Randomly vary the width of a batch of images during training

layer_random_width(
object,
factor,
interpolation = "bilinear",
seed = NULL,
...
)

Arguments

object 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.

Details

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()