jmerkow commented on issue #14421: Updating mxnet from 1.0.0, networks give different outputs URL: https://github.com/apache/incubator-mxnet/issues/14421#issuecomment-494088153 Doing some more analysis, it looks like there are some differences at the convolution layer described above, but the changes are relatively minor. However, there is a global pooling layer at the end of the network which seems to have a VERY VERY large difference. I'm using the following to calculate error: ``` for n in layers: x, y = output[n], mx140_outputs[n] print(n, np.mean([np.abs(((xi-yi)/(xi+1e-10)).sum()) for xi, yi in zip(x,y)])) ``` the error values are all less than 0.1, except after the global pooling layer i get `global_avgpool_output 8.13365e+11` Was there some change to global pooling that would cause this? ```{u'attr': {u'global_pool': u'True', u'kernel': u'(8, 8)', u'pad': u'(1, 1)', u'pool_type': u'avg'}, u'inputs': [[1229, 0, 0]], u'name': u'global_avgpool', u'op': u'Pooling'}```
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