_variable_with_weight_decay

查看具体实现：

def _variable_with_weight_decay(name, shape, stddev, wd, use_xavier=True):
  """Helper to create an initialized Variable with weight decay.

  Note that the Variable is initialized with a truncated normal distribution.
  A weight decay is added only if one is specified.

  Args:
    name: name of the variable
    shape: list of ints
    stddev: standard deviation of a truncated Gaussian
    wd: add L2Loss weight decay multiplied by this float. If None, weight
        decay is not added for this Variable.
    use_xavier: bool, whether to use xavier initializer

  Returns:
    Variable Tensor
  """
  if use_xavier:
    initializer = tf.contrib.layers.xavier_initializer()
  else:
    initializer = tf.truncated_normal_initializer(stddev=stddev)
  var = _variable_on_cpu(name, shape, initializer)
  if wd is not None:
    weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
    tf.add_to_collection('losses', weight_decay)
  return var

第一，初始化一个initializer ，通过_variable_on_cpu，对initializer进行初始化

def _variable_on_cpu(name, shape, initializer, use_fp16=False):
  """Helper to create a Variable stored on CPU memory.
  Args:
    name: name of the variable
    shape: list of ints
    initializer: initializer for Variable
  Returns:
    Variable Tensor
  """
  with tf.device('/cpu:0'):
    dtype = tf.float16 if use_fp16 else tf.float32
    var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype)
  return var

从源码中可以看出，对name为“weight”的tensor进行初始化。参考tf.get_variable

再与wd进行元素相乘（wd为之前创建的bn_decay，指数递增的learning_rate,常量tensor参考）得到name为“weight_loss”的tetensor中。然后通过tf.add_to_collection('losses', weight_decay)将“weight_loss”加入在同一个graph的tensor“losses”中。

最后返回weight矩阵[1,3,1,64]