MNIST¶

class paddle.vision.datasets. MNIST ( image_path=None, label_path=None, mode='train', transform=None, download=True, backend=None ) [source]

Implementation of MNIST dataset.

Parameters

• image_path (str, optional) – Path to image file, can be set None if download is True. Default: None, default data path: ~/.cache/paddle/dataset/mnist.

• label_path (str, optional) – Path to label file, can be set None if download is True. Default: None, default data path: ~/.cache/paddle/dataset/mnist.

• mode (str, optional) – Either train or test mode. Default ‘train’.

• transform (Callable, optional) – Transform to perform on image, None for no transform. Default: None.

• download (bool, optional) – Download dataset automatically if image_path label_path is not set. Default: True.

• backend (str, optional) – Specifies which type of image to be returned: PIL.Image or numpy.ndarray. Should be one of {‘pil’, ‘cv2’}. If this option is not set, will get backend from paddle.vision.get_image_backend, default backend is ‘pil’. Default: None.

Returns

Dataset. An instance of MNIST dataset.

Examples

import itertools
import paddle.vision.transforms as T
from paddle.vision.datasets import MNIST


mnist = MNIST()
print(len(mnist))
# 60000

for i in range(5):  # only show first 5 images
    img, label = mnist[i]
    # do something with img and label
    print(type(img), img.size, label)
    # <class 'PIL.Image.Image'> (28, 28) [5]


transform = T.Compose(
    [
        T.ToTensor(),
        T.Normalize(
            mean=[127.5],
            std=[127.5],
        ),
    ]
)

mnist_test = MNIST(
    mode="test",
    transform=transform,  # apply transform to every image
    backend="cv2",  # use OpenCV as image transform backend
)
print(len(mnist_test))
# 10000

for img, label in itertools.islice(iter(mnist_test), 5):  # only show first 5 images
    # do something with img and label
    print(type(img), img.shape, label)
    # <class 'paddle.Tensor'> [1, 28, 28] [7]