Very basic neural network module that I made in Python using NumPy. It allows you to create neural networks and train them with a simple API, inspired by TensorFlow's Keras.
I tried to keep a clean and extendable project structure, allowing for new additions of layers to be added easily. I have currently implemented dense layers, multiple activation functions, multiple loss functions, and a network class to bring them all together.
This project was mostly made with the goal of getting a basic understanding how neural networks and deep learning works, and just to say I did it from scratch.
Currently Implemented:
- Fully Connect / Dense Layer (a layer in a neural network that connects each neuron in a layer to every neuron in the previous layer)
- Many common activation functions (Sigmoid, Tanh, ReLU, Softmax, etc)
- Common loss functions (MSE, Categorical Cross Entropy, Binary Cross Entropy)
- A network class that allows for easy usages of implemented layers, and allows for easy training with back propagation and easy usage with forward propagation.
- Storing network by saving and loading it from file
TODO:
- Convolutional, maxpool, and dropout layers for convolutional neural networks (CNNs)
Handwritten Digit Classifier ml_projects/mnist
This is a demo of a model trained to classify handwritten digits from the MNIST dataset. It correctly labels 96.45% of the test data, which I am pretty happy with. I made the drawing GUI with Tkinter and all additional plots/graphs with Matplotlib. This was the first real data I attempted to train my network with.
Character Predictions ml_projects/charter-continue
This is a demo of the model trained to predict the next character. It was based on a variety of text files I found on the internet, including the Shrek and Bee Movie script, the entirety of the Bible and Harry Potter, all Shakespeare's works, and some other random stuff I found. To interact (as shown in the demo) you start typing out some characters, and it keeps on guessing what will come next until it guesses a new line character (\n) which terminates the message. I am pretty happy it even generates words but it is definitely overfit on the data.
