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Nested inputs are not supported (e.g. lists of list or dicts of dict). 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(MyModel, self).__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(MyModel, self).__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_executioncsNt||r2|tkr2ddlm}|j|ddi|Stt|j|f||SdS)Nr)rH skip_initT)rZrNrMrHrOsuperrS)rTr@rArH) __class__rCrDrSs z Model.__new__c sd|_ddlm}t|rt||jsdddddgfd d D}fd d D}t|j|jj|f||g}d }x0|jj D]$}t ||jrd}q|r| |qW|rx2|D]}|j|f||qWn|rt d |dStdddddddhtt|jfd |_d|_d|_d|_d|_d |_d|_d|_d|_d |_|dd |ddtrt|_ nd|_ d|_!d|_"|#d|_$d|_%t&j't()|d|_*d|_+|,d|_-dS)NTr)rHrXrWnameZ trainabler_csi|]}|kr||qSrCrC)rKk)rAsupported_kwargsrCrD sz"Model.__init__..csi|]}|kr||qSrCrC)rKrc)rArdrCrDresFz4The following keyword arguments aren't supported: {}dtypedynamicZautocast _is_compiled optimizer)root).Z_is_model_for_instrumentationrMrHrZ isinstancerOrJra__init__rR issubclassappend TypeErrorr=r%Zvalidate_kwargsr`rN_is_graph_networkrXrW input_names output_names stop_traininghistory compiled_losscompiled_metricsZ _compute_output_and_mask_jointlyZ_maybe_create_attribute ds_context has_strategy get_strategy_distribution_strategy_cluster_coordinator _run_eagerly_reset_compile_cache_training_state_saved_model_inputs_spectrackable_utilsZ Checkpointweakrefref _checkpointr^_init_batch_counters_base_model_initialized) r?r@rArHZ model_kwargsZ other_kwargsZ clz_to_initZfound_functional_classZclz)ra)rArdrDrlsh          zModel.__init__cCsBtjj}tjdd|d|_tjdd|d|_tjdd|d|_dS)Nrint64)rf aggregation)r0VariableAggregationV2ONLY_FIRST_REPLICAVariabler[r\r])r?ZaggrCrCrDr=s zModel._init_batch_counterscsxt|dds"tt|||dStddt|Drby |jWntk r`t dYnXtt|||dS)NZ_self_setattr_trackingTcss*|]"}t|tjtjfp t|VqdS)N)rkrLayerr0rrZ has_weights)rKvrCrCrDrLMsz$Model.__setattr__..zsIt looks like you are subclassing `Model` and you forgot to call `super().__init__()`. 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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 tensor or None (no mask). Returns: A tensor if there is a single output, or a list of tensors if there are more than one outputs. zIWhen subclassing the `Model` class, you should implement a `call` method.N)NotImplementedError)r?rXrmaskrCrCrDrsz Model.callrmspropc Ks|jd|kr,td|s,|d}|dd} |j||f|||_|||_t j |||j d|_ t j |||j | d|_||pd|d|_|pi|_Wd QRXd S) aEConfigures the model for training. Args: optimizer: String (name of optimizer) or optimizer instance. See `tf.keras.optimizers`. loss: String (name of objective function), objective function or `tf.keras.losses.Loss` instance. See `tf.keras.losses`. An objective function is any callable with the signature `loss = fn(y_true, y_pred)`, where y_true = ground truth values with shape = `[batch_size, d0, .. dN]`, except sparse loss functions such as sparse categorical crossentropy where shape = `[batch_size, d0, .. dN-1]`. y_pred = predicted values with shape = `[batch_size, d0, .. dN]`. It returns a weighted loss float tensor. If a custom `Loss` instance is used and reduction is set to `None`, return value has the 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. 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). **kwargs: Arguments supported for backwards compatibility only. 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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)rhrurrv_flatten_layersextend_metrics)r?rlrCrCrDrys%    z Model.metricscCsdd|jDS)aHReturns 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|] }|jqSrC)rb)rKmrCrCrDrsz'Model.metrics_names..)r)r?rCrCrD metrics_namess&zModel.metrics_namescCs|jp tS)z:The `tf.distribute.Strategy` this model was created under.)rzrwry)r?rCrCrDrszModel.distribute_strategycCsJ|jr|jdkrtd|jr,|jr,td|jpH|jpHtoH|jdkS)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)rgr|r<r{r Zfunctions_run_eagerly)r?rCrCrDrs  zModel.run_eagerlycCs ||_dS)N)r|)r?rrCrCrDrsc Cst|}t|\}}}t&}||dd}|j||||jd}WdQRX|jj||j |d|j |||i}x6|j D],} | } t| tr|| q~| || j<q~W|S)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 happends 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}`. T)r)regularization_lossesN)tape)r expand_1dunpack_x_y_sample_weightr Z GradientTaperulossesriZminimizetrainable_variablesrv update_staterresultrkrupdaterb) r?datary sample_weightry_predrreturn_metricsmetricrrCrCrD train_steps      zModel.train_stepcsjdk rjSfddjdkr>fddnfddjsftjdd __jrfd d _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. 