PEP8 fixed line length examples/ (#10724)

* PEP8 fixed line length examples/

* revert addition_rnn

* Fix PEP8 in babi_memnn

examples/addition_rnn.py

@@ -24,7 +24,7 @@ Four digits reversed:
 
 Five digits reversed:
 + One layer LSTM (128 HN), 550k training examples = 99% train/test accuracy in 30 epochs
-'''
+'''  # noqa
 
 from __future__ import print_function
 from keras.models import Sequential

examples/babi_memnn.py

@@ -17,7 +17,8 @@ from __future__ import print_function
 
 from keras.models import Sequential, Model
 from keras.layers.embeddings import Embedding
-from keras.layers import Input, Activation, Dense, Permute, Dropout, add, dot, concatenate
+from keras.layers import Input, Activation, Dense, Permute, Dropout
+from keras.layers import add, dot, concatenate
 from keras.layers import LSTM
 from keras.utils.data_utils import get_file
 from keras.preprocessing.sequence import pad_sequences
@@ -79,7 +80,8 @@ def get_stories(f, only_supporting=False, max_length=None):
     '''
     data = parse_stories(f.readlines(), only_supporting=only_supporting)
     flatten = lambda data: reduce(lambda x, y: x + y, data)
-    data = [(flatten(story), q, answer) for story, q, answer in data if not max_length or len(flatten(story)) < max_length]
+    data = [(flatten(story), q, answer) for story, q, answer in data
+            if not max_length or len(flatten(story)) < max_length]
     return data
 
 
@@ -94,19 +96,24 @@ def vectorize_stories(data):
             np.array(answers))
 
 try:
-    path = get_file('babi-tasks-v1-2.tar.gz', origin='https://s3.amazonaws.com/text-datasets/babi_tasks_1-20_v1-2.tar.gz')
+    path = get_file('babi-tasks-v1-2.tar.gz',
+                    origin='https://s3.amazonaws.com/text-datasets/'
+                           'babi_tasks_1-20_v1-2.tar.gz')
 except:
     print('Error downloading dataset, please download it manually:\n'
-          '$ wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz\n'
+          '$ wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2'
+          '.tar.gz\n'
           '$ mv tasks_1-20_v1-2.tar.gz ~/.keras/datasets/babi-tasks-v1-2.tar.gz')
     raise
 
 
 challenges = {
     # QA1 with 10,000 samples
-    'single_supporting_fact_10k': 'tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_{}.txt',
+    'single_supporting_fact_10k': 'tasks_1-20_v1-2/en-10k/qa1_'
+                                  'single-supporting-fact_{}.txt',
     # QA2 with 10,000 samples
-    'two_supporting_facts_10k': 'tasks_1-20_v1-2/en-10k/qa2_two-supporting-facts_{}.txt',
+    'two_supporting_facts_10k': 'tasks_1-20_v1-2/en-10k/qa2_'
+                                'two-supporting-facts_{}.txt',
 }
 challenge_type = 'single_supporting_fact_10k'
 challenge = challenges[challenge_type]

examples/babi_rnn.py

@@ -123,7 +123,8 @@ def get_stories(f, only_supporting=False, max_length=None):
     '''
     data = parse_stories(f.readlines(), only_supporting=only_supporting)
     flatten = lambda data: reduce(lambda x, y: x + y, data)
-    data = [(flatten(story), q, answer) for story, q, answer in data if not max_length or len(flatten(story)) < max_length]
+    data = [(flatten(story), q, answer) for story, q, answer in data
+            if not max_length or len(flatten(story)) < max_length]
     return data
 
 
@@ -140,7 +141,8 @@ def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):
         xs.append(x)
         xqs.append(xq)
         ys.append(y)
-    return pad_sequences(xs, maxlen=story_maxlen), pad_sequences(xqs, maxlen=query_maxlen), np.array(ys)
+    return (pad_sequences(xs, maxlen=story_maxlen),
+            pad_sequences(xqs, maxlen=query_maxlen), np.array(ys))
 
