def train(params):
# status measure
if params.recorder.estop or \
params.recorder.epoch > params.epoches or \
params.recorder.step > params.max_training_steps:
tf.logging.info(
"Stop condition reached, you have finished training your model.")
return 0.
# loading dataset
tf.logging.info("Begin Loading Training and Dev Dataset")
start_time = time.time()
train_dataset = Dataset(params.src_train_file,
params.tgt_train_file,
params.src_vocab,
params.tgt_vocab,
params.max_len,
batch_or_token=params.batch_or_token)
dev_dataset = Dataset(params.src_dev_file,
params.src_dev_file,
params.src_vocab,
params.src_vocab,
1e6,
batch_or_token='batch')
tf.logging.info(
"End Loading dataset, within {} seconds".format(time.time() -
start_time))
# Build Graph
with tf.Graph().as_default():
lr = tf.placeholder(tf.float32, [], "learn_rate")
train_features = {
"source": tf.placeholder(tf.int32, [None, None], "source"),
"target": tf.placeholder(tf.int32, [None, None], "target"),
}
eval_features = {
"source": tf.placeholder(tf.int32, [None, None], "source"),
}
# session info
sess = util.get_session(params.gpus)
tf.logging.info("Begining Building Training Graph")
start_time = time.time()
# create global step
global_step = tf.train.get_or_create_global_step()
# set up optimizer
optimizer = tf.train.AdamOptimizer(lr,
beta1=params.beta1,
beta2=params.beta2,
epsilon=params.epsilon)
# set up training graph
loss, gradients = tower_train_graph(train_features, optimizer, params)
# apply pseudo cyclic parallel operation
vle, ops = cycle.create_train_op({"loss": loss}, gradients, optimizer,
global_step, params)
tf.logging.info(
"End Building Training Graph, within {} seconds".format(
time.time() - start_time))
tf.logging.info("Begin Building Inferring Graph")
start_time = time.time()
# set up infer graph
eval_seqs, eval_scores, eval_mask = tower_infer_graph(
eval_features, params)
tf.logging.info(
"End Building Inferring Graph, within {} seconds".format(
time.time() - start_time))
# initialize the model
sess.run(tf.global_variables_initializer())
# log parameters
util.variable_printer()
# create saver
train_saver = saver.Saver(checkpoints=params.checkpoints,
output_dir=params.output_dir)
tf.logging.info("Training")
lrate = params.lrate
cycle_counter = 1
cum_loss = []
cum_gnorm = []
# restore parameters
tf.logging.info("Trying restore existing parameters")
train_saver.restore(sess)
start_time = time.time()
for epoch in range(1, params.epoches + 1):
if epoch < params.recorder.epoch:
tf.logging.info(
"Passing {}-th epoch according to record".format(epoch))
continue
params.recorder.epoch = epoch
tf.logging.info("Training the model for epoch {}".format(epoch))
size = params.batch_size if params.batch_or_token == 'batch' \
else params.token_size
train_batcher = train_dataset.batcher(
size,
buffer_size=params.buffer_size,
shuffle=params.shuffle_batch)
train_queue = queuer.EnQueuer(train_batcher)
train_queue.start(workers=params.nthreads,
max_queue_size=params.max_queue_size)
for lidx, data in enumerate(train_queue.get()):
