# layer_random_translation

## Randomly translate each image during training

## Description

Randomly translate each image during training

## Usage

```
layer_random_translation(
object,
height_factor,
width_factor, fill_mode = "reflect",
interpolation = "bilinear",
seed = NULL,
fill_value = 0,
... )
```

## Arguments

Arguments | Description |
---|---|

object | What to compose the new `Layer` instance with. Typically a Sequential model or a Tensor (e.g., as returned by `layer_input()` ). The return value depends on `object` . If `object` is: - missing or `NULL` , the `Layer` instance is returned. - a `Sequential` model, the model with an additional layer is returned. - a Tensor, the output tensor from `layer_instance(object)` is returned. |

height_factor | a float represented as fraction of value, or a list 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 = c(-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 list 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 = c(-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"` . |

… | standard layer arguments. |

## See Also

https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomTranslation

https://keras.io/api/layers/preprocessing_layers/

Other image augmentation layers:

`layer_random_brightness()`

,`layer_random_contrast()`

,`layer_random_crop()`

,`layer_random_flip()`

,`layer_random_height()`

,`layer_random_rotation()`

,`layer_random_width()`

,`layer_random_zoom()`

Other preprocessing layers:`layer_category_encoding()`

,`layer_center_crop()`

,`layer_discretization()`

,`layer_hashing()`

,`layer_integer_lookup()`

,`layer_normalization()`

,`layer_random_brightness()`

,`layer_random_contrast()`

,`layer_random_crop()`

,`layer_random_flip()`

,`layer_random_height()`

,`layer_random_rotation()`

,`layer_random_width()`

,`layer_random_zoom()`

,`layer_rescaling()`

,`layer_resizing()`

,`layer_string_lookup()`

,`layer_text_vectorization()`