def loadModel(inputDir,
trainData,
model,
LoadModel,
sampleDatasets=None,
removedVars=None):
inputModel = '%s/KERAS_check_best_model.h5' % inputDir
# inputModel = '%s/KERAS_model.h5'%inputDir
inputWeights = '%s/KERAS_check_best_model_weights.h5' % inputDir
from DeepJetCore.DataCollection import DataCollection
traind = DataCollection()
traind.readFromFile(trainData)
traind.dataclass.regressiontargetclasses = range(0, NBINS)
print traind.getNRegressionTargets()
if (LoadModel):
evalModel = load_model(inputModel, custom_objects=global_loss_list)
shapes = traind.getInputShapes()
else:
shapes = traind.getInputShapes()
train_inputs = []
for s in shapes:
train_inputs.append(keras.layers.Input(shape=s))
evalModel = model(train_inputs, traind.getNClassificationTargets(),
traind.getNRegressionTargets(), sampleDatasets,
removedVars)
evalModel.load_weights(inputWeights)
return evalModel
def loadModel(inputDir,
trainData,
model,
LoadModel,
sampleDatasets=None,
removedVars=None,
adv=False):
inputModel = '%s/KERAS_check_best_model.h5' % inputDir
from DeepJetCore.DataCollection import DataCollection
traind = DataCollection()
traind.readFromFile(trainData)
traind.dataclass.regressiontargetclasses = range(0, NBINS)
print(traind.getNRegressionTargets())
if (LoadModel):
evalModel = load_model(inputModel, custom_objects=global_loss_list)
shapes = traind.getInputShapes()
else:
shapes = traind.getInputShapes()
train_inputs = []
for s in shapes:
train_inputs.append(keras.layers.Input(shape=s))
modelargs = {}
if adv:
modelargs.update({
'nRegTargets': NBINS,
'discTrainable': True,
'advTrainable': True
})
evalModel = model(train_inputs, traind.getNClassificationTargets(),
traind.getNRegressionTargets(), sampleDatasets,
removedVars, **modelargs)
evalModel.load_weights(inputModel)
return evalModel