问题描述
我正在尝试通过将“likelihoods.BernoulliLikelihood”更改为“likelihoods.softmaxLikelihood”来制作基于 notebook 的多类分类器。
但是，我找不到参数 num_features 的合适值。我尝试了不同的值，但都给出了错误。如果您能在这个问题上指导我，我将不胜感激。
代码：
import torch
import gpytorch
from gpytorch.models import AbstractvariationalGP
from gpytorch.variational import CholeskyVariationaldistribution
from gpytorch.variational import VariationalStrategy
from gpytorch.mlls.variational_elbo import VariationalELBO
"""
Data
"""
train_x = torch.linspace(0,1,10)
train_y = torch.tensor([1,-1,1])
num_classes = 3
num_features = 1
"""
Model
"""
class GPClassificationModel(AbstractvariationalGP):
def __init__(self,train_x):
variational_distribution = CholeskyVariationaldistribution(train_x.size(0))
variational_strategy = VariationalStrategy(self,train_x,variational_distribution)
super(GPClassificationModel,self).__init__(variational_strategy)
self.mean_module = gpytorch.means.ConstantMean()
self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())
def forward(self,x):
mean_x = self.mean_module(x)
covar_x = self.covar_module(x)
latent_pred = gpytorch.distributions.Multivariatenormal(mean_x,covar_x)
return latent_pred
# Initialize model and likelihood
model = GPClassificationModel(train_x)
likelihood = gpytorch.likelihoods.softmaxLikelihood(num_features = num_features,num_classes=num_classes)
"""
Train
"""
model.train()
likelihood.train()
optimizer = torch.optim.Adam(model.parameters(),lr=0.1)
# "Loss" for GPs - the marginal log likelihood
# train_y.numel() refers to the amount of training data
mll = VariationalELBO(likelihood,model,train_y.numel())
training_iter = 50
for i in range(training_iter):
# Zero backpropped gradients from prevIoUs iteration
optimizer.zero_grad()
# Get predictive output
output = model(train_x)
# Calc loss and backprop gradients
loss = -mll(output,train_y)
loss.backward()
print('Iter %d/%d - Loss: %.3f' % (i + 1,training_iter,loss.item()))
optimizer.step()
解决方法
暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！
如果你已经找到好的解决方法，欢迎将解决方案带上本链接一起发送给小编。
小编邮箱:dio#foxmail.com (将#修改为@）