Question

我正在使用此example创建一个神经网络，并且在以下行中收到错误“ ValueError：无法将形状（11253,1）的输入数组广播到形状（11253）”中： 1}}我的代码是：

trainPredictPlot[look_back:len(trainPredict)+look_back] = trainPredicty

对于X1，我有16,800个值，如下所示：

import csv
import math
import numpy as np 
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
import datetime
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

X1 = [1:16801] #16,800 values
Y1 = [1:16801]#16,800 values

train_size = int(len(X1) * 0.67)
test_size = len(X1) - train_size

train, test = X1[0:train_size,], X1[train_size:len(X1),]
def Data(X1, look_back=1):
     dataX, dataY = [], []
     for i in range(len(X1)-look_back-1):
         a = X1[i:(i+look_back), 0]
         dataX.append(a)
         dataY.append(Y1[i + look_back, 0])
     return numpy.array(dataX), numpy.array(dataY)

look_back = 1
trainX, testX = Data(train, look_back)

testX, testY = Data(test, look_back)

look_back = 1
trainX, testX = Data(train, look_back)

testX, testY = Data(test, look_back)


trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

trainPredictPlot = numpy.empty_like(Y1)
trainPredictPlot[look_back:len(trainPredict)+look_back] = trainPredict

testPredictPlot = numpy.empty_like(Y1) 
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(X1)-1] = testPredict

我的Y1数据如下：

[0.03454225 0.02062136 0.00186715 ... 0.92857565 0.64930691 0.20325924]

我的回溯错误消息是：

[ 2.25226244  1.44078451  0.99174488 ... 12.8397099   9.75722427 7.98525797]

Answer 1

在分配前将trainPredict从2D数组转换为1D向量

trainPredictPlot[look_back:len(trainPredict)+look_back] = trainPredict.ravel()

ValueError：无法将形状（11253,1）的输入数组广播到形状（11253）

1 个答案: