Rise v3.3

[QueensGambit]

This PR introduces the RISEv3.3 architecture as an improvement over the RISEv2 architecture.

The development process was influenced by the following papers.
However, most of the proposals turned out to be not beneficial for chess neural networks or suboptimal when applied for GPU inference.

• MixConv: Mixed Depthwise Convolutional Kernels, Mingxing Tan, Quoc V. Le, https://arxiv.org/abs/1907.09595
• Direct Neural Architecture Search on Target Task and Hardware, Han Cai, Ligeng Zhu, Song Han.
  https://arxiv.org/abs/1812.
• MnasNet: Platform-Aware Neural Architecture Search for Mobile,
  Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le
  http://openaccess.thecvf.com/content_CVPR_2019/html/Tan_MnasNet_Platform-Aware_Neural_Architecture_Search_for_Mobile_CVPR_2019_paper.html
• FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search,
  Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer,
  http://openaccess.thecvf.com/content_CVPR_2019/html/Wu_FBNet_Hardware-Aware_Efficient_ConvNet_Design_via_Differentiable_Neural_Architecture_Search_CVPR_2019_paper.html
• MobileNetV3: Searching for MobileNetV3,
  Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam.
  https://arxiv.org/abs/1905.02244
• Convolutional Block Attention Module (CBAM),
  Sanghyun Woo, Jongchan Park, Joon-Young Lee, In So Kweon
  https://arxiv.org/pdf/1807.06521.pdf
• ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks (ecaSE) - Wang et al.
  https://arxiv.org/abs/1910.03151

The changes which where incorporated in RISEv3.3 where the following:

• Replacing squeeze excitation modules by efficient squeeze excitation modules as proposed by Wang et al. (https://arxiv.org/abs/1910.03151)
• Replacing sigmoid by hard-sigmoid as recommend in Searching for MobileNetV3 by Howard et al. (https://arxiv.org/abs/1905.02244)
• Making use of 5x5 convolutions in deeper layers as recommended in Platform-Aware Neural Architecture Search for Mobile by Tan et al. (http://openaccess.thecvf.com/content_CVPR_2019/html/Tan_MnasNet_Platform-Aware_Neural_Architecture_Search_for_Mobile_CVPR_2019_paper.html)
• Using the flag boolean flag global for average pooling layers
• Using a higher initial channel size, more residual blocks but a lower increase of number of channels per layer (32 instead of 64).

The architecture resulted in an ~150 Elo improvement when trained on the same data set, here Kingbase2019lite.
The other only difference other difference was changing the value loss ratio from 0.01 to 0.1.

Score of ClassicAra 0.9.1 - Risev3.3 vs ClassicAra 0.9.1 - Risev2: 81 - 15 - 64 [0.706]
Elo difference: 152.4 +/- 42.8, LOS: 100.0 %, DrawRatio: 40.0 %

160 of 1000 games finished.