table = PrettyTable([“Modules”, “Parameters”])total_params = 0 for name, parameter in model.named_parameters():if not parameter.requires_grad: continue params = parameter.numel()table.add_row([name, params])total_params+=params print(table)print(f”Total Trainable Params: {total_params}...
model = models.resnet50(pretrained=True, requires_grad=False).to(device) # total parameters and trainable parameters total_params = sum(p.numel() for p in model.parameters()) print(f"{total_params:,} total parameters.") total_trainable_params = sum( p.numel() for p in model.parameters...
table = PrettyTable([“Modules”, “Parameters”]) total_params = 0 for name, parameter in model.named_parameters(): if not parameter.requires_grad: continue params = parameter.numel() table.add_row([name, params]) total_params+=params print(table) print(f”Total Trainable Params: {total_...
2.2 计算模型参数数量 一旦定义了模型,就可以通过parameters()方法获取参数,并使用numpy计算总数量。以下是计算模型参数数量的示例代码: defcount_parameters(model):returnsum(p.numel()forpinmodel.parameters()ifp.requires_grad)num_params=count_parameters(model)print(f'Total number of trainable parameters:{num_...
[10, 256] | [10, 512]2 | net.1 | BatchNorm1d | 1.0 K | [10, 512] | [10, 512]---132 K Trainable params0 Non-trainable params132 K Total params0.530 Total estimated model params size (MB)""" 1.2.3 初始化回调函数 callbacks 配置并管理回调函数,提供一系列 hook 的接口: self.cal...
#模型体系print(model)def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad) print(f'The model has {count_parameters(model):,} trainable parameters')#初始化预训练embeddingpretrained_embeddings = TEXT.vocab.vectorsmodel.embedding.weight.data.copy...
# Our trainable Parameters self.blend_weight = torch.nn.Parameter(weights) self.means = torch.nn.Parameter(means) self.stdevs = torch.nn.Parameter(stdevs) def forward(self, x): blend_weight = torch.distributions.Categorical( torch.nn.functional.relu( self.blend_weight)) ...
想要简单地得到模型的参数量,在pytorch可以通过简单调用得到,还能区分所有参数和训练参数: # Find total parameters and trainable parameterstotal_params=sum(p.numel()forpinmodel.parameters())print(f'{total_params:,}total parameters.')total_trainable_params=sum(p.numel()forpinmodel.parameters()ifp.require...
# Our trainable Parameters self.blend_weight = torch.nn.Parameter(weights) self.means = torch.nn.Parameter(means) self.stdevs = torch.nn.Parameter(stdevs) def forward(self, x): blend_weight = torch.distributions.Categorical( torch.nn.functional.relu( self.blend_weight)) ...
stoi[TEXT.pad_token]#PAD_IDX = 1 为pad的索引model=WordAVGModel(INPUT_DIM, EMBEDDING_DIM, OUTPUT_DIM, PAD_IDX)defcount_parameters(model):#统计参数,可以不用管returnsum(p.numel()forpinmodel.parameters()ifp.requires_grad)print(f'The model has {count_parameters(model):,} trainable parameters...