Fixes for differential_privacy

differential_privacy/multiple_teachers/analysis.py

@@ -216,10 +216,10 @@ def main(unused_argv):
   # If we are reproducing results from paper https://arxiv.org/abs/1610.05755,
   # download the required binaries with label information.
   ##################################################################
-  
+
   # Binaries for MNIST results
   paper_binaries_mnist = \
-    ["https://github.com/npapernot/multiple-teachers-for-privacy/blob/master/mnist_250_teachers_labels.npy?raw=true", 
+    ["https://github.com/npapernot/multiple-teachers-for-privacy/blob/master/mnist_250_teachers_labels.npy?raw=true",
     "https://github.com/npapernot/multiple-teachers-for-privacy/blob/master/mnist_250_teachers_100_indices_used_by_student.npy?raw=true"]
   if FLAGS.counts_file == "mnist_250_teachers_labels.npy" \
     or FLAGS.indices_file == "mnist_250_teachers_100_indices_used_by_student.npy":
@@ -254,7 +254,7 @@ def main(unused_argv):
   total_log_mgf_nm = np.array([0.0 for _ in l_list])
   total_ss_nm = np.array([0.0 for _ in l_list])
   noise_eps = FLAGS.noise_eps
-  
+
   for i in indices:
     total_log_mgf_nm += np.array(
         [logmgf_from_counts(counts_mat[i], noise_eps, l)

differential_privacy/multiple_teachers/deep_cnn.py

@@ -95,9 +95,9 @@ def inference(images, dropout=False):
 
   # conv1
   with tf.variable_scope('conv1') as scope:
-    kernel = _variable_with_weight_decay('weights', 
+    kernel = _variable_with_weight_decay('weights',
                                          shape=first_conv_shape,
-                                         stddev=1e-4, 
+                                         stddev=1e-4,
                                          wd=0.0)
     conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
     biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
@@ -108,25 +108,25 @@ def inference(images, dropout=False):
 
 
   # pool1
-  pool1 = tf.nn.max_pool(conv1, 
-                         ksize=[1, 3, 3, 1], 
+  pool1 = tf.nn.max_pool(conv1,
+                         ksize=[1, 3, 3, 1],
                          strides=[1, 2, 2, 1],
-                         padding='SAME', 
+                         padding='SAME',
                          name='pool1')
-  
+
   # norm1
-  norm1 = tf.nn.lrn(pool1, 
-                    4, 
-                    bias=1.0, 
-                    alpha=0.001 / 9.0, 
+  norm1 = tf.nn.lrn(pool1,
+                    4,
+                    bias=1.0,
+                    alpha=0.001 / 9.0,
                     beta=0.75,
                     name='norm1')
 
   # conv2
   with tf.variable_scope('conv2') as scope:
-    kernel = _variable_with_weight_decay('weights', 
+    kernel = _variable_with_weight_decay('weights',
                                          shape=[5, 5, 64, 128],
-                                         stddev=1e-4, 
+                                         stddev=1e-4,
                                          wd=0.0)
     conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')
     biases = _variable_on_cpu('biases', [128], tf.constant_initializer(0.1))
@@ -137,28 +137,28 @@ def inference(images, dropout=False):
 
 
   # norm2
-  norm2 = tf.nn.lrn(conv2, 
-                    4, 
-                    bias=1.0, 
-                    alpha=0.001 / 9.0, 
+  norm2 = tf.nn.lrn(conv2,
+                    4,
+                    bias=1.0,
+                    alpha=0.001 / 9.0,
                     beta=0.75,
                     name='norm2')
-  
+
   # pool2
-  pool2 = tf.nn.max_pool(norm2, 
+  pool2 = tf.nn.max_pool(norm2,
                          ksize=[1, 3, 3, 1],
-                         strides=[1, 2, 2, 1], 
-                         padding='SAME', 
+                         strides=[1, 2, 2, 1],
+                         padding='SAME',
                          name='pool2')
 
   # local3
   with tf.variable_scope('local3') as scope:
     # Move everything into depth so we can perform a single matrix multiply.
     reshape = tf.reshape(pool2, [FLAGS.batch_size, -1])
     dim = reshape.get_shape()[1].value
-    weights = _variable_with_weight_decay('weights', 
+    weights = _variable_with_weight_decay('weights',
                                           shape=[dim, 384],
-                                          stddev=0.04, 
+                                          stddev=0.04,
                                           wd=0.004)
     biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1))
     local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
@@ -167,9 +167,9 @@ def inference(images, dropout=False):
 
   # local4
   with tf.variable_scope('local4') as scope:
-    weights = _variable_with_weight_decay('weights', 
+    weights = _variable_with_weight_decay('weights',
                                           shape=[384, 192],
-                                          stddev=0.04, 
+                                          stddev=0.04,
                                           wd=0.004)
     biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1))
     local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name)
@@ -178,11 +178,11 @@ def inference(images, dropout=False):
 
   # compute logits
   with tf.variable_scope('softmax_linear') as scope:
-    weights = _variable_with_weight_decay('weights', 
+    weights = _variable_with_weight_decay('weights',
                                           [192, FLAGS.nb_labels],
-                                          stddev=1/192.0, 
+                                          stddev=1/192.0,
                                           wd=0.0)
-    biases = _variable_on_cpu('biases', 
+    biases = _variable_on_cpu('biases',
                               [FLAGS.nb_labels],
                               tf.constant_initializer(0.0))
     logits = tf.add(tf.matmul(local4, weights), biases, name=scope.name)
@@ -386,7 +386,7 @@ def train_op_fun(total_loss, global_step):
   """
   # Variables that affect learning rate.
   nb_ex_per_train_epoch = int(60000 / FLAGS.nb_teachers)
-  
+
   num_batches_per_epoch = nb_ex_per_train_epoch / FLAGS.batch_size
   decay_steps = int(num_batches_per_epoch * FLAGS.epochs_per_decay)
 

differential_privacy/multiple_teachers/input.py

@@ -47,7 +47,7 @@ def create_dir_if_needed(dest_directory):
 def maybe_download(file_urls, directory):
   """
   Download a set of files in temporary local folder
-  :param directory: the directory where to download 
+  :param directory: the directory where to download
   :return: a tuple of filepaths corresponding to the files given as input
   """
   # Create directory if doesn't exist
@@ -73,7 +73,7 @@ def maybe_download(file_urls, directory):
     result.append(filepath)
 
     # Test if file already exists
-    if not gfile.Exists(filepath):
+    if not tf.gfile.Exists(filepath):
       def _progress(count, block_size, total_size):
         sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename,
             float(count * block_size) / float(total_size) * 100.0))
@@ -124,7 +124,7 @@ def extract_svhn(local_url):
   :return:
   """
 
-  with gfile.Open(local_url, mode='r') as file_obj:
+  with tf.gfile.Open(local_url, mode='r') as file_obj:
     # Load MATLAB matrix using scipy IO
     dict = loadmat(file_obj)
 

differential_privacy/multiple_teachers/train_teachers.py

@@ -64,11 +64,11 @@ def train_teacher(dataset, nb_teachers, teacher_id):
   else:
     print("Check value of dataset flag")
     return False
-    
+
   # Retrieve subset of data for this teacher
-  data, labels = input.partition_dataset(train_data, 
-                                         train_labels, 
-                                         nb_teachers, 
+  data, labels = input.partition_dataset(train_data,
+                                         train_labels,
+                                         nb_teachers,
                                          teacher_id)
 
   print("Length of training data: " + str(len(labels)))