Package: nnweaver
<p align="center"><img src="https://gvinciguerra.github.io/nnweaver/_static/logo.svg"></p># NNWeaver #[![Coverage Status](https://coveralls.io/repos/github/gvinciguerra/nnweaver/badge.svg?branch=master)](https://coveralls.io/github/gvinciguerra/nnweaver?branch=master)[![Build Status](https://travis-ci.org/gvinciguerra/nnweaver.svg?branch=master)](https://travis-ci.org/gvinciguerra/nnweaver)NNWeaver is a *tiny* Python library to create and train feedforward neural networks. We developed this library as a project for a Machine Learning course.Some of its features are:1. Simple API, easy to learn.2. Validation functions included.3. Lightweight and with few dependencies.4. Live loss/epoch curve display.## Installation ##You can install NNWeaver from the GitHub source with the following commands:git clone https://github.com/gvinciguerra/nnweaver.gitcd nnweaverpython3 setup.py installYou can also run the test suite with `python3 setup.py test`.## Getting started ##### Specify a Neural Network Topology ###You can create a feedforward neural network specifying the number of inputs as the argument of [`NN`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.nn.NN), and the number of outputs by adding a [`Layer`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.nn.Layer):from nnweaver import *nn = NN(3)nn.add_layer(Layer(5, Linear))You can always add more layers, specify an activation function and a weights initializer, as the following lines of code show:nn.add_layer(Layer(7, Sigmoid))nn.add_layer(Layer(6, Rectifier, uniform(0, 0.05)))nn.add_layer(Layer(42, TanH, glorot_uniform()))See [`activations`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#module-nnweaver.activations) for the list of available activation functions.### Train the Neural Network ###Now, choose a [`Loss`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.losses.Loss) function, pass it to an [`Optimizer`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.optimizers.Optimizer) (like the stochastic gradient descent) and start the training:sgd = SGD(MSE)sgd.train(nn, x, y, learning_rate=0.3)There are other arguments to pass to the [`SGD.train()`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.optimizers.SGD.train) method, for example:sgd.train(nn, x_train, y_train,learning_rate_time_based(0.25, 0.001),batch_size=10, epochs=100, momentum=0.85)Also, you may want to control the model complexity. [`SGD.train()`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.optimizers.SGD.train) has a `regularizer` argument, that accepts an instance of the [`L1L2Regularizer`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.regularizers.L1L2Regularizer) class.### A very, very simple example ###<img src="https://github.com/gvinciguerra/nnweaver/blob/gh-pages/_images/nnweaver.gif?raw=true" width="550" />## Documentation ##For more information, tutorials, and API reference, please visit [NNweaver's online documentation](https://gvinciguerra.github.io/nnweaver/index.html) or build your own offline copy executing `python3 setup.py docs`.## License ##This project is licensed under the terms of the MIT License.