#NEWLINE# def evaluate( self , x , y , batch_size = 32 , verbose = 1 , sample_weight = None ) : #NEWLINE# #INDENT# 'Returns the loss value and metrics values for the model\n        in test mode. Computation in done in batches.\n\n        # Arguments\n            x: Numpy array of test data,\n                or list of Numpy arrays if the model has multiple inputs.\n                If all inputs in the model are named, you can also pass a dictionary\n                mapping input names to Numpy arrays.\n            y: Numpy array of target data,\n                or list of Numpy arrays if the model has multiple outputs.\n                If all outputs in the model are named, you can also pass a dictionary\n                mapping output names to Numpy arrays.\n            batch_size: integer. Number of samples per gradient update.\n\n        # Returns\n            Scalar test loss (if the model has a single output and no metrics)\n            or list of scalars (if the model has multiple outputs\n            and/or metrics). The attribute `model.metrics_names` will give you\n            the display labels for the scalar outputs.\n        ' #NEWLINE# ( x , y , sample_weights ) = self . _standardize_user_data ( x , y , sample_weight = sample_weight , check_batch_dim = False , batch_size = batch_size ) #NEWLINE# if self . uses_learning_phase : #NEWLINE# #INDENT# ins = ( ( ( x + y ) + sample_weights ) + [ 0.0 ] ) #NEWLINE# #UNINDENT# else : #NEWLINE# #INDENT# ins = ( ( x + y ) + sample_weights ) #NEWLINE# #UNINDENT# self . _make_test_function ( ) #NEWLINE# f = self . test_function #NEWLINE# return self . _test_loop ( f , ins , batch_size = batch_size , verbose = verbose )