[GitHub] [incubator-mxnet] anirudhacharya commented on issue #14301: SoftmaxOutput crashes with normalization "valid"

[GitBox <gi...@apache.org>]

anirudhacharya commented on issue #14301: SoftmaxOutput crashes with normalization "valid"
URL: https://github.com/apache/incubator-mxnet/issues/14301#issuecomment-469422711
 
 
   I can also confirm this issue, it happens only when `normalization-"valid"` and while executing the `Executor.backward` function call. For instance this sample code works fine - 
   
   ```
   import mxnet as mx                                                                                                                                            
   import numpy as np
   
   xpu = mx.cpu()
   x_nd = mx.nd.array([[1, 6, 4, 2],[1, 6, 4, 2]], ctx=xpu)    
   grad_x = mx.nd.zeros((2,4), ctx=xpu)    
   label_nd = mx.nd.array([1,1], ctx=xpu)
   
   x_nd.attach_grad()
   
   with mx.autograd.record():
       y = mx.nd.SoftmaxOutput(data=x_nd, label=label_nd, ignore_label=0, use_ignore=True) #, normalization="valid")
   
   y.backward()
   print(x_nd.grad)
   ```
   
   So the bug is with the gradient calculation of softmax output when `normalization="valid"`

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