def MultiFNN(ANNSetup, test, train):
TrainMultiClass = to_categorical(train.MultiClass)
TestMultiClass = to_categorical(test.MultiClass)
#ClassWeights = GetClassWeights(train.MultiClass,train.Weights)
TrainWeights = GetTrainWeights(train.MultiClass, train.Weights)
model = Sequential()
model.Y_test = TestMultiClass[:, 0]
model.X_train = train.Events
model.Y_train = TrainMultiClass[:, 0]
model.W_train = train.Weights #Original weights!
model.add(
Dense(ANNSetup.Neurons[0],
activation='selu',
input_dim=ANNSetup.InputDim))
if (ANNSetup.Dropout != None):
model.add(Dropout(ANNSetup.Dropout))
for i in range(1, len(ANNSetup.Neurons)):
if (i == len(ANNSetup.Neurons) - 1):
model.add(Dense(ANNSetup.Neurons[i], activation='softmax'))
else:
model.add(Dense(ANNSetup.Neurons[i], activation='selu'))
Opti = GetOpti(ANNSetup.Optimizer, ANNSetup.LearnRate.Lr)
lrate = GetLearnRate(ANNSetup.LearnRate, ANNSetup.Epochs)
Roc = Histories()
Lcallbacks = [Roc, lrate]
model.compile(optimizer=Opti,
loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(train.Events,
TrainMultiClass,
sample_weight=TrainWeights,
validation_data=(test.Events, TestMultiClass,
test.Weights),
epochs=int(ANNSetup.Epochs),
batch_size=int(ANNSetup.Batch),
verbose=2,
callbacks=Lcallbacks) #, sample_weight=TrainWeights
LAuc = Roc.TestAucs
LTrainAuc = Roc.TrainAucs
print("Best Roc {0:.4f} at Epoch {1}".format(max(LAuc),
LAuc.index(max(LAuc)) + 1))
print("Train Auc {0:.4f}".format(LTrainAuc[LAuc.index(max(LAuc))]))
# print("Test Rocs: {0}".format(LAuc))
# print("Test Loss: {0}".format(Roc.TestLosses))
# print("Train Rocs: {0}".format(LTrainAuc))
# print("Train Loss: {0}".format(Roc.TrainLosses))
model.save(ANNSetup.SavePath)
return model, Roc
def LSTMNN(ANNSetup, test, train, VarList):
TrainWeights = GetTrainWeights(train.OutTrue,train.Weights)
model = Sequential()
model.X_train = train.Events
model.Y_train = train.OutTrue
model.W_train = train.Weights #Original weights!
model.X_test = test.Events
model.Y_test = test.OutTrue
model.W_test = test.Weights
LSTMNeurons = ANNSetup.Neurons[0]
DenseNeurons = ANNSetup.Neurons[1]
width = train.Events.shape[1]
Seq = train.Events.shape[2]
model.add(LSTM(LSTMNeurons[0], input_shape=(width, Seq),kernel_regularizer=l1(ANNSetup.Regu), return_sequences=True)) # kernel_regularizer=l2(ANNSetup.Regu)
if(ANNSetup.Dropout[0] != 0):
model.add(Dropout(ANNSetup.Dropout[0]))
for i in range(1,len(LSTMNeurons)):
if(i == len(LSTMNeurons)-1): # Add last LSTMLayer
model.add(LSTM(LSTMNeurons[i]))
else:
model.add(LSTM(LSTMNeurons[i],return_sequences=True,dropout=ANNSetup.Dropout[i], recurrent_regularizer=l2(ANNSetup.Regu)))
if(ANNSetup.Dropout[i] != 0):
model.add(Dropout(ANNSetup.Dropout[i]))
for j in range(len(DenseNeurons)):
model.add(Dense(DenseNeurons[j], activation='selu'))
Opti = GetOpti(ANNSetup.Optimizer,ANNSetup.LearnRate.Lr)
lrate = GetLearnRate(ANNSetup.LearnRate,ANNSetup.Epochs)
Roc = Histories()
if(lrate == None):
Lcallbacks = [Roc]
else:
