TF fixes and style fixes

Author: fchollet

examples/mnist_siamese_graph.py

@@ -9,7 +9,7 @@
 
 Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_siamese_graph.py
 
-Get to 99.5% test accuracy after 20 epochs.
+Gets to 99.5% test accuracy after 20 epochs.
 3 seconds per epoch on a Titan X GPU
 '''
 from __future__ import absolute_import
@@ -20,30 +20,30 @@
 import random
 from keras.datasets import mnist
 from keras.models import Sequential, Graph
-from keras.layers.core import *
+from keras.layers.core import Dense, Dropout, Lambda
 from keras.optimizers import SGD, RMSprop
 from keras import backend as K
 
 
 def euclidean_distance(inputs):
-    assert len(inputs) == 2, \
-        'Euclidean distance needs 2 inputs, %d given' % len(inputs)
+    assert len(inputs) == 2, ('Euclidean distance needs '
+                              '2 inputs, %d given' % len(inputs))
     u, v = inputs.values()
-    return K.sqrt((K.square(u - v)).sum(axis=1, keepdims=True))
+    return K.sqrt(K.sum(K.square(u - v), axis=1, keepdims=True))
 
 
 def contrastive_loss(y, d):
-    """ Contrastive loss from Hadsell-et-al.'06
-        http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
-    """
+    '''Contrastive loss from Hadsell-et-al.'06
+    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
+    '''
     margin = 1
     return K.mean(y * K.square(d) + (1 - y) * K.square(K.maximum(margin - d, 0)))
 
 
 def create_pairs(x, digit_indices):
-    """ Positive and negative pair creation.
-        Alternates between positive and negative pairs.
-    """
+    '''Positive and negative pair creation.
+    Alternates between positive and negative pairs.
+    '''
     pairs = []
     labels = []
     n = min([len(digit_indices[d]) for d in range(10)]) - 1
@@ -59,11 +59,11 @@ def create_pairs(x, digit_indices):
     return np.array(pairs), np.array(labels)
 
 
-def create_base_network(in_dim):
-    """ Base network to be shared (eq. to feature extraction).
-    """
+def create_base_network(input_dim):
+    '''Base network to be shared (eq. to feature extraction).
+    '''
     seq = Sequential()
-    seq.add(Dense(128, input_shape=(in_dim,), activation='relu'))
+    seq.add(Dense(128, input_shape=(input_dim,), activation='relu'))
     seq.add(Dropout(0.1))
     seq.add(Dense(128, activation='relu'))
     seq.add(Dropout(0.1))
@@ -72,8 +72,8 @@ def create_base_network(in_dim):
 
 
 def compute_accuracy(predictions, labels):
-    """ Compute classification accuracy with a fixed threshold on distances.
-    """
+    '''Compute classification accuracy with a fixed threshold on distances.
+    '''
     return labels[predictions.ravel() < 0.5].mean()
 
 
@@ -85,7 +85,7 @@ def compute_accuracy(predictions, labels):
 X_test = X_test.astype('float32')
 X_train /= 255
 X_test /= 255
-in_dim = 784
+input_dim = 784
 nb_epoch = 20
 
 # create training+test positive and negative pairs
@@ -96,11 +96,11 @@ def compute_accuracy(predictions, labels):
 te_pairs, te_y = create_pairs(X_test, digit_indices)
 
 # network definition
-base_network = create_base_network(in_dim)
+base_network = create_base_network(input_dim)
 
 g = Graph()
-g.add_input(name='input_a', input_shape=(in_dim,))
-g.add_input(name='input_b', input_shape=(in_dim,))
+g.add_input(name='input_a', input_shape=(input_dim,))
+g.add_input(name='input_b', input_shape=(input_dim,))
 g.add_shared_node(base_network, name='shared', inputs=['input_a', 'input_b'],
                   merge_mode='join')
 g.add_node(Lambda(euclidean_distance), name='d', input='shared')
@@ -111,7 +111,8 @@ def compute_accuracy(predictions, labels):
 g.compile(loss={'output': contrastive_loss}, optimizer=rms)
 g.fit({'input_a': tr_pairs[:, 0], 'input_b': tr_pairs[:, 1], 'output': tr_y},
       validation_data={'input_a': te_pairs[:, 0], 'input_b': te_pairs[:, 1], 'output': te_y},
-      batch_size=128, nb_epoch=nb_epoch)
+      batch_size=128,
+      nb_epoch=nb_epoch)
 
 # compute final accuracy on training and test sets
 pred = g.predict({'input_a': tr_pairs[:, 0], 'input_b': tr_pairs[:, 1]})['output']