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

`RandomWidth`

class```
tf.keras.layers.experimental.preprocessing.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 4-D 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**

4D tensor with shape: `(samples, height, width, channels)`

(data_format='channels_last').

**Output shape**

4D tensor with shape: `(samples, height, random_width, channels)`

.