1. embeddingbag

词袋embeddingbag 是在embedding词表的基础上演变起来的,nn.embedding的作用是构建一个词表，通过输入index序号来索引词对应的词向量，是可以根据词索引index进行forward计算的，embeddingbag的作用是可以根据offset来按照序号将索引按照一批批来计算，offset相当于菜刀，根据offset索引将引入的词向量求均值或者求和表示出来

• excel 表示 ：
  

2. pytorch

• pytorch源码：

import torch
import torch.nn as nn
import torch.nn.functional as F

torch.set_printoptions(precision=3, sci_mode=False)
torch.manual_seed(323424)

if __name__ == "__main__":
    run_code = 0
    vocab_size = 10
    feature_dim = 5
    my_embedding_bag = nn.EmbeddingBag(num_embeddings=vocab_size, embedding_dim=feature_dim, mode='mean')
    my_weight_total = vocab_size * feature_dim
    my_weight = torch.arange(my_weight_total).reshape((vocab_size, feature_dim)).to(torch.float32)
    my_input = torch.tensor([0, 1, 2, 2, 3, 3, 5], dtype=torch.long)
    my_embedding_bag.weight = nn.Parameter(my_weight)
    print(f"my_embedding_bag.weight=\n{my_embedding_bag.weight}")
    input_embedding = torch.tensor([0, 2, 5], dtype=torch.long)
    output_embedding = my_embedding_bag(my_input, input_embedding)
    print(f"my_input={my_input}")
    print(f"input_embedding={input_embedding}")
    print(f"output_embedding=\n{output_embedding}")

• 结果：

my_embedding_bag.weight=
Parameter containing:
tensor([[ 0.,  1.,  2.,  3.,  4.],
        [ 5.,  6.,  7.,  8.,  9.],
        [10., 11., 12., 13., 14.],
        [15., 16., 17., 18., 19.],
        [20., 21., 22., 23., 24.],
        [25., 26., 27., 28., 29.],
        [30., 31., 32., 33., 34.],
        [35., 36., 37., 38., 39.],
        [40., 41., 42., 43., 44.],
        [45., 46., 47., 48., 49.]], requires_grad=True)
my_input=tensor([0, 1, 2, 2, 3, 3, 5])
input_embedding=tensor([0, 2, 5])
output_embedding=
tensor([[ 2.500,  3.500,  4.500,  5.500,  6.500],
        [11.667, 12.667, 13.667, 14.667, 15.667],
        [20.000, 21.000, 22.000, 23.000, 24.000]],
       grad_fn=<EmbeddingBagBackward0>)