a SicSV@sdZddlZddlZddlZddlmmZddl m Z ddl m Z ddl m Z ddl mZddlmZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddl m!Z!ddl"m#Z#ddl$m%Z%ddl&m'Z'ddl&m(Z(ddl&m)Z)ddl&m*Z*ddl&m+Z+ddl,m-Z-ddl.m/Z0zddl1m2Z2Wne3ydZ2Yn0Gdddej4Z4Gd d!d!e4Z5Gd"d#d#Z6Gd$d%d%Z7d&d'Z8d(d)Z9d*d+Z:d,d-Z;dS).z-V1 Training-related part of the Keras engine.N)backend)losses)metrics) optimizers)distributed_training_utils)distributed_training_utils_v1) base_layer)training)training_arrays_v1)training_distributed_v1)training_eager_v1)training_generator_v1)training_utils)training_utils_v1)loss_scale_optimizer) optimizer_v1) optimizer_v2) saving_utils)model_serialization) data_utils) layer_utils) losses_utils) tf_inspect)tf_utils)ModeKeys) tf_logging)issparsecs,eZdZdZfddZddZejjj ddZ dd Z dfd d Z ejjj dddZ ejjj ddZefddZefddZeddZejddZddZdd!d"Zdd#d$Zdd%d&Zd'd(Zdd)d*Zdd+d,Zd-d.Zdd/d0Zdd1d2Zdd3d4Zd5d6Zd7d8Z d9d:Z!d;d<Z"d=d>Z#d?d@Z$ddAdBZ%dCdDZ&ejjj ddEdFZ'dGdHZ(dIdJZ)dKdLZ*dMdNZ+ejjj dOdPZ,dQdRZ-ddSdTZ.dUdVZ/dWdXZ0dYdZZ1d[d\Z2d]d^Z3dd_d`Z4ddadbZ5dcddZ6dedfZ7dgdhZ8didjZ9dkdlZ:ddmdnZ;ddpdqZdvdwZ?ddxdyZ@ejjj dzd{ZAejjj d|d}ZBed~dZCeddZDeddZEeddZFeddZGeddZHeddZIeddZJeddZKeddZLddZMddZNddZOddZPeddZQdddZReddZSZTS)Modelaf`Model` groups layers into an object with training and inference features. 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) ``` 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() ``` cshtj|i|d|_d|_tjjrDtj rD| tj d|_ d|_ tjj|_d|_dS)NF)super__init___distribution_strategy#_compile_time_distribution_strategytfcompatv1#executing_eagerly_outside_functions distribute has_strategy _set_strategy get_strategy_compile_distribution _run_eagerly_experimental_run_tf_function_v1_compile_was_called)selfargskwargs __class__T/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/engine/training_v1.pyrys  zModel.__init__cCsdSNr3r.r3r3r4_init_batch_countersszModel._init_batch_counterscCs ||_dSr5)r!r.strategyr3r3r4r(szModel._set_strategycCsP|jp |j}|rD|tj|WdS1s:0Ytj|S)zgRetrieves the weights of the model. Returns: A flat list of Numpy arrays. N)r r!scoperLayer get_weightsr8r3r3r4r<s *zModel.get_weightsFcs<t|jr,|jjjdkr,t|s,tdt |||S)a Loads all layer weights, either from a TensorFlow or an HDF5 weight file. If `by_name` is False weights are loaded based on the network's topology. This means the architecture should be the same as when the weights were saved. Note that layers that don't have weights are not taken into account in the topological ordering, so adding or removing layers is fine as long as they don't have weights. If `by_name` is True, weights are loaded into layers only if they share the same name. This is useful for fine-tuning or transfer-learning models where some of the layers have changed. Only topological loading (`by_name=False`) is supported when loading weights from the TensorFlow format. Note that topological loading differs slightly between TensorFlow and HDF5 formats for user-defined classes inheriting from `tf.keras.Model`: HDF5 loads based on a flattened list of weights, while the TensorFlow format loads based on the object-local names of attributes to which layers are assigned in the `Model`'s constructor. Args: filepath: String, path to the weights file to load. For weight files in TensorFlow format, this is the file prefix (the same as was passed to `save_weights`). by_name: Boolean, whether to load weights by name or by topological order. Only topological loading is supported for weight files in TensorFlow format. skip_mismatch: Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weight (only valid when `by_name=True`). Returns: When loading a weight file in TensorFlow format, returns the same status object as `tf.train.Checkpoint.restore`. When graph building, restore ops are run automatically as soon as the network is built (on first call for user-defined classes inheriting from `Model`, immediately if it is already built). When loading weights in HDF5 format, returns `None`. Raises: ImportError: If h5py is not available and the weight file is in HDF5 format. ValueError: If `skip_mismatch` is set to `True` when `by_name` is `False`. zULoad weights is not yet supported with TPUStrategy with steps_per_run greater than 1.) ris_tpu_strategyr extended steps_per_runris_hdf5_filepath ValueErrorr load_weights)r.filepathby_name skip_mismatchr1r3r4rCs/ zModel.load_weightsrmspropNc Ks|| dd|_| dd|_d|_| dd| dd|_hd} t| | } | rntd | f| |_ |j rd|_|j rt d |j f| |t d d tj|jD} | rtjjrt d |d|dustjjsd|_|dur2tjjs|jrt dtd||_d|_n$tjrVtjrVtj|_t |jtjjjj!j"rvt#dt |jtjj!j"rt#d|js|$|j |||t |jtjj%j&r|j'|jddd|pi|_(||_)||_*|pg|_+||_,|j r|durt dg|_-|.|_/i|_0i|_1|2t3s\|jdur\t45|j|6|j7r||j8r||j9sdSd|_:t;j<=ddt>?|j(|j@|_A|B|}tC|j9|j@|jA|D]6\} }}}tD| ||}|jE||j d|j-F|qt>G|j-||j r(|H|||dSt4IJ|K|||L|jM|j9|jN|O|Pdt>Q|j-||Rd|_Sd|_Td|_U|jV|_W|jr|js|jXD]*}|j}|jYZ|st d||fqWdn1s0YdS)a Configures 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 `scalar_loss = fn(y_true, y_pred)`. 