Python 如何在 Keras 中处理 LSTM 的多个输入？

How to work with multiple inputs for LSTM in Keras?

提问by Jvr

I'm trying to predict the water usage of a population.

我正在尝试预测人口的用水量。

I have 1 main input:

我有 1 个主要输入：

• Water volume

• 水量

and 2 secondary inputs:

和 2 个辅助输入：

• Temperature
• Rainfall

• 温度
• 雨量

In theory they have a relation with the water supply.

理论上它们与供水有关系。

It must be said that each rainfall and temperature data correspond with the water volume. So this is a time series problem.

必须说，每次降雨和温度数据都与水量相对应。所以这是一个时间序列问题。

The problem is that I don't know how to use 3 inputs from just one .csv file, with 3 columns, each one for each input, as the code below is made. When I have just one input (e.g.water volume) the network works more or less good with this code, but not when I have more than one. (So if you run this code with the csv file below, it will show a dimension error).

问题是我不知道如何仅使用一个 .csv 文件中的 3 个输入，其中有 3 列，每个输入一列，如下面的代码所示。当我只有一个输入（例如水量）时，网络在这段代码中或多或少地工作得很好，但当我有一个以上时就不行了。（因此，如果您使用下面的 csv 文件运行此代码，它将显示尺寸错误）。

Reading some answers from:

阅读一些答案：

• Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras
• Time Series Forecast Case Study with Python: Annual Water Usage in Baltimore

• 使用 Keras 在 Python 中使用 LSTM 循环神经网络进行时间序列预测
• 使用 Python 进行时间序列预测案例研究：巴尔的摩的年度用水量

it seems to be that many people have the same problem.

似乎很多人都有同样的问题。

The code:

编码：

EDIT:Code has been updated

编辑：代码已更新

import numpy
import matplotlib.pyplot as plt
import pandas
import math

from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error


# convert an array of values into a dataset matrix

def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset) - look_back - 1):
        a = dataset[i:(i + look_back), 0]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 2])
    return numpy.array(dataX), numpy.array(dataY)



# fix random seed for reproducibility
numpy.random.seed(7)


# load the dataset
dataframe = pandas.read_csv('datos.csv', engine='python') 
dataset = dataframe.values

# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# split into train and test sets
train_size = int(len(dataset) * 0.67) 
test_size = len(dataset) - train_size
train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :]

# reshape into X=t and Y=t+1
look_back = 3
trainX, trainY = create_dataset(train, look_back)  
testX, testY = create_dataset(test, look_back)

# reshape input to be  [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], look_back, 3))
testX = numpy.reshape(testX, (testX.shape[0],look_back, 3))

# create and fit the LSTM network

model = Sequential()
model.add(LSTM(4, input_dim=look_back))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
history= model.fit(trainX, trainY,validation_split=0.33, nb_epoch=200, batch_size=32)

# Plot training
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('pérdida')
plt.xlabel('época')
plt.legend(['entrenamiento', 'validación'], loc='upper right')
plt.show()

# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

# Get something which has as many features as dataset
trainPredict_extended = numpy.zeros((len(trainPredict),3))
# Put the predictions there
trainPredict_extended[:,2] = trainPredict[:,0]
# Inverse transform it and select the 3rd column.
trainPredict = scaler.inverse_transform(trainPredict_extended) [:,2]  
print(trainPredict)
# Get something which has as many features as dataset
testPredict_extended = numpy.zeros((len(testPredict),3))
# Put the predictions there
testPredict_extended[:,2] = testPredict[:,0]
# Inverse transform it and select the 3rd column.
testPredict = scaler.inverse_transform(testPredict_extended)[:,2]   


trainY_extended = numpy.zeros((len(trainY),3))
trainY_extended[:,2]=trainY
trainY=scaler.inverse_transform(trainY_extended)[:,2]


testY_extended = numpy.zeros((len(testY),3))
testY_extended[:,2]=testY
testY=scaler.inverse_transform(testY_extended)[:,2]


# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(trainY, trainPredict))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY, testPredict))
print('Test Score: %.2f RMSE' % (testScore))

# shift train predictions for plotting
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, 2] = trainPredict

# shift test predictions for plotting
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, 2] = testPredict



#plot

 serie,=plt.plot(scaler.inverse_transform(dataset)[:,2])  
prediccion_entrenamiento,=plt.plot(trainPredictPlot[:,2],linestyle='--')  
prediccion_test,=plt.plot(testPredictPlot[:,2],linestyle='--')
plt.title('Consumo de agua')
plt.ylabel('cosumo (m3)')
plt.xlabel('dia')
plt.legend([serie,prediccion_entrenamiento,prediccion_test],['serie','entrenamiento','test'], loc='upper right')

This is the csv file I have created, if it helps.

这是我创建的 csv 文件，如果有帮助的话。

datos.csv

数据文件

After changing the code, I fixed all the errors, but I'm not really sure about the results. This is a zoom in the prediction plot:

更改代码后，我修复了所有错误，但我不确定结果。这是预测图的放大图：

which shows that there is a "displacement" in the values predicted and in the real ones. When there is a max in the real time series, there is a min in the forecast for the same time, but it seems like it corresponds to the previous time step.

这表明预测值和实际值存在“位移”。当实时序列中有一个最大值时，同时预测中也有一个最小值，但它似乎对应于前一个时间步长。

采纳答案by Nassim Ben

Change

改变

a = dataset[i:(i + look_back), 0]

To

到

a = dataset[i:(i + look_back), :]

If you want the 3 features in your training data.

如果您想要训练数据中的 3 个特征。

Then use

然后使用

model.add(LSTM(4, input_shape=(look_back,3)))

To specify that you have look_backtime steps in your sequence, each with 3 features.

指定look_back您的序列中有时间步长，每个时间步长有 3 个特征。

It should run

它应该运行

EDIT :

编辑 ：

Indeed, sklearn.preprocessing.MinMaxScaler()'s function : inverse_transform()takes an input which has the same shape as the object you fitted. So you need to do something like this :

事实上，sklearn.preprocessing.MinMaxScaler()的功能：inverse_transform()接受与您安装的对象具有相同形状的输入。所以你需要做这样的事情：

# Get something which has as many features as dataset
trainPredict_extended = np.zeros((len(trainPredict),3))
# Put the predictions there
trainPredict_extended[:,2] = trainPredict
# Inverse transform it and select the 3rd column.
trainPredict = scaler.inverse_transform(trainPredict_extended)[:,2]

I guess you will have other issues like this below in your code but nothing that you can't fix :) the ML part is fixed and you know where the error comes from. Just check the shapes of your objects and try to make them match.

我想您的代码中还会有类似下面的其他问题，但没有什么是您无法修复的:) ML 部分已修复，您知道错误来自哪里。只需检查对象的形状并尝试使它们匹配即可。

回答by Askquestions

The displacement could be due to the lag in predicting maximums/minimums given the randomness in the data.

考虑到数据的随机性，位移可能是由于预测最大值/最小值的滞后造成的。

回答by Corey Levinson

You can change what you are optimizing, for maybe better results. For example, try predicting binary 0,1 if there will be a 'spike up' for the next day. Then feed the probability of a 'spike up' as a feature to predict the usage itself.

您可以更改正在优化的内容，以获得更好的结果。例如，尝试预测二进制 0,1 是否会在第二天出现“峰值”。然后将“飙升”的概率作为特征来预测使用情况本身。