tf.keras.layers.RandomTranslation  |  TensorFlow v2.16.1 (original) (raw)

tf.keras.layers.RandomTranslation

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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
Retraining an Image Classifier

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.

Input shape
3D unbatched) or 4D (batched) tensor with shape (..., height, width, channels), in "channels_last" format, or (..., channels, height, width), in "channels_first" format.
Output shape
3D unbatched) or 4D (batched) tensor with shape (..., target_height, target_width, channels), or (..., channels, target_height, target_width), in "channels_first" format.
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. 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 byfill_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".
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".
data_format string, 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 theimage_data_format value found in your Keras config file at~/.keras/keras.json. If you never set it, then it will be"channels_last".
**kwargs Base layer keyword arguments, such as name and dtype.
Attributes
input Retrieves the input tensor(s) of a symbolic operation.Only returns the tensor(s) corresponding to the _first time_the operation was called.
output Retrieves the output tensor(s) of a layer.Only returns the tensor(s) corresponding to the _first time_the operation was called.

Methods

from_config

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@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
config A Python dictionary, typically the output of get_config.
Returns
A layer instance.

symbolic_call

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symbolic_call(
    *args, **kwargs
)