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
self.n_features = n_features self.n_components = n_components weights = torch.ones( n_components) means = torch.randn( n_components, n_features) * self.init_scale stdevs = torch.rand( n_components, n_features) * self.init_scale # # Our trainable Parameters self.blend_weight = torch...
具体来说,torchsummary 库主要用于以下几个方面: 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 para...
# update parameters self.optimizer.step() self.optimizer.zero_grad()return train_metrics@torch.no_grad() def evaluate_step(self, features,labels):self.eval()if self.device: features = features.to(self.device) labels = labels.to(self.device)...
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...
iafs = nn.ModuleList(iafs) # define a (trainable) parameters z_0 and z_q_0 that help define the probability # distributions p(z_1) and q(z_1) # (since for t = 1 there are no previous latents to condition on) self.z_0 = nn.Parameter(torch.zeros(z_dim)) self.z_q_0 = ...
:param model: nn.Module containing trainable parameters :return: number of trainable parameters in model """num_parameters =0forparameterinmodel.parameters(): num_parameters += torch.numel(parameter)returnnum_parameters 开发者ID:bayesiains,项目名称:nsf,代码行数:12,代码来源:torchutils.py ...
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...
示例10: reset_parameters ▲點讚 6▼ # 需要導入模塊: import torch [as 別名]# 或者: from torch importmanual_seed[as 別名]defreset_parameters(self, init_shared=lambda x: normal(x, std=0.1), init_importance=lambda x: normal(x, std=0.0005)):"""Resets the trainable parameters."""defset_...