c = df.getDataConst(dbCsv.varConst, doNorm=True, rmNan=True)
y = df.getDataTs('SMAP_AM', doNorm=True, rmNan=False)
nx = x.shape[-1] + c.shape[-1]
ny = 1
model = rnn.CudnnLstmModel(nx=nx, ny=ny, hiddenSize=64)
lossFun = crit.RmseLoss()
model = train.trainModel(model,
x,
y,
c,
lossFun,
nEpoch=nEpoch,
miniBatch=[100, 30])
modelName = 'test-LSTM'
train.saveModel(outFolder, model, nEpoch, modelName=modelName)
for k in dLst:
sd = utils.time.t2dt(ty1[0]) - dt.timedelta(days=k)
ed = utils.time.t2dt(ty1[1]) - dt.timedelta(days=k)
df2 = hydroDL.data.dbCsv.DataframeCsv(rootDB=rootDB,
subset='CONUSv4f1',
tRange=[sd, ed])
obs = df2.getDataTs('SMAP_AM', doNorm=True, rmNan=False)
model = rnn.LstmCloseModel(nx=nx, ny=ny, hiddenSize=64)
lossFun = crit.RmseLoss()
model = train.trainModel(model, (x, obs),
y,
c,
lossFun,
# z2 = interp.interpNan1d(z2, mode='pre')
xz2 = np.concatenate([x1, z2], axis=2)
ny = 1
nx = x1.shape[-1] + c1.shape[-1]
lossFun = crit.RmseLoss()
# model1 = rnn.CudnnLstmModel(nx=nx, ny=ny, hiddenSize=64)
# model1 = train.trainModel(
# model1, x1, y1, c1, lossFun, nEpoch=nEpoch, miniBatch=(50, 365))
# train.saveModel(outFolder, model1, nEpoch, modelName='LSTM')
model2 = rnn.CudnnLstmModel(nx=nx + 1, ny=ny, hiddenSize=64)
model2 = train.trainModel(
model2, xz1, y1, c1, lossFun, nEpoch=nEpoch, miniBatch=(50, 365))
train.saveModel(outFolder, model2, nEpoch, modelName='DA-1')
model3 = rnn.CudnnLstmModel(nx=nx + 1, ny=ny, hiddenSize=64)
model3 = train.trainModel(
model3, xz2, y1, c1, lossFun, nEpoch=nEpoch, miniBatch=(50, 365))
train.saveModel(outFolder, model3, nEpoch, modelName='DA-7')
if 'test' in doLst:
df2 = camels.DataframeCamels(subset='all', tRange=[20050101, 20150101])
x2 = df2.getDataTS(varLst=camels.forcingLst, doNorm=True, rmNan=True)
c2 = df2.getDataConst(varLst=camels.attrLstSel, doNorm=True, rmNan=True)
yt2 = df2.getDataObs(doNorm=False, rmNan=False).squeeze()
dfz1 = camels.DataframeCamels(subset='all', tRange=[20041231, 20141231])
z1 = dfz1.getDataObs(doNorm=True, rmNan=True)
# z1 = interp.interpNan1d(z1, mode='pre')