For step 1, ReLU is most commonly used because of its non-linear behavior. The output layer activation function depends on the expected output behavior. Step 4 targets this too. 

For step 2, Leaky ReLU is an improved version of ReLU and is used to avoid the zero gradient problem. However, you might observe a performance drop. We use Leaky ReLU if dead neurons are observed during training. Dead neurons are referred to as neurons with a zero gradient for all possible inputs, which makes them useless for training.

For step 3, the tanh and sigmoid activation functions are similar and are used in feed-forward networks. If you use these activation functions, then make sure you add regularization to network layers to avoid the vanishing gradient problem. These are generally used for classifier problems.