W3cubDocs

tf.keras.layers.RandomTranslation

A preprocessing layer which randomly translates images during training.

Inherits From: Layer, Module

View aliases

Main aliases

tf.keras.layers.experimental.preprocessing.RandomTranslation

tf.keras.layers.RandomTranslation(
    height_factor,
    width_factor,
    fill_mode='reflect',
    interpolation='bilinear',
    seed=None,
    fill_value=0.0,
    **kwargs
)

This layer will apply random translations to each image during training, filling empty space according to fill_mode.

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.

Args
`height_factor`	a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for shifting vertically. A negative value means shifting image up, while a positive value means shifting image down. When represented as a single positive float, this value is used for both the upper and lower bound. For instance, `height_factor=(-0.2, 0.3)` results in an output shifted by a random amount in the range `[-20%, +30%]`. `height_factor=0.2` results in an output height shifted by a random amount in the range `[-20%, +20%]`.
`width_factor`	a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for shifting horizontally. A negative value means shifting image left, while a positive value means shifting image right. When represented as a single positive float, this value is used for both the upper and lower bound. For instance, `width_factor=(-0.2, 0.3)` results in an output shifted left by 20%, and shifted right by 30%. `width_factor=0.2` results in an output height shifted left or right by 20%.
`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"`.

Input shape:

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.

Attributes

Attributes
`auto_vectorize`	Control whether automatic vectorization occurs. By default the `call()` method leverages the `tf.vectorized_map()` function. Auto-vectorization can be disabled by setting `self.auto_vectorize = False` in your `__init__()` method. When disabled, `call()` instead relies on `tf.map_fn()`. For example: class SubclassLayer(BaseImageAugmentationLayer): def __init__(self): super().__init__() self.auto_vectorize = False

auto_vectorize

Control whether automatic vectorization occurs.

By default the call() method leverages the tf.vectorized_map() function. Auto-vectorization can be disabled by setting self.auto_vectorize = False in your __init__() method. When disabled, call() instead relies on tf.map_fn(). For example:

class SubclassLayer(BaseImageAugmentationLayer):
  def __init__(self):
    super().__init__()
    self.auto_vectorize = False

© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/layers/RandomTranslation