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=0.2 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"}).
|
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.
View source on GitHub