在Pytorch中使用样本权重(sample_weight)的正确方法
step:
1.将标签转换为one-hot形式。
2.将每一个one-hot标签中的1改为预设样本权重的值
即可在Pytorch中使用样本权重。
eg:
对于单个样本:loss=-Q*log(P),如下:
P=[0.1,0.2,0.4,0.3] Q=[0,0,1,0] loss=-Q*np.log(P)
增加样本权重则为loss=-Q*log(P)*sample_weight
P=[0.1,0.2,0.4,0.3] Q=[0,0,sample_weight,0] loss_samle_weight=-Q*np.log(P)
在pytorch中示例程序
train_data=np.load(open('train_data.npy','rb')) train_labels=[] foriinrange(8): train_labels+=[i]*100 train_labels=np.array(train_labels) train_labels=to_categorical(train_labels).astype("float32") sample_1=[random.random()foriinrange(len(train_data))] foriinrange(len(train_data)): floor=i/100 train_labels[i][floor]=sample_1[i] train_data=torch.from_numpy(train_data) train_labels=torch.from_numpy(train_labels) dataset=dataf.TensorDataset(train_data,train_labels) trainloader=dataf.DataLoader(dataset,batch_size=batch_size,shuffle=True)
对应one-target的多分类交叉熵损失函数如下:
defmy_loss(outputs,targets): output2=outputs-torch.max(outputs,1,True)[0] P=torch.exp(output2)/torch.sum(torch.exp(output2),1,True)+1e-10 loss=-torch.mean(targets*torch.log(P)) returnloss
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