Small style fixes

examples/cifar10_cnn.py

@@ -82,16 +82,16 @@
 
     # this will do preprocessing and realtime data augmentation
     datagen = ImageDataGenerator(
-        featurewise_center=True, # set input mean to 0 over the dataset
-        samplewise_center=False, # set each sample mean to 0
-        featurewise_std_normalization=True, # divide inputs by std of the dataset
-        samplewise_std_normalization=False, # divide each input by its std
-        zca_whitening=False, # apply ZCA whitening
-        rotation_range=20, # randomly rotate images in the range (degrees, 0 to 180)
-        width_shift_range=0.2, # randomly shift images horizontally (fraction of total width)
-        height_shift_range=0.2, # randomly shift images vertically (fraction of total height)
-        horizontal_flip=True, # randomly flip images
-        vertical_flip=False) # randomly flip images
+        featurewise_center=True,  # set input mean to 0 over the dataset
+        samplewise_center=False,  # set each sample mean to 0
+        featurewise_std_normalization=True,  # divide inputs by std of the dataset
+        samplewise_std_normalization=False,  # divide each input by its std
+        zca_whitening=False,  # apply ZCA whitening
+        rotation_range=20,  # randomly rotate images in the range (degrees, 0 to 180)
+        width_shift_range=0.2,  # randomly shift images horizontally (fraction of total width)
+        height_shift_range=0.2,  # randomly shift images vertically (fraction of total height)
+        horizontal_flip=True,  # randomly flip images
+        vertical_flip=False)  # randomly flip images
 
     # compute quantities required for featurewise normalization
     # (std, mean, and principal components if ZCA whitening is applied)

examples/imdb_lstm.py

@@ -1,7 +1,7 @@
 from __future__ import absolute_import
 from __future__ import print_function
 import numpy as np
-np.random.seed(1337) # for reproducibility
+np.random.seed(1337)  # for reproducibility
 
 from keras.preprocessing import sequence
 from keras.optimizers import SGD, RMSprop, Adagrad
@@ -32,7 +32,7 @@
 '''
 
 max_features = 20000
-maxlen = 100 # cut texts after this number of words (among top max_features most common words)
+maxlen = 100  # cut texts after this number of words (among top max_features most common words)
 batch_size = 32
 
 print("Loading data...")
@@ -49,7 +49,7 @@
 print('Build model...')
 model = Sequential()
 model.add(Embedding(max_features, 128))
-model.add(LSTM(128, 128)) # try using a GRU instead, for fun
+model.add(LSTM(128, 128))  # try using a GRU instead, for fun
 model.add(Dropout(0.5))
 model.add(Dense(128, 1))
 model.add(Activation('sigmoid'))

examples/kaggle_otto_nn.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 import pandas as pd
-np.random.seed(1337) # for reproducibility
+np.random.seed(1337)  # for reproducibility
 
 from keras.models import Sequential
 from keras.layers.core import Dense, Dropout, Activation
@@ -41,7 +41,7 @@ def load_data(path, train=True):
     df = pd.read_csv(path)
     X = df.values.copy()
     if train:
-        np.random.shuffle(X) # https://youtu.be/uyUXoap67N8
+        np.random.shuffle(X)  # https://youtu.be/uyUXoap67N8
         X, labels = X[:, 1:-1].astype(np.float32), X[:, -1]
         return X, labels
     else:

examples/mnist_cnn.py

@@ -1,7 +1,7 @@
 from __future__ import absolute_import
 from __future__ import print_function
 import numpy as np
-np.random.seed(1337) # for reproducibility
+np.random.seed(1337)  # for reproducibility
 
 from keras.datasets import mnist
 from keras.models import Sequential

examples/mnist_irnn.py

@@ -1,7 +1,7 @@
 from __future__ import absolute_import
 from __future__ import print_function
 import numpy as np
-np.random.seed(1337) # for reproducibility
+np.random.seed(1337)  # for reproducibility
 
 from keras.datasets import mnist
 from keras.models import Sequential

examples/mnist_mlp.py

@@ -1,7 +1,7 @@
 from __future__ import absolute_import
 from __future__ import print_function
 import numpy as np
-np.random.seed(1337) # for reproducibility
+np.random.seed(1337)  # for reproducibility
 
 from keras.datasets import mnist
 from keras.models import Sequential

examples/reuters_mlp.py

@@ -1,7 +1,7 @@
 from __future__ import absolute_import
 from __future__ import print_function
 import numpy as np
-np.random.seed(1337) # for reproducibility
+np.random.seed(1337)  # for reproducibility
 
