def main(_):
config = get_config()
name = create_name(config)
logdir = wb.mkdir('./trf_nn_char/' + name, is_recreate=False)
sys.stdout = wb.std_log(logdir + '/trf.log')
config.pprint()
print(logdir)
# word-embedding
# config.config_trf.load_embedding_path = os.path.join(logdir, 'word_emb.txt')
# config.config_trf.update_embedding = False
# data.word2vec(config.config_trf.load_embedding_path, config.config_trf.embedding_dim, 0)
# write data
data.write_vocab(logdir + '/vocab.txt')
data.write_char_vocab(logdir + '/vocab_char.txt',
logdir + '/vocab_w2c.txt')
data.write_data(data.datas[0], logdir + '/train.id')
data.write_data(data.datas[1], logdir + '/valid.id')
data.write_data(data.datas[2], logdir + '/test.id')
data.write_data(nbest_list, logdir + '/nbest.id')
with tf.Graph().as_default():
m = trf.TRF(config, data, logdir=logdir, device=['/gpu:0'])
sv = tf.train.Supervisor(logdir=logdir + '/logs',
summary_op=None,
global_step=m.global_steps)
# sv.summary_writer.add_graph(tf.get_default_graph()) # write the graph to logs
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
session_config.gpu_options.allow_growth = True
with sv.managed_session(config=session_config) as session:
m.set_session(session)
# print(m.get_log_probs(data.datas[1][1]))
# train_seqs = data.datas[0][0: config.config_trf.train_batch_size]
# sample_seqs = data.datas[0][0: config.config_trf.sample_batch_size]
# inputs, lengths = reader.produce_data_to_trf(train_seqs + sample_seqs)
# m.net_trf.run_train(session, inputs, lengths, len(train_seqs))
# m.pre_train(sv, session, batch_size=20, max_epoch=10, lr=1e-3)
m.train(sv,
session,
print_per_epoch=0.1,
nbest=nbest,
nbest_list=nbest_list)
def main(_):
# print(data.datas[0][0: 10])
# print(data.get_max_len())
# return
config = get_config()
name = create_name(config)
logdir = wb.mkdir('./trf_cnn_new/' + name, is_recreate=True)
sys.stdout = wb.std_log(logdir + '/trf.log')
config.pprint()
print(logdir)
# word-embedding
# config.config_trf.load_embedding_path = os.path.join(logdir, 'word_emb.txt')
# config.config_trf.update_embedding = False
# data.word2vec(config.config_trf.load_embedding_path, config.config_trf.embedding_dim, 0)
# write data
data.write_vocab(logdir + '/vocab.txt')
data.write_data(data.datas[0], logdir + '/train.id')
data.write_data(data.datas[1], logdir + '/valid.id')
data.write_data(data.datas[2], logdir + '/test.id')
data.write_data(nbest_list, logdir + '/nbest.id')
with tf.Graph().as_default():
m = trf.TRF(config, data, logdir=logdir, device='/gpu:0')
sv = tf.train.Supervisor(logdir=logdir + '/logs',
summary_op=None,
global_step=m.global_steps)
# sv.summary_writer.add_graph(tf.get_default_graph()) # write the graph to logs
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
session_config.gpu_options.allow_growth = True
with sv.managed_session(config=session_config) as session:
m.set_session(session)
# m.pre_train(sv, session, batch_size=100, max_epoch=3, lr=1.)
m.train(sv,
session,
print_per_epoch=0.1,
nbest=nbest,
nbest_list=nbest_list)
def main(_):
config = get_config()
name = create_name(config)
logdir = wb.mkdir('./trf_nn_char/' + name, is_recreate=True)
sys.stdout = wb.std_log(logdir + '/trf.log')
config.pprint()
print(logdir)
# write data
data.write_vocab(logdir + '/vocab.txt')
data.write_char_vocab(logdir + '/vocab_char.txt',
logdir + '/vocab_w2c.txt')
data.write_data(data.datas[0], logdir + '/train.id')
data.write_data(data.datas[1], logdir + '/valid.id')
data.write_data(data.datas[2], logdir + '/test.id')
data.write_data(nbest_list, logdir + '/nbest.id')
with tf.Graph().as_default():
m = trf.TRF(config, data, logdir=logdir, device=['/gpu:0'])
sv = tf.train.Supervisor(logdir=logdir + '/logs',
summary_op=None,
global_step=m.global_steps)
# sv.summary_writer.add_graph(tf.get_default_graph()) # write the graph to logs
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
session_config.gpu_options.allow_growth = True
with sv.managed_session(config=session_config) as session:
m.set_session(session)
# print(m.get_log_probs(data.datas[1][1]))
m.train(sv,
session,
print_per_epoch=0.1,
nbest=nbest,
nbest_list=nbest_list)