def main(_):
print("-" * 80)
if not os.path.isdir(FLAGS.output_dir):
print("Path {} does not exist. Creating.".format(FLAGS.output_dir))
os.makedirs(FLAGS.output_dir)
elif FLAGS.reset_output_dir:
print("Path {} exists. Remove and remake.".format(FLAGS.output_dir))
shutil.rmtree(FLAGS.output_dir)
os.makedirs(FLAGS.output_dir)
print("-" * 80)
log_file = os.path.join(FLAGS.output_dir, "stdout")
print("Logging to {}".format(log_file))
sys.stdout = Logger(log_file)
print_user_flags()
train()
def main(_):
logger.info(time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime()))
logger.info("-" * 80)
# if not os.path.isdir(FLAGS.output_dir):
# logger.info("Path {} does not exist. Creating.".format(FLAGS.output_dir))
# os.makedirs(FLAGS.output_dir)
# elif FLAGS.reset_output_dir:
# logger.info("Path {} exists. Remove and remake.".format(FLAGS.output_dir))
# shutil.rmtree(FLAGS.output_dir)
# os.makedirs(FLAGS.output_dir)
# logger.info("-" * 80)
# log_file = os.path.join(FLAGS.output_dir, "stdout")
# logger.info("Logging to {}".format(log_file))
# sys.stdout = Logger(log_file)
utils.print_user_flags()
train()
logger.info('End.')
logger.info(time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime()))
def train():
"""
Train driver - no need to change.
"""
print("-" * 80)
if not os.path.isdir(FLAGS.output_dir):
print("Path {0} does not exist. Creating.".format(FLAGS.output_dir))
os.makedirs(FLAGS.output_dir)
elif FLAGS.reset_output_dir:
print("Path {0} exists. Remove and remake.".format(FLAGS.output_dir))
shutil.rmtree(FLAGS.output_dir)
os.makedirs(FLAGS.output_dir)
print_user_flags()
# --------------------------------
# DATASET
# --------------------------------
data_dict = load_dataset(FLAGS.data_path,
is_toy=FLAGS.is_toy,
num_examples=FLAGS.n_loaded_sentences)
batched_datasets = create_batched_dataset(data_dict,
batch_size=FLAGS.batch_size)
# --------------------------------
# MODEL
# --------------------------------
nmt_model = model(data_dict,
checkpoint_dir=FLAGS.output_dir,
emb_dim=FLAGS.emb_dim,
n_hidden=FLAGS.n_hidden,
type=FLAGS.cell_type)
encoder, decoder = nmt_model['encoder'], nmt_model['decoder']
# --------------------------------
# RESTORE
# --------------------------------
# try to restore saved model
if FLAGS.restore_checkpoint:
try:
nmt_model['train_ckpt']. \
restore(tf.train.latest_checkpoint(FLAGS.output_dir))
print("\n\nTRYING TO RESTORE THE LATEST MODEL FROM: "
"'{}'\n\n".format(FLAGS.output_dir))
except Exception as e:
print("Error in restoring checkpoint: {}. Training from "
"scratch".format(e))
# --------------------------------
# TRAIN
# --------------------------------
# (continue to) train now
for epoch in range(1, FLAGS.n_epochs + 1):
start = time.time()
total_loss = 0
total_ppl = 0
# reset encoder's hidden state at the beginning of every epoch to ZEROs
hidden = tf.zeros((FLAGS.batch_size, FLAGS.n_hidden))
for (batch_num, (src, tgt)) in enumerate(batched_datasets['train']):
loss = 0
with tf.GradientTape() as tape:
loss = teacher_forcing(data_dict,
encoder,
decoder,
hidden,
loss,
src,
tgt)# forward
batch_loss = (loss / int(tgt.shape[1]))
batch_ppl = np.exp(batch_loss)
total_loss += batch_loss
total_ppl += batch_ppl
# backward
variables = encoder.variables + decoder.variables
gradients = tape.gradient(loss, variables)
nmt_model['optim'].apply_gradients(zip(gradients, variables))
if batch_num % 100 == 0:
print('Epoch {} Batch {} Loss {:.4f} '
'PPL {:.2f}'.format(epoch,
batch_num,
batch_loss.numpy(),
batch_ppl))
# evaluate some sentences
if (batch_num + 1) % FLAGS.eval_every == 0:
sentences = draw_random_sentences(os.path.join(FLAGS.data_path,
'val.txt'),
size=2)
for sentence in sentences:
# change False to True for Attention Plots
translate(sentence, encoder, decoder, data_dict, FLAGS.is_toy,
FLAGS.is_attention, False)
# saving (checkpoint) the model every 2 epochs
if (batch_num + 1) % FLAGS.save_every == 0:
nmt_model['train_ckpt'].save(file_prefix=nmt_model['ckpt_prefix'])
print("Model saved to '{}".format(FLAGS.output_dir))
print('Epoch {} Loss {:.4f} PPL {:.2f}'.format(epoch,
total_loss / batch_num + 1,
total_ppl / batch_num + 1))
print("Saving for this epoch")
nmt_model['train_ckpt'].save(file_prefix=nmt_model['ckpt_prefix'])
print('Time taken for this epoch {:.2f} sec\n'.format(time.time() - start))
print("-" * 80)
sys.stdout.flush()
# final test after training
# you can inject this into the loop to do it at every epoch's end
if epoch == FLAGS.n_epochs:
