tf.keras.layers.experimental.preprocessing.RandomContrast
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Adjust the contrast of an image or images by a random factor.
Inherits From: Layer
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.
|
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Last updated 2020-10-01 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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 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/v2.3.0/tensorflow/python/keras/layers/preprocessing/image_preprocessing.py#L1031-L1098) |\n\nAdjust the contrast of an image or images by a random factor.\n\nInherits From: [`Layer`](../../../../../tf/keras/layers/Layer)\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`](/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"]]
View source on GitHub