def test_network(FLAGS):
(x_train, y_train, x_test,
y_test) = load_mnist_data(FLAGS.start_batch, FLAGS.batch_size)
# Load saved model
tf.import_graph_def(load_pb_file(FLAGS.model_file))
print("loaded model")
print_nodes()
# Get input / output tensors
x_input = tf.compat.v1.get_default_graph().get_tensor_by_name(
FLAGS.input_node)
y_output = tf.compat.v1.get_default_graph().get_tensor_by_name(
FLAGS.output_node)
# Create configuration to encrypt input
FLAGS, unparsed = server_argument_parser().parse_known_args()
config = server_config_from_flags(FLAGS, x_input.name)
with tf.compat.v1.Session(config=config) as sess:
sess.run(tf.compat.v1.global_variables_initializer())
start_time = time.time()
y_hat = y_output.eval(feed_dict={x_input: x_test})
elasped_time = time.time() - start_time
print("total time(s)", np.round(elasped_time, 3))
if not FLAGS.enable_client:
y_test_label = np.argmax(y_test, 1)
if FLAGS.batch_size < 60:
print("y_hat", np.round(y_hat, 2))
y_pred = np.argmax(y_hat, 1)
correct_prediction = np.equal(y_pred, y_test_label)
error_count = np.size(correct_prediction) - np.sum(correct_prediction)
test_accuracy = np.mean(correct_prediction)
print("Error count", error_count, "of", FLAGS.batch_size, "elements.")
print("Accuracy: %g " % test_accuracy)
'./gc-emp-test/bin/argmax_1', '1', '12345',
f'{out_server_name}{FLAGS.port}.txt',
f'{out_final_name}{FLAGS.port}.txt'
])
# time.sleep(15)
process.wait()
argmax_time_end = time.time()
with open(argmax_times_name, 'a') as outfile:
outfile.write(str(argmax_time_end - argmax_time_start))
outfile.write("\n")
log_timing(stage='server_client:finished 2PC',
log_file=FLAGS.log_timing_file)
if __name__ == "__main__":
FLAGS, unparsed = server_argument_parser().parse_known_args()
if unparsed:
print("Unparsed flags:", unparsed)
exit(1)
if FLAGS.encrypt_server_data and FLAGS.enable_client:
raise Exception(
"encrypt_server_data flag only valid when client is not enabled. Note: the client can specify whether or not to encrypt the data using 'encrypt' or 'plain' in the configuration map"
)
if FLAGS.model_file == "":
raise Exception("FLAGS.model_file must be set")
if not FLAGS.from_pytorch:
# (x_train, y_train), (x_test, y_test) = client_data.get_dataset(
# FLAGS.dataset)
raise ValueError('must be from pytroch')
else:
x_test = x_test[:FLAGS.batch_size]
y_test = y_test[:FLAGS.batch_size]
start_time = time.time()
y_conv_val = y_conv.eval(feed_dict={x: x_test, y_: y_test})
elasped_time = (time.time() - start_time)
print("total time(s)", np.round(elasped_time, 3))
print('y_conv_val', np.round(y_conv_val, 2))
y_test_batch = y_test[:FLAGS.batch_size]
y_label_batch = np.argmax(y_test_batch, 1)
correct_prediction = np.equal(np.argmax(y_conv_val, 1), y_label_batch)
error_count = np.size(correct_prediction) - np.sum(correct_prediction)
test_accuracy = np.mean(correct_prediction)
print('Using average of 4 partitions_repeated sampling')
print('Error count:', error_count, 'of', FLAGS.batch_size, 'elements.')
print('Accuracy: ', test_accuracy)
if __name__ == '__main__':
parser = server_argument_parser()
FLAGS, unparsed = parser.parse_known_args()
if unparsed:
print('Unparsed flags:', unparsed)
if FLAGS.encrypt_server_data and FLAGS.enable_client:
raise Exception(
"encrypt_server_data flag only valid when client is not enabled. Note: the client can specify whether or not to encrypt the data using 'encrypt' or 'plain' in the configuration map"
)
test_mnist_cnn(FLAGS)