https://github.com/nirum/deep-learning-demos
:city_sunset: toying around with deep learning
https://github.com/nirum/deep-learning-demos
Last synced: 5 days ago
JSON representation
:city_sunset: toying around with deep learning
- Host: GitHub
- URL: https://github.com/nirum/deep-learning-demos
- Owner: nirum
- Created: 2016-01-30T00:10:24.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2017-05-19T09:01:19.000Z (about 9 years ago)
- Last Synced: 2025-02-25T00:51:23.050Z (over 1 year ago)
- Language: Python
- Size: 15.6 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
### deep-learning-demos
demos of neural networks, in python
#### walkthrough
The `toynn.py` script is the main script, it generates data from a toy classification problem and trains a multilayered neural network to solve it.
The network is built using the code in `layers.py`, which contains two classes: a `Network` class which contains a stack of `Layer`s. Each layer implements one of the layers in the network, and keeps track of the parameters of that layer. the `Network` connects these layers together so that you can compute forward or backward passes through the network.
The `Layer` class lets you specify the size (# of input and output dimensions) for each layer. The nonlinearity is assumed to be a sigmoid (see the function in `utils.py`) and the parameters are optimized using [adam](http://arxiv.org/abs/1412.6980).
The toy classification problem consists of trying to classify points where the decision boundary is given by the 2D boundary of a norm ball.
#### setup
```bash
$ git clone https://github.com/nirum/deep-learning-demos
$ cd deep-learning-demos
$ pip install -r requirements.txt
```
#### requirements
- numpy
- matplotlib
- [tqdm](https://github.com/tqdm/tqdm)
- [toolz](https://github.com/pytoolz/toolz)
- [descent](https://github.com/nirum/descent)