本文重点

接下来我们将介绍三种文本分类的算法模型，其中已经完成了前两种模型，本文将详细介绍最后一种算法模型。

算法模型

代码实现

我们定义了一个名为 DeepCbow 的类，该类继承自 nn.Module。以下是该类的详细说明：

```python class DeepCbow(nn.Module): def init(self, nwords, ntags, nlayers, embsize, hidsize): super(DeepCbow, self).init() self.nlayers = nlayers self.embedding = nn.Embedding(nwords, embsize) nn.init.xavieruniform_(self.embedding.weight)

    self.linears = nn.ModuleList(
        [nn.Linear(emb_size if i == 0 else hid_size, hid_size) for i in range(nlayers)]
    )
    for i in range(nlayers):
        nn.init.xavier_uniform_(self.linears[i].weight)

    self.out = nn.Linear(hid_size, ntags)
    nn.init.xavier_uniform_(self.out.weight)

def forward(self, words):
    emb = self.embedding(words)
    emb_sum = torch.sum(emb, dim=0)
    h = emb_sum.view(1, -1)

    for i in range(self.nlayers):
        h = self.linears[i](h)
        h = torch.tanh(h)

    out = self.out(h)
    return out

```

代码解析

• self.linears = nn.ModuleList([nn.Linear(emb_size if i == 0 else hid_size, hid_size) for i in range(nlayers)])

  这行代码表示要构建一个包含多个隐藏层的神经网络。第一层的输入神经元数为 emb_size，而后续各层的输入神经元数均为 hid_size。

维度解析

• 输入的 words 的维度为 [文本单词数]
• 经过 embedding 层处理后，维度变为 [文本单词数, embedding 编码维度]
• 对 emb 在 dim=0 维度求和后，维度变为 [embedding 编码维度]
• 将其重塑为 [1, embedding 编码维度]
• 最终全连接层的输入维度为 [1, ntags]

全部代码

以下是完整的代码实现：

```python import torch from torch import nn import random import numpy as np from collections import defaultdict

class DeepCbow(nn.Module): def init(self, nwords, ntags, nlayers, embsize, hidsize): super(DeepCbow, self).init() self.nlayers = nlayers self.embedding = nn.Embedding(nwords, embsize) nn.init.xavieruniform_(self.embedding.weight)

    self.linears = nn.ModuleList(
        [nn.Linear(emb_size if i == 0 else hid_size, hid_size) for i in range(nlayers)]
    )
    for i in range(nlayers):
        nn.init.xavier_uniform_(self.linears[i].weight)

    self.out = nn.Linear(hid_size, ntags)
    nn.init.xavier_uniform_(self.out.weight)

def forward(self, words):
    emb = self.embedding(words)
    emb_sum = torch.sum(emb, dim=0)
    h = emb_sum.view(1, -1)

    for i in range(self.nlayers):
        h = self.linears[i](h)
        h = torch.tanh(h)

    out = self.out(h)
    return out

w2i = defaultdict(lambda: len(w2i)) t2i = defaultdict(lambda: len(t2i)) UNK = w2i[""]

def read_dataset(filename): with open(filename, "r") as f: for line in f: tag, words = line.lower().strip().split(" ||| ") yield ([w2i[x] for x in words.split(" ")], t2i[tag])

train = list(readdataset("train.txt")) w2i = defaultdict(lambda: UNK, w2i) dev = list(readdataset("test.txt"))

nwords = len(w2i) ntags = len(t2i) EMBSIZE = 64 HIDSIZE = 64 NLAYERS = 2

model = DeepCbow(nwords, ntags, NLAYERS, EMBSIZE, HIDSIZE) criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters())

for epoch in range(100): random.shuffle(train) train_loss = 0.0

for words, tag in train:
    words = torch.tensor(words)
    tag = torch.tensor([tag])
    score = model(words)
    loss = criterion(score, tag)
    train_loss += loss.item()

optimizer.zero_grad()
loss.backward()
optimizer.step()

print("current epoch %r, current loss %.4f" % (epoch, train_loss / len(train)))

test_correct = 0.0

for words, tag in dev:
    words = torch.tensor(words)
    scores = model(words).detach().numpy()
    predict = np.argmax(scores)

    if predict == tag:
        test_correct += 1

print("current epoch %r, test accuracy %.4f" % (epoch, test_correct / len(dev)))

```

以上是对原文内容的改写，旨在保留原文的核心信息，同时提高文章的可读性和紧凑性。