我有一个在时间序列中包含 3 个特征的数据集。数据集的维度为 1000 x 3(1000 个时间步长和 3 个特征)。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一维(1个特征(A))数组。但是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：预期dense_1具有形状(无，3)，但得到具有形状的数组(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))
示例数据： v77.txt
需要帮助。谢谢
您的 Y 形状与模型中的最后一层不匹配。你的Y是(num_samples, 1)的形式，这意味着对于每个样本，它输出一个长度为1的向量。
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，它输出一个长度为 3 的向量。
由于神经网络的输出和 y 数据的格式不同，因此神经网络无法训练。
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，将所有特征添加到 y 而不是仅一个，将 y 数据的形状转换为神经网络的输出：
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)
由于您声明要预测 3 个特征，因此您很可能会使用第二个选项。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，但您的模型训练良好。