Lcallbacks = [Roc,lrate]
model.summary()
model.compile(optimizer=Opti, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(train.Events, train.OutTrue, sample_weight=TrainWeights, validation_data=(test.Events, test.OutTrue, test.Weights), epochs=int(ANNSetup.Epochs),
batch_size=int(ANNSetup.Batch), verbose=2, callbacks=Lcallbacks)
LAuc = Roc.TestAucs
LTrainAuc = Roc.TrainAucs
print("Best Roc {0:.4f} at Epoch {1}".format(max(LAuc),LAuc.index(max(LAuc))+1)) #0:.4f
print("Train Auc {0:.4f}".format(LTrainAuc[LAuc.index(max(LAuc))]))
#print("Test Rocs: {0}".format(LAuc))
for i in range(len(LAuc)):
print("Auc at Epoch {0}: {1:.4f} Ov: {2:.3f}".format(i,LAuc[i],1-LAuc[i]/LTrainAuc[i]))
model.save(ANNSetup.SavePath)
return model, Roc
def FNN(ANNSetup, test, train):
# ClassWeights = GetClassWeights(train.OutTrue, train.Weights)
TrainWeights = GetTrainWeights(train.OutTrue,
train.Weights) # Defined below
# tf.debugging.set_log_device_placement(True) # Check if system is running on the correct device
model = Sequential(
) # from tensorflow.keras.models #
model.X_train = train.Events
model.Y_train = train.OutTrue
model.W_train = train.Weights # Original weights!
model.X_test = test.Events
model.Y_test = test.OutTrue
model.W_test = test.Weights
model = BuildModel(ANNSetup, model) # Defined below
Opti = GetOpti(ANNSetup.Optimizer, ANNSetup.LearnRate.Lr) # Defined below
lrate = GetLearnRate(ANNSetup.LearnRate, ANNSetup.Epochs) # Defined below
Roc = Histories() # Defined in Callbacks.py
# Roc = RedHistory()
if lrate == None:
Lcallbacks = [Roc]
# Lcallbacks = []
else:
Lcallbacks = [Roc, lrate]
# Lcallbacks = [lrate]
model.summary()
model.compile(optimizer=Opti,
loss='binary_crossentropy',
metrics=['accuracy'])
start = time.clock() # from time
history = model.fit(train.Events,
train.OutTrue,
sample_weight=TrainWeights,
validation_data=(test.Events, test.OutTrue,
test.Weights),
epochs=int(ANNSetup.Epochs),
batch_size=int(ANNSetup.Batch),
verbose=2,
callbacks=Lcallbacks) # sample_weight=TrainWeights
# history = model.fit(train.Events, train.OutTrue, batch_size=4000, epochs=2)
end = time.clock() # from time
print("The training took {} seconds".format(end - start))
LAuc = Roc.TestAucs
LTrainAuc = Roc.TrainAucs
print("Best Test Auc {0:.4f} at Epoch {1}".format(
max(LAuc), (LAuc.index(max(LAuc)) + 1))) # 0:.4f
print("Best Train Auc {0:.4f}".format(LTrainAuc[LAuc.index(max(LAuc))]))
for i in range(len(LAuc)):
print("Auc at Epoch {0}: {1:.4f} Ov: {2:.3f}".format(
i, LAuc[i], 1 - LAuc[i] / LTrainAuc[i]))
model.save(ANNSetup.SavePath)
return model, Roc
def LSTMNN(ANNSetup, test, train, VarList):
""" Bulding a Keras for the Recurrent Neural Networks with LSTM layers """
TrainWeights = GetTrainWeights(train.OutTrue,train.Weights) # Transformation of the Monte Carlo weights for training
#Create the model and pass it the data for Callbacks
model = Sequential()
model.X_train = train.Events
model.Y_train = train.OutTrue
model.W_train = train.Weights #Original weights!
