用python代码实现基于手写数字识别数据集MNIST的测试集（10000张图片）实验用Pytorch框架实现卷积神经网络的搭建。使用自注意力模型替换卷积神经网络中的一层或多层。调整深层架构相关超参数使模型能正常运行并优化模型。进行十折交叉验证并汇报最终识别正确率。以图的形式汇报随机搜索优化过程中训练和测试错误的变化。

这是一个非常复杂的任务，需要大量代码实现和数据处理。以下是简要的步骤概述：

1. 数据处理：下载MNIST数据集并将其转换为PyTorch数据集，并将其拆分为训练集和测试集。使用数据增强技术（如旋转、翻转、缩放等）来增加数据量和多样性。

2. 构建模型：使用PyTorch构建卷积神经网络模型，使用自注意力模型替换其中的一层或多层。调整深层架构相关超参数，如层数、卷积核大小、池化大小等，以优化模型性能。

3. 训练模型：使用训练集对模型进行训练，并使用验证集来调整超参数。使用十折交叉验证来评估模型的泛化能力。

4. 测试模型：使用测试集对模型进行测试，并计算模型的准确性。在测试过程中，记录训练和测试错误的变化，并以图的形式汇报结果。

以下是一个基本的代码框架，用于实现这个任务：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from sklearn.model_selection import KFold

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

# 构建模型
class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
        self.attn = nn.MultiheadAttention(embed_dim=32, num_heads=4)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(64*7*7, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = x.view(-1, 32, 28*28).transpose(0, 2, 1)
        x, _ = self.attn(x, x, x)
        x = x.transpose(0, 2, 1).view(-1, 32, 28, 28)
        x = nn.functional.relu(self.conv2(x))
        x = nn.functional.max_pool2d(x, kernel_size=2)
        x = x.view(-1, 64*7*7)
        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, criterion):
    model.train()
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    return running_loss / len(train_loader)

def test(model, test_loader, criterion):
    model.eval()
    correct = 0
    total_loss = 0.0
    with torch.no_grad():
        for inputs, labels in test_loader:
            outputs = model(inputs)
            total_loss += criterion(outputs, labels).item()
            _, predicted = torch.max(outputs.data, 1)
            correct += (predicted == labels).sum().item()
    return correct / len(test_loader.dataset), total_loss / len(test_loader)

# 训练和测试循环
def train_loop(model, train_loader, test_loader, optimizer, criterion, num_epochs=10):
    train_losses = []
    test_losses = []
    test_accs = []
    for epoch in range(num_epochs):
        train_loss = train(model, train_loader, optimizer, criterion)
        train_losses.append(train_loss)
        test_acc, test_loss = test(model, test_loader, criterion)
        test_losses.append(test_loss)
        test_accs.append(test_acc)
        print('Epoch [{}/{}], Train Loss: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'.format(epoch+1, num_epochs, train_loss, test_loss, test_acc))
    return train_losses, test_losses, test_accs

# 交叉验证循环
def cross_validation(model, dataset, num_folds=10, num_epochs=10, batch_size=64, lr=0.01):
    kfold = KFold(n_splits=num_folds, shuffle=True)
    fold_results = []
    for fold, (train_idx, test_idx) in enumerate(kfold.split(dataset)):
        print('Fold:', fold+1)
        train_dataset = torch.utils.data.Subset(dataset, train_idx)
        test_dataset = torch.utils.data.Subset(dataset, test_idx)
        train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
        test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
        model.apply(weights_init)
        optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
        criterion = nn.CrossEntropyLoss()
        train_losses, test_losses, test_accs = train_loop(model, train_loader, test_loader, optimizer, criterion, num_epochs=num_epochs)
        fold_results.append((train_losses, test_losses, test_accs))
    return fold_results

# 训练和测试
model = Model()
results = cross_validation(model, train_dataset, num_folds=10, num_epochs=10, batch_size=64, lr=0.01)

# 绘制训练和测试错误的变化图
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 5))
for i, (train_losses, test_losses, test_accs) in enumerate(results):
    plt.subplot(2, 5, i+1)
    plt.plot(train_losses, label='Train')
    plt.plot(test_losses, label='Test')
    plt.title('Fold {}'.format(i+1))
plt.tight_layout()
plt.show()

# 汇报最终识别正确率
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transforms)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
model.eval()
with torch.no_grad():
    correct = 0
    for inputs, labels in test_loader:
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        correct += (predicted == labels).sum().item()
    acc = correct / len(test_loader.dataset)
print('Test Accuracy:', acc)

希望这可以帮助你开始实现这个任务。