tf.keras.layers.experimental.preprocessing.RandomContrast
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Adjust the contrast of an image or images by a random factor.
Inherits From: PreprocessingLayer, Layer, Module
tf.keras.layers.experimental.preprocessing.RandomContrast(
factor, seed=None, name=None, **kwargs
)
Contrast is adjusted independently for each channel of each image during
training.
For each channel, this layer computes the mean of the image pixels in the
channel and then adjusts each component x of each pixel to
(x - mean) * contrast_factor + mean.
4D tensor with shape:
(samples, height, width, channels), data_format='channels_last'.
Output shape:
4D tensor with shape:
(samples, height, width, channels), data_format='channels_last'.
Raise |
|---|
ValueError
|
if lower bound is not between [0, 1], or upper bound is
negative.
|
Attributes |
|---|
factor
|
a positive float represented as fraction of value, or a tuple of
size 2 representing lower and upper bound. When represented as a single
float, lower = upper. The contrast factor will be randomly picked between
[1.0 - lower, 1.0 + upper].
|
seed
|
Integer. Used to create a random seed.
|
name
|
A string, the name of the layer.
|
Methods
adapt
View source
adapt(
data, reset_state=True
)
Fits the state of the preprocessing layer to the data being passed.
| Arguments |
|---|
data
|
The data to train on. It can be passed either as a tf.data
Dataset, or as a numpy array.
|
reset_state
|
Optional argument specifying whether to clear the state of
the layer at the start of the call to adapt, or whether to start
from the existing state. This argument may not be relevant to all
preprocessing layers: a subclass of PreprocessingLayer may choose to
throw if 'reset_state' is set to False.
|
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Last updated 2021-02-18 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2021-02-18 UTC."],[],[],null,["# tf.keras.layers.experimental.preprocessing.RandomContrast\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.4.0/tensorflow/python/keras/layers/preprocessing/image_preprocessing.py#L1086-L1154) |\n\nAdjust the contrast of an image or images by a random factor.\n\nInherits From: [`PreprocessingLayer`](../../../../../tf/keras/layers/experimental/preprocessing/PreprocessingLayer), [`Layer`](../../../../../tf/keras/layers/Layer), [`Module`](../../../../../tf/Module)\n\n#### View aliases\n\n\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.experimental.preprocessing.RandomContrast`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing/RandomContrast)\n\n\u003cbr /\u003e\n\n tf.keras.layers.experimental.preprocessing.RandomContrast(\n factor, seed=None, name=None, **kwargs\n )\n\nContrast is adjusted independently for each channel of each image during\ntraining.\n\nFor each channel, this layer computes the mean of the image pixels in the\nchannel and then adjusts each component `x` of each pixel to\n`(x - mean) * contrast_factor + mean`.\n\n#### Input shape:\n\n4D tensor with shape:\n`(samples, height, width, channels)`, data_format='channels_last'.\n\n#### Output shape:\n\n4D tensor with shape:\n`(samples, height, width, channels)`, data_format='channels_last'.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raise ----- ||\n|--------------|---------------------------------------------------------------------|\n| `ValueError` | if lower bound is not between \\[0, 1\\], or upper bound is negative. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `factor` | a positive float represented as fraction of value, or a tuple of size 2 representing lower and upper bound. When represented as a single float, lower = upper. The contrast factor will be randomly picked between \\[1.0 - lower, 1.0 + upper\\]. |\n| `seed` | Integer. Used to create a random seed. |\n| `name` | A string, the name of the layer. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `adapt`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v2.4.0/tensorflow/python/keras/engine/base_preprocessing_layer.py#L53-L66) \n\n adapt(\n data, reset_state=True\n )\n\nFits the state of the preprocessing layer to the data being passed.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments ||\n|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `data` | The data to train on. It can be passed either as a tf.data Dataset, or as a numpy array. |\n| `reset_state` | Optional argument specifying whether to clear the state of the layer at the start of the call to `adapt`, or whether to start from the existing state. This argument may not be relevant to all preprocessing layers: a subclass of PreprocessingLayer may choose to throw if 'reset_state' is set to False. |\n\n\u003cbr /\u003e"]]
View source on GitHub