本文主要介绍一篇将CNN应用到NLP领域的一篇论文 Convolutional Neural Networks for Sentence Classification
，然后给出 PyTorch 实现
论文比较短，总体流程也不复杂，最主要的是下面这张图，只要理解了这张图，就知道如何写代码了。如果你不了解CNN，请先看我的这篇文章 CS231n笔记：通俗理解CNN
下图的feature map是将一句话中的各个词通过WordEmbedding得到的，feature map的宽为embedding的维度，长为一句话的单词数量。例如下图中，很明显就是用一个6维的向量去编码每个词，并且一句话中有9个词
之所以有两张feature map，你可以理解为batchsize为2
其中，红色和橙色的框代表的就是卷积核。而且很明显可以看出，这是一个长宽不等的卷积核。有意思的是，卷积核的宽可以认为是n-gram，比方说下图卷积核宽为2，所以同时考虑了”wait”和”for”两个单词的词向量，因此可以认为该卷积是一个类似于bigram的模型
后面的部分就是传统CNN的步骤，激活、池化、Flatten，没什幺好说的
代码实现（PyTorch版）
源码来自于 nlp-tutorial
，我在其基础上进行了修改（原本的代码感觉有很多问题）
'''
code by Tae Hwan Jung(Jeff Jung) @graykode, modify by wmathor
'''
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as Data
import torch.nn.functional as F
dtype = torch.FloatTensor
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
下面代码就是定义一些数据，以及设置一些常规参数
# 3 words sentences (=sequence_length is 3)
sentences = ["i love you", "he loves me", "she likes baseball", "i hate you", "sorry for that", "this is awful"]
labels = [1, 1, 1, 0, 0, 0] # 1 is good, 0 is not good.
# TextCNN Parameter
embedding_size = 2
sequence_length = len(sentences[0]) # every sentences contains sequence_length(=3) words
num_classes = len(set(labels)) # num_classes=2
batch_size = 3
word_list = " ".join(sentences).split()
vocab = list(set(word_list))
word2idx = {w: i for i, w in enumerate(vocab)}
vocab_size = len(vocab)
数据预处理
def make_data(sentences, labels):
inputs = []
for sen in sentences:
inputs.append([word2idx[n] for n in sen.split()])
targets = []
for out in labels:
targets.append(out) # To using Torch Softmax Loss function
return inputs, targets
input_batch, target_batch = make_data(sentences, labels)
input_batch, target_batch = torch.LongTensor(input_batch), torch.LongTensor(target_batch)
dataset = Data.TensorDataset(input_batch, target_batch)
loader = Data.DataLoader(dataset, batch_size, True)
构建模型
class TextCNN(nn.Module):
def __init__(self):
super(TextCNN, self).__init__()
self.W = nn.Embedding(vocab_size, embedding_size)
output_channel = 3
self.conv = nn.Sequential(
# conv : [input_channel(=1), output_channel, (filter_height, filter_width), stride=1]
nn.Conv2d(1, output_channel, (2, embedding_size)),
nn.ReLU(),
# pool : ((filter_height, filter_width))
nn.MaxPool2d((2, 1)),
)
# fc
self.fc = nn.Linear(output_channel, num_classes)
def forward(self, X):
'''
X: [batch_size, sequence_length]
'''
batch_size = X.shape[0]
embedding_X = self.W(X) # [batch_size, sequence_length, embedding_size]
embedding_X = embedding_X.unsqueeze(1) # add channel(=1) [batch, channel(=1), sequence_length, embedding_size]
conved = self.conv(embedding_X) # [batch_size, output_channel*1*1]
flatten = conved.view(batch_size, -1)
output = self.fc(flatten)
return output
下面详细介绍一下数据在网络中流动的过程中维度的变化。输入数据是个矩阵，矩阵维度为[batch_size, seqence_length]，输入矩阵的数字代表的是某个词在整个词库中的索引（下标）
首先通过Embedding层，也就是查表，将每个索引转为一个向量，比方说12可能会变成[0.3,0.6,0.12,…]，因此整个数据无形中就增加了一个维度，变成了[batch_size, sequence_length, embedding_size]
之后使用 unsqueeze(1)
函数使数据增加一个维度，变成[batch_size, 1, sequence_length, embedding_size]。现在的数据才能做卷积，因为在传统CNN中，输入数据就应该是[batch_size, in_channel, height, width]这种维度
[batch_size, 1, 3, 2]的输入数据通过 nn.Conv2d(1, 3, (2, 2))
的卷积之后，得到的就是[batch_size, 3, 2, 1]的数据，由于经过ReLU激活函数是不改变维度的，所以就没画出来。最后经过一个 nn.MaxPool2d((2, 1))
池化，得到的数据维度就是[batch_size, 3, 1, 1]
训练
model = TextCNN().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
# Training
for epoch in range(5000):
for batch_x, batch_y in loader:
batch_x, batch_y = batch_x.to(device), batch_y.to(device)
pred = model(batch_x)
loss = criterion(pred, batch_y)
if (epoch + 1) % 1000 == 0:
print('Epoch:', '%04d' % (epoch + 1), 'loss =', '{:.6f}'.format(loss))
optimizer.zero_grad()
loss.backward()
optimizer.step()
测试
# Test
test_text = 'i hate me'
tests = [[word2idx[n] for n in test_text.split()]]
test_batch = torch.LongTensor(tests).to(device)
# Predict
model = model.eval()
predict = model(test_batch).data.max(1, keepdim=True)[1]
if predict[0][0] == 0:
print(test_text,"is Bad Mean...")
else:
print(test_text,"is Good Mean!!")
