飞桨库中如何使用lstm

在飞桨库中使用LSTM，可以使用paddle.fluid.dygraph.LSTM类。该类是一个LSTM层，可以在动态图模式下使用。以下是使用LSTM进行文本分类的示例代码：

import paddle.fluid as fluid
import numpy as np

class LSTMModel(fluid.dygraph.Layer):
    def __init__(self, vocab_size, num_classes, emb_dim, hidden_size):
        super(LSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.embedding = fluid.dygraph.Embedding(size=[vocab_size, emb_dim], dtype='float32')
        self.lstm = fluid.dygraph.LSTMCell(input_size=emb_dim, hidden_size=hidden_size)
        self.fc = fluid.dygraph.Linear(input_dim=hidden_size, output_dim=num_classes)

    def forward(self, inputs):
        inputs = self.embedding(inputs)
        batch_size = inputs.shape[0]
        state = self.lstm.get_initial_states(batch_size)
        for i in range(inputs.shape[1]):
            out, state = self.lstm(inputs[:, i, :], state)
        out = self.fc(out)
        return out

# 定义超参数
vocab_size = 10000
num_classes = 2
emb_dim = 128
hidden_size = 128
batch_size = 32
learning_rate = 0.001
epochs = 10

# 定义模型
model = LSTMModel(vocab_size, num_classes, emb_dim, hidden_size)

# 定义优化器
optimizer = fluid.optimizer.AdamOptimizer(learning_rate=learning_rate, parameter_list=model.parameters())

# 开始训练
for epoch in range(epochs):
    np.random.shuffle(train_corpus)
    total_loss = 0
    for i in range(0, len(train_corpus), batch_size):
        batch_data = train_corpus[i:i+batch_size]
        batch_size = len(batch_data)
        inputs = np.zeros([batch_size, 500], dtype='int64')
        labels = np.zeros([batch_size, 1], dtype='int64')
        for j in range(batch_size):
            inputs[j, :] = np.array([word_dict.get(word, 0) for word in batch_data[j][0].split()])
            labels[j, 0] = batch_data[j][1]
        inputs = fluid.dygraph.to_variable(inputs)
        labels = fluid.dygraph.to_variable(labels)
        logits = model(inputs)
        loss = fluid.layers.sigmoid_cross_entropy_with_logits(logits, labels)
        avg_loss = fluid.layers.mean(loss)
        avg_loss.backward()
        optimizer.minimize(avg_loss)
        model.clear_gradients()
        total_loss += avg_loss.numpy()[0]
    print("Epoch %d, loss=%.4f" % (epoch+1, total_loss))