def create_eval_graph(scope=None):
graph = tf.Graph()
with graph.as_default():
src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(
hparams.src_vocab_file, hparams.tgt_vocab_file, hparams.share_vocab)
src_file_placeholder = tf.placeholder(shape=(), dtype=tf.string)
tgt_file_placeholder = tf.placeholder(shape=(), dtype=tf.string)
src_dataset = tf.data.TextLineDataset(src_file_placeholder)
tgt_dataset = tf.data.TextLineDataset(tgt_file_placeholder)
iterator = iterator_utils.get_iterator(
src_dataset,
tgt_dataset,
src_vocab_table,
tgt_vocab_table,
hparams.batch_size,
sos=hparams.sos,
eos=hparams.eos,
random_seed=hparams.random_seed,
num_buckets=hparams.num_buckets,
src_max_len=hparams.src_max_len_infer,
tgt_max_len=hparams.tgt_max_len_infer)
final_model = model.Model(hparams,
iterator=iterator,
mode=tf.contrib.learn.ModeKeys.EVAL,
source_vocab_table=src_vocab_table,
target_vocab_table=tgt_vocab_table,
scope=scope)
return EvalGraph(graph=graph,
model=final_model,
src_file_placeholder=src_file_placeholder,
tgt_file_placeholder=tgt_file_placeholder,
iterator=iterator)
def create_infer_graph(scope=None):
graph = tf.Graph()
with graph.as_default():
src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(hparams.src_vocab_file,
hparams.tgt_vocab_file,
hparams.share_vocab)
reverse_tgt_vocab_table = lookup_ops.index_to_string_table_from_file(hparams.tgt_vocab_file,
default_value=vocab_utils.UNK)
src_placeholder = tf.placeholder(shape=[None], dtype=tf.string)
batch_size_placeholder = tf.placeholder(shape=[], dtype=tf.int64)
src_dataset = tf.data.Dataset.from_tensor_slices(src_placeholder)
iterator = iterator_utils.get_infer_iterator(src_dataset,
src_vocab_table,
batch_size=batch_size_placeholder,
eos=hparams.eos,
src_max_len=hparams.src_max_len_infer)
final_model = model.Model(hparams,
iterator=iterator,
mode=tf.contrib.learn.ModeKeys.INFER,
source_vocab_table=src_vocab_table,
target_vocab_table=tgt_vocab_table,
reverse_target_vocab_table=reverse_tgt_vocab_table,
scope=scope)
return InferGraph(graph=graph,
model=final_model,
src_placeholder=src_placeholder,
batch_size_placeholder=batch_size_placeholder,
iterator=iterator)
def create_train_graph(scope=None):
graph = tf.Graph()
with graph.as_default():
src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(hparams.src_vocab_file,
hparams.tgt_vocab_file,
share_vocab=False)
src_dataset = tf.data.TextLineDataset(hparams.src_file)
tgt_dataset = tf.data.TextLineDataset(hparams.tgt_file)
skip_count_placeholder = tf.placeholder(shape=(), dtype=tf.int64)
iterator = iterator_utils.get_iterator(src_dataset, tgt_dataset, src_vocab_table, tgt_vocab_table,
batch_size=hparams.batch_size,
sos=hparams.sos,
eos=hparams.eos,
random_seed=None,
num_buckets=hparams.num_buckets,
src_max_len=hparams.src_max_len,
tgt_max_len=hparams.tgt_max_len,
skip_count=skip_count_placeholder)
final_model = model.Model(hparams,
iterator=iterator,
mode=tf.contrib.learn.ModeKeys.TRAIN,
source_vocab_table=src_vocab_table,
target_vocab_table=tgt_vocab_table,
scope=scope)
return TrainGraph(graph=graph, model=final_model, iterator=iterator, skip_count_placeholder=skip_count_placeholder)