NervanaSystems/neon: Generative Adversarial Networks, 3D Deconvolution, doc updates and bug fixes

Add support for 3D deconvolution Generative Adversarial Networks (GAN) implementation, and MNIST DCGAN example, following GoodFellow 2014 (http://arXiv.org/abs/1406.2661) Implement Wasserstein GAN cost function and make associated API changes for GAN models Add a new benchmarking script with per-layer timings Add weight clipping for GDM, RMSProp, Adagrad, Adadelta and Adam optimizers Make multicost an explicit choice in mnist_branch.py example Enable NMS kernels to work with normalized boxes and offset Fix missing links in api.rst [#366] Fix docstring for --datatype option to neon [#367] Fix perl shebang in maxas.py and allow for build with numpy 1.12 [#356] Replace os.path.join for Windows interoperability [#351] Update aeon to 0.2.7 to fix a seg fault on terminatio