Module keras.api.keras.applications.mobilenet_v2

Public API for tf.keras.applications.mobilenet_v2 namespace.

Expand source code
# This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Public API for tf.keras.applications.mobilenet_v2 namespace.
"""

from __future__ import print_function as _print_function

import sys as _sys

from keras.applications.mobilenet_v2 import MobileNetV2
from keras.applications.mobilenet_v2 import decode_predictions
from keras.applications.mobilenet_v2 import preprocess_input

del _print_function

from tensorflow.python.util import module_wrapper as _module_wrapper

if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper):
  _sys.modules[__name__] = _module_wrapper.TFModuleWrapper(
      _sys.modules[__name__], "keras.applications.mobilenet_v2", public_apis=None, deprecation=True,
      has_lite=False)

Functions

def MobileNetV2(input_shape=None, alpha=1.0, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs)

Instantiates the MobileNetV2 architecture.

MobileNetV2 is very similar to the original MobileNet, except that it uses inverted residual blocks with bottlenecking features. It has a drastically lower parameter count than the original MobileNet. MobileNets support any input size greater than 32 x 32, with larger image sizes offering better performance.

Reference: - MobileNetV2: Inverted Residuals and Linear Bottlenecks (CVPR 2018)

This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For MobileNetV2, call tf.keras.applications.mobilenet_v2.preprocess_input on your inputs before passing them to the model. mobilenet_v2.preprocess_input will scale input pixels between -1 and 1.

Args

input_shape
Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels (224, 224, 3). You can also omit this option if you would like to infer input_shape from an input_tensor. If you choose to include both input_tensor and input_shape then input_shape will be used if they match, if the shapes do not match then we will throw an error. E.g. (160, 160, 3) would be one valid value.
alpha
Float between 0 and 1. controls the width of the network. This is known as the width multiplier in the MobileNetV2 paper, but the name is kept for consistency with applications.MobileNetV1 model in Keras. - If alpha < 1.0, proportionally decreases the number of filters in each layer. - If alpha > 1.0, proportionally increases the number of filters in each layer. - If alpha = 1.0, default number of filters from the paper are used at each layer.
include_top
Boolean, whether to include the fully-connected layer at the top of the network. Defaults to True.
weights
String, one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.
input_tensor
Optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
pooling
String, optional pooling mode for feature extraction when include_top is False. - None means that the output of the model will be the 4D tensor output of the last convolutional block. - avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. - max means that global max pooling will be applied.
classes
Integer, optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.
classifier_activation
A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax".
**kwargs
For backwards compatibility only.

Returns

A keras.Model instance.

Expand source code
@keras_export('keras.applications.mobilenet_v2.MobileNetV2',
              'keras.applications.MobileNetV2')
def MobileNetV2(input_shape=None,
                alpha=1.0,
                include_top=True,
                weights='imagenet',
                input_tensor=None,
                pooling=None,
                classes=1000,
                classifier_activation='softmax',
                **kwargs):
  """Instantiates the MobileNetV2 architecture.

  MobileNetV2 is very similar to the original MobileNet,
  except that it uses inverted residual blocks with
  bottlenecking features. It has a drastically lower
  parameter count than the original MobileNet.
  MobileNets support any input size greater
  than 32 x 32, with larger image sizes
  offering better performance.

  Reference:
  - [MobileNetV2: Inverted Residuals and Linear Bottlenecks](
      https://arxiv.org/abs/1801.04381) (CVPR 2018)

  This function returns a Keras image classification model,
  optionally loaded with weights pre-trained on ImageNet.

  For image classification use cases, see
  [this page for detailed examples](
    https://keras.io/api/applications/#usage-examples-for-image-classification-models).

  For transfer learning use cases, make sure to read the
  [guide to transfer learning & fine-tuning](
    https://keras.io/guides/transfer_learning/).

  Note: each Keras Application expects a specific kind of input preprocessing.
  For MobileNetV2, call `tf.keras.applications.mobilenet_v2.preprocess_input`
  on your inputs before passing them to the model.
  `mobilenet_v2.preprocess_input` will scale input pixels between -1 and 1.

