train=trainName,
syr=2015,
eyr=2015,
var='varLst_Forcing',
varC='varConstLst_Noah',
dr=0.6,
modelOpt='relu',
model='cudnn',
loss='sigma')
k = 0
for j, i in zip(C1Lst, C2Lst):
opt['out'] = trainName + \
'_y15_Forcing_dr60_invGamma_'+str(j)+'_'+str(i)
opt['lossPrior'] = 'invGamma+' + str(j) + '+' + str(i)
runTrainLSTM.runCmdLine(opt=opt,
cudaID=k % 3,
screenName=opt['lossPrior'])
# rnnSMAP.funLSTM.trainLSTM(opt)
k = k + 1
#################################################
if 'test' in doOpt:
torch.cuda.empty_cache()
dsLst = list()
statErrLst = list()
statSigmaLst = list()
statConfLst = list()
statNormLst = list()
for k in range(0, nCase):
out = outLst[k]
ds = rnnSMAP.classDB.DatasetPost(rootDB=rootDB,
rootOut=rootOut,
syr=2015,
eyr=2015,
var='varLst_Forcing',
varC='varConstLst_Noah',
train='CONUSv4f1',
dr=0.5,
modelOpt='relu',
model='cudnn',
loss='sigma',
)
for k in range(0, len(drLst)):
opt['dr'] = drLst[k]
opt['out'] = 'CONUSv4f1_y15_Forcing_dr' + drStrLst[k]
cudaID = k % 3
runTrainLSTM.runCmdLine(opt=opt, cudaID=cudaID, screenName=opt['out'])
#################################################
if 'test' in doOpt:
predField = 'LSTM'
targetField = 'SMAP'
dsLst = list()
statErrLst = list()
statSigmaLst = list()
statConfLst = list()
for k in range(0, len(drStrLst)):
if drStrLst[k] is '50':
out = 'CONUSv4f1_y15_Forcing'
else:
out = 'CONUSv4f1_y15_Forcing_dr' + drStrLst[k]
testName = testName
rootOut=rnnSMAP.kPath['OutSigma_L3_NA'],
syr=2015,
eyr=2015,
var='varLst_soilM',
varC='varConstLst_Noah',
dr=0.5,
modelOpt='relu',
model='cudnn',
loss='sigma')
cudaIdLst = [1, 2]
for k in range(0, len(hucLst)):
trainName = hucLst[k] + '_v2f1'
opt['train'] = trainName
opt['out'] = trainName + '_y15_soilM'
print(trainName)
runTrainLSTM.runCmdLine(opt=opt, cudaID=cudaIdLst[k], screenName=trainName)
#################################################
# Test
rootOut = rnnSMAP.kPath['OutSigma_L3_NA']
rootDB = rnnSMAP.kPath['DB_L3_NA']
dsTuple = ([], [], [])
dsTuple2 = ([], [])
for k in range(0, len(hucLst)):
trainName = hucLst[k] + '_v2f1'
out = trainName + '_y15_soilM'
rootOut = rnnSMAP.kPath['OutSigma_L3_NA']
outLst = [
trainName + '_y15_soilM', trainName + '_y15_soilM',
'CONUSv2f1_y15_soilM'