tf.keras.layers.RandomHeight
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Randomly vary the height of a batch of images during training.
Inherits From: Layer, Module
tf.keras.layers.RandomHeight(
factor, interpolation='bilinear', seed=None, **kwargs
)
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.
Args |
|---|
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.
|
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).
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
Last updated 2021-08-16 UTC.
[null,null,["Last updated 2021-08-16 UTC."],[],[],null,["# tf.keras.layers.RandomHeight\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/master/keras/layers/preprocessing/image_preprocessing.py#L1165-L1260) |\n\nRandomly vary the height of a batch of images during training.\n\nInherits From: [`Layer`](../../../tf/keras/layers/Layer), [`Module`](../../../tf/Module)\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.keras.layers.experimental.preprocessing.RandomHeight`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomHeight)\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.layers.RandomHeight`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomHeight), [`tf.compat.v1.keras.layers.experimental.preprocessing.RandomHeight`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomHeight)\n\n\u003cbr /\u003e\n\n tf.keras.layers.RandomHeight(\n factor, interpolation='bilinear', seed=None, **kwargs\n )\n\nAdjusts the height of a batch of images by a random factor. The input\nshould be a 3D (unbatched) or 4D (batched) tensor in the `\"channels_last\"`\nimage data format.\n\nBy default, this layer is inactive during inference.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------|\n| `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%\\]`. \u003c/td\u003e \u003c/tr\u003e\u003ctr\u003e \u003ctd\u003e`interpolation`\u003c/td\u003e \u003ctd\u003e String, the interpolation method. Defaults to`\"bilinear\"`. Supports`\"bilinear\"`,`\"nearest\"`,`\"bicubic\"`,`\"area\"`,`\"lanczos3\"`,`\"lanczos5\"`,`\"gaussian\"`,`\"mitchellcubic\"`. \u003c/td\u003e \u003c/tr\u003e\u003ctr\u003e \u003ctd\u003e`seed\\` | Integer. Used to create a random seed. |\n\n\u003cbr /\u003e\n\n#### Input shape:\n\n3D (unbatched) or 4D (batched) tensor with shape:\n`(..., height, width, channels)`, in `\"channels_last\"` format.\n\n#### Output shape:\n\n3D (unbatched) or 4D (batched) tensor with shape:\n`(..., random_height, width, channels)`."]]
View source on GitHub