(is_training=True,
                      trainable=True,
                      weight_decay=0.00004,
                      stddev=0.09,
                      dropout_keep_prob=0.8,
                      bn_decay=0.997)
Defines Mobilenet training scope.
  In default. We do not use BN

  ReWrite the scope.
Defines Mobilenet training scope.
  In default. We do not use BN
Defines Mobilenet training scope . In default . We do not use BN
def mobilenetv2_scope(is_training=True,
                      trainable=True,
                      weight_decay=0.00004,
                      stddev=0.09,
                      dropout_keep_prob=0.8,
                      bn_decay=0.997):
  """Defines Mobilenet training scope.
  In default. We do not use BN

  ReWrite the scope.
  """
  batch_norm_params = {
      'is_training': False,
      'trainable': False,
      'decay': bn_decay,
  }
  with slim.arg_scope(training_scope(is_training=is_training, weight_decay=weight_decay)):
      with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.separable_conv2d],
                          trainable=trainable):
          with slim.arg_scope([slim.batch_norm], **batch_norm_params) as sc:
              return sc
def mobilenetv2_scope ( is_training = True , trainable = True , weight_decay = 0.00004 , stddev = 0.09 , dropout_keep_prob = 0.8 , bn_decay = 0.997 ) : batch_norm_params = { 'is_training' : False , 'trainable' : False , 'decay' : bn_decay , } with slim . arg_scope ( training_scope ( is_training = is_training , weight_decay = weight_decay ) ) : with slim . arg_scope ( [ slim . conv2d , slim . fully_connected , slim . separable_conv2d ] , trainable = trainable ) : with slim . arg_scope ( [ slim . batch_norm ] , * * batch_norm_params ) as sc : return sc