RandomTranslation layer

[source]

RandomTranslation class

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

A preprocessing layer which randomly translates images during training.

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.

Note: This layer is safe to use inside a tf.data pipeline (independently of which backend you're using).

Arguments

• 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 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".
• 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 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".
• **kwargs: Base layer keyword arguments, such as name and dtype.