tf.raw_ops.OneHot

Returns a one-hot tensor.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.raw_ops.OneHot

tf.raw_ops.OneHot(
    indices, depth, on_value, off_value, axis=-1, name=None
)

The locations represented by indices in indices take value on_value, while all other locations take value off_value.

If the input indices is rank N, the output will have rank N+1, The new axis is created at dimension axis (default: the new axis is appended at the end).

If indices is a scalar the output shape will be a vector of length depth.

If indices is a vector of length features, the output shape will be:

features x depth if axis == -1
depth x features if axis == 0

If indices is a matrix (batch) with shape [batch, features], the output shape will be:

batch x features x depth if axis == -1
batch x depth x features if axis == 1
depth x batch x features if axis == 0

Examples

Suppose that

indices = [0, 2, -1, 1]
depth = 3
on_value = 5.0
off_value = 0.0
axis = -1

Then output is [4 x 3]:

output =
  [5.0 0.0 0.0]  // one_hot(0)
  [0.0 0.0 5.0]  // one_hot(2)
  [0.0 0.0 0.0]  // one_hot(-1)
  [0.0 5.0 0.0]  // one_hot(1)

Suppose that

indices = [0, 2, -1, 1]
depth = 3
on_value = 0.0
off_value = 3.0
axis = 0

Then output is [3 x 4]:

output =
  [0.0 3.0 3.0 3.0]
  [3.0 3.0 3.0 0.0]
  [3.0 3.0 3.0 3.0]
  [3.0 0.0 3.0 3.0]
//  ^                one_hot(0)
//      ^            one_hot(2)
//          ^        one_hot(-1)
//              ^    one_hot(1)

Suppose that

indices = [[0, 2], [1, -1]]
depth = 3
on_value = 1.0
off_value = 0.0
axis = -1

Then output is [2 x 2 x 3]:

output =
  [
    [1.0, 0.0, 0.0]  // one_hot(0)
    [0.0, 0.0, 1.0]  // one_hot(2)
  ][
    [0.0, 1.0, 0.0]  // one_hot(1)
    [0.0, 0.0, 0.0]  // one_hot(-1)
  ]