 from keras.datasets import reuters
 from keras.models import Sequential

examples/skipgram_word_embeddings.py

@@ -43,10 +43,10 @@
 from six.moves import range
 from six.moves import zip
 
-max_features = 50000 # vocabulary size: top 50,000 most common words in data
-skip_top = 100 # ignore top 100 most common words
+max_features = 50000  # vocabulary size: top 50,000 most common words in data
+skip_top = 100  # ignore top 100 most common words
 nb_epoch = 1
-dim_proj = 256 # embedding space dimension
+dim_proj = 256  # embedding space dimension
 
 save = True
 load_model = False
@@ -196,23 +196,23 @@ def closest_to_word(w, nb_closest=10):
 '''
 
 words = [
-    "article", # post, story, hn, read, comments
-    "3", # 6, 4, 5, 2
-    "two", # three, few, several, each
-    "great", # love, nice, working, looking
-    "data", # information, memory, database
-    "money", # company, pay, customers, spend
-    "years", # ago, year, months, hours, week, days
-    "android", # ios, release, os, mobile, beta
-    "javascript", # js, css, compiler, library, jquery, ruby
-    "look", # looks, looking
-    "business", # industry, professional, customers
-    "company", # companies, startup, founders, startups
-    "after", # before, once, until
-    "own", # personal, our, having
-    "us", # united, country, american, tech, diversity, usa, china, sv
-    "using", # javascript, js, tools (lol)
-    "here", # hn, post, comments
+    "article",  # post, story, hn, read, comments
+    "3",  # 6, 4, 5, 2
+    "two",  # three, few, several, each
+    "great",  # love, nice, working, looking
+    "data",  # information, memory, database
+    "money",  # company, pay, customers, spend
+    "years",  # ago, year, months, hours, week, days
+    "android",  # ios, release, os, mobile, beta
+    "javascript",  # js, css, compiler, library, jquery, ruby
+    "look",  # looks, looking
+    "business",  # industry, professional, customers
+    "company",  # companies, startup, founders, startups
+    "after",  # before, once, until
+    "own",  # personal, our, having
+    "us",  # united, country, american, tech, diversity, usa, china, sv
+    "using",  # javascript, js, tools (lol)
+    "here",  # hn, post, comments
 ]
 
 for w in words:

keras/callbacks.py

@@ -106,8 +106,8 @@ def on_train_begin(self, logs={}):
     def on_epoch_begin(self, epoch, logs={}):
         if self.verbose:
             print('Epoch %d' % epoch)
-            self.progbar = Progbar(target=self.params['nb_sample'], \
-                verbose=self.verbose)
+            self.progbar = Progbar(target=self.params['nb_sample'],
+                                   verbose=self.verbose)
         self.seen = 0
         self.totals = {}
 
@@ -193,7 +193,7 @@ def on_epoch_end(self, epoch, logs={}):
                 if current < self.best:
                     if self.verbose > 0:
                         print("Epoch %05d: %s improved from %0.5f to %0.5f, saving model to %s"
-                            % (epoch, self.monitor, self.best, current, self.filepath))
+                              % (epoch, self.monitor, self.best, current, self.filepath))
                     self.best = current
                     self.model.save_weights(self.filepath, overwrite=True)
                 else:

keras/datasets/cifar10.py

@@ -4,6 +4,7 @@
 import numpy as np
 import os
 
+
 def load_data():
     dirname = "cifar-10-batches-py"
     origin = "http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
@@ -20,11 +21,11 @@ def load_data():
         data, labels = load_batch(fpath)
         X_train[(i-1)*10000:i*10000, :, :, :] = data
         y_train[(i-1)*10000:i*10000] = labels
-    
+
     fpath = os.path.join(path, 'test_batch')
     X_test, y_test = load_batch(fpath)
 
     y_train = np.reshape(y_train, (len(y_train), 1))
     y_test = np.reshape(y_test, (len(y_test), 1))
 
-    return (X_train, y_train), (X_test, y_test) 
+    return (X_train, y_train), (X_test, y_test)

keras/datasets/cifar100.py

@@ -4,6 +4,7 @@
 import numpy as np
 import os
 
+
 def load_data(label_mode='fine'):
     if label_mode not in ['fine', 'coarse']:
         raise Exception('label_mode must be one of "fine" "coarse".')
@@ -24,4 +25,4 @@ def load_data(label_mode='fine'):
     y_train = np.reshape(y_train, (len(y_train), 1))
     y_test = np.reshape(y_test, (len(y_test), 1))
 
