This is a simple implementation of a neural network to recognize handwritten digits from the MNIST dataset. The neural network is implemented in Python using the NumPy and SciPy libraries.
Run the main.py file to train and test the neural network:
python main.py
By default, the neural network is trained for 5 epochs with a learning rate of 0.1. You can change these parameters by editing the corresponding variables in the main.py file.
The neural network has an input layer of 784 nodes (corresponding to the 28x28 pixel images in the MNIST dataset), a hidden layer of 100 nodes, and an output layer of 10 nodes (corresponding to the digits 0-9).
The activation function used in the hidden and output layers is the sigmoid function, implemented using the SciPy expit function.
After training the neural network on the MNIST dataset, it achieves an accuracy of around 95% on the test set.
During testing, the neural network outputs its guess for each digit, along with the confidence of the guess and whether it was correct or not.