Further formatting fixes

keras/applications/imagenet_utils.py

@@ -1,3 +1,5 @@
+"""Utilities for ImageNet data preprocessing & prediction decoding.
+"""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

keras/applications/nasnet.py

@@ -324,7 +324,7 @@ def NASNetLarge(input_shape=None,
             layer at the top of the network.
         weights: `None` (random initialization) or
             `imagenet` (ImageNet weights)
-        input_tensor:Ooptional Keras tensor (i.e. output of
+        input_tensor: Optional Keras tensor (i.e. output of
             `layers.Input()`)
             to use as image input for the model.
         pooling: Optional pooling mode for feature extraction
@@ -533,7 +533,7 @@ def _adjust_block(p, ip, filters, block_id=None):
 
 
 def _normal_a_cell(ip, p, filters, block_id=None):
-    '''Adds a Normal cell for NASNet-A (Fig. 4 in the paper)
+    '''Adds a Normal cell for NASNet-A (Fig. 4 in the paper).
 
     # Arguments
         ip: Input tensor `x`
@@ -594,7 +594,7 @@ def _normal_a_cell(ip, p, filters, block_id=None):
 
 
 def _reduction_a_cell(ip, p, filters, block_id=None):
-    '''Adds a Reduction cell for NASNet-A (Fig. 4 in the paper)
+    '''Adds a Reduction cell for NASNet-A (Fig. 4 in the paper).
 
     # Arguments
         ip: Input tensor `x`

keras/datasets/reuters.py

@@ -77,9 +77,9 @@ def load_data(path='reuters.npz', num_words=None, skip_top=0,
     # reserve 'index_from' (=3 by default) characters:
     # 0 (padding), 1 (start), 2 (OOV)
     if oov_char is not None:
-        xs = [[w if (skip_top <= w < num_words) else oov_char for w in x] for x in xs]
+        xs = [[w if skip_top <= w < num_words else oov_char for w in x] for x in xs]
     else:
-        xs = [[w for w in x if (skip_top <= w < num_words)] for x in xs]
+        xs = [[w for w in x if skip_top <= w < num_words] for x in xs]
 
     idx = int(len(xs) * (1 - test_split))
     x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])

keras/engine/training.py

@@ -697,10 +697,10 @@ def compile(self, optimizer, loss, metrics=None, loss_weights=None,
                                          'dictionary: "' + name + '". '
                                          'Only expected the following keys: ' +
                                          str(self.output_names))
-                _target_tensors = []
+                tmp_target_tensors = []
                 for name in self.output_names:
-                    _target_tensors.append(target_tensors.get(name, None))
-                target_tensors = _target_tensors
+                    tmp_target_tensors.append(target_tensors.get(name, None))
+                target_tensors = tmp_target_tensors
             else:
                 raise TypeError('Expected `target_tensors` to be '
                                 'a list or dict, but got:', target_tensors)
@@ -1020,6 +1020,9 @@ def _check_num_samples(self, ins, batch_size=None, steps=None, steps_name='steps
             processed based on the size of the first dimension of the
             first input numpy array. When steps is not `None` and
             `batch_size` is `None`, returns `None`.
+
+        # Raises
+            ValueError: In case of invalid arguments.
         """
         if steps is not None:
             num_samples = None
@@ -1696,7 +1699,6 @@ def evaluate(self, x=None, y=None,
                 before declaring the evaluation round finished.
                 Ignored with the default value of `None`.
 
-
         # Returns
             Scalar test loss (if the model has a single output and no metrics)
             or list of scalars (if the model has multiple outputs

keras/preprocessing/image.py

@@ -956,7 +956,7 @@ def _recursive_list(subpath):
         return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0])
 
     samples = 0
-    for root, _, files in _recursive_list(directory):
+    for _, _, files in _recursive_list(directory):
         for fname in files:
             is_valid = False
             for extension in white_list_formats:

keras/utils/layer_utils.py

@@ -48,7 +48,8 @@ def print_summary(model, line_length=None, positions=None, print_fn=None):
         nodes = []
         for v in nodes_by_depth:
             if (len(v) > 1) or (len(v) == 1 and len(v[0].inbound_layers) > 1):
-                # if the model has multiple nodes or if the nodes have multiple inbound_layers
+                # if the model has multiple nodes
+                # or if the nodes have multiple inbound_layers
                 # the model is no longer sequential
                 sequential_like = False
                 break