Merge pull request #924 from h4ck3rm1k3/master

untie

differential_privacy/dp_sgd/dp_optimizer/dp_pca.py

@@ -27,7 +27,7 @@ def ComputeDPPrincipalProjection(data, projection_dims,
   Args:
     data: the input data, each row is a data vector.
     projection_dims: the projection dimension.
-    sanitizer: the sanitizer used for acheiving privacy.
+    sanitizer: the sanitizer used for achieving privacy.
     eps_delta: (eps, delta) pair.
     sigma: if not None, use noise sigma; otherwise compute it using
       eps_delta pair.

differential_privacy/multiple_teachers/analysis.py

@@ -287,7 +287,7 @@ def main(unused_argv):
   if min(eps_list_nm) == eps_list_nm[-1]:
     print "Warning: May not have used enough values of l"
 
-  # Data indpendent bound, as mechanism is
+  # Data independent bound, as mechanism is
   # 2*noise_eps DP.
   data_ind_log_mgf = np.array([0.0 for _ in l_list])
   data_ind_log_mgf += num_examples * np.array(

differential_privacy/multiple_teachers/deep_cnn.py

@@ -84,7 +84,7 @@ def inference(images, dropout=False):
   """Build the CNN model.
   Args:
     images: Images returned from distorted_inputs() or inputs().
-    dropout: Boolean controling whether to use dropout or not
+    dropout: Boolean controlling whether to use dropout or not
   Returns:
     Logits
   """
@@ -194,7 +194,7 @@ def inference_deeper(images, dropout=False):
   """Build a deeper CNN model.
   Args:
     images: Images returned from distorted_inputs() or inputs().
-    dropout: Boolean controling whether to use dropout or not
+    dropout: Boolean controlling whether to use dropout or not
   Returns:
     Logits
   """

differential_privacy/privacy_accountant/tf/accountant.py

@@ -152,7 +152,7 @@ class MomentsAccountant(object):
   We further assume that at each step, the mechanism operates on a random
   sample with sampling probability q = batch_size / total_examples. Then
     E[exp(L X)] = E[(Pr[M(D)==x / Pr[M(D')==x])^L]
-  By distinguishign two cases of wether D < D' or D' < D, we have
+  By distinguishing two cases of whether D < D' or D' < D, we have
   that
     E[exp(L X)] <= max (I1, I2)
   where

im2txt/im2txt/data/build_mscoco_data.py

@@ -424,7 +424,7 @@ def _load_and_process_metadata(captions_file, image_dir):
         (len(id_to_filename), captions_file))
 
   # Process the captions and combine the data into a list of ImageMetadata.
-  print("Proccessing captions.")
+  print("Processing captions.")
   image_metadata = []
   num_captions = 0
   for image_id, base_filename in id_to_filename:

inception/inception/inception_distributed_train.py

@@ -89,7 +89,7 @@ RMSPROP_EPSILON = 1.0              # Epsilon term for RMSProp.
 
 def train(target, dataset, cluster_spec):
   """Train Inception on a dataset for a number of steps."""
-  # Number of workers and parameter servers are infered from the workers and ps
+  # Number of workers and parameter servers are inferred from the workers and ps
   # hosts string.
   num_workers = len(cluster_spec.as_dict()['worker'])
   num_parameter_servers = len(cluster_spec.as_dict()['ps'])

inception/inception/inception_eval.py

@@ -77,7 +77,7 @@ def _eval_once(saver, summary_writer, top_1_op, top_5_op, summary_op):
       #   /my-favorite-path/imagenet_train/model.ckpt-0,
       # extract global_step from it.
       global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
-      print('Succesfully loaded model from %s at step=%s.' %
+      print('Successfully loaded model from %s at step=%s.' %
             (ckpt.model_checkpoint_path, global_step))
     else:
       print('No checkpoint file found')

inception/inception/inception_train.py

@@ -290,7 +290,7 @@ def train(dataset):
     variable_averages = tf.train.ExponentialMovingAverage(
         inception.MOVING_AVERAGE_DECAY, global_step)
 
-    # Another possiblility is to use tf.slim.get_variables().
+    # Another possibility is to use tf.slim.get_variables().
     variables_to_average = (tf.trainable_variables() +
                             tf.moving_average_variables())
     variables_averages_op = variable_averages.apply(variables_to_average)

inception/inception/slim/ops.py

@@ -15,7 +15,7 @@
 """Contains convenience wrappers for typical Neural Network TensorFlow layers.
 
