我正在尝试LSTM使用tensorflow遵循本教程的模型。

当我尝试反转缩放以进行预测时（本教程中的第 86 行），我无法理解为什么我的测试集中出现错误。

加载数据集并创建特色后，以下是我为reframed数据集所做的，

# split into train and test sets
values = reframed.values
split_point = len(reframed)- 168

train = values[ : split_point, :]
test = values[split_point: , :]

# 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)
>> (2399, 1, 39) (2399,) (168, 1, 39) (168,)


# 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=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)

# make a prediction
yhat = model.predict(test_X)
test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))

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

在此步骤之后，以下代码已scaler.invert_transform引发错误。

inv_yhat = scaler.inverse_transform(inv_yhat)

ValueError：操作数无法与形状一起广播 (168,39) (41,) (​​168,39)

形状，

test_X.shape
>> (168, 1, 39)

yhat.shape
>>(168,1)

inv_yhat.shape
>> (168,39)

任何帮助或建议将不胜感激！