文章目录
在之前的所有笔记中，我们已经对只有两个类别的数据集(正面或负面)进行了情感分析。当我们只有两个类时，我们的输出可以是一个标量，限制在0和1之间，表示示例属于哪个类。当我们有两个以上的分类时，我们的输出必须是一个 C C C维向量，其中 C C C是类的数目。
在本笔记中，我们将对一个有6个类的数据集进行分类。请注意，这个数据集实际上不是一个情感分析数据集，它是一个问题数据集，任务是对问题所属的类别进行分类。但是，本笔记本中所涵盖的内容适用于包含属于 C C C类之一的输入序列的示例的任何数据集。
下面，我们设置字段并加载数据集。
第一个区别是我们不需要在LABEL字段中设置dtype。在处理多类问题时，PyTorch希望标签是数值化的长张量。
第二个不同之处在于我们使用TREC而不是IMDB来加载TREC数据集。fine_grained参数允许我们使用细粒度（fine-grained）标签(其中有50个类)或不使用细粒度标签(在这种情况下，它们将是6个类)。
准备数据
import torch
from torchtext import data
from torchtext import datasets
SEED = 1234
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
TEXT = data.Field(tokenize='spacy', tokenizer_language='en_core_web_sm')
LABEL = data.LabelField()
train_data, test_data = datasets.TREC.splits(TEXT, LABEL, fine_grained = False)
train_data, valid_data = train_data.split()
让我们看一下训练集中的一个例子。
print(vars(train_data[-1]))
{'text': ['What', 'is', 'a', 'Cartesian', 'Diver', '?'], 'label': 'DESC'}
接下来，我们将建立词汇表。由于这个数据集很小(只有约3800个训练示例)，它也有一个非常小的词汇表(约7500个唯一标记)，这意味着我们不需要像以前那样为词汇表设置max_size。
MAX_VOCAB_SIZE = 25_000
TEXT.build_vocab(train_data,
max_size = MAX_VOCAB_SIZE,
vectors = "glove.6B.100d",
unk_init = torch.Tensor.normal_)
LABEL.build_vocab(train_data)
接下来，我们可以检查标签。
这6个标签(对于非细粒度的案例)对应数据集中的6种问题类型:
- HUM 表示关于人类的问题类别
- ENTY 表示关于实体的问题类别
- DESC 表示要求描述的问题类别
- NUM 表示答案是数字的问题类别
- LOC 表示答案是地点的问题类别
- ABBR 表示关于缩写的问题类别
print(LABEL.vocab.stoi)
defaultdict(<function _default_unk_index at 0x7f0a50190d08>, {'HUM': 0, 'ENTY': 1, 'DESC': 2, 'NUM': 3, 'LOC': 4, 'ABBR': 5})
和往常一样，我们设置迭代器。
BATCH_SIZE = 64
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(
(train_data, valid_data, test_data),
batch_size = BATCH_SIZE,
device = device)
我们将使用以前的笔记中的CNN模型，然而，模型将工作在这个数据集。唯一的区别是现在output_dim将是 C C C而不是 1 1 1。
搭建模型
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim, dropout, pad_inx):
super(CNN, self).__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.convs = nn.ModuleList([
nn.Conv2d(in_channels=1, out_channels=n_filters, kernel_size=(fs, embedding_dim))
for fs in filter_sizes
])
self.fc = nn.Linear(n_filters * len(filter_sizes), output_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, text):
# text = [sent_len, batch_size]
text = text.permute(1, 0)
# text = [batch_size, sent_len]
embedded = self.embedding(text)
# embedded = [batch_size, sent_len, emb_dim]
embedded = embedded.unsqueeze(1)
# embedded = [batch_size, 1, sent_len, emb_dim]
convd = [conv(embedded).squeeze(3) for conv in self.convs]
# conv_n = [batch_size, n_filters, sent_len - fs + 1]
pooled = [F.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in convd]
# pooled_n = [batch_size, n_filters]
cat = self.dropout(torch.cat(pooled, dim=1))
# cat = [batch_size, n_filters * len(filter_sizes)]
return self.fc(cat)
我们定义模型，确保将OUTPUT_DIM设置为 C C C。通过使用标签（LABEL）词汇表的大小，我们可以很容易地获得 C C C，就像我们使用文本（TEXT）词汇表的大小来获得输入词汇表的大小一样。
这个数据集中的示例通常来说比IMDb数据集中的示例小得多，因此我们将使用更小的过滤器。
