t |tjr||_ntj|||jd|_tj|||j| d|_||pd |d|_|pi|_|j s|jr|rtd n||_Wd n1s 0Yd S) aZConfigures 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 loss function used and the model output shape. 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. Defaults to `False`. 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`. steps_per_execution: Int. Defaults to 1. The number of batches to run during each `tf.function` call. Running multiple batches inside a single `tf.function` call can greatly improve performance on TPUs or 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). 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. This option cannot be enabled with `run_eagerly=True`. 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. **kwargs: Arguments supported for backwards compatibility only. compileT experimental_steps_per_executionzThe argument `steps_per_execution` is no longer experimental. Pass `steps_per_execution` instead of `experimental_steps_per_execution`.from_serializedF)rT)rTrzCYou cannot enable `run_eagerly` and `jit_compile` at the same time.N)rrFrGrHdistribute_strategyscopeloggingwarningpop_validate_compilera_get_optimizerrrIrLossesContainerrWrTMetricsContainerrX_configure_steps_per_executionrbrClossrBrrm) rqrrmetrics loss_weightsweighted_metrics run_eagerlysteps_per_execution jit_compiler0rr3r3r4rPsDl     z Model.compilecsfdd}tj||S)z7Wraps `optimizer` in `LossScaleOptimizer` if necessary.cs0t|}jjdkr,t|tjs,t|}|S)N mixed_float16)rget dtype_policyr8rIlsoBaseLossScaleOptimizer)optrr3r4_get_single_optimizers   z3Model._get_optimizer.._get_single_optimizerr[rr)rqrrr3rr4rs zModel._get_optimizercCs&d|_d|_d|_d|_||_dSr)train_function test_functionpredict_functiontrain_tf_function_get_trainable_state_compiled_trainable_staterr3r3r4rbs zModel._reset_compile_cachecCstj|dtjjd|_dS)Nrwrx)r[r|rzr{r%)rqrr3r3r4r s z$Model._configure_steps_per_executioncCsdS)NFr3rr3r3r4_should_compute_maskszModel._should_compute_maskcCsTg}|jr6|jdur ||jj7}|jdur6||jj7}|D]}||jq>|S)agReturns the model's metrics added using `compile()`, `add_metric()` APIs. 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'] N)rCrWrrX_flatten_layersextend_metrics)rqrlr3r3r4rs%     z Model.metricscCsdd|jDS)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'] cSsg|] }|jqSr3r8)r;mr3r3r4rpz'Model.metrics_names..)rrr3r3r4 metrics_namesJs&zModel.metrics_namescCs|jptjS)z:The `tf.distribute.Strategy` this model was created under.)r_r[r\r^rr3r3r4rrszModel.distribute_strategycCsL|jr|jdkrtd|jr,|jr,td|jpJ|jpJtjoJ|jduS)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)rBrarr`r[configfunctions_run_eagerlyrr3r3r4rws  zModel.run_eagerlycCs ||_dSr)ra)rqrr3r3r4rscCs6|jr"|dur"td|jdn|dur2tddS)aHRaises error if target or loss is not found. This method verifies that the target and loss are properly populated when applicable, or raises errors. Args: y: the target for training. loss: the total loss tensor including loss added via `compile` and `add_loss`. Nz:Target data is missing. Your model was compiled with loss=z>, 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)rqyrr3r3r4_validate_target_and_losss zModel._validate_target_and_losscCst|\}}}t,}||dd}|||||}Wdn1sJ0Y||||jj||j|d| ||||S)aRThe 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/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) r unpack_x_y_sample_weightr[ GradientTape compute_lossrrminimizetrainable_variablescompute_metrics)rqdatarr sample_weightry_predrr3r3r4 train_steps  . zModel.train_stepcCs~|j||||jdS)a Compute 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)rWlosses)rqrrrr r3r3r4rs3 zModel.compute_losscCsN~|j|||i}|jD],}|}t|tr>||q|||j<q|S)aVUpdate 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}`. )rX update_staterresultrIrupdater8)rqrrrr return_metricsmetricrr3r3r4r  s#    zModel.compute_metricscsjdur|sjSfddjdus<jdkrfddjsdtjdd_jr|fd d _q_nnjrćfd djstjdd_fd d _n.fd