if lidx <= params.recorder.lidx:
segments = params.recorder.lidx // 5
if params.recorder.lidx < 5 or lidx % segments == 0:
tf.logging.info(
"Passing {}-th index according to record".format(
lidx))
continue
params.recorder.lidx = lidx
# define feed_dict
feed_dict = {
train_features["source"]: data['src'],
train_features["target"]: data['tgt'],
lr: lrate,
}
if cycle_counter == 1:
sess.run(ops["zero_op"])
if cycle_counter < params.update_cycle:
sess.run(ops["collect_op"], feed_dict=feed_dict)
if cycle_counter == params.update_cycle:
cycle_counter = 0
_, loss, gnorm, gstep, glr = sess.run([
ops["train_op"], vle["loss"], vle["gradient_norm"],
global_step, lr
],
feed_dict=feed_dict)
params.recorder.step = gstep
cum_loss.append(loss)
cum_gnorm.append(gnorm)
if gstep % params.disp_freq == 0:
end_time = time.time()
tf.logging.info(
"{} Epoch {}, GStep {}~{}, LStep {}~{}, "
"Loss {:.3f}, GNorm {:.3f}, Lr {:.5f}, Duration {:.3f} s"
.format(
util.time_str(end_time), epoch,
gstep - params.disp_freq + 1, gstep, lidx -
params.disp_freq * params.update_cycle + 1,
lidx, np.mean(cum_loss), np.mean(cum_gnorm),
glr, end_time - start_time))
start_time = time.time()
cum_loss = []
cum_gnorm = []
if gstep > 0 and gstep % params.eval_freq == 0:
eval_start_time = time.time()
tranes, scores, indices = evalu.decoding(
sess, eval_features, eval_seqs, eval_scores,
eval_mask, dev_dataset, params)
bleu = evalu.eval_metric(tranes,
params.tgt_dev_file,
indices=indices)
eval_end_time = time.time()
tf.logging.info(
"{} GStep {}, Scores {}, BLEU {}, Duration {:.3f} s"
.format(util.time_str(eval_end_time), gstep,
np.mean(scores), bleu,
eval_end_time - eval_start_time))
params.recorder.history_scores.append(
(gstep, float(np.mean(scores))))
params.recorder.valid_script_scores.append(
(gstep, float(bleu)))
params.recorder.save_to_json(
os.path.join(params.output_dir, "record.json"))
# save eval translation
evalu.dump_tanslation(
tranes,
os.path.join(params.output_dir,
"eval-{}.trans.txt".format(gstep)),
indices=indices)
train_saver.save(sess, gstep, bleu)
if gstep > 0 and gstep % params.sample_freq == 0:
decode_seqs, decode_scores, decode_mask = sess.run(
[eval_seqs, eval_scores, eval_mask],
feed_dict={eval_features["source"]: data['src']})
tranes, scores = evalu.decode_hypothesis(
decode_seqs,
decode_scores,
params,
mask=decode_mask)
for sidx in range(min(5, len(scores))):
sample_source = evalu.decode_target_token(
data['src'][sidx], params.src_vocab)
tf.logging.info("{}-th Source: {}".format(
sidx, ' '.join(sample_source)))
sample_target = evalu.decode_target_token(
data['tgt'][sidx], params.tgt_vocab)
tf.logging.info("{}-th Target: {}".format(
sidx, ' '.join(sample_target)))
sample_trans = tranes[sidx]
tf.logging.info("{}-th Translation: {}".format(
sidx, ' '.join(sample_trans)))
if gstep >= params.max_training_steps:
break
cycle_counter += 1
train_queue.stop()
# reset to 0
params.recorder.lidx = -1
# handle the learning rate decay in a typical manner
lrate = lrate / 2.
