Enforce quoting style in Travis. (#4589)

examples/parameter_server/model.py

@@ -38,16 +38,16 @@ class SimpleCNN(object):
             # Build the graph for the deep net
             self.y_conv, self.keep_prob = deepnn(self.x)
 
-            with tf.name_scope('loss'):
+            with tf.name_scope("loss"):
                 cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
                     labels=self.y_, logits=self.y_conv)
             self.cross_entropy = tf.reduce_mean(cross_entropy)
 
-            with tf.name_scope('adam_optimizer'):
+            with tf.name_scope("adam_optimizer"):
                 self.optimizer = tf.train.AdamOptimizer(learning_rate)
                 self.train_step = self.optimizer.minimize(self.cross_entropy)
 
-            with tf.name_scope('accuracy'):
+            with tf.name_scope("accuracy"):
                 correct_prediction = tf.equal(
                     tf.argmax(self.y_conv, 1), tf.argmax(self.y_, 1))
                 correct_prediction = tf.cast(correct_prediction, tf.float32)
@@ -133,32 +133,32 @@ def deepnn(x):
     # Reshape to use within a convolutional neural net.
     # Last dimension is for "features" - there is only one here, since images
     # are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
-    with tf.name_scope('reshape'):
+    with tf.name_scope("reshape"):
         x_image = tf.reshape(x, [-1, 28, 28, 1])
 
     # First convolutional layer - maps one grayscale image to 32 feature maps.
-    with tf.name_scope('conv1'):
+    with tf.name_scope("conv1"):
         W_conv1 = weight_variable([5, 5, 1, 32])
         b_conv1 = bias_variable([32])
         h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
 
     # Pooling layer - downsamples by 2X.
-    with tf.name_scope('pool1'):
+    with tf.name_scope("pool1"):
         h_pool1 = max_pool_2x2(h_conv1)
 
     # Second convolutional layer -- maps 32 feature maps to 64.
-    with tf.name_scope('conv2'):
+    with tf.name_scope("conv2"):
         W_conv2 = weight_variable([5, 5, 32, 64])
         b_conv2 = bias_variable([64])
         h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
 
     # Second pooling layer.
-    with tf.name_scope('pool2'):
+    with tf.name_scope("pool2"):
         h_pool2 = max_pool_2x2(h_conv2)
 
     # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
     # is down to 7x7x64 feature maps -- maps this to 1024 features.
-    with tf.name_scope('fc1'):
+    with tf.name_scope("fc1"):
         W_fc1 = weight_variable([7 * 7 * 64, 1024])
         b_fc1 = bias_variable([1024])
 
@@ -167,12 +167,12 @@ def deepnn(x):
 
     # Dropout - controls the complexity of the model, prevents co-adaptation of
     # features.
-    with tf.name_scope('dropout'):
+    with tf.name_scope("dropout"):
         keep_prob = tf.placeholder(tf.float32)
         h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 
     # Map the 1024 features to 10 classes, one for each digit
-    with tf.name_scope('fc2'):
+    with tf.name_scope("fc2"):
         W_fc2 = weight_variable([1024, 10])
         b_fc2 = bias_variable([10])
 
@@ -182,13 +182,13 @@ def deepnn(x):
 
 def conv2d(x, W):
     """conv2d returns a 2d convolution layer with full stride."""
-    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
+    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
 
 
 def max_pool_2x2(x):
     """max_pool_2x2 downsamples a feature map by 2X."""
     return tf.nn.max_pool(
-        x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+        x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
 
 
 def weight_variable(shape):