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. metrics: List of metrics to be evaluated by the model during training and testing. Typically you will use `metrics=['accuracy']`. 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 (len = len(outputs)) of lists of metrics such as `metrics=[['accuracy'], ['accuracy', 'mse']]` or `metrics=['accuracy', ['accuracy', 'mse']]`. 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 tensor, it is expected to map output names (strings) to scalar coefficients. sample_weight_mode: If you need to do timestep-wise sample weighting (2D weights), set this to `"temporal"`. `None` defaults to sample-wise weights (1D). If the model has multiple outputs, you can use a different `sample_weight_mode` on each output by passing a dictionary or a list of modes. weighted_metrics: List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing. target_tensors: By default, Keras will create placeholders for the model's target, which will be fed with the target data during training. If instead you would like to use your own target tensors (in turn, Keras will not expect external Numpy data for these targets at training time), you can specify them via the `target_tensors` argument. It can be a single tensor (for a single-output model), a list of tensors, or a dict mapping output names to target tensors. distribute: NOT SUPPORTED IN TF 2.0, please create and compile the model under distribution strategy scope instead of passing it to compile. **kwargs: Any additional arguments. Raises: ValueError: In case of invalid arguments for `optimizer`, `loss`, `metrics` or `sample_weight_mode`. run_eagerlyNexperimental_run_tf_functionTcloningfrom_serializedF> feed_dict run_metadataoptionsfetchesz,Invalid keyword argument(s) in `compile`: %szsSession keyword arguments are not supported when `run_eagerly=True`. You passed the following Session arguments: %scss(|] }t|tjot|tj VqdSr5) isinstancer Optimizer TFOptimizer.0optr3r3r4 As z Model.compile..z `tf.compat.v1.keras` Optimizer (zs) is not supported when eager execution is enabled. 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You cannot set `run_eagerly=False`.)r+r"rrBdynamicconfigfunctions_run_eagerlyr6r3r3r4rHDs"   zModel.run_eagerlycCs ||_dSr5)r+r.valuer3r3r4rHqscCst|tjjjjtjjfr"td|jrF|r>t t St St |rXtSt|rjtS|jrxtStSdS)z>Select training loop for fit/eval/predict based on the inputs.aFor performance reasons Keras `fit`, `evaluate` and`predict` accept tf.data `Datasets` as input but not iterators that have been manually generated from Datasets by users. Please directly pass in the original `Dataset` object instead of passing in `iter(dataset)`.N)rPr"r#r$dataIteratorrBr _in_multi_worker_moder #DistributionMultiWorkerTrainingLoop$DistributionSingleWorkerTrainingLoopris_generator_or_sequencer GeneratorOrSequenceTrainingLoopris_eager_dataset_or_iterator"EagerDatasetOrIteratorTrainingLooprHGeneratorLikeTrainingLoopr ArrayLikeTrainingLoop)r.rr3r3r4_select_training_loopus$   zModel._select_training_loopr=Tr cKs|tjddd|vr6td|d}|rJtdt || | d| |}|j |||||||||| | | | | |||||dS)a#Trains 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)`. 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 symbolic tensors, 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: 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. 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`. 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_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. `validation_data` will override `validation_split`. `validation_data` could be: - tuple `(x_val, y_val)` of Numpy arrays or tensors - tuple `(x_val, y_val, val_sample_weights)` of Numpy arrays - dataset For the first two cases, `batch_size` must be provided. For the last case, `validation_steps` could be provided. shuffle: Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). '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. In this case you should make sure to specify `sample_weight_mode="temporal"` in `compile()`. 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. This argument is not supported with array inputs. 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 a infinite dataset, it will run into a 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_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. If 0, will execute the generator on the main thread. 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. **kwargs: Used for backwards compatibility. 