def train_and_validate():
print('Seizure Detection Learning')
print('---------------------------------------------------------------')
do_train = True
ds_seizure = SeizureDataset(FLAGS)
if do_train:
X_train, y_train = ds_seizure.load_train_data(FLAGS.train_set)
print('Data sample size:', X_train[0].shape)
print('------------------------------------')
print('Batch size: ', FLAGS.batch_size)
print('------------------------------------')
# Note : Make sure dataset is divisible by batch_size
try:
assert(len(X_train) % FLAGS.batch_size == 0)
except:
print("Make sure dataset size is divisble by batch_size!")
sys.exit(1)
with tf.Graph().as_default():
# create and train the network
rnn_net = SeizureClassifier(FLAGS)
rnn_net.setup_loss()
rnn_net.do_train(ds_seizure, X_train, y_train, FLAGS)
#print('Final evaluation on the training data')
# print('---------------------------------------------------------------')
#rnn_net.do_eval(X_train, y_train)
# At this point we are done with the training data
del X_train
del y_train
print('Testing')
print('---------------------------------------------------------------')
X_test, ids = ds_seizure.load_test_data(FLAGS.test_set)
with tf.Graph().as_default():
# create and train the network
rnn_net = SeizureClassifier(FLAGS)
rnn_net.setup_loss()
predictions = rnn_net.predict(X_test, FLAGS)
# Save the results
frame = pd.DataFrame(
{'File': ids,
'Class': predictions
})
cols = frame.columns.tolist()
cols = cols[-1:] + cols[:-1]
frame = frame[cols]
frame['Class'] = frame['Class'].astype(float)
frame.to_csv(FLAGS.test_set + '_res.csv', index=False)
print('Saved results in: ', FLAGS.test_set)
def main(_):
username = os.getlogin()
user_dir = '/home/' + username
instances_count = 1
total_patient = 3
for patient in xrange(total_patient):
patient_id = 1 + patient
FLAGS.patient_id = patient_id
FLAGS.train_set = 'image_train_{0}_1000/single_side_fft/'.format(
patient_id)
FLAGS.test_set = 'image_test_{0}_1000/single_side_fft/'.format(
patient_id)
predictions = 0
for instances in xrange(instances_count):
ds_seizure = SeizureDataset(FLAGS)
X_train, y_train = ds_seizure.load_train_data(FLAGS.train_set)
X_test, y_ids = ds_seizure.load_test_data(FLAGS.test_set)
ds = NetworkDataset(X_train, y_train, X_test, y_ids)
FLAGS.model_dir = user_dir + '/seizure_models/single_side_fft/patient_{0}/model_{1}.cpkd'.format(
FLAGS.patient_id, instances)
predictions += np.array(train_and_validate(ds, instances))
store_total_avg_pred(ds, instances_count, predictions)
def train_and_validate():
height = 300
width = 300
depth = 16
output_dim = 1
patch_size_1 = 5
patch_size_2 = 5
feature_size_1 = 32
feature_size_2 = 64
fc_size = 1024
print('Seizure Detection Learning')
print('---------------------------------------------------------------')
do_train = True
ds_seizure = SeizureDataset(FLAGS)
if do_train:
X_train, y_train = ds_seizure.load_train_data(FLAGS.train_set)
train_set_size = int(round(FLAGS.train_ds_ratio * len(X_train)))
X_train_set = X_train[:train_set_size]
y_train_set = y_train[:train_set_size]
X_val_set = X_train[train_set_size:]
y_val_set = y_train[train_set_size:]
print('Data sample size = ', X_train[0].shape)
print('Trainig samples count = ', len(y_train_set))
print('Validation samples count = ', len(y_val_set))
print('------------------------------------')
print('Batch size: ', FLAGS.batch_size)
print('------------------------------------')
# Note : Make sure dataset is divisible by batch_size
# try:
# assert(len(X_train) % FLAGS.batch_size == 0)
# except:
# print("Make sure dataset size is divisble by batch_size!")
# sys.exit(1)
y_1s_train_cnt = np.sum(y_train_set)
y_0s_train_cnt = train_set_size - y_1s_train_cnt
FLAGS.pos_weight = (1. * y_0s_train_cnt) / y_1s_train_cnt
print('Positive weight = ', FLAGS.pos_weight)
with tf.Graph().as_default():
# create and train the network
cnn_net = SeizureClassifier(FLAGS, height, width, depth,
output_dim, patch_size_1, patch_size_2,
feature_size_1, feature_size_2,
fc_size)
cnn_net.setup_loss_and_trainOp(FLAGS)
cnn_net.do_train(ds_seizure, X_train_set, y_train_set, X_val_set,
y_val_set, FLAGS)
# Start a new graph
with tf.Graph().as_default():
cnn_net = SeizureClassifier(FLAGS, height, width, depth, output_dim,
patch_size_1, patch_size_2, feature_size_1,
feature_size_2, fc_size)
cnn_net.setup_loss_and_trainOp(FLAGS)
cnn_net.predict(ds_seizure, FLAGS)