djstjdd__jS)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}`. NcsPfdd}jr"tj|ddd}t|}jj||fd}t|jdd}|S)zRuns a single training step.csH|}tt|jdWdn1s:0Y|SNr)rr[control_dependencies_minimum_control_depsr" assign_addr r7r5r3r4run_stepns *zBModel.make_train_function..step_function..run_stepTrreduce_retracingr/first reductionrmr[functionnextrrunreduce_per_replicar5iteratorrr r7rrr4 step_functionks z0Model.make_train_function..step_functionrcs |S)z-Runs a training execution with a single step.r3r*rqr+r3r4rsz1Model.make_train_function..train_functionTrcsjj|fdSNr r`scheduleitrqrr3r4sz+Model.make_train_function..cst|D]}|}q |Sz.Runs a training execution with multiple steps.r[ranger*r_r7r-r3r4rs csjj|jfdSr/r`r1r%rr2r4r3r4r5scs tjD]}|}q |Sr6r[r8r%r*r:r7r-r3r4rs ) rr%numpyitemrr[r%rr`rqforcer3)rqr+rr4make_train_functionOsB  zModel.make_train_functionrautoTr c"Cstjddtdd||dtdt ||j }|rn|durnt j ||| f|d\\}}} }|rt |\}}}|j jrtjjj|j |_|j t|t j||| || | || | |||||jd}t|tjstj|d|dk||||jd}d |_||_ |j!"d|#d}| p@|j}|$|| \|_%|_&d}|'D]\}}|(|)||*|j&p|+|_&|,D]}tj-jj.d |||d d h|/|| |}|j0rt12|}||j3}|4|||jrWdq|!|?|| | }|jrbq8qbt|j@tAjBrV|j@C|jDt:|dddurl|`;|jE|d|jFWdWdS1s0YWdn1s0YdS)a8Trains the model for a fixed number of epochs (iterations on a dataset). 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 `tf.keras.utils.experimental.DatasetCreator`, which wraps a callable that takes a single argument of type `tf.distribute.InputContext`, and returns a `tf.data.Dataset`. `DatasetCreator` should be used when users prefer to specify the per-replica batching and sharding logic for the `Dataset`. See `tf.keras.utils.experimental.DatasetCreator` doc for more information. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given below. If these include `sample_weights` as a third component, note that sample weighting applies to the `weighted_metrics` argument but not the `metrics` argument in `compile()`. If using `tf.distribute.experimental.ParameterServerStrategy`, only `DatasetCreator` type is supported for `x`. 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 `x`). batch_size: Integer or `None`. Number of samples per gradient update. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` if your data is in the form of datasets, generators, or `keras.utils.Sequence` instances (since they generate batches). epochs: Integer. Number of epochs to train the model. An epoch is an iteration over the entire `x` and `y` data provided (unless the `steps_per_epoch` flag is set to something other than None). Note that in conjunction with `initial_epoch`, `epochs` is to be understood as "final epoch". The model is not trained for a number of iterations given by `epochs`, but merely until the epoch of index `epochs` is reached. verbose: 'auto', 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. 'auto' defaults to 1 for most cases, but 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 (eg, in a production environment). callbacks: List of `keras.callbacks.Callback` instances. List of callbacks to apply during training. See `tf.keras.callbacks`. Note `tf.keras.callbacks.ProgbarLogger` and `tf.keras.callbacks.History` callbacks are created automatically and need not be passed into `model.fit`. `tf.keras.callbacks.ProgbarLogger` is created or not based on `verbose` argument to `model.fit`. Callbacks with batch-level calls are currently unsupported with `tf.distribute.experimental.ParameterServerStrategy`, and users are advised to implement epoch-level calls instead with an appropriate `steps_per_epoch` value. validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the `x` and `y` data provided, before shuffling. This argument is not supported when `x` is a dataset, generator or `keras.utils.Sequence` instance. If both `validation_data` and `validation_split` are provided, `validation_data` will override `validation_split`. `validation_split` is not yet supported