tf.keras.layers.RandomTranslation

A preprocessing layer which randomly translates images during training.

Inherits From: Layer, Operation

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

Used in the notebooks

Used in the tutorials

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 integer or floating point dtype. By default, the layer will output floats.

3Dunbatched) or 4D (batched) tensor with shape

(..., height, width, channels), in "channels_last" format, or (..., channels, height, width), in "channels_first" format.

3Dunbatched) or 4D (batched) tensor with shape

(..., target_height, target_width, channels), or (..., channels, target_height, target_width), in "channels_first" format.

height_factora 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_factora 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_modePoints outside the boundaries of the input are filled according to the given mode. Available methods are "constant", "nearest", "wrap" and "reflect". Defaults to "constant".

  • "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 specified by fill_value.
  • "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. Note that when using torch backend, "reflect" is redirected to "mirror" (c d c b | a b c d | c b a b) because torch does not support "reflect". Note that torch backend does not support "wrap".
interpolationInterpolation mode. Supported values: "nearest", "bilinear".
seedInteger. Used to create a random seed.
fill_valuea float represents the value to be filled outside the boundaries when fill_mode="constant".
data_formatstring, either "channels_last" or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch, height, width, channels) while "channels_first" corresponds to inputs with shape (batch, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".
**kwargsBase layer keyword arguments, such as name and dtype.

inputRetrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

outputRetrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

View source

@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
configA Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

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symbolic_call(
    *args, **kwargs
)