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shape: 150, 150, 3filters: 32kernel size: 3x3activation: ReLUpool size: 2x2units: 64kernel size: 3x3activation: ReLUpool size: 2x2units: 64activation: ReLUdropout: 0.5units: 1activation: sigmoidTrain the model for 10 epochs (Adam) withloss function binary crossentropyApply the modelon new dataoutput >= 0.5 Caoutput < 0.5 DogEvaluate the modelaccuracyResize to 150x150Path to training imagesNormalize between 0..1Read imagesRemove superfluous columnsSave modelSet up condaenvironmentEncode classeswith 0 and 1Node 348Keras Input Layer Keras Convolution2D Layer Keras Max Pooling2D Layer Keras Convolution2D Layer Keras Max Pooling2D Layer Keras Flatten Layer Keras Dense Layer Keras Dropout Layer Keras Dense Layer Keras NetworkLearner Keras NetworkExecutor Rule Engine Scorer Image Resizer Paths to images Image Calculator Image Reader(Table) Column Filter Partitioning Keras NetworkWriter Conda EnvironmentPropagation Rule Engine Visualize results Read Images shape: 150, 150, 3filters: 32kernel size: 3x3activation: ReLUpool size: 2x2units: 64kernel size: 3x3activation: ReLUpool size: 2x2units: 64activation: ReLUdropout: 0.5units: 1activation: sigmoidTrain the model for 10 epochs (Adam) withloss function binary crossentropyApply the modelon new dataoutput >= 0.5 Caoutput < 0.5 DogEvaluate the modelaccuracyResize to 150x150Path to training imagesNormalize between 0..1Read imagesRemove superfluous columnsSave modelSet up condaenvironmentEncode classeswith 0 and 1Node 348Keras Input Layer Keras Convolution2D Layer Keras Max Pooling2D Layer Keras Convolution2D Layer Keras Max Pooling2D Layer Keras Flatten Layer Keras Dense Layer Keras Dropout Layer Keras Dense Layer Keras NetworkLearner Keras NetworkExecutor Rule Engine Scorer Image Resizer Paths to images Image Calculator Image Reader(Table) Column Filter Partitioning Keras NetworkWriter Conda EnvironmentPropagation Rule Engine Visualize results Read Images

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Created by: Rohit
Created at: 2023-02-23
On NodePit since: 2023-02-23
Last update: 2024-12-21
Created with KNIME version: v4.7.1

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