|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/applications/mobilenet_v2.py#L16-L392) | Instantiates the MobileNetV2 architecture. #### View aliases **Main aliases** [`tf.keras.applications.mobilenet_v2.MobileNetV2`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/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', name=None ) ### Used in the notebooks | Used in the guide | Used in the tutorials | |---|---| | - [Estimators](https://www.tensorflow.org/guide/estimator) | - [Adversarial example using FGSM](https://www.tensorflow.org/tutorials/generative/adversarial_fgsm) - [Image segmentation](https://www.tensorflow.org/tutorials/images/segmentation) - [Transfer learning and fine-tuning](https://www.tensorflow.org/tutorials/images/transfer_learning) | 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](https://arxiv.org/abs/1801.04381) (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](https://keras.io/api/applications/#usage-examples-for-image-classification-models). For transfer learning use cases, make sure to read the [guide to transfer learning \& fine-tuning](https://keras.io/guides/transfer_learning/). > [!NOTE] > **Note:** each Keras Application expects a specific kind of input preprocessing. For MobileNetV2, call [`keras.applications.mobilenet_v2.preprocess_input`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/mobilenet_v2/preprocess_input) on your inputs before passing them to the model. [`mobilenet_v2.preprocess_input`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/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()`](https://www.tensorflow.org/api_docs/python/tf/keras/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"`. | | `name` | String, the name of the model. |


| ## Returns || |---|---| | A model instance. ||