我是神經(jīng)網(wǎng)絡(luò)的初學(xué)者，對(duì)縮放矩陣后端的數(shù)學(xué)不是很了解scaler.inverse_變換. 我正在使用一個(gè)教程來對(duì)我的數(shù)據(jù)應(yīng)用LSTM，并預(yù)測(cè)其中一個(gè)變量的時(shí)間序列。當(dāng)我縮放時(shí)，我在預(yù)測(cè)上遇到了這個(gè)問題。代碼如下。在

我就是這樣訓(xùn)練數(shù)據(jù)的。在from keras.models import Sequential

from keras.layers import Dense

from keras.layers import LSTM

# split into train and test sets

values = reframed.values

n_train_sec = 5000

train = values[:n_train_sec, :]

test = values[n_train_sec:, :]

# split into input and outputs

train_X, train_y = train[:, :-1], train[:, -1]

test_X, test_y = test[:, :-1], test[:, -1]

# reshape input to be 3D [samples, timesteps, features]

train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))

test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))

print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)

我就是這樣設(shè)計(jì)模型的。在

^{pr2}$

這就是我試圖預(yù)測(cè)的from math import sqrt

from numpy import concatenate

# make a prediction

yhat = model.predict(test_X)

test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))

# invert scaling for forecast

inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)

inv_yhat = scaler.inverse_transform(inv_yhat)

inv_yhat = inv_yhat[:,0]

# invert scaling for actual

test_y = test_y.reshape((len(test_y), 1))

inv_y = concatenate((test_y, test_X[:, 1:]), axis=1)

inv_y = scaler.inverse_transform(inv_y)

inv_y = inv_y[:,0]

# calculate RMSE

rmse = sqrt(mean_squared_error(inv_y, inv_yhat))

print('Test RMSE: %.3f' % rmse)

我得到了。在ValueError: operands could not be broadcast together with shapes (4599,12)

(11,) (4599,12)

最初測(cè)試的形狀是(4599，1，12)。如果有人有興趣了解更多關(guān)于數(shù)據(jù)，我可以發(fā)送數(shù)據(jù)和iPython文件的html。在

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