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Merge pull request #96 from berleon/patch-1
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Update mxts only once
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AvantiShri authored Jan 26, 2020
2 parents 667f00b + 9159782 commit d0d5f36
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Showing 4 changed files with 12 additions and 6 deletions.
6 changes: 3 additions & 3 deletions deeplift.egg-info/PKG-INFO
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@@ -1,13 +1,13 @@
Metadata-Version: 2.1
Name: deeplift
Version: 0.6.9.3
Version: 0.6.10.0
Summary: DeepLIFT (Deep Learning Important FeaTures)
Home-page: https://github.com/kundajelab/deeplift
License: UNKNOWN
Description: Algorithms for computing importance scores in deep neural networks.

Implements the methods in "Learning Important Features Through Propagating Activation Differences" by Shrikumar, Greenside & Kundaje, as well as other commonly-used methods such as gradients, guided backprop and integrated gradients. See https://github.com/kundajelab/deeplift for documentation and FAQ.

Platform: UNKNOWN
Provides-Extra: tensorflow
Provides-Extra: tensorflow with gpu
2 changes: 1 addition & 1 deletion deeplift/__init__.py
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@@ -1 +1 @@
__version__ = '0.6.9.3'
__version__ = '0.6.10.0'
8 changes: 7 additions & 1 deletion deeplift/layers/core.py
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Expand Up @@ -69,7 +69,13 @@ def _initialize_mxts(self):

def reset_mxts_updated(self):
for output_layer in self._output_layers:
output_layer.reset_mxts_updated()
# only update layer if needed
# if output_layer was already called by another layer:
# output_layer._mxts_updated == False
# otherwise we call it
if output_layer._mxts_updated:
output_layer.reset_mxts_updated()

self._initialize_mxts()
self._mxts_updated = False
self._mxts_for_inputs_updated = False
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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -8,7 +8,7 @@
Implements the methods in "Learning Important Features Through Propagating Activation Differences" by Shrikumar, Greenside & Kundaje, as well as other commonly-used methods such as gradients, guided backprop and integrated gradients. See https://github.com/kundajelab/deeplift for documentation and FAQ.
""",
url='https://github.com/kundajelab/deeplift',
version='0.6.9.3',
version='0.6.10.0',
packages=['deeplift',
'deeplift.layers', 'deeplift.visualization',
'deeplift.conversion'],
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