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Train MNIST classifier

Deep learning TensorFlow Image classification

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This workflow trains a simple convolutional neural network (CNN) on the MNIST dataset via TensorFlow. 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) You also need a local Python installation that includes TensorFlow. Please refer to https://www.knime.com/deeplearning/tensorflow for installation recommendations and further information. Acknowledgements: The architecture of the created 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/) [1]. [1] 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
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    KNIME Core Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

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    KNIME Deep Learning - Keras Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

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    KNIME Deep Learning - TensorFlow Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

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    KNIME Image Processing Trusted extension

    University of Konstanz / KNIME

    Version 1.8.1

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