Some popular activation and loss functions are implemented. After from NeuralNetwork import NeuralNetwork as NN you can access them with NN.AFunc.func_name or NN.LFunc.func_name . Use d_func_name for derivative and func_name_pair for tuple (func_name, d_func_name) .

In /examples directory there are examples of using the neural network on some popular datasets that are stored in /datasets .

git clone https://github.com/michal-wegrzyn/neural_network_using_numpy.git
cd neural_network_using_numpy
pip install -r requirements.txt
cd examples
python mnist.py

The dataset may be too small to effectively train the neural network. In DatasetAugmentation.py there are functions that can help you augment it.

Use shift function to create more data by shifting e.g. images of digits from the MNIST dataset by a few pixels in different directions.

You can also use flip function e.g. for the Fashion MNIST dataset to expand it with symmetrical (flipped) images.