多输出LSTM时间序列预测

Question

我有一个包含3个特性的数据集。数据集的维度是1000 x 3（1000个时间步长和3个特性）。基本上是1000行3列

数据如下： A B C 131 111 100 131 110 120 131 100 100 ... 131 100 100 问题是如何训练前25步并预测后25步，以得到（A，B，C）3个特征预测的输出。我成功地训练和预测了一维（1个特征（A））阵列。但是我不知道如何使用相同的数据集来预测这三个特征。在

我得到了一个错误：

Error when checking target: expected dense_1 to have shape (None, 3) but got array with shape (21, 1)

代码如下：

# -*- coding: utf-8 -*-
import numpy as np
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, :])
    return numpy.array(dataX), numpy.array(dataY)



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


# load the dataset
dataframe = pandas.read_csv('v77.csv', engine='python',skiprows=0) 
dataset = dataframe.values
print dataset
# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# split into train and test sets
train_size = 10
test_size = 10
train, test = dataset[0:train_size, :], dataset[train_size:train_size+test_size, :]
print (train_size,test_size)

# 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)
print trainX

# 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(32, input_shape=(3,3)))
model.add(Dense(3))
model.compile(loss='mean_squared_error', optimizer='adam')
history= model.fit(trainX, trainY,validation_split=0.33, nb_epoch=10, batch_size=16)

# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# print testPredict
# print np.shape(testPredict)
# Get something which has as many features as dataset
trainPredict_extended = numpy.zeros((len(trainPredict),3))
print trainPredict_extended
print np.shape(trainPredict_extended[:,2])
print np.shape(trainPredict[:,0])
# Put the predictions there
trainPredict_extended[:,2] = trainPredict[:,0]
# Inverse transform it and select the 3rd coumn.
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]   
# print testPredict_extended

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]
# print 

# print testY
# 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))

样本数据： v77.txt

需要帮助。谢谢

Answer 1

Y形与模型中的最后一层不匹配。Y的形式是(num_samples, 1)，这意味着对于每个样本，它输出一个长度为1的向量。在

但是，最后一层是Dense(3)层，它输出(num_samples, 3)，这意味着对于每个样本它都输出一个长度为3的向量。在

由于神经网络的输出和y数据的格式不同，所以神经网络无法训练。在

您可以通过两种方式解决此问题：

1.通过将Dense(3)替换为Dense(1)，将神经网络的输出转换为y数据的形状：

model = Sequential()
model.add(LSTM(32, input_shape=(3,3)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')history= model.fit(trainX, trainY,validation_split=0.33, nb_epoch=10, batch_size=16)

2.通过修改create_dataset()函数，将y数据的形状转换为神经网络的输出，以便将所有特征都添加到y而不是只添加一个：

^{pr2}$

既然你说你想预测3个特性，你很可能会使用第二个选项。请注意，第二个选项确实中断了代码的最后一部分来扩展y，但是您的模型训练得很好。在