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 = []
for i in range(8):
train_labels += [i] *100
train_labels = np.array(train_labels)
train_labels = to_categorical(train_labels).astype("float32")
sample_1 = [random.random() for i in range(len(train_data))]
for i in range(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：
def my_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)) return loss
以上这篇在Pytorch中使用样本权重(sample_weight)的正确方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。
