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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
)
```

Randomly vary the width of a batch of images during training.

Adjusts the width 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.

By default, this layer is inactive during inference.

**Arguments**

**factor**: A positive float (fraction of original height), 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:
`(..., random_height, width, channels)`

.