(input_tensor,
              num_classes=1001,
              depth_multiplier=1.0,
              scope='MobilenetV2',
              conv_defs=None,
              finegrain_classification_mode=False,
              min_depth=None,
              divisible_by=None,
              **kwargs)
Creates mobilenet V2 network.

  Inference mode is created by default. To create training use training_scope
  below.

  with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
     logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  Args:
    input_tensor: The input tensor
    num_classes: number of classes
    depth_multiplier: The multiplier applied to scale number of
    channels in each layer. Note: this is called depth multiplier in the
    paper but the name is kept for consistency with slim's model builder.
    scope: Scope of the operator
    conv_defs: Allows to override default conv def.
    finegrain_classification_mode: When set to True, the model
    will keep the last layer large even for small multipliers. Following
    https://arxiv.org/abs/1801.04381
    suggests that it improves performance for ImageNet-type of problems.
      *Note* ignored if final_endpoint makes the builder exit earlier.
    min_depth: If provided, will ensure that all layers will have that
    many channels after application of depth multiplier.
    divisible_by: If provided will ensure that all layers # channels
    will be divisible by this number.
    **kwargs: passed directly to mobilenet.mobilenet:
      prediciton_fn- what prediction function to use.
      reuse-: whether to reuse variables (if reuse set to true, scope
      must be given).
  Returns:
    logits/endpoints pair

  Raises:
    ValueError: On invalid arguments
Creates mobilenet V2 network.
Creates mobilenet V2 network .
def mobilenet(input_tensor,
              num_classes=1001,
              depth_multiplier=1.0,
              scope='MobilenetV2',
              conv_defs=None,
              finegrain_classification_mode=False,
              min_depth=None,
              divisible_by=None,
              **kwargs):
  """Creates mobilenet V2 network.

  Inference mode is created by default. To create training use training_scope
  below.

  with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
     logits, endpoints = mobilenet_v2.mobilenet(input_tensor)

  Args:
    input_tensor: The input tensor
    num_classes: number of classes
    depth_multiplier: The multiplier applied to scale number of
    channels in each layer. Note: this is called depth multiplier in the
    paper but the name is kept for consistency with slim's model builder.
    scope: Scope of the operator
    conv_defs: Allows to override default conv def.
    finegrain_classification_mode: When set to True, the model
    will keep the last layer large even for small multipliers. Following
    https://arxiv.org/abs/1801.04381
    suggests that it improves performance for ImageNet-type of problems.
      *Note* ignored if final_endpoint makes the builder exit earlier.
    min_depth: If provided, will ensure that all layers will have that
    many channels after application of depth multiplier.
    divisible_by: If provided will ensure that all layers # channels
    will be divisible by this number.
    **kwargs: passed directly to mobilenet.mobilenet:
      prediciton_fn- what prediction function to use.
      reuse-: whether to reuse variables (if reuse set to true, scope
      must be given).
  Returns:
    logits/endpoints pair

  Raises:
    ValueError: On invalid arguments
  """
  if conv_defs is None:
    conv_defs = V2_DEF
  if 'multiplier' in kwargs:
    raise ValueError('mobilenetv2 doesn\'t support generic '
                     'multiplier parameter use "depth_multiplier" instead.')
  if finegrain_classification_mode:
    conv_defs = copy.deepcopy(conv_defs)
    if depth_multiplier < 1:
      conv_defs['spec'][-1].params['num_outputs'] /= depth_multiplier

  depth_args = {}
  # NB: do not set depth_args unless they are provided to avoid overriding
  # whatever default depth_multiplier might have thanks to arg_scope.
  if min_depth is not None:
    depth_args['min_depth'] = min_depth
  if divisible_by is not None:
    depth_args['divisible_by'] = divisible_by

  with slim.arg_scope((lib.depth_multiplier,), **depth_args):
    return lib.mobilenet(
        input_tensor,
        num_classes=num_classes,
        conv_defs=conv_defs,
        scope=scope,
        multiplier=depth_multiplier,
        **kwargs)
def mobilenet ( input_tensor , num_classes = 1001 , depth_multiplier = 1.0 , scope = 'MobilenetV2' , conv_defs = None , finegrain_classification_mode = False , min_depth = None , divisible_by = None , * * kwargs ) : if conv_defs is None : conv_defs = V2_DEF if 'multiplier' in kwargs : raise ValueError ( 'mobilenetv2 doesn\'t support generic ' 'multiplier parameter use "depth_multiplier" instead.' ) if finegrain_classification_mode : conv_defs = copy . deepcopy ( conv_defs ) if depth_multiplier < 1 : conv_defs [ 'spec' ] [ - 1 ] . params [ 'num_outputs' ] /= depth_multiplier depth_args = { } # NB: do not set depth_args unless they are provided to avoid overriding # whatever default depth_multiplier might have thanks to arg_scope. if min_depth is not None : depth_args [ 'min_depth' ] = min_depth if divisible_by is not None : depth_args [ 'divisible_by' ] = divisible_by with slim . arg_scope ( ( lib . depth_multiplier , ) , * * depth_args ) : return lib . mobilenet ( input_tensor , num_classes = num_classes , conv_defs = conv_defs , scope = scope , multiplier = depth_multiplier , * * kwargs )