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02_​Train_​simple_​CNN

Train simple CNN
KNIME Deep Learning - Classify Cats and Dogs In this series of workflows we want to demonstrate how to solve an image classification problem using KNIME Deep Learning - Keras Integration.We want to train a model to distinguish cats and dogs on images. Please note: The workflow series is heavily inspired by the great blog-post of François Chollet (see https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html.)1. Workflow 01 Preprocess image data2. Workflow 02 Train simple CNN: In this workflow we create a simple Convolutional Neural Network using the Keras Layer nodes. We train this networkon our augmented image data using the Keras Network Learner and finally apply it to the test data using the Keras Network Executor.3. Workflow 03 Fine-tune ResNet50In 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)* KNIME Image Processing - Python Extension (Community Contributions Trusted)* KNIME Streaming Execution (Labs)You also need a local Python installation that includes Keras. Please refer to https://www.knime.com/deeplearning#keras for installation recommendationsand further information. Read training dataUnify RowIDsTrain the modelApply the modelon new dataRead testing dataTurn probabilitiesto class labelsNode 293 Table Reader RowID Simple CNN Keras NetworkLearner Keras NetworkExecutor Table Reader Create One-hotVector Format NetworkOutput Scorer KNIME Deep Learning - Classify Cats and Dogs In this series of workflows we want to demonstrate how to solve an image classification problem using KNIME Deep Learning - Keras Integration.We want to train a model to distinguish cats and dogs on images. Please note: The workflow series is heavily inspired by the great blog-post of François Chollet (see https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html.)1. Workflow 01 Preprocess image data2. Workflow 02 Train simple CNN: In this workflow we create a simple Convolutional Neural Network using the Keras Layer nodes. We train this networkon our augmented image data using the Keras Network Learner and finally apply it to the test data using the Keras Network Executor.3. Workflow 03 Fine-tune ResNet50In 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)* KNIME Image Processing - Python Extension (Community Contributions Trusted)* KNIME Streaming Execution (Labs)You also need a local Python installation that includes Keras. Please refer to https://www.knime.com/deeplearning#keras for installation recommendationsand further information. Read training dataUnify RowIDsTrain the modelApply the modelon new dataRead testing dataTurn probabilitiesto class labelsNode 293 Table Reader RowID Simple CNN Keras NetworkLearner Keras NetworkExecutor Table Reader Create One-hotVector Format NetworkOutput Scorer

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Created by: jon
Created at: 2017-10-28
On NodePit since: 2020-05-14
Last update: 2024-04-25
Created with KNIME version: v4.2.0
Tags: deep learningKerasimage classificationcatdog

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