请写出预测某地温度的python代码,需要满足的功能如下:第一,该代码是基于lstm模型的预测,第二,需要结合注意力机制,第三,请加上适当的注释.

以下是基于LSTM模型和注意力机制的预测某地温度的Python代码，注释已经加上：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout, Activation, Input, concatenate
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
from keras import backend as K

# 读取数据
data = pd.read_csv('temperature.csv')

# 数据预处理
data['date'] = pd.to_datetime(data['date'])
data.set_index('date', inplace=True)
data = data.resample('D').mean()
data = data.interpolate()

# 划分训练集和测试集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 归一化
max_value = train_data.max()
min_value = train_data.min()
train_data = (train_data - min_value) / (max_value - min_value)
test_data = (test_data - min_value) / (max_value - min_value)

# 定义函数，将数据转换为监督学习数据
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset) - look_back):
        X.append(dataset[i:(i + look_back)])
        Y.append(dataset[i + look_back])
    return np.array(X), np.array(Y)

# 定义参数
look_back = 30
batch_size = 32
epochs = 100

# 转换数据
train_X, train_Y = create_dataset(train_data.values, look_back)
test_X, test_Y = create_dataset(test_data.values, look_back)

# 定义LSTM模型
input_layer = Input(shape=(look_back, 1))
lstm_layer = LSTM(64, return_sequences=True)(input_layer)
attention_layer = Dense(1, activation='tanh')(lstm_layer)
attention_layer = Activation('softmax')(attention_layer)
context_layer = concatenate([lstm_layer, attention_layer], axis=2)
output_layer = Dense(1)(context_layer)
model = Model(inputs=input_layer, outputs=output_layer)

# 定义损失函数
def attention_loss(y_true, y_pred):
    attention = K.sum(K.exp(y_pred), axis=1)
    attention = attention / K.sum(attention, axis=0)
    attention = K.expand_dims(attention, axis=1)
    mse = K.mean(K.square(y_true - y_pred), axis=1)
    loss = K.mean(K.sum(mse * attention, axis=0))
    return loss

# 编译模型
model.compile(loss=attention_loss, optimizer=Adam(lr=0.001))

# 训练模型
early_stopping = EarlyStopping(monitor='val_loss', patience=10)
history = model.fit(train_X, train_Y, batch_size=batch_size, epochs=epochs, validation_data=(test_X, test_Y), callbacks=[early_stopping])

# 预测结果
train_predict = model.predict(train_X)
test_predict = model.predict(test_X)
train_predict = train_predict * (max_value - min_value) + min_value
train_Y = train_Y * (max_value - min_value) + min_value
test_predict = test_predict * (max_value - min_value) + min_value
test_Y = test_Y * (max_value - min_value) + min_value

# 绘制结果
plt.plot(train_Y, label='true_train')
plt.plot(train_predict, label='predict_train')
plt.plot(test_Y, label='true_test')
plt.plot(test_predict, label='predict_test')
plt.legend()
plt.show()

注意：本代码仅供参考，具体实现需要根据实际情况进行调整。