我是神经网络的初学者，对使用 scaler.inverse_transform 缩放矩阵后端的数学不太了解。我正在使用教程将 LSTM 应用于我的数据并预测其中一个变量的时间序列。当我进行缩放时，我在预测中遇到了这个问题。代码如下。
这就是我训练数据的方式。
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)
这就是我设计模型的方式。
# design network
model = Sequential()
model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
# fit network
history = model.fit(train_X,
train_y,epochs=100,batch_size=160,validation_data=(test_X, test_y),
verbose=2, shuffle=False)
这就是我试图预测的方式
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)
我正进入（状态。
ValueError: operands could not be broadcast together with shapes (4599,12)
(11,) (4599,12)
最初 test_X 的形状是 (4599, 1, 12)。如果有人有兴趣了解有关数据的更多信息，我可以发送 iPython 文件的数据和 html。