https://github.com/andresberejnoi/netbuilder

Create fully connected neural networks that can be trained in just a few lines of code
https://github.com/andresberejnoi/netbuilder

educational-project feedforward-neural-network machine-learning netbuilder neural-network neuralnetwork numpy python python3

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Create fully connected neural networks that can be trained in just a few lines of code

• Host: GitHub
• URL: https://github.com/andresberejnoi/netbuilder
• Owner: andresberejnoi
• License: mit
• Created: 2016-02-20T15:33:13.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2022-05-05T00:02:59.000Z (over 2 years ago)
• Last Synced: 2024-11-12T22:09:44.719Z (3 days ago)
• Topics: educational-project, feedforward-neural-network, machine-learning, netbuilder, neural-network, neuralnetwork, numpy, python, python3
• Language: Python
• Homepage:
• Size: 404 KB
• Stars: 2
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
#          NetBuilder

The neural network class is in NeuralNet.py.

It allows to easily create fully connected feedforward networks of

any size allowed by available memory. It uses numpy arrays as the primary

data structure for the weight matrices.

With this package you can create deep neural networks very quickly.

## Installation

Package can be installed with pip:

```sh

pip install netbuilder

```

## How to Use

The project's documentation was built using Sphinx and stored at readthedocs.org but for some reason it stopped working there. I need to rebuild it and that will happen when I find the time. In the meantime, the same documentation is spread throughout the source code as docstrings. I will include a small piece below:

To use the package, it has to be imported first like:

```

  >>> import netbuilder

  or

  >>> import netbuilder as nb

  With the following lines, you can create a neural network for

  a binary gate:

  >>> net = nb.Network()

  >>> net.init(topology=[2,1])

  The  first line above will create a `Network` object. The parameters

  of the network are not defined yet. The second line tells it to initialize

  weights for a shape of two input nodes for the first layer and one output

  node at the final layer.

  To create hidden layers, just add them to the topology parameter when

  initializing the network:

  >>> net = nb.Network()

  >>> net.init(topology=[2,5,5,1])

  The above lines will create a `Network` object with 4 layers: one input layer

  with 2 nodes, two hidden layers with 5 nodes each, and an output layer with

  one node.

  To perform a feedforward propagation an input array is needed. If the array

  is a numpy array with shape [number of samples x number of features], then

  the `feedfoward` method can be used:

  >>> x = numpy.array([[0,1]])

  >>> net.feedforward(x)

  array([[ 0.82683518]])

  Note above that the following format for x will cause an error because the

  shape is (,2) when it should be (1,2):

  >>> x = numpy.array([0,1])

  The method `predict` is available for quick testing without worry about the

  format of the input array:

  >>> x = [0,1]

  >>> net.predict(x)

  array([[ 0.82683518]])

```

And documentation built with Sphinx can be found at (not working for now):

http://netbuilder.readthedocs.io/en/latest/