def translate_file(load_from: str, input_file_handle: io.TextIOWrapper,
output_file_handle: io.TextIOWrapper, **kwargs):
"""
Translates all lines that can be read from an open file handle. Translations
are written directly to an output file handle.
"""
source_vocab, target_vocab = load_vocabs(load_from)
# fix batch_size to 1
encoder_inputs, decoder_targets, decoder_inputs, _, _, decoder_logits, _ = compgraph.define_computation_graph(
source_vocab.size, target_vocab.size, 1)
saver = tf.train.Saver()
with tf.Session() as session:
# load model
saver.restore(session, os.path.join(load_from, C.MODEL_FILENAME))
for line in input_file_handle:
# new: this didn't work for train().
# Hence, discarded here.
# line = reversed(line)
translation = translate_line(session, line, source_vocab,
target_vocab, encoder_inputs,
decoder_inputs, decoder_targets,
decoder_logits)
output_file_handle.write(translation + "\n")
def translate_lines(load_from: str,
input_lines: List[str],
train_mode: bool = False,
**kwargs) -> List[str]:
"""
Translates a list of strings.
"""
source_vocab, target_vocab = load_vocabs(load_from)
# fix batch_size to 1
encoder_inputs, decoder_targets, decoder_inputs, _, _, decoder_logits, _ = compgraph.define_computation_graph(
source_vocab.size, target_vocab.size, 1)
saver = tf.train.Saver()
with tf.Session() as session:
# load model
saver.restore(session, os.path.join(load_from, C.MODEL_FILENAME))
translations = []
for line in input_lines:
translation = translate_line(session, line, source_vocab,
target_vocab, encoder_inputs,
decoder_inputs, decoder_targets,
decoder_logits)
translations.append(translation)
return translations
def score(source_data: str,
target_data: str,
load_from: str,
corpus_average: bool,
normalize: float = 0.6,
**kwargs):
"""Scores a text using a trained translation model. See argument description in `bin/daikon`."""
# fix batch size at 1 to get individual scores for sentences
batch_size = 1
source_vocab = Vocabulary()
target_vocab = Vocabulary()
source_vocab.load(os.path.join(load_from, C.SOURCE_VOCAB_FILENAME))
target_vocab.load(os.path.join(load_from, C.TARGET_VOCAB_FILENAME))
reader_ids = list(
reader.read_parallel(source_data, target_data, source_vocab,
target_vocab, C.SCORE_MAX_LEN))
encoder_inputs, decoder_targets, decoder_inputs, loss, _, _, _ = define_computation_graph(
source_vocab.size, target_vocab.size, batch_size)
saver = tf.train.Saver()
with tf.Session() as session:
# load model
saver.restore(session, os.path.join(load_from, C.MODEL_FILENAME))
losses = []
total_iter = 0
for x, y, z in reader.iterate(reader_ids, batch_size, shuffle=False):
feed_dict = {
encoder_inputs: x,
decoder_inputs: y,
decoder_targets: z
}
l = session.run([loss], feed_dict=feed_dict)
# first session variable
l = l[0]
if normalize:
# normalize by length of target sequence (including EOS token)
l /= np.power(y.shape[1], normalize)
losses.append(l)
total_iter += 1
if corpus_average:
total_loss = np.sum(losses)
perplexity = np.exp(total_loss / total_iter)
return perplexity
else:
return np.exp(losses)
def _early_stopping(val_reader_ids, source_vocab, target_vocab, batch_size):
val_graph_components = define_computation_graph(source_vocab.size,
target_vocab.size,
batch_size)
val_encoder_inputs, val_decoder_targets, val_decoder_inputs, val_loss, val_train_step, val_decoder_logits, val_summary = val_graph_components
patience = 3
val_total_loss = 0
val_epoch = 0
best_val_perplexity = float()
no_imp_count = 0
with tf.Session() as session:
for x, y, z in reader.iterate(val_reader_ids, batch_size,
shuffle=True):
val_feed_dict = {
val_encoder_inputs: x,
val_decoder_inputs: y,
val_decoder_targets: z
}
l, _, s = session.run([val_loss, val_train_step, val_summary],
val_feed_dict=val_feed_dict)
val_total_loss += l
val_epoch += 1
current_val_perplexity = np.exp(val_total_loss / val_epoch)
logger.info("Perplexity on validation data: %.2f",
current_val_perplexity)
if current_val_perplexity < best_val_perplexity:
logger.info("Lowest perplexity on validation data achieved")
best_val_perplexity = current_val_perplexity
no_imp_count = 0
else:
global save_model
save_model = False
no_imp_count += 1
if no_imp_count >= patience:
logger.info(
"Stopped improving on validation data for %d epochs: terminating training",
no_imp_count)
def score(source_data: str, target_data: str, load_from: str, corpus_average: bool, normalize: bool, **kwargs):
"""Scores a text using a trained translation model. See argument description in `bin/daikon`."""
