# data controls, can go up to 2000000 total for full dataset
train, val, test = 75000, 10000, 15000
# train, val, test = 1000000, 200000, 200000
# network architecture parameters
Phi_sizes, F_sizes = (100, 100, 128), (100, 100, 100)
# Phi_sizes, F_sizes = (100, 100, 256), (100, 100, 100)
# network training parameters
num_epoch = 5
batch_size = 500
###############################################################################
# load data
X, y = qg_jets.load(train + val + test)
# ignore pid information
X = X[:, :, :3]
# convert labels to categorical
Y = to_categorical(y, num_classes=2)
print('Loaded quark and gluon jets')
# preprocess by centering jets and normalizing pts
for x in X:
mask = x[:, 0] > 0
yphi_avg = np.average(x[mask, 1:3], weights=x[mask, 0], axis=0)
x[mask, 1:3] -= yphi_avg
x[mask, 0] /= x[:, 0].sum()
# data controls
num_data = 100000
val_frac, test_frac = 0.1, 0.15
# network architecture parameters
ppm_sizes = (100, 100)
dense_sizes = (100, 100)
# network training parameters
num_epoch = 5
batch_size = 100
################################################################################
# load data
X, y = qg_jets.load(num_data=num_data)
# ignore pid information
X = X[:, :, :3]
# convert labels to categorical
Y = to_categorical(y, num_classes=2)
print()
print('Loaded quark and gluon jets')
# preprocess by centering jets and normalizing pts
for x in X:
mask = x[:, 0] > 0
yphi_avg = np.average(x[mask, 1:3], weights=x[mask, 0], axis=0)
x[mask, 1:3] -= yphi_avg