exp

#pretraining
ver=1.0.0.0
python -m sigr.train_sigimg_actimg_fast exp --log log --snapshot model \
  --root .cache/ninapro-db1-rawsemg_feature_multisource_multistream_sigimgv2-20-1-universal-one-fold-intra-subject \
  --batch-size 1000 --decay-all --dataset ninapro-db1-rawdata-semgfeature-multisource \
  --num-filter 64 \
  --num-epoch 28 --lr-step 16 --lr-step 24 --snapshot-period 28 \
  --balance-gesture 1 \
  --feature-name 'rawsemg_feature_multisource_multistream_sigimgv2' \
  --fusion-type 'fuse_7' \
  --window 1 \
  --num-semg-row 1 --num-semg-col 10 \
  --wd 0.00015 \
  --preprocess 'ninapro-lowpass' \
  crossval --crossval-type universal-one-fold-intra-subject --fold 0 

#training
ver=1.0.0.1
for i in $(seq 0 26); do
   python -m sigr.train_sigimg_actimg_fast exp --log log --snapshot model \
   --root .cache/ninapro-db1-rawsemg_feature_multisource_multistream_sigimgv2-20-1-one-fold-intra-subject-fold-$i-v$ver \
   --params .cache/ninapro-db1-rawsemg_feature_multisource_multistream_sigimgv2-20-1-universal-one-fold-intra-subject/model-0028.params \
   --batch-size 1000 --decay-all --dataset ninapro-db1-rawdata-semgfeature-multisource \
   --num-filter 64 \
   --num-epoch 28 --lr-step 16 --lr-step 24 --snapshot-period 28 \
   --balance-gesture 1 \
   --feature-name 'rawsemg_feature_multisource_multistream_sigimgv2' \
   --fusion-type 'fuse_7' \
   --window 1 \
   --num-semg-row 1 --num-semg-col 10 \
   --dropout 0.65  \
   --wd 0.00015 \
   --preprocess 'ninapro-lowpass' \
   crossval --crossval-type one-fold-intra-subject --fold $i 
done