个人网站制作总体设计,青岛seo外包公司,网站安全怎么做,长沙0731手机平台网利用pytorch实现卷积形式的ResNet 1. 导入必需的库2. 定义残差块3. 构建 ResNet 网络4. 实例化网络和训练 要使用 PyTorch 实现卷积形式的 ResNet#xff08;残差网络#xff09;#xff0c;你需要遵循几个主要步骤。首先#xff0c;让我们概述 ResNet 的基本结构。ResNet … 利用pytorch实现卷积形式的ResNet 1. 导入必需的库2. 定义残差块3. 构建 ResNet 网络4. 实例化网络和训练 要使用 PyTorch 实现卷积形式的 ResNet残差网络你需要遵循几个主要步骤。首先让我们概述 ResNet 的基本结构。ResNet 通过添加所谓的“残差连接”或跳跃连接来解决深度神经网络中的梯度消失/爆炸问题。这些连接允许梯度直接流过网络从而改善了训练过程。
1. 导入必需的库
import torch
import torch.nn as nn
import torch.nn.functional as F2. 定义残差块
残差块包括两个卷积层和一个跳跃连接。
class ResidualBlock(nn.Module):def __init__(self, in_channels, out_channels, stride1, downsampleNone):super(ResidualBlock, self).__init__()self.conv1 nn.Conv2d(in_channels, out_channels, kernel_size3, stridestride, padding1, biasFalse)self.bn1 nn.BatchNorm2d(out_channels)self.conv2 nn.Conv2d(out_channels, out_channels, kernel_size3, stride1, padding1, biasFalse)self.bn2 nn.BatchNorm2d(out_channels)self.downsample downsampledef forward(self, x):residual xout self.conv1(x)out F.relu(self.bn1(out))out self.conv2(out)out self.bn2(out)if self.downsample:residual self.downsample(x)out residualout F.relu(out)return out3. 构建 ResNet 网络
这里以 ResNet-18 为例但可以根据需要调整层数。
class ResNet(nn.Module):def __init__(self, block, layers, num_classes1000):super(ResNet, self).__init__()self.in_channels 64self.conv nn.Conv2d(3, 64, kernel_size7, stride2, padding3, biasFalse)self.bn nn.BatchNorm2d(64)self.relu nn.ReLU(inplaceTrue)self.maxpool nn.MaxPool2d(kernel_size3, stride2, padding1)self.layer1 self.make_layer(block, 64, layers[0])self.layer2 self.make_layer(block, 128, layers[1], 2)self.layer3 self.make_layer(block, 256, layers[2], 2)self.layer4 self.make_layer(block, 512, layers[3], 2)self.avgpool nn.AdaptiveAvgPool2d((1, 1))self.fc nn.Linear(512, num_classes)def make_layer(self, block, out_channels, blocks, stride1):downsample Noneif (stride ! 1) or (self.in_channels ! out_channels):downsample nn.Sequential(nn.Conv2d(self.in_channels, out_channels, kernel_size1, stridestride, biasFalse),nn.BatchNorm2d(out_channels))layers []layers.append(block(self.in_channels, out_channels, stride, downsample))self.in_channels out_channelsfor i in range(1, blocks):layers.append(block(out_channels, out_channels))return nn.Sequential(*layers)def forward(self, x):x self.conv(x)x self.bn(x)x self.relu(x)x self.maxpool(x)x self.layer1(x)x self.layer2(x)x self.layer3(x)x self.layer4(x)x self.avgpool(x)x x.view(x.size(0), -1)x self.fc(x)return x
4. 实例化网络和训练
创建 ResNet 实例并进行训练。
model ResNet(ResidualBlock, [2, 2, 2, 2]) # ResNet-18
# 接下来是训练代码包括数据加载、损失函数、优化器等
