Remove TensorFlow2v1

Signed-off-by: Beat Buesser <[email protected]>

Author: beat-buesser

docs/conf.py

@@ -58,7 +58,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = "English"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -81,8 +81,6 @@
         import sphinx_rtd_theme
     except ImportError:
         pass  # assume we have sphinx >= 1.3
-    else:
-        html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
     html_theme = "sphinx_rtd_theme"
 
 # Theme options are theme-specific and customize the look and feel of a theme

examples/README.md

@@ -8,7 +8,7 @@ for accuracy.
 
 
 ### TensorFlow
-[get_started_tensorflow.py](get_started_tensorflow.py) demonstrates a simple example of using ART with TensorFlow v1.x. 
+[get_started_tensorflow_v2.py](get_started_tensorflow_v2.py) demonstrates a simple example of using ART with TensorFlow v2.x. 
 
 ### Keras
 [get_started_keras.py](get_started_keras.py) demonstrates a simple example of using ART with Keras.
@@ -49,8 +49,3 @@ on it.
 
 [mnist_poison_detection.py](mnist_poison_detection.py) generates a backdoor for MNIST dataset, then trains a 
 convolutional neural network on the poisoned dataset and runs activation defence to find poison.
-
-[mnist_transferability.py](mnist_transferability.py) trains a convolutional neural network on the MNIST dataset using 
-the Keras backend, then generates adversarial images using DeepFool and uses them to attack a convolutional neural 
-network trained on MNIST using TensorFlow. This is to show how to perform a black-box attack: the attack never has
-access to the parameters of the TensorFlow model.

examples/adversarial_training_cifar10.py

@@ -8,10 +8,6 @@
 
 import logging
 
-import tensorflow as tf
-
-tf.compat.v1.disable_eager_execution()
-
 from keras.models import Sequential
 from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Activation, Dropout
 import numpy as np

examples/adversarial_training_data_augmentation.py

@@ -2,10 +2,6 @@
 This is an example of how to use ART and Keras to perform adversarial training using data generators for CIFAR10
 """
 
-import tensorflow as tf
-
-tf.compat.v1.disable_eager_execution()
-
 import keras
 import numpy as np
 from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D, Input, BatchNormalization

examples/get_started_keras.py

@@ -5,13 +5,10 @@
 The parameters are chosen for reduced computational requirements of the script and not optimised for accuracy.
 """
 
-import tensorflow as tf
-
-tf.compat.v1.disable_eager_execution()
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
-from tensorflow.keras.losses import categorical_crossentropy
-from tensorflow.keras.optimizers.legacy import Adam
+from keras.models import Sequential
+from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+from keras.losses import categorical_crossentropy
+from keras.optimizers import Adam
 import numpy as np
 
 from art.attacks.evasion import FastGradientMethod

examples/get_started_tensorflow.py

@@ -7,9 +7,7 @@
 
 import pprint
 import json
-import tensorflow as tf
 
-tf.compat.v1.disable_eager_execution()
 from keras.models import Sequential
 from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout
 import numpy as np