Neural Network with Feed Forward

So we have created linear layers and activation functions with forward methods. Our final class will be to create a NeuralNet which will contain everything it needs and provide an API to the user.

We will follow the Scikit-Learner API style for this. So there will be a predict method and a fit method (let's not worry about all other utility methods for now). As we have just implemented the forward methods for all the classes, let's create the predict method using those forward methods of all classes.

1class NeuralNet():
2    def __init__(self, layers=[]):
3        self.layers = layers
4    
5    def predict(X):
6        for layer in layers:
7            A = layer.forward(A)
8        return A

This looks easy, right? But now we will implement a little difficult part of the NeuralNet. The Backward Propagation.