Merge pull request #236 from tensorflow/cleverhans_tutorials

rename "tutorials" to "cleverhans_tutorials"

README.md

@@ -73,20 +73,20 @@ currently [test it](https://travis-ci.org/tensorflow/cleverhans) with Python
 ## Tutorials
 
 To help you get started with the functionalities provided by this library, the
-`tutorials/' folder comes with the following tutorials:
-* **MNIST with FGSM** ([code](tutorials/mnist_tutorial_tf.py)): this
+`cleverhans_tutorials/' folder comes with the following tutorials:
+* **MNIST with FGSM** ([code](cleverhans_tutorials/mnist_tutorial_tf.py)): this
 tutorial covers how to train a MNIST model using TensorFlow,
 craft adversarial examples using the [fast gradient sign method](https://arxiv.org/abs/1412.6572),
 and make the model more robust to adversarial examples using adversarial training.
-* **MNIST with FGSM using Keras** ([code](tutorials/mnist_tutorial_keras_tf.py)): this
+* **MNIST with FGSM using Keras** ([code](cleverhans_tutorials/mnist_tutorial_keras_tf.py)): this
 tutorial covers how to define a MNIST model with Keras and train it using TensorFlow,
 craft adversarial examples using the [fast gradient sign method](https://arxiv.org/abs/1412.6572),
 and make the model more robust to adversarial
 examples using adversarial training.
-* **MNIST with JSMA** ([code](tutorials/mnist_tutorial_jsma.py)): this second
+* **MNIST with JSMA** ([code](cleverhans_tutorials/mnist_tutorial_jsma.py)): this second
 tutorial covers how to define a MNIST model with Keras and train it using TensorFlow and
 craft adversarial examples using the [Jacobian-based saliency map approach](https://arxiv.org/abs/1511.07528).
-* **MNIST using a black-box attack** ([code](tutorials/mnist_blackbox.py)):
+* **MNIST using a black-box attack** ([code](cleverhans_tutorials/mnist_blackbox.py)):
 this tutorial implements the black-box
 attack described in this [paper](https://arxiv.org/abs/1602.02697).
 The adversary train a substitute model: a copy that imitates the black-box

cleverhans/attacks_tf.py

@@ -403,7 +403,7 @@ def jacobian_augmentation(sess, x, X_sub_prev, Y_sub, grads, lmbda,
     Augment an adversary's substitute training set using the Jacobian
     of a substitute model to generate new synthetic inputs.
     See https://arxiv.org/abs/1602.02697 for more details.
-    See tutorials/mnist_blackbox.py for example use case
+    See cleverhans_tutorials/mnist_blackbox.py for example use case
     :param sess: TF session in which the substitute model is defined
     :param x: input TF placeholder for the substitute model
     :param X_sub_prev: substitute training data available to the adversary

cleverhans/devtools/list_files.py

@@ -32,7 +32,7 @@ def list_files(suffix=""):
     file_list = _list_files(cleverhans_path, suffix)
 
     repo_path = os.path.abspath(os.path.join(cleverhans_path, os.pardir))
-    tutorials_path = os.path.join(repo_path, "tutorials")
+    tutorials_path = os.path.join(repo_path, "cleverhans_tutorials")
     tutorials_files = _list_files(tutorials_path, suffix)
     tutorials_files = [os.path.join(os.pardir, path) for path in
                        tutorials_files]

tutorials/mnist_blackbox.py → cleverhans_tutorials/mnist_blackbox.py

@@ -18,8 +18,8 @@
 from cleverhans.attacks import FastGradientMethod
 from cleverhans.attacks_tf import jacobian_graph, jacobian_augmentation
 
-from tutorials.tutorial_models import make_basic_cnn, MLP
-from tutorials.tutorial_models import Flatten, Linear, ReLU, Softmax
+from cleverhans_tutorials.tutorial_models import make_basic_cnn, MLP
+from cleverhans_tutorials.tutorial_models import Flatten, Linear, ReLU, Softmax
 
 FLAGS = flags.FLAGS
 

tutorials/mnist_tutorial_cw.py → cleverhans_tutorials/mnist_tutorial_cw.py

@@ -16,7 +16,7 @@
 from cleverhans.utils import set_log_level
 from cleverhans.utils_mnist import data_mnist
 from cleverhans.utils_tf import model_train, model_eval, tf_model_load
-from tutorials.tutorial_models import make_basic_cnn
+from cleverhans_tutorials.tutorial_models import make_basic_cnn
 
 FLAGS = flags.FLAGS
 

tutorials/mnist_tutorial_jsma.py → cleverhans_tutorials/mnist_tutorial_jsma.py

@@ -16,7 +16,7 @@
 from cleverhans.utils_mnist import data_mnist
 from cleverhans.utils_tf import model_train, model_eval, model_argmax
 from cleverhans.utils_keras import KerasModelWrapper, cnn_model
-from tutorials.tutorial_models import make_basic_cnn
+from cleverhans_tutorials.tutorial_models import make_basic_cnn
 
 FLAGS = flags.FLAGS
 

tutorials/mnist_tutorial_tf.py → cleverhans_tutorials/mnist_tutorial_tf.py

@@ -19,7 +19,7 @@
 from cleverhans.utils_mnist import data_mnist
 from cleverhans.utils_tf import model_train, model_eval
 from cleverhans.attacks import FastGradientMethod
-from tutorials.tutorial_models import make_basic_cnn
+from cleverhans_tutorials.tutorial_models import make_basic_cnn
 from cleverhans.utils import AccuracyReport, set_log_level
 
 import os

tests_tf/test_mnist_blackbox.py

@@ -4,7 +4,7 @@
 
 class TestMNISTBlackboxF(unittest.TestCase):
     def test_mnist_blackbox(self):
-        from tutorials import mnist_blackbox
+        from cleverhans_tutorials import mnist_blackbox
 
         np.random.seed(42)
         import tensorflow as tf

tests_tf/test_mnist_tutorial_cw.py

@@ -3,7 +3,7 @@
 
 class TestMNISTTutorialCW(unittest.TestCase):
     def test_mnist_tutorial_cw(self):
-        from tutorials import mnist_tutorial_cw
+        from cleverhans_tutorials import mnist_tutorial_cw
 
         # Run the MNIST tutorial on a dataset of reduced size
         # and disable visualization.

tests_tf/test_mnist_tutorial_jsma.py

@@ -9,7 +9,7 @@ def test_mnist_tutorial_jsma(self):
         import tensorflow as tf
         tf.set_random_seed(42)
 
-        from tutorials import mnist_tutorial_jsma
+        from cleverhans_tutorials import mnist_tutorial_jsma
 
         # Run the MNIST tutorial on a dataset of reduced size
         # and disable visualization.

tests_tf/test_mnist_tutorial_keras_tf.py

@@ -9,7 +9,7 @@ def test_mnist_tutorial_keras_tf(self):
         import tensorflow as tf
         tf.set_random_seed(42)
 
-        from tutorials import mnist_tutorial_keras_tf
+        from cleverhans_tutorials import mnist_tutorial_keras_tf
 
         # Run the MNIST tutorial on a dataset of reduced size
         test_dataset_indices = {'train_start': 0,

tests_tf/test_mnist_tutorial_tf.py

@@ -9,7 +9,7 @@ def test_mnist_tutorial_tf(self):
         import tensorflow as tf
         tf.set_random_seed(42)
 
-        from tutorials import mnist_tutorial_tf
+        from cleverhans_tutorials import mnist_tutorial_tf
 
         # Run the MNIST tutorial on a dataset of reduced size
         test_dataset_indices = {'train_start': 0,