具有多输出的LSTM时序预测

Question

我有一个数据集，在一个时间序列中有三个特性。数据集的维度为1000×3(1000个时间步骤和3个特性)。基本上，1000行3列

数据看起来是这样的：A B C 131 111 100 131 110 120 131 100 100 ... 131 100 100，问题是如何训练前25个步骤，并预测接下来的25个步骤，以便得到三个特征预测的输出，即A、B和C。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性。

我得到了一个错误：

检查目标时出错:期望dense_1具有形状(无，3)，但得到形状为(21，1)的数组

守则如下：

# -*- 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

您的Y形状与模型中的最后一层不匹配。Y是(num_samples, 1)的形式，这意味着它对每一个样本输出一个长度为1的向量。

然而，最后一层是Dense(3)层，它输出(num_samples, 3)，这意味着它为每个样本输出一个长度为3的向量。

由于神经网络和y-数据的输出格式不同，神经网络无法进行训练。

您可以通过两种方法解决这个问题：

1.通过用Dense(3)替换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，从而将所有特性添加到y中，而不仅仅是一个：

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)

既然您说您希望预测3个特性，那么您将最有可能使用第二个选项。请注意，第二个选项确实破坏了代码的最后一部分以扩展y，但是您的模型训练得很好。