def train():
output_model_params()
loadFile = True
ifLoad, data = False, None
if loadFile:
ifLoad, data = load_file(cfg.processed_path, 'data', 'pickle')
if not ifLoad or not loadFile:
data_object = Dataset(cfg.train_data_path, cfg.dev_data_path)
data_object.save_dict(cfg.dict_path)
save_file({'data_obj': data_object}, cfg.processed_path)
else:
data_object = data['data_obj']
emb_mat_token, emb_mat_glove = data_object.emb_mat_token, data_object.emb_mat_glove
with tf.variable_scope(network_type) as scope:
if network_type in model_set:
model = Model(emb_mat_token, emb_mat_glove,
len(data_object.dicts['token']),
len(data_object.dicts['char']),
data_object.max_lens['token'], scope.name)
graphHandler = GraphHandler(model)
evaluator = Evaluator(model)
performRecoder = PerformRecoder(5)
if cfg.gpu_mem < 1.:
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=cfg.gpu_mem, allow_growth=True)
else:
gpu_options = tf.GPUOptions()
graph_config = tf.ConfigProto(gpu_options=gpu_options,
allow_soft_placement=True)
sess = tf.Session(config=graph_config)
graphHandler.initialize(sess)
# begin training
steps_per_epoch = int(
math.ceil(1.0 * len(data_object.digitized_train_data_list) /
cfg.train_batch_size))
num_steps = steps_per_epoch * cfg.max_epoch or cfg.num_steps
global_step = 0
# debug or not
if cfg.debug:
sess = tf_debug.LocalCLIDebugWrapperSession(sess)
for sample_batch, batch_num, data_round, idx_b in Dataset.generate_batch_sample_iter(
data_object.digitized_train_data_list, num_steps):
global_step = sess.run(model.global_step) + 1
if_get_summary = global_step % (cfg.log_period or steps_per_epoch) == 0
loss, summary, train_op = model.step(sess,
sample_batch,
get_summary=if_get_summary)
if global_step % 10 == 0:
_logger.add('data round: %d: %d/%d, global step:%d -- loss: %.4f' %
(data_round, idx_b, batch_num, global_step, loss))
if if_get_summary:
graphHandler.add_summary(summary, global_step)
# Occasional evaluation
if global_step % (cfg.eval_period or steps_per_epoch) == 0:
# ---- dev ----
dev_loss, dev_accu = evaluator.get_evaluation(
sess, data_object.digitized_dev_data_list, 'dev', global_step)
_logger.add('==> for dev, loss: %.4f, accuracy: %.4f' %
(dev_loss, dev_accu))
# ---- test ----
if cfg.test_data_name != None:
test_loss, test_accu = evaluator.get_evaluation(
sess, data_object.digitized_test_data_list, 'test',
global_step)
_logger.add('~~> for test, loss: %.4f, accuracy: %.4f' %
(test_loss, test_accu))
is_in_top, deleted_step = performRecoder.update_top_list(
global_step, dev_accu, sess)
this_epoch_time, mean_epoch_time = cfg.time_counter.update_data_round(
data_round)
if this_epoch_time is not None and mean_epoch_time is not None:
_logger.add('##> this epoch time: %f, mean epoch time: %f' %
(this_epoch_time, mean_epoch_time))
def test():
output_model_params()
loadFile = True
ifLoad, data = False, None
if loadFile:
ifLoad, data = load_file(cfg.processed_path, 'processed data',
'pickle')
if not ifLoad or not loadFile:
train_data_obj = Dataset(cfg.train_data_path, 'train')
dev_data_obj = Dataset(cfg.dev_data_path, 'dev', train_data_obj.dicts)
test_data_obj = Dataset(cfg.test_data_path, 'test',
train_data_obj.dicts)
save_file(
{
'train_data_obj': train_data_obj,
'dev_data_obj': dev_data_obj,
'test_data_obj': test_data_obj
}, cfg.processed_path)
train_data_obj.save_dict(cfg.dict_path)
else:
train_data_obj = data['train_data_obj']
dev_data_obj = data['dev_data_obj']
test_data_obj = data['test_data_obj']
emb_mat_token, emb_mat_glove = train_data_obj.emb_mat_token, train_data_obj.emb_mat_glove
with tf.variable_scope(network_type) as scope:
if network_type in model_set:
model = Model(emb_mat_token, emb_mat_glove,
len(train_data_obj.dicts['token']),
len(train_data_obj.dicts['char']),
train_data_obj.max_lens['token'], scope.name)
graphHandler = GraphHandler(model)
evaluator = Evaluator(model)
if cfg.gpu_mem < 1.:
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=cfg.gpu_mem, allow_growth=True)
else:
gpu_options = tf.GPUOptions()
graph_config = tf.ConfigProto(gpu_options=gpu_options,
allow_soft_placement=True)
# graph_config.gpu_options.allow_growth = True
sess = tf.Session(config=graph_config)
graphHandler.initialize(sess)
# ---- dev ----
dev_loss, dev_accu = evaluator.get_evaluation(sess, dev_data_obj, 1)
_logger.add('==> for dev, loss: %.4f, accuracy: %.4f' %
(dev_loss, dev_accu))
# ---- test ----
test_loss, test_accu = evaluator.get_evaluation(sess, test_data_obj, 1)
_logger.add('~~> for test, loss: %.4f, accuracy: %.4f' %
(test_loss, test_accu))
# ---- train ----
train_loss, train_accu, train_sent_accu = evaluator.get_evaluation(
sess, train_data_obj, 1)
_logger.add('--> for test, loss: %.4f, accuracy: %.4f' %
(train_loss, train_accu))