Removed unused regularization ops

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)