Image resizing layer
layer_resizing(
object,
height,
width,
interpolation = "bilinear",
crop_to_aspect_ratio = FALSE,
...
)What to compose the new Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()).
The return value depends on object. If object is:
missing or NULL, the Layer instance is returned.
a Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object) is returned.
Integer, the height of the output shape.
Integer, the width of the output shape.
String, the interpolation method. Defaults to "bilinear".
Supports "bilinear", "nearest", "bicubic", "area", "lanczos3",
"lanczos5", "gaussian", and "mitchellcubic".
If TRUE, resize the images without aspect
ratio distortion. When the original aspect ratio differs from the target
aspect ratio, the output image will be cropped so as to return the largest
possible window in the image (of size (height, width)) that matches
the target aspect ratio. By default (crop_to_aspect_ratio = FALSE),
aspect ratio may not be preserved.
standard layer arguments.
Resize the batched image input to target height and width. The input should
be a 4D (batched) or 3D (unbatched) tensor in "channels_last" format.
https://www.tensorflow.org/api_docs/python/tf/keras/layers/Resizing
https://keras.io/api/layers/preprocessing_layers/image_preprocessing/resizing
Other image preprocessing layers:
layer_center_crop(),
layer_rescaling()
Other preprocessing layers:
layer_category_encoding(),
layer_center_crop(),
layer_discretization(),
layer_hashing(),
layer_integer_lookup(),
layer_normalization(),
layer_random_brightness(),
layer_random_contrast(),
layer_random_crop(),
layer_random_flip(),
layer_random_height(),
layer_random_rotation(),
layer_random_translation(),
layer_random_width(),
layer_random_zoom(),
layer_rescaling(),
layer_string_lookup(),
layer_text_vectorization()