with `tf.distribute.experimental.ParameterServerStrategy`. validation_data: Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. Thus, note the fact that the validation loss of data provided using `validation_split` or `validation_data` is not affected by regularization layers like noise and dropout. `validation_data` will override `validation_split`. `validation_data` could be: - A tuple `(x_val, y_val)` of Numpy arrays or tensors. - A tuple `(x_val, y_val, val_sample_weights)` of NumPy arrays. - A `tf.data.Dataset`. - A Python generator or `keras.utils.Sequence` returning `(inputs, targets)` or `(inputs, targets, sample_weights)`. `validation_data` is not yet supported with `tf.distribute.experimental.ParameterServerStrategy`. shuffle: Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). This argument is ignored when `x` is a generator or an object of tf.data.Dataset. 'batch' is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized chunks. Has no effect when `steps_per_epoch` is not `None`. class_weight: Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class. sample_weight: Optional Numpy array of weights for the training samples, used for weighting the loss function (during training only). 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, generator, or `keras.utils.Sequence` instance, instead provide the sample_weights as the third element of `x`. Note that sample weighting does not apply to metrics specified via the `metrics` argument in `compile()`. To apply sample weighting to your metrics, you can specify them via the `weighted_metrics` in `compile()` instead. initial_epoch: Integer. Epoch at which to start training (useful for resuming a previous training run). steps_per_epoch: Integer or `None`. Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors such as TensorFlow data tensors, the default `None` is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a `tf.data` dataset, and 'steps_per_epoch' is None, the epoch will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the `steps_per_epoch` argument. If `steps_per_epoch=-1` the training will run indefinitely with an infinitely repeating dataset. This argument is not supported with array inputs. When using `tf.distribute.experimental.ParameterServerStrategy`: * `steps_per_epoch=None` is not supported. validation_steps: Only relevant if `validation_data` is provided and is a `tf.data` dataset. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch. If 'validation_steps' is None, validation will run until the `validation_data` dataset is exhausted. In the case of an infinitely repeated dataset, it will run into an infinite loop. If 'validation_steps' is specified and only part of the dataset will be consumed, the evaluation will start from the beginning of the dataset at each epoch. This ensures that the same validation samples are used every time. validation_batch_size: Integer or `None`. Number of samples per validation batch. If unspecified, will default to `batch_size`. Do not specify the `validation_batch_size` if your data is in the form of datasets, generators, or `keras.utils.Sequence` instances (since they generate batches). validation_freq: Only relevant if validation data is provided. Integer or `collections.abc.Container` instance (e.g. list, tuple, etc.). If an integer, specifies how many training epochs to run before a new validation run is performed, e.g. `validation_freq=2` runs validation every 2 epochs. If a Container, specifies the epochs on which to run validation, e.g. `validation_freq=[1, 2, 10]` runs validation at the end of the 1st, 2nd, and 10th epochs. 