Optimized CPU performance on macOS with new MKLML support, improved SSD CPU performance

• Further optimized MKL backend performance for SSD inference
• Updated MKLML to version 20171227
• Enabled neon install with MKLML on Mac OSX

Improved CPU performance for SSD and inference with batchnorm, Docker file

• Optimized SSD MKL backend performance (~3X boost version over version)
• Bumped aeon version to v1.3.0
• Fixed inference performance issue of MKL batchnorm
• Fixed batch prediction issue for gpu backend
• Enabled subset_pct for MNIST_DCGAN example
• Updated "make clean" to clean up mkl artifacts
• Added dockerfile for IA mkl

pip install support, performance improvements

• Enabled pip install through pypi
• Updated MKLML to version 20171007 with performance improvement of ~3X for mnist datalayer/nondatalayer and ~1.6X for DCGAN/WGAN datalayer
• Updated resnet model to optimize performance with MKLML 20171007
• Updated Alexnet weight file and fixed bug for deep dream
• Fixed faster-rcnn inference model loading issue
• Added data_loading time measurement and enabled GAN networks benchmarking
• Updated to Aeon version 1.2.0
• Enabled neon build with mklEngine on Windows systems

CPU optimizations bringing 7x gain in Deep Speech 2 performance and gains in Alexnet, VGG

• Optimized DeepSpeech2 MKL backend performance (~7X improvement over the CPU backend)
• Fused convolution and bias layer which significantly boosted AlexNet and VGG performance on Intel architectures with MKL backend
• Made SSD and Faster-RNN use VGG weight files in new format
• Fixed use of reset_cells hyperparameter
• Fixed MKL backend bug for GAN and Faster-RCNN models

SSD example, Deep Speech 2 IA optimizations, aeon v1.0.0

• Update MKLML version 20170908 that fixes a bug related to data conversions
• Add SSD example for bounding box object detection that works for both GPU and MKL backend
• Add DeepSpeech2 MKL backend optimization that features ~3X improvement
• Update aeon to 1.0.0 including new version of manifest (doc/source/loading_data.rst#aeon-dataloader)
• Add CHWD Support for Batch Normalization in mkl backend
• Modify ResNet-50 model's last layer to match the original ResNet-50 model paper
• Enable Seq2Seq testing and benchmarking

Default to MKL backend on Linux/CPU and update to MKLML release 20170720

• Set MKL backend (-b mkl) as the default CPU backend on Linux (use -b cpu to specify original CPU backend)
• Update MKLML version 20170720 (AVX512 code paths enabled by default and conversion optimizations)
• Simplify ResNet example
• Makefiles now check for virtualenv and pkg-config (#383)
• Fix Deep Speech2 model on MKL backend
• Fix MKL installation for "make sysinstall"

MKL backend boosting CPU performance, WGAN model examples

• Added support for MKL backend (-b mkl) on Linux, which boosts neon CPU performance significantly
• Added WGAN model examples for LSUN and MNIST data
• Enabled WGAN and DCGAN model examples for Python3
• Added fix (using file locking) to prevent race conditions running multiple jobs on the same machine with multiple GPUs
• Added functionality to display some information about hardware, OS and model used
• Updated appdirs to 1.4.3 to be compatibile on Centos 7.3 for appliance

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

Support greyscale videos and bug fixes to examples

• Make the whale calls example stable and shuffle dataset before splitting into subsets
• Reduce default depth in cifar_msra example to 2
• Fix the formatting of the conv layer description
• Fix documentation error in the video-c3d example
• Support greyscale videos

Dilated convolution and Speech model bug fixes

• Bug fix: Add dilation to object dict and assign defaults to dil_w = dil_h = 1 [#335, #336]
• Bug fix: Prevent GPU backend from ignoring non-zero slope in Rectlinclip and change default slope to 0
• Bug fix: Nesterov momentum was updating velocities incorrectly