out=self.conv1(x)out=self.bn1(out)out=self.relu(out)out=self.conv2(out)out=self.bn2(out)ifself.downsampleisnotNone:identity=self.downsample(x)out+=identity out=self.relu(out)returnout# 定义ResNet-18模型classResNet(nn.Module):def__init__(self,block,layers,num_classes=10):super(ResN...
importtorchimporttorch.nnasnnimporttorchvision.modelsasmodels# 加载预训练的ResNet18模型resnet=models.resnet18(pretrained=True)# 替换最后一层全连接层num_classes=10resnet.fc=nn.Linear(resnet.fc.in_features,num_classes)# 定义损失函数和优化器criterion=nn.CrossEntropyLoss()optimizer=torch.optim.SGD(re...
classResNet(keras.Model):def__init__(self,layer_dims,num_classes=10):super(ResNet, self).__init__()# 预处理层self.stem=Sequential([ layers.Conv2D(64,(3,3),strides=(1,1)), layers.BatchNormalization(), layers.Activation('relu'), layers.MaxPool2D(pool_size=(2,2),strides=(1,1)...
=outchannel:self.shortcut=nn.Sequential(nn.Conv2d(inchannel,outchannel,kernel_size=1,stride=stride,bias=False),nn.BatchNorm2d(outchannel))defforward(self,x):out=self.left(x)out+=self.shortcut(x)out=F.relu(out)returnoutclassResNet(nn.Module):def__init__(self,ResidualBlock,num_classes=1...
classResNet(nn.Module):def__init__(self,ResBlock,num_classes=10):super(ResNet,self).__init__()self.inchannel=64self.conv1=nn.Sequential(nn.Conv2d(3,64,kernel_size=3,stride=1,padding=1,bias=False),nn.BatchNorm2d(64),nn.ReLU())self.layer1=self.make_layer(ResBlock,64,2,stride=...
classResNet(keras.Model):def__init__(self,layer_dims,num_classes=10):super(ResNet,self).__init__()# 预处理层 self.stem=Sequential([layers.Conv2D(64,(3,3),strides=(1,1)),layers.BatchNormalization(),layers.Activation('relu'),layers.MaxPool2D(pool_size=(2,2),strides=(1,1),paddin...
datasets.CIFAR10('./data', download=True, train=False, transform=transform_test) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False) # classes = ('plane'...
def __init__(self, num_classes=10): super(ResNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, ...
num_epochs =200train_model(net, data_loaders, criterion, optimizer, num_epochs) 4.10 查看训练结果 5. 测试模型 5.1 加载模型 net = torchvision.models.resnet18(pretrained=True) net.fc = nn.Linear(512,10) load_path ="./models/vgg16_40_1.0.pth"load_weights = torch.load(load_path) net....
代码如下: class ResNet18_1(nn.Layer): # 继承paddle.nn.Layer定义网络结构 def __init__(self,num_classes=11): super(ResNet18_1, self).__init__() # 初始化函数(根网络,预处理) # x:[b, c, h ,w]=[b,3,224,224] self.features = nn.Sequential( nn.Conv2D(in_channels=3, out_...