INPUT_DIM = len(TEXT.vocab)
EMBEDDING_DIM = 100
N_FILTERS = 100
FILTER_SIZES = [2,3,4]
OUTPUT_DIM = len(LABEL.vocab)
DROPOUT = 0.5
PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]
model = CNN(INPUT_DIM, EMBEDDING_DIM, N_FILTERS, FILTER_SIZES, OUTPUT_DIM, DROPOUT, PAD_IDX)
检查参数的数量，我们可以看到较小的过滤器意味着我们在IMDb数据集上的CNN模型的参数大约只有之前模型的三分之一。
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f'The model has {count_parameters(model):,} trainable parameters')
The model has 834,206 trainable parameters
接下来，我们将加载预先训练好的嵌入。
pretrained_embeddings = TEXT.vocab.vectors
model.embedding.weight.data.copy_(pretrained_embeddings)
tensor([[-0.1117, -0.4966, 0.1631, ..., 1.2647, -0.2753, -0.1325],
[-0.8555, -0.7208, 1.3755, ..., 0.0825, -1.1314, 0.3997],
[ 0.1638, 0.6046, 1.0789, ..., -0.3140, 0.1844, 0.3624],
...,
[-0.3110, -0.3398, 1.0308, ..., 0.5317, 0.2836, -0.0640],
[ 0.0091, 0.2810, 0.7356, ..., -0.7508, 0.8967, -0.7631],
[ 0.4306, 1.2011, 0.0873, ..., 0.8817, 0.3722, 0.3458]])
然后将《unk》和《pad》标记的初始权值归零。
UNK_IDX = TEXT.vocab.stoi[TEXT.unk_token]
model.embedding.weight.data[UNK_IDX] = torch.zeros(EMBEDDING_DIM)
model.embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)
训练模型
另一个不同于之前笔记的是我们的损失函数(又名准则（criterion）)。以前我们使用BCEWithLogitsLoss，但是现在我们使用CrossEntropyLoss。不需要了解太多细节，CrossEntropyLoss在我们的模型输出上执行一个softmax函数，损失由它和标签之间的交叉熵给出。
通常来说：
- 当我们的示例只属于 C C C类中的一个时，使用CrossEntropyLoss
- BCEWithLogitsLoss用于我们的示例只属于两个类(0和1)，也用于示例属于0和 C C C类(又名多标签分类)的情况。
import torch.optim as optim
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()
model = model.to(device)
criterion = criterion.to(device)
以前，我们有一个函数，在二进制标签的情况下计算准确率，我们说，如果值大于0.5，那么我们假设它是正的。在有2个以上类的情况下，我们的模型输出一个 C C C维向量，其中每个元素的值表示示例属于这个类程度。
例如，在我们的标签中有:‘HUM’ = 0， ‘ENTY’ = 1， ‘DESC’ = 2， ‘NUM’ = 3， ‘LOC’ = 4和’ABBR’ = 5。如果我们的模型输出类似于:[5.1,0.3,0.1,2.1,0.2,0.6]，这意味着模型强烈地认为该示例属于第0类，即关于human的问题，稍微地认为该示例属于第3类，即numerical问题。
我们通过执行argmax获取batch处理中每个元素的预测最大值的索引来计算准确率，然后计算它等于实际标签的次数，然后我们在这batch中求平均值。
def categorical_accuracy(preds, y):
"""
Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8
"""
max_preds = preds.argmax(dim = 1, keepdim = True) # get the index of the max probability
correct = max_preds.squeeze(1).eq(y)
return correct.sum() / torch.FloatTensor([y.shape[0]])
训练循环与以前类似，不需要压缩（squeeze ）模型预测值，因为CrossEntropyLoss预期输入为[batch_size，n classes]，标签为[batch size]。
标签（Label）需要是一个LongTensor，默认情况下是这样的，因为我们没有像之前那样将dtype设置为FloatTensor。
epoch_loss = 0
epoch_acc = 0
model.train()
for batch in iterator:
optimizer.zero_grad()
predictions = model(batch.text)
loss = criterion(predictions, batch.label)
acc = categorical_accuracy(predictions, batch.label)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
epoch_acc += acc.item()
return epoch_loss / len(iterator), epoch_acc / len(iterator)
评估循环与前面类似。
def evaluate(model, iterator, criterion):
epoch_loss = 0
epoch_acc = 0
