About add the feature variances

[yuyijie1995]

I am trying to add the learnable feature variances in the python code,but I found that the acc will decreased a lot.
The add strategy as below:
1.I reshape the var,mean as the (1,num_classes,feature_dim),reshape the input_feature as (batchsize,1,feature_dim)
2. using the input_feature subtract the mean and divise the var get a tensor with shape ((batchsize,num_classes,feature_dim)).
3.using the tensor batch_dot the mean and get the tensor with shape (batchsize,num_classes,num_classes)
4.Then I take the value of the diagonal of the two or three dimensions and get the final tensor as the margin_distance.
BUT the acc result is terrible,can you give me some advice?

[yuyijie1995]

This is my code:
reshape_var=F.reshape(var,(1,-1,self._feature_dim))
reshape_mean=F.reshape(mean,(1,-1,self._feature_dim))
expand_data=F.expand_dims(x,1)
t=expand_data-reshape_mean#(8,10,2)
m_distance=F.batch_dot(t/(reshape_var+1e-8),t,transpose_b=True)
index=F.array([i for i in range(self._num_class)])
distance=m_distance[:,index,index]
ALPHA = F.one_hot(y, self._num_class, on_value=self._alpha, dtype='float32')
K = ALPHA + F.ones((N, self._num_class), dtype='float32')
logits_with_margin = distance * K
batch_mean = F.take(mean, y)
likelihood_reg_loss = self._lamda * (F.sum(F.square(x - batch_mean)) / 2.0) * (1. / N)