def main():
if len(sys.argv) != 2 or sys.argv[1] not in {
"autoencoder", "cluster", "lifetime"
}:
print("USAGE: python main.py <Model Type>")
print("<Model Type>: [autoencoder/cluster/lifetime]")
return
'''
Read numpy array type data which has n_events x 2700 (time slices) x 4 (number of channels) and desolves it into (1) pulse data, n_pulses x 2700 (time slices),
(2) label, n_pulses dimension which represents how many channels detect signal per events, (3) event index, n_pulses dimension which represents where desolved pulses come from,
(4) channel index, n_pulses dimension which represents which channels(scintillator ind.) desolved pulses come from.
Without specification, the number of test dataset is 0.01 of that of training dataset.
Depending on modes (autoencoder, cluster and lifetime), the main function runs autoencoder training, cluster training, lifetime calculation respectively, taken previously saved checkpoint.
'''
#pulse_data, label, test_data, test_label, train_evt_ind, test_evt_ind, train_ch_ind, test_ch_ind = preprocess.get_data("../testData11_14bit_100mV.npy")
pulse_data1, label1, test_data1, test_label1, train_evt_ind1, test_evt_ind1, train_ch_ind1, test_ch_ind1 = preprocess.get_data(
filename="../DL_additional_data/muon_data_deep_learning_0_1.npy",
make_delta_t=False)
print('first data loaded')
pulse_data2, label2, test_data2, test_label2, train_evt_ind2, test_evt_ind2, train_ch_ind2, test_ch_ind2 = preprocess.get_data(
filename="../DL_additional_data/muon_data_deep_learning_0_2.npy",
make_delta_t=False)
print('second data loaded')
pulse_data3, label3, test_data3, test_label3, train_evt_ind3, test_evt_ind3, train_ch_ind3, test_ch_ind3 = preprocess.get_data(
filename="../DL_additional_data/muon_data_deep_learning_1_1.npy",
make_delta_t=False)
print('third data loaded')
pulse_data4, label4, test_data4, test_label4, train_evt_ind4, test_evt_ind4, train_ch_ind4, test_ch_ind4 = preprocess.get_data(
filename="../DL_additional_data/muon_data_deep_learning_1_2.npy",
make_delta_t=False)
print('fourth data loaded')
pulse_data5, label5, test_data5, test_label5, train_evt_ind5, test_evt_ind5, train_ch_ind5, test_ch_ind5 = preprocess.get_data(
filename="../DL_additional_data/muon_data_deep_learning_2_1.npy",
make_delta_t=False)
print('data loading finished')
pulse_data = np.concatenate(
[pulse_data1, pulse_data2, pulse_data3, pulse_data4, pulse_data5])
del pulse_data1, pulse_data2, pulse_data3, pulse_data4, pulse_data5
label = np.concatenate([label1, label2, label3, label4, label5])
del label1, label2, label3, label4, label5
test_data = np.concatenate(
[test_data1, test_data2, test_data3, test_data4, test_data5])
del test_data1, test_data2, test_data3, test_data4, test_data5
test_label = np.concatenate(
[test_label1, test_label2, test_label3, test_label4, test_label5])
del test_label1, test_label2, test_label3, test_label4, test_label5
train_evt_ind = np.concatenate([
train_evt_ind1, train_evt_ind2, train_evt_ind3, train_evt_ind4,
train_evt_ind5
])
del train_evt_ind1, train_evt_ind2, train_evt_ind3, train_evt_ind4, train_evt_ind5
#test_evt_ind = np.concat([test_evt_ind1, test_evt_ind2, test_evt_ind3, test_evt_ind4, test_evt_ind5])
del test_evt_ind1, test_evt_ind2, test_evt_ind3, test_evt_ind4, test_evt_ind5
train_ch_ind = np.concatenate([
train_ch_ind1, train_ch_ind2, train_ch_ind3, train_ch_ind4,
train_ch_ind5
])
del train_ch_ind1, train_ch_ind2, train_ch_ind3, train_ch_ind4, train_ch_ind5
#test_ch_ind = np.concat([test_ch_ind1, test_ch_ind2, test_ch_ind3, test_ch_ind4, test_ch_ind5])
del test_ch_ind1, test_ch_ind2, test_ch_ind3, test_ch_ind4, test_ch_ind5
#delta_t_origin = preprocess.get_delta_t("../testData11_14bit_100mV.npz", train_evt_ind)
train_delta_t1, test_delta_t1 = preprocess.get_delta_t2(
"../DL_additional_data/muon_data_deep_learning_0_1.npz"