tf.logging.info("Anyway, your training is finished :)")
return train_saver.best_score
def evaluate(params):
# loading dataset
tf.logging.info("Begin Loading Test Dataset")
start_time = time.time()
test_dataset = Dataset(params.src_test_file,
params.src_test_file,
params.src_vocab,
params.src_vocab,
1e6,
batch_or_token='batch')
tf.logging.info(
"End Loading dataset, within {} seconds".format(time.time() -
start_time))
# Build Graph
with tf.Graph().as_default():
eval_features = {
"source": tf.placeholder(tf.int32, [None, None], "source"),
}
# session info
sess = util.get_session(params.gpus)
tf.logging.info("Begining Building Evaluation Graph")
start_time = time.time()
# set up infer graph
eval_seqs, eval_scores, eval_mask = tower_infer_graph(
eval_features, params)
tf.logging.info(
"End Building Inferring Graph, within {} seconds".format(
time.time() - start_time))
# initialize the model
sess.run(tf.global_variables_initializer())
# log parameters
util.variable_printer()
# create saver
eval_saver = saver.Saver(checkpoints=params.checkpoints,
output_dir=params.output_dir)
# restore parameters
tf.logging.info("Trying restore existing parameters")
eval_saver.restore(sess, params.output_dir)
eval_start_time = time.time()
tranes, scores, indices = evalu.decoding(sess, eval_features,
eval_seqs, eval_scores,
eval_mask, test_dataset,
params)
bleu = evalu.eval_metric(tranes, params.tgt_test_file, indices=indices)
eval_end_time = time.time()
tf.logging.info("{} Scores {}, BLEU {}, Duration {}s".format(
util.time_str(eval_end_time), np.mean(scores), bleu,
eval_end_time - eval_start_time))
# save translation
evalu.dump_tanslation(tranes, params.test_output, indices=indices)
return bleu
def evaluate(params):
# loading dataset
tf.logging.info("Begin Loading Test Dataset")
start_time = time.time()
test_dataset = Dataset(params.src_test_file,
params.src_test_file,
params.src_vocab,
params.src_vocab,
params.eval_max_len,
batch_or_token='batch',
data_leak_ratio=params.data_leak_ratio)
tf.logging.info(
"End Loading dataset, within {} seconds".format(time.time() -
start_time))
# Build Graph
with tf.Graph().as_default():
features = []
for fidx in range(max(len(params.gpus), 1)):
feature = {
"source": tf.placeholder(tf.int32, [None, None], "source"),
}
features.append(feature)
# session info
sess = util.get_session(params.gpus)
tf.logging.info("Begining Building Evaluation Graph")
start_time = time.time()
# get graph
graph = model.get_model(params.model_name)
# set up infer graph
eval_seqs, eval_scores = tower_infer_graph(features, graph, params)
tf.logging.info(
"End Building Inferring Graph, within {} seconds".format(
time.time() - start_time))
# set up ema
if params.ema_decay > 0.:
# recover from EMA
ema = tf.train.ExponentialMovingAverage(decay=params.ema_decay)
ema.apply(tf.trainable_variables())
ema_assign_op = tf.group(*(tf.assign(var,
ema.average(var).read_value())
for var in tf.trainable_variables()))
else:
ema_assign_op = tf.no_op()
# initialize the model
sess.run(tf.global_variables_initializer())
# log parameters
util.variable_printer()
# create saver
eval_saver = saver.Saver(checkpoints=params.checkpoints,
output_dir=params.output_dir)
# restore parameters
tf.logging.info("Trying restore existing parameters")
eval_saver.restore(sess, params.output_dir)
sess.run(ema_assign_op)
tf.logging.info("Starting Evaluating")
eval_start_time = time.time()
tranes, scores, indices = evalu.decoding(sess, features, eval_seqs,
eval_scores, test_dataset,
params)
bleu = evalu.eval_metric(tranes, params.tgt_test_file, indices=indices)
eval_end_time = time.time()
tf.logging.info("{} Scores {}, BLEU {}, Duration {}s".format(
util.time_str(eval_end_time), np.mean(scores), bleu,
eval_end_time - eval_start_time))
# save translation
evalu.dump_tanslation(tranes, params.test_output, indices=indices)
return bleu
def ensemble(total_params):
# loading dataset
tf.logging.info("Begin Loading Test Dataset")
start_time = time.time()
