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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.15.0/keras/applications/mobilenet.py#L82-L335) |

Instantiates the MobileNet architecture.

#### View aliases


**Main aliases**

[`tf.keras.applications.MobileNet`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/MobileNet)

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    tf.keras.applications.mobilenet.MobileNet(
        input_shape=None,
        alpha=1.0,
        depth_multiplier=1,
        dropout=0.001,
        include_top=True,
        weights='imagenet',
        input_tensor=None,
        pooling=None,
        classes=1000,
        classifier_activation='softmax',
        **kwargs
    )

#### Reference:

- [MobileNets: Efficient Convolutional Neural Networks
  for Mobile Vision Applications](https://arxiv.org/abs/1704.04861)

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 MobileNet, call [`tf.keras.applications.mobilenet.preprocess_input`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/keras/applications/mobilenet/preprocess_input) on your inputs before passing them to the model. [`mobilenet.preprocess_input`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/keras/applications/mobilenet/preprocess_input) will scale input pixels between -1 and 1.

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| ## 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`. |
| `depth_multiplier` | Depth multiplier for depthwise convolution. This is called the resolution multiplier in the MobileNet paper. Defaults to `1.0`. |
| `dropout` | Dropout rate. Defaults to `0.001`. |
| `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/versions/r2.15/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`. <br /> - `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"`. |
| `**kwargs` | For backwards compatibility only. |

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| ## Returns ||
|---|---|
| A [`keras.Model`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/keras/Model) instance. ||

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