* #288 - add new layer loss_multiclass_log_matrixoutput for semantic-segmentation purposes

* In semantic segmentation, add capability to ignore individual pixels when computing gradients

* In semantic segmentation, 65535 classes ought to be enough for anybody

* Divide matrix output loss by matrix dimensions too, in order to make losses related to differently sized matrices more comparable
- note that this affects the required learning rate as well!

* Review fix: avoid matrix copy

* Review fix: rename to loss_multiclass_log_per_pixel

* Review fix: just use uint16_t as the label type

* Add more tests: check that network params and outputs are correct

* Improve error message when output and truth matrix dimensions do not match

* Add test case verifying that a single call of loss_multiclass_log_per_pixel equals multiple corresponding calls of loss_multiclass_log

* Fix test failure by training longer

* Remove the test case that fails on Travis for some reason, even though it works on AppVeyor and locally