|TensorFlow 1 version||View source on GitHub|
Instantiates the MobileNetV2 architecture.
See Migration guide for more details.
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 )
Used in the notebooks
|Used in the guide||Used in the tutorials|
Optionally loads weights pre-trained on ImageNet.
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.MobileNetV1model in Keras.
alpha< 1.0, proportionally decreases the number of filters in each layer.
alpha> 1.0, proportionally increases the number of filters in each layer.
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
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
Nonemeans that the output of the model will be the 4D tensor output of the last convolutional block.
avgmeans 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.
maxmeans that global max pooling will be applied.
classes: Integer, optional number of classes to classify images into, only to be specified if
include_topis True, and if no
weightsargument is specified.
stror callable. The activation function to use on the "top" layer. Ignored unless
classifier_activation=Noneto return the logits of the "top" layer.
**kwargs: For backwards compatibility only.
ValueError: in case of invalid argument for
weights, or invalid input shape or invalid alpha, rows when weights='imagenet'
Nonewhen using a pretrained top layer.