CIFAR-10 TensorFlow ResNet
This program performs image classification on the CIFAR-10 dataset. It uses a ResNet with identity mappings, similar to the one described by Kaiming He et. al. in Deep Residual Learning for Image Recognition and Identity Mappings in Deep Residual Networks. In order to facilitate automated tuning and experimentation, all settings and hyperparameters are defined in params.py.
- Download the python version of the CIFAR-10 dataset.
- Extract cifar-10-batches-py
- Edit params.py so that DATA_DIR points to the directory containing cifar-10-batches-py.
- Run train.py.
Python 3.6, TensorFlow 1.5, and NumPy should be installed before running this program.
- Sean Soleyman - seansoleyman.com
Copyright (c) 2018 Sean Soleyman
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- Aurelien Geron, Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Kaiming He et. al., Deep Residual Learning for Image Recognition
- Kaiming He et. al., Identity Mappings in Deep Residual Networks
- Alex Krizhevsky, Learning Multiple Layers of Features from Tiny Images