选择’sum’时即为L1 loss; loss_func = torch.nn.L1Loss() input = torch.autograd.Variable(torch.randn(3,4)) target = torch.autograd.Variable(torch.randn(3,4)) loss = loss_func(input, target) print(input); print(targe
loss_functoin = nn.CrossEntropyLoss() #①处 1. 先通过nn.CrossEntropyLoss构建损失函数赋给loss_function,紧接着在训练过程中通过 loss = loss_functoin(outputs, labels) #②处 1. 进行计算其损失函数,输入神经网络模型的输出outputs的值和标签进行loss。 在①②处设置断点,step into①处时,进入loss.py...
loss_functoin = nn.CrossEntropyLoss() 1. 就构建了一个loss_function,从loss_function的构建过程中知道,nn.CrossEntropyLoss是一个Module。Debug程序到: loss = loss_functoin(outputs, labels) 1. 因为loss_functoin()是一个Module,所以输入outputs和labels,其实就是执行一个forward(),一个模型模块必须有...
'''代码示例'''loss_func=torch.nn.NLLLoss(weight=None,reduction='mean')# note:# weight同上,如公式中的w代表各个类在损失中占有的权重,即类的重要程度,若不赋予权重w,则各类同等重要,上述公式中的w[class]去掉。 # reduction同上。 nn.CrossEntropyLoss (交叉熵损失):如上述二进制交叉熵所示,随着预测的...
loss_function_sum = nn.L1Loss(reduction="sum") loss_none = loss_function_none(input, target) loss_mean = loss_function_mean(input, target) loss_sum = loss_function_sum(input, target) print("\nloss_none\n", loss_none) print("\nloss_mean\n", loss_mean) ...
for images, target in tqdm(train_dataloader): images, target = images.to(device), target.to(device) images.requires_grad=True optimizer.zero_grad() output = model(images) loss = loss_func(output, target) loss.backward() optimizer.step() if os.path.exists('grad_c...
loss/=len_data metric/=len_data return loss, metric 4.最后,定义train_val函数用于评估模型性能: python 复制代码 def train_val(epochs, model, loss_func, opt, train_dl, val_dl): for epoch in range(epochs): model.train() train_loss, train_metric=loss_epoch(model,loss_func,train_dl,opt...
选择’sum’时即为L1 loss; loss_func = torch.nn.L1Loss() input = torch.autograd.Variable(torch.randn(3,4)) target = torch.autograd.Variable(torch.randn(3,4)) loss = loss_func(input, target) print(input); print(targ...
loss = loss_func(output, target) loss.backward() optimizer.step() if os.path.exists('grad_checkpoints/') is False: os.mkdir('grad_checkpoints') torch.save(model.state_dict(), 'grad_checkpoints/epoch_'+str(epoch)+'.pt') #Test the model on validation data. ...
batch_loss = loss_func(y_pred_batch, y_true_batch, reduction='mean')# 返回的 loss 为 一个 batch 的平均损失total_loss += batch_loss.item()# total lost 为所有 batch 的平均损失的和,而不是整个训练集的平均损失print('Sum of batch loss:', total_loss)# 每一 batch 的损失函数设置 reducti...