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-picklable arguments to the generator as they can't be passed easily to children processes. Unpacking behavior for iterator-like inputs: A common pattern is to pass a tf.data.Dataset, generator, or tf.keras.utils.Sequence to the `x` argument of fit, which will in fact yield not only features (x) but optionally targets (y) and sample weights. Keras requires that the output of such iterator-likes be unambiguous. The iterator should return a tuple of length 1, 2, or 3, where the optional second and third elements will be used for y and sample_weight respectively. Any other type provided will be wrapped in a length one tuple, effectively treating everything as 'x'. When yielding dicts, they should still adhere to the top-level tuple structure. e.g. `({"x0": x0, "x1": x1}, y)`. Keras will not attempt to separate features, targets, and weights from the keys of a single dict. A notable unsupported data type is the namedtuple. The reason is that it behaves like both an ordered datatype (tuple) and a mapping datatype (dict). So given a namedtuple of the form: `namedtuple("example_tuple", ["y", "x"])` it is ambiguous whether to reverse the order of the elements when interpreting the value. Even worse is a tuple of the form: `namedtuple("other_tuple", ["x", "y", "z"])` where it is unclear if the tuple was intended to be unpacked into x, y, and sample_weight or passed through as a single element to `x`. As a result the data processing code will simply raise a ValueError if it encounters a namedtuple. (Along with instructions to remedy the issue.) Returns: A `History` object. Its `History.history` attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable). Raises: RuntimeError: 1. If the model was never compiled or, 2. If `model.fit` is wrapped in `tf.function`. ValueError: In case of mismatch between the provided input data and what the model expects or when the input data is empty. fitTr!N)validation_split)rrr  batch_sizesteps_per_epoch initial_epochepochsshuffle class_weightmax_queue_sizeworkersuse_multiprocessingr5rr add_history add_progbarr5verboserKstepsFrfr) epoch_numstep_numrH_rzUnexpected result of `train_function` (Empty logs). 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`._eval_data_handler rrr rHrIrJrKrNrOrPr5r) rrr rHrUrrNrOrP return_dict_use_cached_eval_datasetcSsi|]\}}d||qS)val_r3)r;r8valr3r3r4r?SszModel.fit..logs)GrrFrGrHrdisallow_legacy_graph_assert_compile_was_called_check_call_args_disallow_inside_tf_function_get_verbosityrr train_validation_splitr_should_use_with_coordinatorr[r\ experimental coordinatorClusterCoordinatorr`rr RespectCompiledTrainableStateget_data_handlerr%rIcallbacks_module CallbackListinferred_stepsrUrBrr"assignon_train_begin&_maybe_load_initial_counters_from_ckpt_initial_epochZ _initial_stepenumerate_epochs reset_metricson_epoch_begincatch_stop_iteration"_maybe_load_initial_step_from_ckptrUprofilerTraceon_train_batch_begin should_syncr async_waitstep_incrementon_train_batch_endrsync_to_numpy_or_python_typerr _should_evalrrYevaluaterr on_epoch_endroptimizer_experimental Optimizerfinalize_variable_valuesr  on_train_endrV)"rqrrrHrKrTrrGvalidation_datarLrMr rJrIvalidation_stepsvalidation_batch_sizevalidation_freqrNrOrPval_xval_yval_sample_weight data_handler training_logsZsteps_per_epoch_inferredr`epochr*steptmp_logsend_step epoch_logsval_logsr3r3r4rFs&z                 V         z Model.fitcCs<t|\}}}||dd}||||||||||S)a]The 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. Fr)r rrr )rqr rrr rr3r3r4 test_stephs zModel.test_stepcsjdur|sjSfddjdus<jdkr~fddjs^tjddjrvfd d _q_nbjrfd djstjddfd d _n(fd djstjdd_jS)aCreates 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`. NcsPfdd}jr"tj|ddd}t|}jj||fd}t|jdd}|S)Runs a single evaluation step.csH|}tt|jdWdn1s:0Y|Sr)rr[rrr#rrrr3r4rs *zAModel.make_test_function..step_function..run_stepTrr r!r"r$r)rrr4r+s z/Model.make_test_function..step_functionrcs |S)z)Runs a test execution with a single step.r3r,r-r3r4rsz/Model.make_test_function..test_functionTr.csjj|fdSr/r0r2rqrr3r4r5sz*Model.make_test_function..cst|D]}|}q |Sz*Runs a test execution with multiple steps.r7r9r-r3r4rs csjj|jfdSr/r;r2rr3r4r5scs tjD]}|}q |Srr<r=r-r3r4rs )rr%r>r?rr[r%r`r@r3)rqr+rr4make_test_functions<  zModel.make_test_functionc Ksdtjddtdd||d|||t d| dd} | rjt dt | |jjrtjjj|j|_t||j}|j| rt|dddur|j}n$tj|||||d d || | ||jd }t|tjs tj|d|d k||d |j d }i}|!