nModes = 32
nTS = 1
smooth = 0
learning_range = CTimeRange.new( "1980-1-1", "2005-12-30" )
prediction_lag = CDuration.months(1)
nInterationsPerProc = 10
batchSize = 100
nEpocs = 200
validation_fraction = 0.15
hiddenLayers = [100]
activation = "relu"
plotPrediction = True
variables = [ Variable("ts"), Variable( "zg", 50000 ) ]
project = Project(outDir,projectName)
pcDataset = ClimateleDataset([Experiment(project, start_year, end_year, nModes, variable) for variable in variables], nts = nTS, smooth = smooth, timeRange = learning_range)
td = IITMDataSource( "AI", "monthly" )
trainingDataset = TrainingDataset.new( [td], pcDataset, prediction_lag, decycle=True )
#ref_time_range = ( "1980-1-1", "2014-12-1" )
#ref_ts = ProjectDataSource( "HadISST_1.cvdp_data.1980-2017", [ "nino34" ], ref_time_range )
def learning_model_factory( weights = None ):
return LearningModel( pcDataset, trainingDataset, batch=batchSize, epocs=nEpocs, vf=validation_fraction, hidden=hiddenLayers, activation=activation, weights=weights )
result = LearningModel.parallel_execute( learning_model_factory, nInterationsPerProc )
print "Got Best result, valuation loss = " + str( result.val_loss ) + " training loss = " + str( result.train_loss )
if plotPrediction:
pname = "20CRv2c"
projectName = pname + "_EOFs"
nModes = 10
start_year = 1851
end_year = 2012
nTS = 1
smooth = 0
freq = "Y" # Yearly input/outputs
filter = "ja" # Filter months out of each year.
learning_range = CTimeRange.new("1851-1-1", "2005-12-1")
variables = [
Variable("ts"), Variable("zg", 80000)
] # [ Variable("ts"), Variable( "zg", 80000 ), Variable( "zg", 50000 ), Variable( "zg", 25000 ) ]
project = Project.new(outDir, projectName)
pcDataset = ClimateleDataset(projectName, [
Experiment(project, start_year, end_year, 64, variable)
for variable in variables
],
nts=nTS,
smooth=smooth,
filter=filter,
nmodes=nModes,
freq=freq,
timeRange=learning_range)
inputDataset = InputDataset([pcDataset])
prediction_lag = CDuration.years(1)
nInterationsPerProc = 25
batchSize = 200