DBN pytorch代码

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深度信念网络（DBN）及其PyTorch实现

深度信念网络（DBN）是一种无监督的深度学习模型，由多层的受限玻尔兹曼机（RBM）堆叠而成。DBN能够有效地从数据中学习特征并进行分类，广泛应用于图像识别、语音识别等领域。本文将介绍DBN的基本原理，并提供PyTorch中的实现代码示例。

DBN的基本原理

DBN由多个RBM分层构成。每个RBM层独立训练，之后将上一层的输出作为下一层的输入。训练完成后，DBN可以通过微调来提高分类性能。以下是DBN的基本训练流程：

flowchart TD
    A[原始数据] --> B[训练RBM]
    B --> C[堆叠RBM层]
    C --> D[微调DBN]
    D --> E[分类或生成]

PyTorch实现DBN

下面是一个简单的PyTorch代码示例，演示如何实现DBN。代码包括RBM类和DBN类的定义，使用MNIST数据集进行训练。

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

class RBM(nn.Module):
    def __init__(self, n_visible, n_hidden):
        super(RBM, self).__init__()
        self.W = nn.Parameter(torch.randn(n_hidden, n_visible) * 1e-2)
        self.h_bias = nn.Parameter(torch.zeros(n_hidden))
        self.v_bias = nn.Parameter(torch.zeros(n_visible))

    def sample_h(self, v):
        activation = torch.mm(v, self.W.t()) + self.h_bias
        return torch.sigmoid(activation)

    def sample_v(self, h):
        activation = torch.mm(h, self.W) + self.v_bias
        return torch.sigmoid(activation)

    def free_energy(self, v):
        return -torch.mm(v, self.v_bias.unsqueeze(0).t()) - torch.sum(torch.log(1 + torch.exp(self.sample_h(v))), dim=1)

class DBN(nn.Module):
    def __init__(self, layer_sizes):
        super(DBN, self).__init__()
        self.rbm_layers = nn.ModuleList()
        for i in range(len(layer_sizes)-1):
            self.rbm_layers.append(RBM(layer_sizes[i], layer_sizes[i+1]))

    def forward(self, x):
        for rbm in self.rbm_layers:
            x = rbm.sample_h(x)
        return x

# 训练DBN
def train_dbn(dbn, data_loader, epochs, lr):
    optimizer = optim.SGD(dbn.parameters(), lr=lr)
    for epoch in range(epochs):
        for data, _ in data_loader:
            optimizer.zero_grad()
            output = dbn(data.view(data.size(0), -1))
            loss = -dbn.free_energy(data.view(data.size(0), -1)).sum()
            loss.backward()
            optimizer.step()
        print(f'Epoch {epoch+1}/{epochs}, Loss: {loss.item()}')

# 数据加载与模型训练示例
if __name__ == "__main__":
    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
    train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
    train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)

    dbn = DBN([784, 500, 200, 10])  # 输入层784，隐藏层500，200，输出层10
    train_dbn(dbn, train_loader, epochs=5, lr=0.01)