self.bn8_2= torch.nn.BatchNorm2d(512) self.relu8_2=torch.nn.ReLU()#第18层self.fc = torch.nn.Linear(512, num_classes)defforward(self, x):#batch_size, 3, 224, 224x = self.conv0_1(x)#bs, 64, 112, 112x =self.bn0_1(x) x=self.relu0_1(x) x1= self.dmp(x)#bs, 64,...
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 = ...
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: ...
# 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...
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...
调整模型和数据集的配置,使其与 Cifar-10 一起工作。 # Configure model exp_config.task.model.num_classes = 10 exp_config.task.model.input_size = list(ds_info.features["image"].shape) exp_config.task.model.backbone.resnet.model_id = 18 ...
def __init__(self, layer_dims, num_classes= 10): #[2,2,2,2] super(ResNet, self).__init__() # 根网络,预处理 self.stem= Sequential([ layers.Conv2D(64, kernel_size= (3,3), strides= (1,1)), layers.BatchNormalization(), ...
(1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) #网络参数初始化...