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Building CNN from scratch with Keras Layer nodes

KNIME Deep Learning - Train MNIST classifier 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#kerasfor 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 documentrecognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998. train for 3 epochswith AdamMNIST imagesMNIST imagestransform probabilitiesto predicted classes’ labelsopenView: Confusion MatrixNode 195Node 196Node 197Node 198Node 199Node 200Node 210 Keras NetworkLearner Preparetraining data Prepare test data Format results Scorer Image Viewer Keras Input Layer Keras Max Pooling2D Layer Keras Flatten Layer Keras Dropout Layer Keras Dropout Layer Keras Convolution2D Layer Keras Convolution2D Layer Keras Dense Layer Keras Dense Layer Keras NetworkExecutor KNIME Deep Learning - Train MNIST classifier 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#kerasfor 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 documentrecognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998. train for 3 epochswith AdamMNIST imagesMNIST imagestransform probabilitiesto predicted classes’ labelsopenView: Confusion MatrixNode 195Node 196Node 197Node 198Node 199Node 200Node 210Keras NetworkLearner Preparetraining data Prepare test data Format results Scorer Image Viewer Keras Input Layer Keras Max Pooling2D Layer Keras Flatten Layer Keras Dropout Layer Keras Dropout Layer Keras Convolution2D Layer Keras Convolution2D Layer Keras Dense Layer Keras Dense Layer Keras NetworkExecutor

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Created by: helfrich
Created at: 2019-01-28
On NodePit since: 2019-10-03
Last update: 2024-11-21
Created with KNIME version: v4.0.0

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