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Function one_hot

imperative/python/megengine/functional/nn.py:1644–1666  ·  view source on GitHub ↗

r"""Performs one-hot encoding for the input tensor. Args: inp: input tensor. num_classes: number of classes denotes the last dimension of the output tensor. Examples: >>> import numpy as np >>> x = Tensor(np.arange(1, 4, dtype=np.int32)) >>> F.one_ho

(inp: Tensor, num_classes: int)

Source from the content-addressed store, hash-verified

1642
1643
1644def one_hot(inp: Tensor, num_classes: int) -> Tensor:
1645 r"""Performs one-hot encoding for the input tensor.
1646
1647 Args:
1648 inp: input tensor.
1649 num_classes: number of classes denotes the last dimension of the output tensor.
1650
1651 Examples:
1652 >>> import numpy as np
1653 >>> x = Tensor(np.arange(1, 4, dtype=np.int32))
1654 >>> F.one_hot(x, num_classes=4)
1655 Tensor([[0 1 0 0]
1656 [0 0 1 0]
1657 [0 0 0 1]], dtype=int32, device=xpux:0)
1658 """
1659 zeros_tensor = zeros(
1660 list(inp.shape) + [num_classes], dtype=inp.dtype, device=inp.device
1661 )
1662 ones_tensor = ones(list(inp.shape) + [1], dtype=inp.dtype, device=inp.device)
1663
1664 op = builtin.IndexingSetOneHot(axis=inp.ndim, ndim=inp.ndim)
1665 (result,) = apply(op, zeros_tensor, inp, ones_tensor)
1666 return result
1667
1668
1669def embedding(

Callers

nothing calls this directly

Calls 4

listFunction · 0.85
zerosFunction · 0.70
onesFunction · 0.70
applyFunction · 0.50

Tested by

no test coverage detected