The KNIME Deep Learning - TensorFlow Integration gives easy access to the powerful machine learning library TensorFlow within KNIME (since version 3.6.0). This enables users to execute, build, and train state of the art deep learning models.
The extension contains the following nodes:
The TensorFlow Network Reader node for reading TensorFlow SavedModels.
The TensorFlow Network Writer node for writing TensorFlow SavedModels.
The Keras to TensorFlow Network Converter node for converting Keras models to TensorFlow.
Additionally, the DL Python nodes provided in KNIME Deep Learning can be used to create, edit, execute and train models with user-supplied Python scripts.
Installation instructions for the KNIME Deep Learning - Tensorflow Integration can be found here.
Example workflows and more info can be found on the KNIME Hub.
DL Python Network Executor scripting node outputs wrong numerical values when using Flatbuffers serialization library.
Flatbuffers in KNIME does not support float32 data at the moment. We recommend to use Apache Arrow instead. Install it via: conda install -n py35_knime -c conda-forge pyarrow=0.7.0 , where "py35_knime" is the name of your conda environment. Then select it in KNIME via File > Preferences > KNIME > Python > Serialization library.
This will be fixed in a future version of KNIME.