Building CNN Predictor for cats and dogs images - Deep Learning
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Classifying Images of Cats and Dogs - Training
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.
Reference:
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
- Rule Engine3 ×
- Keras Convolution 2D Layer2 ×
- Keras Dense Layer2 ×
- Keras Max Pooling 2D Layer2 ×
- Column Filter1 ×
- Conda Environment Propagation1 ×
- Image Calculator1 ×
- Image Reader (Table)1 ×
- Image Resizer1 ×
- Keras Dropout Layer1 ×
- Keras Flatten Layer1 ×
- Keras Input Layer1 ×
- Keras Network Executor1 ×
- Keras Network Learner1 ×
- Keras Network Writer1 ×
- List Files/Folders1 ×
- Partitioning1 ×
- Path to String1 ×
- Row Sampling1 ×
- Scorer1 ×
- Sorter1 ×
Extensions
- FeatureKNIME Base nodes
- FeatureKNIME Conda Integration
- FeatureKNIME Deep Learning - Keras Integration
- FeatureKNIME Image Processing
- FeatureKNIME Javasnippet
Links
- No links available