本文介紹了tensorflow中next_batch的具體使用，分享給大家，具體如下：

此處給出了幾種不同的next_batch方法，該文章只是做出代碼片段的解釋，以備以后查看：

 def next_batch(self, batch_size, fake_data=False):  """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  self._index_in_epoch += batch_size  if self._index_in_epoch > self._num_examples: # epoch中的句子下標是否大于所有語料的個數，如果為True,開始新一輪的遍歷   # Finished epoch   self._epochs_completed += 1   # Shuffle the data   perm = numpy.arange(self._num_examples) # arange函數用于創建等差數組   numpy.random.shuffle(perm) # 打亂   self._images = self._images[perm]   self._labels = self._labels[perm]   # Start next epoch   start = 0   self._index_in_epoch = batch_size   assert batch_size <= self._num_examples  end = self._index_in_epoch  return self._images[start:end], self._labels[start:end]

該段代碼摘自mnist.py文件，從代碼第12行start = self._index_in_epoch開始解釋，_index_in_epoch-1是上一次batch個圖片中最后一張圖片的下邊，這次epoch第一張圖片的下標是從 _index_in_epoch開始，最后一張圖片的下標是_index_in_epoch+batch, 如果 _index_in_epoch 大于語料中圖片的個數，表示這個epoch是不合適的，就算是完成了語料的一遍的遍歷，所以應該對圖片洗牌然后開始新一輪的語料組成batch開始

def ptb_iterator(raw_data, batch_size, num_steps): """Iterate on the raw PTB data. This generates batch_size pointers into the raw PTB data, and allows minibatch iteration along these pointers. Args:  raw_data: one of the raw data outputs from ptb_raw_data.  batch_size: int, the batch size.  num_steps: int, the number of unrolls. Yields:  Pairs of the batched data, each a matrix of shape [batch_size, num_steps].  The second element of the tuple is the same data time-shifted to the  right by one. Raises:  ValueError: if batch_size or num_steps are too high. """ raw_data = np.array(raw_data, dtype=np.int32) data_len = len(raw_data) batch_len = data_len // batch_size #有多少個batch data = np.zeros([batch_size, batch_len], dtype=np.int32) # batch_len 有多少個單詞 for i in range(batch_size): # batch_size 有多少個batch  data[i] = raw_data[batch_len * i:batch_len * (i + 1)] epoch_size = (batch_len - 1) // num_steps # batch_len 是指一個batch中有多少個句子 #epoch_size = ((len(data) // model.batch_size) - 1) // model.num_steps # // 表示整數除法 if epoch_size == 0:  raise ValueError("epoch_size == 0, decrease batch_size or num_steps") for i in range(epoch_size):  x = data[:, i*num_steps:(i+1)*num_steps]  y = data[:, i*num_steps+1:(i+1)*num_steps+1]  yield (x, y)