def compute_perplexity(model, sess, name):
"""Compute perplexity of the output of the model.
Args:
model: model for compute perplexity.
sess: tensorflow session to use.
name: name of the batch.
Returns:
The perplexity of the eval outputs.
"""
total_loss = 0
total_predict_count = 0
start_time = time.time()
while True:
try:
output_tuple = model.eval(sess)
total_loss += output_tuple.eval_loss * output_tuple.batch_size
total_predict_count += output_tuple.predict_count
except tf.errors.OutOfRangeError:
break
perplexity = utils.safe_exp(total_loss / total_predict_count)
utils.print_time(" eval %s: perplexity %.2f" % (name, perplexity),
start_time)
return perplexity
def _decode_inference_indices(model, sess, output_infer,
output_infer_summary_prefix, inference_indices,
tgt_eos, subword_option):
"""Decoding only a specific set of sentences."""
utils.print_out(" decoding to output %s , num sents %d." %
(output_infer, len(inference_indices)))
start_time = time.time()
with codecs.getwriter("utf-8")(tf.gfile.GFile(output_infer,
mode="wb")) as trans_f:
trans_f.write("") # Write empty string to ensure file is created.
for decode_id in inference_indices:
nmt_outputs, infer_summary = model.decode(sess)
# get text translation
assert nmt_outputs.shape[0] == 1
translation = nmt_utils.get_translation(
nmt_outputs,
sent_id=0,
tgt_eos=tgt_eos,
subword_option=subword_option)
if infer_summary is not None: # Attention models
image_file = output_infer_summary_prefix + str(
decode_id) + ".png"
utils.print_out(" save attention image to %s*" % image_file)
image_summ = tf.Summary()
image_summ.ParseFromString(infer_summary)
with tf.gfile.GFile(image_file, mode="w") as img_f:
# pylint: disable=no-member
img_f.write(image_summ.value[0].image.encoded_image_string)
trans_f.write("%s\n" % translation)
utils.print_out(translation + b"\n")
utils.print_time(" done", start_time)
def train(hparams, scope=None, target_session=""):
"""Train a translation model."""
log_device_placement = hparams.log_device_placement
out_dir = hparams.out_dir
num_train_steps = hparams.num_train_steps
steps_per_stats = hparams.steps_per_stats
steps_per_external_eval = hparams.steps_per_external_eval
steps_per_eval = 10 * steps_per_stats
avg_ckpts = hparams.avg_ckpts
if not steps_per_external_eval:
steps_per_external_eval = 5 * steps_per_eval
# Create model
model_creator = get_model_creator(hparams)
train_model = model_helper.create_train_model(model_creator, hparams,
scope)
eval_model = model_helper.create_eval_model(model_creator, hparams, scope)
infer_model = model_helper.create_infer_model(model_creator, hparams,
scope)
# Preload data for sample decoding.
dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
sample_src_data = inference.load_data(dev_src_file)
sample_tgt_data = inference.load_data(dev_tgt_file)
summary_name = "train_log"
model_dir = hparams.out_dir
# Log and output files
log_file = os.path.join(out_dir, "log_%d" % time.time())
log_f = tf.gfile.GFile(log_file, mode="a")
utils.print_out("# log_file=%s" % log_file, log_f)
# TensorFlow model
config_proto = utils.get_config_proto(
log_device_placement=log_device_placement,
num_intra_threads=hparams.num_intra_threads,
num_inter_threads=hparams.num_inter_threads)
train_sess = tf.Session(target=target_session,
config=config_proto,
graph=train_model.graph)
eval_sess = tf.Session(target=target_session,
config=config_proto,
graph=eval_model.graph)
infer_sess = tf.Session(target=target_session,
config=config_proto,
graph=infer_model.graph)
with train_model.graph.as_default():
loaded_train_model, global_step = model_helper.create_or_load_model(
train_model.model, model_dir, train_sess, "train")
# Summary writer
summary_writer = tf.summary.FileWriter(os.path.join(out_dir, summary_name),
train_model.graph)
# First evaluation
run_full_eval(model_dir, infer_model, infer_sess, eval_model, eval_sess,
hparams, summary_writer, sample_src_data, sample_tgt_data,
avg_ckpts)
last_stats_step = global_step
last_eval_step = global_step
last_external_eval_step = global_step
# This is the training loop.
stats, info, start_train_time = before_train(loaded_train_model,
train_model, train_sess,
global_step, hparams, log_f)
while global_step < num_train_steps:
# Run a step #
start_time = time.time()
try:
step_result = loaded_train_model.train(train_sess)
hparams.epoch_step += 1
except tf.errors.OutOfRangeError:
# Finished going through the training dataset. Go to next epoch.
hparams.epoch_step = 0
utils.print_out(
"# Finished an epoch, step %d. Perform external evaluation" %
global_step)
run_sample_decode(infer_model, infer_sess, model_dir, hparams,
summary_writer, sample_src_data, sample_tgt_data)
run_external_eval(infer_model, infer_sess, model_dir, hparams,
summary_writer)
if avg_ckpts:
run_avg_external_eval(infer_model, infer_sess, model_dir,
hparams, summary_writer, global_step)
train_sess.run(train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: 0})
continue
# Process step_result, accumulate stats, and write summary
global_step, info[
"learning_rate"], step_summary, pruning_stats = update_stats(
stats, start_time, step_result)
summary_writer.add_summary(step_summary, global_step)
if hparams.pruning_hparams is not None:
summary_writer.add_summary(pruning_stats, global_step)
