def main():
keras.backend.set_image_dim_ordering('th')
# We can't use argparse in a test because it reads the arguments to nosetests
# e.g., nosetests -v passes the -v to the test
args = {
"batch_size": 128,
"nb_epochs": 2,
"learning_rate": .5
}
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist()
X_train = X_train[:10000]
Y_train = Y_train[:10000]
X_test = X_test[:2000]
Y_test = Y_test[:2000]
assert Y_train.shape[1] == 10.
label_smooth = .1
Y_train = Y_train.clip(label_smooth / 9., 1. - label_smooth)
# Define input Theano placeholder
x_shape = (None, 1, 28, 28)
x = T.tensor4('x')
y = T.matrix('y')
# Define Theano model graph
model = cnn_model()
model.build(x_shape)
predictions = model(x)
print("Defined Theano model graph.")
# Train an MNIST model
th_model_train(x, y, predictions, model.trainable_weights,
X_train, Y_train, args=args)
accuracy = th_model_eval(x, y, predictions, X_test, Y_test, args=args)
assert accuracy > 0.8, accuracy
def main():
"""
Test the accuracy of the MNIST cleverhans tutorial model
:return:
"""
if not hasattr(backend, "theano"):
raise RuntimeError("This tutorial requires keras to be configured"
" to use the Theano backend.")
# Image dimensions ordering should follow the Theano convention
if keras.backend.image_dim_ordering() != 'th':
keras.backend.set_image_dim_ordering('th')
print(
"INFO: '~/.keras/keras.json' sets 'image_dim_ordering' to 'tf', temporarily setting to 'th'"
)
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size',
'-b',
default=128,
help='Size of training batches')
parser.add_argument('--train_dir',
'-d',
default='/tmp',
help='Directory storing the saved model.')
parser.add_argument('--filename',
'-f',
default='mnist.ckpt',
help='Filename to save model under.')
parser.add_argument('--nb_epochs',
'-e',
default=6,
type=int,
help='Number of epochs to train model')
parser.add_argument('--learning_rate',
'-lr',
default=0.5,
type=float,
help='Learning rate for training')
args = parser.parse_args()
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist()
print("Loaded MNIST test data.")
# Define input Theano placeholder
x_shape = (None, 1, 28, 28)
y_shape = (None, 10)
x = T.tensor4('x')
y = T.matrix('y')
# Define Theano model graph
model = model_mnist()
model.build(x_shape)
predictions = model(x)
print("Defined Theano model graph.")
# Train an MNIST model
th_model_train(x,
y,
predictions,
model.trainable_weights,
X_train,
Y_train,
args=args)
# Evaluate the accuracy of the MNIST model on legitimate test examples
accuracy = th_model_eval(x, y, predictions, X_test, Y_test, args=args)
assert float(accuracy) >= 0.98, accuracy
# Craft adversarial examples using Fast Gradient Sign Method (FGSM)
adv_x = fgsm(x, predictions, eps=0.3, back='th')
X_test_adv, = batch_eval([x], [adv_x], [X_test], args=args)
assert X_test_adv.shape[0] == 10000, X_test_adv.shape
# Evaluate the accuracy of the MNIST model on adversarial examples
accuracy = th_model_eval(x, y, predictions, X_test_adv, Y_test, args=args)
assert float(accuracy) <= 0.1, accuracy
print("Defined Theano model graph.")
def evaluate():
# Evaluate the accuracy of the CIFAR model on legitimate test examples
accuracy = th_model_eval(x, y, predictions, X_test, Y_test, args=args)
assert X_test.shape[0] == 10000, X_test.shape
print('Test accuracy on legitimate test examples: ' + str(accuracy))
pass
# Train a CIFAR model
th_model_train(x,
y,
predictions,
model.trainable_weights,
X_train,
Y_train,
evaluate=evaluate,
args=args)
# Craft adversarial examples using Fast Gradient Sign Method (FGSM)
adv_x = fgsm(x, predictions, eps=0.3)
X_test_adv, = batch_eval([x], [adv_x], [X_test], args=args)
assert X_test_adv.shape[0] == 10000, X_test_adv.shape
# Evaluate the accuracy of the CIFAR model on adversarial examples
accuracy = th_model_eval(x, y, predictions, X_test_adv, Y_test, args=args)
print('Test accuracy on adversarial examples: ' + str(accuracy))
print("Repeating the process, using adversarial training")
x_2 = T.tensor4('x_2')
y_2 = T.matrix('y_2')
def main():
"""
Test the accuracy of the MNIST cleverhans tutorial model
:return:
"""
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size',
'-b',
default=128,
help='Size of training batches')
parser.add_argument('--train_dir',
'-d',
default='/tmp',
help='Directory storing the saved model.')
parser.add_argument('--filename',
'-f',
default='mnist.ckpt',
help='Filename to save model under.')
parser.add_argument('--nb_epochs',
'-e',
default=6,
type=int,
help='Number of epochs to train model')
parser.add_argument('--learning_rate',
'-lr',
default=0.5,
type=float,
help='Learning rate for training')
args = parser.parse_args()
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist()
print("Loaded MNIST test data.")