  Args:
    input_shape: Optional shape tuple, to be specified if you would
      like to use a model with an input image resolution that is not
      (224, 224, 3).
      It should have exactly 3 inputs channels (224, 224, 3).
      You can also omit this option if you would like
      to infer input_shape from an input_tensor.
      If you choose to include both input_tensor and input_shape then
      input_shape will be used if they match, if the shapes
      do not match then we will throw an error.
      E.g. `(160, 160, 3)` would be one valid value.
    alpha: Float between 0 and 1. controls the width of the network.
      This is known as the width multiplier in the MobileNetV2 paper,
      but the name is kept for consistency with `applications.MobileNetV1`
      model in Keras.
      - If `alpha` < 1.0, proportionally decreases the number
          of filters in each layer.
      - If `alpha` > 1.0, proportionally increases the number
          of filters in each layer.
      - If `alpha` = 1.0, default number of filters from the paper
          are used at each layer.
    include_top: Boolean, whether to include the fully-connected
      layer at the top of the network. Defaults to `True`.
    weights: String, one of `None` (random initialization),
      'imagenet' (pre-training on ImageNet),
      or the path to the weights file to be loaded.
    input_tensor: Optional Keras tensor (i.e. output of
      `layers.Input()`)
      to use as image input for the model.
    pooling: String, optional pooling mode for feature extraction
      when `include_top` is `False`.
      - `None` means that the output of the model
          will be the 4D tensor output of the
          last convolutional block.
      - `avg` means that global average pooling
          will be applied to the output of the
          last convolutional block, and thus
          the output of the model will be a
          2D tensor.
      - `max` means that global max pooling will
          be applied.
    classes: Integer, optional number of classes to classify images
      into, only to be specified if `include_top` is True, and
      if no `weights` argument is specified.
    classifier_activation: A `str` or callable. The activation function to use
      on the "top" layer. Ignored unless `include_top=True`. Set
      `classifier_activation=None` to return the logits of the "top" layer.
      When loading pretrained weights, `classifier_activation` can only
      be `None` or `"softmax"`.
    **kwargs: For backwards compatibility only.

  Returns:
    A `keras.Model` instance.
  """
  global layers
  if 'layers' in kwargs:
    layers = kwargs.pop('layers')
  else:
    layers = VersionAwareLayers()
  if kwargs:
    raise ValueError('Unknown argument(s): %s' % (kwargs,))
  if not (weights in {'imagenet', None} or tf.io.gfile.exists(weights)):
    raise ValueError('The `weights` argument should be either '
                     '`None` (random initialization), `imagenet` '
                     '(pre-training on ImageNet), '
                     'or the path to the weights file to be loaded.')

  if weights == 'imagenet' and include_top and classes != 1000:
    raise ValueError('If using `weights` as `"imagenet"` with `include_top` '
                     'as true, `classes` should be 1000')

  # Determine proper input shape and default size.
  # If both input_shape and input_tensor are used, they should match
  if input_shape is not None and input_tensor is not None:
    try:
      is_input_t_tensor = backend.is_keras_tensor(input_tensor)
    except ValueError:
      try:
        is_input_t_tensor = backend.is_keras_tensor(
            layer_utils.get_source_inputs(input_tensor))
      except ValueError:
        raise ValueError('input_tensor: ', input_tensor,
                         'is not type input_tensor')
    if is_input_t_tensor:
      if backend.image_data_format() == 'channels_first':
        if backend.int_shape(input_tensor)[1] != input_shape[1]:
          raise ValueError('input_shape: ', input_shape, 'and input_tensor: ',
                           input_tensor,
                           'do not meet the same shape requirements')
      else:
        if backend.int_shape(input_tensor)[2] != input_shape[1]:
          raise ValueError('input_shape: ', input_shape, 'and input_tensor: ',
                           input_tensor,
                           'do not meet the same shape requirements')
    else:
      raise ValueError('input_tensor specified: ', input_tensor,
                       'is not a keras tensor')

