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https://issues.apache.org/jira/browse/MADLIB-1450?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Frank McQuillan updated MADLIB-1450:
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Priority: Minor (was: Major)
> Remove dependency on keras
> --------------------------
>
> Key: MADLIB-1450
> URL: https://issues.apache.org/jira/browse/MADLIB-1450
> Project: Apache MADlib
> Issue Type: Improvement
> Components: Deep Learning
> Reporter: Domino Valdano
> Priority: Minor
> Fix For: v1.18.0
>
>
> Presently, we require that users install both standalone "multi-backend"
> keras and tensorflow python modules in order to use the DL features of madlib.
> But there is no good reason for the keras requirement, as keras has come
> included with tensorflow since 2017, starting with version 1.4.0:
> [https://github.com/tensorflow/tensorflow/tree/v1.4.0/tensorflow/python/keras]
> In fact, the multi-backend version of keras is now a dead project.
> As it says on [https://github.com/keras-team/keras]:
> *Multi-backend Keras has been discontinued. At this time, we recommend that
> Keras users who use multi-backend Keras with the TensorFlow backend switch to
> {{tf.keras}}*
> Keras 2.2.5 was the last release to support backends other than tensorflow,
> now it is just maintained as a part of the tensorflow module.
> Because madlib 1.17.0 does not import tensorflow directly, users still must
> have the old multi-backend keras module installed in order to use our DL
> features.
> In order to fix this, dropping the keras dependency, in 1.18.0 we should
> switch from using:
>
> {code:python}
> import keras
> from keras.backend import tf
> {code}
>
> to:
>
> {code:python}
> import tensorflow as tf
> from tensorflow import keras
> {code}
>
> We can continue using both tf and keras as before, without touching anything
> but the import statements.
> Another motivation for this change is that keras 2.2.5 no longer includes
> keras.backend.tf, so our current method of importing only works up through
> keras 2.2.4.
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