|  View source on GitHub | 
Randomly translate each image during training.
tf.keras.layers.RandomTranslation(
    height_factor, width_factor, fill_mode='reflect',
    interpolation='bilinear', seed=None, fill_value=0.0, **kwargs
)
| 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=0.2results 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=0.2results 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"}).
 | 
| 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.