DenseNet框架体系与构造：构建网络的多样性

2023-12-17 20:45:45

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传统的卷积神经网络在一个前向过程中每层只有一个连接，ResNet增加了残差连接从而增加了信息从一层到下一层的流动。FractalNets重复组合几个有不相同深度和宽度且共享权重的模块来学习多分形模式。但这些方法通常只能固定网络结构。

DenseNet（Dense Convolutional Network）框架体系提出了DenseNet框架体系与构造，使得网络具备更强的泛化能力。通过在网络中建立更多的连接，可以使网络学习到更多的特征，从而提高网络的性能。

DenseNet框架体系

DenseNet框架体系是一个由多个Dense Block组成的网络结构，每个Dense Block由多个Dense Layer组成。Dense Layer是一种特殊的卷积层，它将前一层的所有输出与本层的输入进行连接，从而形成一个更加密集的网络结构。

DenseNet结构构造

DenseNet结构构造是通过在网络中建立更多的连接来实现的。在传统的卷积神经网络中，每层只与前一层相连，而在DenseNet中，每层与前面的所有层都相连。这种连接方式可以使网络学习到更多的特征，从而提高网络的性能。

示例代码

import torch
import torch.nn as nn
import torch.nn.functional as F

class DenseBlock(nn.Module):
    def __init__(self, in_channels, growth_rate, num_layers):
        super(DenseBlock, self).__init__()
        self.in_channels = in_channels
        self.growth_rate = growth_rate
        self.num_layers = num_layers

        self.layers = nn.ModuleList()
        for i in range(num_layers):
            self.layers.append(nn.Conv2d(in_channels + i * growth_rate, growth_rate, kernel_size=3, padding=1))

    def forward(self, x):
        for layer in self.layers:
            y = layer(x)
            x = torch.cat([x, y], dim=1)
        return x

class DenseNet(nn.Module):
    def __init__(self, in_channels, growth_rate, num_layers, num_classes):
        super(DenseNet, self).__init__()
        self.in_channels = in_channels
        self.growth_rate = growth_rate
        self.num_layers = num_layers
        self.num_classes = num_classes

        self.conv1 = nn.Conv2d(in_channels, growth_rate, kernel_size=7, padding=3)
        self.denseblocks = nn.ModuleList()
        for i in range(num_layers):
            self.denseblocks.append(DenseBlock(in_channels + i * growth_rate, growth_rate, num_layers))
        self.classifier = nn.Linear(in_channels + num_layers * growth_rate, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        for denseblock in self.denseblocks:
            x = denseblock(x)
        x = F.avg_pool2d(x, kernel_size=8)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x