MNIST数据集上神经网络性能分析代码

以下是一个使用PyTorch框架实现的MNIST数据集上神经网络性能分析代码，包括了卷积神经网络（CNN）和全连接神经网络（FCN）两种模型结构以及不同的参数配置和优化算法：

import torch
from torch import nn
from torch.optim import Adam
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from torch.utils.data import DataLoader

# 加载MNIST数据集
train_dataset = MNIST(root='data/', train=True, transform=ToTensor(), download=True)
test_dataset = MNIST(root='data/', train=False, transform=ToTensor(), download=True)

# 定义CNN模型
class CNNModel(nn.Module):
    def __init__(self):
        super(CNNModel, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.fc1 = nn.Linear(1600, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.relu(self.conv2(x))
        x = x.view(-1, 1600)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 定义FCN模型
class FCNModel(nn.Module):
    def __init__(self):
        super(FCNModel, self).__init__()
        self.fc1 = nn.Linear(28*28, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = x.view(-1, 28*28)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 训练和测试函数
def train(model, train_loader, optimizer):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = nn.functional.cross_entropy(output, target)
        loss.backward()
        optimizer.step()

def test(model, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = model(data)
            test_loss += nn.functional.cross_entropy(output, target, reduction='sum').item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)
    return test_loss, accuracy

# 训练和测试CNN模型
def train_cnn():
    model = CNNModel()
    optimizer = Adam(model.parameters(), lr=0.01)
    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True)

    for epoch in range(10):
        train(model, train_loader, optimizer)
        test_loss, accuracy = test(model, test_loader)
        print('CNN Epoch: {}, Test Loss: {:.4f}, Accuracy: {:.2f}%'.format(epoch, test_loss, accuracy))

# 训练和测试FCN模型
def train_fcn():
    model = FCNModel()
    optimizer = Adam(model.parameters(), lr=0.001)
    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True)

    for epoch in range(10):
        train(model, train_loader, optimizer)
        test_loss, accuracy = test(model, test_loader)
        print('FCN Epoch: {}, Test Loss: {:.4f}, Accuracy: {:.2f}%'.format(epoch, test_loss, accuracy))

if __name__ == '__main__':
    train_cnn()
    train_fcn()

在这个代码中，我们首先定义了CNN模型和FCN模型，并且分别实现了训练和测试函数。在训练函数中，我们使用交叉熵损失函数和Adam优化算法进行训练，而在测试函数中，我们计算了测试集上的损失和准确率。最后，我们分别调用train_cnn()和train_fcn()函数进行CNN和FCN模型的训练和测试，并输出了在测试集上的表现结果。

需要注意的是，在实现过程中，我们还需要对输入数据进行合适的预处理和数据加载等操作。此外，为了进一步提高模型性能，我们可以尝试使用更复杂的模型结构、更优化的算法和参数配置等策略，同时需要进行充分的实验验证和综合评估。