  # If input_shape is None, infer shape from input_tensor
  if input_shape is None and input_tensor is not None:

    try:
      backend.is_keras_tensor(input_tensor)
    except ValueError:
      raise ValueError('input_tensor: ', input_tensor, 'is type: ',
                       type(input_tensor), 'which is not a valid type')

    if input_shape is None and not backend.is_keras_tensor(input_tensor):
      default_size = 224
    elif input_shape is None and backend.is_keras_tensor(input_tensor):
      if backend.image_data_format() == 'channels_first':
        rows = backend.int_shape(input_tensor)[2]
        cols = backend.int_shape(input_tensor)[3]
      else:
        rows = backend.int_shape(input_tensor)[1]
        cols = backend.int_shape(input_tensor)[2]

      if rows == cols and rows in [96, 128, 160, 192, 224]:
        default_size = rows
      else:
        default_size = 224

  # If input_shape is None and no input_tensor
  elif input_shape is None:
    default_size = 224

  # If input_shape is not None, assume default size
  else:
    if backend.image_data_format() == 'channels_first':
      rows = input_shape[1]
      cols = input_shape[2]
    else:
      rows = input_shape[0]
      cols = input_shape[1]

    if rows == cols and rows in [96, 128, 160, 192, 224]:
      default_size = rows
    else:
      default_size = 224

  input_shape = imagenet_utils.obtain_input_shape(
      input_shape,
      default_size=default_size,
      min_size=32,
      data_format=backend.image_data_format(),
      require_flatten=include_top,
      weights=weights)

  if backend.image_data_format() == 'channels_last':
    row_axis, col_axis = (0, 1)
  else:
    row_axis, col_axis = (1, 2)
  rows = input_shape[row_axis]
  cols = input_shape[col_axis]

  if weights == 'imagenet':
    if alpha not in [0.35, 0.50, 0.75, 1.0, 1.3, 1.4]:
      raise ValueError('If imagenet weights are being loaded, '
                       'alpha can be one of `0.35`, `0.50`, `0.75`, '
                       '`1.0`, `1.3` or `1.4` only.')

    if rows != cols or rows not in [96, 128, 160, 192, 224]:
      rows = 224
      logging.warning('`input_shape` is undefined or non-square, '
                      'or `rows` is not in [96, 128, 160, 192, 224].'
                      ' Weights for input shape (224, 224) will be'
                      ' loaded as the default.')

  if input_tensor is None:
    img_input = layers.Input(shape=input_shape)
  else:
    if not backend.is_keras_tensor(input_tensor):
      img_input = layers.Input(tensor=input_tensor, shape=input_shape)
    else:
      img_input = input_tensor

  channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1

  first_block_filters = _make_divisible(32 * alpha, 8)
  x = layers.Conv2D(
      first_block_filters,
      kernel_size=3,
      strides=(2, 2),
      padding='same',
      use_bias=False,
      name='Conv1')(img_input)
  x = layers.BatchNormalization(
      axis=channel_axis, epsilon=1e-3, momentum=0.999, name='bn_Conv1')(
          x)
  x = layers.ReLU(6., name='Conv1_relu')(x)

  x = _inverted_res_block(
      x, filters=16, alpha=alpha, stride=1, expansion=1, block_id=0)

  x = _inverted_res_block(
      x, filters=24, alpha=alpha, stride=2, expansion=6, block_id=1)
  x = _inverted_res_block(
      x, filters=24, alpha=alpha, stride=1, expansion=6, block_id=2)

  x = _inverted_res_block(
      x, filters=32, alpha=alpha, stride=2, expansion=6, block_id=3)
  x = _inverted_res_block(
      x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=4)
  x = _inverted_res_block(
      x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=5)

  x = _inverted_res_block(
      x, filters=64, alpha=alpha, stride=2, expansion=6, block_id=6)
  x = _inverted_res_block(
      x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=7)
  x = _inverted_res_block(
      x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=8)
  x = _inverted_res_block(
      x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=9)

  x = _inverted_res_block(
      x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=10)
  x = _inverted_res_block(
      x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=11)
  x = _inverted_res_block(
      x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=12)

  x = _inverted_res_block(
      x, filters=160, alpha=alpha, stride=2, expansion=6, block_id=13)
  x = _inverted_res_block(
      x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=14)
  x = _inverted_res_block(
      x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=15)

  x = _inverted_res_block(
      x, filters=320, alpha=alpha, stride=1, expansion=6, block_id=16)

