def evaluate(experiment_name, step=''):
logging.info('*' * 50)
logging.info('RUNNING EVALUATION 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()
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 = []
list_labels = []
for docs, labels in dataset_manager.get_test_by_batch(
batch_size=FLAGS.BATCH_SIZE):
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: docs})
list_predictions.extend(prediction)
list_labels.extend(labels)
logging.debug('-- Prediction length: %s/%s',
len(list_predictions),
dataset_manager.test_y.shape[0])
logging.info('-- Report for model: %s', experiment_name)
logging.info(
classification_report(y_true=list_labels,
y_pred=list_predictions,
digits=4))
logging.info(
confusion_matrix(y_true=list_labels, y_pred=list_predictions))