    Additionally it maintains a collection with update_ops that need to be
-   updated after the ops have been computed, for exmaple to update moving means
+   updated after the ops have been computed, for example to update moving means
    and moving variances of batch_norm.
 
    Ops that have different behavior during training or eval have an is_training

namignizer/data_utils.py

@@ -58,7 +58,7 @@ def _letter_to_number(letter):
 def namignizer_iterator(names, counts, batch_size, num_steps, epoch_size):
     """Takes a list of names and counts like those output from read_names, and
     makes an iterator yielding a batch_size by num_steps array of random names
-    separated by an end of name token. The names are choosen randomly according
+    separated by an end of name token. The names are chosen randomly according
     to their counts. The batch may end mid-name
 
     Args:

namignizer/names.py

@@ -14,7 +14,7 @@
 """A library showing off sequence recognition and generation with the simple
 example of names.
 
-We use recurrent neural nets to learn complex functions able to recogize and
+We use recurrent neural nets to learn complex functions able to recognize and
 generate sequences of a given form. This can be used for natural language
 syntax recognition, dynamically generating maps or puzzles and of course
 baby name generation.

neural_programmer/data_utils.py

@@ -223,7 +223,7 @@ def list_join(a):
 
 
 def group_by_max(table, number):
-  #computes the most frequently occuring entry in a column
+  #computes the most frequently occurring entry in a column
   answer = []
   for i in range(len(table)):
     temp = []

neural_programmer/model.py

@@ -135,7 +135,7 @@ class Graph():
     #Attention on quetsion to decide the question number to passed to comparison ops
     def compute_ans(op_embedding, comparison):
       op_embedding = tf.expand_dims(op_embedding, 0)
-      #dot product of operation embedding with hidden state to the left of the number occurence
+      #dot product of operation embedding with hidden state to the left of the number occurrence
       first = tf.transpose(
           tf.matmul(op_embedding,
                     tf.transpose(

slim/deployment/model_deploy.py

@@ -304,7 +304,7 @@ def optimize_clones(clones, optimizer,
       regularization_losses = None
   # Compute the total_loss summing all the clones_losses.
   total_loss = tf.add_n(clones_losses, name='total_loss')
-  # Sum the gradients accross clones.
+  # Sum the gradients across clones.
   grads_and_vars = _sum_clones_gradients(grads_and_vars)
   return total_loss, grads_and_vars
 

slim/nets/inception_resnet_v2.py

@@ -191,7 +191,7 @@ def inception_resnet_v2(inputs, num_classes=1001, is_training=True,
         end_points['Mixed_6a'] = net
         net = slim.repeat(net, 20, block17, scale=0.10)
 
-        # Auxillary tower
+        # Auxiliary tower
         with tf.variable_scope('AuxLogits'):
           aux = slim.avg_pool2d(net, 5, stride=3, padding='VALID',
                                 scope='Conv2d_1a_3x3')

slim/nets/inception_v4.py

@@ -269,7 +269,7 @@ def inception_v4(inputs, num_classes=1001, is_training=True,
     reuse: whether or not the network and its variables should be reused. To be
       able to reuse 'scope' must be given.
     scope: Optional variable_scope.
-    create_aux_logits: Whether to include the auxilliary logits.
+    create_aux_logits: Whether to include the auxiliary logits.
 
   Returns:
     logits: the logits outputs of the model.