https://github.com/atulapra/learnkeras
Learn keras with two simple examples
https://github.com/atulapra/learnkeras
cifar-10 deep-learning keras keras-models keras-tutorials pima-indians-dataset
Last synced: about 1 month ago
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Learn keras with two simple examples
- Host: GitHub
- URL: https://github.com/atulapra/learnkeras
- Owner: atulapra
- Created: 2017-12-24T08:04:54.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-05-12T15:54:41.000Z (about 8 years ago)
- Last Synced: 2025-10-10T01:11:18.482Z (8 months ago)
- Topics: cifar-10, deep-learning, keras, keras-models, keras-tutorials, pima-indians-dataset
- Language: Python
- Homepage:
- Size: 69.3 KB
- Stars: 3
- Watchers: 0
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# LearnKeras
This repository contains some code for you to get started with keras with a few simple datasets.
## Compatibility
* This code runs on Python 3.5 and Keras 2.0.4 and has been tested on Ubuntu 16.04.
## Intro
* The folder `Intro` contains the keras implementation for the analysis of the pima-indians-diabetes dataset.
* It involves the prediction of a binary output variable using 8 input variables.
* The code `learnkeras1.py` steps you through the training process and also shows you how to save your model checkpoint.
* The code `learnkeras2.py` shows you how to load the above trained model and find accuracy.
## CIFAR-10
* The folder `CIFAR-10` contains the code for classifying images into 10 categories of the famous CIFAR-10 dataset.
* I have used a simple model architecture for easy understanding of code.
* The code can be run in train or test mode.
* To run in train mode, type `python cifar10.py --mode train`. This trains the model and saves the checkpoint in the folder. It will also make predictions on test data and print the accuracy.
* To run in test mode, type `python cifar10.py --mode test`. This will output the test accuracy using weights from the checkpoint saved.
* Download the checkpoint for 10 epochs [here.](https://drive.google.com/open?id=1NsZDL8dKEwSFRdQ1nfAisRbXqe-46Lmo)
