def main(argv):
del argv # unused
embeddings_path = FLAGS.embeddings_path
preprocessor = text_preprocessor.TextPreprocessor(embeddings_path)
nltk.download("punkt")
train_preprocess_fn = preprocessor.train_preprocess_fn(nltk.word_tokenize)
dataset = tfrecord_input.TFRecordInputWithTokenizer(
train_preprocess_fn=train_preprocess_fn)
# TODO: Move embedding *into* Keras model.
model_tf = tf_gru_attention_multiclass.TFRNNModel(dataset.labels())
model = preprocessor.add_embedding_to_model(model_tf,
base_model.TOKENS_FEATURE_KEY)
trainer = model_trainer.ModelTrainer(dataset, model)
trainer.train_with_eval()
serving_input_fn = serving_input.create_serving_input_fn(
word_to_idx=preprocessor._word_to_idx,
unknown_token=preprocessor._unknown_token,
text_feature_name=base_model.TOKENS_FEATURE_KEY,
example_key_name=base_model.EXAMPLE_KEY)
trainer.export(serving_input_fn, base_model.EXAMPLE_KEY)
def main(argv):
del argv # unused
embeddings_path = FLAGS.embeddings_path
text_feature_name = FLAGS.text_feature_name
key_name = FLAGS.key_name
preprocessor = text_preprocessor.TextPreprocessor(embeddings_path)
nltk.download("punkt")
train_preprocess_fn = preprocessor.train_preprocess_fn(nltk.word_tokenize)
dataset = tfrecord_input.TFRecordInput(
train_path=FLAGS.train_path,
validate_path=FLAGS.validate_path,
text_feature=text_feature_name,
labels=LABELS,
train_preprocess_fn=train_preprocess_fn,
batch_size=FLAGS.batch_size)
# TODO: Move embedding *into* Keras model.
model_keras = keras_gru_attention.KerasRNNModel(
set(LABELS.keys()),
preprocessor._embedding_size)
model = preprocessor.add_embedding_to_model(
model_keras, text_feature_name)
trainer = model_trainer.ModelTrainer(dataset, model)
trainer.train_with_eval(FLAGS.train_steps, FLAGS.eval_period, FLAGS.eval_steps)
serving_input_fn = serving_input.create_serving_input_fn(
word_to_idx=preprocessor._word_to_idx,
unknown_token=preprocessor._unknown_token,
text_feature_name=text_feature_name,
key_name=key_name)
trainer.export(serving_input_fn)
