一个商城网站开发要多少时间,软件工程的定义,1m带宽可以建设电商网站吗,东莞勒流网站制作在 PyTorch 中#xff0c;named_children、named_modules 和 named_parameters 是用于获取神经网络模型组件和参数的三种不同的方法。下面是它们各自的作用和区别#xff1a;
named_parameters#xff1a;递归地列出所有参数名称和tensornamed_modules#xff1a;递归地列…在 PyTorch 中named_children、named_modules 和 named_parameters 是用于获取神经网络模型组件和参数的三种不同的方法。下面是它们各自的作用和区别
named_parameters递归地列出所有参数名称和tensornamed_modules递归地列出所有子层其中第一个返回值就是模型本身named_children列出模型的第一层级的子层不往下进行深入递归
1. named_children:
named_children 返回一个生成器它包含模型中所有直接子模块的名称和模块对。它只返回一层级的子模块不递归到更深层次的子模块。这个方法通常用于迭代模型的直接子模块并对其进行操作或检查。
示例:
from torchvision.models import resnet18model resnet18()
# Print each layer name and its module
# Note that named_children method only returns the first level submodules
for name, layer in model.named_children():print(name.ljust(10), --, type(layer))输出
conv1 -- class torch.nn.modules.conv.Conv2d
bn1 -- class torch.nn.modules.batchnorm.BatchNorm2d
relu -- class torch.nn.modules.activation.ReLU
maxpool -- class torch.nn.modules.pooling.MaxPool2d
layer1 -- class torch.nn.modules.container.Sequential
layer2 -- class torch.nn.modules.container.Sequential
layer3 -- class torch.nn.modules.container.Sequential
layer4 -- class torch.nn.modules.container.Sequential
avgpool -- class torch.nn.modules.pooling.AdaptiveAvgPool2d
fc -- class torch.nn.modules.linear.Linear2. named_modules:
named_modules 返回一个生成器它包含模型中所有模块的名称和模块对包括子模块的子模块。它递归地遍历整个模型返回所有模块的名称和引用。这个方法适用于当你需要对模型中的所有模块进行操作或检查时无论它们位于哪一层级。
示例:
from torchvision.models import resnet18model resnet18()
# The first layer is the model itself
for name, layer in model.named_modules():print(name.ljust(15), --, type(layer))输出 -- class torchvision.models.resnet.ResNet
conv1 -- class torch.nn.modules.conv.Conv2d
bn1 -- class torch.nn.modules.batchnorm.BatchNorm2d
relu -- class torch.nn.modules.activation.ReLU
maxpool -- class torch.nn.modules.pooling.MaxPool2d
layer1 -- class torch.nn.modules.container.Sequential
layer1.0 -- class torchvision.models.resnet.BasicBlock
layer1.0.conv1 -- class torch.nn.modules.conv.Conv2d
layer1.0.bn1 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer1.0.relu -- class torch.nn.modules.activation.ReLU
layer1.0.conv2 -- class torch.nn.modules.conv.Conv2d
layer1.0.bn2 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer1.1 -- class torchvision.models.resnet.BasicBlock
layer1.1.conv1 -- class torch.nn.modules.conv.Conv2d
layer1.1.bn1 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer1.1.relu -- class torch.nn.modules.activation.ReLU
layer1.1.conv2 -- class torch.nn.modules.conv.Conv2d
layer1.1.bn2 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer2 -- class torch.nn.modules.container.Sequential
layer2.0 -- class torchvision.models.resnet.BasicBlock
layer2.0.conv1 -- class torch.nn.modules.conv.Conv2d
layer2.0.bn1 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer2.0.relu -- class torch.nn.modules.activation.ReLU
layer2.0.conv2 -- class torch.nn.modules.conv.Conv2d
layer2.0.bn2 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer2.0.downsample -- class torch.nn.modules.container.Sequential
layer2.0.downsample.0 -- class torch.nn.modules.conv.Conv2d
layer2.0.downsample.1 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer2.1 -- class torchvision.models.resnet.BasicBlock
layer2.1.conv1 -- class torch.nn.modules.conv.Conv2d
layer2.1.bn1 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer2.1.relu -- class torch.nn.modules.activation.ReLU
layer2.1.conv2 -- class torch.nn.modules.conv.Conv2d
layer2.1.bn2 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer3 -- class torch.nn.modules.container.Sequential
layer3.0 -- class torchvision.models.resnet.BasicBlock
layer3.0.conv1 -- class torch.nn.modules.conv.Conv2d
layer3.0.bn1 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer3.0.relu -- class torch.nn.modules.activation.ReLU
layer3.0.conv2 -- class torch.nn.modules.conv.Conv2d
layer3.0.bn2 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer3.0.downsample -- class torch.nn.modules.container.Sequential
layer3.0.downsample.0 -- class torch.nn.modules.conv.Conv2d
layer3.0.downsample.1 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer3.1 -- class torchvision.models.resnet.BasicBlock
layer3.1.conv1 -- class torch.nn.modules.conv.Conv2d
layer3.1.bn1 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer3.1.relu -- class torch.nn.modules.activation.ReLU