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}`. NcsHfdd}t|}jj||fd}t|jdd}t|jd|S)zRuns a single training step.c s4|}tt|jdWdQRX|S)NrV)rrcontrol_dependencies_minimum_control_depsr[ assign_add)rrW)modelrCrDrun_stepGs zBModel.make_train_function..step_function..run_step)r@first) reduction)step)nextrrunreduce_per_replicawrite_scalar_summariesr[)riteratorrrrW)r?)rrD step_functionDs z0Model.make_train_function..step_functionrVcs |S)z(Runs a training execution with one step.rC)r)r?rrCrDrWsz1Model.make_train_function..train_functioncs$xtjD]}|}qW|S)z.Runs a training execution with multiple steps.)r-ranger^)r_rW)r?rrCrDr]sT)experimental_relax_shapescsjj|fdS)N)r@)r{schedule)r)r?rrCrDksz+Model.make_train_function..) rr^numpyitemrr functionrr{)r?rC)r?rrrDmake_train_function-s   zModel.make_train_functionrVautoTr Fc!Cstdd||dtd|dkr@|jjrPq>Wt0|dd d k r|`1|j6|d|j7SQRXWd QRXd S)a1Trains 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 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. 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. `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`. 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. rNfitrrUrV)validation_split)rrr batch_sizesteps_per_epoch initial_epochepochsshuffle class_weightmax_queue_sizeworkersuse_multiprocessingrrTr) add_history add_progbarrverboserstepsFNtrain)Z epoch_numstep_numr_rz,Expect x to be a non-empty array or dataset._eval_data_handler) rrrrrrrrrrrr) rrrrrrrrr return_dict_use_cached_eval_datasetcSsi|]\}}|d|qS)Zval_rC)rKrbvalrCrCrDreszModel.fit..)logs)8r(disallow_legacy_graph_assert_compile_was_called_check_call_args_disallow_inside_tf_functionr_should_use_with_coordinatorrZtrain_validation_splitrr ClusterCoordinatorr{rrRespectCompiledTrainableStateget_data_handlerr^rkcallbacks_module CallbackListinferred_stepsrsrrr[assignZon_train_begin#_maybe_load_initial_epoch_from_ckptZ_initial_epochenumerate_epochs reset_metricsZon_epoch_begincatch_stop_iterationrr2TraceZon_train_batch_begin should_syncr async_waitstep_incrementZon_train_batch_endr'sync_to_numpy_or_python_typer<copy _should_evalrrevaluaterrZ on_epoch_endZ on_train_endrt)!r?rrrrrrrvalidation_datarrrrrvalidation_stepsZvalidation_batch_sizevalidation_freqrrrZval_xZval_yZval_sample_weight data_handlerZ training_logsr epochrrtmp_logsend_stepZ epoch_logsZval_logsrCrCrDrpse                       z Model.fitc Cst|}t|\}}}||dd}|j||||jd|j|||i}x6|jD],}|}t |t rx| |qV|||j <qVW|S)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. F)r)r) rrrrurrvrrrrkrrrb) r?rrrrrrrrrCrCrD test_steps     zModel.test_stepcsjdk rjSfddjdkr>fddnfddjs`tjdd _jr|fd d _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. 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`. Ncs:fdd}t|}jj||fd}t|jdd}|S)zRuns a single evaluation step.c s4|}tt|jdWdQRX|S)NrV)r+rrrr\r)rrW)rrCrDrs zAModel.make_test_function..step_function..run_step)r@r)r)rrrr)rrrrrW)r?)rrDrs  z/Model.make_test_function..step_functionrVcs |S)z+Runs an evaluation execution with one step.rC)r)r?rrCrDr-sz/Model.make_test_function..test_functioncs$xtjD]}|}qW|S)z1Runs an evaluation execution with multiple steps.)r-rr^)rrrW)r?rrCrDr3sT)rcsjj|fdS)N)r@)r{r)r)r?rrCrDr@sz*Model.make_test_function..)rr^rrrr rr{)r?rC)r?rrrDmake_test_functions   zModel.make_test_functionc Kstdd||dtd| dd} | rDtd| f|jjrZt |j|_ |j ^| rt |dddk r|j}n$tj|||||dd || | ||jd }t|tjstj|d |dk||d |jd }i}||_|jd|x|D]\}}||txl|D]`}tj d |d dD|!