NeuralNetwork

Description: We designed a configurable neural network framework that can be used in mulitple problems. The framework supports mulitple activation Fns and also multiple optimization algorithms as well as a comprehensive evaluation metrics. It's tested and measured using the MNIST dataset with the final model accuracy reaching over 80%.

Table Of Contents: 1-Activation Functions 2-Optimization Algorithms 3-Evaluation Metrics 4-Visualization 5-Usage

1-Activation Functions:

Implemented Activation Functions Include: -Relu -Tanh -Sigmoid -Softmax

Other Functions can be easily added and passed through the activation fns .py.

2-Optimization Algorithms:

Implemented Optimization Algorithms Include: -Gradient Descent -AdaGrad -Mommentum Based -Nesstrove -RMSProp -AdaDelta

3-Evaluation Metrics:

Implemented Evaluation Metrics Include: -Confusion Matrix -Accuracy -Percision -Recall -f1 Score

4-Visualization:

-Simple plotting of any two vectors

5-Usage

a- To add a model use DeepNeuralNetwork Class passing it the needed epochs, learning rate and the optimization algorithms along as well as its parameters. b- use the .add function to add different FC layers just by passing the size and the desired activation fn. c- use the .addout function to add softmax layers with passing the size. d- use the .train function to start the training process with passing the required training features and labels as well as the testing features and labels