|_"|j#$d |%|&D]\}}|'|(|)D]z}tj*jj+d |d dN|,||"|}|j-rt./|}||j0}|1||Wdn1s0YqXWdn1s0Yq6t23|}|j4|d| r&|WdSt5||j6WdSWdn1sV0YdS)amReturns 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"` defaults to 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). 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](/api_docs/python/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-picklable 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`. rTr!r\FzInvalid keyword arguments: rYNrrrZrQtest)rWrXr_)7rrFrGrHrrarbrc_check_sample_weight_warningrdrrOrkeysrrgr[r\rhrirjr`rerrrYr rlr%rIrmrnrorrr#rp on_test_beginrtrurwrUryrzon_test_batch_beginr|rr}r~on_test_batch_endrr on_test_endflatten_metrics_in_orderr)rqrrrHrTr rUrrNrOrPr[r0use_cached_eval_datasetrr`r:r*rrrr3r3r4rsf              T  zModel.evaluatecCst|\}}}||ddS)aThe 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. Fr)r r)rqr rr:r3r3r4 predict_stepszModel.predict_stepcszjdur|sjSfddjdus<jdkrLfdd}nfdd}jsntj|dd }|_jS) aCreates 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`. NcsPfdd}jr"tj|ddd}t|}jj||fd}t|jdd}|S)rcsH|}tt|jdWdn1s:0Y|Sr)rr[rrr$rrrr3r4rs *zDModel.make_predict_function..step_function..run_stepTrr concatr"r$r)rrr4r+s z2Model.make_predict_function..step_functionrcs |S)z0Runs an evaluation execution with a single step.r3r,r-r3r4rsz5Model.make_predict_function..predict_functioncsf|}tjdD]F}tjjj|tjdd|fgd|}tjdd||}q|S)z1Runs an evaluation execution with multiple steps.rcSstj|ddjS)NT) dynamic_batch)rget_tensor_specr)tr3r3r4r5szGModel.make_predict_function..predict_function..)shape_invariantscSs t||gSr)r)t1t2r3r3r4r5r)r[r8r% autographrhset_loop_optionsrr)r*r7r: step_outputsr-r3r4rs    Tr.)rr%r>r?rr[r%)rqrArr3r-r4make_predict_functions zModel.make_predict_functionc  Cstjddtdd|dtdd} |jj rH|j} d|_ |j rTd|_ t ||j}d} |j tjjjjtjjf} |st|jrt|| rz,tj} tjjjj} | | j_|| }Wn tytjdddYn0t j!|||dd |||||j"d }t|t#j$s.cSs ||Sr)rN)outputrr3r3r4r5sr7zUnexpected 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`.)>> 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_state)rqrr3r3r4rus zModel.reset_metricsc Cs||dtd|r&||jbt|8t |j||||}| |_ | |}Wdn1sz0YWdn1s0Yt |}|r|St||jSdS)a.Runs 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. 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)rbrcrdrurrr rkr single_batch_iteratorrBrrrrr) rqrrr rMrur[r*r`r3r3r4r s"0   F zModel.train_on_batchcCs||dtd|r&||j6t|j|||}||_ | |}Wdn1sl0Yt |}|r|St ||j SdS)a?Test 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)rbrcrdrurrr rrrrrrr)rqrrr rur[r*r`r3r3r4rU s+    ( zModel.test_on_batchcCsh|dtd|j2t|j|}||_||}Wdn1sT0Yt |S)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) rcrdrrr rrrrr)rqrr*r7r3r3r4r s   (zModel.predict_on_batchcCs4tjddd|j||||||||| | | | | |dS)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) rIrKrTrrrrrMrNrOrPrLrJ)rrrF)rq generatorrIrKrTrrrrrMrNrOrPrLrJr3r3r4 fit_generator s(zModel.fit_generatorc Cs0tjddd|d|j|||||||dS)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_generatorrUrNrOrPrTr)rrrcrrqrrUrrNrOrPrTr3r3r4r s zModel.evaluate_generatorc Cs&tjddd|j|||||||dS)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)rrrrr3r3r4predict_generator szModel.predict_generatorcCs@||jsgSg}|jD]}||j7}q||j7}||Sr)_assert_weights_created _trainable_self_tracked_trackablesr _trainable_weights_dedup_weights)rqr  trackable_objr3r3r4trainable_weights% s   zModel.trainable_weightscCsl|g}|jD]}||j7}q|jsXg}|jD]}||j7}q2||j||j}n ||j}||Sr)rrnon_trainable_variablesrr r_non_trainable_weightsr)rqrrr r3r3r4non_trainable_weights0 s&    zModel.non_trainable_weightscs8|jtWdS1s*0YdS)zgRetrieves the weights of the model. Returns: A