|---|
| [ View source on GitHub](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/preprocessing/image_preprocessing/random_zoom.py#L16-L420) |
A preprocessing layer which randomly zooms images during training.
Inherits From: [`Layer`](https://www.tensorflow.org/api_docs/python/tf/keras/Layer.md.txt), [`Operation`](https://www.tensorflow.org/api_docs/python/tf/keras/Operation.md.txt)
```python
tf.keras.layers.RandomZoom(
height_factor,
width_factor=None,
fill_mode='reflect',
interpolation='bilinear',
seed=None,
fill_value=0.0,
data_format=None,
**kwargs
)
```
### Used in the notebooks
| Used in the guide | Used in the tutorials |
|---|---|
| - [Working with preprocessing layers](https://www.tensorflow.org/guide/keras/preprocessing_layers.md.txt) | - [Image classification](https://www.tensorflow.org/tutorials/images/classification.md.txt) - [Retraining an Image Classifier](https://www.tensorflow.org/hub/tutorials/tf2_image_retraining.md.txt) |
This layer will randomly zoom in or out on each axis of an image
independently, filling empty space according to `fill_mode`.
Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
of integer or floating point dtype.
By default, the layer will output floats.
> [!NOTE]
> **Note:** This layer is safe to use inside a [`tf.data`](https://www.tensorflow.org/api_docs/python/tf/data.md.txt) or `grain` pipeline (independently of which backend you're using).
| ## Input shape ||
|---|---|
| `3D` | `unbatched) or 4D (batched) tensor with shape` `(..., height, width, channels)`, in `"channels_last"` format, or `(..., channels, height, width)`, in `"channels_first"` format. |
| ## Output shape ||
|---|---|
| `3D` | `unbatched) or 4D (batched) tensor with shape` `(..., target_height, target_width, channels)`, or `(..., channels, target_height, target_width)`, in `"channels_first"` format. |
| ## Args ||
|---|---|
| `height_factor` | a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for zooming vertically. When represented as a single float, this value is used for both the upper and lower bound. A positive value means zooming out, while a negative value means zooming in. For instance, `height_factor=(0.2, 0.3)` result in an output zoomed out by a random amount in the range `[+20%, +30%]`. `height_factor=(-0.3, -0.2)` result in an output zoomed in by a random amount in the range `[+20%, +30%]`. |
| `width_factor` | a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for zooming horizontally. When represented as a single float, this value is used for both the upper and lower bound. For instance, `width_factor=(0.2, 0.3)` result in an output zooming out between 20% to 30%. `width_factor=(-0.3, -0.2)` result in an output zooming in between 20% to 30%. `None` means i.e., zooming vertical and horizontal directions by preserving the aspect ratio. Defaults to `None`. |
| `fill_mode` | Points outside the boundaries of the input are filled according to the given mode. Available methods are `"constant"`, `"nearest"`, `"wrap"` and `"reflect"`. Defaults to `"reflect"`. - `"reflect"`: `(d c b a | a b c d | d c b a)` The input is extended by reflecting about the edge of the last pixel. - `"constant"`: `(k k k k | a b c d | k k k k)` The input is extended by filling all values beyond the edge with the same constant value k specified by `fill_value`. - `"wrap"`: `(a b c d | a b c d | a b c d)` The input is extended by wrapping around to the opposite edge. - `"nearest"`: `(a a a a | a b c d | d d d d)` The input is extended by the nearest pixel. Note that when using torch backend, `"reflect"` is redirected to `"mirror"` `(c d c b | a b c d | c b a b)` because torch does not support `"reflect"`. Note that torch backend does not support `"wrap"`. |
| `interpolation` | Interpolation mode. Supported values: `"nearest"`, `"bilinear"`. |
| `seed` | Integer. Used to create a random seed. |
| `fill_value` | a float that represents the value to be filled outside the boundaries when `fill_mode="constant"`. |
| `data_format` | string, either `"channels_last"` or `"channels_first"`. The ordering of the dimensions in the inputs. `"channels_last"` corresponds to inputs with shape `(batch, height, width, channels)` while `"channels_first"` corresponds to inputs with shape `(batch, channels, height, width)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be `"channels_last"`. |
| `**kwargs` | Base layer keyword arguments, such as `name` and `dtype`. |
#### Example:
```python
input_img = np.random.random((32, 224, 224, 3))
layer = keras.layers.RandomZoom(.5, .2)
out_img = layer(input_img)
```
| ## Attributes ||
|---|---|
| `input` | Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the *first time* the operation was called. |
| `output` | Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the *first time* the operation was called. |
## Methods
### `convert_weight`
[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/preprocessing/data_layer.py#L153-L159)
```python
convert_weight(
weight
)
```
Convert the weight if it is from the a different backend.
### `from_config`
[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/ops/operation.py#L322-L362)
```python
@classmethod
from_config(
config
)
```
Creates an operation from its config.
This method is the reverse of `get_config`, capable of instantiating the
same operation from the config dictionary.
> [!NOTE]
> **Note:** If you override this method, you might receive a serialized dtype config, which is a `dict`. You can deserialize it as follows:
```python
if "dtype" in config and isinstance(config["dtype"], dict):
policy = dtype_policies.deserialize(config["dtype"])
```
| Args ||
|---|---|
| `config` | A Python dictionary, typically the output of `get_config`. |