"trained_word2vec.model")
if not os.path.exists(trained_word2vec_model_file):
print("Word2vec model file \'{}\' doesn't exist!".format(
trained_word2vec_model_file))
print("Using word2vec model file : {}".format(trained_word2vec_model_file))
# validate training params file
training_params_file = os.path.join(FLAGS.checkpoint_dir, "..",
"training_params.pickle")
if not os.path.exists(training_params_file):
print("Training params file \'{}\' is missing!".format(
training_params_file))
print("Using training params file : {}".format(training_params_file))
# Load params
params = data_helpers.loadDict(training_params_file)
num_labels = int(params['num_labels'])
max_document_length = int(params['max_document_length'])
# Load data
if FLAGS.eval_train:
x_raw, y_test = data_helpers.load_data_and_labels(
FLAGS.input_text_file, FLAGS.input_label_file, num_labels)
else:
x_raw = [
"a masterpiece four years in the making", "everything is off."
]
y_test = [1, 0]
# Get Embedding vector x_test
sentences, max_document_length = data_helpers.padding_sentences(
print("Using checkpoint file : {}".format(checkpoint_file))
# validate word2vec model file
trained_word2vec_model_file = os.path.join(FLAGS.checkpoint_dir, "..", "trained_word2vec.model")
if not os.path.exists(trained_word2vec_model_file):
print("Word2vec model file \'{}\' doesn't exist!".format(trained_word2vec_model_file))
print("Using word2vec model file : {}".format(trained_word2vec_model_file))
# validate training params file
training_params_file = os.path.join(FLAGS.checkpoint_dir, "..", "training_params.pickle")
if not os.path.exists(training_params_file):
print("Training params file \'{}\' is missing!".format(training_params_file))
print("Using training params file : {}".format(training_params_file))
# Load params
params = data_helpers.loadDict(training_params_file)
num_labels = int(params['num_labels'])
max_document_length = int(params['max_document_length'])
# Load data
if FLAGS.eval_train:
x_raw, y_test = data_helpers.load_data_and_labels(FLAGS.input_text_file, FLAGS.input_label_file, num_labels)
else:
x_raw = ["a masterpiece four years in the making", "everything is off."]
y_test = [1, 0]
# Get Embedding vector x_test
sentences, max_document_length = data_helpers.padding_sentences(x_raw, '<PADDING>', padding_sentence_length = max_document_length)
x_test = np.array(word2vec_helpers.embedding_sentences(sentences, file_to_load = trained_word2vec_model_file))
print("x_test.shape = {}".format(x_test.shape))