def predict(list_docs, experiment_name, step='', batch_size=64):
logging.info('*' * 50)
logging.info('RUNNING PREDICT FOR MODEL: %s', experiment_name)
if step == '':
interesting_checkpoint = tf.train.latest_checkpoint(os.path.join(CURRENT_DIR, '..', 'checkpoint', experiment_name))
else:
interesting_checkpoint = os.path.join(CURRENT_DIR, '..', 'checkpoint', experiment_name, 'step-{}'.format(step))
dataset_manager = DatasetManager()
dataset_manager.boot()
list_preprocessed_sentences = preprocessor.preprocess(list_docs)
list_vecs = dataset_manager.text2vec.doc_to_vec(list_preprocessed_sentences)
print(dataset_manager.text2vec.vec_to_doc(list_vecs))
list_vecs = dataset_manager.equalize_vector_length_to_np(list_vectors=list_vecs,
max_length=model_v6.SENTENCE_LENGTH_MAX)
with tf.Graph().as_default() as gr:
logging.info('-- Restoring graph for model: %s', interesting_checkpoint)
saver = tf.train.import_meta_graph('{}.meta'.format(interesting_checkpoint))
logging.info('-- Restored graph for model named: %s', interesting_checkpoint)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default() as sess:
saver.restore(sess=sess, save_path=interesting_checkpoint)
logging.info('-- Restored variables for model named: %s', interesting_checkpoint)
list_predictions = []
num_steps = len(list_vecs) // batch_size
logging.info('There will be %s steps', num_steps + 1)
for i in range(num_steps + 1):
tf_input = gr.get_tensor_by_name('input/tf_input:0')
tf_predictions = gr.get_tensor_by_name('prediction:0')
prediction = sess.run(tf_predictions, feed_dict={
tf_input: list_vecs[i*batch_size: (i+1)*batch_size]
})
list_predictions.extend([dataset_manager.LABEL_UNMAPPING[p] for p in prediction])
return list_predictions
def predict(list_sentences,
output_file,
experiment_name,
step='',
list_labels=[]):
dataset_manager = DatasetManager()
dataset_manager.boot()
list_preprocessed_sentences = preprocessor.preprocess(list_sentences)
list_vecs = dataset_manager.text2vec.doc_to_vec(
list_preprocessed_sentences)
list_vecs = dataset_manager.equalize_vector_length_to_np(
list_vectors=list_vecs, max_length=model_v1.SENTENCE_LENGTH_MAX)
list_labels = dataset_manager.convert_labels_to_np(list_labels)
if step == '':
interesting_checkpoint = tf.train.latest_checkpoint(
os.path.join(CURRENT_DIR, '..', 'checkpoint', experiment_name))
else:
interesting_checkpoint = os.path.join(CURRENT_DIR, '..', 'checkpoint',
experiment_name,
'step-{}'.format(step))
with tf.Graph().as_default() as gr:
logging.info('-- Restoring graph for model: %s',
interesting_checkpoint)
saver = tf.train.import_meta_graph(
'{}.meta'.format(interesting_checkpoint))
logging.info('-- Restored graph for model named: %s',
interesting_checkpoint)
with tf.Session(config=tf.ConfigProto(
allow_soft_placement=True)).as_default() as sess:
saver.restore(sess=sess, save_path=interesting_checkpoint)
logging.info('-- Restored variables for model named: %s',
interesting_checkpoint)
tf_input = gr.get_tensor_by_name('input/tf_input:0')
tf_predictions = gr.get_tensor_by_name('prediction:0')
prediction = sess.run(tf_predictions,
feed_dict={tf_input: list_vecs})
if len(list_labels) != 0:
logging.info('-- Report for model: %s', experiment_name)
logging.info(
classification_report(y_true=list_labels,
y_pred=prediction))
result_dict = dict()
result_dict['sentence'] = list_sentences
result_dict['pre-processed'] = list_preprocessed_sentences
result_dict[
'pre-processed_recover'] = dataset_manager.text2vec.vec_to_doc(
list_vecs)
result_dict['predict'] = prediction
if len(list_labels) != 0:
result_dict['label'] = list_labels
pd.DataFrame(result_dict).to_csv(output_file, index=None)
logging.debug('Saved result at %s', output_file)
