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一个forward()函数,通过步进调试进入查看具体是怎么...
# forward:前向传播 inputs,labels = data outputs = net(inputs) # backward:反向传播,更新模型参数 optimizer.zero_grad() loss = loss_function(outputs,labels) loss.backward() # 更新模型参数 optimizer.step() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. inputs,labels,outputs都是一个batchsize的数...
首先说明下,doc string里写的是NLL Loss,但实际调用的是CE Loss,这是因为在Pytorch实现中,CE Loss实质上就是将Log-Softmax操作和NLL Loss封装在了一起,如果直接使用NLL Loss,那么需要先对预测结果作Log-Softmax操作,而使用CELoss则直接免去了这一步。 loss_labels(i) 其次,要理解红框部分的_get_src_permutat...
首先说明下,doc string里写的是NLL Loss,但实际调用的是CE Loss,这是因为在Pytorch实现中,CE Loss实质上就是将Log-Softmax操作和NLL Loss封装在了一起,如果直接使用NLL Loss,那么需要先对预测结果作Log-Softmax操作,而使用CELoss则直接免去了这一步。 loss_labels(i) 其次,要理解红框部分的_get_src_permutat...
labels=torch.randn(100,1) # 存储Loss值 losses=[] # 迭代训练 forepochinrange(100): # 清零梯度 optimizer.zero_grad() # 前向传播 outputs=model(inputs) # 计算损失 loss=criterion(outputs, labels) # 反向传播 loss.backward() # 更新模型参数 optimizer.step() # 存储Loss值 losses.append(loss....
if activation_function == None : outputs = Wx_plus_b else:无锡人流费用 http://www.xasgfk120.com/ outputs = activation_function(Wx_plus_b) tf.summary.histogram(layer_name+"/outputs",outputs) return outputs def compute_accuracy(x_data,y_data): ...
Soft labels (soft_label = True) $$loss_j=loss_j\sum_{i}\left(weight[label_i]*logits_i\right)$$ 还原 2.1 如果 reduction 标签是none 直接返回原始结果 2.2 如果reduction 标签是 sum 返回前一个结果的和 $$loss=\sum_{j}loss_j$$ 2.3 如果reduction标签是mean , 它将根据weight 参数进行处理,...
As far as I understand subset accuracy needs the explicit classes {0, 1}, but your model outputs probabilities, how did you choose the threshold to binarize the labels? Have you tried usingLRAPto evaluate your model? Sorry, something went wrong. ...
This outputs: <class 'mmdet.models.losses.smooth_l1_loss.L1Loss'> None Did you make any modifications on the code or config? Did you understand what you have modified? This is raw minified example. Environment sys.platform: linux Python: 3.11.2 (main, Mar 13 2023, 12:18:29) [GCC ...
function def test_step(texts, labels): predictions = model(texts) t_loss = loss_object(labels, predictions) test_loss(t_loss) test_accuracy(labels, predictions) # 定义好上述的function后,就可以开始进行相应的train和evaluate了 EPOCHS = 10 for epoch in range(EPOCHS): # 在每一个epoch开始时,...