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Fix custom loss for MXNet backend. Fix bug in Concat layer #110
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roywei
reviewed
May 31, 2018
keras/engine/training.py
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| # If sample_weights shape is like (100, ), we convert it to (100, 1). | ||
| # Because, MXNet treats the shape (100, ) as (100) leading to broadcast operator | ||
| # failures in below operations. | ||
| if K.backend() == 'mxnet' and K.ndim(weights) == 1: |
There was a problem hiding this comment.
we can use weight_ndim already calculated:
if K.backend() == 'mxnet' and weight_ndim== 1:
| @@ -82,7 +82,7 @@ def test_batchnorm_correctness_2d(): | |||
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| @pytest.mark.skipif((K.backend() == 'mxnet'), | |||
| reason='MXNet backend does not allow predict() before compile()') | |||
| reason='MXNet backend uses native BatchNorm operator. Donot do updates in the model.') | |||
| @@ -160,7 +160,7 @@ def test_batchnorm_convnet_no_center_no_scale(): | |||
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| @pytest.mark.skipif((K.backend() == 'mxnet'), | |||
| reason='MXNet backend uses native BatchNorm operator. Do do updates in the model.') | |||
| reason='MXNet backend uses native BatchNorm operator. Donot do updates in the model.') | |||
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@roywei - Fixed your comments |
roywei
approved these changes
May 31, 2018
sandeep-krishnamurthy
added a commit
that referenced
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Jun 15, 2018
* Fix custom loss usage in MXNet backend. Issue - 25 * Fix CR comments * Fix CR comments
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@roywei @kalyanee