|||}|j"r\t#$|}||j%}|&||WdQRXq"WWdQRXqWt'(|}|j)|d| r|St*||j+SWdQRXdS)a6Returns 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: 0 or 1. Verbosity mode. 0 = silent, 1 = progress bar. 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`. `Model.evaluate` is not yet supported with `tf.distribute.experimental.ParameterServerStrategy`. 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 `tf.function`. ValueError: in case of invalid arguments. rNr#r FzInvalid keyword arguments: %srNrrV) rrrrrrrrrrrrT)rrrrrrtest)rr)r ),r(r r rrrrorrr rr{rrrrrr^rkrrrr,rr\rZ on_test_beginrrrrr2rZon_test_batch_beginrr rrZon_test_batch_endr'r Z on_test_endflatten_metrics_in_orderr)r?rrrrrrrrrrrrAZuse_cached_eval_datasetr'r rrrr)r*rCrCrDr#Esr^             *  zModel.evaluatecCs&t|}t|\}}}||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. F)r)rrr)r?rrrrCrCrD predict_steps zModel.predict_stepcsvjdk rjSfddjdks8jdkrHfdd}nfdd}jsjtj|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. Returns: Function. The function created by this method should accept a `tf.data.Iterator`, and return the outputs of the `Model`. Ncs:fdd}t|}jj||fd}t|jdd}|S)zRuns a single evaluation step.c s4|}tt|jdWdQRX|S)NrV)r/rrrr]r)rrW)rrCrDrs zDModel.make_predict_function..step_function..run_step)r@concat)r)rrrr)rrrrrW)r?)rrDrs  z2Model.make_predict_function..step_functionrVcs |S)z+Runs an evaluation execution with one step.rC)r)r?rrCrDr'sz5Model.make_predict_function..predict_functioncs`|}xPtjdD]<}tjddt|Dd|}tdd||}qW|S)z1Runs an evaluation execution with multiple steps.rVcSs g|]}|tj|ddjfqS)T) dynamic_batch)r'get_tensor_specr)rKtrCrCrDr2szIModel.make_predict_function..predict_function..)Zshape_invariantscSs t||gS)N)r0)t1t2rCrCrDr6zGModel.make_predict_function..predict_function..)r-rr^rZset_loop_optionsr7rr)rrWrZ step_outputs)r?rrCrDr-s    T)r)rr^rrrr r)r?rrC)r?rrDmake_predict_functions     zModel.make_predict_functionc  Cs.tdd|dtdd} |jjr6|j} d|_|jrBd|_d} |jt j t j f} | srt |jrt|| ry&t} tjj} | | j_|| }Wntk rtdYnXtj|||dd|||||jd }t|tjstj|d|dk||d|jd }||_ |j!"d|#d}x|$D]\}}|%x|&D]x}|'|| |}|j(r~t)*|}| dkrt+,d d |} nt+-|d d | |||j.}|/|d |iqVWWdQRXq:W|dkrtd|0WdQRXt+-|t1| }| dk r$| |_t23|S)a Generates output predictions for the input samples. Computation is done in batches. This method is designed for performance in large scale inputs. For small amount of inputs that fit in one batch, directly using `__call__` is recommended for faster execution, e.g., `model(x)`, or `model(x, training=False)` if you have layers such as `tf.keras.layers.BatchNormalization` that behaves differently during inference. Also, note the fact that test loss is not affected by regularization layers like noise and dropout. 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: Verbosity mode, 0 or 1. 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](/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. 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 `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. rNpredictNzUsing Model.predict with MultiWorkerDistributionStrategy or TPUStrategy and AutoShardPolicy.FILE might lead to out-of-order result. Consider setting it to AutoShardPolicy.DATA.rrV) rrrrrrrrrrT)rrrrrrcSs|gS)NrC) batch_outputrCrCrDrr6zModel.predict..cSs ||S)N)rn)outputr9rCrCrDrr6rWz,Expect x to be a non-empty array or dataset.)4r(r rrrrrzr{rrZ DatasetV1Z DatasetV2r;_is_tpu_multi_hostrk options_libOptionsZAutoShardPolicyZDATAZexperimental_distributeZauto_shard_policyZ with_optionsr<warningswarnrrr^rrrr7rr]rZon_predict_beginrrrZon_predict_batch_beginrr rr7rZmap_structure_up_torZon_predict_batch_endZon_predict_endr0r'r )r?rrrrrrrrZoriginal_pss_strategyrWZ dataset_typesrZ data_optionr'Z batch_outputsrrrZtmp_batch_outputsr*Z all_outputsrCrCrDr8AsJ              &  z Model.predictcCsx|jD] }|qWdS)aOResets 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)rZ reset_state)r?rrCrCrDrs zModel.reset_metricsc Cs||dtd|jDt|.t|j||||}| |_ | |}WdQRXWdQRX|rz| t |}|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). 