model.X_test = test.Events
model.Y_test = test.OutTrue
model.W_test = test.Weights
# Building the model from the predefinite configuration (RNN.py)
LSTMNeurons = ANNSetup.Neurons[0]
DenseNeurons = ANNSetup.Neurons[1]
width = train.Events.shape[1]
Seq = train.Events.shape[2]
model.add(LSTM(LSTMNeurons[0], input_shape=(width, Seq),kernel_regularizer=l1(ANNSetup.Regu), return_sequences=True)) # kernel_regularizer=l2(ANNSetup.Regu)
if(ANNSetup.Dropout[0] != 0):
model.add(Dropout(ANNSetup.Dropout[0]))
for i in range(1,len(LSTMNeurons)):
if(i == len(LSTMNeurons)-1): # Add last LSTMLayer
model.add(LSTM(LSTMNeurons[i]))
else:
model.add(LSTM(LSTMNeurons[i],return_sequences=True,dropout=ANNSetup.Dropout[i], recurrent_regularizer=l2(ANNSetup.Regu)))
if(ANNSetup.Dropout[i] != 0):
model.add(Dropout(ANNSetup.Dropout[i]))
for j in range(len(DenseNeurons)):
model.add(Dense(DenseNeurons[j], activation='selu'))
Opti = GetOpti(ANNSetup.Optimizer,ANNSetup.LearnRate.Lr) # Set the optimizer
lrate = GetLearnRate(ANNSetup.LearnRate,ANNSetup.Epochs) # Set a learning rate schedule
Roc = Histories() # Definite history for AUC at each training step
if(lrate == None):
Lcallbacks = [Roc]
else:
Lcallbacks = [Roc,lrate]
model.summary()
model.compile(optimizer=Opti, loss='binary_crossentropy', metrics=['accuracy'])
start = time.clock() # start clock to track training time
history = model.fit(train.Events, train.OutTrue, sample_weight=TrainWeights, validation_data=(test.Events, test.OutTrue, test.Weights), nb_epoch=int(ANNSetup.Epochs),
batch_size=int(ANNSetup.Batch), verbose=2, callbacks=Lcallbacks)
end = time.clock()
print("The training took {} seconds".format(end-start))
LAuc = Roc.TestAucs
LTrainAuc = Roc.TrainAucs
print("Best Roc {0:.4f} at Epoch {1}".format(max(LAuc),LAuc.index(max(LAuc))+1)) #0:.4f
print("Train Auc {0:.4f}".format(LTrainAuc[LAuc.index(max(LAuc))]))
#print("Test Rocs: {0}".format(LAuc))
for i in range(len(LAuc)):
print("Auc at Epoch {0}: {1:.4f} Ov: {2:.3f}".format(i,LAuc[i],1-LAuc[i]/LTrainAuc[i]))
model.save(ANNSetup.SavePath)
return model, Roc
def MultiFNN(ANNSetup, test, train):
""" Bulding a Keras for multi-classifer """
#One hot encoding
TrainMultiClass = to_categorical(train.MultiClass)
TestMultiClass = to_categorical(test.MultiClass)
#ClassWeights = GetClassWeights(train.MultiClass,train.Weights)
TrainWeights = GetTrainWeights(
train.MultiClass, train.Weights
) # Transformation of the Monte Carlo weights for training
#Create the model and pass it the data for Callbacks
model = Sequential()
model.Y_test = TestMultiClass[:, 0]
model.X_train = train.Events
model.Y_train = TrainMultiClass[:, 0]
model.W_train = train.Weights #Original weights!
