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03_​Fine-tune_​VGG16_​Python

Workflow

Fine-tune VGG16 (Python)
Instead of creating our own network architecture as in the previous workflow "Train simple CNN", in this workflow we use the pre-trained network architecture VGG16.
deep learning Keras image classification
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 Preprocessing2. Workflow 02 Trains simple CNN3. Workflow 03 Fine-tune VGG16 Python: Instead of creating our own network architecture as in the previous workflow "Train simple CNN", in this workflow we use the pre-trained network architecture VGG16. (https://keras.io/applications/#vgg16, released by VGG (http://www.robots.ox.ac.uk/~vgg/research/very_deep/) at Oxford under the CreativeCommons Attribution License (https://creativecommons.org/licenses/by/4.0/).4. Workflow 04 Fine-tune VGG16In 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)You also need a local Python installation that includes Keras. Please refer to https://www.knime.com/deeplearning#keras for installation recommendations and furtherinformation. Replace layers.Fine-tune last convolutional.Training DataApply trainedmodel on test data.After 20 Epochs>= 0.5 is dogDownload network from Keras.Alternatively, you can also use theDL Keras Network Readerand load any other stored network. DL PythonNetwork Editor DL PythonNetwork Learner Table Reader DL Python NetworkExecutor Joiner Scorer Shuffle Partitioning Rule Engine Column Filter RowID DL PythonNetwork Creator Category To Number 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 Preprocessing2. Workflow 02 Trains simple CNN3. Workflow 03 Fine-tune VGG16 Python: Instead of creating our own network architecture as in the previous workflow "Train simple CNN", in this workflow we use the pre-trained network architecture VGG16. (https://keras.io/applications/#vgg16, released by VGG (http://www.robots.ox.ac.uk/~vgg/research/very_deep/) at Oxford under the CreativeCommons Attribution License (https://creativecommons.org/licenses/by/4.0/).4. Workflow 04 Fine-tune VGG16In 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)You also need a local Python installation that includes Keras. Please refer to https://www.knime.com/deeplearning#keras for installation recommendations and furtherinformation. Replace layers.Fine-tune last convolutional.Training DataApply trainedmodel on test data.After 20 Epochs>= 0.5 is dogDownload network from Keras.Alternatively, you can also use theDL Keras Network Readerand load any other stored network.DL PythonNetwork Editor DL PythonNetwork Learner Table Reader DL Python NetworkExecutor Joiner Scorer Shuffle Partitioning Rule Engine Column Filter RowID DL PythonNetwork Creator Category To Number

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Nodes

03_​Fine-tune_​VGG16_​Python consists of the following 15 nodes(s):

Plugins

03_​Fine-tune_​VGG16_​Python contains nodes provided by the following 3 plugin(s):