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