forward_propagation.jl

"""
    Forward the design matrix through the network layers using the parameters.
"""
function forward_propagate_model_weights(DMatrix, parameters)
    master_cache = []
    A = DMatrix
    L = Int(length(parameters) / 2)

    # Forward propagate until the last (output) layer
    for l = 1 : (L-1)
        A_prev = A
        A, cache = linear_forward_activation(A_prev,
                                             parameters[string("W_", (l))],
                                             parameters[string("b_", (l))],
                                             "relu")
        push!(master_cache , cache)
    end

    # Make predictions in the output layer
    Ŷ, cache = linear_forward_activation(A,
                                         parameters[string("W_", (L))],
                                         parameters[string("b_", (L))],
                                         "sigmoid")
    push!(master_cache, cache)

    return Ŷ, master_cache
end