# Define input Theano placeholder
x_shape = (None, 1, 28, 28)
y_shape = (None, 10)
x = T.tensor4('x')
y = T.matrix('y')
# Define Theano model graph
model = model_mnist()
model.build(x_shape)
predictions = model(x)
print("Defined Theano model graph.")
# Train an MNIST model
th_model_train(x,
y,
predictions,
model.trainable_weights,
X_train,
Y_train,
args=args)
# Evaluate the accuracy of the MNIST model on legitimate test examples
accuracy = th_model_eval(x, y, predictions, X_test, Y_test, args=args)
assert float(accuracy) >= 0.98, accuracy
# Craft adversarial examples using Fast Gradient Sign Method (FGSM)
adv_x = fgsm(x, predictions, eps=0.3, back='th')
X_test_adv, = batch_eval([x], [adv_x], [X_test], args=args)
assert X_test_adv.shape[0] == 10000, X_test_adv.shape
# Evaluate the accuracy of the MNIST model on adversarial examples
accuracy = th_model_eval(x, y, predictions, X_test_adv, Y_test, args=args)
assert float(accuracy) <= 0.1, accuracy
def main():
"""
MNIST CleverHans tutorial
:return:
"""
if not hasattr(backend, "theano"):
raise RuntimeError("This tutorial requires keras to be configured"
" to use the Theano backend.")
# Image dimensions ordering should follow the Theano convention
if keras.backend.image_dim_ordering() != 'th':
keras.backend.set_image_dim_ordering('th')
print("INFO: '~/.keras/keras.json' sets 'image_dim_ordering' to "
"'tf', temporarily setting to 'th'")
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', '-b', default=128,
help='Size of training batches')
parser.add_argument('--train_dir', '-d', default='/tmp',
help='Directory storing the saved model.')
parser.add_argument('--filename', '-f', default='mnist.ckpt',
help='Filename to save model under.')
parser.add_argument('--nb_epochs', '-e', default=6, type=int,
help='Number of epochs to train model')
parser.add_argument('--learning_rate', '-lr', default=0.5, type=float,
help='Learning rate for training')
args = parser.parse_args()
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist()
print("Loaded MNIST test data.")
assert Y_train.shape[1] == 10.
label_smooth = .1
Y_train = Y_train.clip(label_smooth / 9., 1. - label_smooth)
# Define input Theano placeholder
x_shape = (None, 1, 28, 28)
x = T.tensor4('x')
y = T.matrix('y')
# Define Theano model graph
model = cnn_model()
model.build(x_shape)
predictions = model(x)
print("Defined Theano model graph.")
def evaluate():
# Evaluate the accuracy of the MNIST model on legitimate test examples
accuracy = th_model_eval(x, y, predictions, X_test, Y_test, args=args)
assert X_test.shape[0] == 10000, X_test.shape
print('Test accuracy on legitimate test examples: ' + str(accuracy))
pass
# Train an MNIST model
th_model_train(x, y, predictions, model.trainable_weights, X_train,
Y_train, evaluate=evaluate, args=args)
# Initialize the Fast Gradient Sign Method (FGSM) attack object and graph
fgsm = FastGradientMethod(model, back='th')
adv_x = fgsm.generate(x, params={'eps': 0.3})
# Evaluate the accuracy of the MNIST model on adversarial examples
accuracy = th_model_eval(x, y, model(adv_x), X_test, Y_test, args=args)
print('Test accuracy on adversarial examples: ' + str(accuracy))
print("Repeating the process, using adversarial training")
# Redefine Theano model graph
model_2 = cnn_model()
model_2.build(x_shape)
preds_2 = model_2(x)
fgsm = FastGradientMethod(model_2, back='th')
preds_2_adv = model_2(fgsm.generate(x, params={'eps': 0.3}))
def evaluate_2():
# Evaluate the accuracy of the adversarialy trained MNIST model on
# legitimate test examples
accuracy = th_model_eval(x, y, preds_2, X_test, Y_test, args=args)
print('Test accuracy on legitimate test examples: ' + str(accuracy))
# Evaluate the accuracy of the adversarially trained MNIST model on
# adversarial examples
acc_adv = th_model_eval(x, y, preds_2_adv, X_test, Y_test, args=args)
print('Test accuracy on adversarial examples: ' + str(acc_adv))
# Perform adversarial training
th_model_train(x, y, preds_2, model_2.trainable_weights, X_train, Y_train,
predictions_adv=preds_2_adv, evaluate=evaluate_2, args=args)
def main():
"""
MNIST cleverhans tutorial
:return:
"""
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', '-b', default=1000, type=int, help='Size of training batches')
parser.add_argument('--train_dir', '-d', default='/tmp', help='Directory storing the saved model.')
parser.add_argument('--filename', '-f', default='mnist.ckpt', help='Filename to save model under.')
