Merge branch 'master' into dataset

research/textsum/README.md

@@ -2,7 +2,7 @@ Sequence-to-Sequence with Attention Model for Text Summarization.
 
 Authors:
 
-Xin Pan (xpan@google.com, github:panyx0718),
+Xin Pan
 Peter Liu (peterjliu@google.com, github:peterjliu)
 
 <b>Introduction</b>

tutorials/image/cifar10/cifar10.py

@@ -204,7 +204,7 @@ def inference(images):
     kernel = _variable_with_weight_decay('weights',
                                          shape=[5, 5, 3, 64],
                                          stddev=5e-2,
-                                         wd=0.0)
+                                         wd=None)
     conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
     biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
     pre_activation = tf.nn.bias_add(conv, biases)
@@ -223,7 +223,7 @@ def inference(images):
     kernel = _variable_with_weight_decay('weights',
                                          shape=[5, 5, 64, 64],
                                          stddev=5e-2,
-                                         wd=0.0)
+                                         wd=None)
     conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')
     biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1))
     pre_activation = tf.nn.bias_add(conv, biases)
@@ -262,7 +262,7 @@ def inference(images):
   # and performs the softmax internally for efficiency.
   with tf.variable_scope('softmax_linear') as scope:
     weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES],
-                                          stddev=1/192.0, wd=0.0)
+                                          stddev=1/192.0, wd=None)
     biases = _variable_on_cpu('biases', [NUM_CLASSES],
                               tf.constant_initializer(0.0))
     softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name)

tutorials/image/cifar10/cifar10_input.py

@@ -157,6 +157,7 @@ def distorted_inputs(data_dir, batch_size):
   # Create a queue that produces the filenames to read.
   filename_queue = tf.train.string_input_producer(filenames)
 
+  with tf.name_scope('data_augmentation'):
     # Read examples from files in the filename queue.
     read_input = read_cifar10(filename_queue)
     reshaped_image = tf.cast(read_input.uint8image, tf.float32)
@@ -226,6 +227,7 @@ def inputs(eval_data, data_dir, batch_size):
     if not tf.gfile.Exists(f):
       raise ValueError('Failed to find file: ' + f)
 
+  with tf.name_scope('input'):
     # Create a queue that produces the filenames to read.
     filename_queue = tf.train.string_input_producer(filenames)