y) # 反向传播和优化 optimizer.zero_grad() loss.backward() optimizer...
命令: python3 -m pip install transformers --index-url https://mirrors.aliyun.com/pypi/simple/ 这里需要注意的:如果是从huggingface.co下载模型,由于国内不能访问,所以建议先配置一下环境变量(国内镜像站点https://hf-mirror.com),export HF_ENDPOINT=https://hf-mirror.com 2.2、生成式模型 以下是一段古...
from ignite.engine import create_supervised_trainer, create_supervised_evaluator epochs = 1000 train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size) trainer = create_supervised_trainer(model, optimizer, F.nll_loss) evaluator = create_supervised_evaluator(model) trainer.run(...
model=CNN().to(device) optimizer=optim.SGD(model.parameters(),lr=lr,momentum=momentum) 1. 2. 3. 4. 5. 4.2网络训练 def train(model,device,train_loader,optimizer,epoch,losses): model.train() for idx,(t_data,t_target) in enumerate(train_loader): t_data,t_target=t_data.to(device),...
optimizer.zero_grad()ifbatch %100==0: loss, current = loss.item(), (batch +1) *len(X)print(f"loss:{loss:>7f}[{current:>5d}/{size:>5d}]") 我们还会检查模型在测试数据集上的表现,以确保它正在学习。 deftest(dataloader, model, loss_fn): ...
optimizer= torch.optim.Adam(net.parameters,lr=0.001), metrics_dict = {"acc":Accuracy} ) fromtorchkerasimportsummary summary(model,input_data=features); # if gpu/mps is available, will auto use it, otherwise cpu will be used. dfhistory=model.fit(train_data=dl_train, ...
1criterion =nn.CrossEntropyLoss()2#分类交叉熵Cross-Entropy 作损失函数3optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)#lr learning rate 学习速率,学习速率0.01比较小的时候比较稳定45forepochinrange(2):#loop over the dataset multiple times,进行2轮的学习训练6running_loss = 0.07for...
x=self.fc2(x)returnF.log_softmax(x)model=Net()ifargs.cuda:model.cuda()# 将所有的模型参数移动到GPU上 optimizer=optim.SGD(model.parameters(),lr=args.lr,momentum=args.momentum)deftrain(epoch):model.train()# 把module设成training模式,对Dropout和BatchNorm有影响forbatch_idx,(data,target)in...
optimizer.step() # 统计该轮训练效果 print(f"第{epoch}轮的训练准确率为{correctSampleNum/totalSampleNum}") epoch += 1 # 保存网络 torch.save(net.state_dict(), './numberSample/trainedCNN.pth') 1. 2. 3. 4. 5. 6. 7. 8. 9. ...
train_loss = train(model, train_loader, optimizer, criterion, device) print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {train_loss:.4f}') 八、 数据预测 8.1预测函数及预测 编写预测函数: def predict(model, dataloader, device): model.eval() ...