importtorchclassmyResNet(torch.nn.Module):def__init__(self, in_channels=3, num_classes=10): super(myResNet, self).__init__()#第1层self.conv0_1 = torch.nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3) self.bn0_1= torch.nn.BatchNorm2d(64) self.relu0_1=torc...
x = layers.GlobalAveragePooling2D()(x) output = layers.Dense(num_classes, activation='softmax')(x) model = Model(input, output) return model # 加载CIFAR-10数据集并预处理 (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train = x_train.astype('float32') / 255.0...
model = Resnet18(num_classes=10, improved=args.my_improved) else: model = resnet18(pretrained=False, num_classes=10) 这里通过配置pretrained决定是否加载预训练权重。另外,pytorch内置resnet18最后一个全连接层是1000个输出,而分类cifar10我们需要设定全连接层为10个输出,所以我们加载权重的时候不加载全连接...
def resnet(input_shape,depth,num_classes=10): """ResNet Arguments: input_shape (tensor): 输入尺寸 depth (int): 网络层数 num_classes (int): 预测类别数 Return: model (Model): 模型 """ if (depth - 2) % 6 != 0: raise ValueError('depth should be 6n+2') #超参数 num_filters = ...
# 假设新任务的类别数为num_classes num_classes = 10 # 示例值,请根据实际情况修改 # 获取原模型全连接层的输入特征数 n_features = model.fc.in_features # 替换原模型的全连接层 model.fc = torch.nn.Linear(n_features, num_classes) 3. 冻结ResNet18模型中除最后一层外的所有层 在迁移学习中,我...
out=self.relu(out)returnout# 定义ResNet-18模型classResNet(nn.Module):def__init__(self,block,layers,num_classes=10):super(ResNet,self).__init__() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.
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...
(self,ResidualBlock,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(ResidualBlock,64,2,stride=1)self.layer2=self.make_...
num_classes = 10 # 根据具体问题设置类别数 model = CustomResNet18(num_classes) 定义损失函数和优化器: 代码语言:txt 复制 criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) 加载数据集并进行数据预处理: ...
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=...