FashionMNIST¶
- class paddle.vision.datasets. FashionMNIST ( image_path=None, label_path=None, mode='train', transform=None, download=True, backend=None ) [源代码] ¶
Fashion-MNIST 数据集的实现。
参数¶
image_path (str,可选) - 图像文件路径,如果
download
参数设置为True
,image_path
参数可以设置为None
。默认值为None
,默认存放在:~/.cache/paddle/dataset/fashion-mnist
。label_path (str,可选) - 标签文件路径,如果
download
参数设置为True
,label_path
参数可以设置为None
。默认值为None
,默认存放在:~/.cache/paddle/dataset/fashion-mnist
。mode (str,可选) -
'train'
或'test'
模式两者之一,默认值为'train'
。transform (Callable,可选) - 图片数据的预处理,若为
None
即为不做预处理。默认值为None
。download (bool,可选) - 当
data_file
是None
时,该参数决定是否自动下载数据集文件。默认值为True
。backend (str,可选) - 指定要返回的图像类型:PIL.Image 或 numpy.ndarray。必须是 {'pil','cv2'} 中的值。如果未设置此选项,将从 paddle.vision.get_image_backend 获得这个值。默认值为
None
。
代码示例¶
import itertools
import paddle.vision.transforms as T
from paddle.vision.datasets import FashionMNIST
fashion_mnist = FashionMNIST()
print(len(fashion_mnist))
# 60000
for i in range(5): # only show first 5 images
img, label = fashion_mnist[i]
# do something with img and label
print(type(img), img.size, label)
# <class 'PIL.Image.Image'> (28, 28) [9]
transform = T.Compose(
[
T.ToTensor(),
T.Normalize(
mean=[127.5],
std=[127.5],
),
]
)
fashion_mnist_test = FashionMNIST(
mode="test",
transform=transform, # apply transform to every image
backend="cv2", # use OpenCV as image transform backend
)
print(len(fashion_mnist_test))
# 10000
for img, label in itertools.islice(iter(fashion_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] [9]