Keras is a high level neural network API, supporting popular deep learning libraries like Tensorflow, Microsoft Cognitive Toolkit, and Theano.
The RapidMiner Keras extension provides a set of operators that allow an easy visual configuration of Deep Learning network structures and layers. Calculations are pushed into the Python-based backend libraries, so you can leverage the computing power of GPUs and grid environments.
The extension makes use of an existing Keras installation. This article shows how to do a simple deployment of Keras and how to configure the Keras extension to connect to it.
Let's review several options:
Anaconda on MacOS
Warning: As of version 1.2, TensorFlow no longer provides GPU support on macOS.
Create a new environment by typing in command line: conda create –n keras
Activate the created environment by typing in the command line: source activate keras
Install pandas by typing in the command line: conda install pandas
Install scikit-learn by typing in the command line: conda install scikit-learn
Install keras by typing in the command line: conda install -c conda-forge keras
Install graphviz by typing in the command line: conda install –c anaconda graphviz
Install pydotplus by typing in the commandline conda install –c conda-forge pydotplus
In RapidMiner Studio Keras and Python Scripting panels in preferences, specify the path to your new conda environment Python executable.
You’re good to go!
Anaconda on Windows
Warning: Due to issues with package dependencies, it is not currently possible to install graphviz and pydot in a conda environment on Windows, and consequently to visualise the model graph in the results panel.