(self, batch_size, fake_data=False, shuffle=True)
Return the next `batch_size` examples from this data set.
Return the next `batch_size` examples from this data set.
Return the next batch_size examples from this data set .
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
              range(batch_size)], [fake_label for _ in range(batch_size)]
    start = self._index_in_epoch
    # Shuffle for the first epoch
    if self._epochs_completed == 0 and start == 0 and shuffle:
      perm0 = np.arange(self._num_examples)
      np.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 = np.arange(self._num_examples)
        np.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 np.concatenate(
          (images_rest_part, images_new_part), axis=0), np.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]
def next_batch ( self , batch_size , fake_data = False , shuffle = True ) : 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 range ( batch_size ) ] , [ fake_label for _ in range ( batch_size ) ] start = self . _index_in_epoch # Shuffle for the first epoch if self . _epochs_completed == 0 and start == 0 and shuffle : perm0 = np . arange ( self . _num_examples ) np . 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 = np . arange ( self . _num_examples ) np . 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 np . concatenate ( ( images_rest_part , images_new_part ) , axis = 0 ) , np . 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 ]