我使用以下代码构建了一个Keras回归器:

from keras.models import Sequentialfrom keras.layers import Densefrom keras.wrappers.scikit_learn import KerasRegressorfrom sklearn.model_selection import cross_val_scorefrom sklearn.model_selection import KFoldfrom sklearn.preprocessing import StandardScalerfrom sklearn.pipeline import Pipelineimport numpy as nyimport pandasfrom numpy.random import seedseed(1)from tensorflow import set_random_seedset_random_seed(2)X = ny.array([[1,2], [3,4], [5,6], [7,8], [9,10]])sc_X=StandardScaler()X_train = sc_X.fit_transform(X)Y = ny.array([3, 4, 5, 6, 7])Y=ny.reshape(Y,(-1,1))sc_Y=StandardScaler()Y_train = sc_Y.fit_transform(Y)N = 5def brain():    #Create the brain    br_model=Sequential()    br_model.add(Dense(3, input_dim=2, kernel_initializer='normal',activation='relu'))    br_model.add(Dense(2, kernel_initializer='normal',activation='relu'))    br_model.add(Dense(1,kernel_initializer='normal'))    #Compile the brain    br_model.compile(loss='mean_squared_error',optimizer='adam')    return br_modeldef predict(X,sc_X,sc_Y,estimator):    prediction = estimator.predict(sc_X.fit_transform(X))    return sc_Y.inverse_transform(prediction)estimator = KerasRegressor(build_fn=brain, epochs=1000, batch_size=5,verbose=0)# print "Done"estimator.fit(X_train,Y_train)prediction = estimator.predict(X_train)print predict(X,sc_X,sc_Y,estimator)X_test = ny.array([[1.5,4.5], [7,8], [9,10]])print predict(X_test,sc_X,sc_Y,estimator)

我遇到的问题是代码每次预测的值不相同（例如，它在第一次预测（X）中对[9,10]预测为6.64，而在第二次预测（X_test）中预测为6.49）。完整的输出如下:

[2.9929883 4.0016675 5.0103474 6.0190268 6.6434317][3.096634  5.422326  6.4955378]

为什么我得到不同的值？我该如何解决这个问题？

回答：

问题出在这一行代码:

prediction = estimator.predict(sc_X.fit_transform(X))

每次你预测新数据的值时，你都在拟合一个新的缩放器。这就是差异的来源。试试:

prediction = estimator.predict(sc_X.transform(X))

在这种情况下，你使用的是预训练的缩放器。