# fix batch size at 1 to get individual scores for sentences
batch_size = 1
source_vocab = Vocabulary()
target_vocab = Vocabulary()
source_vocab.load(os.path.join(load_from, C.SOURCE_VOCAB_FILENAME))
target_vocab.load(os.path.join(load_from, C.TARGET_VOCAB_FILENAME))
reader_ids = list(reader.read_parallel(source_data, target_data, source_vocab, target_vocab, C.SCORE_MAX_LEN))
encoder_inputs, decoder_targets, decoder_inputs, loss, _, _, _ = define_computation_graph(source_vocab.size, target_vocab.size, batch_size)
saver = tf.train.Saver()
with tf.Session() as session:
# load model
saver.restore(session, os.path.join(load_from, C.MODEL_FILENAME))
losses = []
total_iter = 0
for x, y, z in reader.iterate(reader_ids, batch_size, shuffle=False):
feed_dict = {encoder_inputs: x,
decoder_inputs: y,
decoder_targets: z}
l = session.run([loss], feed_dict=feed_dict)
# first session variable
l = l[0]
if normalize:
# normalize by length of target sequence (including EOS token)
l /= y.shape[1]
losses.append(l)
total_iter += 1
if corpus_average:
total_loss = np.sum(losses)
perplexity = np.exp(total_loss / total_iter)
return perplexity
else:
return np.exp(losses)
def train(source_data: str,
target_data: str,
epochs: int,
batch_size: int,
source_vocab_max_size: int,
target_vocab_max_size: int,
save_to: str,
log_to: str,
sample_after_epoch: bool,
**kwargs) -> None:
"""Trains a language model. See argument description in `bin/romanesco`."""
# create folders for model and logs if they don't exist yet
for folder in [save_to, log_to]:
if not os.path.exists(folder):
os.makedirs(folder)
logger.info("Creating vocabularies.")
# create vocabulary to map words to ids, for source and target
source_vocab = create_vocab(source_data, source_vocab_max_size, save_to, C.SOURCE_VOCAB_FILENAME)
target_vocab = create_vocab(target_data, target_vocab_max_size, save_to, C.TARGET_VOCAB_FILENAME)
logger.info("Source vocabulary: %s", source_vocab)
logger.info("Target vocabulary: %s", target_vocab)
# convert training data to list of word ids
logger.info("Reading training data.")
reader_ids = list(reader.read_parallel(source_data, target_data, source_vocab, target_vocab, C.MAX_LEN))
# define computation graph
logger.info("Building computation graph.")
graph_components = define_computation_graph(source_vocab.size, target_vocab.size, batch_size)
encoder_inputs, decoder_targets, decoder_inputs, loss, train_step, decoder_logits, summary = graph_components
saver = tf.train.Saver()
with tf.Session() as session:
# init
session.run(tf.global_variables_initializer())
# write logs (@tensorboard)
summary_writer = tf.summary.FileWriter(log_to, graph=tf.get_default_graph())
logger.info("Starting training.")
