Returns locations of nonzero / true values in a tensor.
tf.raw_ops.Where(
condition, name=None
)
This operation returns the coordinates of true elements in condition. The
coordinates are returned in a 2-D tensor where the first dimension (rows)
represents the number of true elements, and the second dimension (columns)
represents the coordinates of the true elements. Keep in mind, the shape of
the output tensor can vary depending on how many true values there are in
condition. Indices are output in row-major order.
For example:
# 'input' tensor is [[True, False]
# [True, False]]
# 'input' has two true values, so output has two coordinates.
# 'input' has rank of 2, so coordinates have two indices.
where(input) ==> [[0, 0],
[1, 0]]
# `condition` tensor is [[[True, False]
# [True, False]]
# [[False, True]
# [False, True]]
# [[False, False]
# [False, True]]]
# 'input' has 5 true values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
[0, 1, 0],
[1, 0, 1],
[1, 1, 1],
[2, 1, 1]]
# `condition` tensor is [[[1.5, 0.0]
# [-0.5, 0.0]]
# [[0.0, 0.25]
# [0.0, 0.75]]
# [[0.0, 0.0]
# [0.0, 0.01]]]
# 'input' has 5 nonzero values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
[0, 1, 0],
[1, 0, 1],
[1, 1, 1],
[2, 1, 1]]
# `condition` tensor is [[[1.5 + 0.0j, 0.0 + 0.0j]
# [0.0 + 0.5j, 0.0 + 0.0j]]
# [[0.0 + 0.0j, 0.25 + 1.5j]
# [0.0 + 0.0j, 0.75 + 0.0j]]
# [[0.0 + 0.0j, 0.0 + 0.0j]
# [0.0 + 0.0j, 0.01 + 0.0j]]]
# 'input' has 5 nonzero magnitude values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
[0, 1, 0],
[1, 0, 1],
[1, 1, 1],
[2, 1, 1]]
Returns | |
|---|---|
A Tensor of type int64.
|