# tf.keras.applications.inception_v3.InceptionV3

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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/inception_v3.py#L47-L401) |

Instantiates the Inception v3 architecture.

#### View aliases


**Main aliases**

[`tf.keras.applications.InceptionV3`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/inception_v3/InceptionV3)
**Compat aliases for migration**

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

\`tf.compat.v1.keras.applications.InceptionV3\`, \`tf.compat.v1.keras.applications.inception_v3.InceptionV3\`

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    tf.keras.applications.inception_v3.InceptionV3(
        include_top=True,
        weights='imagenet',
        input_tensor=None,
        input_shape=None,
        pooling=None,
        classes=1000,
        classifier_activation='softmax'
    )

#### Reference:

- [Rethinking the Inception Architecture for Computer Vision](http://arxiv.org/abs/1512.00567) (CVPR 2016)

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 `InceptionV3`, call [`tf.keras.applications.inception_v3.preprocess_input`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/applications/inception_v3/preprocess_input) on your inputs before passing them to the model. [`inception_v3.preprocess_input`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/applications/inception_v3/preprocess_input) will scale input pixels between -1 and 1.

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| ## Args ||
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| `include_top` | Boolean, whether to include the fully-connected layer at the top, as the last layer 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/r2.13/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`. |
| `input_shape` | Optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(299, 299, 3)` (with `channels_last` data format) or `(3, 299, 299)` (with `channels_first` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 75. E.g. `(150, 150, 3)` would be one valid value. `input_shape` will be ignored if the `input_tensor` is provided. |
| `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"`. |

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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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