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

`RandomHeight`

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

A preprocessing layer which randomly varies image height during training.

This layer adjusts the height 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 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 height changed by a random amount in the range`[20%, 30%]`

.`factor=(-0.2, 0.3)`

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

.`factor=0.2`

results in an output with height 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)`

.