[GitHub] [incubator-singa] dcslin commented on issue #540: added softmax with axis

2019-10-09 Thread GitBox
dcslin commented on issue #540: added softmax with axis
URL: https://github.com/apache/incubator-singa/pull/540#issuecomment-539904174
 
 
   Hi @joddiy, thank you for the review. kindly find the updated version which 
fixed the mentioned issue.


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[GitHub] [incubator-singa] dcslin commented on issue #540: added softmax with axis

2019-10-08 Thread GitBox
dcslin commented on issue #540: added softmax with axis
URL: https://github.com/apache/incubator-singa/pull/540#issuecomment-539628035
 
 
   > > > still has some problems, the output of multiple dimension inputs is 
not correct.
   > > > please check:
   > > > ```
   > > > x_0 = np.array([[0, 1, 2, 3], [1, 10001, 10002, 
10003]]).astype(np.float32)
   > > > # axis is 1
   > > > # expected output [[0.0320586, 0.08714432, 0.23688284, 0.64391428],
   > > > # [0.0320586, 0.08714432, 0.23688284, 0.64391428]]
   > > > ```
   > > 
   > > 
   > > Hi @joddiy , this is updated, could you please help to review?
   > 
   > Hi shicong, the axis and output are almost correct. However, the result 
will overflow if the input values are too big. For example, for input of [0, 1, 
2, 3], the result is correct. But for input of 1, 10001, 10002, 10003], the 
result will be [nan, nan, nan, nan].
   > 
   > Please use this formation:
   > 
   > ```
   > def softmax(x):
   > """Compute softmax values for each sets of scores in x."""
   > e_x = np.exp(x - np.max(x))
   > return e_x / e_x.sum()
   > ```
   > 
   > **not directly use x as input, instead subtracting the max value of x from 
x to avoid the overflow.**
   
   Hi @joddiy , thank you for the comment. I am still looking into this issue, 
as `max()` is not currently implemented in backend yet.


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[GitHub] [incubator-singa] dcslin commented on issue #540: added softmax with axis

2019-10-07 Thread GitBox
dcslin commented on issue #540: added softmax with axis
URL: https://github.com/apache/incubator-singa/pull/540#issuecomment-538955169
 
 
   > still has some problems, the output of multiple dimension inputs is not 
correct.
   > please check:
   > 
   > ```
   > x_0 = np.array([[0, 1, 2, 3], [1, 10001, 10002, 
10003]]).astype(np.float32)
   > # axis is 1
   > # expected output [[0.0320586, 0.08714432, 0.23688284, 0.64391428],
   > # [0.0320586, 0.08714432, 0.23688284, 0.64391428]]
   > ```
   
   Hi @joddiy , this is updated, could you please help to review?


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