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fk_train.sh
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fk_train.sh
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## Parameters of GPU
gpu=0
threads=8
# Parameters of DMFT
count=20
iota=0
momentum=0.5
momDisor=0
maxEpoch=200
milestone=30
f_filling=0.5
d_filling=None
tol_sc=1e-6
tol_bi=1e-7
gap=1
# Parameters of training
L=12
data=FK_${L}_QPT
Net=Naive_
input_size=$(($L*$L))
embedding_size=100
hidden_size=64
output_size=2
restr=False # False: fc, 1: 1D NN, 2: 2D NN, 3: 1D NNN, 4: 2D NNN
diago=False # True, False, 1: 1D NN, 2: 2D NN, 3: 1D NNN, 4: 2D NNN
hermi=True # True, False, 0: naive hermi
bound=0.05 # initial bound
entanglement=False # False, int or float
delta=0
tc=None
gradsnorm=False
loss=CE # NLL, CE, BCE, BCEWL
opt=Adam
lr=1e-3
wd=0
betas=0.9,0.999
sch=StepLR
gamma=0.5
ss=20
drop=0
disor=0
epochs=10
workers=8
batchsize=128
print_freq=7
save_freq=1
seed=0
preNet=Naive_h_4-
checkpointID=checkpoint_0100
# paths
pretrained="models/${data}/${preNet}/model_best.pth.tar"
resume="models/${data}/${preNet}/${checkpointID}.pth.tar"
source activate
#source /opt/anaconda3/etc/profile.d/conda.sh
conda activate pytorch
python FK_Train.py \
-t $threads -j $workers -b $batchsize -p $print_freq -s $save_freq --epochs $epochs --gpu $gpu --seed $seed \
--count $count --iota $iota --momentum $momentum --momDisor $momDisor --maxEpoch $maxEpoch --milestone $milestone \
--f_filling $f_filling --d_filling $d_filling --tol_sc $tol_sc --tol_bi $tol_bi --gap $gap --disor $disor \
--loss $loss --opt $opt --lr $lr --wd $wd --betas $betas --sch $sch --gamma $gamma --ss $ss --drop $drop \
--data $data --Net $Net --entanglement $entanglement --delta $delta --tc $tc --gradsnorm $gradsnorm \
--input_size $input_size --embedding_size $embedding_size --hidden_size $hidden_size --output_size $output_size \
--init_bound $bound --restr $restr --hermi $hermi --diago $diago --double --scale