3.The way we compute the activation function of the weighted sum is with the single `h1` line; this is to multiply our input pixels by their respective weights for each neuron:
![Exploring the single hidden layer model](img/00028.jpg)
4.Now it's just your friendly logistic regression again; you already know what to do. These newly computed 128 features need their own set of weights and biases to compute a score on the output class, that's `W2` and `b2`, respectively. Note how the shape matches the shape of the neurons 128, and the number of the output class is `5`: