Neural Network from scratch in Python exclusively using Numpy.

This project consists of a neural network implementation from scratch. Modules are organized in a way that intends to provide both an understandable implementation of neural networks and a user-friendly API. The project is structured as follows:

• nnn
  • core/: Network main functionalities.
    • neural_network.py: Implementation of the neural network, including functionalities such as building a custom network and training it with backpropagation + stochastic gradient descent.
    • layers.py: Defines layers that can take part in the neural network by describing its behavior at the forward and backward steps.
    • initializers.py: Functions used to initialize network's weights.
    • activations.py: Activation functions that may be used to add nonlinearities to the model.
    • objectives.py: Functions to be optimized by adjusting network's parameters.
  • utils/: Other utilities.
    • plots.py: Module with plotting tools. It contains a function to plot the classification boundaries of a 2d classifier, among others.
    • loss_grid.py: Computation of the loss grid for a given neural network and objective function.
  • examples: Simple instructive examples. See nnn/examples/.
    • a_greater_than_b.py: Example demonstrating that a linearly separable dataset can be classified using a neural network without any hidden layer.
    • a_aprox_b.py: Example demonstrating that a non linearly separable dataset requires at least a hidden layer in order to classify samples correctly.

• Python 3.5
• Pip 9.0.1

Note: Not tested with other python versions.

Once you have met the prerequisites, a single step is required to install this software:

1. Run python setup.py install

This will install numpy (the only required external library to run the neural network) and matplotlib (only needed to plot classifier boundaries when running an example).

There are several functionalities that may be implemented to make this software more useful:

• Other types of layers: LSTM, CNN, embeddings,...
• Batches
• More optimizers other than Stochastic Gradient Descent
• More activations
• More initializers
• More objective functions
• Regularization
• Parallelization