tf.keras.layers.RandomRotation
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A preprocessing layer which randomly rotates images during training.
Inherits From: Layer, Module
tf.keras.layers.RandomRotation(
factor,
fill_mode='reflect',
interpolation='bilinear',
seed=None,
fill_value=0.0,
**kwargs
)
This layer will apply random rotations to each image, filling empty space
according to fill_mode.
By default, random rotations are only applied during training.
At inference time, the layer does nothing. If you need to apply random
rotations at inference time, set training to True when calling the layer.
Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and
of interger or floating point dtype. By default, the layer will output floats.
For an overview and full list of preprocessing layers, see the preprocessing
guide.
|
|---|
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:
(..., height, width, channels), in "channels_last" format
|
Args |
|---|
seed
|
optional integer, used to create RandomGenerator.
|
force_generator
|
boolean, default to False, whether to force the
RandomGenerator to use the code branch of tf.random.Generator.
|
**kwargs
|
other keyword arguments that will be passed to the parent class
|
Attributes |
|---|
factor
|
a float represented as fraction of 2 Pi, or a tuple of size 2
representing lower and upper bound for rotating clockwise and
counter-clockwise. A positive values means rotating counter clock-wise,
while a negative value means clock-wise. 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 rotation by a random
amount in the range [-20% * 2pi, 30% * 2pi]. factor=0.2 results in an
output rotating by a random amount in the range [-20% * 2pi, 20% * 2pi].
|
fill_mode
|
Points outside the boundaries of the input are filled according
to the given mode (one of {"constant", "reflect", "wrap", "nearest"}).
- reflect:
(d c b a | a b c d | d c b a) The input is extended by
reflecting about the edge of the last pixel.
- constant:
(k k k k | a b c d | k k k k) The input is extended by
filling all values beyond the edge with the same constant value k = 0.
- wrap:
(a b c d | a b c d | a b c d) The input is extended by
wrapping around to the opposite edge.
- nearest:
(a a a a | a b c d | d d d d) The input is extended by the
nearest pixel.
|
interpolation
|
Interpolation mode. Supported values: "nearest",
"bilinear".
|
seed
|
Integer. Used to create a random seed.
|
fill_value
|
a float represents the value to be filled outside the boundaries
when fill_mode="constant".
|
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Last updated 2022-09-07 UTC.
[null,null,["Last updated 2022-09-07 UTC."],[],[],null,["# tf.keras.layers.RandomRotation\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v2.8.0/keras/layers/preprocessing/image_preprocessing.py#L782-L909) |\n\nA preprocessing layer which randomly rotates 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.RandomRotation`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomRotation)\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.RandomRotation\\`, \\`tf.compat.v1.keras.layers.experimental.preprocessing.RandomRotation\\`\n\n\u003cbr /\u003e\n\n tf.keras.layers.RandomRotation(\n factor,\n fill_mode='reflect',\n interpolation='bilinear',\n seed=None,\n fill_value=0.0,\n **kwargs\n )\n\nThis layer will apply random rotations to each image, filling empty space\naccording to `fill_mode`.\n\nBy default, random rotations are only applied during training.\nAt inference time, the layer does nothing. If you need to apply random\nrotations at inference time, set `training` to True when calling the layer.\n\nInput pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and\nof interger or floating point dtype. By default, the layer will output floats.\n\nFor an overview and full list of preprocessing layers, see the preprocessing\n[guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Input shape ----------- ||\n|---|---|\n| 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `\"channels_last\"` format ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Output shape ------------ ||\n|---|---|\n| 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `\"channels_last\"` format ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------------|----------------------------------------------------------------------------------------------------------------|\n| `seed` | optional integer, used to create RandomGenerator. |\n| `force_generator` | boolean, default to False, whether to force the RandomGenerator to use the code branch of tf.random.Generator. |\n| `**kwargs` | other keyword arguments that will be passed to the parent class |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `factor` | a float represented as fraction of 2 Pi, or a tuple of size 2 representing lower and upper bound for rotating clockwise and counter-clockwise. A positive values means rotating counter clock-wise, while a negative value means clock-wise. 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 rotation by a random amount in the range `[-20% * 2pi, 30% * 2pi]`. `factor=0.2` results in an output rotating by a random amount in the range `[-20% * 2pi, 20% * 2pi]`. |\n| `fill_mode` | Points outside the boundaries of the input are filled according to the given mode (one of `{\"constant\", \"reflect\", \"wrap\", \"nearest\"}`). \u003cbr /\u003e - *reflect* : `(d c b a | a b c d | d c b a)` The input is extended by reflecting about the edge of the last pixel. - *constant* : `(k k k k | a b c d | k k k k)` The input is extended by filling all values beyond the edge with the same constant value k = 0. - *wrap* : `(a b c d | a b c d | a b c d)` The input is extended by wrapping around to the opposite edge. - *nearest* : `(a a a a | a b c d | d d d d)` The input is extended by the nearest pixel. |\n| `interpolation` | Interpolation mode. Supported values: `\"nearest\"`, `\"bilinear\"`. |\n| `seed` | Integer. Used to create a random seed. |\n| `fill_value` | a float represents the value to be filled outside the boundaries when `fill_mode=\"constant\"`. |"]]
View source on GitHub