Posted in Python onAugust 17, 2019
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三水点靠木。
在Pytorch中使用样本权重(sample_weight)的正确方法
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