model.eval()
with torch.no_grad():
for batch in iterator:
predictions = model(batch.text)
loss = criterion(predictions, batch.label)
acc = categorical_accuracy(predictions, batch.label)
epoch_loss += loss.item()
epoch_acc += acc.item()
return epoch_loss / len(iterator), epoch_acc / len(iterator)
import time
def epoch_time(start_time, end_time):
elapsed_time = end_time - start_time
elapsed_mins = int(elapsed_time / 60)
elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
return elapsed_mins, elapsed_secs
接下来，我们训练我们的模型。
N_EPOCHS = 5
best_valid_loss = float('inf')
for epoch in range(N_EPOCHS):
start_time = time.time()
train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(), 'tut5-model.pt')
print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')
print(f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')
print(f'\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}%')
Epoch: 01 | Epoch Time: 0m 3s
Train Loss: 1.281 | Train Acc: 49.74%
Val. Loss: 0.940 | Val. Acc: 66.27%
Epoch: 02 | Epoch Time: 0m 3s
Train Loss: 0.855 | Train Acc: 69.60%
Val. Loss: 0.772 | Val. Acc: 73.10%
Epoch: 03 | Epoch Time: 0m 3s
Train Loss: 0.645 | Train Acc: 77.69%
Val. Loss: 0.645 | Val. Acc: 77.02%
Epoch: 04 | Epoch Time: 0m 3s
Train Loss: 0.476 | Train Acc: 84.39%
Val. Loss: 0.556 | Val. Acc: 80.35%
Epoch: 05 | Epoch Time: 0m 3s
Train Loss: 0.364 | Train Acc: 88.34%
Val. Loss: 0.513 | Val. Acc: 81.40%
最后，让我们在测试集中运行我们的模型!
model.load_state_dict(torch.load('tut5-model.pt'))
test_loss, test_acc = evaluate(model, test_iterator, criterion)
print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')
Test Loss: 0.390 | Test Acc: 86.57%
用户输入
类似于我们如何建立一个函数来预测任何给定句子的情绪，我们现在可以建立一个函数来预测所给出的问题的类型。
这里唯一的区别是，我们不是使用sigmoid函数来压缩0到1之间的输入，而是使用argmax来获得最高的预测类索引。然后，我们将这个索引与标签词汇表一起使用，以获得人类可读的标签。
import spacy
nlp = spacy.load('en_core_web_sm')
def predict_class(model, sentence, min_len = 4):
model.eval()
tokenized = [tok.text for tok in nlp.tokenizer(sentence)]
if len(tokenized) < min_len:
tokenized += ['<pad>'] * (min_len - len(tokenized))
indexed = [TEXT.vocab.stoi[t] for t in tokenized]
tensor = torch.LongTensor(indexed)
tensor = tensor.unsqueeze(1)
preds = model(tensor)
max_preds = preds.argmax(dim = 1)
return max_preds.item()
现在，让我们来回答几个不同的问题……
pred_class = predict_class(model, "Who is Keyser Söze?")
print(f'Predicted class is: {pred_class} = {LABEL.vocab.itos[pred_class]}')
Predicted class is: 0 = HUM
pred_class = predict_class(model, "How many minutes are in six hundred and eighteen hours?")
print(f'Predicted class is: {pred_class} = {LABEL.vocab.itos[pred_class]}')
Predicted class is: 3 = NUM
pred_class = predict_class(model, "What continent is Bulgaria in?")
print(f'Predicted class is: {pred_class} = {LABEL.vocab.itos[pred_class]}')
Predicted class is: 4 = LOC
pred_class = predict_class(model, "What does WYSIWYG stand for?")
