如何在 Keras 中为 LSTM 处理多个输入? [英] How to work with multiple inputs for LSTM in Keras?

问题描述

我正在尝试预测人口的用水量.

我有 1 个主要输入:

• 水量

和 2 个辅助输入:

• 温度
• 降雨

理论上它们与供水有关.

不得不说，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.

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，网络使用此代码或多或少地工作得很好，但当我有多个输入时就不行了.(因此，如果您使用下面的 csv 文件运行此代码，则会显示尺寸错误).

阅读一些答案:

• 这表明预测值和真实值存在位移".当实时序列有一个最大值时，同时预测中也有一个最小值，但好像对应上一个时间步.

  解决方案

  更改

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

  到

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

  如果您想要训练数据中的 3 个特征.

  然后使用

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

  要指定您的序列中有 look_back 时间步长，每个步长有 3 个特征.

  它应该运行

  确实，sklearn.preprocessing.MinMaxScaler() 的函数:inverse_transform() 接受一个输入，该输入与您拟合的对象具有相同的形状.所以你需要做这样的事情:

  # 获取与数据集一样多特征的东西trainPredict_extended = np.zeros((len(trainPredict),3))# 把预测放在那里trainPredict_extended[:,2] = trainPredict# 对其进行逆变换并选择第 3 列.trainPredict = scaler.inverse_transform(trainPredict_extended)[:,2]

  我想您的代码中还会有其他类似的问题，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.

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

  I have 1 main input:

  • Water volume

  and 2 secondary inputs:

  • Temperature
  • Rainfall

  In theory they have a relation with the water supply.

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

  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).

  Reading some answers from:

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

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

  The code:

  EDIT: Code has been updated

  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.

  datos.csv

  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:

  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.

  解决方案

  Change

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

  To

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

  If you want the 3 features in your training data.

  Then use

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

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

  It should run

  EDIT :

  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 :

  # 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.

  这篇关于如何在 Keras 中为 LSTM 处理多个输入?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！