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tf.ragged.range

Returns a RaggedTensor containing the specified sequences of numbers.

tf.ragged.range(
    starts,
    limits=None,
    deltas=1,
    dtype=None,
    name=None,
    row_splits_dtype=tf.dtypes.int64
)

Used in the notebooks

Used in the guide

Each row of the returned RaggedTensor contains a single sequence:

ragged.range(starts, limits, deltas)[i] ==
    tf.range(starts[i], limits[i], deltas[i])

If start[i] < limits[i] and deltas[i] > 0, then output[i] will be an empty list. Similarly, if start[i] > limits[i] and deltas[i] < 0, then output[i] will be an empty list. This behavior is consistent with the Python range function, but differs from the tf.range op, which returns an error for these cases.

Examples:

tf.ragged.range([3, 5, 2]).to_list()
[[0, 1, 2], [0, 1, 2, 3, 4], [0, 1]]
tf.ragged.range([0, 5, 8], [3, 3, 12]).to_list()
[[0, 1, 2], [], [8, 9, 10, 11]]
tf.ragged.range([0, 5, 8], [3, 3, 12], 2).to_list()
[[0, 2], [], [8, 10]]

The input tensors starts, limits, and deltas may be scalars or vectors. The vector inputs must all have the same size. Scalar inputs are broadcast to match the size of the vector inputs.

starts Vector or scalar Tensor. Specifies the first entry for each range if limits is not None; otherwise, specifies the range limits, and the first entries default to 0. limits Vector or scalar Tensor. Specifies the exclusive upper limits for each range. deltas Vector or scalar Tensor. Specifies the increment for each range. Defaults to 1. dtype The type of the elements of the resulting tensor. If not specified, then a value is chosen based on the other args. name A name for the operation. row_splits_dtype dtype for the returned RaggedTensor's row_splits tensor. One of tf.int32 or tf.int64.

A RaggedTensor of type dtype with ragged_rank=1.