(inputs,
                  num_classes=None,
                  is_training=True,
                  global_pool=True,
                  output_stride=None,
                  spatial_squeeze=True,
                  reuse=None,
                  scope='resnet_v2_200')
return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
                   global_pool=global_pool, output_stride=output_stride,
                   include_root_block=True, spatial_squeeze=spatial_squeeze,
                   reuse=reuse, scope=scope)
ResNet-200 model of [2]. See resnet_v2() for arg and return description.
ResNet-200 model of [2]. See resnet_v2() for arg and return description.
ResNet - 200 model of [ 2 ] . See resnet_v2 () for arg and return description .
def resnet_v2_200(inputs,
                  num_classes=None,
                  is_training=True,
                  global_pool=True,
                  output_stride=None,
                  spatial_squeeze=True,
                  reuse=None,
                  scope='resnet_v2_200'):
  """ResNet-200 model of [2]. See resnet_v2() for arg and return description."""
  blocks = [
      resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),
      resnet_v2_block('block2', base_depth=128, num_units=24, stride=2),
      resnet_v2_block('block3', base_depth=256, num_units=36, stride=2),
      resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),
  ]
  return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
                   global_pool=global_pool, output_stride=output_stride,
                   include_root_block=True, spatial_squeeze=spatial_squeeze,
                   reuse=reuse, scope=scope)
def resnet_v2_200 ( inputs , num_classes = None , is_training = True , global_pool = True , output_stride = None , spatial_squeeze = True , reuse = None , scope = 'resnet_v2_200' ) : blocks = [ resnet_v2_block ( 'block1' , base_depth = 64 , num_units = 3 , stride = 2 ) , resnet_v2_block ( 'block2' , base_depth = 128 , num_units = 24 , stride = 2 ) , resnet_v2_block ( 'block3' , base_depth = 256 , num_units = 36 , stride = 2 ) , resnet_v2_block ( 'block4' , base_depth = 512 , num_units = 3 , stride = 1 ) , ] return resnet_v2 ( inputs , blocks , num_classes , is_training = is_training , global_pool = global_pool , output_stride = output_stride , include_root_block = True , spatial_squeeze = spatial_squeeze , reuse = reuse , scope = scope )