def run():
total_time = time.time()
FLAGS.num_gpus = 1 if FLAGS.core_type == 'GPU' else FLAGS.num_gpus
LOGGER.debug("Load Data")
data_load_time = time.time()
data_sets = load_data(input_data, FLAGS.one_hot)
data_load_time = time.time() - data_load_time
with tf.Graph().as_default():
images_placeholder, labels_placeholder = placeholder_inputs(FLAGS.one_hot, FLAGS.height*FLAGS.width*FLAGS.channels, FLAGS.num_classes)
LOGGER.debug("Build Model")
if FLAGS.model_type == 'lenet':
model = Lenet(images_placeholder, model_config['lenet'])
model.build_model()
else:
model = MLP(images_placeholder, model_config['mlp'])
model.build_model()
sess, model, train_time = train(model, images_placeholder, labels_placeholder, data_sets.train)
test_time = time.time()
do_eval(sess, model.model, images_placeholder, labels_placeholder, data_sets.test)
test_time = time.time() - test_time
sess.close
total_time = time.time() - total_time
print("****************Example finished********************")
print_time('Data load', data_load_time)
print_time('Train', train_time)
print_time('Test', test_time)
print_time('Total', total_time)
def run(argv=None): # pylint: disable=unused-argument
total_time = time.time()
download_and_extract()
if tf.gfile.Exists(FLAGS.train_dir):
tf.gfile.DeleteRecursively(FLAGS.train_dir)
tf.gfile.MakeDirs(FLAGS.train_dir)
train_time, data_load_time = train()
test_time, data_load_time2 = evaluate()
print("****************Example finished********************")
print_time('Data load', data_load_time)
print_time('Train', train_time)
print_time('Test', test_time)
print_time('Total', total_time)
def run(argv=None): # pylint: disable=unused-argument
total_time = time.time()
download_and_extract()
if tf.gfile.Exists(FLAGS.train_dir):
tf.gfile.DeleteRecursively(FLAGS.train_dir)
tf.gfile.MakeDirs(FLAGS.train_dir)
train_time, data_load_time = train()
test_time, data_load_time2 = evaluate()
print("****************Example finished********************")
print_time('Data load', data_load_time)
print_time('Train', train_time)
print_time('Test', test_time)
print_time('Total', total_time)
def run():
total_time = time.time()
FLAGS.num_gpus = 1 if FLAGS.core_type == 'GPU' else FLAGS.num_gpus
LOGGER.debug("Load Data")
data_load_time = time.time()
data_sets = load_data(input_data, FLAGS.one_hot)
data_load_time = time.time() - data_load_time
with tf.Graph().as_default():
images_placeholder, labels_placeholder = placeholder_inputs(
FLAGS.one_hot, FLAGS.height * FLAGS.width * FLAGS.channels,
FLAGS.num_classes)
LOGGER.debug("Build Model")
if FLAGS.model_type == 'lenet':
model = Lenet(images_placeholder, model_config['lenet'])
model.build_model()
else:
model = MLP(images_placeholder, model_config['mlp'])
model.build_model()
sess, model, train_time = train(model, images_placeholder,
labels_placeholder, data_sets.train)
test_time = time.time()
do_eval(sess, model.model, images_placeholder, labels_placeholder,
data_sets.test)
test_time = time.time() - test_time
sess.close
total_time = time.time() - total_time
print("****************Example finished********************")
print_time('Data load', data_load_time)
print_time('Train', train_time)
print_time('Test', test_time)
print_time('Total', total_time)