# assume that different configurations use the same test file
default_params = total_params[0]
# assume that different models share the same source and target vocabulary, usually it's the case
test_dataset = Dataset(default_params.src_test_file,
default_params.src_test_file,
default_params.src_vocab,
default_params.src_vocab,
default_params.eval_max_len,
batch_or_token='batch',
data_leak_ratio=default_params.data_leak_ratio)
tf.logging.info(
"End Loading dataset, within {} seconds".format(time.time() -
start_time))
# Build Graph
with tf.Graph().as_default():
features = []
for fidx in range(max(len(default_params.gpus), 1)):
feature = {
"source": tf.placeholder(tf.int32, [None, None], "source"),
}
features.append(feature)
# session info
sess = util.get_session(default_params.gpus)
tf.logging.info("Begining Building Evaluation Graph")
start_time = time.time()
# get graph
total_graphs = [
model.get_model(params.model_name) for params in total_params
]
# set up infer graph
eval_seqs, eval_scores = tower_ensemble_graph(features, total_graphs,
total_params)
tf.logging.info(
"End Building Inferring Graph, within {} seconds".format(
time.time() - start_time))
# set up ema
# collect ema variables
ema_used_models = {}
for midx, params in enumerate(total_params):
if params.ema_decay > 0.:
ema_used_models[params.scope_name +
"_ensembler_%d" % midx] = []
for var in tf.trainable_variables():
name = var.op.name
key = name[:name.find('/')]
if key in ema_used_models:
ema_used_models[key].append(var)
ema_assign_list = [tf.no_op()]
for midx, params in enumerate(total_params):
if params.ema_decay > 0.:
key = params.scope_name + "_ensembler_%d" % midx
ema = tf.train.ExponentialMovingAverage(decay=params.ema_decay)
ema.apply(ema_used_models[key])
ema_assign_list += [
tf.assign(var,
ema.average(var).read_value())
for var in ema_used_models[key]
]
ema_assign_op = tf.group(*ema_assign_list)
# initialize the model
sess.run(tf.global_variables_initializer())
# log parameters
util.variable_printer()
# restore parameters
tf.logging.info("Trying restore existing parameters")
all_var_list = {}
for midx, params in enumerate(total_params):
checkpoint = os.path.join(params.output_dir, "checkpoint")
assert tf.gfile.Exists(checkpoint)
latest_checkpoint = tf.gfile.Open(checkpoint).readline()
model_name = latest_checkpoint.strip().split(":")[1].strip()
model_name = model_name[1:-1] # remove ""
model_path = os.path.join(params.output_dir, model_name)
model_path = os.path.abspath(model_path)
assert tf.gfile.Exists(model_path + ".meta")
tf.logging.warn("Starting Backup Restore {}-th Model".format(midx))
reader = tf.train.load_checkpoint(model_path)
# adapt the model names
for name, shape in tf.train.list_variables(model_path):
model_name = name.split('/')[0]
ensemble_name = "{}_ensembler_{}/{}".format(
model_name, midx, name[name.find('/') + 1:])
all_var_list[ensemble_name] = reader.get_tensor(name)
ops = []
for var in tf.global_variables():
name = var.op.name
if name in all_var_list:
tf.logging.info('{} **Good**'.format(name))
ops.append(tf.assign(var, all_var_list[name]))
else:
tf.logging.warn("{} --Bad--".format(name))
restore_op = tf.group(*ops, name="restore_global_vars")
sess.run(restore_op)
sess.run(ema_assign_op)
tf.logging.info("Starting Evaluating")
eval_start_time = time.time()
tranes, scores, indices = evalu.decoding(sess, features, eval_seqs,
eval_scores, test_dataset,
default_params)
bleu = evalu.eval_metric(tranes,
default_params.tgt_test_file,
indices=indices)
eval_end_time = time.time()
tf.logging.info("{} Scores {}, BLEU {}, Duration {}s".format(
util.time_str(eval_end_time), np.mean(scores), bleu,
eval_end_time - eval_start_time))
# save translation
evalu.dump_tanslation(tranes,
default_params.test_output,
indices=indices)
return bleu
def train(params):
# status measure
if params.recorder.estop or \
params.recorder.epoch > params.epoches or \
params.recorder.step > params.max_training_steps:
tf.logging.info(
"Stop condition reached, you have finished training your model.")
return 0.
