The workflow builds, trains, and saves an RNN with an LSTM layer to generate new fictive mountain names. The brown nodes define the network structure. The "Pre-Processing" metanode reads original mountain names and index-encodes them. The Keras Network Learner node trains the network using index-encoded original mountain names. Finally, the trained network is prepared for deployment, transformed into a TensorFlow model, and saved to a file.
In order to run the example, please make sure you have the following KNIME extensions installed:
* KNIME Deep Learning - Keras Integration (Labs)
* KNIME Deep Learning - TensorFlow Integration
* KNIME Python Integration
You also need a local Python installation that includes Keras. Please refer to https://www.knime.com/deeplearning#keras for installation recommendations and further information.
Used extensions & nodes
Created with KNIME Analytics Platform version 4.3.1
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KNIME Base nodes
KNIME AG, Zurich, Switzerland
Version 4.3.1
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KNIME Deep Learning - Keras Integration
KNIME AG, Zurich, Switzerland
Version 4.3.1
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KNIME Deep Learning - TensorFlow Integration
KNIME AG, Zurich, Switzerland
Version 4.3.0
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KNIME Javasnippet
KNIME AG, Zurich, Switzerland
Version 4.3.0
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KNIME Math Expression (JEP)
KNIME AG, Zurich, Switzerland
Version 4.3.0
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KNIME Textprocessing
KNIME AG, Zurich, Switzerland
Version 4.3.0
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