從 Tensorflow 提供的 MNIST Dataset 修改來的類別，可以加入自己的數據集，並用 next_batch 提取數據集的子集。

Dataset.py

import numpy
class DatasetNoLabel(object):
    def __init__(self, data):
        self._data = data
        self._num_examples = data.shape[0]
        self._epochs_completed = 0
        self._index_in_epoch = 0
    @property
    def data(self):
        return self._data
    @property
    def num_examples(self):
        return self._num_examples
    @property
    def epochs_completed(self):
        return self._epochs_completed
    def next_batch(self, batch_size):
        start = self._index_in_epoch
        self._index_in_epoch += batch_size
        if self._index_in_epoch > self._num_examples:
            # Finished epoch
            self._epochs_completed += 1
            # Shuffle the data
            perm = numpy.arange(self._num_examples)
            numpy.random.shuffle(perm)
            self._data = self._data[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._data[start:end]

class Dataset(object):
    def __init__(self, features, labels):
        assert features.shape[0] == labels.shape[0], (
        'features.shape: %s labels.shape: %s' % (features.shape,labels.shape))
        self._num_examples = features.shape[0]
        
        features = features.astype(numpy.float32)
        self._features = features
        self._labels = labels
        self._epochs_completed = 0
        self._index_in_epoch = 0
    @property
    def features(self):
        return self._features
    @property
    def labels(self):
        return self._labels
    @property
    def num_examples(self):
        return self._num_examples
    @property
    def epochs_completed(self):
        return self._epochs_completed
    def next_batch(self, batch_size):
        start = self._index_in_epoch
        self._index_in_epoch += batch_size
        if self._index_in_epoch > self._num_examples:
          # Finished epoch
          self._epochs_completed += 1
          # Shuffle the data
          perm = numpy.arange(self._num_examples)
          numpy.random.shuffle(perm)
          self._features = self._features[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._features[start:end], self._labels[start:end]