This workflow trains a simple convolutional neural network (CNN) on the MNIST dataset via Keras.
In order to run the example, please make sure you have the following KNIME extensions installed:
* KNIME Deep Learning - Keras Integration (Labs)
* 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.
In order to run the example, please make sure you have the following KNIME extensions installed:
* KNIME Deep Learning - Keras Integration (Labs)
* 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.