Keras-TypeError：只有整数标量数组才能转换为标量索引

Question

我正在尝试学习keras，特别是LSTM用于时间序列中的异常检测，为此，我一直在网上跟踪示例。但由于某种原因，它没有起作用。我已经按照之前一篇关于TypeError: only integer scalar arrays can be converted to a scalar index的文章中的建议做了，但是没有任何效果。从那看来，我想这和努比有关。这是我的代码：

import numpy
import pandas
import matplotlib.pyplot as plt
import math
import tensorflow as tf
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

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

#load the dataset
dataframe = pandas.read_csv('international-airline-passengers.csv', usecols=[1], engine='python', skipfooter=3)
dataset = dataframe.values
dataset = dataset.astype('float32')

#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 = create_dataset(train, look_back)[0]
trainY = create_dataset(train, look_back)[0]
testX = create_dataset(test, look_back)[0]
testY = create_dataset(test, look_back)[0]

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

# create and fit the LSTM network
model = Sequential()[0]
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))[0]
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)

#make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

#invert predictions
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])[0]

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

从中我得到了错误：

Using TensorFlow backend.
96 48
Traceback (most recent call last):
  File "C:\Users\fires\Anaconda3\envs\python3.5\lib\site-packages\numpy\core\fromnumeric.py", line 57, in _wrapfunc
    return getattr(obj, method)(*args, **kwds)
TypeError: only integer scalar arrays can be converted to a scalar index


During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:/Users/fires/PycharmProjects/RSI/Test 1.py", line 52, in <module>
    trainX = numpy.reshape(trainX, (trainX[0], 1, trainX.shape[1]))[0]
  File "C:\Users\fires\Anaconda3\envs\python3.5\lib\site-packages\numpy\core\fromnumeric.py", line 232, in reshape
    return _wrapfunc(a, 'reshape', newshape, order=order)
  File "C:\Users\fires\Anaconda3\envs\python3.5\lib\site-packages\numpy\core\fromnumeric.py", line 67, in _wrapfunc
    return _wrapit(obj, method, *args, **kwds)
  File "C:\Users\fires\Anaconda3\envs\python3.5\lib\site-packages\numpy\core\fromnumeric.py", line 47, in _wrapit
    result = getattr(asarray(obj), method)(*args, **kwds)
TypeError: only integer scalar arrays can be converted to a scalar index

Answer 1

您正在尝试将数组重塑为非整数长度。

你已经写了以下代码

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

但是我怀疑你的意思是trainX.shape[0]，而不是trainX[0]。这将修复only integer arrays can be converted to a scalar index错误。但是在下面的行中，您已经编写了testX = numpy.reshape(testX)，这是无效的，因为numpy.reshape需要一个shape参数。我不确定你到底想用这句话来达到什么目的，但希望能引起你的注意来解决你的问题！