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,该温度数据集需要提供下载地址

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，需要提供温度数据集的下载地址：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM, Attention
from keras.callbacks import EarlyStopping

# 加载温度数据集
data_url = "https://example.com/temperature.csv"
df = pd.read_csv(data_url)

# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(df["Temperature"].values.reshape(-1, 1))

# 划分训练集和测试集
train_size = int(len(scaled_data) * 0.8)
test_size = len(scaled_data) - train_size
train_data = scaled_data[0:train_size, :]
test_data = scaled_data[train_size:len(scaled_data), :]

# 定义函数生成LSTM数据集
def create_lstm_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset) - look_back):
        a = dataset[i:(i + look_back), 0]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return np.array(dataX), np.array(dataY)

# 创建训练集和测试集的LSTM数据集
look_back = 30
trainX, trainY = create_lstm_dataset(train_data, look_back)
testX, testY = create_lstm_dataset(test_data, look_back)

# 转换为3D格式
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))

# 定义LSTM模型
model = Sequential()
model.add(LSTM(64, input_shape=(look_back, 1), return_sequences=True))
model.add(Attention())
model.add(Dense(1))
model.compile(loss="mean_squared_error", optimizer="adam")

# 训练LSTM模型
early_stopping = EarlyStopping(monitor="loss", patience=3, verbose=1)
history = model.fit(trainX, trainY, epochs=100, batch_size=32, callbacks=[early_stopping])

# 预测温度
train_predict = model.predict(trainX)
test_predict = model.predict(testX)

# 反归一化
train_predict = scaler.inverse_transform(train_predict)
trainY = scaler.inverse_transform([trainY])
test_predict = scaler.inverse_transform(test_predict)
testY = scaler.inverse_transform([testY])

# 绘制预测结果
plt.plot(df["Temperature"].values, label="True")
plt.plot([None for _ in range(look_back)] + [x for x in train_predict], label="Train")
plt.plot([None for _ in range(look_back)] + [None for _ in train_predict] + [x for x in test_predict], label="Test")
plt.legend()
plt.show()

请将`data_url`替换为您的温度数据集的下载地址。