Module keras.api.keras.applications.mobilenet

Public API for tf.keras.applications.mobilenet 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 namespace.
"""

from __future__ import print_function as _print_function

import sys as _sys

from keras.applications.mobilenet import MobileNet
from keras.applications.mobilenet import decode_predictions
from keras.applications.mobilenet 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", public_apis=None, deprecation=True,
      has_lite=False)

Functions

def MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, dropout=0.001, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs)

Instantiates the MobileNet architecture.

Reference: - MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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 MobileNet, call tf.keras.applications.mobilenet.preprocess_input on your inputs before passing them to the model. mobilenet.preprocess_input will scale input pixels between -1 and 1.

Args

input_shape
Optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with channels_last data format) or (3, 224, 224) (with channels_first data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value. Default to None. input_shape will be ignored if the input_tensor is provided.
alpha
Controls the width of the network. This is known as the width multiplier in the MobileNet paper. - 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, default number of filters from the paper are used at each layer. Default to 1.0.
depth_multiplier
Depth multiplier for depthwise convolution. This is called the resolution multiplier in the MobileNet paper. Default to 1.0.
dropout
Dropout rate. Default to 0.001.
include_top
Boolean, whether to include the fully-connected layer at the top of the network. Default to True.
weights
One of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. Default to imagenet.
input_tensor
Optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model. input_tensor is useful for sharing inputs between multiple different networks. Default to None.
pooling
Optional pooling mode for feature extraction when include_top is False. - None (default) 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
Optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified. Defaults to 1000.
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.MobileNet',
              'keras.applications.MobileNet')
def MobileNet(input_shape=None,
              alpha=1.0,
              depth_multiplier=1,
              dropout=1e-3,
              include_top=True,
              weights='imagenet',
              input_tensor=None,
              pooling=None,
              classes=1000,
              classifier_activation='softmax',
              **kwargs):
  """Instantiates the MobileNet architecture.

  Reference:
  - [MobileNets: Efficient Convolutional Neural Networks
     for Mobile Vision Applications](
      https://arxiv.org/abs/1704.04861)

  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 MobileNet, call `tf.keras.applications.mobilenet.preprocess_input`
  on your inputs before passing them to the model.
  `mobilenet.preprocess_input` will scale input pixels between -1 and 1.

  Args:
    input_shape: Optional shape tuple, only to be specified if `include_top`
      is False (otherwise the input shape has to be `(224, 224, 3)` (with
      `channels_last` data format) or (3, 224, 224) (with `channels_first`
      data format). It should have exactly 3 inputs channels, and width and
      height should be no smaller than 32. E.g. `(200, 200, 3)` would be one
      valid value. Default to `None`.
      `input_shape` will be ignored if the `input_tensor` is provided.
    alpha: Controls the width of the network. This is known as the width
      multiplier in the MobileNet paper. - 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, default number of filters from the paper are used at each
      layer. Default to 1.0.
    depth_multiplier: Depth multiplier for depthwise convolution. This is
      called the resolution multiplier in the MobileNet paper. Default to 1.0.
    dropout: Dropout rate. Default to 0.001.
    include_top: Boolean, whether to include the fully-connected layer at the
      top of the network. Default to `True`.
    weights: One of `None` (random initialization), 'imagenet' (pre-training
      on ImageNet), or the path to the weights file to be loaded. Default to
      `imagenet`.
    input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`) to
      use as image input for the model. `input_tensor` is useful for sharing
      inputs between multiple different networks. Default to None.
    pooling: Optional pooling mode for feature extraction when `include_top`
      is `False`.
      - `None` (default) 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: Optional number of classes to classify images into, only to be
      specified if `include_top` is True, and if no `weights` argument is
      specified. Defaults to 1000.
    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 input_shape is None:
    default_size = 224
  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 [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 depth_multiplier != 1:
      raise ValueError('If imagenet weights are being loaded, '
                       'depth multiplier must be 1')

    if alpha not in [0.25, 0.50, 0.75, 1.0]:
      raise ValueError('If imagenet weights are being loaded, '
                       'alpha can be one of'
                       '`0.25`, `0.50`, `0.75` or `1.0` only.')

    if rows != cols or rows not in [128, 160, 192, 224]:
      rows = 224
      logging.warning('`input_shape` is undefined or non-square, '
                      'or `rows` is not in [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

  x = _conv_block(img_input, 32, alpha, strides=(2, 2))
  x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1)

  x = _depthwise_conv_block(
      x, 128, alpha, depth_multiplier, strides=(2, 2), block_id=2)
  x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3)

  x = _depthwise_conv_block(
      x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4)
  x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5)

  x = _depthwise_conv_block(
      x, 512, alpha, depth_multiplier, strides=(2, 2), block_id=6)
  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7)
  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8)
  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9)
  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10)
  x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11)

  x = _depthwise_conv_block(
      x, 1024, alpha, depth_multiplier, strides=(2, 2), block_id=12)
  x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13)

  if include_top:
    if backend.image_data_format() == 'channels_first':
      shape = (int(1024 * alpha), 1, 1)
    else:
      shape = (1, 1, int(1024 * alpha))

    x = layers.GlobalAveragePooling2D()(x)
    x = layers.Reshape(shape, name='reshape_1')(x)
    x = layers.Dropout(dropout, name='dropout')(x)
    x = layers.Conv2D(classes, (1, 1), padding='same', name='conv_preds')(x)
    x = layers.Reshape((classes,), name='reshape_2')(x)
    imagenet_utils.validate_activation(classifier_activation, weights)
    x = layers.Activation(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='mobilenet_%0.2f_%s' % (alpha, rows))

  # Load weights.
  if weights == 'imagenet':
    if alpha == 1.0:
      alpha_text = '1_0'
    elif alpha == 0.75:
      alpha_text = '7_5'
    elif alpha == 0.50:
      alpha_text = '5_0'
    else:
      alpha_text = '2_5'

    if include_top:
      model_name = 'mobilenet_%s_%d_tf.h5' % (alpha_text, rows)
      weight_path = BASE_WEIGHT_PATH + model_name
      weights_path = data_utils.get_file(
          model_name, weight_path, cache_subdir='models')
    else:
      model_name = 'mobilenet_%s_%d_tf_no_top.h5' % (alpha_text, rows)
      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.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.preprocess_input')
def preprocess_input(x, data_format=None):
  return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')