Tensor Transformations > Slicing and Joining

tf.one_hot(indices, depth, on_value=None, off_value=None, axis=None, dtype=None, name=None)

`tf.one_hot(indices, depth, on_value=None, off_value=None, axis=None, dtype=None, name=None)`

See the guide: Tensor Transformations > Slicing and Joining

Returns a one-hot tensor.

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

on_value and off_value must have matching data types. If dtype is also provided, they must be the same data type as specified by dtype.

If on_value is not provided, it will default to the value 1 with type dtype

If off_value is not provided, it will default to the value 0 with type dtype

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

If dtype is not provided, it will attempt to assume the data type of on_value or off_value, if one or both are passed in. If none of on_value, off_value, or dtype are provided, dtype will default to the value tf.float32.

Note: If a non-numeric data type output is desired (tf.string, tf.bool, etc.), both on_value and off_value must be provided to one_hot.

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 = 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)
  ]

Using default values for on_value and off_value:

  indices = [0, 1, 2]
  depth = 3
  The output will be

  output =
  [[1., 0., 0.],
   [0., 1., 0.],
   [0., 0., 1.]]

Args:

indices: A Tensor of indices.
depth: A scalar defining the depth of the one hot dimension.
on_value: A scalar defining the value to fill in output when indices[j] = i. (default: 1)
off_value: A scalar defining the value to fill in output when indices[j] != i. (default: 0)
axis: The axis to fill (default: -1, a new inner-most axis).
dtype: The data type of the output tensor.

Returns:

output: The one-hot tensor.

Raises:

TypeError: If dtype of either on_value or off_value don't match dtype
TypeError: If dtype of on_value and off_value don't match one another

Defined in tensorflow/python/ops/array_ops.py.