tf.keras.applications.MobileNetV2

Instantiates the MobileNetV2 architecture.

View aliases

Main aliases

tf.keras.applications.mobilenet_v2.MobileNetV2

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'
)

Used in the notebooks

Used in the guide	Used in the tutorials
Estimators	Adversarial example using FGSM Image segmentation Transfer learning and fine-tuning

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 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, 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. Defaults 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. Defaults to 1.0.
include_top	Boolean, whether to include the fully-connected layer at the top of the network. Defaults to True.
weights	One of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults 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. Defaults 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".

Returns
A model instance.