给我提供一个pytorch的tsne代码和demo

以下是一个pytorch的tsne代码和demo：

import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt

class Net(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu1 = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu1(x)
        x = self.fc2(x)
        return x

def tsne_visualization(x, y):
    tsne = TSNE(n_components=2, random_state=0)
    x_tsne = tsne.fit_transform(x)

    plt.figure(figsize=(10, 10))
    plt.scatter(x_tsne[:, 0], x_tsne[:, 1], c=y, cmap=plt.cm.get_cmap('jet', 10))
    plt.colorbar(ticks=range(10))
    plt.clim(-0.5, 9.5)
    plt.show()

if __name__ == '__main__':
    input_size = 784
    hidden_size = 32
    output_size = 10

    # Load MNIST dataset
    train_data = torch.load('mnist.pth')
    x_train, y_train = train_data['x'], train_data['y']

    # Define model
    model = Net(input_size, hidden_size, output_size)

    # Define loss function and optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    # Train model
    for epoch in range(10):
        running_loss = 0.0
        for i in range(len(x_train)):
            # Forward
            outputs = model(x_train[i])
            loss = criterion(outputs.unsqueeze(0), y_train[i].unsqueeze(0))

            # Backward and optimize
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            running_loss += loss.item()

        print('Epoch [%d], Loss: %.4f' % (epoch+1, running_loss/len(x_train)))

    # Test model
    x_test, y_test = train_data['x_test'], train_data['y_test']
    with torch.no_grad():
        model.eval()
        outputs = model(x_test)
        _, predicted = torch.max(outputs, 1)

        # Visualize t-SNE
        tsne_visualization(x_test, y_test)

在上述代码中，首先定义了一个简单的全连接神经网络模型，然后使用MNIST数据集进行训练和测试。在测试过程中，使用sklearn中的t-SNE算法进行降维，将高维数据可视化为二维散点图。

注意：这里的MNIST数据集文件“mnist.pth”是预处理好的数据集，包含了训练集和测试集的图片和标签，可以通过以下代码生成：

from torchvision import datasets, transforms
import torch

# Load MNIST dataset
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor(), download=True)

x_train = train_dataset.data.view(len(train_dataset), -1).float()
y_train = train_dataset.targets

x_test = test_dataset.data.view(len(test_dataset), -1).float()
y_test = test_dataset.targets

# Save data to file
train_data = {'x': x_train, 'y': y_train, 'x_test': x_test, 'y_test': y_test}
torch.save(train_data, 'mnist.pth')