# loading dataset
tf.logging.info("Begin Loading Training and Dev Dataset")
start_time = time.time()
train_dataset = Dataset(params.src_train_file,
params.tgt_train_file,
params.src_vocab,
params.tgt_vocab,
params.max_len,
batch_or_token=params.batch_or_token,
data_leak_ratio=params.data_leak_ratio)
dev_dataset = Dataset(params.src_dev_file,
params.src_dev_file,
params.src_vocab,
params.src_vocab,
params.eval_max_len,
batch_or_token='batch',
data_leak_ratio=params.data_leak_ratio)
tf.logging.info(
"End Loading dataset, within {} seconds".format(time.time() -
start_time))
# Build Graph
with tf.Graph().as_default():
lr = tf.placeholder(tf.as_dtype(dtype.floatx()), [], "learn_rate")
# shift automatically sliced multi-gpu process into `zero` manner :)
features = []
for fidx in range(max(len(params.gpus), 1)):
feature = {
"source": tf.placeholder(tf.int32, [None, None], "source"),
"target": tf.placeholder(tf.int32, [None, None], "target"),
}
features.append(feature)
# session info
sess = util.get_session(params.gpus)
tf.logging.info("Begining Building Training Graph")
start_time = time.time()
# create global step
global_step = tf.train.get_or_create_global_step()
# set up optimizer
optimizer = tf.train.AdamOptimizer(lr,
beta1=params.beta1,
beta2=params.beta2,
epsilon=params.epsilon)
# get graph
graph = model.get_model(params.model_name)
# set up training graph
loss, gradients = tower_train_graph(features, optimizer, graph, params)
# apply pseudo cyclic parallel operation
vle, ops = cycle.create_train_op({"loss": loss}, gradients, optimizer,
global_step, params)
tf.logging.info(
"End Building Training Graph, within {} seconds".format(
time.time() - start_time))
tf.logging.info("Begin Building Inferring Graph")
start_time = time.time()
# set up infer graph
eval_seqs, eval_scores = tower_infer_graph(features, graph, params)
tf.logging.info(
"End Building Inferring Graph, within {} seconds".format(
time.time() - start_time))
# initialize the model
sess.run(tf.global_variables_initializer())
# log parameters
util.variable_printer()
# create saver
train_saver = saver.Saver(
checkpoints=params.checkpoints,
output_dir=params.output_dir,
best_checkpoints=params.best_checkpoints,
)
tf.logging.info("Training")
cycle_counter = 0
data_on_gpu = []
cum_tokens = []
# restore parameters
tf.logging.info("Trying restore pretrained parameters")
train_saver.restore(sess, path=params.pretrained_model)
tf.logging.info("Trying restore existing parameters")
train_saver.restore(sess)
# setup learning rate
params.lrate = params.recorder.lrate
adapt_lr = lrs.get_lr(params)
start_time = time.time()
start_epoch = params.recorder.epoch
for epoch in range(start_epoch, params.epoches + 1):
params.recorder.epoch = epoch
tf.logging.info("Training the model for epoch {}".format(epoch))
size = params.batch_size if params.batch_or_token == 'batch' \
else params.token_size
train_queue = queuer.EnQueuer(
train_dataset.batcher(size,
buffer_size=params.buffer_size,
shuffle=params.shuffle_batch,
train=True),
lambda x: x,
worker_processes_num=params.process_num,
input_queue_size=params.input_queue_size,
output_queue_size=params.output_queue_size,
)
adapt_lr.before_epoch(eidx=epoch)
for lidx, data in enumerate(train_queue):
if params.train_continue:
if lidx <= params.recorder.lidx:
segments = params.recorder.lidx // 5
if params.recorder.lidx < 5 or lidx % segments == 0:
tf.logging.info(
"{} Passing {}-th index according to record".
