Add Input2d layer

[milancurcic]

Adds input2d layer.

This also changes how input layers of various ranks are constructed.

Currently Fortran generic name can't distinguish between input([10, 10]) and input([10, 10, 10]) (1-d array as input), but it can distinguish between input(10, 10) and input(10, 10, 10) (multiple scalars as input).

Before:

input(32)  ! input1d layer
input([3, 32, 32]) ! input3d layer

This PR:

input(32) ! input1d layer
input(32, 32) ! input2d layer
input(3, 32, 32) ! input3d layer

@OneAdder please review.

This PR is in support of removing the crutch in #197.

TODO:

• network % {forward,predict,predict_batch}_2d
• input2d tests
• input2d example
• Update CMake
• Update README.md

[OneAdder]

Thank you for your effort, Milan!

Great job figuring out such a handsome solution for generics! Looks really nice.

However, there are several pieces missing for the plumbing part:

nf_network.f90::forward_2d
nf_network.f90::predict_2d
nf_network.f90::predict_batch_2d

nf_layer.f90::backward_2d

Should I add them together with an example in my PR?

[milancurcic]

@OneAdder indeed, you're right. If a network begins with a 2-d input layer, then the specific forward, predict, and backward methods need to be added as well. If you don't mind, yes, please go ahead and add them. You should be able to push directly to this PR (rather than a separate PR) by cloning my fork at milancurcic/neural-fortran and checking out the input2d branch.

[OneAdder]

@milancurcic I made the updates for input2d_layer, added tests and updated CMakeLists and readme

[milancurcic]

Awesome, thanks! I'll take another read through and test locally tonight.