def evaluate():
# Evaluate the accuracy of the MNIST model on legitimate test examples
accuracy = model_eval(sess,
x,
y,
predictions,
X_test,
Y_test,
args=eval_params)
print('Test accuracy on legitimate test examples: %0.4f' % accuracy)
# Accuracy of the model on Wasserstein adversarial examples
# accuracy_adv_wass = model_eval(sess, x, y, predictions_adv_wrm, X_test, \
# Y_test, args=eval_params)
# print('Test accuracy on Wasserstein examples: %0.4f' % accuracy_adv_wass)
# Accuracy of the model on FGSM adversarial examples
accuracy_adv_fgsm = model_eval(sess, x, y, preds_adv_fgsm, X_test, \
Y_test, args=eval_params)
print('Test accuracy on fgsm examples: %0.4f' % accuracy_adv_fgsm)
# Accuracy of the model on IFGM adversarial examples
accuracy_adv_ifgm = model_eval(sess, x, y, preds_adv_ifgm, X_test, \
Y_test, args=eval_params)
print('Test accuracy on ifgm examples: %0.4f' % accuracy_adv_ifgm)
def evaluate_adv():
# Accuracy of adversarially trained model on legitimate test inputs
accuracy = model_eval(sess, x, y, predictions_adv, X_test, Y_test, args=eval_params)
print('Test accuracy on legitimate test examples: %0.4f' % accuracy)
# Accuracy of the adversarially trained model on Wasserstein adversarial examples
accuracy_adv_wass = model_eval(sess, x, y, predictions_adv_adv_wrm, \
X_test, Y_test, args=eval_params)
print('Test accuracy on Wasserstein examples: %0.4f\n' % accuracy_adv_wass)
def evaluate():
# Evaluate the accuracy of the MNIST model on legitimate test examples
accuracy = model_eval(sess, x, y, predictions, X_test, Y_test, args=eval_params)
print('Test accuracy on legitimate test examples: %0.4f' % accuracy)
# Accuracy of the model on Wasserstein adversarial examples
accuracy_adv_wass = model_eval(sess, x, y, predictions_adv_wrm, X_test, \
Y_test, args=eval_params)
print('Test accuracy on Wasserstein examples: %0.4f\n' % accuracy_adv_wass)
def evaluate():
# Evaluate the accuracy of the MNIST model on legitimate test examples
accuracy = model_eval(sess, x, y, predictions, X_test, Y_test, args=eval_params)
print('Test accuracy on legitimate test examples: %0.4f' % accuracy)
# Accuracy of the model on Wasserstein adversarial examples
accuracy_adv_wass = model_eval(sess, x, y, predictions_adv_eval, X_test,
Y_test, args=eval_params)
print('Test accuracy on Wasserstein examples: %0.4f\n' % accuracy_adv_wass)
f = open(file, 'a')
f_writter = csv.writer(f)
f_writter.writerow((accuracy, accuracy_adv_wass))