Cleanup examples

examples/addition_rnn.py

@@ -1,13 +1,5 @@
 # -*- coding: utf-8 -*-
-from __future__ import print_function
-from keras.models import Sequential, slice_X
-from keras.layers.core import Activation, TimeDistributedDense, RepeatVector
-from keras.layers import recurrent
-import numpy as np
-from six.moves import range
-
-"""
-An implementation of sequence to sequence learning for performing addition
+'''An implementation of sequence to sequence learning for performing addition
 Input: "535+61"
 Output: "596"
 Padding is handled by using a repeated sentinel character (space)
@@ -32,16 +24,23 @@
 Five digits inverted:
 + One layer LSTM (128 HN), 550k training examples = 99% train/test accuracy in 30 epochs
 
-"""
+'''
+
+from __future__ import print_function
+from keras.models import Sequential, slice_X
+from keras.layers.core import Activation, TimeDistributedDense, RepeatVector
+from keras.layers import recurrent
+import numpy as np
+from six.moves import range
 
 
 class CharacterTable(object):
-    """
+    '''
     Given a set of characters:
     + Encode them to a one hot integer representation
     + Decode the one hot integer representation to their character output
     + Decode a vector of probabilties to their character output
-    """
+    '''
     def __init__(self, chars, maxlen):
         self.chars = sorted(set(chars))
         self.char_indices = dict((c, i) for i, c in enumerate(self.chars))
@@ -150,7 +149,8 @@ class colors:
     print()
     print('-' * 50)
     print('Iteration', iteration)
-    model.fit(X_train, y_train, batch_size=BATCH_SIZE, nb_epoch=1, validation_data=(X_val, y_val), show_accuracy=True)
+    model.fit(X_train, y_train, batch_size=BATCH_SIZE, nb_epoch=1,
+              validation_data=(X_val, y_val), show_accuracy=True)
     ###
     # Select 10 samples from the validation set at random so we can visualize errors
     for i in range(10):

examples/babi_memnn.py

@@ -1,17 +1,4 @@
-from __future__ import print_function
-from keras.models import Sequential
-from keras.layers.embeddings import Embedding
-from keras.layers.core import Activation, Dense, Merge, Permute, Dropout
-from keras.layers.recurrent import LSTM
-from keras.datasets.data_utils import get_file
-from keras.preprocessing.sequence import pad_sequences
-from functools import reduce
-import tarfile
-import numpy as np
-import re
-
-"""
-Train a memory network on the bAbI dataset.
+'''Train a memory network on the bAbI dataset.
 
 References:
 - Jason Weston, Antoine Bordes, Sumit Chopra, Tomas Mikolov, Alexander M. Rush,
@@ -24,7 +11,19 @@
 
 Reaches 93% accuracy on task 'single_supporting_fact_10k' after 70 epochs.
 Time per epoch: 3s on CPU (core i7).
-"""
+'''
+
+from __future__ import print_function
+from keras.models import Sequential
+from keras.layers.embeddings import Embedding
+from keras.layers.core import Activation, Dense, Merge, Permute, Dropout
+from keras.layers.recurrent import LSTM
+from keras.datasets.data_utils import get_file
+from keras.preprocessing.sequence import pad_sequences
+from functools import reduce
+import tarfile
+import numpy as np
+import re
 
 
 def tokenize(sent):
@@ -200,4 +199,3 @@ def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):
            nb_epoch=70,
            show_accuracy=True,
            validation_data=([inputs_test, queries_test, inputs_test], answers_test))
-

examples/babi_rnn.py

@@ -1,21 +1,4 @@
-from __future__ import absolute_import
-from __future__ import print_function
-from functools import reduce
-import re
-import tarfile
-
-import numpy as np
-np.random.seed(1337)  # for reproducibility
-
-from keras.datasets.data_utils import get_file
-from keras.layers.embeddings import Embedding
-from keras.layers.core import Dense, Merge
-from keras.layers import recurrent
-from keras.models import Sequential
-from keras.preprocessing.sequence import pad_sequences
-
-'''
-Trains two recurrent neural networks based upon a story and a question.
+'''Trains two recurrent neural networks based upon a story and a question.
 The resulting merged vector is then queried to answer a range of bAbI tasks.
 
 The results are comparable to those for an LSTM model provided in Weston et al.:
@@ -73,6 +56,21 @@
 This becomes especially obvious on QA2 and QA3, both far longer than QA1.
 '''
 
+from __future__ import print_function
+from functools import reduce
+import re
+import tarfile
+
+import numpy as np
+np.random.seed(1337)  # for reproducibility
+
+from keras.datasets.data_utils import get_file
+from keras.layers.embeddings import Embedding
+from keras.layers.core import Dense, Merge
+from keras.layers import recurrent
+from keras.models import Sequential
+from keras.preprocessing.sequence import pad_sequences
+
 
 def tokenize(sent):
     '''Return the tokens of a sentence including punctuation.

examples/cifar10_cnn.py

@@ -1,4 +1,16 @@
-from __future__ import absolute_import
+'''Train a simple deep CNN on the CIFAR10 small images dataset.
+
+GPU run command:
+    THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cifar10_cnn.py
+
+It gets down to 0.65 test logloss in 25 epochs, and down to 0.55 after 50 epochs.
+(it's still underfitting at that point, though).
+
+Note: the data was pickled with Python 2, and some encoding issues might prevent you
+from loading it in Python 3. You might have to load it in Python 2,
+save it in a different format, load it in Python 3 and repickle it.
+'''
+
 from __future__ import print_function
 from keras.datasets import cifar10
 from keras.preprocessing.image import ImageDataGenerator
@@ -9,20 +21,6 @@
 from keras.utils import np_utils, generic_utils
 from six.moves import range
 