-    return (X_train, y_train), (X_test, y_test) 
+    return (X_train, y_train), (X_test, y_test)

keras/datasets/imdb.py

@@ -6,8 +6,9 @@
 from six.moves import zip
 import numpy as np
 
-def load_data(path="imdb.pkl", nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113, 
-    start_char=1, oov_char=2, index_from=3):
+
+def load_data(path="imdb.pkl", nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113,
+              start_char=1, oov_char=2, index_from=3):
 
     path = get_file(path, origin="https://s3.amazonaws.com/text-datasets/imdb.pkl")
 
@@ -63,4 +64,3 @@ def load_data(path="imdb.pkl", nb_words=None, skip_top=0, maxlen=None, test_spli
     y_test = labels[int(len(X)*(1-test_split)):]
 
     return (X_train, y_train), (X_test, y_test)
-

keras/datasets/mnist.py

@@ -4,6 +4,7 @@
 import six.moves.cPickle
 import sys
 
+
 def load_data(path="mnist.pkl.gz"):
     path = get_file(path, origin="https://s3.amazonaws.com/img-datasets/mnist.pkl.gz")
 
@@ -19,4 +20,4 @@ def load_data(path="mnist.pkl.gz"):
 
     f.close()
 
-    return data # (X_train, y_train), (X_test, y_test)
+    return data  # (X_train, y_train), (X_test, y_test)

keras/datasets/reuters.py

@@ -9,6 +9,7 @@
 from six.moves import zip
 import numpy as np
 
+
 def make_reuters_dataset(path=os.path.join('datasets', 'temp', 'reuters21578'), min_samples_per_topic=15):
     import re
     from ..preprocessing.text import Tokenizer
@@ -24,8 +25,7 @@ def make_reuters_dataset(path=os.path.join('datasets', 'temp', 'reuters21578'),
             while tag in s:
                 s = s[s.find(tag)+len(tag):]
                 topics = s[:s.find('</')]
-
-                if topics and not '</D><D>' in topics:
+                if topics and '</D><D>' not in topics:
                     topic = topics.replace('<D>', '').replace('</D>', '')
                     wire_topics.append(topic)
                     topic_counts[topic] = topic_counts.get(topic, 0) + 1
@@ -39,7 +39,7 @@ def make_reuters_dataset(path=os.path.join('datasets', 'temp', 'reuters21578'),
 
     # only keep most common topics
     items = list(topic_counts.items())
-    items.sort(key = lambda x: x[1])
+    items.sort(key=lambda x: x[1])
     kept_topics = set()
     for x in items:
         print(x[0] + ': ' + str(x[1]))
@@ -75,16 +75,15 @@ def make_reuters_dataset(path=os.path.join('datasets', 'temp', 'reuters21578'),
     reverse_word_index = dict([(v, k) for k, v in tokenizer.word_index.items()])
     print(' '.join(reverse_word_index[i] for i in X[10]))
 
-    dataset = (X, labels) 
+    dataset = (X, labels)
     print('-')
     print('Saving...')
     six.moves.cPickle.dump(dataset, open(os.path.join('datasets', 'data', 'reuters.pkl'), 'w'))
     six.moves.cPickle.dump(tokenizer.word_index, open(os.path.join('datasets', 'data', 'reuters_word_index.pkl'), 'w'))
 
 
-
 def load_data(path="reuters.pkl", nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113,
-    start_char=1, oov_char=2, index_from=3):
+              start_char=1, oov_char=2, index_from=3):
 
     path = get_file(path, origin="https://s3.amazonaws.com/text-datasets/reuters.pkl")
     f = open(path, 'rb')

keras/layers/containers.py

@@ -102,15 +102,15 @@ class Graph(Layer):
             - set_weights
     '''
     def __init__(self):
-        self.namespace = set() # strings
-        self.nodes = {} # layer-like
-        self.inputs = {} # layer-like
-        self.input_order = [] # strings
-        self.outputs = {} # layer-like
-        self.output_order = [] # strings
-        self.input_config = [] # dicts
-        self.output_config = [] # dicts
-        self.node_config = [] # dicts
+        self.namespace = set()  # strings
+        self.nodes = {}  # layer-like
+        self.inputs = {}  # layer-like
+        self.input_order = []  # strings
+        self.outputs = {}  # layer-like
+        self.output_order = []  # strings
+        self.input_config = []  # dicts
+        self.output_config = []  # dicts
+        self.node_config = []  # dicts
 
         self.params = []
         self.regularizers = []