# tf.keras.applications.efficientnet.EfficientNetB7

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.14.0/keras/applications/efficientnet.py#L803-L831) |

Instantiates the EfficientNetB7 architecture.

#### View aliases


**Main aliases**

[`tf.keras.applications.EfficientNetB7`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/efficientnet/EfficientNetB7)

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    tf.keras.applications.efficientnet.EfficientNetB7(
        include_top=True,
        weights='imagenet',
        input_tensor=None,
        input_shape=None,
        pooling=None,
        classes=1000,
        classifier_activation='softmax',
        **kwargs
    )

#### Reference:

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) (ICML 2019)

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 EfficientNet, input preprocessing is included as part of the model (as a `Rescaling` layer), and thus [`tf.keras.applications.efficientnet.preprocess_input`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/applications/efficientnet/preprocess_input) is actually a pass-through function. EfficientNet models expect their inputs to be float tensors of pixels with values in the \[0-255\] range.

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| ## Args ||
|---|---|
| `include_top` | 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/r2.14/api_docs/python/tf/keras/Input)) to use as image input for the model. |
| `input_shape` | Optional shape tuple, only to be specified if `include_top` is False. It should have exactly 3 inputs channels. |
| `pooling` | Optional pooling mode for feature extraction when `include_top` is `False`. Defaults to `None`. <br /> - `None` means that the output of the model will be the 4D tensor output of the last convolutional layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, 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. 1000 is how many ImageNet classes there are. 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. Defaults to 'softmax'. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`. |

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| ## Returns ||
|---|---|
| A [`keras.Model`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/Model) instance. ||

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