Randomly vary the width of a batch of images during training
layer_random_width( object, factor, interpolation = "bilinear", seed = NULL, ... )
What to compose the new
Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by
The return value depends on
Layer instance is returned.
Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from
layer_instance(object) is returned.
A positive float (fraction of original height), or a list 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 = c(0.2, 0.3) results in an output with
width changed by a random amount in the range
factor=(-0.2, 0.3) results in an output with width changed by a random amount in the
factor = 0.2 results in an output with width changed
by a random amount in the range
String, the interpolation method. Defaults to
Integer. Used to create a random seed.
standard layer arguments.
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
image data format.
By default, this layer is inactive during inference.
Other preprocessing layers: