手写数字识别代码（可直接使用）_手写数字识别代码 不用torch

       搭建一个简单的神经网络实现手写数字识别，是对《深度学习之PyTorch实战计算机视觉》一书中的项目进行的复现。

       这里使用的GPU进行运算的，如果无法使用GPU运行，把代码中的.cuda()删掉就好。

import torch
from torchvision import datasets, transforms
import torchvision.transforms
from torch.autograd import Variable
import matplotlib.pyplot as plt
 
transform = transforms.Compose([transforms.ToTensor(),
                                transforms.Lambda(lambda x: x.repeat(3, 1, 1)),
                                transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
 
data_train = datasets.MNIST(root = "./data/",
                            transform = transform,
                            train = True,
                            download = True)
data_test = datasets.MNIST(root="./data/",
                            transform = transform,
                            train = False)
data_loader_train = torch.utils.data.DataLoader(dataset = data_train,
                                                batch_size = 64,
                                                shuffle = True)
data_loader_test = torch.utils.data.DataLoader(dataset = data_test,
                                                batch_size = 64,
                                                shuffle = True)
 
images, labels = next(iter(data_loader_train))
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[i] for i in range(64)])
# plt.imshow(img)
# plt.show()
 
class Model(torch.nn.Module):
 
    def __init__(self):
        super(Model, self).__init__()
        self.conv1=torch.nn.Sequential(
            torch.nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
            torch.nn.ReLU(),
            torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(stride=2, kernel_size=2)
        )
 
        self.dense=torch.nn.Sequential(
            torch.nn.Linear(14*14*128, 1024),
            torch.nn.ReLU(),
            torch.nn.Dropout(p=0.5),
            torch.nn.Linear(1024, 10)
        )
 
    def forward(self, x):
        x = self.conv1(x)
        x = x.view(-1, 14*14*128)
        x = self.dense(x)
        return x
 
model = Model()
model = model.cuda()  #转GPU跑
cost = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
n_epochs = 5
 
for epoch in range(n_epochs):
    running_loss = 0.0
    running_correct = 0
    print("Epoch {}/{}".format(epoch, n_epochs))
    print("-"*10)
    for data in data_loader_train:
        X_train, Y_train = data
        X_train, Y_train = Variable(X_train.cuda()), Variable(Y_train.cuda())
        outputs = model(X_train)
        _, pred = torch.max(outputs.data, 1)
        optimizer.zero_grad()
        loss = cost(outputs, Y_train)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        running_correct += torch.sum(pred == Y_train.data)
    testing_correct = 0
    for data in data_loader_test:
        X_test, Y_test = data
        X_test, Y_test = Variable(X_test.cuda()), Variable(Y_test.cuda())
        outputs = model(X_test)
        _, pred = torch.max(outputs.data, 1)
        testing_correct += torch.sum(pred == Y_test.data)
    print("Loss is:{:.4f}, Train Accuracy is:{:.4f}%, Test Accuracy is:{:.4f}%"
          .format(running_loss/len(data_train),
                  100*running_correct/len(data_train),
                  100*testing_correct/len(data_test)))