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# tf.keras.layers.experimental.preprocessing.RandomTranslation

Randomly translate each image during training.

Inherits From: `Layer`

`height_factor` a positive float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for shifting vertically. When represented as a single float, this value is used for both the upper and lower bound. For instance, `height_factor=(0.2, 0.3)` results in an output height varying in the range `[original - 20%, original + 30%]`. `height_factor=0.2` results in an output height varying in the range `[original - 20%, original + 20%]`.
`width_factor` a positive float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for shifting horizontally. When represented as a single float, this value is used for both the upper and lower bound.
`fill_mode` Points outside the boundaries of the input are filled according to the given mode (one of `{'constant', 'reflect', 'wrap'}`).

• 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.
`interpolation` Interpolation mode. Supported values: "nearest", "bilinear".
`seed` Integer. Used to create a random seed.
`name` A string, the name of the layer.

#### Input shape:

4D tensor with shape: `(samples, height, width, channels)`, data_format='channels_last'.

#### Output shape:

4D tensor with shape: `(samples, height, width, channels)`, data_format='channels_last'.

`ValueError` if lower bound is not between [0, 1], or upper bound is negative.

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