用mmd迁移样本pytorch

MMD（最大均值差异）是用于度量两个概率分布之间距离的一种方法。在深度学习中，我们可以使用MMD来比较两个数据集之间的差异。如果我们想在PyTorch中使用MMD，可以通过以下步骤实现：

1. 安装必要的库

!pip install torch
!pip install numpy
!pip install scipy

2. 导入库

import torch
import numpy as np
from scipy.spatial.distance import cdist

3. 定义计算高斯核的函数

def gaussian_kernel(source, target, kernel_mul=2.0, kernel_num=5, fix_sigma=None):
    '''
    计算MMD中的高斯核
    :param source: 源数据
    :param target: 目标数据
    :param kernel_mul: 高斯核初始值
    :param kernel_num: 高斯核个数
    :param fix_sigma: 是否固定高斯核值
    :return:
    '''
    n_samples = int(source.size()[0])+int(target.size()[0])
    total = torch.cat([source, target], dim=0)
    total0 = total.unsqueeze(0).expand(
        int(total.size(0)), int(total.size(0)), int(total.size(1)))
    total1 = total.unsqueeze(1).expand(
        int(total.size(0)), int(total.size(0)), int(total.size(1)))
    L2_distance = ((total0-total1)**2).sum(2)
    if fix_sigma:
        bandwidth = fix_sigma
    else:
        bandwidth = torch.sum(L2_distance.detach()) / (n_samples**2-n_samples)
    bandwidth /= kernel_mul**(kernel_num//2)
    bandwidth_list = [bandwidth * (kernel_mul**i) for i in range(kernel_num)]
    kernel_val = [torch.exp(-L2_distance / bandwidth_temp) for bandwidth_temp in bandwidth_list]
    return sum(kernel_val)#/len(kernel_val)

4. 定义计算MMD的函数

def MMD(source, target, kernel_mul=2.0, kernel_num=5, fix_sigma=None):
    '''
    计算最大均值差异
    :param source: 源数据
    :param target: 目标数据
    :param kernel_mul: 高斯核初始值
    :param kernel_num: 高斯核个数
    :param fix_sigma: 是否固定高斯核值
    :return:
    '''
    batch_size = int(source.size()[0])
    kernels = gaussian_kernel(source, target,
                              kernel_mul=kernel_mul, kernel_num=kernel_num, fix_sigma=fix_sigma)
    loss = 0
    for i in range(batch_size):
        s1, s2 = i, (i+1) % batch_size
        t1, t2 = s1+batch_size, s2+batch_size
        loss += kernels[s1, s2]+kernels[t1, t2]
        loss -= kernels[s1, t2]+kernels[s2, t1]
    return loss/(batch_size*2)

5. 加载数据集并计算MMD

# 加载数据集
source_data = np.load('source_data.npy')
target_data = np.load('target_data.npy')

# 转为Tensor
source_data = torch.tensor(source_data).float()
target_data = torch.tensor(target_data).float()

# 计算MMD
mmd_loss = MMD(source_data, target_data)

这就是在PyTorch中使用MMD的基本步骤。需要注意的是，在实际应用中，我们需要根据具体情况来设置高斯核参数和核数等超参数。