Deep learning 错误显示:';ResNet';对象没有属性';分类器&x27;
我下载Resnet18模型来训练一个模型 我打字的时候Deep learning 错误显示:';ResNet';对象没有属性';分类器&x27;,deep-learning,pytorch,torch,resnet,torchvision,Deep Learning,Pytorch,Torch,Resnet,Torchvision,我下载Resnet18模型来训练一个模型 我打字的时候 model 它表明 ResNet( (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=Tru
model
它表明
ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=512, out_features=1000, bias=True)
(classifer): Sequential(
(fc1): Linear(in_features=512, out_features=256, bias=True)
(relu): ReLU()
(fc5): Linear(in_features=128, out_features=2, bias=True)
(output): LogSoftmax()
)
)
正如您所看到的,它清楚地显示了分类器
但当我这么做的时候
optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)
它显示了一个错误
AttributeError: 'ResNet' object has no attribute 'classifier'
我不知道我犯了什么错误,如果你能帮忙,那就太好了。如果您需要,我可以提供一些额外的详细信息。删除
分类器
并仅保留它model.parameters()
optimizer = optim.Adam(model.parameters(), lr=0.001)
要构造一个
优化器
,您必须给它一个包含要优化的参数的iterable。假设您只想训练分类器,您可以冻结不想更改的参数。
对于你的情况,你可以这样做
for name, param in model.named_parameters() :
param.requires_grad = False
if name.startswith('classifier') :
param.requires_grad = True
这将冻结除分类器外的所有参数
然后,您可以执行建议的操作,即将所有参数传递给优化器
optimizer = optim.Adam(model.parameters(), lr=0.001)
在优化阶段之前,您是否可能在模型上使用
dataparallel
?