Randomly rotate each image

```
layer_random_rotation(
object,
factor,
fill_mode = "reflect",
interpolation = "bilinear",
seed = NULL,
fill_value = 0,
...
)
```

- 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.

- factor
a float represented as fraction of 2 Pi, or a list of size 2 representing lower and upper bound for rotating clockwise and counter-clockwise. A positive values means rotating counter clock-wise, while a negative value means clock-wise. When represented as a single float, this value is used for both the upper and lower bound. For instance,

`factor = c(-0.2, 0.3)`

results in an output rotation by a random amount in the range`[-20% * 2pi, 30% * 2pi]`

.`factor = 0.2`

results in an output rotating by a random amount in the range`[-20% * 2pi, 20% * 2pi]`

.- 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.

By default, random rotations are only applied during training.
At inference time, the layer does nothing. If you need to apply random
rotations at inference time, set `training`

to TRUE when calling the layer.

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

Other image augmentation layers:
`layer_random_contrast()`

,
`layer_random_crop()`

,
`layer_random_flip()`

,
`layer_random_height()`

,
`layer_random_translation()`

,
`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_contrast()`

,
`layer_random_crop()`

,
`layer_random_flip()`

,
`layer_random_height()`

,
`layer_random_translation()`

,
`layer_random_width()`

,
`layer_random_zoom()`

,
`layer_rescaling()`

,
`layer_resizing()`

,
`layer_string_lookup()`

,
`layer_text_vectorization()`