|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/random_crop_ops.py#L85-L135) | Randomly crops a tensor to a given size in a deterministic manner. tf.image.stateless_random_crop( value, size, seed, name=None ) ### Used in the notebooks | Used in the tutorials | |---| | - [Data augmentation](https://www.tensorflow.org/tutorials/images/data_augmentation) | Slices a shape `size` portion out of `value` at a uniformly chosen offset. Requires `value.shape >= size`. If a dimension should not be cropped, pass the full size of that dimension. For example, RGB images can be cropped with `size = [crop_height, crop_width, 3]`. Guarantees the same results given the same `seed` independent of how many times the function is called, and independent of global seed settings (e.g. [`tf.random.set_seed`](https://www.tensorflow.org/api_docs/python/tf/random/set_seed)). #### Usage Example: image = [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]] seed = (1, 2) tf.image.stateless_random_crop(value=image, size=(1, 2, 3), seed=seed)


| ## Args || |---|---| | `value` | Input tensor to crop. | | `size` | 1-D tensor with size the rank of `value`. | | `seed` | A shape \[2\] Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.) | | `name` | A name for this operation (optional). |



| ## Returns || |---|---| | A cropped tensor of the same rank as `value` and shape `size`. ||