print("Testing the whole test set for epoch {}".format(epoch))
test_lines = open(os.path.join(FLAGS.data_path, 'target.txt')).\
read().strip().split('\n')
with open(os.path.join(FLAGS.data_path, 'translated_{}.txt'.format(epoch)),
'w') as f:
for line in test_lines:
result, _, _ = evaluate(line, encoder, decoder, data_dict,
FLAGS.is_toy, FLAGS.is_attention)
f.write(' '.join(result.split()[:-1]) + '\n')
def main(_):
print_user_flags()
tf.logging.set_verbosity(tf.logging.INFO)
processors = {
"ar": ARProcessor,
}
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
raise ValueError(
"At least one of `do_train`, `do_eval` or `do_predict' must be True.")
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
print('before train', time.localtime())
train_examples = None
eval_examples = None
num_train_steps = None
num_warmup_steps = None
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
# if FLAGS.do_eval:
# valsize = int(len(train_examples) / 9) # take 10% as validation
# eval_examples = train_examples[:valsize]
# train_examples = train_examples[valsize:]
num_train_steps = int(
len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
model_fn = model_fn_builder(
bert_config=bert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
if not FLAGS.use_record:
file_based_convert_examples_to_features(train_examples, label_list,
FLAGS.max_seq_length,
tokenizer, train_file)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
train_input_fn = file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
val_file = os.path.join(FLAGS.output_dir, "val.tf_record")
val_examples = processor.get_dev_examples(FLAGS.data_dir)
if not FLAGS.use_record:
file_based_convert_examples_to_features(val_examples, label_list,
FLAGS.max_seq_length,
tokenizer, val_file)
tf.logging.info(" Num val examples = %d", len(val_examples))
tf.logging.info(" Batch size val = %d", FLAGS.eval_batch_size)
val_drop_remainder = True if FLAGS.use_tpu else False
val_input_fn = file_based_input_fn_builder(
input_file=val_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=val_drop_remainder)
train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn,
max_steps=num_train_steps)
val_spec = tf.estimator.EvalSpec(input_fn=val_input_fn)
tf.estimator.train_and_evaluate(estimator, train_spec, val_spec)
# estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
# result = estimator.evaluate(input_fn=val_input_fn, steps=val_steps)
print('before eval', time.localtime())
if FLAGS.do_eval:
eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
if eval_examples == None:
eval_examples = processor.get_mytest_examples(FLAGS.data_dir)
if not FLAGS.use_record:
file_based_convert_examples_to_features(eval_examples, label_list,
FLAGS.max_seq_length,
tokenizer, eval_file)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d", len(eval_examples))
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if FLAGS.use_tpu:
# Eval will be slightly WRONG on the TPU because it will truncate
# the last batch.
eval_steps = int(len(eval_examples) / FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder)
result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
with tf.gfile.GFile(output_eval_file, "w") as writer:
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
print('before predict', time.localtime())
if FLAGS.do_predict:
if not FLAGS.use_record:
predict_examples = processor.get_test_examples(FLAGS.data_dir)
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
file_based_convert_examples_to_features(predict_examples, label_list,
FLAGS.max_seq_length,
tokenizer,
predict_file)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d", len(predict_examples))
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
if FLAGS.use_tpu:
# Warning: According to tpu_estimator.py Prediction on TPU is an
# experimental feature and hence not supported here
raise ValueError("Prediction in TPU not supported")
predict_drop_remainder = True if FLAGS.use_tpu else False
predict_input_fn = file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder)
result = estimator.predict(input_fn=predict_input_fn)
output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
with tf.gfile.GFile(output_predict_file, "w") as writer:
tf.logging.info("***** Predict results *****")
for prediction in result:
output_line = "\t".join(
str(class_probability) for class_probability in prediction) + "\n"
writer.write(output_line)
print('End', time.localtime())
