imdb双向lstm paddle2.3实现

以下是使用PaddlePaddle 2.3实现IMDB情感分析的双向LSTM代码：

首先，我们需要安装PaddlePaddle和相关的依赖项：

!pip install paddlepaddle==2.3.0
!pip install paddlehub==2.1.0
!pip install paddlenlp==2.0.5
!pip install --upgrade paddlenlp

然后，我们可以导入必要的库和下载IMDB数据集：

import paddle
import paddle.nn as nn
import paddle.optim as optim
from paddle.io import DataLoader
import paddlenlp as ppnlp
from paddlenlp.datasets import load_dataset
from paddlenlp.data import Pad, Stack

train_ds, test_ds = load_dataset('imdb', splits=('train', 'test'))

接下来，我们需要定义数据预处理器和数据集的转换器：

tokenizer = ppnlp.data.Tokenizer()
vocab = ppnlp.data.Vocab.from_dict(tokenizer.vocab)
trans_fn = lambda x: (tokenizer(x['text'], vocab), x['label'])

train_ds = train_ds.map(trans_fn)
test_ds = test_ds.map(trans_fn)

接下来，我们将定义一个双向LSTM模型：

class BiLSTM(nn.Layer):
    def __init__(self, vocab_size, num_classes, hidden_size=128, num_layers=2):
        super(BiLSTM, self).__init__()

        # 定义嵌入层
        self.embedding = nn.Embedding(
            num_embeddings=vocab_size, embedding_dim=hidden_size)

        # 定义双向LSTM层
        self.bilstm = nn.LSTM(
            input_size=hidden_size,
            hidden_size=hidden_size,
            num_layers=num_layers,
            direction='bidirectional')

        # 定义全连接层
        self.fc = nn.Linear(hidden_size * 2, num_classes)

    def forward(self, inputs):
        # 嵌入层输入
        embeds = self.embedding(inputs)

        # 双向LSTM层输入
        lstm_out, _ = self.bilstm(embeds)

        # 取最后一个时间步的输出
        last_out = lstm_out[:, -1, :]

        # 全连接层输出
        output = self.fc(last_out)

        return output

接下来，我们将定义训练和测试函数：

def train(model, data_loader, optimizer, criterion):
    model.train()
    loss_list = []
    acc_list = []
    for batch in data_loader:
        input_ids, labels = batch
        logits = model(input_ids)
        loss = criterion(logits, labels)
        loss.backward()
        optimizer.step()
        optimizer.clear_grad()

        loss_list.append(loss.numpy())
        acc_list.append(
            paddle.metric.accuracy(
                input=logits, label=labels.reshape([-1, 1])).numpy())

    avg_loss = sum(loss_list) / len(loss_list)
    avg_acc = sum(acc_list) / len(acc_list)

    return avg_loss, avg_acc


def evaluate(model, data_loader, criterion):
    model.eval()
    loss_list = []
    acc_list = []
    for batch in data_loader:
        input_ids, labels = batch
        logits = model(input_ids)
        loss = criterion(logits, labels)

        loss_list.append(loss.numpy())
        acc_list.append(
            paddle.metric.accuracy(
                input=logits, label=labels.reshape([-1, 1])).numpy())

    avg_loss = sum(loss_list) / len(loss_list)
    avg_acc = sum(acc_list) / len(acc_list)

    return avg_loss, avg_acc

现在，我们可以开始训练模型：

# 定义超参数
batch_size = 128
learning_rate = 1e-3
epochs = 10

# 定义模型
model = BiLSTM(len(vocab), 2)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(parameters=model.parameters(), learning_rate=learning_rate)

# 定义数据加载器
train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, collate_fn=Stack(), num_workers=4)
test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False, collate_fn=Stack(), num_workers=4)

# 训练模型
for epoch in range(1, epochs+1):
    train_loss, train_acc = train(model, train_loader, optimizer, criterion)
    test_loss, test_acc = evaluate(model, test_loader, criterion)
    print(f'Epoch {epoch}: Train Loss {train_loss:.4f}, Train Acc {train_acc:.4f}, Test Loss {test_loss:.4f}, Test Acc {test_acc:.4f}')

在训练完成后，我们可以使用测试集评估模型的性能：

test_loss, test_acc = evaluate(model, test_loader, criterion)
print(f'Test Loss {test_loss:.4f}, Test Acc {test_acc:.4f}')

这就是使用PaddlePaddle 2.3实现IMDB情感分析的双向LSTM代码。