做化妆品网站,定制v教程免费,在线外链工具,谁能给做网站这是一个面向小白(比如#xff0c;本人)的关于情感分析的系列教程 [1]。老鸽子整理了“4 - Convolutional Sentiment Analysis.ipynb”中的内容。本文任务#xff1a;使用卷积神经网络(CNN)来实现句子分类。简介CNN用于分析图像#xff0c;包含一个或多个卷积层#xff0c;… 这是一个面向小白(比如本人)的关于情感分析的系列教程 [1]。老鸽子整理了“4 - Convolutional Sentiment Analysis.ipynb”中的内容。本文任务使用卷积神经网络(CNN)来实现句子分类。简介CNN用于分析图像包含一个或多个卷积层紧跟一个或多个线性层。在卷积层中使用滤波器扫描图像。扫描后的图像送入另一个卷积层或线性层。每个滤波器都有大小如每次使用大小的滤波器来扫描大小的图像区域滤波器有9个对应的权重。类似于使用滤波器查看图像区域使用大小的滤波器来查看文本中两个连续的词(bi-gram)。准备数据不同于FastText模型不用明确创建bi-grams并添加到句尾。因为卷积层的第一个维度是batch所以可以设置field中的batch_first为True。划分训练集和验证集构建词汇表创建迭代器。import torchfrom torchtext import datafrom torchtext import datasetsimport randomimport numpy as npSEED 1234random.seed(SEED)np.random.seed(SEED)torch.manual_seed(SEED)torch.backends.cudnn.deterministic TrueTEXT data.Field(tokenizespacy, batch_firstTrue)LABEL data.LabelField(dtypetorch.float)train_data, test_data datasets.IMDB.splits(TEXT, LABEL)train_data, valid_data train_data.split(random_state random.seed(SEED))#-----------------------------MAX_VOCAB_SIZE 25_000TEXT.build_vocab( train_data, max_size MAX_VOCAB_SIZE, vectors glove.6B.100d, unk_init torch.Tensor.normal_ )LABEL.build_vocab(train_data)#-----------------------------BATCH_SIZE 64device 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_sizeBATCH_SIZE, devicedevice )构建模型在二维中可视化句中的词。每个词沿一个轴嵌入沿另一个轴。使用一个大小的滤波器每次扫描n个词。下图有4个词每个词有5维嵌入因此得到一个大小的“图像”张量。使用一个大小的滤波器每次扫描两个词(黄色)。滤波器的每个元素包含一个权重它的输出为10个嵌入元素的加权和。滤波器向下扫描(跨句)到下一个bi-gram输出另一个加权和。滤波器再次向下扫描计算最后的加权和。滤波器的宽度等于“图像”的宽度输出一个向量它的长度等于“图像”的高度减滤波器的高度加1。本文所用的模型将有不同大小的滤波器它们的高度分别为3、4和5每个大小有100个从而能够寻找与评论的情感相关的不同的tri-grams、4-grams和5-grams。为了确定与情绪相关的最重要的n-gram最大池化卷积层的输出。因为模型有300个不同且重要的n-grams所以可以认为全连接层用于加权这些n-grams从而给出最终的判断。应用细节使用nn.Conv2d来实现卷积层in_channels为输入通道数图像的通道数为3(红-绿-蓝)而文本的通道数为1out_channels为输出通道数kernel_size为滤波器大小其中的为n-grams的大小。RNN的batch位于第二维而CNN的batch位于第一维。CNN的第二维为通道数(为嵌入添加大小为1的新维度与卷积层的in_channels1一致)。卷积后的激活函数为ReLU。池化层可以处理不同长度的句子卷积层的输出取决于它的输入不同的batches包含了不同长度的句子。当没有最大池化层时线性层的输入取决于输入句子的大小。为了解决该问题可以修剪或填充所有句子到等长。当有最大池化层时线性层的输入数等于滤波器个数。当句子比最大的滤波器还短时必须填充句子到最大的滤波器长度。因为IMDb数据中的评论都大于5个字长所以不用担心该情况。对级联的滤波器输出使用Dropout再经过线性层得到预测结果。import torch.nn as nnimport torch.nn.functional as Fclass CNN(nn.Module): def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim, dropout, pad_idx): super().__init__() self.embedding nn.Embedding( vocab_size, embedding_dim, padding_idxpad_idx ) self.conv_0 nn.Conv2d( in_channels1, out_channelsn_filters, kernel_size(filter_sizes[0], embedding_dim) ) self.conv_1 nn.Conv2d( in_channels1, out_channelsn_filters, kernel_size(filter_sizes[1], embedding_dim) ) self.conv_2 nn.Conv2d( in_channels1, out_channelsn_filters, kernel_size(filter_sizes[2], embedding_dim) ) self.fc nn.Linear(len(filter_sizes) * n_filters, output_dim) self.dropout nn.Dropout(dropout) def forward(self, text): # text: [batch size, sent len] # embedded: [batch size, sent len, emb dim] embedded self.embedding(text) # embedded: [batch size, 1, sent len, emb dim] embedded embedded.unsqueeze(1) # conved_n: [batch size, n_filters, sent len - filter_sizes[n] 1] conved_0 F.relu(self.conv_0(embedded).squeeze(3)) conved_1 F.relu(self.conv_1(embedded).squeeze(3)) conved_2 F.relu(self.conv_2(embedded).squeeze(3)) # pooled_n: [batch size, n_filters] pooled_0 F.max_pool1d(conved_0, conved_0.shape[2]).squeeze(2) pooled_1 F.max_pool1d(conved_1, conved_1.shape[2]).squeeze(2) pooled_2 F.max_pool1d(conved_2, conved_2.shape[2]).squeeze(2) # cat: [batch size, n_filters * len(filter_sizes)] cat self.dropout(torch.cat((pooled_0, pooled_1, pooled_2), dim1)) return self.fc(cat)CNN模型仅用了3种不同大小的滤波器。为了使用任意数量的滤波器将所有的卷积层放入nn.ModuleList。class CNN2d(nn.Module): def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim, dropout, pad_idx): super().