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. 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 `tf.function`. ValueError: In case of invalid user-provided arguments. train_on_batchN)r rrrrrrrsingle_batch_iteratorrrrr'r r.r) r?rrrrrrrr rCrCrDr@s /      zModel.train_on_batchc Cs||dtd|j,t|j|||}||_||}WdQRX|rb| t |}|rt|St ||j SdS)aTest 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 `tf.function`. ValueError: In case of invalid user-provided arguments. test_on_batchN)r rrrrrrAr,rrr'r r.r)r?rrrrrrr rCrCrDrB@s)     zModel.test_on_batchc CsT|dtd|j(t|j|}||_||}WdQRXt |S)aJReturns 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 `tf.function`. ValueError: In case of mismatch between given number of inputs and expectations of the model. predict_on_batchN) rrrrrrAr7rr'r )r?rrrWrCrCrDrCzs   zModel.predict_on_batchcCs0td|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.) rrrrr$r%r&rrrrrr)r>r?r)r? generatorrrrrr$r%r&rrrrrrrCrCrD fit_generators  zModel.fit_generatorc Cs,td|d|j|||||||dS)zEvaluates the model on a data generator. DEPRECATED: `Model.evaluate` now supports generators, so there is no longer any need to use this endpoint. z`Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.evaluate_generator)rrrrrr)r>r?rr#)r?rDrrrrrrrCrCrDrFs  zModel.evaluate_generatorc Cs"td|j|||||||dS)zGenerates predictions for the input samples from a data generator. DEPRECATED: `Model.predict` now supports generators, so there is no longer any need to use this endpoint. z`Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.)rrrrrr)r>r?r8)r?rDrrrrrrrCrCrDpredict_generators zModel.predict_generatorcCsD||jsgSg}x|jD]}||j7}qW||j7}||S)N)_assert_weights_created _trainable_self_tracked_trackablesr_trainable_weights_dedup_weights)r?r trackable_objrCrCrDtrainable_weightss  zModel.trainable_weightscCst|g}x|jD]}||j7}qW|js`g}x|jD]}||j7}q8W||j||j}n ||j}||S)N)rHrJnon_trainable_variablesrIrrK_non_trainable_weightsrL)r?rOrMrrCrCrDnon_trainable_weightss   zModel.non_trainable_weightsc s$|jtt|SQRXdS)z[Retrieves the weights of the model. Returns: A flat list of Numpy arrays. N)rrr`rN get_weights)r?)rarCrDrRs zModel.get_weightsc Cst||||||||dS)a!Saves 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!Z save_model)r?filepath overwriteZinclude_optimizer save_formatZ signaturesrZ save_tracesrCrCrDr!!s3z Model.savec Cs@|t|}t|}|dkr2|r,d}qhd}n6|}|dkrLd}n|dkrZd}ntd|f|dkr|rtd||dkrtdkrtd|dkr|d }n|}|st j |rt |}|sdS|dkrt |d } t| |jWdQRXn>tst|jj||d tjt j ||d |gd dS)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. ValueError: For invalid/unknown format arguments. Nh5tf)Z tensorflowrW)Zhdf5rVZkerasz7Unknown format "%s". 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Please use `model.to_json()` instead.N)r)r?rArCrCrDto_yamlh sz Model.to_yamlcCs2x,|jD]"}t|drt|ddr|qWdS)N reset_statesstatefulF)rchasattrrr)r?rtrCrCrDr s zModel.reset_statescCsBtdg}x.|jD]$}t|ddrt|dr||j7}qW|S)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.rFupdates)r>r?rcrrr)r? state_updatesrtrCrCrDr 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. 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Nz+Provide only a layer name or a layer index.z%Was asked to retrieve layer at index z but model only has z layers.zNo such layer: .z+Provide either a layer name or layer index.)r<rYrcstrrb)r?rbindexrtrCrCrD get_layer s    zModel.get_layercCs|jdk rdS|j}|s"t|}t|}g}x,t||D]\}}|tj |d|dq.)rrqrZcreate_pseudo_input_namesr7rziprnr'r2Zpack_sequence_asrar>Z_build_input_shaper)r?rXrqZ flat_inputsspecsrbZtensorrCrCrD_set_save_spec s       zModel._set_save_speccCs8|jr dSd|jjkr4|jtkr4|js4td|jdS)aaAsserts 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. 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