parm={}forname,parametersinmodel.named_parameters():print(name,':',parameters.size()) parm[name]=parameters.detach().numpy() 一些方法 model.state_dict()和model.named_parameters()的不一样: Pytorch中state_dict()、named_parameters()和parameters()的区别 bert中,load参数函数中:state_dict是预训练...
AI代码解释 # List named parameters model_0.state_dict()>>>OrderedDict([('weights',tensor([0.3367])),('bias',tensor([0.1288]))]) 请注意model_0.state_dict()中的weights和bias的值是随机分配的初始值。 本质上,我们希望从随机参数开始,让模型将它们更新为最适合我们数据的参数(我们在创建直线数据时...
named_parameters()][0]) nn.init.uniform_(m.weight, -10, 10) # >=5的权重×自身,<5的为0,也是一种正则化 m.weight.data *= m.weight.data.abs() >= 5 net.apply(my_init) net[0].weight[:2] 输出结果: Init weight torch.Size([8, 4])Init weight torch.Size([1, 8])tensor([[ ...
有一个方法可以做到这一点,称为named_parameters: # In[18]:forname, paraminseq_model.named_parameters():print(name, param.shape)# Out[18]:0.weight torch.Size([13,1])0.bias torch.Size([13])2.weight torch.Size([1,13])2.bias torch.Size([1]) Sequential中每个模块的名称只是模块在参数中...
model.load_state_dict(best_model_wts)returnmodel,val_acc_history 3.2 设置模型参数的`.requires_grad`属性 当我们进行特征提取时,此辅助函数将模型中参数的 .requires_grad 属性设置为False。 默认情况下,当我们加载一个预训练模型时,所有参数都是.requires_grad = True,如果我们从头开始训练或微调,这种设置就...
location=Nonetorch_model.load_state_dict(model_zoo.load_url(model_url,map_location=map_location)) 1.2.1.3 缺点 单进程、效率慢 不支持多机情况 不支持模型并行 1.2.1.4 注意事项 此处的batch_size 应该是每个GPU的batch_size的总和 1.2.2 方式二:torch.nn.parallel.DistributedDataParallel(推荐)...
for name, w in model.named_parameters(): print(name)print(model.parameters) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. embedding.weightconvs.0.weightconvs.0.biasconvs.1.weightconvs.1.biasconvs.2.weightconvs.2.biasfc.weightfc.bias<bound method ...
(weights_dict.keys()): if "head" in k: del weights_dict[k] print(model.load_state_dict(weights_dict, strict=False)) if args.freeze_layers: for name, para in model.named_parameters(): #除head外,其他权重全部冻结 if "head" not in name: para.requires_grad_(False) else: print("...
import osepochs = 5if (os.path.isfile('pretrained/MNIST_net.t7')): print ('Loading model') model.load_state_dict(torch.load('pretrained/MNIST_net.t7', map_location=lambda storage, loc: storage)) acc, loss = test(model, 1, criterion, test_loader)else: print ('Training model') ...
for name, w in model.named_parameters(): if exclude not in name: # 对于embedding,保留预训练的embedding if 'weight' in name: if method == 'xavier': nn.init.xavier_normal_(w) elif method == 'kaiming': nn.init.kaiming_normal_(w) ...