在mxnet中实现对比损失函数

import mxnet as mx
from mxnet import gluon

# 定义对比损失函数
class ContrastiveLoss(gluon.loss.Loss):
    def __init__(self, margin, weight=1, batch_axis=0, **kwargs):
        super(ContrastiveLoss, self).__init__(weight, batch_axis, **kwargs)
        self.margin = margin

    def hybrid_forward(self, F, output1, output2, label):
        euclidean_distance = F.sqrt(F.sum(F.square(output1 - output2), axis=1))
        loss = (1 - label) * 0.5 * F.square(euclidean_distance) + label * 0.5 * F.square(F.maximum(0, self.margin - euclidean_distance))
        return F.mean(loss, axis=self._batch_axis, exclude=True)

# 创建模型和数据
net = gluon.nn.Sequential()
net.add(gluon.nn.Dense(128))
net.initialize()

data1 = mx.nd.random.uniform(shape=(10, 128))
data2 = mx.nd.random.uniform(shape=(10, 128))
label = mx.nd.random.randint(0, 2, shape=(10,))

# 创建对比损失函数实例
loss = ContrastiveLoss(margin=1)

# 计算损失
with mx.autograd.record():
    output1 = net(data1)
    output2 = net(data2)
    l = loss(output1, output2, label)

# 打印损失值
print(l)