代码解析 模型定义:上述代码中,我们定义了一个SourceModel和一个TargetModel类,均有两个线性层。 参数复制函数:copy_model_parameters函数逐层遍历源模型和目标模型的参数,并使用target_param.data.copy_(source_param.data)实现了参数的复制。 验证步骤:我们在复制参数之后,打印源模型和目标模型的参数,以确保复制成功。
迭代打印model.named_parameters()将会打印每一次迭代元素的名字和param(元素是 torch.nn.parameter.Parameter 类型) for name, param in model.named_parameters(): print(name,param.requires_grad) param.requires_grad=False # 顺便改下属性 model.parameters() [parameters(recurse: bool = True) → Iterator[t...
model = nn.DataParallel(model.to(device), device_ids=gpus, output_device=gpus[0]) optimizer = optim.SGD(model.parameters) forepochinrange(100): forbatch_idx, (data, target)inenumerate(train_loader): images = images.cuda(non_blocking=True) target = target.cuda(non_blocking=True) ... ou...
所以最后网络结构是预处理的conv层和bn层,以及接下去的三个stage,每个stage分别是三层,最后是avgpool和全连接层 1、model.named_parameters(),迭代打印model.named_parameters()将会打印每一次迭代元素的名字和param forname, paraminnet.named_parameters():print(name,param.requires_grad) param.requires_grad=False...
model = get_model()optimizer = torch.optim.Adam(model.parameters())criterion = torch.nn.CrossEntropyLoss()train_loader = get_data(batch_size) # copy the model to the GPUmodel = model.to(device)if compile_model:# compile modelmodel = torch.c...
By design, the learning rate is attached to the model parameters. Since the optimizers' goal is to update the model parameters, the optimizer is not making a copy, and modify the parameters directly. If you want to modify a copy, you can do so before passing the parameters to an optimize...
同样的model.conv1是nn.Conv2d同样继承了Module,conv1除了自己的方法和属性外,同样具有8个属性和那些方法,我们可以使用model.conv1.weight,model.fc1.weight,model.conv1.in_channels,model.fc1.in_features, model.conv1._modules,model.conv1.modules(),model.parameters()等,可以nn.init.kaiming_normal_(mode...
(self.model.parameters(),lr=self.learning_rate)# 定义优化器#...forepochinrange(self.epochs):fori,(x,y)inenumerate(train_iter):x,y=x.to(device),y.to(device)logits=self.model(x)# ...print("Epochs[{}/{}]--acc on test{:.4}".format(epoch,self.epochs,self.evaluate(test_iter,sel...
#加载预训练模型model = torchvision.models.resnet18(pretrained=True)#遍历每一个参数,将其设置为不更新参数,即不学习forparaminmodel.parameters(): param.requires_grad=False#将全连接层改为mnist所需的10类,注意:这样更改后requires_grad默认为Truemodel.fc = nn.Linear(512, 10)#优化optimizer = optim.SGD...
2. 编译模型# 定义二元交叉熵损失函数criterion=nn.BCELoss()# 定义Adam优化器,学习率为0.001optimizer=optim.Adam(model.parameters(),lr=0.0001)# 3. 准备数据# 生成500个训练样本,每个样本有6个特征X_train=np.random.rand(5000,6).astype(np.float32)# 生成500个训练标签,值为0或1y_train=np.random....