Closes #28

PiperOrigin-RevId: 238274605

research/pate_2017/analysis.py

@@ -44,7 +44,7 @@ import numpy as np
 from six.moves import xrange
 import tensorflow as tf
 
-from differential_privacy.multiple_teachers.input import maybe_download
+import maybe_download
 
 # These parameters can be changed to compute bounds for different failure rates
 # or different model predictions.

research/pate_2017/deep_cnn.py

@@ -24,7 +24,7 @@ from six.moves import xrange
 import tensorflow as tf
 import time
 
-from differential_privacy.multiple_teachers import utils
+import utils
 
 FLAGS = tf.app.flags.FLAGS
 

research/pate_2017/train_student.py

@@ -13,20 +13,17 @@
 # limitations under the License.
 # ==============================================================================
 
-
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-
+import aggregation
+import deep_cnn
+import input  # pylint: disable=redefined-builtin
+import metrics
 import numpy as np
 from six.moves import xrange
 import tensorflow as tf
 
-from differential_privacy.multiple_teachers import aggregation
-from differential_privacy.multiple_teachers import deep_cnn
-from differential_privacy.multiple_teachers import input
-from differential_privacy.multiple_teachers import metrics
-
 FLAGS = tf.flags.FLAGS
 
 tf.flags.DEFINE_string('dataset', 'svhn', 'The name of the dataset to use')

research/pate_2017/train_teachers.py

@@ -13,17 +13,14 @@
 # limitations under the License.
 # ==============================================================================
 
-
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-
+import deep_cnn
+import input  # pylint: disable=redefined-builtin
+import metrics
 import tensorflow as tf
 
-from differential_privacy.multiple_teachers import deep_cnn
-from differential_privacy.multiple_teachers import input
-from differential_privacy.multiple_teachers import metrics
-
 
 tf.flags.DEFINE_string('dataset', 'svhn', 'The name of the dataset to use')
 tf.flags.DEFINE_integer('nb_labels', 10, 'Number of output classes')