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Keras API reference /
Layers API /
Preprocessing layers /
Image augmentation layers /
RandomWidth layer

`RandomWidth`

class```
tf.keras.layers.RandomWidth(
factor, interpolation="bilinear", seed=None, **kwargs
)
```

A preprocessing layer which randomly varies image width during training.

This layer will randomly adjusts the width of a batch of images of a
batch of images by a random factor. The input should be a 3D (unbatched) or
4D (batched) tensor in the `"channels_last"`

image data format. 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.

By default, this layer is inactive during inference.

For an overview and full list of preprocessing layers, see the preprocessing guide.

**Arguments**

**factor**: A positive float (fraction of original width), or a tuple of size 2 representing lower and upper bound for resizing vertically. When represented as a single float, this value is used for both the upper and lower bound. For instance,`factor=(0.2, 0.3)`

results in an output with width changed by a random amount in the range`[20%, 30%]`

.`factor=(-0.2, 0.3)`

results in an output with width changed by a random amount in the range`[-20%, +30%]`

.`factor=0.2`

results in an output with width changed by a random amount in the range`[-20%, +20%]`

.**interpolation**: String, the interpolation method. Defaults to`bilinear`

. Supports`"bilinear"`

,`"nearest"`

,`"bicubic"`

,`"area"`

,`"lanczos3"`

,`"lanczos5"`

,`"gaussian"`

,`"mitchellcubic"`

.**seed**: Integer. Used to create a random seed.

**Input shape**

3D (unbatched) or 4D (batched) tensor with shape:
`(..., height, width, channels)`

, in `"channels_last"`

format.

**Output shape**

3D (unbatched) or 4D (batched) tensor with shape:
`(..., height, random_width, channels)`

.