# tf.keras.layers.RandomTranslation

Randomly translate each image during training.

Inherits From: `Layer`, `Module`

### Used in the notebooks

Used in the tutorials

`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.

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