tic = time.time()
num_batches = math.floor(len(reader_ids) / batch_size)
prev_perplexity = 10000.0
counter = 0
# iterate over training data `epochs` times
for epoch in range(1, epochs + 1):
total_loss = 0.0
total_iter = 0
iter_tic = time.time()
for x, y, z in reader.iterate(reader_ids, batch_size, shuffle=True):
feed_dict = {encoder_inputs: x,
decoder_inputs: y,
decoder_targets: z}
l, _, s = session.run([loss, train_step, summary],
feed_dict=feed_dict)
summary_writer.add_summary(s, total_iter)
total_loss += l
total_iter += 1
if total_iter % C.LOGGING_INTERVAL == 0 or total_iter == num_batches:
iter_taken = time.time() - iter_tic
logger.debug("Epoch=%s, iteration=%s/%s, samples/second=%.2f", epoch, total_iter, num_batches, batch_size * C.LOGGING_INTERVAL / float(iter_taken))
iter_tic = time.time()
perplexity = np.exp(total_loss / total_iter)
logger.info("Perplexity on training data after epoch %s: %.2f", epoch, perplexity)
saver.save(session, os.path.join(save_to, C.MODEL_FILENAME))
if sample_after_epoch:
# sample from model after epoch
thread = threading.Thread(target=_sample_after_epoch, args=[reader_ids, source_vocab, target_vocab, save_to, epoch])
thread.start()
# early stopping
if perplexity > prev_perplexity:
counter += 1
else:
if counter != 0:
counter -= 1
if counter == 2:
taken = time.time() - tic
m, s = divmod(taken, 60)
h, m = divmod(m, 60)
logger.info("Training finished early. Overall time taken to train: %d:%02d:%02d" % (h, m, s))
taken = time.time() - tic
m, s = divmod(taken, 60)
h, m = divmod(m, 60)
logger.info("Training finished. Overall time taken to train: %d:%02d:%02d" % (h, m, s))
def train(source_data: str,
target_data: str,
epochs: int,
batch_size: int,
source_vocab_max_size: int,
target_vocab_max_size: int,
save_to: str,
log_to: str,
sample_after_epoch: bool,
source_val_data: str = None,
target_val_data: str = None,
val_epochs: int = C.VAL_EPOCHS,
patience: int = C.PATIENCE,
overwrite: bool = False,
**kwargs) -> None:
"""Trains a translation model. See argument description in `bin/daikon`."""
# enable early stopping if validation data files were specified
early_stopping = source_val_data is not None and target_val_data is not None
# create folders for model and logs if they don't exist yet
for folder in [save_to, log_to]:
if not os.path.exists(folder):
os.makedirs(folder)
if early_stopping:
val_model_dir = os.path.join(save_to, C.VALIDATION_MODEL_DIR)
if not os.path.exists(val_model_dir):
os.makedirs(val_model_dir)
# create a new graph if the overwrite option is enabled or there is no
# existing model in the save_to directory
checkpoint_file = os.path.join(save_to, C.MODEL_CHECKPOINT)
initialize_graph = overwrite or not os.path.exists(checkpoint_file)
source_vocab = Vocabulary()
target_vocab = Vocabulary()
if not initialize_graph:
# load existing vocabulary that maps words to ids, for source and target
logger.info("Loading vocabularies.")
source_vocab.load(os.path.join(save_to, C.SOURCE_VOCAB_FILENAME))
target_vocab.load(os.path.join(save_to, C.TARGET_VOCAB_FILENAME))
else:
# create vocabulary to map words to ids, for source and target
logger.info("Creating vocabularies.")
source_vocab = create_vocab(source_data, source_vocab_max_size,
save_to, C.SOURCE_VOCAB_FILENAME)
target_vocab = create_vocab(target_data, target_vocab_max_size,
save_to, C.TARGET_VOCAB_FILENAME)
logger.info("Source vocabulary: %s", source_vocab)
logger.info("Target vocabulary: %s", target_vocab)
if early_stopping:
# create copies of vocabulary files used for checking validation
# data performance
source_vocab.save(os.path.join(val_model_dir, C.SOURCE_VOCAB_FILENAME))
target_vocab.save(os.path.join(val_model_dir, C.TARGET_VOCAB_FILENAME))
# convert training data to list of word ids
logger.info("Reading training data.")
reader_ids = list(
reader.read_parallel(source_data, target_data, source_vocab,
target_vocab, C.MAX_LEN))
# define computation graph
logger.info("Building computation graph.")
graph_components = define_computation_graph(source_vocab.size,
target_vocab.size, batch_size)
encoder_inputs, decoder_targets, decoder_inputs, loss, train_step, decoder_logits, summary = graph_components
saver = tf.train.Saver()
with tf.Session() as session:
if initialize_graph:
# init
session.run(tf.global_variables_initializer())
else:
# load/restore model for further training
saver.restore(session, os.path.join(save_to, C.MODEL_FILENAME))
# write logs (@tensorboard)
summary_writer = tf.summary.FileWriter(log_to,
graph=tf.get_default_graph())
logger.info("Starting training.")
