Dataloader传入数据(这个数据包括:训练数据和标签),batchsize(代表的是将数据分成batch=[len(train_ids[0])除以batchsize],每一份包括的数据是batchsize) 三. 对enumerate的理解: enumerate返回值有两个:一个是序号,也就是在这里的batch地址,一个是数据train_ids for i, data in enumerate(train_loader,1): ...
for i in range(num_epochs):total_loss = 0for bidx, (x,_) in enumerate(tqdm(train_loader, desc=f"Epoch {i+1}/{num_epochs}")):x = x.cuda()x = F.pad(x, (2,2,2,2))t = torch.randint(0,num_time_steps,(batch...
def train(epoch): model.train() running_loss = 0.0 # enumerate计算下标从0开始 for batch_idx, data in enumerate(train_loader, 0): # 得到数据 images, labels = data # 前向传播 outputs = model(images) #计算损失函数 loss = criterion(outputs, labels) # 反向传播 #优化器梯度清零 optimizer.z...
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=...) model = ... model = nn.DataParallel(model.to(device), device_ids=gpus, output_device=gpus[0]) optimizer = optim.SGD(model.parameters) forepochinrange(100): forbatch_idx, (data, target)inenumerate(train_loader):...
tensorinenumerate(data_loader): data, target = tensor# Specify features and labels in a tensoroptimizer.zero_grad()# Reset optimizer stateout = model(data)# Pass the data through the networkloss = loss_criteria(out, target)# Calculate the losstrain_loss += loss.item()# Keep a running to...
local_model.train() pid = os.getpid()forbatch_idx, (data, target)inenumerate(train_loader): optimizer.zero_grad() output = local_model(data.to(device)) loss = F.nll_loss(output, target.to(device)) loss.backward() optimizer.step()ifbatch_idx % log_interval ==0:print('{}\tTrain ...
train_sampler.set_epoch(epoch)forbatch_idx, (data, target)inenumerate(train_loader): data=data.cuda() target=target.cuda() ... output=model(images) loss=criterion(output, target) ... optimizer.zero_grad() loss.backward() optimizer.step()if__name__=="__main__": ...
print("The model will be running on", device,"device")# Convert model parameters and buffers to CPU or Cudamodel.to(device)forepochinrange(num_epochs):# loop over the dataset multiple timesrunning_loss =0.0running_acc =0.0fori, (images, labels)inenumerate(train_loader,0):# get the ...
for batch_idx, (data, target) in enumerate(train_loader): images = images.cuda(non_blocking=True) target = target.cuda(non_blocking=True) ... output = model(images) loss = criterion(output, target) ... optimizer.zero_grad() loss.backward() ...
def train(epoch): for i, data in enumerate(train_loader): # 一个batch inputs, labels, inputs_path = data inputs = inputs.unsqueeze(1) labels = labels.unsqueeze(1) # 将这些数据转换成Variable类型 inputs, labels = Variable(inputs), Variable(labels) ...