Jordan RNN implementation

Implementation of Jordan neural network.

It uses numpy for matrix multiplication. Gradient descent algorithm, forward propagation and dataset loader are created without usage of Deep Learning algorithms. Supports only CPU computing.

Getting Started

To download project:

git clone https://github.com/Vadbeg/jordan_nn.git

Installing

To install all libraries you need, print in autoencoder directory:

pip install -r requirements.txt

It will install all essential libraries

Config

To use project with start_training.py you need to setup config. Config is located in config.py Config class. Example:

class Config:
    learning_rate = 0.0003
    momentum = 0.1
    num_epochs = 200_000

    min_error = 0.05
    dataset = 'fibonacci'

    num_of_precalculated_values = 5
    num_of_input_elements = 1

    num_of_hidden_neurons = 7

Usage

After libraries installation you can ran training and evaluation for different sequences. All settings for it are settled in config dictionaries separately for every sequence. Script will produce learning plots for each of the sequences:

python test.py

Or you can run start_training.py script for sequence you want:

python start_training.py

Sequences are listed below:

• fibonacci
• periodical function (1, 0, -1, 0, 1, 0, -1, ...)
• factorial
• exponential

Built With

• numpy - The math framework used.

Authors

• Vadim Titko aka Vadbeg - GitHub | LinkedIn