of the pyramid image.

output tensor coordinates to use dpoint instead of point.  This way, we can
obtain sub-pixel coordinates if we need them.

better results when run on float and double images.  There was needless
rounding to integers happening in the bilinear interpolation.  Now if you work
with a float image the entire process will run without integer rounding.

* Make noncopyable constructor and destructor default

C++11 provides the functionality.
Defining empty functions cause all classes derived from noncopyable
to be non-trivially constructible and non-trivially destructible.

For example, matrix with compile-time layout by definition does not
require an explicit destructor and should be trivially destructible
; however, deriving from noncopyable makes it non-trivially
destrutible. This also affects vector<T, 2> and vector<T, 3>.

* Delete array2d copy constructor and assignment operators

PNG_LIBRARY set by libpng's FindPNG.cmake does not contain zlib dependancy.  This causes the CHECK_FUNCTION_EXISTS(png_create_read_struct LIBPNG_IS_GOOD) to fail with liner errors, and for dlib to use it's internal copy of PNG.

Updated to use libpng's PNG_LIBRARIES variable.  This also sets both PNG and ZLib libraries in dlib_needed_libraries.

Fixed linker errors when building pyhton on windows.  This fixes a bug that was introduced in a recent PR.

Also fixed compiler errors that occurred in visual studio.

in variance when the learning rate resets.

coordinate mappings work on networks that contain skip layers.

to 500.  Now that we have a better history management of loss values in the
trainer it's much more sensible to have a larger value here.

when it determines that there have been a lot of steps without progress and
shrinks the learning rate.  Instead, it removes only the oldest 100.  The
problem with the old way of removing all the loss values in the history was
that if you set the steps without progress threshold to a really high number
you would often observe that the last few learning rate values were obviously
not making progress, however, since all the previous loss values were forgotten
the trainer needed to fully populate it's loss history from scratch before it
would figure this out.  This new style makes the trainer not waste time running
this excessive optimization of obviously useless mini-batches.

which it was in all ways except you couldn't deserialize directly like you
would expect.  This has now been fixed.

across scales regardless of the input image size.  Previously, if you gave
really large images or really small images it had a bias towards giving only
large patches or small patches respectively.