 RNN = recurrent.LSTM
 EMBED_HIDDEN_SIZE = 50
@@ -154,10 +156,13 @@ print('RNN / Embed / Sent / Query = {}, {}, {}, {}'.format(RNN,
                                                            QUERY_HIDDEN_SIZE))
 
 try:
-    path = get_file('babi-tasks-v1-2.tar.gz', origin='https://s3.amazonaws.com/text-datasets/babi_tasks_1-20_v1-2.tar.gz')
+    path = get_file('babi-tasks-v1-2.tar.gz',
+                    origin='https://s3.amazonaws.com/text-datasets/'
+                           'babi_tasks_1-20_v1-2.tar.gz')
 except:
     print('Error downloading dataset, please download it manually:\n'
-          '$ wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz\n'
+          '$ wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2'
+          '.tar.gz\n'
           '$ mv tasks_1-20_v1-2.tar.gz ~/.keras/datasets/babi-tasks-v1-2.tar.gz')
     raise
 

examples/cifar10_cnn.py

@@ -85,19 +85,26 @@ else:
         zca_whitening=False,  # apply ZCA whitening
         zca_epsilon=1e-06,  # epsilon for ZCA whitening
         rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
-        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
-        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
+        # randomly shift images horizontally (fraction of total width)
+        width_shift_range=0.1,
+        # randomly shift images vertically (fraction of total height)
+        height_shift_range=0.1,
         shear_range=0.,  # set range for random shear
         zoom_range=0.,  # set range for random zoom
         channel_shift_range=0.,  # set range for random channel shifts
-        fill_mode='nearest',  # set mode for filling points outside the input boundaries
+        # set mode for filling points outside the input boundaries
+        fill_mode='nearest',
         cval=0.,  # value used for fill_mode = "constant"
         horizontal_flip=True,  # randomly flip images
         vertical_flip=False,  # randomly flip images
-        rescale=None,  # set rescaling factor (applied before any other transformation)
-        preprocessing_function=None,  # set function that will be applied on each input
-        data_format=None,  # image data format, either "channels_first" or "channels_last"
-        validation_split=0.0)  # fraction of images reserved for validation (strictly between 0 and 1)
+        # set rescaling factor (applied before any other transformation)
+        rescale=None,
+        # set function that will be applied on each input
+        preprocessing_function=None,
+        # image data format, either "channels_first" or "channels_last"
+        data_format=None,
+        # fraction of images reserved for validation (strictly between 0 and 1)
+        validation_split=0.0)
 
     # Compute quantities required for feature-wise normalization
     # (std, mean, and principal components if ZCA whitening is applied).

examples/cifar10_cnn_capsule.py

@@ -211,14 +211,19 @@ else:
         shear_range=0.,  # set range for random shear
         zoom_range=0.,  # set range for random zoom
         channel_shift_range=0.,  # set range for random channel shifts
-        fill_mode='nearest',  # set mode for filling points outside the input boundaries
+        # set mode for filling points outside the input boundaries
+        fill_mode='nearest',
         cval=0.,  # value used for fill_mode = "constant"
         horizontal_flip=True,  # randomly flip images
         vertical_flip=False,  # randomly flip images
-        rescale=None,  # set rescaling factor (applied before any other transformation)
-        preprocessing_function=None,  # set function that will be applied on each input
-        data_format=None,  # image data format, either "channels_first" or "channels_last"
-        validation_split=0.0)  # fraction of images reserved for validation (strictly between 0 and 1)
+        # set rescaling factor (applied before any other transformation)
+        rescale=None,
+        # set function that will be applied on each input
+        preprocessing_function=None,
+        # image data format, either "channels_first" or "channels_last"
+        data_format=None,
+        # fraction of images reserved for validation (strictly between 0 and 1)
+        validation_split=0.0)
 
     # Compute quantities required for feature-wise normalization
     # (std, mean, and principal components if ZCA whitening is applied).

pytest.ini

@@ -14,11 +14,6 @@ norecursedirs= build
 
 pep8ignore=* E402 \
            * E731 \
-           examples/addition_rnn.py E501 \
-           examples/babi_memnn.py E501 \
-           examples/babi_rnn.py E501 \
-           examples/cifar10_cnn.py E501 \
-           examples/cifar10_cnn_capsule.py E501 \
            examples/conv_filter_visualization.py E501 \
            examples/deep_dream.py E501 \
            examples/image_ocr.py E501 \