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: If the model was never compiled. ValueError: In case of mismatch between the provided input data and what the model expects. fitTnb_epochz;The `nb_epoch` argument in `fit` has been renamed `epochs`.z Unrecognized keyword arguments: )xy batch_sizeepochsverbose callbacksvalidation_splitvalidation_datashuffle class_weight sample_weight initial_epochsteps_per_epochvalidation_stepsvalidation_freqmax_queue_sizeworkersuse_multiprocessing)r^rrrrarlrmr_rcstr_assert_compile_was_called_check_call_argsrr)r.rrrrrrrrrrrrrrrrrrr0funcr3r3r4rsD,   z Model.fitc CsV|tjdd||d||} | j|||||||||| | d S)aBReturns 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. - A generator or `keras.utils.Sequence` instance. 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 symbolic tensors, 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. In this case you should make sure to specify `sample_weight_mode="temporal"` in `compile()`. 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. If 0, will execute the generator on the main thread. 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. 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: ValueError: in case of invalid arguments. evaluateT) rrrrrstepsrrrr) r^rrrrarrrr) r.rrrrrrrrrrrr3r3r4rms$[  zModel.evaluatec CsJ|tjdd|d||} | j|||||||||d S)a Generates output predictions for the input samples. Computation is done in batches (see the `batch_size` arg.) 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. 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 symbolic tensors, 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. If 0, will execute the generator on the main thread. 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. Returns: Numpy array(s) of predictions. Raises: 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. predictT)rrrrrrrr)r^rrrrarrr) r.rrrrrrrrrr3r3r4rsA  z Model.predictcCs.|}|D] }|q |jr*t|dS)zResets the state of metrics.N)_get_training_eval_metrics reset_stater r_reset_metrics)r.rrr3r3r4 reset_metrics.s  zModel.reset_metricsc Cs||d|jr*tjr*td|j||||dd\}}}|jsP|jrt j |||||j d}|d|d|d}d d |D}n`t |}|t|pgt|pg} ttts| dg7} |j|d ||| }|r|t|d kr|d S|S)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. - A `tf.data` dataset. 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, `y` should not be specified (since targets will be obtained from the iterator). 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. In this case you should make sure to specify sample_weight_mode="temporal" in compile(). This argument is not supported when `x` is a dataset. 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. 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Returns: A list of metric result tensors. )weightsraN)rrrKrcall_metric_functionr) r.rrbrcrarmetric_resultsrr metric_resultr3r3r4_handle_per_output_metricsWs  *z Model._handle_per_output_metricsc Cs|pdgt|}g}tdtt|D]} || r.updatesrX)rDrrPrWrrBrHr|_set_trainable_stater} _feed_inputs _feed_targets_feed_sample_weightsrrr rrrK get_updatesrrget_updates_forrrfunctionrdsetattr)r.has_recompiledcurrent_trainable_staterrrmetrics_tensorsfnr3r3r4r sR   .&  ,zModel._make_train_functioncCs|}t|dddus|r|j|j|j}t&|}dd|D}Wdn1sf0Yt dF|j }tj ||j g|f|dd|j }t|d|Wdn1s0YdS)NrcSsg|]}t|dr|jqSrrrr3r3r4r s z-Model._make_test_function.. evaluationr)rDrHrrrrrrrrK state_updatesrrrdr)r.rrrrrrr3r3r4r s2$  zModel._make_test_functioncCs|t|dsd|_|jdurx|j}t|di}ttj0tj||j f|j dd||_Wdn1sn0YdS)Nrrdr) rrrrHrrKrPREDICTrrr)r.rr0r3r3r4r( s   zModel._make_predict_functioncCsL|tjkr||jS|tjkr0||jS|tjkrH||j SdSr5) rTRAINr rTESTrrrrr)r.moder3r3r4_make_execution_function9 s   zModel._make_execution_functionc Cs|r td|dur0|r0t|jr0tdt|tjjrL|rLt ||j} | @tj j rpd} nt} tj|d} t| tjrjt|}|durt|}|durt|}|||f} q||f} n|} | jj| | d}|r|td|d}|dkr||}| o*| jj}t| rZ|sZ| jd}||dkrZd }|j||d }n$t|tjjs~Jt ||||Wdn1s0Y|S) aRuns validation checks on input and target data passed by the user. 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If `x` is a dataset, `y` should not be specified (since targets will be obtained from the iterator). sample_weight: An optional sample-weight array passed by the user to weight the importance of each sample in `x`. class_weight: An optional class-weight array by the user to weight the importance of samples in `x` based on the class they belong to, as conveyed by `y`. If both `sample_weight` and `class_weight` are provided, the weights are multiplied. batch_size: Integer batch size. If provided, it is used to run additional validation checks on stateful models. check_steps: boolean, True if we want to check for validity of `steps` and False, otherwise. For example, when we are standardizing one batch of data for train_on_batch/predict_on_batch/test_on_batch APIs, `steps` value is not required and we should not check for its validity in these cases. steps_name: The public API's parameter name for `steps`. steps: Integer or `None`. 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