Restart study deep learn(MNIST)

1 Restart study Deep learn

reference link

#!/usr/bin/env python
# -*- coding: utf-8 -*- 
# @Time : 5/23/2022 8:19 PM 
# @File : mnist1.py
import torch
import torch.nn as nn
import torchvision.datasets
from torch.autograd import Variable
import torch.utils.data as Data
import matplotlib.pyplot as plt

# Hyper Parameters
EPOCH = 1
BATCH_SIZE = 50
LR = 0.001  # learning rate
DOWNLOAD_MNIST = False

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

# plot one example

# plt.imshow(train_data.train_data[0].numpy(),cmap='gray')
# plt.title("%i"%train_data.train_labels[0])
# plt.show()

train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)

test_data = torchvision.datasets.MNIST(root='./mnist', train=False)
test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1)).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(kernel_size=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


def main():
    cnn = CNN()
    print(cnn)

    optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)
    loss_func = nn.CrossEntropyLoss()

    for epeoch 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) / test_y.size(0)
                print('Epoch:', epeoch, "|train loss:%.4f" % loss.item(), "|test accuracy:%.4f" % accuracy.item())
    test_output = cnn(test_x[:10])
    pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
    print(pred_y, 'prediction number')
    print(test_y[:10].numpy(), 'real numpy')
    a = 0


if __name__ == '__main__':
    main()

利用训练好的模型，去预测MNIST数据集

# utilize pt file to predict


from mnist1 import CNN
import torch
import torchvision.datasets
from torch.autograd import Variable
def   main():
    PATH = r'my_model.pth'
    cnnmodel = torch.load(PATH)
    cnnmodel.eval()
    train_data = torchvision.datasets.MNIST(
        root='./mnist',
        train=True,
        transform=torchvision.transforms.ToTensor(),
        download=False
    )
    
    test_data = torchvision.datasets.MNIST(root='./mnist', train=False)
    test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1)).type(torch.FloatTensor)[:2000] / 255
    test_y = test_data.test_labels[:2000]
    test_output = cnnmodel(test_x[:10])
    pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
    print(pred_y, 'prediction number')
    print(test_y[:10].numpy(), 'real numpy')

if __name__ == '__main__':
    main()

主要重新导入训练前的network class