实验五：全连接神经网络手写数字识别实验

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【实验目的】

理解神经网络原理，掌握神经网络前向推理和后向传播方法；

掌握使用pytorch框架训练和推理全连接神经网络模型的编程实现方法。

【实验内容】

1.使用pytorch框架，设计一个全连接神经网络，实现Mnist手写数字字符集的训练与识别。

 

【实验报告要求】

修改神经网络结构，改变层数观察层数对训练和检测时间，准确度等参数的影响；
修改神经网络的学习率，观察对训练和检测效果的影响；
修改神经网络结构，增强或减少神经元的数量，观察对训练的检测效果的影响。

import torch
import numpy as np
from matplotlib import pyplot as plt
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision import datasets
import torch.nn.functional as F

batch_size = 64
learning_rate = 0.01
momentum = 0.5
EPOCH = 10

transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_dataset = datasets.MNIST(root='./data/mnist', train=True, transform=transform) 
test_dataset = datasets.MNIST(root='./data/mnist', train=False, transform=transform) 

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = torch.nn.Sequential(
            torch.nn.Conv2d(1, 10, kernel_size=5),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size=2),
        )
        self.conv2 = torch.nn.Sequential(
            torch.nn.Conv2d(10, 20, kernel_size=5),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size=2),
        )
        self.fc = torch.nn.Sequential(
            torch.nn.Linear(320, 50),
            torch.nn.Linear(50, 10),
        )

    def forward(self, x):
        batch_size = x.size(0)
        x = self.conv1(x)  
        x = self.conv2(x)  
        x = x.view(batch_size, -1)  
        x = self.fc(x)
        return x 

model = Net()

criterion = torch.nn.CrossEntropyLoss()  
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)  

def train(epoch):
    running_loss = 0.0  
    running_total = 0
    running_correct = 0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        optimizer.zero_grad()
       
        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()
      
        running_loss += loss.item()
       
        _, predicted = torch.max(outputs.data, dim=1)
        running_total += inputs.shape[0]
        running_correct += (predicted == target).sum().item()
        if batch_idx % 300 == 299: 
            print('[%d, %5d]: loss: %.3f , acc: %.2f %%'
                  % (epoch + 1, batch_idx + 1, running_loss / 300, 100 * running_correct / running_total))
            running_loss = 0.0  
            running_total = 0
            running_correct = 0 
def test():
    correct = 0
    total = 0
    with torch.no_grad():  
        for data in test_loader:
            images, labels = data
            outputs = model(images)
            _, predicted = torch.max(outputs.data, dim=1) 
            total += labels.size(0)  
            correct += (predicted == labels).sum().item()
    acc = correct / total
    print('[%d / %d]: Accuracy on test set: %.1f %% ' % (epoch+1, EPOCH, 100 * acc))  
    return acc

if __name__ == '__main__':
    acc_list_test = []
    for epoch in range(EPOCH):
        train(epoch)
        acc_test = test()
        acc_list_test.append(acc_test)

 

 

 

 

 

 

y_test=acc_list_test
plt.plot(y_test)
plt.xlabel("Epoch")
plt.ylabel("Accuracy On TestSet")
plt.show()