mindspore.mint.nn.functional.embedding

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mindspore.mint.nn.functional.embedding(input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False)[source]

Retrieve the word embeddings in weight using indices specified in input.

Warning

On Ascend, the behavior is unpredictable when the value of input is invalid.

Parameters
  • input (Tensor) – The indices used to lookup in the weight. The data type must be mindspore.int32 or mindspore.int64, and the value should be in range [0, weight.shape[0]).

  • weight (Parameter) – The matrix where to lookup from. The shape must be 2D.

  • padding_idx (int, optional) – If the value is not None, the corresponding row of weight will not be updated in training. The value should be in range [-weight.shape[0], weight.shape[0]) if it's not None. Default None.

  • max_norm (float, optional) – If not None, firstly get the p-norm result of the weight specified by input where p is specified by norm_type; if the result is larger then max_norm, update the weight with \(\frac{max\_norm}{result+1e^{-7}}\) in-place. Default None.

  • norm_type (float, optional) – Indicates the value of p in p-norm. Default 2.0.

  • scale_grad_by_freq (bool, optional) – If True the gradients will be scaled by the inverse of frequency of the index in input. Default False.

Returns

Tensor, has the same data type as weight, the shape is \((*input.shape, weight.shape[1])\).

Raises
Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, Parameter, mint
>>> input = Tensor([[1, 0, 1, 1], [0, 0, 1, 0]])
>>> weight = Parameter(np.random.randn(3, 3).astype(np.float32))
>>> output = mint.nn.functional.embedding(input, weight, max_norm=0.4)
>>> print(output)
[[[ 5.49015924e-02,  3.47811311e-01, -1.89771220e-01],
  [ 2.09307984e-01, -2.24846993e-02,  3.40124398e-01],
  [ 5.49015924e-02,  3.47811311e-01, -1.89771220e-01],
  [ 5.49015924e-02,  3.47811311e-01, -1.89771220e-01]],
 [[ 2.09307984e-01, -2.24846993e-02,  3.40124398e-01],
  [ 2.09307984e-01, -2.24846993e-02,  3.40124398e-01],
  [ 5.49015924e-02,  3.47811311e-01, -1.89771220e-01],
  [ 2.09307984e-01, -2.24846993e-02,  3.40124398e-01]]]