net_model.py 文件源码

def variable_with_weight_decay(name, shape, stddev, wd):
    """
    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.
    Rtns:
        var    -> variable tensor
    """
    dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
    var   = variable_on_cpu(name,shape,
                    tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))
    if wd is not None:
        weight_decay = tf.mul(tf.nn.l2_loss(var),wd,name='weight_loss')
        tf.add_to_collection('losses', weight_decay)
    return var