我有以下神经网络

def customLoss(yTrue,yPred):    loss_value = np.divide(abs(yTrue - yPred) , yTrue)    loss_value = tf.reduce_mean(loss_value)    return loss_valuedef model(inp_size):   inp = Input(shape=(inp_size,))   x1 = Dense(100, activation='relu')((inp))   x1 = Dense(50, activation='relu')(x1)   x1 = Dense(20, activation='relu')(x1)   x1 = Dense(1, activation = 'linear')(x1)    x2 = Dense(100, activation='relu')(inp)    x2 = Dense(50, activation='relu')(x2)    x2 = Dense(20, activation='relu')(x2)    x2 = Dense(1, activation = 'linear')(x2)    x3 = Dense(100, activation='relu')(inp)    x3 = Dense(50, activation='relu')(x3)    x3 = Dense(20, activation='relu')(x3)    x3 = Dense(1, activation = 'linear')(x3)    x4 = Dense(100, activation='relu')(inp)    x4 = Dense(50, activation='relu')(x4)    x4 = Dense(20, activation='relu')(x4)    x4 = Dense(1, activation = 'linear')(x4)    x1 = Lambda(lambda x: x * baseline[0])(x1)    x2 = Lambda(lambda x: x * baseline[1])(x2)    x3 = Lambda(lambda x: x * baseline[2])(x3)    x4 = Lambda(lambda x: x * baseline[3])(x4)    out = Add()([x1, x2, x3, x4])    return Model(inputs = inp, outputs = out)y_train=y_train.astype('float32')y_test=y_test.astype('float32')NN_model = Sequential()NN_model = model(X_train.shape[1])NN_model.compile(loss=customLoss, optimizer= 'Adamax', metrics=    [customLoss])NN_model.fit(X_train, y_train, epochs=500,verbose = 1)train_predictions = NN_model.predict(X_train)predictions = NN_model.predict(X_test)MAE  = customLoss (y_test, predictions)

最后的输出是3663/3663 [==============================] – 0s 103us/step – loss: 0.0055 – customLoss: 0.0055

然而，当我打印 customLoss (y_train , train_predictions) 时

我得到 0.06469738

我读到过，训练期间的损失是整个轮次的平均值，但最终结果肯定不应该更差，更不用说相差一个数量级了？我对keras还比较新手，任何建议都欢迎，谢谢！

回答：

事实证明，训练预测的形状是(3000 , 1)，而 y_train 是(3000, )train_predictions = NN_model.predict(X_train).flatten()

解决了这个问题