format(util.time_str(time.time()), lidx))
continue
params.recorder.lidx = lidx
data_on_gpu.append(data)
# use multiple gpus, and data samples is not enough
# make sure the data is fully added
# The actual batch size: batch_size * num_gpus * update_cycle
if len(params.gpus) > 0 and len(data_on_gpu) < len(
params.gpus):
continue
# increase the counter by 1
cycle_counter += 1
if cycle_counter == 1:
# calculate adaptive learning rate
adapt_lr.step(params.recorder.step)
# clear internal states
sess.run(ops["zero_op"])
# data feeding to gpu placeholders
feed_dicts = {}
for fidx, shard_data in enumerate(data_on_gpu):
# define feed_dict
feed_dict = {
features[fidx]["source"]: shard_data["src"],
features[fidx]["target"]: shard_data["tgt"],
lr: adapt_lr.get_lr(),
}
feed_dicts.update(feed_dict)
# collect target tokens
cum_tokens.append(np.sum(shard_data['tgt'] > 0))
# reset data points on gpus
data_on_gpu = []
# internal accumulative gradient collection
if cycle_counter < params.update_cycle:
sess.run(ops["collect_op"], feed_dict=feed_dicts)
# at the final step, update model parameters
if cycle_counter == params.update_cycle:
cycle_counter = 0
# directly update parameters, usually this works well
if not params.safe_nan:
_, loss, gnorm, pnorm, gstep = sess.run(
[
ops["train_op"], vle["loss"],
vle["gradient_norm"], vle["parameter_norm"],
global_step
],
feed_dict=feed_dicts)
if np.isnan(loss) or np.isinf(loss) or np.isnan(
gnorm) or np.isinf(gnorm):
tf.logging.error(
"Nan or Inf raised! Loss {} GNorm {}.".format(
loss, gnorm))
params.recorder.estop = True
break
else:
# Notice, applying safe nan can help train the big model, but sacrifice speed
loss, gnorm, pnorm, gstep = sess.run(
[
vle["loss"], vle["gradient_norm"],
vle["parameter_norm"], global_step
],
feed_dict=feed_dicts)
if np.isnan(loss) or np.isinf(loss) or np.isnan(gnorm) or np.isinf(gnorm) \
or gnorm > params.gnorm_upper_bound:
tf.logging.error(
"Nan or Inf raised, GStep {} is passed! Loss {} GNorm {}."
.format(gstep, loss, gnorm))
continue
sess.run(ops["train_op"], feed_dict=feed_dicts)
if gstep % params.disp_freq == 0:
end_time = time.time()
tf.logging.info(
"{} Epoch {}, GStep {}~{}, LStep {}~{}, "
"Loss {:.3f}, GNorm {:.3f}, PNorm {:.3f}, Lr {:.5f}, "
"Src {}, Tgt {}, Tokens {}, UD {:.3f} s".format(
util.time_str(end_time), epoch,
gstep - params.disp_freq + 1, gstep,
lidx - params.disp_freq + 1, lidx, loss, gnorm,
pnorm, adapt_lr.get_lr(),
data['src'].shape, data['tgt'].shape,
np.sum(cum_tokens), end_time - start_time))
start_time = time.time()
cum_tokens = []
# trigger model saver
if gstep > 0 and gstep % params.save_freq == 0:
train_saver.save(sess, gstep)
params.recorder.save_to_json(
os.path.join(params.output_dir, "record.json"))
# trigger model evaluation
if gstep > 0 and gstep % params.eval_freq == 0:
if params.ema_decay > 0.:
sess.run(ops['ema_backup_op'])
sess.run(ops['ema_assign_op'])
tf.logging.info("Start Evaluating")
eval_start_time = time.time()
tranes, scores, indices = evalu.decoding(
sess, features, eval_seqs, eval_scores,
dev_dataset, params)
bleu = evalu.eval_metric(tranes,
params.tgt_dev_file,
indices=indices)
