trainable_params = [] for name, param in model.named_parameters(): if param.requires_grad: trainable_params.append((name, param)) print(f"Trainable parameter: {name}, shape: {param.shape}") # 也可以选择将可训练参数打印出来 for name, param in trainable_params: print(f"Trainable parameter...
macs))print('{:<30} {:<8}'.format('Number of parameters: ', params))#Computational complexity: 0.05 GMac#Number of parameters: 1.26 M"""torchsummary 用来计算网络的计算参数等信息"""fromtorchsummaryimportsummary
This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses. Parameters param_group (dict)– Specifies what Tensors should be optimized along with group optimization options. (specific)– load_state_dict(...
def num_trainable_params(model): nums = sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6 return nums # Calculate the number of trainable parameters in the embedding, LSTM, and fully connected layers of the LanguageModel instance 'model' num_trainable_params(model.embed...
1. 显示模型结构:torchsummary 可以显示 PyTorch 模型的层次结构,包括每一层的类型、输入形状、输出形状以及参数数量等信息,有助于用户理解模型的组成和架构。 2. 统计参数数量:通过 torchsummary,用户可以快速了解模型中各个层的参数数量,包括可训练参数(trainable parameters)和非可训练参数(non-trainable parameters),...
ubuntu20.04 Atlas 300V Pro Video python版本3.10.13 torch-npu2.1.0 torch2.1.0 llama-factory,0.9.1 glm-4-9b-chat-hf 三、测试步骤: llama界面训练 四、日志信息: [INFO|trainer.py:2322] 2024-12-25 02:45:52,413 >> Number of trainable parameters = 23,797,760 ...
fc(x) return x model = DummyModel().cuda() optimizer = SGD([ {'params': model.base.parameters()}, {'params': model.fc.parameters(), 'lr': 1e-3} #对 fc的参数设置不同的学习率 ], lr=1e-2, momentum=0.9) 1.2.3 step 此方法主要完成一次模型参数的更新...
# Some modules do the computation themselves using parameters or the parameters of children, treat these as layers layer_modules = (torch.nn.MultiheadAttention, ) defsummary(model, input_shape, input_dtype = torch.FloatTensor, batch_size=-1, ...
"""def__init__(self, dim, init_value=1.0, trainable=True, use_nchw=True):super().__init__() self.shape = (dim,1,1)ifuse_nchwelse(dim,)# static shape, which should be updated after pruningself.scale = nn.Parameter(init_value * torch.ones(dim), requires_grad=trainable)defforward...
当我这样做时len(list(bert.parameters())),它给了我 199。所以让我们假设 79 是参数的 40%。我可以做这样的事情: for param in list(bert.parameters())[-79:]: # total trainable 199 Params: 79 is 40% param.requires_grad = False Run Code Online (Sandbox Code Playgroud) 我认为它会冻结前...