utils.py 文件源码

def load_mnist(self):

        data_dir = os.path.join("./data", "mnist")

        fd = open(os.path.join(data_dir, 'train-images-idx3-ubyte'))
        loaded = np.fromfile(file=fd , dtype=np.uint8)
        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)

        seed = 547
        np.random.seed(seed)
        np.random.shuffle(X)
        np.random.seed(seed)
        np.random.shuffle(y)

        #convert label to one-hot

        y_vec = np.zeros((len(y), 10), dtype=np.float)
        for i, label in enumerate(y):
            y_vec[i, int(y[i])] = 1.0

        return X / 255. , y_vec