写一份基于LSTM的车流量预测代码

好的，以下是一个简单的基于LSTM的车流量预测代码：

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM

# 读取数据
df = pd.read_csv('traffic_volume.csv')

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

# 创建训练集和测试集
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，将数据转换为时间序列数据
def create_dataset(dataset, time_step=1):
    dataX, dataY = [], []
    for i in range(len(dataset) - time_step - 1):
        a = dataset[i:(i + time_step), 0]
        dataX.append(a)
        dataY.append(dataset[i + time_step, 0])
    return np.array(dataX), np.array(dataY)

# 创建时间序列数据
time_step = 100
X_train, y_train = create_dataset(train_data, time_step)
X_test, y_test = create_dataset(test_data, time_step)

# 将数据重构为LSTM模型的输入格式
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))

# 创建LSTM模型
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(time_step, 1)))
model.add(LSTM(50, return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练模型
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=64, verbose=1)

# 使用模型进行预测
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

# 反归一化
train_predict = scaler.inverse_transform(train_predict)
y_train = scaler.inverse_transform([y_train])
test_predict = scaler.inverse_transform(test_predict)
y_test = scaler.inverse_transform([y_test])

# 计算均方根误差（RMSE）
train_score = np.sqrt(mean_squared_error(y_train[0], train_predict[:, 0]))
test_score = np.sqrt(mean_squared_error(y_test[0], test_predict[:, 0]))
print('Train Score: %.2f RMSE' % (train_score))
print('Test Score: %.2f RMSE' % (test_score))

这个代码假设你有一个名为`traffic_volume.csv`的csv文件，其中包含有关车流量的数据。您需要更改代码以适应您的数据。