比vgg16更好的网络模型

### 更优的神经网络模型 #### ResNet (Residual Network) ResNet引入了残差连接的概念，解决了深层网络中的梯度消失问题。通过跳跃连接（skip connections），允许信息绕过某些层直接传递给后续层。这种设计使得训练非常深的网络成为可能，并显著提高了图像分类的效果[^1]。 ```python import torch.nn as nn class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_planes, planes, stride=1): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.shortcut = nn.Sequential() if stride != 1 or in_planes != self.expansion*planes: self.shortcut = nn.Sequential( nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion*planes) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) out = F.relu(out) return out ``` #### DenseNet (Densely Connected Convolutional Networks) DenseNet进一步改进了特征传播机制，在每一层与其后的所有层之间建立直接连接。这不仅增强了特征重用，还减少了参数数量并改善了正则化效果。实验表明，DenseNet在多个基准测试集上均取得了优异的成绩[^3]。 ```python def dense_block(net, num_layers, growth_rate, bn_size, drop_rate): block = [] for i in range(num_layers): layer = _DenseLayer(growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate, efficient=self.efficient) block.append(layer) net.add_module('denseblock_%d' % (len(list(net.children())) + 1), _DenseBlock(block)) def transition_layer(net, input_features, output_features): net.add_module('transition_%d' % (len(list(net.children())) + 1), Transition(input_channels=input_features, output_channels=output_features)) ``` #### EfficientNet EfficientNet采用复合缩放的方法来优化网络宽度、深度以及分辨率之间的关系。相比于简单地增加层数或扩大滤波器尺寸的传统做法，这种方法能够在保持计算成本可控的同时大幅提升模型性能。研究表明，EfficientNet系列模型可以在多种任务中超越其他先进架构的表现[^4]。 ```python from tensorflow.keras.applications import EfficientNetB0 model = EfficientNetB0(weights='imagenet') ```