08-16-2017 12:54 PM
I just tried it and got the following error: the script could not be parsed. No module named keras.callbacks
I was trying to run the s&p 500 regression example. When I test the keras extension in preferences, I get:
Keras is not installed.
None of the backends CNTK, TensorFlow or Theano are installed
Graphviz is not installed
Pydot is not installed
08-17-2017 04:08 AM
So far, so good, with a few hiccups! Some reflections.
Running RapidMiner 7.6 / Ubuntu 16.04, Keras with Tensorflow as the back end
I have started with the most complex example and then went down to the simplest. In all cases, I have added the Performance operator at the end and in s&p 500 I have also added Filter Example to remove missing values generated in the prediction.
Example: s&p 500 (regression)
Example: Iris (classification)
Example: Boston housing prices (regression)
You have beatten me to this extension as I've been working through a similar interface to Keras but stopped at Python interfaces Note that my earlier comments about cleaning Python processes and GPU memory is something that I really struggled with, so this is a really clean job!
08-17-2017 04:17 AM
@lbookman You need to first install Python / Anaconda and then Tensorflow (or Theano or CNTK) and Keras on top, in the process you may need to install quite a few different libraries as well, depending on if you are running it on Windows or Linux. Graphviz and Pydot are needed to plot the Keras model (the Graph option for the Keras model). RapidMiner will not install any of these for you, I am afraid.
08-17-2017 08:58 AM
I tried to install it using Anaconda and Windows and didn't have success. When I was testing it internally a month ago I had to install it on Windows without using Anaconda and got it to work. I think I'm going to try it on a Ubuntu VM and see how it goes.
08-18-2017 06:12 AM
08-22-2017 07:59 AM - edited 08-22-2017 07:59 AM
Well, this is a great news!!
(To RapidMiner team) Hopefully in the future they will implement extensions for TensorFlow, Theano, Statsmodel, and Scikit-Learn. It would also be nice to build extensions for R statistical packages.