# tf.keras.applications.mobilenet_v2.MobileNetV2

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/applications/mobilenet_v2.py#L96-L485) |

Instantiates the MobileNetV2 architecture.

#### View aliases


**Main aliases**

[`tf.keras.applications.MobileNetV2`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/mobilenet_v2/MobileNetV2)
**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

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

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    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](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 [`tf.keras.applications.mobilenet_v2.preprocess_input`](https://www.tensorflow.org/versions/r2.13/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/versions/r2.13/api_docs/python/tf/keras/applications/mobilenet_v2/preprocess_input) will scale input pixels between -1 and 1.

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| ## 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. <br /> - 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()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/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. |

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| ## Returns ||
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| A [`keras.Model`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/Model) instance. ||

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