完整代码如下：
'''
code by Tae Hwan Jung(Jeff Jung) @graykode, modify by wmathor
'''
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as Data
import torch.nn.functional as F
dtype = torch.FloatTensor
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 3 words sentences (=sequence_length is 3)
sentences = ["i love you", "he loves me", "she likes baseball", "i hate you", "sorry for that", "this is awful"]
labels = [1, 1, 1, 0, 0, 0] # 1 is good, 0 is not good.
# TextCNN Parameter
embedding_size = 2
sequence_length = len(sentences[0]) # every sentences contains sequence_length(=3) words
num_classes = 2 # 0 or 1
batch_size = 3
word_list = " ".join(sentences).split()
vocab = list(set(word_list))
word2idx = {w: i for i, w in enumerate(vocab)}
vocab_size = len(vocab)
def make_data(sentences, labels):
inputs = []
for sen in sentences:
inputs.append([word2idx[n] for n in sen.split()])
targets = []
for out in labels:
targets.append(out) # To using Torch Softmax Loss function
return inputs, targets
input_batch, target_batch = make_data(sentences, labels)
input_batch, target_batch = torch.LongTensor(input_batch), torch.LongTensor(target_batch)
dataset = Data.TensorDataset(input_batch, target_batch)
loader = Data.DataLoader(dataset, batch_size, True)
class TextCNN(nn.Module):
def __init__(self):
super(TextCNN, self).__init__()
self.W = nn.Embedding(vocab_size, embedding_size)
output_channel = 3
self.conv = nn.Sequential(
# conv : [input_channel(=1), output_channel, (filter_height, filter_width), stride=1]
nn.Conv2d(1, output_channel, (2, embedding_size)),
nn.ReLU(),
# pool : ((filter_height, filter_width))
nn.MaxPool2d((2, 1)),
)
# fc
self.fc = nn.Linear(output_channel, num_classes)
def forward(self, X):
'''
X: [batch_size, sequence_length]
'''
batch_size = X.shape[0]
embedding_X = self.W(X) # [batch_size, sequence_length, embedding_size]
embedding_X = embedding_X.unsqueeze(1) # add channel(=1) [batch, channel(=1), sequence_length, embedding_size]
conved = self.conv(embedding_X) # [batch_size, output_channel, 1, 1]
flatten = conved.view(batch_size, -1) # [batch_size, output_channel*1*1]
output = self.fc(flatten)
return output
model = TextCNN().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
# Training
for epoch in range(5000):
for batch_x, batch_y in loader:
batch_x, batch_y = batch_x.to(device), batch_y.to(device)
pred = model(batch_x)
loss = criterion(pred, batch_y)
if (epoch + 1) % 1000 == 0:
print('Epoch:', '%04d' % (epoch + 1), 'loss =', '{:.6f}'.format(loss))
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Test
test_text = 'i hate me'
tests = [[word2idx[n] for n in test_text.split()]]
test_batch = torch.LongTensor(tests).to(device)
# Predict
model = model.eval()
predict = model(test_batch).data.max(1, keepdim=True)[1]
if predict[0][0] == 0:
print(test_text,"is Bad Mean...")
else:
print(test_text,"is Good Mean!!")
如果你仔细看过我参考的源码，就会发现他写的很奇怪
for filter_size in filter_sizes:
# conv : [input_channel(=1), output_channel(=3), (filter_height, filter_width), bias_option]
conv = nn.Conv2d(1, num_filters, (filter_size, embedding_size), bias=True)(embedded_chars)
h = F.relu(conv)
# mp : ((filter_height, filter_width))
mp = nn.MaxPool2d((sequence_length - filter_size + 1, 1))
# pooled : [batch_size(=6), output_height(=1), output_width(=1), output_channel(=3)]
pooled = mp(h).permute(0, 3, 2, 1)
pooled_outputs.append(pooled)
他使用了一个循环，对原始数据做了多次卷积，得到多个feature map。这个做法奇怪在于，如果说想要得到更多feature map，修改 nn.Conv2d()
中output_channel参数即可，为什幺要这样做多次循环？
如果作者本来的意思是想搞一个深层卷积神经网络，也说不通，因为他这个写法就没有这样的效果，他的循环始终是对原始输入数据做运算，而不是对卷积后的数据再运算
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