# tf.keras.applications.efficientnet_v2.EfficientNetV2L

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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_v2.py#L1296-L1323) |

Instantiates the EfficientNetV2L architecture.

#### View aliases


**Main aliases**

[`tf.keras.applications.EfficientNetV2L`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/efficientnet_v2/EfficientNetV2L)

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    tf.keras.applications.efficientnet_v2.EfficientNetV2L(
        include_top=True,
        weights='imagenet',
        input_tensor=None,
        input_shape=None,
        pooling=None,
        classes=1000,
        classifier_activation='softmax',
        include_preprocessing=True
    )

#### Reference:

- [EfficientNetV2: Smaller Models and Faster Training](https://arxiv.org/abs/2104.00298) (ICML 2021)

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 EfficientNetV2, by default input preprocessing is included as a part of the model (as a `Rescaling` layer), and thus [`tf.keras.applications.efficientnet_v2.preprocess_input`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/applications/efficientnet_v2/preprocess_input) is actually a pass-through function. In this use case, EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the \[0-255\] range. At the same time, preprocessing as a part of the model (i.e. `Rescaling` layer) can be disabled by setting `include_preprocessing` argument to False. With preprocessing disabled EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the \[-1, 1\] range.

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| ## Args ||
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| `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/r2.14/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`. <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. Defaults to `None`. |
| `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 string 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"`. Defaults to `"softmax"`. |

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

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