7 import tensorflow
as tf
16 def get_model(number_of_features, number_of_spectators, number_of_events, training_fraction, parameters):
18 tf.reset_default_graph()
19 x = tf.placeholder(tf.float32, [
None, number_of_features])
20 y = tf.placeholder(tf.float32, [
None, 1])
22 def layer(x, shape, name, unit=tf.sigmoid):
23 with tf.name_scope(name)
as scope:
24 weights = tf.Variable(tf.truncated_normal(shape, stddev=1.0 / np.sqrt(float(shape[0]))), name=
'weights')
25 biases = tf.Variable(tf.constant(0.0, shape=[shape[1]]), name=
'biases')
26 layer = unit(tf.matmul(x, weights) + biases)
29 inference_hidden1 = layer(x, [number_of_features, number_of_features + 1],
'inference_hidden1')
30 inference_activation = layer(inference_hidden1, [number_of_features + 1, 1],
'inference_sigmoid', unit=tf.sigmoid)
33 inference_loss = -tf.reduce_sum(y * tf.log(inference_activation + epsilon) +
34 (1.0 - y) * tf.log(1 - inference_activation + epsilon))
36 inference_optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
37 inference_minimize = inference_optimizer.minimize(inference_loss)
39 init = tf.global_variables_initializer()
41 config = tf.ConfigProto()
42 config.gpu_options.allow_growth =
True
43 session = tf.Session(config=config)
46 state = State(x, y, inference_activation, inference_loss, inference_minimize, session)
50 def partial_fit(state, X, S, y, w, epoch):
52 Pass received data to tensorflow session
54 feed_dict = {state.x: X, state.y: y}
55 state.session.run(state.optimizer, feed_dict=feed_dict)
58 avg_cost = state.session.run(state.cost, feed_dict=feed_dict)
59 print(
"Epoch:",
'%04d' % (epoch),
"cost=",
"{:.9f}".format(avg_cost))
63 if __name__ ==
"__main__":
64 from basf2
import conditions
66 conditions.testing_payloads = [
67 'localdb/database.txt'
70 general_options = basf2_mva.GeneralOptions()
71 general_options.m_datafiles = basf2_mva.vector(
"train.root")
72 general_options.m_identifier =
"Simple"
73 general_options.m_treename =
"tree"
74 variables = [
'M',
'p',
'pt',
'pz',
75 'daughter(0, p)',
'daughter(0, pz)',
'daughter(0, pt)',
76 'daughter(1, p)',
'daughter(1, pz)',
'daughter(1, pt)',
77 'daughter(2, p)',
'daughter(2, pz)',
'daughter(2, pt)',
78 'chiProb',
'dr',
'dz',
79 'daughter(0, dr)',
'daughter(1, dr)',
80 'daughter(0, dz)',
'daughter(1, dz)',
81 'daughter(0, chiProb)',
'daughter(1, chiProb)',
'daughter(2, chiProb)',
82 'daughter(0, kaonID)',
'daughter(0, pionID)',
83 'daughterInvariantMass(0, 1)',
'daughterInvariantMass(0, 2)',
'daughterInvariantMass(1, 2)']
84 general_options.m_variables = basf2_mva.vector(*variables)
85 general_options.m_target_variable =
"isSignal"
87 specific_options = basf2_mva.PythonOptions()
88 specific_options.m_framework =
"tensorflow"
89 specific_options.m_steering_file =
'mva/examples/tensorflow/simple.py'
90 specific_options.m_nIterations = 100
91 specific_options.m_mini_batch_size = 100
92 specific_options.m_normalize =
True
93 training_start = time.time()
94 basf2_mva.teacher(general_options, specific_options)
95 training_stop = time.time()
96 training_time = training_stop - training_start
98 inference_start = time.time()
99 test_data = [
"test.root"] * 10
100 p, t = method.apply_expert(basf2_mva.vector(*test_data), general_options.m_treename)
101 inference_stop = time.time()
102 inference_time = inference_stop - inference_start
104 print(
"Tensorflow", training_time, inference_time, auc)