flat list of Numpy arrays. N)rrr, get_weightsrr1r3r4rJ s zModel.get_weightsc Cst||||||||dS)aSaves the model to Tensorflow SavedModel or a single HDF5 file. Please see `tf.keras.models.save_model` or the [Serialization and Saving guide]( https://keras.io/guides/serialization_and_saving/) for details. Args: filepath: String, PathLike, path to SavedModel or H5 file to save the model. overwrite: Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt. include_optimizer: If True, save optimizer's state together. save_format: Either `'tf'` or `'h5'`, indicating whether to save the model to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X, and 'h5' in TF 1.X. signatures: Signatures to save with the SavedModel. Applicable to the 'tf' format only. Please see the `signatures` argument in `tf.saved_model.save` for details. options: (only applies to SavedModel format) `tf.saved_model.SaveOptions` object that specifies options for saving to SavedModel. 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 from keras.models import load_model model.save('my_model.h5') # creates a HDF5 file 'my_model.h5' del model # deletes the existing model # returns a compiled model # identical to the previous one model = load_model('my_model.h5') ``` N)r save_model)rqfilepath overwriteinclude_optimizer save_format signaturesr save_tracesr3r3r4rS s7z Model.savec Csd|t|}t|}|dur4|r.d}qld}n8|}|dvrNd}n|dvr\d}ntd|d|dkr|rtd|d |dkrtdurt d |dkr|d }n|}|st j |rt |}|sdS|dkrt|d } t| |Wdn1s0YnBts0t|jj||d tjjjt j ||d|gddS)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` defaults to 'tf'. 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. Nh5r[) tensorflowr[)hdf5rkerasz(Unknown format. Received: `save_format`=z$. Was expecting one of {"tf", "h5"}.zBsave_weights got save_format="tf"/"tensorflow", but the filepath (zT) looks like an HDF5 file. Omit the ".h5"/".keras" when saving in TensorFlow format.zi`save_weights` requires h5py when saving in hdf5, but h5py is not available. Try installing h5py package.z.indexw)rT)save_dirmodel_checkpoint_pathsave_relative_pathsall_model_checkpoint_paths)rrpath_to_stringris_hdf5_filepathlowerstriprh5py ImportErrorospathisfileask_to_proceed_with_overwriteFilersave_weights_to_hdf5_groupr[rr get_sessionrjwriterrfupdate_checkpoint_statedirname) rqrrrrfilepath_is_h5 user_formatcheck_filepathproceedfr3r3r4 save_weights sX@        .  zModel.save_weightsc CsZt|jr<|jjjdkr.r8)r6r7r8zUnable to revive model from config. When overriding the `get_config()`, 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` method to specify how to create an instance of z> from the config. Error encountered during deserialization: )rr)rrdeserialize_keras_objectr*r'rSharedObjectLoadingScoperreconstruct_from_configrconnect_ancillary_layersrOr&r)rr)r.rcustom_objectsrrZoptimizer_dict loss_dictrr'Zfunctional_model_keysr6r7rr5rr3r/r4 from_config sJ             zModel.from_configcKs |}tj|fdtji|S)abReturns 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)r(jsondumpsr get_json_type)rqr0r'r3r3r4to_json s z Model.to_jsoncKs tddS)aReturns 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.N)r)rqr0r3r3r4to_yaml sz Model.to_yamlcCs.|jD]"}t|drt|ddr|qdS)N reset_statesstatefulF)rhasattrrr=)rqr r3r3r4r=/ s   zModel.reset_statescCsBtjdddg}|jD]$}t|ddrt|dr||j7}q|S)a9Deprecated, 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@)rq state_updatesr r3r3r4rA6 s    zModel.state_updatescCs ||jS)zReturns the list of all layer variables/weights. Note: This will not track the weights of nested `tf.Modules` that are not themselves Keras layers. Returns: A list of variables. )r_undeduplicated_weightsrr3r3r4weightsQ s z Model.weightscCs6|g}|jD]}||j7}q||j|j7}|S)z?Returns the undeduplicated list of all layer variables/weights.)rr variablesrr)rqrCr r3r3r4rB] s   zModel._undeduplicated_weightsc Cs*|jstdtj|||||||ddS)aPrints 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, defaults to `[.33, .55, .67, 1.]