为什么 TensorFlow tf.data.Dataset.shuffle 函数的 reshuffle_each_iteration 布尔参数默认为 None，而不是 True？

Question

documentation for the tf.Dataset.data.shuffle function 声明如下：

reshuffle_each_iteration: (Optional.) A boolean, which if true indicates that the dataset should be pseudorandomly reshuffled each time it is iterated over. (Defaults to True.)

但是，函数中的默认值为 None，如同一页和 the actual code 中所述：

def shuffle(self, buffer_size, seed=None, reshuffle_each_iteration=None):

函数调用ShuffleDatasetclass，其__init__函数也sets the same argument to None by default, and uses the following logic将参数的默认值设置为True:

if reshuffle_each_iteration is None:
  self._reshuffle_each_iteration = True
else:
  self._reshuffle_each_iteration = reshuffle_each_iteration

为什么函数和 class 中的参数不默认设置为 True？这将使上面的代码块变得多余，并且只允许用 self._reshuffle_each_iteration = reshuffle_each_iteration.

替换它

Answer 1

@mrry 回答here:

In general I prefer None for default arguments, because it makes it easier to write wrappers around an API. If the default argument is set to True, it's difficult for a wrapper to use the default of the wrapped function, without making a copy of the default at each layer of wrapping. Using None and implementing the default at the innermost level is simpler to maintain.

Using None is also consistent with the necessary style when the default argument can be a mutable type (like a list) and it is unsafe to put the default value in the argument list.

为什么 TensorFlow tf.data.Dataset.shuffle 函数的 reshuffle_each_iteration 布尔参数默认为 None，而不是 True？

Why does the TensorFlow tf.data.Dataset.shuffle function's reshuffle_each_iteration boolean argument default to None, as opposed to True?

python

tensorflow

tensorflow-datasets