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 termination