ANN - Artifical Neural Network

General ANN with many settings In main MNIST Digit Recognition is implemented, but ANN can be used for many purposes There are not convolutional layers implementation, MNIST Digit recognition got 97.21% accuracy.

Files and classes:

network.py - class containing ANN class with functions:

1. init - constructor
2. fit - learning+testing of ANN
3. test - testing of ANN
4. softmax - applies softmax on layer

activation_function.py - abstract class of activation function specified in layer:

1. ReLU
2. Sigmoid
3. Tanh You can add activation functions by inheritating from activation_function class

cost_function.py - abstract class of cost function of ANN:

1. Squared Sum
2. Cross Entropy You can add cost functions by inheritating from cost_function class

MNIST Digit Recognition Part:

mnist.pkl.gz - contains MNIST data

mnist_loader.py - script for loading MNIST data

main.py - creating, training, and testing ANN for MNIST Digit recognition