Branch-AI/one_exercise_per_file/week03/day01/ex02/readme.md

Exerice 2 Neural network

The goal of this exercise is to understand how to combine three neurons to form a neural network. A neural newtwork is nothing else than neurons connected together. As shown in the figure the neural network is composed of layers:

• Input layer: it only represents input data. It doesn't contain neurons.
• Output layer: it represents the last layer. It contains a neuron (in some cases more than 1).
• Hidden layer: any layer between the input (first) layer and output (last) layer. Many hidden layers can be stacked. When there are many hidden layers, the neural networks is deep.

Notice that the neuron o1 in the output layer takes as input the output of the neurons h1 and h2 in the hidden layer.

In exercise 1, you implemented this neuron.

Now, we add two more neurons:

• h2, the second neuron of the hidden layer
• o1, the neuron of the output layer

1. Implement the function feedforward of the class OurNeuralNetwork that takes as input the input data and returns the output y. Return the output for these neurons:

   neuron_h1 = Neuron(1,2,-1)
   neuron_h2 = Neuron(0.5,1,0)
   neuron_o1 = Neuron(2,0,1)
   

   class OurNeuralNetwork:
   
       def __init__(self, neuron_h1, neuron_h2, neuron_o1):
           self.h1 = neuron_h1
           self.h2 = neuron_h2
           self.o1 = neuron_o1
   
       def feedforward(self, x1, x2):
       # The inputs for o1 are the outputs from h1 and h2
       # TODO
           return y