Randomly crop the images to target height and width
layer_random_crop(object, height, width, 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.
Integer, the height of the output shape.
Integer, the width of the output shape.
Integer. Used to create a random seed.
standard layer arguments.
This layer will crop all the images in the same batch to the same cropping
location.
By default, random cropping is only applied during training. At inference
time, the images will be first rescaled to preserve the shorter side, and
center cropped. If you need to apply random cropping at inference time,
set training to TRUE when calling the layer.
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:
(..., target_height, target_width, channels).
https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomCrop
https://keras.io/api/layers/preprocessing_layers/image_augmentation/random_crop
Other image augmentation layers:
layer_random_contrast(),
layer_random_flip(),
layer_random_height(),
layer_random_rotation(),
layer_random_translation(),
layer_random_width(),
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_flip(),
layer_random_height(),
layer_random_rotation(),
layer_random_translation(),
layer_random_width(),
layer_random_zoom(),
layer_rescaling(),
layer_resizing(),
layer_string_lookup(),
layer_text_vectorization()