This workflow shows how to edit a TensorFlow model using the TensorFlow Python API by adding an additional output to a model. The loaded model does classification on MNIST but only outputs the probabilities for each class. We edit the model such that it outputs the class as well. In order to run the example, please make sure you have the following KNIME extensions installed: * KNIME Deep Learning - TensorFlow Integration (Labs) * KNIME Image Processing (Community Contributions Trusted) * KNIME Image Processing - Deep Learning Extension (Community Contributions Trusted) Acknowledgements: The architecture of the used network was taken but slightly changed from https://www.tensorflow.org/tutorials/layers. The enclosed pictures are from the MNIST dataset (http://yann.lecun.com/exdb/mnist/) .  Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998.
Used extensions & nodes
Created with KNIME Analytics Platform version 4.1.0
Loading ad hoc executions
By using or downloading the workflow, you agree to our terms and conditions.
Discussions are currently not available, please try again later.