parser.add_argument('--nb_epochs', '-e', default=6, type=int, help='Number of epochs to train model')
parser.add_argument('--nb_iters', '-i', default=10000, type=int, help='Number of iterations for crafting adversarial examples')
parser.add_argument('--learning_rate', '-lr', default=0.1, type=float, help='Learning rate for training')
parser.add_argument('--eps', default=0.01, type=float, help='Epsilon for Carlini L2 Attack')
parser.add_argument('--kappa', default=0.01, type=float, help='Kappa for Carlini L2 Attack')
parser.add_argument('--c', default=20, type=float)
parser.add_argument('--load', default=None, type=str, help='Model path to load')
parser.add_argument('--dump', default=None, type=str, help='Model path to dump')
args = parser.parse_args()
np.random.seed(126)
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist()
print("Loaded MNIST test data.")
assert Y_train.shape[1] == 10.
label_smooth = .1
Y_train = Y_train.clip(label_smooth / 9., 1. - label_smooth)
# Define input Theano placeholder
x_shape = (None, 1, 28, 28)
y_shape = (None, 10)
x = T.tensor4('x')
y = T.matrix('y')
if args.load:
model = pickle.load(open(args.load, "rb"))
predictions = model(x)
else:
# Define Theano model graph
model = model_mnist()
model.build(x_shape)
predictions = model(x)
print("Defined Theano model graph.")
def evaluate():
# Evaluate the accuracy of the MNIST model on legitimate test examples
accuracy = th_model_eval(x, y, predictions, X_test, Y_test, args=args)
assert X_test.shape[0] == 10000, X_test.shape
print('Test accuracy on legitimate test examples: ' + str(accuracy))
pass
# Train an MNIST model
th_model_train(x, y, predictions, model.trainable_weights, X_train, Y_train, evaluate=evaluate, args=args)
if args.dump:
pickle.dump(model, open(args.dump, "wb"))
# Craft adversarial examples using Fast Gradient Sign Method (FGSM)
#for i in range(10):
carlini_L2(x, predictions, X_test, Y_test, eps=args.eps, kappa=args.kappa, c=args.c, nb_iters=args.nb_iters, batch_size=args.batch_size)
def main():
"""
MNIST cleverhans tutorial
:return:
"""
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', '-b', default=128, help='Size of training batches')
parser.add_argument('--train_dir', '-d', default='/tmp', help='Directory storing the saved model.')
parser.add_argument('--filename', '-f', default='mnist.ckpt', help='Filename to save model under.')
parser.add_argument('--nb_epochs', '-e', default=6, type=int, help='Number of epochs to train model')
parser.add_argument('--learning_rate', '-lr', default=0.5, type=float, help='Learning rate for training')
args = parser.parse_args()
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist()
print("Loaded MNIST test data.")
assert Y_train.shape[1] == 10.
label_smooth = .1
Y_train = Y_train.clip(label_smooth / 9., 1. - label_smooth)
# Define input Theano placeholder
x_shape = (None, 1, 28, 28)
y_shape = (None, 10)
x = T.tensor4('x')
y = T.matrix('y')
# Define Theano model graph
model = model_mnist()
model.build(x_shape)
predictions = model(x)
print("Defined Theano model graph.")
def evaluate():
# Evaluate the accuracy of the MNIST model on legitimate test examples
accuracy = th_model_eval(x, y, predictions, X_test, Y_test, args=args)
assert X_test.shape[0] == 10000, X_test.shape
print('Test accuracy on legitimate test examples: ' + str(accuracy))
pass
# Train an MNIST model
th_model_train(x, y, predictions, model.trainable_weights, X_train, Y_train, evaluate=evaluate, args=args)
# Craft adversarial examples using Fast Gradient Sign Method (FGSM)
adv_x = fgsm(x, predictions, eps=0.3)
X_test_adv, = batch_eval([x], [adv_x], [X_test], args=args)
assert X_test_adv.shape[0] == 10000, X_test_adv.shape
# Evaluate the accuracy of the MNIST model on adversarial examples
accuracy = th_model_eval(x, y, predictions, X_test_adv, Y_test, args=args)
print('Test accuracy on adversarial examples: ' + str(accuracy))
print("Repeating the process, using adversarial training")
# Redefine Theano model graph
model_2 = model_mnist()
model_2.build(x_shape)
predictions_2 = model_2(x)
adv_x_2 = fgsm(x, predictions_2, eps=0.3)
predictions_2_adv = model_2(adv_x_2)
def evaluate_2():
# Evaluate the accuracy of the adversarialy trained MNIST model on
# legitimate test examples
accuracy = th_model_eval(x, y, predictions_2, X_test, Y_test, args=args)
print('Test accuracy on legitimate test examples: ' + str(accuracy))
# Evaluate the accuracy of the adversarially trained MNIST model on
# adversarial examples
accuracy_adv = th_model_eval(x, y, predictions_2_adv, X_test, Y_test, args=args)
print('Test accuracy on adversarial examples: ' + str(accuracy_adv))
# Perform adversarial training
th_model_train(x, y, predictions_2, model_2.trainable_weights, X_train, Y_train, predictions_adv=predictions_2_adv,
evaluate=evaluate_2, args=args)