tf.keras.applications.MobileNetV2

View source on GitHub

Instantiates the MobileNetV2 architecture.

View aliases

Main aliases

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

Reference:

• MobileNetV2: Inverted Residuals and Linear Bottlenecks (CVPR 2018)

Optionally loads weights pre-trained on ImageNet.

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.