如何在 RNN 中嵌入句子序列？

Question

我正在尝试制作一个 RNN 模型（在 Pytorch 中），它需要几个句子，然后将其分类为Class 0或Class 1。为了这个问题，我们假设句子的 max_len 为 4，时间步长的 max_amount 为 5。因此，每个数据点都在表单上（0 是用于填充填充值的值）：    x[1] = [    # Input features at timestep 1    [1, 48, 91, 0],    # Input features at timestep 2    [20, 5, 17, 32],    # Input features at timestep 3    [12, 18, 0, 0],    # Input features at timestep 4    [0, 0, 0, 0],    # Input features at timestep 5    [0, 0, 0, 0]    ]    y[1] = [1]当我每个目标只有一个句子时：我只是将每个单词传递给嵌入层，然后传递给 LSTM 或 GRU，但是当每个目标有一系列句子时，我有点卡住了怎么办？如何构建可以处理句子的嵌入？

Answer 1

最简单的方法是使用 2 种 LSTM。

准备玩具数据集

xi = [

# Input features at timestep 1

[1, 48, 91, 0],

# Input features at timestep 2

[20, 5, 17, 32],

# Input features at timestep 3

[12, 18, 0, 0],

# Input features at timestep 4

[0, 0, 0, 0],

# Input features at timestep 5

[0, 0, 0, 0]

]

yi = 1

x = torch.tensor([xi, xi])

y = torch.tensor([yi, yi])

print(x.shape)

# torch.Size([2, 5, 4])

print(y.shape)

# torch.Size([2])

然后，x是输入的批次。这里batch_size= 2。

嵌入输入

vocab_size = 1000

embed_size = 100

hidden_size = 200

embed = nn.Embedding(vocab_size, embed_size)

# shape [2, 5, 4, 100]

x = embed(x)

第一个词-LSTM是将每个序列编码成一个向量

# convert x into a batch of sequences

# Reshape into [2, 20, 100]

x = x.view(bs * 5, 4, 100)

wlstm = nn.LSTM(embed_size, hidden_size, batch_first=True)

# get the only final hidden state of each sequence

_, (hn, _) = wlstm(x)

# hn shape [1, 10, 200]

# get the output of final layer

hn = hn[0] # [10, 200]

第二个seq-LSTM是将序列编码成单个向量

# Reshape hn into [bs, num_seq, hidden_size]

hn = hn.view(2, 5, 200)

# Pass to another LSTM and get the final state hn

slstm = nn.LSTM(hidden_size, hidden_size, batch_first=True)

_, (hn, _) = slstm(hn) # [1, 2, 200]

# Similarly, get the hidden state of the last layer

hn = hn[0] # [2, 200]

添加一些分类层

pred_linear = nn.Linear(hidden_size, 1)

# [2, 1]

output = torch.sigmoid(pred_linear(hn))