# Build model from configuration (set in FNN.py)
model.add(
Dense(ANNSetup.Neurons[0],
activation='selu',
input_dim=ANNSetup.InputDim))
if (ANNSetup.Dropout != None):
model.add(Dropout(ANNSetup.Dropout))
for i in range(1, len(ANNSetup.Neurons)):
if (i == len(ANNSetup.Neurons) - 1):
model.add(Dense(ANNSetup.Neurons[i], activation='softmax'))
else:
model.add(Dense(ANNSetup.Neurons[i], activation='selu'))
Opti = GetOpti(ANNSetup.Optimizer, ANNSetup.LearnRate.Lr) # Set optimizer
lrate = GetLearnRate(ANNSetup.LearnRate,
ANNSetup.Epochs) # Set learning rate schedule
Roc = Histories() # Create history for AUC during training
Lcallbacks = [Roc, lrate]
model.compile(optimizer=Opti,
loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(train.Events,
TrainMultiClass,
sample_weight=TrainWeights,
validation_data=(test.Events, TestMultiClass,
test.Weights),
epochs=int(ANNSetup.Epochs),
batch_size=int(ANNSetup.Batch),
verbose=2,
callbacks=Lcallbacks) #, sample_weight=TrainWeights
LAuc = Roc.TestAucs
LTrainAuc = Roc.TrainAucs
print("Best Roc {0:.4f} at Epoch {1}".format(max(LAuc),
LAuc.index(max(LAuc)) + 1))
print("Train Auc {0:.4f}".format(LTrainAuc[LAuc.index(max(LAuc))]))
# print("Test Rocs: {0}".format(LAuc))
# print("Test Loss: {0}".format(Roc.TestLosses))
# print("Train Rocs: {0}".format(LTrainAuc))
# print("Train Loss: {0}".format(Roc.TrainLosses))
model.save(ANNSetup.SavePath)
return model, Roc
def FNN(ANNSetup, test, train):
""" Bulding a Keras for the Feedforward Neural Networks """
#ClassWeights = GetClassWeights(train.OutTrue,train.Weights)
TrainWeights = GetTrainWeights(
train.OutTrue, train.Weights
) # Transformation of the Monte Carlo weights for training
#tf.debugging.set_log_device_placement(True) #Check if system is running on the correct device
#Create the model and pass it the data for Callbacks
model = Sequential()
model.X_train = train.Events
model.Y_train = train.OutTrue
model.W_train = train.Weights #Original weights!
model.X_test = test.Events
model.Y_test = test.OutTrue
model.W_test = test.Weights
model = BuildModel(
ANNSetup, model
) # Building the model from the predefinite configuration (FNN.py)
Opti = GetOpti(ANNSetup.Optimizer,
ANNSetup.LearnRate.Lr) # Set the optimizer
lrate = GetLearnRate(ANNSetup.LearnRate,
ANNSetup.Epochs) # Set a learning rate schedule
Roc = Histories(
) # Definie history for the AUC results at each training step
#Roc = RedHistory()
if (lrate == None):
Lcallbacks = [Roc]
#Lcallbacks = []
else:
Lcallbacks = [Roc, lrate]
#Lcallbacks = [lrate]
model.summary()
model.compile(optimizer=Opti,
loss='binary_crossentropy',
metrics=['accuracy'])
start = time.clock() # start clock to track training time
history = model.fit(train.Events,
train.OutTrue,
sample_weight=TrainWeights,
validation_data=(test.Events, test.OutTrue,
test.Weights),
epochs=int(ANNSetup.Epochs),
batch_size=int(ANNSetup.Batch),
verbose=2,
callbacks=Lcallbacks
) #, callbacks=Lcallbacks , sample_weight=TrainWeights
#history = model.fit(train.Events,train.OutTrue,batch_size=4000,epochs=2)
end = time.clock()
print("The training took {} seconds".format(end - start))
LAuc = Roc.TestAucs
LTrainAuc = Roc.TrainAucs
print("Best Test Auc {0:.4f} at Epoch {1}".format(
max(LAuc), (LAuc.index(max(LAuc)) + 1))) #0:.4f
print("Best Train Auc {0:.4f}".format(LTrainAuc[LAuc.index(max(LAuc))]))
for i in range(len(LAuc)):
print("Auc at Epoch {0}: {1:.4f} Ov: {2:.3f}".format(
i, LAuc[i], 1 - LAuc[i] / LTrainAuc[i]))
model.save(ANNSetup.SavePath)
return model, Roc