`. print_fn: Print function to use. Defaults 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. If not provided, defaults to `False`. show_trainable: Whether to show if a layer is trainable. If not provided, 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)rqrErFrGrHrIrJr3r3r4summaryg s&z Model.summarycCst|jdddS)NF) include_self recursive)rrrr3r3r4r sz Model.layerscCs tddS)NzU`Model.layers` attribute is reserved and should not be used. Please use another name.)r)rqr:r3r3r4r scCs|dur&|dur&td|d|d|durdt|j|krZtd|dt|jdn |j|S|dur|jD]}|j|krr|Sqrtd|d td d |jDdtd dS) axRetrieves 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. Nz.z:Provide either a layer name or layer index at `get_layer`.)rrrr8r)rqr8indexr r3r3r4 get_layer s8    zModel.get_layercCsXi}tjj|\}}|D]4}t|tjr||}ddd|D}|||<q|S)aRetrieve all the variables and their paths for the model. The variable path (string) is a stable key to indentify 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, # } ``` rcSsg|] }|jqSr3r)r;rr3r3r4r rz*Model.get_weight_paths..)r[rrObjectGraphViewbreadth_first_traversalrIr|join)rqr descendantsobject_paths_dict descendanttrackable_references object_pathr3r3r4get_weight_paths s;  zModel.get_weight_pathsc s|jdurdS|pg}|pi}|j}|s2t|}tj|}g}t||D]\}}|t j |d|dqLtj ||}t ||||jjdkr|jdurtjdd||_dS)aQDefines the save spec so that serialization is able to trace model 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)rr8 SequentialcSs|dur dS|jSr)rrr3r3r4r5C rz&Model._set_save_spec..)rdrSrcreate_pseudo_input_namesr[rrziprNrrpack_sequence_asr,_set_save_specr2r&r+r) rqr6r/r0rS flat_inputs inputs_specr8tensorr1r3r4r^ s,    zModel._set_save_speccCs|j|ddS)aNReturns the `tf.TensorSpec` of call inputs 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)rqrr3r3r4 save_specF s(zModel.save_speccCs<|jr dSd|jjvr8|jtkr8|js8td|jddS)aAsserts 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`.)rBr2__dict__r!rrr8rr3r3r4rp s   zModel._assert_weights_createdcCsp|jj}|jr&|jdt|j }n|j}d|vr>|dt|dkrl|dd}td|d|ddS)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)rq method_name fullargspecpositional_args extra_argsr3r3r4rc s   zModel._check_call_argsc Kstddtj|Dr*td|d|dd|dd|dd}|durftd |d |d d}|durtd |d t|d h}|rtd|fd|jrtj rtj }|j D]&}|j |std|d|dq|j}tj|D]>} t| dgD]*}|j |std| d|dqqtj|D]>} t| dgD]*}|j |sdtd|d|dqdqTdS)z7Performs validation checks for the default `compile()`.css|]}t|tjVqdSr)rIr r)r;rr3r3r4r sz*Model._validate_compile..z `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(...)rDzMetric (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.)anyr[rrrrrHrOrr\r]r^rDrvariable_created_in_scoperr) rqrrr0Zdistribute_argZtarget_tensor_arginvalid_kwargsstrategyrrrr3r3r4r sn           zModel._validate_compilecCs*d}|jdur"|jj||tjdS||fS)aMaybe load initial epoch from ckpt considering possible worker recovery. Refer to tensorflow/python/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. rN)mode)rcZ%maybe_load_initial_counters_from_ckptrTRAIN)rqrIrJZ initial_stepr3r3r4rr s  z,Model._maybe_load_initial_counters_from_ckptcCs"t|dddkr|jdSdS)N_callback_steprr)rrvr>rr3r3r4rxsz(Model._maybe_load_initial_step_from_ckptcCs|jstddS)NzZYou must compile your model before training/testing. Use `model.compile(optimizer, loss)`.)rCrrr3r3r4rbsz Model._assert_compile_was_calledcCsN|dup.t|tjjo.t|jto.t|jdk}|rJ|jjdurJt ddS)Na `evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided. Did you mean to pass metrics to `weighted_metrics` in `compile()`? 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There are three 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="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. 3) 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 `"first"`, `"concat"`. Returns: Structure of `Tensor`s, representing the result of reduction. 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