LSTM-Keras错误:ValueError:形状为(67704,1)的不可广播的输出操作数与广播形状(67704,12)不匹配 [英] LSTM-Keras Error: ValueError: non-broadcastable output operand with shape (67704,1) doesn't match the broadcast shape (67704,12)

问题描述

大家早上好.我正在尝试使用Keras和pandas实现此LSTM算法，以便在csv文件中读取.我正在使用的后端是Tensorflow.在预测训练集之前反转结果时，我遇到了问题.下面是我的代码

Good morning everyone. I'm trying to implement this LSTM Algorithm using Keras and pandas as to read in the csv file in. The backend that I'm using is Tensorflow. I'm having a problem when it comes to inversing my results before predicting the training set. Below is my code

import numpy
import matplotlib.pyplot as plt
import pandas
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error


#plt.plot(dataset)
#plt.show()

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

#Load dataset
col_names = ['UserID','SysTouchTime', 'EventTime', 'ActivityTouchID', 'Pointer_count', 'PointerID',
                'ActionID', 'Touch_X', 'Touch_Y', 'Touch_Pressure', 'Contact_Size', 'Phone_Orientation']
dataframe = pandas.read_csv('touchEventsFor5Users.csv', engine='python', header=None, names = col_names, skiprows=1)
#print(dataset.head())
#print(dataset.shape)
dataset = dataframe.values
dataset = dataframe.astype('float32')
print(dataset.isnull().any())
dataset = dataset.fillna(method='ffill')
feature_cols = ['SysTouchTime', 'EventTime', 'ActivityTouchID', 'Pointer_count', 'PointerID', 'ActionID', 'Touch_X', 'Touch_Y', 'Touch_Pressure', 'Contact_Size', 'Phone_Orientation']

X = dataset[feature_cols]
y = dataset['UserID']
print(y.head())
#normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# split into train and test sets

train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size, :], dataset[train_size:len(dataset),:]
print(len(train), len(test))

# 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), 0]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return numpy.array(dataX), numpy.array(dataY)

# reshape into X=t and Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

#reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))

#create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_dim=look_back))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
model.fit(trainX, trainY, epochs=1, batch_size=32, verbose=2)

# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# invert predictions
import gc
gc.collect()

#####problem occurs with the following line of code#############

trainPredict = scaler.inverse_transform(trainPredict)

trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))

#shift train predictions for plotting
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# plot baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()

我得到的错误是

ValueError:形状为(67704,1)的不可广播的输出操作数与广播形状(67704,12)不匹配

ValueError: non-broadcastable output operand with shape (67704,1) doesn't match the broadcast shape (67704,12)

认为你们可以帮助我解决这个问题吗?我对此很陌生，但想学得很烂，而这个错误使我受苦！感谢您提供的任何帮助.

Think you guys could help me solve this problem? I'm very new to this but want to learn it so bad, and this error is making me suffer! Thanks for any help that can be provided.

推荐答案

缩放数据时，它将以不同的方式缩放12个字段.它将取每个字段的minmax并将其转换为0到1的值.

When you scale your data, it will scale the 12 fields differently. It will take the minmax of each field and transform it into 0 to 1 values.

进行invert_transform时，该函数没有意义，因为仅给它一个字段，它不知道该如何处理，它的最小值和最大值是多少...您需要输入12个字段数据集，此预测字段位于正确的位置.

When you make an invert_transform, it makes no sense to the function because you give it only one field, it doesn't know what to do with it, what was its min and max value... You need to feed a 12 fields dataset, with thise predicted field in the right place.

尝试将其添加到有问题的行之前:

Try to add this before the problematic line :

# create empty table with 12 fields
trainPredict_dataset_like = np.zeros(shape=(len(train_predict), 12) )
# put the predicted values in the right field
trainPredict_dataset_like[:,0] = trainPredict[:,0]
# inverse transform and then select the right field
trainPredict = scaler.inverse_transform(trainPredict_dataset_like)[:,0]

这有帮助吗?:)

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