# Once in a while, we print statistics.
if global_step - last_stats_step >= steps_per_stats:
last_stats_step = global_step
is_overflow = process_stats(stats, info, global_step,
steps_per_stats, log_f)
print_step_info(" ", global_step, info, get_best_results(hparams),
log_f)
if is_overflow:
break
# Reset statistics
stats = init_stats()
if global_step - last_eval_step >= steps_per_eval:
last_eval_step = global_step
utils.print_out("# Save eval, global step %d" % global_step)
add_info_summaries(summary_writer, global_step, info)
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir,
"translate.ckpt"),
global_step=global_step)
# Evaluate on dev/test
run_sample_decode(infer_model, infer_sess, model_dir, hparams,
summary_writer, sample_src_data, sample_tgt_data)
run_internal_eval(eval_model, eval_sess, model_dir, hparams,
summary_writer)
if global_step - last_external_eval_step >= steps_per_external_eval:
last_external_eval_step = global_step
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir,
"translate.ckpt"),
global_step=global_step)
run_sample_decode(infer_model, infer_sess, model_dir, hparams,
summary_writer, sample_src_data, sample_tgt_data)
run_external_eval(infer_model, infer_sess, model_dir, hparams,
summary_writer)
if avg_ckpts:
run_avg_external_eval(infer_model, infer_sess, model_dir,
hparams, summary_writer, global_step)
# Done training
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir, "translate.ckpt"),
global_step=global_step)
(result_summary, _, final_eval_metrics) = (run_full_eval(
model_dir, infer_model, infer_sess, eval_model, eval_sess, hparams,
summary_writer, sample_src_data, sample_tgt_data, avg_ckpts))
print_step_info("# Final, ", global_step, info, result_summary, log_f)
utils.print_time("# Done training!", start_train_time)
summary_writer.close()
utils.print_out("# Start evaluating saved best models.")
for metric in hparams.metrics:
best_model_dir = getattr(hparams, "best_" + metric + "_dir")
summary_writer = tf.summary.FileWriter(
os.path.join(best_model_dir, summary_name), infer_model.graph)
result_summary, best_global_step, _ = run_full_eval(
best_model_dir, infer_model, infer_sess, eval_model, eval_sess,
hparams, summary_writer, sample_src_data, sample_tgt_data)
print_step_info("# Best %s, " % metric, best_global_step, info,
result_summary, log_f)
summary_writer.close()
if avg_ckpts:
best_model_dir = getattr(hparams, "avg_best_" + metric + "_dir")
summary_writer = tf.summary.FileWriter(
os.path.join(best_model_dir, summary_name), infer_model.graph)
result_summary, best_global_step, _ = run_full_eval(
best_model_dir, infer_model, infer_sess, eval_model, eval_sess,
hparams, summary_writer, sample_src_data, sample_tgt_data)
print_step_info("# Averaged Best %s, " % metric, best_global_step,
info, result_summary, log_f)
summary_writer.close()
return final_eval_metrics, global_step
def decode_and_evaluate(name,
model,
sess,
trans_file,
ref_file,
metrics,
subword_option,
beam_width,
tgt_eos,
num_translations_per_input=1,
decode=True,
infer_mode="greedy"):
"""Decode a test set and compute a score according to the evaluation task."""
# Decode
if decode:
utils.print_out(" decoding to output %s" % trans_file)
start_time = time.time()
num_sentences = 0
with codecs.getwriter("utf-8")(tf.gfile.GFile(trans_file,
mode="wb")) as trans_f:
trans_f.write("") # Write empty string to ensure file is created.
if infer_mode == "greedy":
num_translations_per_input = 1
elif infer_mode == "beam_search":
num_translations_per_input = min(num_translations_per_input,
beam_width)
while True:
try:
nmt_outputs, _ = model.decode(sess)
if infer_mode != "beam_search":
nmt_outputs = np.expand_dims(nmt_outputs, 0)
batch_size = nmt_outputs.shape[1]
num_sentences += batch_size
for sent_id in range(batch_size):
for beam_id in range(num_translations_per_input):
translation = get_translation(
nmt_outputs[beam_id],
sent_id,
tgt_eos=tgt_eos,
subword_option=subword_option)
trans_f.write(
(translation + b"\n").decode("utf-8"))
except tf.errors.OutOfRangeError:
utils.print_time(
" done, num sentences %d, num translations per input %d"
% (num_sentences, num_translations_per_input),
start_time)
break
# Evaluation
evaluation_scores = {}
if ref_file and tf.gfile.Exists(trans_file):
for metric in metrics:
score = evaluation_utils.evaluate(ref_file,
trans_file,
metric,
subword_option=subword_option)
evaluation_scores[metric] = score
utils.print_out(" %s %s: %.1f" % (metric, name, score))
return evaluation_scores
