neural-network-from-scratch.py

import numpy as np 

class MLP:

    def __init__(self, num_inputs=3, num_hidden=[3,5], num_outputs=2):
        
        self.num_inputs = num_inputs
        self.num_hidden = num_hidden
        self.num_outputs = num_outputs

        layers = [self.num_inputs] + self.num_hidden + [self.num_outputs]

        # initialise random weights
        self.weights = []
        for i in range(len(layers)-1):
            w = np.random.rand(layers[i], layers[i+1])
            self.weights.append([w])

        
    def forward_propogate(self, inputs):
        
        activations = inputs

        for w in self.weights:
            # calculate net inputs
            net_inputs = np.dot(activations, w)

            activations = self._sigmoid(net_inputs)
        
        return activations
    
    def _sigmoid(self, x):
        return 1 / (1 + np.exp(-x))


if __name__ == '__main__':

    # create an MLP
    mlp = MLP()

    # create some inputs
    inputs = np.random.rand(mlp.num_inputs)

    # perform forward propogation
    outputs = mlp.forward_propogate(inputs)

    # print the result
    print("The network input is {}".format(inputs))
    print("The network output is {}".format(outputs))