https://github.com/hereismari/mnist-tensorflow
Exploring mnist dataset with TensorFlow and getting 99% accuracy in tests
https://github.com/hereismari/mnist-tensorflow
classifying-handwritten-digits convolutional-neural-networks mnist tensorflow tutorial
Last synced: 9 months ago
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Exploring mnist dataset with TensorFlow and getting 99% accuracy in tests
- Host: GitHub
- URL: https://github.com/hereismari/mnist-tensorflow
- Owner: hereismari
- Created: 2017-04-12T14:08:07.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2017-09-08T13:47:05.000Z (over 8 years ago)
- Last Synced: 2025-05-09T01:08:49.553Z (9 months ago)
- Topics: classifying-handwritten-digits, convolutional-neural-networks, mnist, tensorflow, tutorial
- Language: Jupyter Notebook
- Size: 10.8 MB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Exploring MNIST with TensorFlow
There are a lot of awesome tutorials about how to classify handwritten
digits from [MNIST database](http://yann.lecun.com/exdb/mnist/),
so my plan is to put some of these tutorials together,
evolving from a very simple model to a nice model that achieves
99% accuracy in the test data.
[Here](https://github.com/mari-linhares/mnist-android-tensorflow) is an
android app that I did as a final result of this exercise :smile:!
Have fun!
> Note: most of the code presented here is originally from the tutorials in the Reference
section, please follow those for more details
## Getting started
For running the "tutorials" You'll need:
* Python 2 or 3
* Numpy
* [TensorFlow](https://www.tensorflow.org/install/)
* Jupyter Notebook
### Getting the data
The MNIST databased used here was the one available at TensorFlow
([read more about it](https://www.tensorflow.org/get_started/mnist/beginners)),
so you don't have to do much.
You can download the data [here](http://yann.lecun.com/exdb/mnist/), if
you want.
For the examples in this repository we'll use all the available
data and train the models with stochastic gradient descent (except 01-KNN).
## Let's do it!
To follow the tutorial samples, run: `jupyter notebook`.
If you want to check TensorBoard: run `tensorboard --logdir=/tmp/tensorflow_log`
## References
Thank you!
0. [TF and DL without a Phd by Martin Gorner, Google](https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#0)
1. [Classifying Handwritten Digits with TF.Learn, by Josh Gordon](https://www.youtube.com/watch?v=Gj0iyo265bc&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal&index=1)
2. [How to Make a TF Image Classifier, by Siraj Raval](https://github.com/llSourcell/How_to_make_a_tensorflow_image_classifier_LIVE)
3. [TensorFlow Tutorials, by Hvass Labs ](https://github.com/Hvass-Labs/TensorFlow-Tutorials)
4. [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.github.io/)
5. [Getting started with TensorFlow](https://www.tensorflow.org/get_started/get_started)
6. [Not another mnist tutorial with tensorflow](https://www.oreilly.com/learning/not-another-mnist-tutorial-with-tensorflow)
7. [Training convolutional neural network for image classification](http://cv-tricks.com/tensorflow-tutorial/training-convolutional-neural-network-for-image-classification/)