kerasModel.py

import numpy
from keras.layers import Input, Dense,Flatten, Dropout, Activation, concatenate, BatchNormalization, GRU, Add, Conv1D, Conv2D, Concatenate
from keras.models import Model, Sequential
from numpy import loadtxt, expand_dims
import matplotlib
import h5py
import tensorflow
from keras.models import load_model
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import sys

#Load in the datasets for training and compiling the sample weights
with h5py.File('/data/t3home000/aidandc/trainingDataver3.h5', 'r') as hf:
    dataset = hf["Training Data"][:]
with h5py.File('/data/t3home000/aidandc/sampleDataver3.h5', 'r') as hf:
    sampleData = hf["Sample Data"][:]

#Separate datasets into inputs and outputs, expand the dimensions of the inputs to be used with Conv1D layers
X = dataset[:,0:len(dataset[0])-1]
y = dataset[:,len(dataset[0])-1]
X = expand_dims(X,axis=3)

#Establish the sample weights
thebins = numpy.linspace(0, 200, 100)
bkgPts = []
sigPts = []
for i in range(len(sampleData)):
    if y[i]==1:
        sigPts.append(sampleData[i][0])
    if y[i]==0:
        bkgPts.append(sampleData[i][0])
bkg_counts, binsbkg = numpy.histogram(bkgPts, bins=thebins)
sig_counts, binssig = numpy.histogram(sigPts, bins=thebins)
a = []
for i in range(len(bkg_counts)):
    tempSig = float(sig_counts[i])
    tempBkg = float(bkg_counts[i])
    if tempBkg!=0:
        a.append(tempSig/tempBkg)
    if tempBkg==0:
        a.append(0)
#Normalize the sample weights above a certain pT
for i in range(42,len(a)):
    a[i]=0.7

#Compile the network
model = Sequential()
model.add(Conv1D(filters=50, kernel_size=14, strides=14, activation='relu',input_shape=(len(dataset[0])-1,1)))
model.add(Conv1D(filters=50, kernel_size=1,activation='relu'))
model.add(GRU(100, return_sequences=False))
model.add(Dense(50, activation = 'relu'))
model.add(Dense(10, activation = 'relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam', metrics=['binary_accuracy'])

#Add in the sample weights, 1-to-1 correspondence with training data
#Sample weight of all signal events being equal to 1
#Sample weight of all background events being equal to the sig/bkg ratio at that jet's pT
weights = []
for i in range(len(sampleData)):
    if y[i]==1:
        weights.append(1)
    if y[i]==0:
        jetPt = sampleData[i][0]
        tempPt = int(jetPt/2)
        if tempPt>98:
            tempPt = 98
        weights.append(a[tempPt])

#Train the network
callback = tensorflow.keras.callbacks.EarlyStopping(monitor='val_loss',verbose=1,patience=5)
model.fit(X,y,epochs=50,batch_size=50,verbose=2,sample_weight=numpy.asarray(weights),validation_split=0.20,callbacks=[callback])

#Save the network
model.save('L1BTagModel.h5')