  Current research suggests that the key factors in designing neural network
architectures involve choosing number of filters for every convolution layer,
number of hidden neurons for every fully connected layer, dropout and pruning.
The default activation function in most cases is the ReLU, as it has
empirically shown faster training convergence. We explore whether ReLU is the
best choice if one is aiming to desire better modularity structure within a
neural network.
