任务描述

本示例教程演示如何在IMDB数据集上使用RNN网络完成文本分类的任务。IMDB数据集包含对电影评论进行正向和负向标注的数据，共有25000条文本数据作为训练集，25000条文本数据作为测试集。数据集的官方地址为：IMDB Dataset

一、环境设置

本示例基于飞桨开源框架2.0版本。

import paddle
import numpy as np
import matplotlib.pyplot as plt
import paddle.nn as nnprint(paddle.__version__)  # 查看当前版本# cpu/gpu环境选择，在 paddle.set_device() 输入对应运行设备。
device = paddle.set_device('gpu')2.0.1

二、数据准备

由于IMDB是NLP领域中常见的数据集，飞桨框架将其内置，路径为paddle.text.datasets.Imdb。通过mode参数可以控制训练集与测试集。

print('loading dataset...')
train_dataset = paddle.text.datasets.Imdb(mode='train')
test_dataset = paddle.text.datasets.Imdb(mode='test')
print('loading finished')

构建了训练集与测试集后，可以通过word_idx获取数据集的词表。

word_dict = train_dataset.word_idx  # 获取数据集的词表# add a pad token to the dict for later padding the sequence
word_dict['<pad>'] = len(word_dict)for k in list(word_dict)[:5]:print("{}:{}".format(k.decode('ASCII'), word_dict[k]))print("...")for k in list(word_dict)[-5:]:print("{}:{}".format(k if isinstance(k, str) else k.decode('ASCII'), word_dict[k]))print("totally {} words".format(len(word_dict)))

2.1 参数设置

在这里设置词表大小、embedding大小、batch_size等参数。

vocab_size = len(word_dict) + 1
print(vocab_size)
emb_size = 256
seq_len = 200
batch_size = 32
epochs = 2
pad_id = word_dict['<pad>']classes = ['negative', 'positive']# 生成句子列表
def ids_to_str(ids):words = []for k in ids:w = list(word_dict)[k]words.append(w if isinstance(w, str) else w.decode('ASCII'))return " ".join(words)

2.2 用padding的方式对齐数据

文本数据中，每一句话的长度都是不一样的，为了方便后续的神经网络的计算，通常使用padding的方式对齐数据。

# 读取数据归一化处理
def create_padded_dataset(dataset):padded_sents = []labels = []for batch_id, data in enumerate(dataset):sent, label = data[0], data[1]padded_sent = np.concatenate([sent[:seq_len], [pad_id] * (seq_len - len(sent))]).astype('int32')padded_sents.append(padded_sent)labels.append(label)return np.array(padded_sents), np.array(labels)# 对train、test数据进行实例化
train_sents, train_labels = create_padded_dataset(train_dataset)
test_sents, test_labels = create_padded_dataset(test_dataset)# 查看数据大小及举例内容
print(train_sents.shape)
print(train_labels.shape)
print(test_sents.shape)
print(test_labels.shape)for sent in train_sents[:3]:print(ids_to_str(sent))

2.3 用Dataset与DataLoader加载

将前面准备好的训练集与测试集用Dataset与DataLoader封装后，完成数据的加载。

class IMDBDataset(paddle.io.Dataset):'''继承paddle.io.Dataset类进行封装数据'''def __init__(self, sents, labels):self.sents = sentsself.labels = labelsdef __getitem__(self, index):data = self.sents[index]label = self.labels[index]return data, labeldef __len__(self):return len(self.sents)train_dataset = IMDBDataset(train_sents, train_labels)
test_dataset = IMDBDataset(test_sents, test_labels)train_loader = paddle.io.DataLoader(train_dataset, return_list=True,shuffle=True, batch_size=batch_size, drop_last=True)
test_loader = paddle.io.DataLoader(test_dataset, return_list=True,shuffle=True, batch_size=batch_size, drop_last=True)

