神经网络高维互信息计算Python实现（MINE）

论文

Belghazi, Mohamed Ishmael, et al. “Mutual information neural estimation.” International Conference on Machine Learning. 2018.

利用神经网络的梯度下降法可以实现快速高维连续随机变量之间互信息的估计，上述论文提出了Mutual Information Neural Estimator (MINE)。NN在维度和样本量上都是线性可伸缩的，MI的计算可以通过反向传播进行训练。

核心

Python实现

现有github上的代码无法计算和估计高维随机变量，只能计算一维随机变量，下面的代码给出的修改方案能够计算真实和估计高维随机变量的真实互信息。

其中，为了计算理论的真实互信息，我们不直接暴力求解矩阵（耗时，这也是为什么要有MINE的原因），我们采用给定生成随机变量的参数计算理论互信息。

SIGNAL_NOISE = 0.2 SIGNAL_POWER = 3

12	SIGNAL_NOISE = 0.2SIGNAL_POWER = 3

完整代码基于pytorch

Python

代码语言：javascript复制

# Name: MINE_simple
# Author: Reacubeth
# Time: 2020/12/15 18:49
# Mail: noverfitting@gmail.com
# Site: www.omegaxyz.com
# *_*coding:utf-8 *_*

import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
import matplotlib.pyplot as plt


SIGNAL_NOISE = 0.2
SIGNAL_POWER = 3

data_dim = 3
num_instances = 20000


def gen_x(num, dim):
    return np.random.normal(0., np.sqrt(SIGNAL_POWER), [num, dim])


def gen_y(x, num, dim):
    return x   np.random.normal(0., np.sqrt(SIGNAL_NOISE), [num, dim])


def true_mi(power, noise, dim):
    return dim * 0.5 * np.log2(1   power/noise)


mi = true_mi(SIGNAL_POWER, SIGNAL_NOISE, data_dim)
print('True MI:', mi)


hidden_size = 10
n_epoch = 500


class MINE(nn.Module):
    def __init__(self, hidden_size=10):
        super(MINE, self).__init__()
        self.layers = nn.Sequential(nn.Linear(2 * data_dim, hidden_size),
                                    nn.ReLU(),
                                    nn.Linear(hidden_size, 1))

    def forward(self, x, y):
        batch_size = x.size(0)
        tiled_x = torch.cat([x, x, ], dim=0)
        idx = torch.randperm(batch_size)

        shuffled_y = y[idx]
        concat_y = torch.cat([y, shuffled_y], dim=0)
        inputs = torch.cat([tiled_x, concat_y], dim=1)
        logits = self.layers(inputs)

        pred_xy = logits[:batch_size]
        pred_x_y = logits[batch_size:]
        loss = - np.log2(np.exp(1)) * (torch.mean(pred_xy) - torch.log(torch.mean(torch.exp(pred_x_y))))
        # compute loss, you'd better scale exp to bit
        return loss


model = MINE(hidden_size)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
plot_loss = []
all_mi = []
for epoch in tqdm(range(n_epoch)):
    x_sample = gen_x(num_instances, data_dim)
    y_sample = gen_y(x_sample, num_instances, data_dim)

    x_sample = torch.from_numpy(x_sample).float()
    y_sample = torch.from_numpy(y_sample).float()

    loss = model(x_sample, y_sample)

    model.zero_grad()
    loss.backward()
    optimizer.step()
    all_mi.append(-loss.item())


fig, ax = plt.subplots()
ax.plot(range(len(all_mi)), all_mi, label='MINE Estimate')
ax.plot([0, len(all_mi)], [mi, mi], label='True Mutual Information')
ax.set_xlabel('training steps')
ax.legend(loc='best')
plt.show()

结果

变量维度为1