-'''
-    Train a (fairly simple) deep CNN on the CIFAR10 small images dataset.
-
-    GPU run command:
-        THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cifar10_cnn.py
-
-    It gets down to 0.65 test logloss in 25 epochs, and down to 0.55 after 50 epochs.
-    (it's still underfitting at that point, though).
-
-    Note: the data was pickled with Python 2, and some encoding issues might prevent you
-    from loading it in Python 3. You might have to load it in Python 2,
-    save it in a different format, load it in Python 3 and repickle it.
-'''
-
 batch_size = 32
 nb_classes = 10
 nb_epoch = 200
@@ -71,19 +69,19 @@
 sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
 model.compile(loss='categorical_crossentropy', optimizer=sgd)
 
-X_train = X_train.astype("float32")
-X_test = X_test.astype("float32")
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
 X_train /= 255
 X_test /= 255
 
 if not data_augmentation:
-    print("Not using data augmentation or normalization")
+    print('Not using data augmentation or normalization')
     model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch)
     score = model.evaluate(X_test, Y_test, batch_size=batch_size)
     print('Test score:', score)
 
 else:
-    print("Using real time data augmentation")
+    print('Using real time data augmentation')
 
     # this will do preprocessing and realtime data augmentation
     datagen = ImageDataGenerator(
@@ -106,16 +104,16 @@
         print('-'*40)
         print('Epoch', e)
         print('-'*40)
-        print("Training...")
+        print('Training...')
         # batch train with realtime data augmentation
         progbar = generic_utils.Progbar(X_train.shape[0])
         for X_batch, Y_batch in datagen.flow(X_train, Y_train):
             loss = model.train_on_batch(X_batch, Y_batch)
-            progbar.add(X_batch.shape[0], values=[("train loss", loss[0])])
+            progbar.add(X_batch.shape[0], values=[('train loss', loss[0])])
 
-        print("Testing...")
+        print('Testing...')
         # test time!
         progbar = generic_utils.Progbar(X_test.shape[0])
         for X_batch, Y_batch in datagen.flow(X_test, Y_test):
             score = model.test_on_batch(X_batch, Y_batch)
-            progbar.add(X_batch.shape[0], values=[("test loss", score[0])])
+            progbar.add(X_batch.shape[0], values=[('test loss', score[0])])

examples/imdb_bidirectional_lstm.py

@@ -1,4 +1,12 @@
-from __future__ import absolute_import
+'''Train a Bidirectional LSTM on the IMDB sentiment classification task.
+
+GPU command:
+    THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_bidirectional_lstm.py
+
+Output after 4 epochs on CPU: ~0.8146
+Time per epoch on CPU (Core i7): ~150s.
+'''
+
 from __future__ import print_function
 import numpy as np
 np.random.seed(1337)  # for reproducibility
@@ -11,21 +19,12 @@
 from keras.layers.recurrent import LSTM
 from keras.datasets import imdb
 
-'''
-    Train a Bidirectional LSTM on the IMDB sentiment classification task.
-
-    GPU command:
-        THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_bidirectional_lstm.py
-
-    Output after 4 epochs on CPU: ~0.8146
-    Time per epoch on CPU (Core i7): ~150s.
-'''
 
 max_features = 20000
 maxlen = 100  # cut texts after this number of words (among top max_features most common words)
 batch_size = 32
 
-print("Loading data...")
+print('Loading data...')
 (X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features,
                                                       test_split=0.2)
 print(len(X_train), 'train sequences')
@@ -53,7 +52,7 @@
 # try using different optimizers and different optimizer configs
 model.compile('adam', {'output': 'binary_crossentropy'})
 
-print("Train...")
+print('Train...')
 model.fit({'input': X_train, 'output': y_train},
           batch_size=batch_size,
           nb_epoch=4)

examples/imdb_cnn.py

@@ -1,4 +1,10 @@
-from __future__ import absolute_import
+'''This example demonstrates the use of Convolution1D for text classification.
+
+Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_cnn.py
+
+Get to 0.835 test accuracy after 2 epochs. 100s/epoch on K520 GPU.
+'''
+
 from __future__ import print_function
 import numpy as np
 np.random.seed(1337)  # for reproducibility
@@ -10,14 +16,6 @@
 from keras.layers.convolutional import Convolution1D, MaxPooling1D
 from keras.datasets import imdb
 
-'''
-    This example demonstrates the use of Convolution1D
-    for text classification.
-
-    Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_cnn.py
-
-    Get to 0.835 test accuracy after 2 epochs. 100s/epoch on K520 GPU.
-'''
 
 # set parameters:
 max_features = 5000
@@ -29,13 +27,13 @@
 hidden_dims = 250
 nb_epoch = 2
 
-print("Loading data...")
+print('Loading data...')
 (X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features,
                                                       test_split=0.2)
 print(len(X_train), 'train sequences')
 print(len(X_test), 'test sequences')
 
-print("Pad sequences (samples x time)")
+print('Pad sequences (samples x time)')
 X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
 X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
 print('X_train shape:', X_train.shape)
@@ -53,8 +51,8 @@
 # word group filters of size filter_length:
 model.add(Convolution1D(nb_filter=nb_filter,
                         filter_length=filter_length,
-                        border_mode="valid",
-                        activation="relu",
+                        border_mode='valid',
+                        activation='relu',
                         subsample_length=1))
 # we use standard max pooling (halving the output of the previous layer):
 model.add(MaxPooling1D(pool_length=2))
@@ -74,7 +72,7 @@
 
 model.compile(loss='binary_crossentropy',
               optimizer='rmsprop',
-              class_mode="binary")
+              class_mode='binary')
 model.fit(X_train, y_train, batch_size=batch_size,
           nb_epoch=nb_epoch, show_accuracy=True,
           validation_data=(X_test, y_test))