Randomly vary the width of a batch of images during training
layer_random_width(
object,
factor,
interpolation = "bilinear",
seed = NULL,
...
)What to call the new Layer instance with. Typically a keras
Model, another Layer, or a tf.Tensor/KerasTensor. If object is
missing, the Layer instance is returned, otherwise, layer(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 [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%].
String, the interpolation method. Defaults to bilinear.
Supports "bilinear", "nearest", "bicubic", "area", "lanczos3",
"lanczos5", "gaussian", "mitchellcubic".
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 "channels_last"
image data format.
By default, this layer is inactive during inference.
Other image augmentation layers:
layer_random_contrast(),
layer_random_crop(),
layer_random_flip(),
layer_random_height(),
layer_random_rotation(),
layer_random_translation(),
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_rotation(),
layer_random_translation(),
layer_random_zoom(),
layer_rescaling(),
layer_resizing(),
layer_string_lookup(),
layer_text_vectorization()