# tf.keras.applications.resnet_rs.ResNetRS420

<br />

|---|
| [![](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/resnet_rs.py#L918-L946) |

Instantiates the ResNetRS420 architecture.

#### View aliases


**Main aliases**

[`tf.keras.applications.ResNetRS420`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet_rs/ResNetRS420)
**Compat aliases for migration**

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

\`tf.compat.v1.keras.applications.ResNetRS420\`, \`tf.compat.v1.keras.applications.resnet_rs.ResNetRS420\`

<br />

    tf.keras.applications.resnet_rs.ResNetRS420(
        include_top=True,
        weights='imagenet',
        classes=1000,
        input_shape=None,
        input_tensor=None,
        pooling=None,
        classifier_activation='softmax',
        include_preprocessing=True
    )

#### Reference:

[Revisiting ResNets: Improved Training and Scaling Strategies](https://arxiv.org/pdf/2103.07579.pdf)

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 ResNetRs, by default input preprocessing is included as a part of the model (as a `Rescaling` layer), and thus [`tf.keras.applications.resnet_rs.preprocess_input`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/applications/resnet_rs/preprocess_input) is actually a pass-through function. In this use case, ResNetRS 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 ResNetRS models expect their inputs to be float tensors of pixels with values in the \[-1, 1\] range.

<br />

<br />

<br />

| ## Args ||
|---|---|
| `depth` | Depth of ResNet network. |
| `input_shape` | optional shape tuple. 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. |
| `bn_momentum` | Momentum parameter for Batch Normalization layers. |
| `bn_epsilon` | Epsilon parameter for Batch Normalization layers. |
| `activation` | activation function. |
| `se_ratio` | Squeeze and Excitation layer ratio. |
| `dropout_rate` | dropout rate before final classifier layer. |
| `drop_connect_rate` | dropout rate at skip connections. |
| `include_top` | whether to include the fully-connected layer at the top of the network. |
| `block_args` | list of dicts, parameters to construct block modules. |
| `model_name` | name of the model. |
| `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. |
| `weights` | one of `None` (random initialization), `'imagenet'` (pre-training on ImageNet), or the path to the weights file to be loaded. Note: one model can have multiple imagenet variants depending on input shape it was trained with. For input_shape 224x224 pass `imagenet-i224` as argument. By default, highest input shape weights are downloaded. |
| `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. |
| `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. |
| `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. |
| `include_preprocessing` | Boolean, whether to include the preprocessing layer (`Rescaling`) at the bottom of the network. Defaults to `True`. Note: Input image is normalized by ImageNet mean and standard deviation. |

<br />

<br />

<br />

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

<br />