parameters: (self,
            model,
            x=None,
            y=None,
            batch_size=None,
            epochs=1,
            verbose=1,
            callbacks=None,
            validation_split=0.,
            validation_data=None,
            shuffle=True,
            class_weight=None,
            sample_weight=None,
            initial_epoch=0,
            steps_per_epoch=None,
            validation_steps=None,
            validation_freq=1,
            **kwargs)
return_statement: return training_arrays_v1.fit_loop(
          model,
          dataset,
          batch_size=batch_size,
          epochs=epochs,
          verbose=verbose,
          callbacks=callbacks,
          val_inputs=val_dataset,
          shuffle=shuffle,
          initial_epoch=initial_epoch,
          steps_per_epoch=steps_per_epoch,
          validation_steps=validation_steps,
          validation_freq=validation_freq,
          steps_name='steps_per_epoch')
docstring: Fit loop for Distribution Strategies.
docstring_summary: Fit loop for Distribution Strategies.
docstring_tokens: Fit loop for Distribution Strategies .
function: def fit(self,
            model,
            x=None,
            y=None,
            batch_size=None,
            epochs=1,
            verbose=1,
            callbacks=None,
            validation_split=0.,
            validation_data=None,
            shuffle=True,
            class_weight=None,
            sample_weight=None,
            initial_epoch=0,
            steps_per_epoch=None,
            validation_steps=None,
            validation_freq=1,
            **kwargs):
      """Fit loop for Distribution Strategies."""
      dist_utils.validate_callbacks(input_callbacks=callbacks,
                                    optimizer=model.optimizer)
      dist_utils.validate_inputs(x, y)
  
      batch_size, steps_per_epoch = dist_utils.process_batch_and_step_size(
          model._distribution_strategy,
          x,
          batch_size,
          steps_per_epoch,
          ModeKeys.TRAIN,
          validation_split=validation_split)
      batch_size = model._validate_or_infer_batch_size(
          batch_size, steps_per_epoch, x)
      dataset = model._distribution_standardize_user_data(
          x, y,
          sample_weight=sample_weight,
          class_weight=class_weight,
          batch_size=batch_size,
          validation_split=validation_split,
          shuffle=shuffle,
          epochs=epochs)
      if not dist_utils.is_distributing_by_cloning(model):
        with model._distribution_strategy.scope():
          (dataset, _, _) = model._standardize_user_data(
              dataset,
              sample_weight=sample_weight,
              class_weight=class_weight,
              batch_size=batch_size,
              validation_split=validation_split,
              shuffle=shuffle)
  
      val_dataset = None
      if validation_data:
        val_x, val_y, val_sample_weights = (
            training_utils_v1.unpack_validation_data(validation_data))
        dist_utils.validate_inputs(val_x, val_y)
        _, validation_steps = dist_utils.process_batch_and_step_size(
            model._distribution_strategy, val_x, batch_size, validation_steps,
            ModeKeys.TEST)
  
        val_dataset = model._distribution_standardize_user_data(
            val_x, val_y,
            sample_weight=val_sample_weights,
            class_weight=None,
            batch_size=batch_size,
            validation_split=validation_split,
            shuffle=shuffle,
            allow_partial_batch=True)
      elif validation_split:
        raise ValueError('validation_split argument is not supported with '
                         'distribution strategies.')
  
      if backend.is_tpu_strategy(model._distribution_strategy):
        steps_per_epoch = training_utils_v1.infer_steps_for_dataset(
            model, dataset, steps_per_epoch, epochs, steps_name='steps_per_epoch')
        if steps_per_epoch is None:
          raise ValueError('Number of steps could not be inferred from the data, '
                           'please pass the steps_per_epoch argument.')
  