eval_end_time = time.time()
tf.logging.info("End Evaluating")
if params.ema_decay > 0.:
sess.run(ops['ema_restore_op'])
tf.logging.info(
"{} GStep {}, Scores {}, BLEU {}, Duration {:.3f} s"
.format(util.time_str(eval_end_time), gstep,
np.mean(scores), bleu,
eval_end_time - eval_start_time))
# save eval translation
evalu.dump_tanslation(
tranes,
os.path.join(params.output_dir,
"eval-{}.trans.txt".format(gstep)),
indices=indices)
# save parameters
train_saver.save(sess, gstep, bleu)
# check for early stopping
valid_scores = [
v[1] for v in params.recorder.valid_script_scores
]
if len(valid_scores
) == 0 or bleu > np.max(valid_scores):
params.recorder.bad_counter = 0
else:
params.recorder.bad_counter += 1
if params.recorder.bad_counter > params.estop_patience:
params.recorder.estop = True
break
params.recorder.history_scores.append(
(int(gstep), float(np.mean(scores))))
params.recorder.valid_script_scores.append(
(int(gstep), float(bleu)))
params.recorder.save_to_json(
os.path.join(params.output_dir, "record.json"))
# handle the learning rate decay in a typical manner
adapt_lr.after_eval(float(bleu))
# trigger temporary sampling
if gstep > 0 and gstep % params.sample_freq == 0:
tf.logging.info("Start Sampling")
decode_seqs, decode_scores = sess.run(
[eval_seqs[:1], eval_scores[:1]],
feed_dict={features[0]["source"]: data["src"][:5]})
tranes, scores = evalu.decode_hypothesis(
decode_seqs, decode_scores, params)
for sidx in range(min(5, len(scores))):
sample_source = evalu.decode_target_token(
data['src'][sidx], params.src_vocab)
tf.logging.info("{}-th Source: {}".format(
sidx, ' '.join(sample_source)))
sample_target = evalu.decode_target_token(
data['tgt'][sidx], params.tgt_vocab)
tf.logging.info("{}-th Target: {}".format(
sidx, ' '.join(sample_target)))
sample_trans = tranes[sidx]
tf.logging.info("{}-th Translation: {}".format(
sidx, ' '.join(sample_trans)))
tf.logging.info("End Sampling")
# trigger stopping
if gstep >= params.max_training_steps:
# stop running by setting EStop signal
params.recorder.estop = True
break
# should be equal to global_step
params.recorder.step = int(gstep)
if params.recorder.estop:
tf.logging.info("Early Stopped!")
break
# reset to 0
params.recorder.lidx = -1
adapt_lr.after_epoch(eidx=epoch)
# Final Evaluation
tf.logging.info("Start Final Evaluating")
if params.ema_decay > 0.:
sess.run(ops['ema_backup_op'])
sess.run(ops['ema_assign_op'])
gstep = int(params.recorder.step + 1)
eval_start_time = time.time()
tranes, scores, indices = evalu.decoding(sess, features, eval_seqs,
eval_scores, dev_dataset, params)
bleu = evalu.eval_metric(tranes, params.tgt_dev_file, indices=indices)
eval_end_time = time.time()
tf.logging.info("End Evaluating")
if params.ema_decay > 0.:
sess.run(ops['ema_restore_op'])
tf.logging.info(
"{} GStep {}, Scores {}, BLEU {}, Duration {:.3f} s".format(
util.time_str(eval_end_time), gstep, np.mean(scores), bleu,
eval_end_time - eval_start_time))
# save eval translation
evalu.dump_tanslation(tranes,
os.path.join(params.output_dir,
"eval-{}.trans.txt".format(gstep)),
indices=indices)
tf.logging.info("Your training is finished :)")
return train_saver.best_score