Hyperopt for a Keras CNN on CIFAR-100
A tutorial and demo using Hyperopt to auto-optimize CNN architecture and hyperparameters for the CIFAR-100 dataset with Keras/TensorFlow.
What is Hyperopt for a Keras CNN on CIFAR-100?
Hyperopt-Keras-CNN-CIFAR-100 is a tutorial and demonstration project that automates neural architecture search and hyperparameter optimization for convolutional neural networks on the CIFAR-100 dataset. It uses Hyperopt with Keras and TensorFlow to meta-optimize both network architecture and training parameters, solving the problem of manual tuning in deep learning projects.
Machine learning practitioners and researchers working on computer vision tasks who want to automate hyperparameter tuning and neural architecture search using Keras/TensorFlow.
Developers choose this project because it provides a complete, working example of automated optimization that goes beyond basic hyperparameter tuning to include architectural decisions, with clear code that can be adapted to other datasets and tasks.
Overview
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
Use Cases
Best For
- Learning Hyperopt integration with Keras and TensorFlow
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