        if not context.executing_eagerly():
          # Run TPU training in a custom loop in graph mode.
          return experimental_tpu_fit_loop(
              model,
              dataset,
              epochs=epochs,
              verbose=verbose,
              callbacks=callbacks,
              val_dataset=val_dataset,
              initial_epoch=initial_epoch,
              steps_per_epoch=steps_per_epoch,
              validation_steps=validation_steps,
              validation_freq=validation_freq)
  
      return training_arrays_v1.fit_loop(
          model,
          dataset,
          batch_size=batch_size,
          epochs=epochs,
          verbose=verbose,
          callbacks=callbacks,
          val_inputs=val_dataset,
          shuffle=shuffle,
          initial_epoch=initial_epoch,
          steps_per_epoch=steps_per_epoch,
          validation_steps=validation_steps,
          validation_freq=validation_freq,
          steps_name='steps_per_epoch')
function_tokens: def fit ( self , model , x = None , y = None , batch_size = None , epochs = 1 , verbose = 1 , callbacks = None , validation_split = 0. , validation_data = None , shuffle = True , class_weight = None , sample_weight = None , initial_epoch = 0 , steps_per_epoch = None , validation_steps = None , validation_freq = 1 , * * kwargs ) : dist_utils . validate_callbacks ( input_callbacks = callbacks , optimizer = model . optimizer ) dist_utils . validate_inputs ( x , y ) batch_size , steps_per_epoch = dist_utils . process_batch_and_step_size ( model . _distribution_strategy , x , batch_size , steps_per_epoch , ModeKeys . TRAIN , validation_split = validation_split ) batch_size = model . _validate_or_infer_batch_size ( batch_size , steps_per_epoch , x ) dataset = model . _distribution_standardize_user_data ( x , y , sample_weight = sample_weight , class_weight = class_weight , batch_size = batch_size , validation_split = validation_split , shuffle = shuffle , epochs = epochs ) if not dist_utils . is_distributing_by_cloning ( model ) : with model . _distribution_strategy . scope ( ) : ( dataset , _ , _ ) = model . _standardize_user_data ( dataset , sample_weight = sample_weight , class_weight = class_weight , batch_size = batch_size , validation_split = validation_split , shuffle = shuffle ) val_dataset = None if validation_data : val_x , val_y , val_sample_weights = ( training_utils_v1 . unpack_validation_data ( validation_data ) ) dist_utils . validate_inputs ( val_x , val_y ) _ , validation_steps = dist_utils . process_batch_and_step_size ( model . _distribution_strategy , val_x , batch_size , validation_steps , ModeKeys . TEST ) val_dataset = model . _distribution_standardize_user_data ( val_x , val_y , sample_weight = val_sample_weights , class_weight = None , batch_size = batch_size , validation_split = validation_split , shuffle = shuffle , allow_partial_batch = True ) elif validation_split : raise ValueError ( 'validation_split argument is not supported with ' 'distribution strategies.' ) if backend . is_tpu_strategy ( model . _distribution_strategy ) : steps_per_epoch = training_utils_v1 . infer_steps_for_dataset ( model , dataset , steps_per_epoch , epochs , steps_name = 'steps_per_epoch' ) if steps_per_epoch is None : raise ValueError ( 'Number of steps could not be inferred from the data, ' 'please pass the steps_per_epoch argument.' ) if not context . executing_eagerly ( ) : # Run TPU training in a custom loop in graph mode. return experimental_tpu_fit_loop ( model , dataset , epochs = epochs , verbose = verbose , callbacks = callbacks , val_dataset = val_dataset , initial_epoch = initial_epoch , steps_per_epoch = steps_per_epoch , validation_steps = validation_steps , validation_freq = validation_freq ) return training_arrays_v1 . fit_loop ( model , dataset , batch_size = batch_size , epochs = epochs , verbose = verbose , callbacks = callbacks , val_inputs = val_dataset , shuffle = shuffle , initial_epoch = initial_epoch , steps_per_epoch = steps_per_epoch , validation_steps = validation_steps , validation_freq = validation_freq , steps_name = 'steps_per_epoch' )