print(f'Predicted class is: {pred_class} = {LABEL.vocab.itos[pred_class]}')
Predicted class is: 5 = ABBR
完整代码
import torch
from torchtext import data
from torchtext import datasets
SEED = 1234
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
TEXT = data.Field(tokenize='spacy', tokenizer_language='en_core_web_sm')
LABEL = data.LabelField()
train_data, test_data = datasets.TREC.splits(TEXT, LABEL, fine_grained = False)
train_data, valid_data = train_data.split()
print(vars(train_data[-1]))
MAX_VOCAB_SIZE = 25_000
TEXT.build_vocab(
train_data,
max_size = MAX_VOCAB_SIZE,
vectors = 'glove.6B.100d',
unk_init = torch.Tensor.normal_
)
LABEL.build_vocab(train_data)
print(LABEL.vocab.stoi)
BATCH_SIZE = 64
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(
(train_data, valid_data, test_data),
batch_size=BATCH_SIZE,
device=device
)
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim, dropout, pad_inx):
super(CNN, self).__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.convs = nn.ModuleList([
nn.Conv2d(in_channels=1, out_channels=n_filters, kernel_size=(fs, embedding_dim))
for fs in filter_sizes
])
self.fc = nn.Linear(n_filters * len(filter_sizes), output_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, text):
# text = [sent_len, batch_size]
text = text.permute(1, 0)
# text = [batch_size, sent_len]
embedded = self.embedding(text)
# embedded = [batch_size, sent_len, emb_dim]
embedded = embedded.unsqueeze(1)
# embedded = [batch_size, 1, sent_len, emb_dim]
convd = [conv(embedded).squeeze(3) for conv in self.convs]
# conv_n = [batch_size, n_filters, sent_len - fs + 1]
pooled = [F.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in convd]
# pooled_n = [batch_size, n_filters]
cat = self.dropout(torch.cat(pooled, dim=1))
# cat = [batch_size, n_filters * len(filter_sizes)]
return self.fc(cat)
INPUT_DIM = len(TEXT.vocab)
EMBEDDING_DIM = 100
N_FILTERS = 100
FILTER_SIZES = [2, 3, 4]
OUTPUT_DIM = len(LABEL.vocab)
DROPOUT = 0.5
PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]
model = CNN(INPUT_DIM, EMBEDDING_DIM, N_FILTERS, FILTER_SIZES, OUTPUT_DIM, DROPOUT, PAD_IDX)
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f'The model has {count_parameters(model):,} trainable parameters')
pretrained_embeddings = TEXT.vocab.vectors
model.embedding.weight.data.copy_(pretrained_embeddings)
UNK_IDX = TEXT.vocab.stoi[TEXT.unk_token]
model.embedding.weight.data[UNK_IDX] = torch.zeros(EMBEDDING_DIM)
model.embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)
import torch.optim as optim
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()
model = model.to(device)
criterion = criterion.to(device)
def categorical_accuracy(preds, y):
"""
Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8
"""
max_preds = preds.argmax(dim = 1, keepdim = True) # get the index of the max probability
correct = max_preds.squeeze(1).eq(y)
return correct.sum() / torch.FloatTensor([y.shape[0]])
def train(model, iterator, optimizer, criterion):
epoch_loss = 0
epoch_acc = 0
model.train()
for batch in iterator:
optimizer.zero_grad()
predictions = model(batch.text)
loss = criterion(predictions, batch.label)
acc = categorical_accuracy(predictions, batch.label)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
epoch_acc += acc.item()
return epoch_loss / len(iterator), epoch_acc / len(iterator)
def evaluate(model, iterator, criterion):
epoch_loss = 0
epoch_acc = 0
model.eval()
with torch.no_grad():
for batch in iterator:
predictions = model(batch.text)
loss = criterion(predictions, batch.label)
acc = categorical_accuracy(predictions, batch.label)
epoch_loss += loss.item()
epoch_acc += acc.item()
return epoch_loss / len(iterator), epoch_acc / len(iterator)
import time
def epoch_time(start_time, end_time):
elapsed_time = end_time - start_time
elapsed_mins = int(elapsed_time / 60)
elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
return elapsed_mins, elapsed_secs
N_EPOCHS = 5
best_valid_loss = float('inf')
for epoch in range(N_EPOCHS):
start_time = time.time()
train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(), 'tut5-model.pt')
print(f'Epoch: {epoch + 1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')
print(f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc * 100:.2f}%')
print(f'\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc * 100:.2f}%')
model.load_state_dict(torch.load('tut5-model.pt'))
test_loss, test_acc = evaluate(model, test_iterator, criterion)
print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')
import spacy
nlp = spacy.load('en_core_web_sm')
def predict_class(model, sentence, min_len = 4):
model.eval()
tokenized = [tok.text for tok in nlp.tokenizer(sentence)]
if len(tokenized) < min_len:
tokenized += ['<pad>'] * (min_len - len(tokenized))
indexed = [TEXT.vocab.stoi[t] for t in tokenized]
tensor = torch.LongTensor(indexed)
tensor = tensor.unsqueeze(1)
preds = model(tensor)
max_preds = preds.argmax(dim = 1)
return max_preds.item()
pred_class = predict_class(model, "Who is Keyser Söze?")
print(f'Predicted class is: {pred_class} = {LABEL.vocab.itos[pred_class]}')
pred_class = predict_class(model, "How many minutes are in six hundred and eighteen hours?")
print(f'Predicted class is: {pred_class} = {LABEL.vocab.itos[pred_class]}')
pred_class = predict_class(model, "What continent is Bulgaria in?")
print(f'Predicted class is: {pred_class} = {LABEL.vocab.itos[pred_class]}')
pred_class = predict_class(model, "What does WYSIWYG stand for?")
print(f'Predicted class is: {pred_class} = {LABEL.vocab.itos[pred_class]}')