基于keras输入形状的LSTM多对一预测

Question

最初，我有一个csv文件，有6列：日期、耗电量和其他4个对消费量有影响的气候特征（如温度、湿度等）

到目前为止，我只能在consumption列上运行LSTM，它给了我非常准确的结果，但是我需要向LSTM提供其他特性。我试图根据前面的注释修改python代码here，但仍然有一个整形错误。在

以下是我修改后的代码：

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), :]
    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('out_meteo.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_shape=(look_back,3)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
history= model.fit(trainX, trainY,validation_split=0.33, nb_epoch=5, batch_size=32)



# 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
# 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 baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()

我得到的错误是

^{pr2}$

请注意，我还是一个新手，重塑的概念对我来说还是有点模棱两可的。在

Answer 1

在策划之前，试着去做

testPredict = scaler.inverse_transform(testPredict)

Answer 2

这是我的最后一个代码，它包含了所有的列

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), :]
     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('out_meteo.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) * 0.7) 
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)


# create and fit the LSTM network

model = Sequential()
model.add(LSTM(20, input_shape=(look_back,6)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
history= model.fit(trainX, trainY,validation_split=0.33, nb_epoch=15, batch_size=15)

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

print(trainPredict)

# 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, :] = trainPredict

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

 # plot baseline and predictions
plt.plot((dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()

到目前为止，它在我所有的csv列上都运行得很好，我还删除了许多行（重塑、MinMAxScaler转换），但仍然无法正确地可视化我的最终数据（使用实际值），它显示的是非常小的值或严格的行。
该数据集的返回序列和测试分数分别为0.03和0.05

Answer 3

为了回答您的问题，我建议您检查一下python/numpy中的多维列表/数组。在

另外，这里有一个链接，可以解释Keras中张量的形状

https://github.com/fchollet/keras/issues/2045