torch.nn.Embedding

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 Parameters

• num_embeddings (int) – size of the dictionary of embeddings

• embedding_dim (int) – the size of each embedding vector

• padding_idx (int, optional) – If specified, the entries at padding_idx do not contribute to the gradient; therefore, the embedding vector at padding_idx is not updated during training, i.e. it remains as a fixed “pad”. For a newly constructed Embedding, the embedding vector at padding_idx will default to all zeros, but can be updated to another value to be used as the padding vector.

• max_norm (float, optional) – If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.

• norm_type (float, optional) – The p of the p-norm to compute for the max_norm option. Default 2.

• scale_grad_by_freq (boolean, optional) – If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default False.

• sparse (bool, optional) – If True, gradient w.r.t. weight matrix will be a sparse tensor. See Notes for more details regarding sparse gradients.

Examples:

import torch
from torch import nn
embedding = nn.Embedding(5, 4) # 假定字典中只有5个词，词向量维度为4
word = [[1, 2, 3],
        [2, 3, 4]] # 每个数字代表一个词，例如 {'!':0,'how':1, 'are':2, 'you':3,  'ok':4}
                    #而且这些数字的范围只能在0～4之间，因为上面定义了只有5个词
embed = embedding(torch.LongTensor(word))
print(embed)
print(embed.size())

 结果：

tensor([[[-0.0436, -1.0037, 0.2681, -0.3834],
[ 0.0222, -0.7280, -0.6952, -0.7877],
[ 1.4341, -0.0511, 1.3429, -1.2345]],

[[ 0.0222, -0.7280, -0.6952, -0.7877],
[ 1.4341, -0.0511, 1.3429, -1.2345],
[-0.2014, -0.4946, -0.0273, 0.5654]]], grad_fn=<EmbeddingBackward0>)
torch.Size([2, 3, 4])