message: Makes inception_v3 in tensorflow/tensorflow using the same initializer as tensorflow/models.
  Experiments show that variance_scaling_initializer, which is used by converges faster than truncated_normal_initializer, which is used by
diff: @@ -20,6 +20,7 @@ from __future__ import print_function
  from tensorflow.contrib import layers
  from tensorflow.contrib.framework.python.ops import arg_scope
  +from tensorflow.contrib.layers.python.layers import initializers
  from tensorflow.contrib.layers.python.layers import layers as layers_lib
  from tensorflow.contrib.layers.python.layers import regularizers
  from tensorflow.python.framework import ops
  @@ -675,14 +676,12 @@ def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
  def inception_v3_arg_scope(weight_decay=0.00004,
  - stddev=0.1,
  batch_norm_var_collection='moving_vars',
  use_fused_batchnorm=True):
  """Defines the default InceptionV3 arg scope.
  Args:
  weight_decay: The weight decay to use for regularizing the model.
  - stddev: The standard deviation of the trunctated normal weight initializer.
  batch_norm_var_collection: The name of the collection for the batch norm
  variables.
  use_fused_batchnorm: Enable fused batchnorm.
  @@ -714,8 +713,7 @@ def inception_v3_arg_scope(weight_decay=0.00004,
  weights_regularizer=regularizers.l2_regularizer(weight_decay)):
  with arg_scope(
  [layers.conv2d],
  - weights_initializer=init_ops.truncated_normal_initializer(
  - stddev=stddev),
  + weights_initializer=initializers.variance_scaling_initializer(),
  activation_fn=nn_ops.relu,
  normalizer_fn=layers_lib.batch_norm,
  normalizer_params=batch_norm_params) as sc:
  