parameters: (self, x, y, sample_weight=None, class_weight=None)
return_statement: return outputs
docstring: Runs a single gradient update on a single batch of data.
  
      Arguments:
          x: Numpy array of training data,
              or list of Numpy arrays if the model has multiple inputs.
              If all inputs in the model are named,
              you can also pass a dictionary
              mapping input names to Numpy arrays.
          y: Numpy array of target data,
              or list of Numpy arrays if the model has multiple outputs.
              If all outputs in the model are named,
              you can also pass a dictionary
              mapping output names to Numpy arrays.
          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().
          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.
  
      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.
docstring_summary: Runs a single gradient update on a single batch of data.
docstring_tokens: Runs a single gradient update on a single batch of data .
function: def train_on_batch(self, x, y, sample_weight=None, class_weight=None):
      """Runs a single gradient update on a single batch of data.
  
      Arguments:
          x: Numpy array of training data,
              or list of Numpy arrays if the model has multiple inputs.
              If all inputs in the model are named,
              you can also pass a dictionary
              mapping input names to Numpy arrays.
          y: Numpy array of target data,
              or list of Numpy arrays if the model has multiple outputs.
              If all outputs in the model are named,
              you can also pass a dictionary
              mapping output names to Numpy arrays.
          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().
          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.
  
      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.
      """
      x, y, sample_weights = self._standardize_user_data(
          x,
          y,
          sample_weight=sample_weight,
          class_weight=class_weight,
          check_batch_axis=True)
      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
        ins = x + y + sample_weights + [1.]
      else:
        ins = x + y + sample_weights
      self._make_train_function()
      outputs = self.train_function(ins)
      if len(outputs) == 1:
        return outputs[0]
      return outputs
function_tokens: def train_on_batch ( self , x , y , sample_weight = None , class_weight = None ) : x , y , sample_weights = self . _standardize_user_data ( x , y , sample_weight = sample_weight , class_weight = class_weight , check_batch_axis = True ) if self . uses_learning_phase and not isinstance ( K . learning_phase ( ) , int ) : ins = x + y + sample_weights + [ 1. ] else : ins = x + y + sample_weights self . _make_train_function ( ) outputs = self . train_function ( ins ) if len ( outputs ) == 1 : return outputs [ 0 ] return outputs