我有一个时间序列中包含3个要素的数据集。数据集的维度为1000 x 3（1000个时间步长和3个要素）。基本上，1000行和3列
数据如下所示：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，以获得3个特征预测的输出，即（A、B和C）。我成功地训练和预测了1-D（1个特征（A））数组。但我不知道如何使用相同的数据集预测3个特征。
我得到了这个错误：
检查目标时出错：dense_1的形状应为（None，3），但得到的数组的形状为（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