bilstm-gru预测模型python

以下是使用PaddlePaddle框架实现BiLSTM-GRU预测模型的Python代码：

import paddle
import paddle.fluid as fluid

# 定义输入数据
words = fluid.layers.data(name='words', shape=[1], dtype='int64', lod_level=1)
target = fluid.layers.data(name='target', shape=[1], dtype='int64', lod_level=1)

# 定义词向量
embedding = fluid.layers.embedding(input=words, size=[vocab_size, embedding_size], is_sparse=True)

# 定义BiLSTM层
lstm_forward = fluid.layers.dynamic_lstm(input=embedding, size=lstm_hidden_size, time_major=False)
lstm_backward = fluid.layers.dynamic_lstm(input=embedding, size=lstm_hidden_size, time_major=False, is_reverse=True)
bi_lstm = fluid.layers.concat(input=[lstm_forward, lstm_backward], axis=1)

# 定义GRU层
gru = fluid.layers.dynamic_gru(input=bi_lstm, size=gru_hidden_size, time_major=False)

# 定义全连接层
fc = fluid.layers.fc(input=gru, size=num_classes, act='softmax')

# 定义损失函数和优化器
cost = fluid.layers.cross_entropy(input=fc, label=target)
avg_cost = fluid.layers.mean(cost)
optimizer = fluid.optimizer.AdamOptimizer(learning_rate=learning_rate)
optimizer.minimize(avg_cost)

# 定义训练程序
train_program = fluid.default_main_program()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

# 训练模型
for epoch in range(num_epochs):
    for batch_id, data in enumerate(train_reader()):
        words_data, target_data = data
        loss = exe.run(train_program, feed={'words': words_data, 'target': target_data}, fetch_list=[avg_cost])
        if batch_id % 100 == 0:
            print("Epoch %d, Batch %d, Loss %.4f" % (epoch, batch_id, loss[0]))

# 保存模型
fluid.io.save_inference_model(dirname=model_save_dir, feeded_var_names=['words'], target_vars=[fc], executor=exe)