) #delta_t_origin[train_evt_ind, train_ch_ind]
train_delta_t2, test_delta_t2 = preprocess.get_delta_t2(
"../DL_additional_data/muon_data_deep_learning_0_2.npz")
train_delta_t3, test_delta_t3 = preprocess.get_delta_t2(
"../DL_additional_data/muon_data_deep_learning_1_1.npz")
train_delta_t4, test_delta_t4 = preprocess.get_delta_t2(
"../DL_additional_data/muon_data_deep_learning_1_2.npz")
train_delta_t5, test_delta_t5 = preprocess.get_delta_t2(
"../DL_additional_data/muon_data_deep_learning_2_1.npz")
train_delta_t = np.concatenate([
train_delta_t1, train_delta_t2, train_delta_t3, train_delta_t4,
train_delta_t5
])
del train_delta_t1, train_delta_t2, train_delta_t3, train_delta_t4, train_delta_t5
#test_delta_t = np.concat([test_delta_t1, test_delta_t2, test_delta_t3, test_delta_t4, test_delta_t5])
del test_delta_t1, test_delta_t2, test_delta_t3, test_delta_t4, test_delta_t5
model = AutoEncoder()
checkpoint_dir = './checkpoint'
checkpoint = tf.train.Checkpoint(model=model)
manager = tf.train.CheckpointManager(checkpoint,
checkpoint_dir,
max_to_keep=3)
if sys.argv[1] == "autoencoder":
start = time.time()
num_epochs = 1
curr_loss = 0
epoch = 0
for i in range(num_epochs):
print(epoch + 1, 'th epoch:')
tot_loss = train(model, pulse_data)
curr_loss += tot_loss
epoch += 1
print("Test loss:", test(model, test_data))
print("Process time : {} s".format(int(time.time() - start)))
print("Saving Checkpoint...")
manager.save()
visualization.plot_1ch(
test_data[7],
tf.squeeze(model.call(tf.reshape(test_data[7],
(1, 1300, 1)))).numpy())
visualization.plot_1ch(
test_data[33],
tf.squeeze(model.call(tf.reshape(test_data[33],
(1, 1300, 1)))).numpy())
visualization.plot_1ch(
test_data[46],
tf.squeeze(model.call(tf.reshape(test_data[46],
(1, 1300, 1)))).numpy())
visualization.plot_1ch(
test_data[25],
tf.squeeze(model.call(tf.reshape(test_data[25],
(1, 1300, 1)))).numpy())
visualization.feature_v_proj(model.encoder, test_data, test_label)
elif sys.argv[1] == "cluster":
checkpoint.restore(manager.latest_checkpoint)
visualization.feature_v_proj(model.encoder, test_data, test_label)
model_cluster = clustering(model.encoder)
checkpoint_dir_cluster = './checkpoint_cluster'
checkpoint_cluster = tf.train.Checkpoint(model=model_cluster)
manager_cluster = tf.train.CheckpointManager(checkpoint_cluster,
checkpoint_dir_cluster,
max_to_keep=3)
if sys.argv[1] == "cluster":
#checkpoint_cluster.restore(manager_cluster.latest_checkpoint)
kmeans = KMeans(n_clusters=3,
init='k-means++',
n_init=20,
max_iter=400)
cluster_pred = kmeans.fit_predict(
model.encoder(
tf.reshape(pulse_data[:min(len(pulse_data), 10000)],
(-1, 1300, 1))))
model_cluster.cluster.set_weights([kmeans.cluster_centers_])
num_iter = 30
cnt_iter = 0
p = None
for i in range(num_iter):
print(cnt_iter + 1, 'th iteration:')
tot_loss, p = train_cluster(model_cluster, model, pulse_data,
cnt_iter, p, train_ch_ind,
train_delta_t)
cnt_iter += 1
prbs = model_cluster.call(
tf.cast(tf.reshape(pulse_data[:10000], (-1, 1300, 1)),
dtype=tf.float32))
ind = tf.argmax(prbs, axis=1)
visualization.feature_v_proj(model.encoder, pulse_data[:10000],
ind)
num_bkgcluster = tf.reduce_sum(
tf.cast(tf.logical_and(tf.not_equal(train_ch_ind[:10000], 0),
tf.not_equal(ind, 0)),
dtype=tf.float32))
num_bkg = tf.reduce_sum(
tf.cast(tf.not_equal(train_ch_ind[:10000], 0),
dtype=tf.float32))
print('%dth epochs, \tAccuracy: %f' %
(cnt_iter + 1,
tf.cast(num_bkgcluster / num_bkg, dtype=tf.float32)))
if cnt_iter % 10 == 0 and cnt_iter != 0:
print("Saving Checkpoint...")
manager_cluster.save()
visualization.feature_v_proj(model.encoder, test_data, test_label)
manager.save()
elif sys.argv[1] == "lifetime":
checkpoint.restore(manager.latest_checkpoint)
checkpoint_cluster.restore(manager_cluster.latest_checkpoint)
lifetime_calc(model_cluster, model.encoder, pulse_data, train_delta_t,
train_evt_ind, train_ch_ind)
