tf.gather_nd

Gather slices from params into a Tensor with shape specified by indices.

tf.gather_nd(
    params, indices, batch_dims=0, name=None
)

indices is an K-dimensional integer tensor, best thought of as a (K-1)-dimensional tensor of indices into params, where each element defines a slice of params:

output[\\(i_0, ..., i_{K-2}\\)] = params[indices[\\(i_0, ..., i_{K-2}\\)]]

Whereas in tf.gather indices defines slices into the first dimension of params, in tf.gather_nd, indices defines slices into the first N dimensions of params, where N = indices.shape[-1].

The last dimension of indices can be at most the rank of params:

indices.shape[-1] <= params.rank

The last dimension of indices corresponds to elements (if indices.shape[-1] == params.rank) or slices (if indices.shape[-1] < params.rank) along dimension indices.shape[-1] of params. The output tensor has shape

indices.shape[:-1] + params.shape[indices.shape[-1]:]

Additionally both 'params' and 'indices' can have M leading batch dimensions that exactly match. In this case 'batch_dims' must be M.

Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, a 0 is stored in the corresponding output value.

Some examples below.

Simple indexing into a matrix:

    indices = [[0, 0], [1, 1]]
    params = [['a', 'b'], ['c', 'd']]
    output = ['a', 'd']

Slice indexing into a matrix:

    indices = [[1], [0]]
    params = [['a', 'b'], ['c', 'd']]
    output = [['c', 'd'], ['a', 'b']]

Indexing into a 3-tensor:

    indices = [[1]]
    params = [[['a0', 'b0'], ['c0', 'd0']],
              [['a1', 'b1'], ['c1', 'd1']]]
    output = [[['a1', 'b1'], ['c1', 'd1']]]


    indices = [[0, 1], [1, 0]]
    params = [[['a0', 'b0'], ['c0', 'd0']],
              [['a1', 'b1'], ['c1', 'd1']]]
    output = [['c0', 'd0'], ['a1', 'b1']]


    indices = [[0, 0, 1], [1, 0, 1]]
    params = [[['a0', 'b0'], ['c0', 'd0']],
              [['a1', 'b1'], ['c1', 'd1']]]
    output = ['b0', 'b1']

The examples below are for the case when only indices have leading extra dimensions. If both 'params' and 'indices' have leading batch dimensions, use the 'batch_dims' parameter to run gather_nd in batch mode.

Batched indexing into a matrix:

    indices = [[[0, 0]], [[0, 1]]]
    params = [['a', 'b'], ['c', 'd']]
    output = [['a'], ['b']]

Batched slice indexing into a matrix:

    indices = [[[1]], [[0]]]
    params = [['a', 'b'], ['c', 'd']]
    output = [[['c', 'd']], [['a', 'b']]]

Batched indexing into a 3-tensor:

    indices = [[[1]], [[0]]]
    params = [[['a0', 'b0'], ['c0', 'd0']],
              [['a1', 'b1'], ['c1', 'd1']]]
    output = [[[['a1', 'b1'], ['c1', 'd1']]],
              [[['a0', 'b0'], ['c0', 'd0']]]]

    indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]
    params = [[['a0', 'b0'], ['c0', 'd0']],
              [['a1', 'b1'], ['c1', 'd1']]]
    output = [[['c0', 'd0'], ['a1', 'b1']],
              [['a0', 'b0'], ['c1', 'd1']]]


    indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]
    params = [[['a0', 'b0'], ['c0', 'd0']],
              [['a1', 'b1'], ['c1', 'd1']]]
    output = [['b0', 'b1'], ['d0', 'c1']]

Examples with batched 'params' and 'indices':

    batch_dims = 1
    indices = [[1], [0]]
    params = [[['a0', 'b0'], ['c0', 'd0']],
              [['a1', 'b1'], ['c1', 'd1']]]
    output = [['c0', 'd0'], ['a1', 'b1']]

    batch_dims = 1
    indices = [[[1]], [[0]]]
    params = [[['a0', 'b0'], ['c0', 'd0']],
              [['a1', 'b1'], ['c1', 'd1']]]
    output = [[['c0', 'd0']], [['a1', 'b1']]]

    batch_dims = 1
    indices = [[[1, 0]], [[0, 1]]]
    params = [[['a0', 'b0'], ['c0', 'd0']],
              [['a1', 'b1'], ['c1', 'd1']]]
    output = [['c0'], ['b1']]

Args
`params`	A `Tensor`. The tensor from which to gather values.
`indices`	A `Tensor`. Must be one of the following types: `int32`, `int64`. Index tensor.
`name`	A name for the operation (optional).
`batch_dims`	An integer or a scalar 'Tensor'. The number of batch dimensions.

tf.gather_nd

Args

Returns