Randomly crop the images to target height and width

layer_random_crop(object, height, width, seed = NULL, ...)

Arguments

object

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.

height

Integer, the height of the output shape.

width

Integer, the width of the output shape.

seed

Integer. Used to create a random seed.

...

standard layer arguments.

Details

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).