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Transfer learning for CNN Predictor for cats and dogs images - Deep Learning (fast execution)

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Classifying Images of Cats and Dog with Transfer Learning - Training (fast execution)

This workflow reads images of cats and dogs, performs some simple preprocessing, and uses Transfer Learning (VGG16) to train the 3 top layers and evaluate a Convolutional Neural Network (CNN) that is able to distinguish cat images from dog images (fast execution).

Read and apply feature extraction part of VGG16 Train, execute, and evalutate network Read and preprocess images Read and preprocess imageshttps://www.kaggle.com/c/dogs-vs-cats/data Classifying Images of Cats and Dogs - TrainingThis workflow reads images of cats and dogs, performs some image preprocessing, and uses Transfer Learning (VGG16) to train the 3 top layers and evaluate a Convolutional Neural Network(CNN) for image classification (fast execution). Read pretrained VGG16Apply VGG16on training dataFlatten output to 8192 neuronsInput: 819264 neuronsReLUDrop Rate = 0.53 neuronsSoftmaxTrain the model for 5 epochs (Adam)Apply the modelon new dataEvaluate the modelAccuracy (91%)output >= 0.5 Catoutput <0.5 DogApply the VGG16on test dataPoin to local folderwith Kaggle image train dataabout cats and dogs DL PythonNetwork Creator Keras NetworkExecutor Keras Flatten Layer Keras Input Layer Keras Dense Layer Keras Dropout Layer Keras Dense Layer Keras NetworkLearner Keras NetworkExecutor Scorer Rule Engine Keras NetworkExecutor Read andpreprocess images Conda EnvironmentPropagation Read and apply feature extraction part of VGG16 Train, execute, and evalutate network Read and preprocess images Read and preprocess imageshttps://www.kaggle.com/c/dogs-vs-cats/data Classifying Images of Cats and Dogs - TrainingThis workflow reads images of cats and dogs, performs some image preprocessing, and uses Transfer Learning (VGG16) to train the 3 top layers and evaluate a Convolutional Neural Network(CNN) for image classification (fast execution). Read pretrained VGG16Apply VGG16on training dataFlatten output to 8192 neuronsInput: 819264 neuronsReLUDrop Rate = 0.53 neuronsSoftmaxTrain the model for 5 epochs (Adam)Apply the modelon new dataEvaluate the modelAccuracy (91%)output >= 0.5 Catoutput <0.5 DogApply the VGG16on test dataPoin to local folderwith Kaggle image train dataabout cats and dogs DL PythonNetwork Creator Keras NetworkExecutor Keras Flatten Layer Keras Input Layer Keras Dense Layer Keras Dropout Layer Keras Dense Layer Keras NetworkLearner Keras NetworkExecutor Scorer Rule Engine Keras NetworkExecutor Read andpreprocess images Conda EnvironmentPropagation

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Created by: roberto.cadili
Created at: 2022-01-26
On NodePit since: 2023-11-30
Last update: 2024-12-17
Created with KNIME version: v5.2.0

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