手書きデジタル認識(pytorch)

import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.utils.data as Data
import torchvision
import matplotlib.pyplot as plt
import numpy as np

EPOCH = 1
BATCH_SIZE = 50
LR = 0.001
DOWNLOAD_MNIST = False #           True

train_data = torchvision.datasets.MNIST(
    root='./mnist',
    train=True,
    transform=torchvision.transforms.ToTensor(),
    download=DOWNLOAD_MNIST
)

train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)
test_data = torchvision.datasets.MNIST(root='./mnist', train=False)

test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1), volatile=True).type(torch.FloatTensor)[:2000]/255.
test_y = test_data.test_labels[:2000]

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(
                in_channels=1,
                out_channels=16,
                kernel_size=5,
                stride=1,
                padding=2,
            ),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2),
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(16, 32, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        self.out = nn.Linear(32*7*7, 10)
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0), -1)
        output = self.out(x)
        return output

if __name__ == '__main__':
    cnn = CNN()
    
    optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)
    loss_func = nn.CrossEntropyLoss()

    for epoch in range(EPOCH):
        for step, (x, y) in enumerate(train_loader):
            b_x = Variable(x)
            b_y = Variable(y)

            output = cnn(b_x)
            loss = loss_func(output, b_y)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            if step % 50 == 0:
                test_output = cnn(test_x)
                pred_y = torch.max(test_output, 1)[1].data.squeeze()
                accuracy = sum(pred_y == test_y).type(torch.FloatTensor)/test_y.size(0)
                print('Epoch:', epoch, '| train loss: %.4f' % loss.item(), '| test_accuracy: %.4f' % accuracy)
    test_output = cnn(test_x[:10])
    pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
    print(pred_y, 'prediction')
    print(test_y[:10].numpy(), 'real')