  # no alpha applied to last conv as stated in the paper:
  # if the width multiplier is greater than 1 we
  # increase the number of output channels
  if alpha > 1.0:
    last_block_filters = _make_divisible(1280 * alpha, 8)
  else:
    last_block_filters = 1280

  x = layers.Conv2D(
      last_block_filters, kernel_size=1, use_bias=False, name='Conv_1')(
          x)
  x = layers.BatchNormalization(
      axis=channel_axis, epsilon=1e-3, momentum=0.999, name='Conv_1_bn')(
          x)
  x = layers.ReLU(6., name='out_relu')(x)

  if include_top:
    x = layers.GlobalAveragePooling2D()(x)
    imagenet_utils.validate_activation(classifier_activation, weights)
    x = layers.Dense(classes, activation=classifier_activation,
                     name='predictions')(x)

  else:
    if pooling == 'avg':
      x = layers.GlobalAveragePooling2D()(x)
    elif pooling == 'max':
      x = layers.GlobalMaxPooling2D()(x)

  # Ensure that the model takes into account
  # any potential predecessors of `input_tensor`.
  if input_tensor is not None:
    inputs = layer_utils.get_source_inputs(input_tensor)
  else:
    inputs = img_input

  # Create model.
  model = training.Model(inputs, x, name='mobilenetv2_%0.2f_%s' % (alpha, rows))

  # Load weights.
  if weights == 'imagenet':
    if include_top:
      model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' +
                    str(float(alpha)) + '_' + str(rows) + '.h5')
      weight_path = BASE_WEIGHT_PATH + model_name
      weights_path = data_utils.get_file(
          model_name, weight_path, cache_subdir='models')
    else:
      model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' +
                    str(float(alpha)) + '_' + str(rows) + '_no_top' + '.h5')
      weight_path = BASE_WEIGHT_PATH + model_name
      weights_path = data_utils.get_file(
          model_name, weight_path, cache_subdir='models')
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)

  return model
def decode_predictions(preds, top=5)

Decodes the prediction of an ImageNet model.

Args

preds
Numpy array encoding a batch of predictions.
top
Integer, how many top-guesses to return. Defaults to 5.

Returns

A list of lists of top class prediction tuples (class_name, class_description, score). One list of tuples per sample in batch input.

Raises

ValueError
In case of invalid shape of the pred array (must be 2D).
Expand source code
@keras_export('keras.applications.mobilenet_v2.decode_predictions')
def decode_predictions(preds, top=5):
  return imagenet_utils.decode_predictions(preds, top=top)
def preprocess_input(x, data_format=None)

Preprocesses a tensor or Numpy array encoding a batch of images.

Usage example with applications.MobileNet:

i = tf.keras.layers.Input([None, None, 3], dtype = tf.uint8)
x = tf.cast(i, tf.float32)
x = tf.keras.applications.mobilenet.preprocess_input(x)
core = tf.keras.applications.MobileNet()
x = core(x)
model = tf.keras.Model(inputs=[i], outputs=[x])

image = tf.image.decode_png(tf.io.read_file('file.png'))
result = model(image)

Args

x
A floating point numpy.array or a tf.Tensor, 3D or 4D with 3 color channels, with values in the range [0, 255]. The preprocessed data are written over the input data if the data types are compatible. To avoid this behaviour, numpy.copy(x) can be used.
data_format
Optional data format of the image tensor/array. Defaults to None, in which case the global setting tf.keras.backend.image_data_format() is used (unless you changed it, it defaults to "channels_last").

Returns

Preprocessed numpy.array or a tf.Tensor with type float32.

The inputs pixel values are scaled between -1 and 1, sample-wise.

Raises

ValueError
In case of unknown data_format argument.
Expand source code
@keras_export('keras.applications.mobilenet_v2.preprocess_input')
def preprocess_input(x, data_format=None):
  return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')