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|
Randomly vary the width of a batch of images during training.
tf.keras.layers.RandomWidth(
factor, interpolation='bilinear', seed=None, **kwargs
)
Adjusts the width of a batch of images by a random factor. The input
should be a 3D (unbatched) or 4D (batched) tensor in the "channels_last"
image data format.
By default, this layer is inactive during inference.
Args | |
|---|---|
factor
|
A positive float (fraction of original height), or a tuple of size 2
representing lower and upper bound for resizing vertically. When
represented as a single float, this value is used for both the upper and
lower bound. For instance, factor=(0.2, 0.3) results in an output with
width changed by a random amount in the range [20%, 30%]. factor=(-0.2,
0.3) results in an output with width changed by a random amount in the
range [-20%, +30%]. factor=0.2 results in an output with width changed
by a random amount in the range [-20%, +20%].
|
interpolation
|
String, the interpolation method. Defaults to bilinear.
Supports "bilinear", "nearest", "bicubic", "area", "lanczos3",
"lanczos5", "gaussian", "mitchellcubic".
|
seed
|
Integer. Used to create a random seed. |
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:
(..., random_height, width, channels).