但如果想保存最优模型的参数,则必须要用deepcopy best_state changes with the model during training in pytorch这位提问者想保存最佳模型参数,结果因为浅拷贝,导致保存的都是最后一轮的模型参数,下面是他的错误代码: def train(): #training steps … if acc > best_acc: best_state = model.state_dict() ...
1.1、dict的copy: dict直接赋值是深拷贝。同样使用from copy import deepcopy()也是深拷贝。而使用copy()是浅拷贝。 深拷贝,原始改动副本不变。浅拷贝原始改动副本也改动 j={'c':1,'g':2} p=j.copy() j['c']=5 print("使用.copy()是浅拷贝",'\n',"只是简单讲第一个dict的地址copy过去。所以原始...
下面函数中, scheduler 参数是 torch.optim.lr_scheduler 中的 LR scheduler 对象。 deftrain_model(model,criterion,optimizer,scheduler,num_epochs=25):since=time.time()best_model_wts=copy.deepcopy(model.state_dict())best_acc=0.0forepochinrange(num_epochs):print('Epoch {}/{}'.format(epoch,num_e...
writer.writerow(batchsummary) # deep copy the model if phase == 'Test' and loss < best_loss: best_loss = loss best_model_wts = copy.deepcopy(model.state_dict()) time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format( time_elapsed // 60, ...
def train_model(model, criterion, optimizer, scheduler, num_epochs=25): since = time.time() best_model_wts = copy.deepcopy(model.state_dict()) best_acc = 0.0 for epoch in range(num_epochs): print(f'Epoch {epoch}/{num_epochs - 1}') ...
这个时候的做法是让model_from中的该层维度跟model_to一样,在代码上的体现就是: deftransfer_weights(model_from,model_to):wf=copy.deepcopy(model_from.state_dict())wt=model_to.state_dict()forkinwt.keys():#if (not k in wf)):if((notkinwf)|(k=='fc.weight')|(k=='fc.bias')):wf[k...
# deep copy the model if phase == 'Test' and loss best_loss = loss best_model_wts = copy.deepcopy(model.state_dict()) time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format( time_elapsed // 60, time_elapsed % 60)) ...
torch.save(model.state_dict(), model_save_path)# 在训练过程中保存某个条件下的最优模型,可以如下操作best_model_state = deepcopy(model.state_dict()) torch.save(best_model_state, model_save_path)# 下面这种方法是错误的,因为best_model_state只是model.state_dict()的引用,会随着训练的改变而改变be...
# deep copy the model if phase == 'Test' and loss < best_loss: best_loss = loss best_model_wts = copy.deepcopy(model.state_dict()) time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format( ...
先说结论,model.state_dict()是浅拷贝,返回的参数仍然会随着网络的训练而变化。应该使用deepcopy(model.state_dict()),或将参数及时序列化到硬盘。 再讲故事,前几天在做一个模型的交叉验证训练时,通过model.state_dict()保存了每一组交叉验证模型的参数,后根据效果选择准确率最佳的模型load回去,结果每一次都是最...