This workflow trains a simple convolutional neural network (CNN) on the MNIST dataset via Keras. It then adds the 'serving' tag to the network and uploads it to S3 as saved model.
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 Image Processing (Community Contributions Trusted)
* KNIME Image Processing - Deep Learning Extension (Community Contributions Trusted)
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.
Acknowledgements:
The architecture of the created network was taken from https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py [1].
The enclosed pictures are from the MNIST dataset (http://yann.lecun.com/exdb/mnist/) [2].
[1] Chollet, Francois and others. Keras. https://github.com/fchollet/keras. 2015.
[2] 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.
Workflow
Create MNIST Model For TensorFlow Serving
Used extensions & nodes
Created with KNIME Analytics Platform version 4.3.2
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