tic = time.time()
num_batches = math.floor(len(reader_ids) / batch_size)
if early_stopping:
# initialize metrics for checking validation data performance
best_val_loss = float("inf")
epochs_without_improvement = 0
# iterate over training data `epochs` times
for epoch in range(1, epochs + 1):
total_loss = 0.0
total_iter = 0
iter_tic = time.time()
for x, y, z in reader.iterate(reader_ids, batch_size,
shuffle=True):
feed_dict = {
encoder_inputs: x,
decoder_inputs: y,
decoder_targets: z
}
l, _, s = session.run([loss, train_step, summary],
feed_dict=feed_dict)
summary_writer.add_summary(s, total_iter)
total_loss += l
total_iter += 1
if total_iter % C.LOGGING_INTERVAL == 0 or total_iter == num_batches:
iter_taken = time.time() - iter_tic
logger.debug(
"Epoch=%s, iteration=%s/%s, samples/second=%.2f",
epoch, total_iter, num_batches,
batch_size * C.LOGGING_INTERVAL / float(iter_taken))
iter_tic = time.time()
perplexity = np.exp(total_loss / total_iter)
logger.info("Perplexity on training data after epoch %s: %.2f",
epoch, perplexity)
save_model = True
if early_stopping and epoch % val_epochs == 0:
# save a copy of the current model that can be used to check
# its performance for the validation data
saver.save(session,
os.path.join(val_model_dir, C.MODEL_FILENAME))
# spin off a thread to call score() for the validation data
threadPool = ThreadPool(processes=1)
scoreRes = threadPool.apply_async(
score, (source_val_data, target_val_data, val_model_dir,
True, False))
latest_val_loss = scoreRes.get()
logging.info(
"Current model perplexity on validation data: %.2f",
latest_val_loss)
if latest_val_loss < best_val_loss:
logging.info(
"Lowest perplexity on validation data achieved")
best_val_loss = latest_val_loss
epochs_without_improvement = 0
else:
save_model = False
epochs_without_improvement += 1
if epochs_without_improvement >= patience:
logging.info(
"No improvement in validation data perplexity for %d epochs: terminating training",
epochs_without_improvement)
return
if save_model:
saver.save(session, os.path.join(save_to, C.MODEL_FILENAME))
if sample_after_epoch:
# sample from model after epoch
thread = threading.Thread(target=_sample_after_epoch,
args=[
reader_ids, source_vocab,
target_vocab, save_to, epoch
])
thread.start()
taken = time.time() - tic
m, s = divmod(taken, 60)
h, m = divmod(m, 60)
logger.info(
"Training finished. Overall time taken to train: %d:%02d:%02d" %
(h, m, s))
def train(source_data: str, target_data: str, epochs: int, batch_size: int,
vocab_max_size: int, save_to: str, log_to: str,
sample_after_epoch: bool, **kwargs) -> None:
"""Trains a language model. See argument description in `bin/romanesco`."""
# create folders for model and logs if they don't exist yet
for folder in [save_to, log_to]:
if not os.path.exists(folder):
os.makedirs(folder)
# create vocabulary to map words to ids, for source and target
source_vocab = create_vocab(source_data, vocab_max_size, save_to,
C.SOURCE_VOCAB_FILENAME)
target_vocab = create_vocab(target_data, vocab_max_size, save_to,
C.TARGET_VOCAB_FILENAME)
# convert training data to list of word ids
reader_ids = list(
reader.read_parallel(source_data, target_data, source_vocab,
target_vocab, C.MAX_LEN))
# define computation graph
logging.info("Building computation graph.")
graph_components = define_computation_graph(source_vocab.size,
target_vocab.size, batch_size)
encoder_inputs, decoder_targets, decoder_inputs, loss, train_step, decoder_logits, summary = graph_components
saver = tf.train.Saver()
with tf.Session() as session:
# init
session.run(tf.global_variables_initializer())
# write logs (@tensorboard)
summary_writer = tf.summary.FileWriter(log_to,
graph=tf.get_default_graph())
logging.info("Starting training.")
