Update NNUE architecture to SFNNv5. Update network to nn-3c0aa92af1da.nnue.

[Sopel97]

STC:
LLR: 2.95 (-2.94,2.94) <0.00,2.50>
Total: 27288 W: 7445 L: 7178 D: 12665
Ptnml(0-2): 159, 3002, 7054, 3271, 158 

LTC:
LLR: 2.95 (-2.94,2.94) <0.50,3.00>
Total: 21792 W: 5969 L: 5727 D: 10096
Ptnml(0-2): 25, 2152, 6294, 2406, 19 

Architecture changes:

• Duplicated activation after the 1024->15 layer with squared crelu (so 15->15*2). As proposed by @vondele.

Trainer changes:

• Added bias to L1 factorization, which was previously missing (no measurable improvement but at least neutral in principle)
• For retraining linearly reduce lambda parameter from 1.0 at epoch 0 to 0.75 at epoch 800.
• reduce max_skipping_rate from 15 to 10 (compared to vondele's outstanding PR)

Note: This network was trained with a ~0.8% error in quantization regarding the newly added activation function. This will be fixed in the released trainer version. Expect a trainer PR tomorrow.

Note: The inference implementation cuts a corner to merge results from two activation functions. This could possibly be resolved nicer in the future. AVX2 implementation likely not necessary, but NEON is missing.

First training session invocation:

python3 train.py \
    ../nnue-pytorch-training/data/nodes5000pv2_UHO.binpack \
    ../nnue-pytorch-training/data/nodes5000pv2_UHO.binpack \
    --gpus "$3," \
    --threads 4 \
    --num-workers 8 \
    --batch-size 16384 \
    --progress_bar_refresh_rate 20 \
    --random-fen-skipping 3 \
    --features=HalfKAv2_hm^ \
    --lambda=1.0 \
    --max_epochs=400 \
    --default_root_dir ../nnue-pytorch-training/experiment_$1/run_$2

Second training session invocation:

python3 train.py \
    ../nnue-pytorch-training/data/T60T70wIsRightFarseerT60T74T75T76.binpack \
    ../nnue-pytorch-training/data/T60T70wIsRightFarseerT60T74T75T76.binpack \
    --gpus "$3," \
    --threads 4 \
    --num-workers 8 \
    --batch-size 16384 \
    --progress_bar_refresh_rate 20 \
    --random-fen-skipping 3 \
    --features=HalfKAv2_hm^ \
    --start-lambda=1.0 \
    --end-lambda=0.75 \
    --gamma=0.995 \
    --lr=4.375e-4 \
    --max_epochs=800 \
    --resume-from-model /data/sopel/nnue/nnue-pytorch-training/data/exp367/nn-exp367-run3-epoch399.pt \
    --default_root_dir ../nnue-pytorch-training/experiment_$1/run_$2