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tf.keras.layers.RandomHeight

A preprocessing layer which randomly varies image height during training.

Inherits From: Layer, Module

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.

By default, this layer is inactive during inference.

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

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=0.2results in an output with height changed by a random amount in the range[-20%, +20%]. </td> </tr><tr> <td>interpolation</td> <td> String, the interpolation method. Defaults to"bilinear". Supports"bilinear","nearest","bicubic","area","lanczos3","lanczos5","gaussian","mitchellcubic". </td> </tr><tr> <td>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).