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A tiny Python library to create and train feedforward neural networks

Project description

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# NNWeaver #

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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
cd nnweaver
python3 install

You can also run the test suite with `python3 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`](, and the number of outputs by adding a [`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`]( for the list of available activation functions.

### Train the Neural Network ###

Now, choose a [`Loss`]( function, pass it to an [`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()`]( 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()`]( has a `regularizer` argument, that accepts an instance of the [`L1L2Regularizer`]( class.

### A very, very simple example ###

<img src="" width="550" />

## Documentation ##

For more information, tutorials, and API reference, please visit [NNweaver's online documentation]( or build your own offline copy executing `python3 docs`.

## License ##

This project is licensed under the terms of the MIT License.

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