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Returns a one-hot tensor.
tf.one_hot( indices, depth, on_value=None, off_value=None, axis=None, dtype=None, name=None )
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 indices
is a RaggedTensor, the 'axis' argument must be positive and refer to a non-ragged axis. The output will be equivalent to applying 'one_hot' on the values of the RaggedTensor, and creating a new RaggedTensor from the result.
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.), bothon_value
andoff_value
must be provided toone_hot
.
indices = [0, 1, 2] depth = 3 tf.one_hot(indices, depth) # output: [3 x 3] # [[1., 0., 0.], # [0., 1., 0.], # [0., 0., 1.]] indices = [0, 2, -1, 1] depth = 3 tf.one_hot(indices, depth, on_value=5.0, off_value=0.0, axis=-1) # output: [4 x 3] # [[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) indices = [[0, 2], [1, -1]] depth = 3 tf.one_hot(indices, depth, on_value=1.0, off_value=0.0, axis=-1) # output: [2 x 2 x 3] # [[[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) indices = tf.ragged.constant([[0, 1], [2]]) depth = 3 tf.one_hot(indices, depth) # output: [2 x None x 3] # [[[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. |
name | A name for the operation (optional). |
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 |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/one_hot