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Transfer learning for CNN Predictor for cats and dogs images - Deep Learning (freeze layer)

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Classifying Images of Cats and Dog with Transfer Learning - Training

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.

URL: Dataset Kaggle https://www.kaggle.com/c/dogs-vs-cats/data

Define network architecture Read and preprocess images Train, execute, and evalutate network IMPORTANT!To execute the workflow you needto download the training datasetfrom Kaggle and update the pathin the List Files / Folders Nodeinside this metanode. The pathshould point to the unzipped folderincluding the images.https://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. Read pretrained VGG16Only train 3 new layers3 neuronsSoftmaxDrop Rate = 0.564 neuronsReLUFlatten output to 8192 neuronsTrain the modelfor 5 epochs (Adam)Apply the modelon new dataoutput >= 0.5 Catoutput <0.5 DogEvaluate the modelAccuracy (90%)Unify RowIDsResize to 150x150List of 4000imagesNormalize between 0..1Read imagesFilter superfluouscolumns DL PythonNetwork Creator Keras Freeze Layers Keras Dense Layer Keras Dropout Layer Keras Dense Layer Keras Flatten Layer Keras NetworkLearner Keras NetworkExecutor Rule Engine Scorer RowID Image Resizer Path to images Image Calculator Image Reader(Table) Column Filter Partitioning Conda EnvironmentPropagation Define network architecture Read and preprocess images Train, execute, and evalutate network IMPORTANT!To execute the workflow you needto download the training datasetfrom Kaggle and update the pathin the List Files / Folders Nodeinside this metanode. The pathshould point to the unzipped folderincluding the images.https://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. Read pretrained VGG16Only train 3 new layers3 neuronsSoftmaxDrop Rate = 0.564 neuronsReLUFlatten output to 8192 neuronsTrain the modelfor 5 epochs (Adam)Apply the modelon new dataoutput >= 0.5 Catoutput <0.5 DogEvaluate the modelAccuracy (90%)Unify RowIDsResize to 150x150List of 4000imagesNormalize between 0..1Read imagesFilter superfluouscolumns DL PythonNetwork Creator Keras Freeze Layers Keras Dense Layer Keras Dropout Layer Keras Dense Layer Keras Flatten Layer Keras NetworkLearner Keras NetworkExecutor Rule Engine Scorer RowID Image Resizer Path to images Image Calculator Image Reader(Table) Column Filter Partitioning Conda EnvironmentPropagation

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Created by: jon
Created at: 2017-10-29
On NodePit since: 2023-11-30
Last update: 2024-04-27
Created with KNIME version: v5.2.0
Tags: deep learningKerasimage classificationcatdog

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