Feedforward Neural Networks- Image Classification Using Keras and Tensorflow

Consider two models:

(1.) A 2-layer feedforward neural network (i.e., 1 hidden layer with $f(x,W_1,b_1,W_2,b_2) = W_2\max(0,W_1x+b_1) + b_2$), and

(2.) Same as before but with leaky ReLU ($f(x) = x$ if $x > 0$, else $ f(x) = 0.01*x$).

a.) Build the above classifiers using Keras and Tensorflow and solve the classification problem for MNIST/Fashion MNIST.

b.) Discuss how optimizer choice influences performance.

c.) What happens when the number of hidden units chosen is much smaller. Similarly, what happens when the number of hidden units chosen is much higher?