t_sne可视化pytorch网络训练前代码和训练后的代码

训练前代码：

import torch
import numpy as np
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt

# 定义模型
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = torch.nn.Linear(784, 512)
        self.fc2 = torch.nn.Linear(512, 256)
        self.fc3 = torch.nn.Linear(256, 10)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        x = x.view(-1, 784)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 加载数据
train_loader = torch.utils.data.DataLoader(
    torchvision.datasets.MNIST('./data', train=True, download=True,
                               transform=torchvision.transforms.Compose([
                                   torchvision.transforms.ToTensor(),
                                   torchvision.transforms.Normalize(
                                       (0.1307,), (0.3081,))
                               ])),
    batch_size=64, shuffle=True)

# 创建模型和优化器
model = Net()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# 训练模型
for epoch in range(10):
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = torch.nn.functional.cross_entropy(output, target)
        loss.backward()
        optimizer.step()

# 获取网络输出
outputs = []
labels = []
for batch_idx, (data, target) in enumerate(train_loader):
    output = model(data)
    outputs.append(output.detach().numpy())
    labels.append(target.numpy())
outputs = np.concatenate(outputs, axis=0)
labels = np.concatenate(labels, axis=0)

# 使用t-SNE降维并可视化
tsne = TSNE(n_components=2, perplexity=30, init='pca', n_iter=5000)
outputs_tsne = tsne.fit_transform(outputs)
plt.scatter(outputs_tsne[:, 0], outputs_tsne[:, 1], c=labels)
plt.show()

训练后代码：

import torch
import numpy as np
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt

# 定义模型
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = torch.nn.Linear(784, 512)
        self.fc2 = torch.nn.Linear(512, 256)
        self.fc3 = torch.nn.Linear(256, 10)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        x = x.view(-1, 784)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 加载数据
train_loader = torch.utils.data.DataLoader(
    torchvision.datasets.MNIST('./data', train=True, download=True,
                               transform=torchvision.transforms.Compose([
                                   torchvision.transforms.ToTensor(),
                                   torchvision.transforms.Normalize(
                                       (0.1307,), (0.3081,))
                               ])),
    batch_size=64, shuffle=True)

# 创建模型和优化器
model = Net()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# 加载已训练好的模型参数
model.load_state_dict(torch.load('model.pth'))

# 获取网络输出
outputs = []
labels = []
for batch_idx, (data, target) in enumerate(train_loader):
    output = model(data)
    outputs.append(output.detach().numpy())
    labels.append(target.numpy())
outputs = np.concatenate(outputs, axis=0)
labels = np.concatenate(labels, axis=0)

# 使用t-SNE降维并可视化
tsne = TSNE(n_components=2, perplexity=30, init='pca', n_iter=5000)
outputs_tsne = tsne.fit_transform(outputs)
plt.scatter(outputs_tsne[:, 0], outputs_tsne[:, 1], c=labels)
plt.show()