About

We use Neural Network all the time, but have you ever implemented your own? Learning the mathematical details of how it works internally is truly an amazing experience and I suggest you trying too!

This is a vanilla Recurrent Neural Network with backpropagation based on the sklearn structure.

How to use

First, you'll need to instantiate the NN class with the desired parameters and then fit() the network using inputs and targets. After that, the predict() method can be used to take a look at how the neurons react based on the input.

The class topology must be built using the format [X,Y,Z,...] where X, Y, and Z are the number of neurons of the respective layer (X neurons for layer 0, Y neurons for layer 1, Z neurons for layer 2 and so on). The network is trained by the fit() function and predict() returns the output layer.

Example

from vanillaNN import VanillaNeuralNetwork

myNeuralNetwork = VanillaNeuralNetwork([4,4,3],numberOfEpochs=2500)
myNeuralNetwork.fit(input_train,targets_train, testAccuracy= True ,testInputs = input_test, testTargets= targets_test)
myNeuralNetwork.predict(input_test[0])
myNeuralNetwork.accuracy(input_test,targets_test)

Requirements:

• Python 3
• Numpy
• sklearn
• Matplotlib