tf.keras.layers.experimental.preprocessing.RandomHeight
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Randomly vary the height of a batch of images during training.
Inherits From: Layer
tf.keras.layers.experimental.preprocessing.RandomHeight(
factor, interpolation='bilinear', seed=None, name=None, **kwargs
)
Adjusts the height of a batch of images by a random factor. The input
should be a 4-D tensor in the "channels_last" image data format.
By default, this layer is inactive during inference.
Arguments |
|---|
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 height
varying in the range [original + 20%, original + 30%]. factor=(-0.2,
0.3) results in an output height varying in the range [original - 20%,
original + 30%]. factor=0.2 results in an output height varying in the
range [original - 20%, original + 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.
|
name
|
A string, the name of the layer.
|
4D tensor with shape: (samples, height, width, channels)
(data_format='channels_last').
Output shape:
4D tensor with shape: (samples, random_height, width, channels).
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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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01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.RandomHeight\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.2.0/tensorflow/python/keras/layers/preprocessing/image_preprocessing.py#L1082-L1171) |\n\nRandomly vary the height of a batch of images during training.\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.RandomHeight`](/api_docs/python/tf/keras/layers/experimental/preprocessing/RandomHeight)\n\n\u003cbr /\u003e\n\n tf.keras.layers.experimental.preprocessing.RandomHeight(\n factor, interpolation='bilinear', seed=None, name=None, **kwargs\n )\n\nAdjusts the height of a batch of images by a random factor. The input\nshould be a 4-D tensor in the \"channels_last\" image 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| Arguments --------- ||\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 height varying in the range `[original + 20%, original + 30%]`. `factor=(-0.2, 0.3)` results in an output height varying in the range `[original - 20%, original + 30%]`. `factor=0.2` results in an output height varying in the range `[original - 20%, original + 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| `name` | A string, the name of the layer. |\n\n\u003cbr /\u003e\n\n#### Input shape:\n\n4D tensor with shape: `(samples, height, width, channels)`\n(data_format='channels_last').\n\n#### Output shape:\n\n4D tensor with shape: `(samples, random_height, width, channels)`."]]
View source on GitHub