emb_x = self.emb(x).view(batch_size, -1)
时间: 2023-10-20 09:03:39 浏览: 52
这行代码的作用是将输入张量 x 映射为一个二维张量 emb_x。首先,self.emb(x) 是将输入张量 x 映射到词嵌入空间的操作,它返回一个三维张量,其形状为 [batch_size, seq_len, emb_dim],其中,batch_size 表示输入的样本数,seq_len 表示输入序列的长度,emb_dim 表示词嵌入的维度。然后,view(batch_size, -1) 操作将三维张量 emb_x 重塑为一个二维张量,其中第一维的大小为 batch_size,第二维的大小为 -1,表示自动计算。由于在重塑前已经知道 batch_size 的大小,所以第二个参数 -1 会自动计算为 $seq\_len \times emb\_dim$,即将每个样本的词嵌入拼接成一行,形状为 [batch_size, seq_len * emb_dim]。最终,emb_x 是一个二维张量,每行表示一个样本在词嵌入空间的表示。
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def forward(self, x): batch_size = x.shape[0] emb_x = self.emb(x).view(batch_size, -1) dnn = self.dnn(emb_x) dcn = self.cross_network(emb_x) return self.stack(torch.cat([dnn, dcn], dim=1)).squeeze(1)
这段代码的作用是对输入张量 x 进行处理,返回一个输出张量。具体来说,它首先将输入张量 x 映射为一个二维张量 emb_x,然后将 emb_x 输入到两个不同的网络中,分别为 dnn 和 dcn。其中,dnn 是一个深度神经网络,dcn 是一个交叉网络。最后,它将 dnn 和 dcn 的输出张量在第二个维度上拼接起来,并通过 stack 和 squeeze 操作将其转换为一个一维张量,作为最终的输出张量返回。
具体来说,torch.cat([dnn, dcn], dim=1) 是在第二个维度上将 dnn 和 dcn 的输出张量拼接起来,形成一个新的张量。self.stack 将这个张量转换为一个三维张量,第一维大小为 1,第二维大小为 batch_size,第三个维度大小为 dnn 和 dcn 输出张量的总大小。最后,squeeze(1) 将第一维的大小压缩为 1,将第二维的大小压缩为 batch_size,返回一个一维张量。
填写以下代码,并给出详细解释Hypothesis = namedtuple('Hypothesis', ['value', 'score']) class NMT(nn.Module): """ Simple Neural Machine Translation Model: - Bidrectional LSTM Encoder - Unidirection LSTM Decoder - Global Attention Model (Luon
The code you provided defines a named tuple `Hypothesis` with two fields, `value` and `score`. This is a convenient way to store and manipulate hypotheses in the context of sequence-to-sequence models.
The `NMT` class is a PyTorch module that implements a simple neural machine translation model. It consists of a bidirectional LSTM encoder, a unidirectional LSTM decoder, and a global attention mechanism based on Luong et al. (2015).
Here's a breakdown of the code:
```python
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
Hypothesis = namedtuple('Hypothesis', ['value', 'score'])
class NMT(nn.Module):
def __init__(self, src_vocab_size, tgt_vocab_size, emb_size, hidden_size):
super(NMT, self).__init__()
self.src_embed = nn.Embedding(src_vocab_size, emb_size)
self.tgt_embed = nn.Embedding(tgt_vocab_size, emb_size)
self.encoder = nn.LSTM(emb_size, hidden_size, bidirectional=True)
self.decoder = nn.LSTMCell(emb_size + hidden_size, hidden_size)
self.attention = nn.Linear(hidden_size * 2, hidden_size)
self.out = nn.Linear(hidden_size, tgt_vocab_size)
self.hidden_size = hidden_size
def forward(self, src, tgt):
batch_size = src.size(0)
src_len = src.size(1)
tgt_len = tgt.size(1)
# Encode the source sentence
src_embedded = self.src_embed(src)
encoder_outputs, (last_hidden, last_cell) = self.encoder(src_embedded)
# Initialize the decoder states
decoder_hidden = last_hidden.view(batch_size, self.hidden_size)
decoder_cell = last_cell.view(batch_size, self.hidden_size)
# Initialize the attention context vector
context = torch.zeros(batch_size, self.hidden_size, device=src.device)
# Initialize the output scores
outputs = torch.zeros(batch_size, tgt_len, self.hidden_size, device=src.device)
# Decode the target sentence
for t in range(tgt_len):
tgt_embedded = self.tgt_embed(tgt[:, t])
decoder_input = torch.cat([tgt_embedded, context], dim=1)
decoder_hidden, decoder_cell = self.decoder(decoder_input, (decoder_hidden, decoder_cell))
attention_scores = self.attention(encoder_outputs)
attention_weights = F.softmax(torch.bmm(attention_scores, decoder_hidden.unsqueeze(2)).squeeze(2), dim=1)
context = torch.bmm(attention_weights.unsqueeze(1), encoder_outputs).squeeze(1)
output = self.out(decoder_hidden)
outputs[:, t] = output
return outputs
```
The `__init__` method initializes the model parameters and layers. It takes four arguments:
- `src_vocab_size`: the size of the source vocabulary
- `tgt_vocab_size`: the size of the target vocabulary
- `emb_size`: the size of the word embeddings
- `hidden_size`: the size of the encoder and decoder hidden states
The model has four main components:
- `src_embed`: an embedding layer for the source sentence
- `tgt_embed`: an embedding layer for the target sentence
- `encoder`: a bidirectional LSTM encoder that encodes the source sentence
- `decoder`: a unidirectional LSTM decoder that generates the target sentence
The attention mechanism is implemented in the `forward` method. It takes two arguments:
- `src`: the source sentence tensor of shape `(batch_size, src_len)`
- `tgt`: the target sentence tensor of shape `(batch_size, tgt_len)`
The method first encodes the source sentence using the bidirectional LSTM encoder. The encoder outputs and final hidden and cell states are stored in `encoder_outputs`, `last_hidden`, and `last_cell`, respectively.
The decoder is initialized with the final hidden and cell states of the encoder. At each time step, the decoder takes as input the embedded target word and the context vector, which is a weighted sum of the encoder outputs based on the attention scores. The decoder output and hidden and cell states are updated using the LSTMCell module.
The attention scores are calculated by applying a linear transform to the concatenated decoder hidden state and encoder outputs, followed by a softmax activation. The attention weights are used to compute the context vector as a weighted sum of the encoder outputs.
Finally, the decoder hidden state is passed through a linear layer to produce the output scores for each target word in the sequence. The output scores are stored in the `outputs` tensor and returned by the method.