python之Tensorflow之关于 mnist.train.next_batch()

当我搜索 mnist.train.next_batch() 时，我发现了这个 https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/learn/python/learn/datasets/mnist.py

在这段代码中

 def next_batch(self, batch_size, fake_data=False, shuffle=True): 
  """Return the next `batch_size` examples from this data set.""" 
  if fake_data: 
  fake_image = [1] * 784 
  if self.one_hot: 
    fake_label = [1] + [0] * 9 
  else: 
    fake_label = 0 
  return [fake_image for _ in xrange(batch_size)], [ 
      fake_label for _ in xrange(batch_size) 
  ] 
start = self._index_in_epoch 
# Shuffle for the first epoch 
if self._epochs_completed == 0 and start == 0 and shuffle: 
  perm0 = numpy.arange(self._num_examples) 
  numpy.random.shuffle(perm0) 
  self._images = self.images[perm0] 
  self._labels = self.labels[perm0] 
# Go to the next epoch 
if start + batch_size > self._num_examples: 
  # Finished epoch 
  self._epochs_completed += 1 
  # Get the rest examples in this epoch 
  rest_num_examples = self._num_examples - start 
  images_rest_part = self._images[start:self._num_examples] 
  labels_rest_part = self._labels[start:self._num_examples] 
  # Shuffle the data 
  if shuffle: 
    perm = numpy.arange(self._num_examples) 
    numpy.random.shuffle(perm) 
    self._images = self.images[perm] 
    self._labels = self.labels[perm] 
  # Start next epoch 
  start = 0 
  self._index_in_epoch = batch_size - rest_num_examples 
  end = self._index_in_epoch 
  images_new_part = self._images[start:end] 
  labels_new_part = self._labels[start:end] 
  return numpy.concatenate((images_rest_part, images_new_part), axis=0) , numpy.concatenate((labels_rest_part, labels_new_part), axis=0) 
else: 
  self._index_in_epoch += batch_size 
  end = self._index_in_epoch 
  return self._images[start:end], self._labels[start:end] 

我知道 mnist.train.next_batch(batch_size=100) 意味着它从 MNIST 数据集中随机选择 100 个数据。现在，这是我的问题

1. shuffle=true 是什么意思？
2. 如果我设置 next_batch(batch_size=100,fake_data=False, shuffle=False) 那么它会从 MNIST 数据集的开始到结束依次选择 100 个数据？不是随机的？

请您参考如下方法:

关于 1，当 shuffle=True 时，数据中示例的顺序是随机的。关于 2，是的，它应该尊重示例在 numpy 数组中的任何顺序。