datasets: mnist

方法一：
直接读取二进制文件
像素点值的范围[0, 255]，需要return X/255.,y_vec

  def load_mnist(self):
    data_dir = os.path.join("./data", self.dataset_name)
    
    fd = open(os.path.join(data_dir,'train-images.idx3-ubyte'))
    # 转化成 numpy 数组, 像素点的范围是[0,255]
    loaded = np.fromfile(file=fd,dtype=np.uint8)
    # 根据 mnist 官网描述的数据格式，图像像素从 16 字节开始
    trX = loaded[16:].reshape((60000,28,28,1)).astype(np.float)

    fd = open(os.path.join(data_dir,'train-labels.idx1-ubyte'))
    loaded = np.fromfile(file=fd,dtype=np.uint8)
    trY = loaded[8:].reshape((60000)).astype(np.float)

    fd = open(os.path.join(data_dir,'t10k-images.idx3-ubyte'))
    loaded = np.fromfile(file=fd,dtype=np.uint8)
    teX = loaded[16:].reshape((10000,28,28,1)).astype(np.float)

    fd = open(os.path.join(data_dir,'t10k-labels.idx1-ubyte'))
    loaded = np.fromfile(file=fd,dtype=np.uint8)
    teY = loaded[8:].reshape((10000)).astype(np.float)

    trY = np.asarray(trY)
    teY = np.asarray(teY)
    
    X = np.concatenate((trX, teX), axis=0)
    y = np.concatenate((trY, teY), axis=0).astype(np.int)
    
    seed = 547
    np.random.seed(seed)
    np.random.shuffle(X)
    np.random.seed(seed)
    np.random.shuffle(y)
    
    y_vec = np.zeros((len(y), self.y_dim), dtype=np.float)
    for i, label in enumerate(y):
      y_vec[i,y[i]] = 1.0
    
    return X/255.,y_vec 

方法二：
通过mnsit.train.next_batch(100)返回的图片像素范围是[0, 1]