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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

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mindspore.ops.embedding

mindspore.ops.embedding(input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False)

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 (Union[Parameter, Tensor]) – 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 $\frac{max\mathrm{\_}norm}{result+1e^{-7}}$ 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 $(\ast input.shape,weight.shape[1])$.

Raises

• ValueError – If padding_idx is out of valid range.

• ValueError – If the shape of weight is invalid.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, Parameter, ops
>>> input = Tensor([[1, 0, 1, 1], [0, 0, 1, 0]])
>>> weight = Parameter(np.random.randn(3, 3).astype(np.float32))
>>> output = ops.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]]]