# path_reviews = os.path.join(args.data_dir, 'reviews_small.txt')
path_reviews = os.path.join(args.data_dir, 'reviews{}.txt'.format(toy))
path_sentiments = os.path.join(args.data_dir, 'sentiments{}.txt'.format(toy))
# path_sentiments = os.path.join(args.data_dir, 'sentiments.txt')
# Load vocabularies
words = tf.contrib.lookup.index_table_from_file(path_words, num_oov_buckets=num_oov_buckets)
sentiments = tf.contrib.lookup.index_table_from_file(path_sentiment_tags)
# Create the input data pipeline
reviews = load_dataset_from_text(path_reviews,words)
review_sentiments = load_dataset_from_text(path_sentiments,sentiments, isLabels=True)
# Specify other parameters for the dataset and the model
params_sentiment.id_pad_word = words.lookup(tf.constant(params_sentiment.pad_word))
params_sentiment.id_pad_tag = words.lookup(tf.constant(params_sentiment.pad_tag))
# Create the iterator over the test set
inputs_sentiment = input_fn('eval', reviews, review_sentiments, params_sentiment)
# Define the model
print('Creating sentiment and era models...')
model_spec_sentiment = model_fn('eval', inputs_sentiment, params_sentiment, reuse=False)
print('Done')
# Evaluate the model...
# evaluate(model-spec, args.model_dir, params, args.restore_from)
# initialize saver to restore model
saver = tf.train.Saver()
path_words = os.path.join(args.data_dir, 'words.txt')
path_tags = os.path.join(args.data_dir, 'tags.txt')
path_eval_sentences = os.path.join(args.data_dir, 'dev/sentences.txt')
path_eval_labels = os.path.join(args.data_dir, 'dev/labels.txt')
# Load Vocabularies
words = tf.contrib.lookup.index_table_from_file(path_words, num_oov_buckets=num_oov_buckets)
tags = tf.contrib.lookup.index_table_from_file(path_tags)
# Create the input data pipeline
logging.info("Creating the dataset...")
test_sentences = load_dataset_from_text(path_eval_sentences, words)
test_labels = load_dataset_from_text(path_eval_labels, tags)
# Specify other parameters for the dataset and the model
params.eval_size = params.test_size
params.id_pad_word = words.lookup(tf.constant(params.pad_word))
params.id_pad_tag = tags.lookup(tf.constant(params.pad_tag))
# Create iterator over the test set
inputs = input_fn('eval', test_sentences, test_labels, params)
logging.info("- done.")
# Define the model
logging.info("Creating the model...")
model_spec = model_fn('eval', inputs, params, reuse=False)
logging.info("- done.")
logging.info("Starting evaluation")
evaluate(model_spec, args.model_dir, params, args.restore_from)
path_reviews = os.path.join(args.data_dir, 'reviews{}.txt'.format(toy))
path_eras = os.path.join(args.data_dir, 'eras{}.txt'.format(toy))
# path_eras = os.path.join(args.data_dir, 'eras.txt')
# Load vocabularies
words = tf.contrib.lookup.index_table_from_file(
path_words, num_oov_buckets=num_oov_buckets)
eras = tf.contrib.lookup.index_table_from_file(path_era_tags)
# Create the input data pipeline
reviews = load_dataset_from_text(path_reviews, words)
review_eras = load_dataset_from_text(path_eras, eras, isLabels=True)
# Specify other parameters for the dataset and the model
params_era.id_pad_word = words.lookup(tf.constant(params_era.pad_word))
params_era.id_pad_tag = words.lookup(tf.constant(params_era.pad_tag))
# Create the iterator over the test set
inputs_era = input_fn('eval', reviews, review_eras, params_era)
# Define the model
print('Creating era models...')
model_spec_era = model_fn('eval', inputs_era, params_era, reuse=False)
print('Done')
print(era_model_path)
print(path_words)
print(path_era_tags)
print(path_reviews)
print(path_eras)
print(os.path.join(args.model_dir, args.restore_from))