# iterate over training data `epochs` times
for epoch in range(1, epochs + 1):
total_loss = 0.0
total_iter = 0
for x, y, z in reader.iterate(reader_ids, batch_size,
shuffle=True):
feed_dict = {
encoder_inputs: x,
decoder_inputs: y,
decoder_targets: z
}
l, _, s = session.run([loss, train_step, summary],
feed_dict=feed_dict)
summary_writer.add_summary(s, total_iter)
total_loss += l
total_iter += 1
if total_iter % 100 == 0:
logging.debug("Epoch=%s, iteration=%s", epoch, total_iter)
perplexity = np.exp(total_loss / total_iter)
logging.info("Perplexity on training data after epoch %s: %.2f",
epoch, perplexity)
saver.save(session, os.path.join(save_to, C.MODEL_FILENAME))
if sample_after_epoch:
# sample from model after epoch
thread = threading.Thread(target=_sample_after_epoch,
args=[
reader_ids, source_vocab,
target_vocab, save_to, epoch
])
thread.start()
logging.info("Training finished.")
def train(source_data: str,
target_data: str,
epochs: int,
batch_size: int,
source_vocab_max_size: int,
target_vocab_max_size: int,
save_to: str,
log_to: str,
sample_after_epoch: bool,
**kwargs) -> None:
"""Trains a language model. See argument description in `bin/romanesco`."""
# create folders for model and logs if they don't exist yet
for folder in [save_to, log_to]:
if not os.path.exists(folder):
os.makedirs(folder)
logger.info("Creating vocabularies.")
# create vocabulary to map words to ids, for source and target
source_vocab = create_vocab(source_data, source_vocab_max_size, save_to, C.SOURCE_VOCAB_FILENAME)
target_vocab = create_vocab(target_data, target_vocab_max_size, save_to, C.TARGET_VOCAB_FILENAME)
logger.info("Source vocabulary: %s", source_vocab)
logger.info("Target vocabulary: %s", target_vocab)
# convert training data to list of word ids
logger.info("Reading training data.")
reader_ids = list(reader.read_parallel(source_data, target_data, source_vocab, target_vocab, C.MAX_LEN))
# define computation graph
logger.info("Building computation graph.")
graph_components = define_computation_graph(source_vocab.size, target_vocab.size, batch_size)
encoder_inputs, decoder_targets, decoder_inputs, loss, train_step, decoder_logits, summary = graph_components
saver = tf.train.Saver()
with tf.Session() as session:
# init
session.run(tf.global_variables_initializer())
# write logs (@tensorboard)
summary_writer = tf.summary.FileWriter(log_to, graph=tf.get_default_graph())
logger.info("Starting training.")
tic = time.time()
num_batches = math.floor(len(reader_ids) / batch_size)
# iterate over training data `epochs` times
for epoch in range(1, epochs + 1):
total_loss = 0.0
total_iter = 0
iter_tic = time.time()
for x, y, z in reader.iterate(reader_ids, batch_size, shuffle=True):
feed_dict = {encoder_inputs: x,
decoder_inputs: y,
decoder_targets: z}
l, _, s = session.run([loss, train_step, summary],
feed_dict=feed_dict)
summary_writer.add_summary(s, total_iter)
total_loss += l
total_iter += 1
if total_iter % C.LOGGING_INTERVAL == 0 or total_iter == num_batches:
iter_taken = time.time() - iter_tic
logger.debug("Epoch=%s, iteration=%s/%s, samples/second=%.2f", epoch, total_iter, num_batches, batch_size * C.LOGGING_INTERVAL / float(iter_taken))
iter_tic = time.time()
perplexity = np.exp(total_loss / total_iter)
logger.info("Perplexity on training data after epoch %s: %.2f", epoch, perplexity)
saver.save(session, os.path.join(save_to, C.MODEL_FILENAME))
if sample_after_epoch:
# sample from model after epoch
thread = threading.Thread(target=_sample_after_epoch, args=[reader_ids, source_vocab, target_vocab, save_to, epoch])
thread.start()
taken = time.time() - tic
m, s = divmod(taken, 60)
h, m = divmod(m, 60)
logger.info("Training finished. Overall time taken to train: %d:%02d:%02d" % (h, m, s))
