diff --git a/network.py b/network.py
new file mode 100644
index 0000000..621b7b6
--- /dev/null
+++ b/network.py
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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import datasets, transforms
+import torchvision
+from torch.autograd import Variable
+from torch.utils.data import DataLoader
+import cv2
+
+# 下载训练集
+train_dataset = datasets.MNIST(root='./num/',
+                               train=True,
+                               transform=transforms.ToTensor(),
+                               download=True)
+# 下载测试集
+test_dataset = datasets.MNIST(root='./num/',
+                              train=False,
+                              transform=transforms.ToTensor(),
+                              download=True)
+# dataset 参数用于指定我们载入的数据集名称
+# batch_size参数设置了每个包中的图片数据个数
+# 在装载的过程会将数据随机打乱顺序并进打包
+batch_size = 64
+# 建立一个数据迭代器
+# 装载训练集
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                           batch_size=batch_size,
+                                           shuffle=True)
+# 装载测试集
+test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
+                                          batch_size=batch_size,
+                                          shuffle=True)
+ #实现单张图片可视化
+images, labels = next(iter(train_loader))
+img = torchvision.utils.make_grid(images)
+
+img = img.numpy().transpose(1, 2, 0)
+std = [0.5, 0.5, 0.5]
+mean = [0.5, 0.5, 0.5]
+img = img * std + mean
+print(labels)
+cv2.imshow('win', img)
+key_pressed = cv2.waitKey(0)
+
+print("运行到这了1")
+
+
+# 卷积层使用 torch.nn.Conv2d
+# 激活层使用 torch.nn.ReLU
+# 池化层使用 torch.nn.MaxPool2d
+# 全连接层使用 torch.nn.Linear
+
+class LeNet(nn.Module):
+    def __init__(self):
+        super(LeNet, self).__init__()
+        self.conv1 = nn.Sequential(nn.Conv2d(1, 6, 3, 1, 2), nn.ReLU(),
+                                   nn.MaxPool2d(2, 2))
+
+        self.conv2 = nn.Sequential(nn.Conv2d(6, 16, 5), nn.ReLU(),
+                                   nn.MaxPool2d(2, 2))
+
+        self.fc1 = nn.Sequential(nn.Linear(16 * 5 * 5, 120),
+                                 nn.BatchNorm1d(120), nn.ReLU())
+
+        self.fc2 = nn.Sequential(
+            nn.Linear(120, 84),
+            nn.BatchNorm1d(84),
+            nn.ReLU(),
+            nn.Linear(84, 10))  # 最后的结果一定要变为 10，因为数字的选项是 0 ~ 9
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = x.view(x.size()[0], -1)
+        x = self.fc1(x)
+        x = self.fc2(x)
+        return x
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+LR = 0.001
+
+net = LeNet().to(device)
+# 损失函数使用交叉熵
+criterion = nn.CrossEntropyLoss()
+# 优化函数使用 Adam 自适应优化算法
+optimizer = optim.Adam(
+    net.parameters(),
+    lr=LR,
+)
+epoch = 1
+if __name__ == '__main__':
+    for epoch in range(epoch):
+        sum_loss = 0.0
+        for i, data in enumerate(train_loader):
+            inputs, labels = data
+            inputs, labels = Variable(inputs).cuda(), Variable(labels).cuda()
+            optimizer.zero_grad()  # 将梯度归零
+            outputs = net(inputs)  # 将数据传入网络进行前向运算
+            loss = criterion(outputs, labels)  # 得到损失函数
+            loss.backward()  # 反向传播
+            optimizer.step()  # 通过梯度做一步参数更新
+            # print(loss)
+            sum_loss += loss.item()
+            if i % 100 == 99:
+                print('[%d,%d] loss:%.03f' %
+                      (epoch + 1, i + 1, sum_loss / 100))
+                sum_loss = 0.0
+    #测试集
+    net.eval()  # 将模型变换为测试模式
+    correct = 0
+    total = 0
+    for data_test in test_loader:
+        images, labels = data_test
+        images, labels = Variable(images).cuda(), Variable(labels).cuda()
+        output_test = net(images)
+        _, predicted = torch.max(output_test, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum()
+    print("correct1: ", correct)
+    print("Test acc: {0}".format(correct.item() /
+                                  len(test_dataset)))
+