RandomCrop layer
RandomCrop class
keras.layers.RandomCrop(
height, width, seed=None, data_format=None, name=None, **kwargs
)
A preprocessing layer which randomly crops images during training.
During training, this layer will randomly choose a location to crop images down to a target size. The layer will crop all the images in the same batch to the same cropping location.
At inference time, and during training if an input image is smaller than the
target size, the input will be resized and cropped so as to return the
largest possible window in the image that matches the target aspect ratio.
If you need to apply random cropping at inference time, set training to
True when calling the layer.
Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and
of integer or floating point dtype. By default, the layer will output
floats.
Note: This layer is safe to use inside a tf.data pipeline
(independently of which backend you're using).
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).
Arguments
- height: Integer, the height of the output shape.
- width: Integer, the width of the output shape.
- seed: Integer. Used to create a random seed.
- **kwargs: Base layer keyword arguments, such as
nameanddtype.