score = MyModel.Model.evaluate_generator(Test_gen,
steps=NTestSamples / BatchSize)
print "Evaluating score on test sample..."
print "Final Score:", score
MyModel.MetaData["FinalScore"] = score
if TestDefaultParam("RunningTime"):
MyModel.MetaData["EpochTime"] = TSCB.history
# Store the parameters used for scanning for easier tables later:
for k in Params:
MyModel.MetaData[k] = Config[k]
# Save Model
MyModel.Save()
else:
print "Skipping Training."
# Analysis
if Analyze:
print "Running Analysis."
Test_genC = MakeGenerator(ECAL,
HCAL,
TestSampleList,
NTestSamples,
LCDNormalization(Norms),
batchsize=BatchSize,
shapes=shapes,
n_threads=n_threads,
steps=NTestSamples /
BatchSize)
print "Evaluating score on test sample..."
print "Final Score:", score
ReconstructionModel.MetaData["FinalScore"] = score
if TestDefaultParam("RunningTime"):
ReconstructionModel.MetaData["EpochTime"] = TSCB.history
# Store the parameters used for scanning for easier tables later:
for k in Params:
ReconstructionModel.MetaData[k] = Config[k]
# Save Model
ReconstructionModel.Save()
else:
print "Skipping Training."
# Analysis
if Analyze:
Test_genC = MakeGenerator(TestSampleList,
NTestSamples,
cachefile=Test_genC.cachefilename
) #"/tmp/LArTPCDNN-LArIAT-TestEvent-Cache.h5")
Test_genC.PreloadData(n_threads_cache)
[Test_X_View1, Test_X_View2], Test_Y = MergeInputs()(tuple(Test_genC.D))
from DLAnalysis.Classification import MultiClassificationAnalysis
result, NewMetaData = MultiClassificationAnalysis(