给我一个基于lstm的时间序列预测模型的Python代码，该模型需要融合注意力机制

以下是一个基于 LSTM 和注意力机制的时间序列预测模型的 Python 代码，供您参考：

import numpy as np
import tensorflow as tf

class AttentionLSTMModel(tf.keras.Model):
    def __init__(self, input_shape, output_shape, units=64):
        super(AttentionLSTMModel, self).__init__()
        self.units = units
        self.lstm_layer = tf.keras.layers.LSTM(units, return_sequences=True, input_shape=input_shape)
        self.attention_layer = tf.keras.layers.Dense(1, activation='tanh')
        self.output_layer = tf.keras.layers.Dense(output_shape)

    def call(self, inputs):
        lstm_output = self.lstm_layer(inputs)
        attention_weights = self.attention_layer(lstm_output)
        attention_weights = tf.keras.layers.Flatten()(attention_weights)
        attention_weights = tf.keras.layers.Activation('softmax')(attention_weights)
        attention_weights = tf.keras.layers.RepeatVector(self.units)(attention_weights)
        attention_weights = tf.keras.layers.Permute([2, 1])(attention_weights)
        weighted_output = tf.keras.layers.Multiply()([lstm_output, attention_weights])
        output = tf.keras.layers.Dense(self.units, activation='relu')(weighted_output)
        output = self.output_layer(output)
        return output

# 以下是使用示例
input_shape = (10, 1)
output_shape = 1
model = AttentionLSTMModel(input_shape, output_shape)
model.compile(optimizer='adam', loss='mse')

# 生成一些随机数据用于训练
X_train = np.random.rand(100, 10, 1)
y_train = np.random.rand(100, 1)

# 训练模型
model.fit(X_train, y_train, epochs=10, batch_size=16)

希望这个代码能够对您有所帮助！