importnumpy as np#model是我们在pytorch定义的神经网络层#model.parameters()取出这个model所有的权重参数para = sum([np.prod(list(p.size()))forpinmodel.parameters()]) #下面的type_size是4,因为我们的参数是float32也就是4B,4个字节print('Model {} : params: {:4f}M'.format(model._get_name(),...
他主要是引用另一个类内成员函数named_parameters(),实现对所有参数的索引包装,生成迭代器,下面看另一个函数: def named_parameters(self, memo=None, prefix=''): r"""Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself Yields: (string,...
另外上面例子给出了三种读取parameter的方法,推荐使用后面两种(这两种的区别可参阅Pytorch: parameters(),children(),modules(),named_*区别),因为是以迭代生成器的方式来读取,第一种方式是一股脑的把参数全丢给你,要是模型很大,估计你的电脑会吃不消。 另外需要介绍的是_parameters是nn.Module在__init__()函数中...
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...
1. model.modules() 2. model.named_modules() 3. model.children() 4. model.named_children() 5. model.parameters() 6. model.named_parameters() 7. model.state_dict() 模型示例: import torch import torch.nn as nn class Net(nn.Module): def __init__(self, num_class=10): super()._...
model = LeNet5().to(device) parameters_to_prune = ( # 刚才讲局部剪枝的时候,可以传入 bias # 你在看这段代码的时候,是否也举一反三的发现这里也可以传入 bias? (model.conv1, 'weight'), (model.conv2, 'weight'), (model.fc1, 'weight'), (model.fc2, 'weight'), (model.fc3, 'weight...
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...
Hi, In the documentation, it is written: If you need to move a model to GPU via .cuda(), please do so before constructing optimizers for it. Parameters of a model after .cuda() will be different objects with those before the call. In gen...
:param model: 模型实例 :param train_loader: 训练集数据加载器 :param val_loader: 验证集数据加载器 :param lr: 学习率 :param epochs: 训练轮数 :return: 训练损失列表和验证损失列表 """criterion=nn.MSELoss()optimizer=optim.Adam(model.parameters(),lr=lr)train_losses=[]val_losses=[]forepochin...
print("The model will be running on", device,"device\n") model.to(device)# Convert model parameters and buffers to CPU or Cuda 在最後一個步驟中,定義用來儲存模型的函式: py複製 # Function to save the modeldefsaveModel():path ="./NetModel.pth"torch.save(model.state_dict(), path) ...