tf.one_hot

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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 ) 

Used in the notebooks

See also tf.fill, tf.eye.

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.

For example:

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.]]]