nn.embedding.weight
时间: 2023-05-01 21:07:13 浏览: 68
'b'nn.embedding.weight' 是指神经网络中的一个叫做 "embedding" 的层的权重参数。这个层主要作用是将输入的离散化的词语或者符号(token)转化为连续向量表示,方便神经网络进行处理和学习。权重参数就是在转换过程中需要学习的参数。
相关问题
torch.nn.embedding.weight.data
torch.nn.embedding.weight.data is a tensor that represents the weights of the embedding layer in a neural network. It is a 2-dimensional tensor of size (num_embeddings, embedding_dim), where num_embeddings is the total number of embeddings in the layer and embedding_dim is the size of each embedding vector.
For example, if the embedding layer has 1000 embeddings and each embedding vector is of size 300, then the size of torch.nn.embedding.weight.data would be (1000, 300).
The values in this tensor are usually randomly initialized and updated during the training process using backpropagation. The embedding layer is used to convert categorical variables (such as words in natural language processing) into continuous vectors that can be fed into a neural network.
nn.Embedding是否需要初始化权重
是的,nn.Embedding需要初始化权重。在PyTorch中,所有的权重都需要初始化。默认情况下,nn.Embedding会使用均匀分布进行初始化,可以在创建时通过指定weight参数来传入自定义的初始化权重。例如:
```
embedding = nn.Embedding(num_embeddings, embedding_dim, weight=custom_weight)
```
其中,num_embeddings表示词汇表的大小,embedding_dim表示每个词向量的维度,custom_weight是一个形状为[num_embeddings, embedding_dim]的张量,表示自定义的嵌入权重。