https://bergstrom.org/tags/cnn/CNN - Tag - Jorgen BergstromHugo -- gohugo.ioen-usJorgen BergstromMon, 14 Sep 2026 00:00:00 -0500https://bergstrom.org/posts/elastomer_thermoplastic_classifier/Mon, 14 Sep 2026 00:00:00 -0500Jorgen Bergstromhttps://bergstrom.org/posts/elastomer_thermoplastic_classifier/Introduction A common way to learn how Convolutional Neural Networks (CNNs) work is to develop a tool that can classify whether an image is a cat or a dog (in this case all images are either of a cat or of a dog, no other options are allowed). I created an example that does just that. Since my background is in polymer mechanics, I wanted a more interesting version of this classification task: given an image of a stress-strain curve, decide whether it comes from a rubber material or a thermoplastic.https://bergstrom.org/posts/cnn_cats_vs_dogs/Sat, 07 Dec 2024 00:00:00 -0500Jorgen Bergstromhttps://bergstrom.org/posts/cnn_cats_vs_dogs/Introduction In this example I will use the Kaggle cats and dogs dataset to train a CNN to classify if an image is a cat or a dog. The code below reads all images from a local PetImages folder and splits them into a training and a validation set. Before training, the images are preprocessed with simple data augmentation: random horizontal flips and small rotations are applied to make the model more robust, and the pixel values are rescaled to the range [0, 1].