我们从Python开源项目中，提取了以下3个代码示例，用于说明如何使用keras.utils.Sequence()。
def __init__(self, X, y, batch_size, process_fn=None): """A `Sequence` implementation that returns balanced `y` by undersampling majority class. Args: X: The numpy array of inputs. y: The numpy array of targets. batch_size: The generator mini-batch size. process_fn: The preprocessing function to apply on `X` """ self.X = X self.y = y self.batch_size = batch_size self.process_fn = process_fn or (lambda x: x) self.pos_indices = np.where(y == 1)[0] self.neg_indices = np.where(y == 0)[0] self.n = min(len(self.pos_indices), len(self.neg_indices)) self._index_array = None
def __getitem__(self, index): assert self.sequence, "This transformer {} has not been called with a Sequence object".format( self.__class__.__name__) batch = self.sequence[index] if self.batch_size is None: # The first batch should be the maximum batch_size i.e. not the last. self.batch_size = get_batch_size(batch) args = self.get_args() for arg in args: arg.update(self.common_args) return apply_fun(batch, self.apply_transformation, self.mask, transformation=self.transformation, args=args)
def __init__(self, X, y, batch_size, process_fn=None): """A `Sequence` implementation that can pre-process a mini-batch via `process_fn` Args: X: The numpy array of inputs. y: The numpy array of targets. batch_size: The generator mini-batch size. process_fn: The preprocessing function to apply on `X` """ self.X = X self.y = y self.batch_size = batch_size self.process_fn = process_fn or (lambda x: x)
