在Pytorch中估计混合高斯模型

ggazkfy8 于 12个月前发布在其他

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实际上，我想估计一个以高斯混合模型为基础分布的归一化流，所以我有点坚持使用torch。然而，你可以在我的代码中重现我的错误，只需在torch中估计高斯混合模型。我的代码如下：

import numpy as np
import matplotlib.pyplot as plt
import sklearn.datasets as datasets

import torch
from torch import nn
from torch import optim
import torch.distributions as D

num_layers = 8
weights = torch.ones(8,requires_grad=True).to(device)
means = torch.tensor(np.random.randn(8,2),requires_grad=True).to(device)#torch.randn(8,2,requires_grad=True).to(device)
stdevs = torch.tensor(np.abs(np.random.randn(8,2)),requires_grad=True).to(device)
mix = D.Categorical(weights)
comp = D.Independent(D.Normal(means,stdevs), 1)
gmm = D.MixtureSameFamily(mix, comp)

num_iter = 10001#30001
num_iter2 = 200001
loss_max1 = 100
for i in range(num_iter):
    x = torch.randn(5000,2)#this can be an arbitrary x samples
    loss2 = -gmm.log_prob(x).mean()#-densityflow.log_prob(inputs=x).mean()
    optimizer1.zero_grad()
    loss2.backward()
    optimizer1.step()

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我得到的错误是：

0
8.089411823514835
Traceback (most recent call last):

  File "/home/cameron/AnacondaProjects/gmm.py", line 183, in <module>
    loss2.backward()

  File "/home/cameron/anaconda3/envs/torch/lib/python3.7/site-packages/torch/tensor.py", line 221, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)

  File "/home/cameron/anaconda3/envs/torch/lib/python3.7/site-packages/torch/autograd/__init__.py", line 132, in backward
    allow_unreachable=True)  # allow_unreachable flag

RuntimeError: Trying to backward through the graph a second time, but the saved intermediate results have already been freed. Specify retain_graph=True when calling backward the first time.

型
之后，如您所见，模型运行了1次迭代。

pytorch

来源：https://stackoverflow.com/questions/65755730/estimating-mixture-of-gaussian-models-in-pytorch

1条答案

按热度按时间

ar7v8xwq1#

代码中存在排序问题，因为您在训练循环之外创建了高斯混合模型，因此在计算损失时，高斯混合模型将尝试使用您在定义模型时设置的参数的初始值，但optimizer1.step()已经修改了该值，因此即使您设置loss2.backward(retain_graph=True)，仍然会出现错误：RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation
这个问题的解决方案是只要在更新参数时创建新的高斯混合模型，示例代码按预期运行：

import numpy as np
import matplotlib.pyplot as plt
import sklearn.datasets as datasets

import torch
from torch import nn
from torch import optim
import torch.distributions as D

num_layers = 8
weights = torch.ones(8,requires_grad=True)
means = torch.tensor(np.random.randn(8,2),requires_grad=True)
stdevs = torch.tensor(np.abs(np.random.randn(8,2)),requires_grad=True)

parameters = [weights, means, stdevs]
optimizer1 = optim.SGD(parameters, lr=0.001, momentum=0.9)

num_iter = 10001
for i in range(num_iter):
    mix = D.Categorical(weights)
    comp = D.Independent(D.Normal(means,stdevs), 1)
    gmm = D.MixtureSameFamily(mix, comp)

    optimizer1.zero_grad()
    x = torch.randn(5000,2)#this can be an arbitrary x samples
    loss2 = -gmm.log_prob(x).mean()#-densityflow.log_prob(inputs=x).mean()
    loss2.backward()
    optimizer1.step()

    print(i, loss2)

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在Pytorch中估计混合高斯模型

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