https://github.com/lucianopalmeida/mnist-number-classification

A simple classification of mnist handwritten digits
https://github.com/lucianopalmeida/mnist-number-classification

coreml-models jupyter-notebook mnist-classification mnist-handwriting-recognition python3 tensorflow

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A simple classification of mnist handwritten digits

• Host: GitHub
• URL: https://github.com/lucianopalmeida/mnist-number-classification
• Owner: LucianoPAlmeida
• Created: 2018-06-03T20:52:41.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-07-29T00:20:01.000Z (over 6 years ago)
• Last Synced: 2024-12-26T13:13:29.722Z (about 2 months ago)
• Topics: coreml-models, jupyter-notebook, mnist-classification, mnist-handwriting-recognition, python3, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 245 MB
• Stars: 2
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# mnist-number-classification

[![license](https://img.shields.io/github/license/mashape/apistatus.svg)](https://opensource.org/licenses/MIT)

This is a Convolutional Neural Network model trained with the [MNIST Dataset](https://en.wikipedia.org/wiki/MNIST_database) of handwritten digits and 

this model was implemented to support the [CoreML-MNIST](https://github.com/LucianoPAlmeida/CoreML-MNIST) Demo Application.

## Tools

* [Tensorflow 1.8](https://www.tensorflow.org/)

* [tf-coreml](https://github.com/tf-coreml/tf-coreml) 

* [coremltools](https://github.com/apple/coremltools)

* [Jupyter Notebook](http://jupyter.org/)

* [Python 3.6](https://www.python.org/)

* [matplotlib](https://matplotlib.org/)

## The Model 

The model was trainned with 70 epochs with a batch size of 512. Achieving 0.984400 of validation accuracy and 0.9861225328947368 of test accuracy.

The [AdamOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer) was used to train this network with a learning rate of 0.00001.

### Architecture 

 * conv2d with filter size 32, strides 5, padding same and relu activation

 * max_pooling2d with pool size of 2 and strides 2

 * conv2d with filter size 64, strides 5, padding same and relu activation

 * max_pooling2d with pool size of 2 and strides 2

 * fully_connected with number of outputs 1024 and relu activation

 * fully_connected with number of outputs 10 and no activation function

 * softmax activation layer

## The CoreML Model

With a trained model and saved .pb file, tf-coreml was used generate a CoreML model. 

The code is available on [coreml_converter.py](https://github.com/LucianoPAlmeida/mnist-number-classification/blob/master/coreml_converter.py)

## Credits and Thanks

- [Udacity](https://www.udacity.com) for their awesome [Deep Learning Nanodegree Foundation](https://www.udacity.com/course/deep-learning-nanodegree--nd101) course.

## Licence 

mnist-number-classification is released under the [MIT License](https://opensource.org/licenses/MIT).