def main():
args = dict()
args['corpus_path'] = '../vector_traing_corpus/clusterTrajectory_100m_5minutes_50eps_5minPts_uniq_2windows.txt'
args['train_data_path'] = '../vector_traing_corpus/clusterTrajectory_100m_5minutes_50eps_5minPts_uniq_2windows.txt'
args['dbscanner_path'] = '../utils/dbscaner_100m_5minutes_50eps_5minPts.pkl'
args['batch_size'] = 1
args['neg_sample_size'] = 5
args['alpha'] = 0.75 # smooth out unigram frequencies
args['table_size'] = 1000 # table size from which to sample neg samples
args['min_frequency'] = 1 # threshold for vocab frequency
args['lr'] = 0.05
args['min_lr'] = 0.005
args['embed_size'] = 128
args['sampling'] = False
args['epoches'] = 70
args['save_every_n'] = 200
args['save_dir'] = './save_windowns{}'.format(args['neg_sample_size'])
dataloader = Dataloader(args)
args['vocab_size'] = dataloader.vocab_size
pickle.dump(dataloader, open('./variable/dataloader.pkl', 'w'))
pickle.dump(args, open('./variable/args.pkl', 'w'))
model = Model(args)
saver = tf.train.Saver(max_to_keep=10)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter('./log', sess.graph)
count = 0
for e in range(args['epoches']):
dataloader.reset_pointer()
for i in range(dataloader.batch_num):
count += 1
start = time.time()
x, y = dataloader.next_batch()
# labels = dataloader.labels
args['lr'] = args['lr'] + dataloader.decay
feed = {model.x: x,
model.y: y,
model.lr: args['lr']}
summary, train, loss = sess.run([model.merged, model.train, model.loss], feed_dict=feed)
summary_writer.add_summary(summary, count)
end = time.time()
if count % 100 == 0:
print('round_num: {}/{}... '.format(e + 1, args['epoches']),
'Training steps: {}... '.format(count),
'Training error: {:.4f}... '.format(loss),
'Learning rate: {:.4f}... '.format(args['lr']),
'{:.4f} sec/batch'.format((end - start)))
if (count % args['save_every_n'] == 0):
saver.save(sess, "{path}/i{counter}.ckpt".format(path = args['save_dir'], counter=count))
saver.save(sess, "{path}/i{counter}.ckpt".format(path=args['save_dir'], counter=count))
summary_writer.close()
def main():
args = dict()
args[
'train_data_path'] = '../predict_training_corpus/clusterTrajectory_100m_5minutes_50eps_5minPts_uniq_3numseqs_train.txt'
args[
'test_data_path'] = '../predict_training_corpus/clusterTrajectory_100m_5minutes_50eps_5minPts_uniq_3numseqs_test.txt'
args[
'dbscanner_path'] = '../utils/dbscaner_100m_5minutes_50eps_5minPts.pkl'
args['save_dir'] = './save_3numsteps'
args['embedding_path'] = '../word2vec/variable/embedding.pkl'
args['batch_size'] = 5
args['lstm_size'] = 128
args['lstm_layer'] = 1
args['weight_decay'] = 0.00001
args['is_training'] = True
args['keep_prob'] = 0.5
args['lr'] = 0.001
args['epochs'] = 70
args['save_every_n'] = 200
dataloader = Dataloader(args)
args['num_steps'] = dataloader.num_steps
args['feature_dim'] = dataloader.feature_dim
args['classes'] = dataloader.classes
pickle.dump(args, open('./utils/args.pkl', 'wb'))
model = Model(args)
saver = tf.train.Saver(max_to_keep=10)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
count = 0
for e in range(args['epochs']):
dataloader.reset()
# new_state = sess.run(model.initial_state)
for i in range(dataloader.batch_num):
count += 1
x, y = dataloader.next_batch()
start = time.time()
feed = {model.input: x, model.target: y}
batch_loss, new_state, _ = sess.run(
[model.loss, model.final_state, model.optimizer],
feed_dict=feed)
end = time.time()
if count % 100 == 0:
print('round_num: {}/{}... '.format(e + 1, args['epochs']),
'Training steps: {}... '.format(count),
'Training error: {:.4f}... '.format(batch_loss),
'{:.4f} sec/batch'.format((end - start)))
if (count % args['save_every_n'] == 0):
# aaa = 1
saver.save(
sess, "{path}/i{counter}_l{lstm_size}.ckpt".format(
path=args['save_dir'],
counter=count,
lstm_size=args['lstm_size']))
saver.save(
sess, "{path}/i{counter}_l{lstm_size}.ckpt".format(
path=args['save_dir'],
counter=count,
lstm_size=args['lstm_size']))
