ยป Keras API reference / Keras Applications / MobileNet and MobileNetV2

MobileNet and MobileNetV2

MobileNet function

tf.keras.applications.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

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in the tf.keras.backend.image_data_format().

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.

Arguments

  • 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.
  • **kwargs: For backwards compatibility only.

Returns

A keras.Model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape.
  • ValueError: if classifier_activation is not softmax or None when using a pretrained top layer.

MobileNetV2 function

tf.keras.applications.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.

Reference

Optionally loads weights pre-trained on ImageNet.

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.

Arguments

  • 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, 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.
  • **kwargs: For backwards compatibility only.

Returns

A keras.Model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape or invalid alpha, rows when weights='imagenet'
  • ValueError: if classifier_activation is not softmax or None when using a pretrained top layer.