tf.keras.layers.RandomWidth
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Randomly vary the width of a batch of images during training.
Inherits From: Layer, Module
tf.keras.layers.RandomWidth(
factor, interpolation='bilinear', seed=None, **kwargs
)
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.
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
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.
|
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).
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Last updated 2021-08-16 UTC.
[null,null,["Last updated 2021-08-16 UTC."],[],[],null,["# tf.keras.layers.RandomWidth\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#L1265-L1359) |\n\nRandomly vary the width 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.RandomWidth`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomWidth)\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.RandomWidth`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomWidth), [`tf.compat.v1.keras.layers.experimental.preprocessing.RandomWidth`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomWidth)\n\n\u003cbr /\u003e\n\n tf.keras.layers.RandomWidth(\n factor, interpolation='bilinear', seed=None, **kwargs\n )\n\nAdjusts the width 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 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%]`. |\n| `interpolation` | String, the interpolation method. Defaults to `bilinear`. Supports `\"bilinear\"`, `\"nearest\"`, `\"bicubic\"`, `\"area\"`, `\"lanczos3\"`, `\"lanczos5\"`, `\"gaussian\"`, `\"mitchellcubic\"`. |\n| `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