parameters: (self, x, y, batch_size=32, verbose=1, sample_weight=None)
return_statement: return self._test_loop(f, ins, batch_size=batch_size, verbose=verbose)
docstring: Returns the loss value & metrics values for the model in test mode.
  
      Computation is done in batches.
  
      Arguments:
          x: Numpy array of test 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.
          batch_size: integer. Number of samples per gradient update.
          verbose: verbosity mode, 0 or 1.
          sample_weight: Array of weights to weight the contribution
              of different samples to the loss and metrics.
  
      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.
docstring_summary: Returns the loss value & metrics values for the model in test mode.
docstring_tokens: Returns the loss value & metrics values for the model in test mode .
function: def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):
      """Returns the loss value & metrics values for the model in test mode.
  
      Computation is done in batches.
  
      Arguments:
          x: Numpy array of test 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.
          batch_size: integer. Number of samples per gradient update.
          verbose: verbosity mode, 0 or 1.
          sample_weight: Array of weights to weight the contribution
              of different samples to the loss and metrics.
  
      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.
      """
      # Validate user data.
      x, y, sample_weights = self._standardize_user_data(
          x,
          y,
          sample_weight=sample_weight,
          check_batch_axis=False,
          batch_size=batch_size)
      # Prepare inputs, delegate logic to `_test_loop`.
      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
        ins = x + y + sample_weights + [0.]
      else:
        ins = x + y + sample_weights
      self._make_test_function()
      f = self.test_function
      return self._test_loop(f, ins, batch_size=batch_size, verbose=verbose)
function_tokens: def evaluate ( self , x , y , batch_size = 32 , verbose = 1 , sample_weight = None ) : # Validate user data. x , y , sample_weights = self . _standardize_user_data ( x , y , sample_weight = sample_weight , check_batch_axis = False , batch_size = batch_size ) # Prepare inputs, delegate logic to `_test_loop`. if self . uses_learning_phase and not isinstance ( K . learning_phase ( ) , int ) : ins = x + y + sample_weights + [ 0. ] else : ins = x + y + sample_weights self . _make_test_function ( ) f = self . test_function return self . _test_loop ( f , ins , batch_size = batch_size , verbose = verbose )