[GitHub] [incubator-mxnet] vjache edited a comment on issue #17143: Wrong sum of array

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

vjache edited a comment on issue #17143: Wrong sum of array
URL: https://github.com/apache/incubator-mxnet/issues/17143#issuecomment-568257313
 
 
   > Hi @vjache , it is not a bug.
   > The reason is that the default data type is `float64` in numpy, but `float32` in MXNet.
   > 
   > ```python
   > >>> import numpy as np
   > >>> import mxnet.ndarray as md
   > >>> a = [1.0504983e+07, 1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 5.0000000e-01, 1.0000000e+01,
   > ... 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00]
   > >>> np.array(a).dtype, np.array(a).sum()
   > (dtype('float64'), 10504999.5)
   > >>> md.array(a).dtype, md.array(a).sum()
   > (<class 'numpy.float32'>,
   > [10505000.]
   > <NDArray 1 @cpu(0)>)
   > >>> np.array(a, dtype=np.float32).sum()
   > 10505000.0
   > >>> md.array(a, dtype=np.float64).sum()
   > 
   > [10504999.5]
   > <NDArray 1 @cpu(0)>
   > ```
   
   Sorry, I've seen your response only after I posted my where I found the bypass. But still I consider this is a serious thing. If I create an array and the accuracy is lost then I should be at least warned, and at best the error raised. It is incorrect to silently do decreasing of accuracy when I create array.

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