kerasModel.py

import h5py
import numpy as np
import tensorflow
import matplotlib.pyplot as plt
from keras.models import Model
from tensorflow.keras.layers import Conv1D, Dense, Flatten, Input, GlobalAveragePooling1D

from dataForgeScripts.dataForge import N_FEAT, N_PART_PER_JET

# Load in the datasets for training and compiling the sample weights
with h5py.File("trainingDataStop_1_30.h5", "r") as hf:
    dataset = hf["Training Data"][:]
with h5py.File("trainingDataQCD_30.h5", "r") as hf:
    datasetQCD = hf["Training Data"][:]
with h5py.File("sampleDataStop_30.h5", "r") as hf:
    sampleData = hf["Sample Data"][:]


dataset = np.concatenate((dataset, datasetQCD))#Put datasets on top of one another
#dataset = np.load("AugTrainingDataPt30.npy")
np.random.shuffle(dataset) #randomize QCD and Stop samples

# 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 = X.reshape((X.shape[0], N_PART_PER_JET, N_FEAT))

# Establish the sample weights
thebins = np.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 = np.histogram(bkgPts, bins=thebins)
sig_counts, binssig = np.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
x = inputs = Input(shape=(N_PART_PER_JET, N_FEAT))
x = Conv1D(
    filters=50,
    kernel_size=1,
    strides=1,
    activation="relu",
)(x)
x = Conv1D(filters=50, kernel_size=1, activation="relu")(x)
x = GlobalAveragePooling1D()(x)
#x = Flatten()(x)
x = Dense(50, activation="relu")(x)
x = Dense(10, activation="relu")(x)
outputs = Dense(1, activation="sigmoid")(x)

model = Model(inputs=inputs, outputs=outputs)
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)
history=model.fit(
    X,
    y,
    epochs=50,
    batch_size=50,
    verbose=2,
    #sample_weight=np.asarray(weights),
    validation_split=0.20,
    callbacks=[callback],
)
plt.figure(figsize=(7,5), dpi=120)
plt.plot(history.history['loss'], label = 'Train')
plt.plot(history.history['val_loss'], label = 'Validation')
plt.title('Model Loss', fontsize=25)
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(loc='best')
plt.savefig("modelLoss.png")

# Save the network
model.save("L1JetTagModel.h5")