resnet 50超参数

ResNet-50 是一个具有 50 层深度的卷积神经网络模型，其超参数如下：

1. Batch size：一般为 32 或 64。
2. Learning rate：通常设置为 0.1 或 0.01，并可以使用学习率调度器进行调整。
3. Momentum：通常设置为 0.9。
4. Weight decay：一般设置为 0.0001。
5. Optimizer：使用随机梯度下降（SGD）优化器。
6. 激活函数：使用 ReLU 激活函数。
7. Dropout：通常设置为 0.5。
8. 输入图像大小：通常为 224x224。

需要注意的是，这些超参数并不是绝对的，可以根据具体的数据集和任务进行微调。

相关问题

pytorch实现resnet 50

### 回答1：
b'pytorch实现resnet50' 可以使用PyTorch库来实现ResNet50。ResNet是深度学习中一种非常流行的卷积神经网络结构，它包含多个Residual Block。在PyTorch中，可以使用torchvision库中的预训练模型或者自行构建网络来实现ResNet50。

### 回答2：
ResNet 50是一种深度卷积神经网络，用于图像分类和目标检测任务。在PyTorch中实现ResNet 50，您可以遵循以下步骤：

1. 导入必要的库：

import torch
import torch.nn as nn
import torch.optim as optim

2. 定义ResNet 50的结构：ResNet 50由多个残差模块组成，每个残差模块包含多个卷积层和标准化层。最后，使用全局平均池化和完全连接层进行分类。定义ResNet 50的结构：

class ResNet50(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet50, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(64, 3)
        self.layer2 = self._make_layer(128, 4, stride=2)
        self.layer3 = self._make_layer(256, 6, stride=2)
        self.layer4 = self._make_layer(512, 3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(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.reshape(x.size(0), -1)
        x = self.fc(x)

        return x

3. 定义ResNet 50的残差模块：使用残差块改善训练深层的模型。定义ResNet 50的残差模块：

class Bottleneck(nn.Module):
    expansion = 4
    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()

        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out

4. 定义损失函数和优化器：使用交叉熵损失函数和随机梯度下降优化器来训练模型。

model = ResNet50().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

5. 训练模型：迭代训练模型，调整损失函数并使用优化器进行优化。

num_epochs = 10
for epoch in range(num_epochs):
    running_loss = 0.0

    for i, data in enumerate(trainloader, 0):
        inputs, labels = data[0].to(device), data[1].to(device)
        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

以上是在PyTorch中实现ResNet 50的基本步骤。要实现所有的步骤，您必须定义ResNet 50的结构，残差块和优化器，并使用迭代训练模型来完成整个过程。

### 回答3：
ResNet-50是ResNet系列的一种，是在ILSVRC 2015比赛中取得了很大的成功的网络结构。它相比于之前的网络结构，主要有两个创新点：全卷积层和残差网络。全卷积层用于增加感受野，使得每个卷积层处理的信息范围更广，从而提取更加抽象和高级的特征。而残差网络则是用于解决深层神经网络的梯度消失问题，并提升模型的准确率。在ResNet-50中，采用了50层的深度，并且在每个Block中包含了经典的残差结构。

下面介绍如何用PyTorch实现ResNet-50

1.导入必要的库和模块

import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torchvision
import torchvision.transforms as transforms

2.定义ResNet-50模型

class Bottleneck(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.conv3 = nn.Conv2d(out_channels, out_channels * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels * 4)
        self.relu = nn.ReLU(inplace=True)
        self.stride = stride

        if in_channels != out_channels * 4 or stride != 1:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * 4, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels * 4)
            )
        else:
            self.shortcut = nn.Sequential()

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.bn3(out)
        out += self.shortcut(residual)
        out = self.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, out_channels, blocks, stride=1):
        layers = []
        layers.append(block(64, out_channels, stride))
        for i in range(1, blocks):
            layers.append(block(out_channels * 4, out_channels))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(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

3.定义训练过程

def train(model, criterion, optimizer, train_loader, epochs):
    for epoch in range(epochs):
        running_loss = 0.0
        for i, (inputs, targets) in enumerate(train_loader):
            inputs, targets = inputs.to(device), targets.to(device)
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()

            if (i + 1) % 100 == 0:
                print('[Epoch %d Batch %5d] Loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
                running_loss = 0.0


device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
epochs = 5
batch_size = 64
lr = 0.001

transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

train_set = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = data.DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=4)

model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=10)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)

model.to(device)
train(model, criterion, optimizer, train_loader, epochs)

4.进行模型评估

def test(model, test_loader):
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))


test_set = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
test_loader = data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=4)
test(model, test_loader)

以上是使用PyTorch实现ResNet-50的过程，通过对训练集的训练和测试集的评估，可以大致了解模型的准确率和性能，进一步提高模型的泛化能力和优化模型的参数。

pytorch实现resnet 18

PyTorch可以通过使用预训练模型和自定义层来实现ResNet-18。

首先，可以使用PyTorch的torchvision模块中的预训练模型来加载ResNet-18。这可以通过以下代码实现：

import torch
import torchvision.models as models

resnet18 = models.resnet18(pretrained=True)

接下来，可以根据需要添加自定义层。例如，可以添加一个全连接层来适应特定的分类任务：

import torch.nn as nn

num_classes = 10

resnet18.fc = nn.Linear(resnet18.fc.in_features, num_classes)

最后，可以使用PyTorch的数据加载器和优化器来训练模型：

import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms

train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

optimizer = optim.SGD(resnet18.parameters(), lr=.001, momentum=.9)
criterion = nn.CrossEntropyLoss()

for epoch in range(10):
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = resnet18(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

这样就可以使用PyTorch实现ResNet-18了。