__init__() self.embedding nn.Embedding( vocab_size, embedding_dim, padding_idxpad_idx ) self.convs nn.ModuleList([ nn.Conv2d( in_channels1, out_channelsn_filters, kernel_size(fs, embedding_dim) ) for fs in filter_sizes ]) self.fc nn.Linear(len(filter_sizes) * n_filters, output_dim) self.dropout nn.Dropout(dropout) def forward(self, text): # text: [batch size, sent len] # embedded: [batch size, sent len, emb dim] embedded self.embedding(text) # embedded: [batch size, 1, sent len, emb dim] embedded embedded.unsqueeze(1) # conved_n: [batch size, n_filters, sent len - filter_sizes[n] 1] conved [F.relu(conv(embedded)).squeeze(3) for conv in self.convs] # pooled_n: [batch size, n_filters] pooled [F.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in conved] # cat: [batch size, n_filters * len(filter_sizes)] cat self.dropout(torch.cat(pooled, dim1)) return self.fc(cat)使用一维卷积层滤波器的“深度”和宽分别为嵌入的维度和句中的词数。class CNN1d(nn.Module): def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim, dropout, pad_idx): super().__init__() self.embedding nn.Embedding( vocab_size, embedding_dim, padding_idxpad_idx ) self.convs nn.ModuleList([ nn.Conv1d( in_channelsembedding_dim, out_channelsn_filters, kernel_sizefs ) for fs in filter_sizes ]) self.fc nn.Linear(len(filter_sizes) * n_filters, output_dim) self.dropout nn.Dropout(dropout) def forward(self, text): # text: [batch size, sent len] # embedded: [batch size, sent len, emb dim] embedded self.embedding(text) # embedded: [batch size, emb dim, sent len] embedded embedded.permute(0, 2, 1) # conved_n: [batch size, n_filters, sent len - filter_sizes[n] 1] conved [F.relu(conv(embedded)) for conv in self.convs] # pooled_n: [batch size, n_filters] pooled [F.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in conved] # cat: [batch size, n_filters * len(filter_sizes)] cat self.dropout(torch.cat(pooled, dim1)) return self.fc(cat)创建CNN2d(或CNN、CNN1d)类的实例打印模型中可训练的参数量清0“”和“”的初始权重。INPUT_DIM len(TEXT.vocab)EMBEDDING_DIM 100N_FILTERS 100FILTER_SIZES [3, 4, 5]OUTPUT_DIM 1DROPOUT 0.5PAD_IDX TEXT.vocab.stoi[TEXT.pad_token]model CNN2d(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(fThe model has {count_parameters(model):,} trainable parameters)#-----------------------------pretrained_embeddings TEXT.vocab.vectorsmodel.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)训练模型、测试和用户输入该部分与前面几节对应的代码相同。当测试模型时损失为0.337准确率为85.69%。用户的预测结果分别为0.11990132927894592(负面情绪)和0.9671174883842468(正面情绪)。import torch.optim as optimoptimizer optim.Adam(model.parameters())criterion nn.BCEWithLogitsLoss()model model.to(device)criterion criterion.to(device)def binary_accuracy(preds, y): # round predictions to the closest integer rounded_preds torch.round(torch.sigmoid(preds)) correct (rounded_preds y).float() # convert into float for division acc correct.sum() / len(correct) return acc#-----------------------------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).squeeze(1) loss criterion(predictions, batch.label) acc binary_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).squeeze(1) loss criterion(predictions, batch.label) acc binary_accuracy(predictions, batch.label) epoch_loss loss.item() epoch_acc acc.item() return epoch_loss / len(iterator), epoch_acc / len(iterator)#-----------------------------import timedef 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 5best_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 valid_loss torch.save(model.state_dict(), tut4-model.pt) print(fEpoch: {epoch1:02} | 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}%)## Test.model.load_state_dict(torch.load(tut4-model.pt))test_loss, test_acc evaluate(model, test_iterator, criterion)print(fTest Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%)## User input.import spacynlp spacy.load(en)def predict_sentiment(model, sentence, min_len5): model.eval() tokenized [tok.text for tok in nlp.tokenizer(sentence)] if len(tokenized) tokenized [] * (min_len - len(tokenized)) indexed [TEXT.vocab.stoi[t] for t in tokenized] tensor torch.LongTensor(indexed).to(device) tensor tensor.unsqueeze(0) prediction torch.sigmoid(model(tensor)) return prediction.item()print(predict_sentiment(model, This film is terrible))print(predict_sentiment(model, This film is great))[1]https://github.com/bentrevett/pytorch-sentiment-analysis