三、模型配置

本示例中使用一个序列特性的RNN网络，在查找到每个词对应的embedding后，取平均作为一个句子的表示。然后用Linear进行线性变换，同时使用Dropout防止过拟合。

class MyRNN(paddle.nn.Layer):def __init__(self):super(MyRNN, self).__init__()self.embedding = nn.Embedding(vocab_size, 256)self.rnn = nn.SimpleRNN(256, 256, num_layers=2, direction='forward',dropout=0.5)self.linear = nn.Linear(in_features=256*2, out_features=2)self.dropout = nn.Dropout(0.5)def forward(self, inputs):emb = self.dropout(self.embedding(inputs))output, hidden = self.rnn(emb)hidden = paddle.concat((hidden[-2,:,:], hidden[-1,:,:]), axis = 1)hidden = self.dropout(hidden)return self.linear(hidden) 

四、模型训练

# 可视化定义
def draw_process(title, color, iters, data, label):plt.title(title, fontsize=24)plt.xlabel("iter", fontsize=20)plt.ylabel(label, fontsize=20)plt.plot(iters, data, color=color, label=label) plt.legend()plt.grid()plt.show()# 对模型进行封装
def train(model):model.train()opt = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())steps = 0Iters, total_loss, total_acc = [], [], []for epoch in range(epochs):for batch_id, data in enumerate(train_loader):steps +=1sent = data[0]label = data[1]logits = model(sent)loss = paddle.nn.functional.cross_entropy(logits, label)acc = paddle.metric.accuracy(logits, label)if batch_id % 500 == 0:  # 500个epoch输出一次结果Iters.append(steps)total_loss.append(loss.numpy()[0])total_acc.append(acc.numpy()[0])print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, loss.numpy()))loss.backward()opt.step()opt.clear_grad()# evaluate model after one epochmodel.eval()accuracies = []losses = []for batch_id, data in enumerate(test_loader):sent = data[0]label = data[1]logits = model(sent)loss = paddle.nn.functional.cross_entropy(logits, label)acc = paddle.metric.accuracy(logits, label)accuracies.append(acc.numpy())losses.append(loss.numpy())avg_acc, avg_loss = np.mean(accuracies), np.mean(losses)print("[validation] accuracy: {}, loss: {}".format(avg_acc, avg_loss))model.train()# 保存模型paddle.save(model.state_dict(), str(epoch) + "_model_final.pdparams")# 可视化查看draw_process("training loss", "red", Iters, total_loss, "training loss")draw_process("training acc", "green", Iters, total_acc, "training acc")model = MyRNN()
train(model)

五、模型评估

model_state_dict = paddle.load('1_model_final.pdparams')  # 导入模型
model = MyRNN()
model.set_state_dict(model_state_dict) 
model.eval()
accuracies = []
losses = []for batch_id, data in enumerate(test_loader):sent = data[0]label = data[1]logits = model(sent)loss = paddle.nn.functional.cross_entropy(logits, label)acc = paddle.metric.accuracy(logits, label)accuracies.append(acc.numpy())losses.append(loss.numpy())avg_acc, avg_loss = np.mean(accuracies), np.mean(losses)
print("[validation] accuracy: {}, loss: {}".format(avg_acc, avg_loss))

六、模型预测

def ids_to_str(ids):words = []for k in ids:w = list(word_dict)[k]words.append(w if isinstance(w, str) else w.decode('UTF-8'))return " ".join(words)label_map = {0: "negative", 1: "positive"}# 导入模型
model_state_dict = paddle.load('1_model_final.pdparams')
model = MyRNN()
model.set_state_dict(model_state_dict) 
model.eval()for batch_id, data in enumerate(test_loader):sent = data[0]results = model(sent)predictions = []for probs in results:# 映射分类labelidx = np.argmax(probs)labels = label_map[idx]predictions.append(labels)for i, pre in enumerate(predictions):print(' 数据: {} \n 情感: {}'.format(ids_to_str(sent[0]), pre))breakbreak

以上是使用RNN完成IMDB电影评论情感分析的示例。通过搭建RNN网络，对文本数据进行预处理、模型训练和评估，最终实现了对电影评论情感的分类。在实际应用中，可以根据需求调整网络结构和超参数，提高模型性能。