tf.keras.applications.mobilenet_v2.MobileNetV2

Instantiates the MobileNetV2 architecture.

View aliases

Main aliases

tf.keras.applications.MobileNetV2

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.applications.MobileNetV2, tf.compat.v1.keras.applications.mobilenet_v2.MobileNetV2

tf.keras.applications.mobilenet_v2.MobileNetV2(
    input_shape=None,
    alpha=1.0,
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax',
    **kwargs
)

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, larger than zero, 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	Optional integer 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.