在多列上执行计算？

Question

我有一个LSTM神经网络来测试该网络的预测能力，并且它可用于一列。但是现在我想对不同的项目使用几列，并为每一列计算“ ABSE”。例如，如果我有两列：

它需要分别为每一列计算“ ABSE”函数。

我下面的代码失败。有人可以帮我吗？

这是我尝试的方法，但出现值错误：

ValueError: non-broadcastable output operand with shape (1,1) doesn't 
match the broadcast shape (1,2)

这发生在行上：

 ---> 51     trainPredict = scaler.inverse_transform(trainPredict)

代码：

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)

def ABSE(a,b):
    ABSE = abs((b-a)/b)
    return numpy.mean(ABSE)

columns = df[['Item1','Item2']]

for i in columns:
    # 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) * 0.5)
    test_size = 1- train_size
    train, test = dataset[0:train_size,:], 
    dataset[train_size:len(dataset),:]
    look_back = 1
    trainX, trainY = create_dataset(train, look_back)
    testX, testY = create_dataset(test, look_back)
    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(1, input_shape=(1, look_back)))
    model.add(Dense(1))
    model.compile(loss='mean_squared_error', optimizer='adam')
    model.fit(trainX, trainY, epochs=1, batch_size = 1, verbose = 0)
    # make predictions
    trainPredict = model.predict(trainX)
    testPredict = model.predict(testX)
    # invert predictions
    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 = ABSE(trainY[0], trainPredict[:,0])
    print('Train Score: %.2f ABSE' % (trainScore))
    testScore = ABSE(testY[0], testPredict[:,0])
    print('Test Score: %.2f ABSE' % (testScore))
    print(testY[0].T,testPredict[:,0].T)