This a fully functional feedforward neural network library. The implemented features are:

• loss functions: cross entropy, mean squared error
• layers: linear, sigmoid, ReLU
• network with forward and backpropagation
• function to one hot encode labels
• confussion matrix visualiser

There are two demos to demonstrate capabilities of the library:

• iris dataset classifier
• handwritten digits (mnist) classifier

There are lots of comments in the code explaining the details

• python 3.x
• numpy
• matplotlib

To install required dependencies: make install.

To run the demo: python3 iris_demo.py.

The demo is based on the iris dataset, the dataset can be found in dataset/iris/. It consists of 150 entries.

The accuracy obtained on the validation set is: 98.7%.

In the demo data is shuffled split into train and validation datasets, the netowork is trained and the performance is displayed as a confusion matrix:

To run the demo: python3 digits_mnist_demo.py.

This demo is based on mnist digits dataset, the dataset can be found in dataset/digits_mnist/. It consists of 60000 train images and 10000 test images.

The accuracy obtained on the test set is: 97.8%.

Example network architecture:

Example confusion matrix: