inceptionv2结构代码

Inception V2 是一种流行的卷积神经网络结构，用于图像分类和其他计算机视觉任务。下面是一个简单的 Inception V2 实现的 PyTorch 代码示例： ```python import torch import torch.nn as nn import torch.nn.functional as F class InceptionBlock(nn.Module): def __init__(self, in_channels): super(InceptionBlock, self).__init__() self.branch1x1 = nn.Conv2d(in_channels, 64, kernel_size=1) self.branch5x5_1 = nn.Conv2d(in_channels, 48, kernel_size=1) self.branch5x5_2 = nn.Conv2d(48, 64, kernel_size=5, padding=2) self.branch3x3_1 = nn.Conv2d(in_channels, 64, kernel_size=1) self.branch3x3_2 = nn.Conv2d(64, 96, kernel_size=3, padding=1) self.branch3x3_3 = nn.Conv2d(96, 96, kernel_size=3, padding=1) self.branch_pool = nn.Conv2d(in_channels, 32, kernel_size=1) def forward(self, x): branch1 = self.branch1x1(x) branch5x5 = self.branch5x5_1(x) branch5x5 = self.branch5x5_2(branch5x5) branch3x3 = self.branch3x3_1(x) branch3x3 = self.branch3x3_2(branch3x3) branch3x3 = self.branch3x3_3(branch3x3) branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) branch_pool = self.branch_pool(branch_pool) outputs = [branch1, branch5x5, branch3x3, branch_pool] return torch.cat(outputs, 1) class InceptionV2(nn.Module): def __init__(self, num_classes=1000): super(InceptionV2, self).__init__() self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1) self.conv2 = nn.Conv2d(32, 32, kernel_size=3, padding=1) self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.inception1 = InceptionBlock(64) self.inception2 = InceptionBlock(128) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(256, num_classes) def forward(self, x): x = F.relu(self.conv1(x)) x = F.relu(self.conv2(x)) x = F.relu(self.conv3(x)) x = self.maxpool(x) x = self.inception1(x) x = self.inception2(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x # 实例化模型 model = InceptionV2(num_classes=1000) ``` 解释 1. Inception Block: 实现了一个 Inception 模块，包括 1x1、5x5、3x3 卷积和一个 1x1 平均池化分支。 2. Inception V2: 该模型使用多个 Inception Modules，进行特征提取，最后通过平均池化和全连接层进行分类。 3. 前向传播: 在 `forward` 方法中，图像数据通过各个卷积层和 Inception 模块进行处理。 请根据自己的需求调整模型的层数、输出类别和超参数。 [2024-10-24 14:35:42 | AI写代码神器 | 1806点数解答]