The workflow builds, trains, and saves an RNN with an LSTM layer to generate new fictive fairy tales. The brown nodes define the network structure. The "Pre-Processing" metanode reads fairy tales and index-encodes them, and creates semi-overlapping sequences. The Keras Network Learner node trains the network using the index-encoded fairy tales. Finally, the trained network is converted into a TensorFlow model, and saved to a file.




EXAMPLES Server: 04_Analytics/14_Deep_Learning/02_Keras/11_Generate_Fairy_Tales/01_Training04_Analytics/14_Deep_Learning/02_Keras/11_Generate_Fairy_Tales/01_Training*
Download a zip-archive




* Find more about the Examples Server here.
The link will open the workflow directly in KNIME Analytics Platform (requirements: Windows; KNIME Analytics Platform must be installed with the Installer version 3.2.0 or higher). In other cases, please use the link to a zip-archive or open the provided path manually