layer3.1.conv2 -- class torch.nn.modules.conv.Conv2d
layer3.1.bn2 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer4 -- class torch.nn.modules.container.Sequential
layer4.0 -- class torchvision.models.resnet.BasicBlock
layer4.0.conv1 -- class torch.nn.modules.conv.Conv2d
layer4.0.bn1 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer4.0.relu -- class torch.nn.modules.activation.ReLU
layer4.0.conv2 -- class torch.nn.modules.conv.Conv2d
layer4.0.bn2 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer4.0.downsample -- class torch.nn.modules.container.Sequential
layer4.0.downsample.0 -- class torch.nn.modules.conv.Conv2d
layer4.0.downsample.1 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer4.1 -- class torchvision.models.resnet.BasicBlock
layer4.1.conv1 -- class torch.nn.modules.conv.Conv2d
layer4.1.bn1 -- class torch.nn.modules.batchnorm.BatchNorm2d
layer4.1.relu -- class torch.nn.modules.activation.ReLU
layer4.1.conv2 -- class torch.nn.modules.conv.Conv2d
layer4.1.bn2 -- class torch.nn.modules.batchnorm.BatchNorm2d
avgpool -- class torch.nn.modules.pooling.AdaptiveAvgPool2d
fc -- class torch.nn.modules.linear.Linear3. named_parameters:
named_parameters 返回一个生成器它包含模型中所有参数的名称和参数值对。它递归地遍历模型返回所有可训练参数的名称和参数张量。这个方法用于获取和检查模型中的参数例如在打印模型参数、保存模型或加载模型时使用。
示例:
from torchvision.models import resnet18model resnet18()
for name, param in model.named_parameconv1.weight -- torch.Size([64, 3, 7, 7])
bn1.weight -- torch.Size([64])
bn1.bias -- torch.Size([64])
layer1.0.conv1.weight -- torch.Size([64, 64, 3, 3])
layer1.0.bn1.weight -- torch.Size([64])
layer1.0.bn1.bias -- torch.Size([64])
layer1.0.conv2.weight -- torch.Size([64, 64, 3, 3])
layer1.0.bn2.weight -- torch.Size([64])
layer1.0.bn2.bias -- torch.Size([64])
layer1.1.conv1.weight -- torch.Size([64, 64, 3, 3])
layer1.1.bn1.weight -- torch.Size([64])
layer1.1.bn1.bias -- torch.Size([64])
layer1.1.conv2.weight -- torch.Size([64, 64, 3, 3])
layer1.1.bn2.weight -- torch.Size([64])
layer1.1.bn2.bias -- torch.Size([64])
layer2.0.conv1.weight -- torch.Size([128, 64, 3, 3])
layer2.0.bn1.weight -- torch.Size([128])
layer2.0.bn1.bias -- torch.Size([128])
layer2.0.conv2.weight -- torch.Size([128, 128, 3, 3])
layer2.0.bn2.weight -- torch.Size([128])
layer2.0.bn2.bias -- torch.Size([128])
layer2.0.downsample.0.weight -- torch.Size([128, 64, 1, 1])
layer2.0.downsample.1.weight -- torch.Size([128])
layer2.0.downsample.1.bias -- torch.Size([128])
layer2.1.conv1.weight -- torch.Size([128, 128, 3, 3])
layer2.1.bn1.weight -- torch.Size([128])
layer2.1.bn1.bias -- torch.Size([128])
layer2.1.conv2.weight -- torch.Size([128, 128, 3, 3])
layer2.1.bn2.weight -- torch.Size([128])
layer2.1.bn2.bias -- torch.Size([128])
layer3.0.conv1.weight -- torch.Size([256, 128, 3, 3])
layer3.0.bn1.weight -- torch.Size([256])
layer3.0.bn1.bias -- torch.Size([256])
layer3.0.conv2.weight -- torch.Size([256, 256, 3, 3])
layer3.0.bn2.weight -- torch.Size([256])
layer3.0.bn2.bias -- torch.Size([256])
layer3.0.downsample.0.weight -- torch.Size([256, 128, 1, 1])
layer3.0.downsample.1.weight -- torch.Size([256])
layer3.0.downsample.1.bias -- torch.Size([256])
layer3.1.conv1.weight -- torch.Size([256, 256, 3, 3])
layer3.1.bn1.weight -- torch.Size([256])
layer3.1.bn1.bias -- torch.Size([256])
layer3.1.conv2.weight -- torch.Size([256, 256, 3, 3])
layer3.1.bn2.weight -- torch.Size([256])
layer3.1.bn2.bias -- torch.Size([256])
layer4.0.conv1.weight -- torch.Size([512, 256, 3, 3])
layer4.0.bn1.weight -- torch.Size([512])
layer4.0.bn1.bias -- torch.Size([512])
layer4.0.conv2.weight -- torch.Size([512, 512, 3, 3])
layer4.0.bn2.weight -- torch.Size([512])
layer4.0.bn2.bias -- torch.Size([512])
layer4.0.downsample.0.weight -- torch.Size([512, 256, 1, 1])
layer4.0.downsample.1.weight -- torch.Size([512])
layer4.0.downsample.1.bias -- torch.Size([512])
layer4.1.conv1.weight -- torch.Size([512, 512, 3, 3])
layer4.1.bn1.weight -- torch.Size([512])
layer4.1.bn1.bias -- torch.Size([512])
layer4.1.conv2.weight -- torch.Size([512, 512, 3, 3])
layer4.1.bn2.weight -- torch.Size([512])
layer4.1.bn2.bias -- torch.Size([512])
fc.weight -- torch.Size([1000, 512])
fc.bias -- torch.Size([1000])总结来说named_children 用于获取模型的直接子模块named_modules 用于获取模型的所有模块包括嵌套的子模块而 named_parameters 用于获取模型中的所有参数。这些方法在模型调试、分析和优化时非常有用。
