def test(session_name=None, is_visualize=False):
if session_name is None:
session_name = input("Session name: ")
config = Configuration() # general settings
data_sets = DataSet(config) # data sets retrieval
model = Clstmnn(config) # model builder
evaluation = Evaluation(config)
visualization = Visualization(config)
with tf.Graph().as_default():
data_set = data_sets.get_data_sets(config.TESTING_BATCH_SIZE)
print('Building model...')
predictions_testing = model.inference(x=data_set.testing_set.x,
mode_name=config.MODE.TESTING,
reuse_lstm=None)
mse = evaluation.loss(predictions=predictions_testing,
labels=data_set.testing_set.y,
mode_name=config.MODE.TESTING)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
merged = tf.summary.merge_all()
saver = tf.train.Saver()
print('Starting session...')
with tf.Session() as sess:
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
summary_writer = tf.summary.FileWriter(
config.OUTPUT_PATH + session_name + '_tested', sess.graph)
sessions_helper = Sessions(config=config,
session=sess,
saver=saver,
session_name=session_name,
summary_writer=summary_writer,
coordinator=coord,
threads=threads)
sessions_helper.restore()
print()
summary = None
start_time = time.time()
mses = []
actual_labels = []
predicted_labels = []
for epoch in range(config.TESTING_EPOCHS):
for step in range(
int(data_set.testing_set.size /
config.TESTING_BATCH_SIZE)):
summary = sess.run(merged)
sys.stdout.write('\r>> Examples tested: {}/{}'.format(
step,
int(data_set.testing_set.size /
config.TESTING_BATCH_SIZE)))
sys.stdout.flush()
example_image, actual_label, predicted_label, mse_result = sess.run(
[
data_set.testing_set.x, data_set.testing_set.y,
predictions_testing, mse
])
mses.append(mse_result)
actual_labels.append(actual_label)
predicted_labels.append(predicted_label)
if is_visualize:
visualization.show_example(predicted_label,
actual_label, example_image,
mse_result)
summary_writer.add_summary(summary, 1)
print()
print('testing completed in %s' % (time.time() - start_time))
print('%s: MSE @ 1 = %.9f' %
(datetime.now(), np.array(mses).mean()))
visualization.display_on_map(actual_labels, predicted_labels,
session_name,
np.array(mses).mean())
sessions_helper.end()
def test(session_name=None, is_visualize=False):
if session_name is None:
session_name = input("Session name: ")
config = Configuration() # general settings
data_sets = DataSet(config) # data sets retrieval
model = Cnn(config) # model builder
evaluation = Evaluation(config)
visualization = Visualization(config)
with tf.Graph().as_default():
data_set = data_sets.get_data_sets(config.TESTING_BATCH_SIZE)
print('Building model...')
predictions_testing = model.inference(x=data_set.testing_set.x,
mode_name=config.MODE.TESTING)
is_correct = evaluation.correct_number(predictions_testing,
data_set.testing_set.y)
predictions_testing = tf.argmax(predictions_testing, 1)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
merged = tf.summary.merge_all()
saver = tf.train.Saver()
print('Starting session...')
with tf.Session() as sess:
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
summary_writer = tf.summary.FileWriter(
config.OUTPUT_PATH + session_name + '_tested', sess.graph)
sessions_helper = Sessions(config=config,
session=sess,
saver=saver,
session_name=session_name,
summary_writer=summary_writer,
coordinator=coord,
threads=threads)
sessions_helper.restore()
print()
true_count = 0
summary = None
start_time = time.time()
labels = []
predictions = []
for epoch in range(config.TESTING_EPOCHS):
for step in range(
int(data_set.testing_set.size /
config.TESTING_BATCH_SIZE)):
summary = sess.run(merged)
sys.stdout.write('\r>> Examples tested: {}/{}'.format(
step,
int(data_set.testing_set.size /
config.TESTING_BATCH_SIZE)))
sys.stdout.flush()
example_image, actual_label, predicted_label, is_correct_result = sess.run(
[
data_set.testing_set.x, data_set.testing_set.y,
predictions_testing, is_correct
])
true_count += np.sum(is_correct_result)
labels.append(actual_label)
predictions.append(predicted_label)
if is_visualize:
visualization.show_example(predicted_label,
actual_label, example_image,
is_correct_result)
summary_writer.add_summary(summary, 1)
np_labels = np.array(labels)
np_predictions = np.array(predictions)
conf_matrix = tf.confusion_matrix(
labels=tf.squeeze(np_labels),
predictions=tf.squeeze(np_predictions),
num_classes=config.NUM_CLASSES)
print()
c_m = sess.run(conf_matrix)
print(c_m)
precision = true_count / data_set.testing_set.size
print()
print('testing completed in %s' % (time.time() - start_time))
print('%s: accuracy @ 1 = %.3f' %
(datetime.now(), precision * 100))
sessions_helper.end()
