Icon

Train_​MNIST_​classifier_​Keras_​Nodes

KNIME Deep Learning - Train MNIST classifier with Keras nodes
KNIME Deep Learning - Train MNIST classifier This workflow trains a simple convolutional neural network (CNN) on the MNIST dataset viaKeras.In order to run the example, please make sure you have the following KNIME extensionsinstalled:* 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 furtherinformation. 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 learningapplied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998. MNIST imagesMNIST imagestransform probabilitiesto predicted classes’ labelssimple, untrainedCNNinput: untrained net andtraining images with labelsoutput: trained net- -5 epochs(increase to improve accuracy)openView: Confusion MatrixNode 97Node 98 Preparetraining data Prepare test data Format results Image Viewer DL PythonNetwork Creator Keras NetworkLearner Scorer (JavaScript) Keras KNIME Nodes Keras NetworkExecutor Number To String KNIME Deep Learning - Train MNIST classifier This workflow trains a simple convolutional neural network (CNN) on the MNIST dataset viaKeras.In order to run the example, please make sure you have the following KNIME extensionsinstalled:* 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 furtherinformation. 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 learningapplied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998. MNIST imagesMNIST imagestransform probabilitiesto predicted classes’ labelssimple, untrainedCNNinput: untrained net andtraining images with labelsoutput: trained net- -5 epochs(increase to improve accuracy)openView: Confusion MatrixNode 97Node 98 Preparetraining data Prepare test data Format results Image Viewer DL PythonNetwork Creator Keras NetworkLearner Scorer (JavaScript) Keras KNIME Nodes Keras NetworkExecutor Number To String

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: marcel
Created at: 2017-07-05
On NodePit since: 2019-10-03
Last update: 2024-04-15
Created with KNIME version: v4.0.1
Tags: deep learningKerasimage classification

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!