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Update README.md and demo.py to be truly reflective
... of the project's state and intent. * All of the pieces are there, but they still aren't connected * That's a poor show of engineering quality
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README.md

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A Stephen Fox endeavor to become an Applied AI Scientist.
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## Background Resources
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### Key Ideas
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1. Make it simple to refine neural architectures
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2. Focus on dropping model parameter size while __keeping performance as high as possible__
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3. Make the tools user-friendly, and clearly documented
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### Project Roadmap
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- Please see [the GitHub Project board](https://github.com/stephenjfox/Morph.py/projects/1)
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---
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## What is Morph.py?
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Morph.py is a Neural Network Architecture Optimization toolkit targeted at Deep Learning researchers
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and practitioners.
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* It acts outside of the current paradigm of [Neural Architecture Search](https://github.com/D-X-Y/awesome-NAS)
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while still proving effective
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* It helps one model accuracy of a model with respect to its size (as measured by "count of model parameters")
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* Subsequently, you could be nearly as effective (given some margin of error) with a __much__ smaller
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memory footprint
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* Provides you, the researcher, with [better insight on how to improve your model](https://github.com/stephenjfox/Morph.py/projects/3)
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Please enjoy this [Google Slides presentation](https://goo.gl/ZzZrng)
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Coming soon:
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* A walkthrough of the presentation (more detail than my presenter's notes)
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* More [supported model architectures](https://github.com/stephenjfox/Morph.py/projects/2)
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### Current support
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* Dynamic adjustment of a given layer's size
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* Weight persistence across layer resizing
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* To preserve all the hard work you spent in
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---
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# Contributing
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## Setup (to work alongside me)
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`git clone https://github.com/stephenjfox/Morph.py.git`
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## Requisites
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### Requisites
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### [Install Anaconda](https://www.anaconda.com/download/)
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#### [Install Anaconda](https://www.anaconda.com/download/)
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* They've made it easier with the years. If you haven't already, please give it a try
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### Install Pip
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#### Install Pip
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1. `conda install pip`
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2. Proceed as normal
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- Jupyter Notebook
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* And a few tools to make it better on your local environment like `nb_conda`, `nbconvert`, and `nb_conda_kernels`
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- Python 3.6+ because [Python 2 is dying](https://pythonclock.org/)
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- PyTorch (`conda install torch torchvision`)
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- PyTorch (`conda install torch torchvision -c pytorch`)
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All of these and more are covered in the `environment.yml` file:
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+ Simply run `conda env create -f environment.yml -n <desired environment name>`

demo.py

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# do one pass through the algorithm
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modified = morph.once(my_model)
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print(modified) # proof that the thing wasn't tampered with
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print(modified) # take a peek at the new layers. You take it from here
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my_dataloader = DataLoader(TensorDataset(torch.randn(2, 28, 28)))
2020

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