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框架实现深度信念网络的搭建。进行十折交叉验证并汇报最终识别正确率。网络的隐藏层个数、每个隐藏层的结点数等设置根据实验结果进一步调整，最后选择结果最优的设置在实验报告中汇报。

好的，以下是用Pytorch实现基于手写数字识别数据集MNIST的测试集（10000张图片）实验的代码：

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

# 定义深度信念网络模型
class DBN(nn.Module):
    def __init__(self, num_visible, hidden_sizes):
        super(DBN, self).__init__()
        self.num_layers = len(hidden_sizes)
        self.rbm_layers = nn.ModuleList()
        self.hidden_layers = nn.ModuleList()
        self.visible_layer = nn.Linear(num_visible, hidden_sizes[0])
        for i in range(self.num_layers):
            input_size = hidden_sizes[i-1] if i > 0 else num_visible
            rbm_layer = nn.Sequential(
                nn.Linear(input_size, hidden_sizes[i]),
                nn.Sigmoid()
            )
            self.rbm_layers.append(rbm_layer)
            hidden_layer = nn.Sequential(
                nn.Linear(input_size, hidden_sizes[i]),
                nn.Sigmoid()
            )
            self.hidden_layers.append(hidden_layer)

    def forward(self, x):
        out = self.visible_layer(x)
        for i in range(self.num_layers):
            out = self.hidden_layers[i](out)
        return out

# 定义训练函数
def train(model, train_loader, optimizer, criterion):
    model.train()
    train_loss = 0.0
    for data, target in tqdm(train_loader):
        data = data.view(data.size(0), -1)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, data)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * len(data)
    train_loss /= len(train_loader.dataset)
    return train_loss

# 定义测试函数
def test(model, test_loader, criterion):
    model.eval()
    test_loss = 0.0
    correct = 0
    with torch.no_grad():
        for data, target in tqdm(test_loader):
            data = data.view(data.size(0), -1)
            output = model(data)
            test_loss += criterion(output, data).item() * len(data)
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(data.argmax(dim=1, keepdim=True)).sum().item()
    test_loss /= len(test_loader.dataset)
    accuracy = correct / len(test_loader.dataset)
    return test_loss, accuracy

# 设置超参数
num_visible = 784
hidden_sizes = [500, 500, 2000]
batch_size = 128
lr = 0.01
epochs = 10
nfolds = 10

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

# 划分数据集并进行交叉验证
n_train = len(train_dataset)
n_test = len(test_dataset)
fold_size = n_train // nfolds
folds = []
for i in range(nfolds):
    train_indices = list(range(i*fold_size, (i+1)*fold_size))
    test_indices = list(set(range(n_train)) - set(train_indices))
    train_sampler = torch.utils.data.sampler.SubsetRandomSampler(train_indices)
    test_sampler = torch.utils.data.sampler.SubsetRandomSampler(test_indices)
    train_loader = DataLoader(train_dataset, batch_size=batch_size, sampler=train_sampler)
    test_loader = DataLoader(test_dataset, batch_size=batch_size, sampler=test_sampler)
    folds.append((train_loader, test_loader))

# 进行交叉验证训练和测试
for i in range(nfolds):
    print(f"Fold {i+1}")
    train_loader, test_loader = folds[i]
    model = DBN(num_visible, hidden_sizes)
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=lr)
    for epoch in range(1, epochs+1):
        train_loss = train(model, train_loader, optimizer, criterion)
        test_loss, accuracy = test(model, test_loader, criterion)
        print(f"Epoch {epoch}: Train Loss={train_loss:.5f}, Test Loss={test_loss:.5f}, Accuracy={accuracy:.5f}")

这里我们定义了一个`DBN`类作为深度信念网络模型，并定义了训练函数`train`和测试函数`test`。接着我们设置了超参数，加载了MNIST数据集，并进行十折交叉验证。在每一折交叉验证中，我们都实例化一个新的`DBN`模型，并使用MSE损失函数和Adam优化器进行训练。在每个epoch结束时，我们计算训练集和测试集的损失和准确率，并输出结果。

最终，我们可以将十折交叉验证的结果进行平均，得到最终的识别正确率。同时，根据实验结果进一步调整网络的隐藏层个数、每个隐藏层的结点数等设置，选择结果最优的设置在实验报告中汇报。