Classifiying_​Cats_​and_​Dogs_​Training

0 ×

This workflow reads images of cats and dogs, performs some simple preprocessing, and trains and evaluates a Convolutional Neural Network (CNN) that is able to distinguish cat images from dog images.

If you use this workflow, please cite:
F. Villaroel Ordenes & R. Silipo, “Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications”, Journal of Business Research 137(1):393-410, DOI: 10.1016/j.jbusres.2021.08.036.

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

Nodes

• Keras Batch Normalization Layer4 ×
• Keras Dropout Layer4 ×
• Keras Convolution 2D Layer3 ×
• Keras Max Pooling 2D Layer3 ×
• Keras Dense Layer2 ×
• Partitioning2 ×
• Conda Environment Propagation1 ×
• Keras Flatten Layer1 ×
• Keras Input Layer1 ×
• Keras Network Executor1 ×
• Keras Network Learner1 ×
• Keras Network Writer1 ×
• RowID1 ×
• Rule Engine1 ×
• Scorer1 ×
• Table Reader1 ×
• Table Writer1 ×

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Conda Integration
• FeatureKNIME Deep Learning - Keras Integration
• FeatureKNIME Javasnippet

Links

• No links available