# 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))