d000bae3a03681b11818e98c29a64a145e0ff1ec,torchsample/modules/example.py,,,#,10

Before Change

import torch.optim as optim

from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train / 255.
x_test = x_test / 255.
x_train = np.expand_dims(x_train,1).astype("float32")

After Change

import os
from torchvision import datasets
ROOT = "./data"
dataset = datasets.MNIST(ROOT, train=True, download=True)
x_train, y_train = torch.load(os.path.join(dataset.root, "processed/training.pt"))
x_test, y_test = torch.load(os.path.join(dataset.root, "processed/test.pt"))

x_train = x_train.float()
y_train = y_train.long()
x_test = x_test.float()

Instances