我对深度学习还很陌生，但是我设法建立了一个多分支的图像分类架构，产生了令人满意的结果。
不太重要：我正在研究KKBox客户流失（https://kaggle.com/c/kkbox-churn-prediction-challenge/data），在那里我将客户行为，交易和静态数据转换为热图，并尝试基于此对流失进行分类。
分类本身可以正常工作。当我尝试应用LIME来查看结果从何而来时，我的问题就来了。在遵循以下代码时：https://marcotcr.github.io/lime/tutorials/Tutorial%20-%20images.html，但我使用输入列表[members [0]，transactions [0]，user_logs [0]]时，出现以下错误： AttributeError：'list '对象没有属性'shape'
浮现在脑海的是，LIME可能不是针对像我这样的多输入体系结构制作的。另一方面，Microsoft Azure也具有多分支体系结构（http://www.freepatentsonline.com/20180253637.pdf?fbclid=IwAR1j30etyDGPCmG-QGfb8qaGRysvnS_f5wLnKz-KdwEbp2Gk0_-OBsSepVc），据称他们使用LIME来解释其结果（https://www.slideshare.net/FengZhu18/predicting-azure-churn-with-deep-learning-and-explaining-predictions-with-lime）。
我试图将图像连接成一个输入，但是这种方法产生的结果要比多输入方法差得多。尽管LIME可以使用这种方法（即使不像通常的图像识别那样可理解）。
DNN架构：
# Members
members_input = Input(shape=(61,4,3), name='members_input')
x1 = Dropout(0.2)(members_input)
x1 = Conv2D(32, kernel_size = (61,4), padding='valid', activation='relu', strides=1)(x1)
x1 = GlobalMaxPooling2D()(x1)
# Transactions
transactions_input = Input(shape=(61,39,3), name='transactions_input')
x2 = Dropout(0.2)(transactions_input)
x2 = Conv2D(32, kernel_size = (61,1,), padding='valid', activation='relu', strides=1)(x2)
x2 = Conv2D(32, kernel_size = (1,39,), padding='valid', activation='relu', strides=1)(x2)
x2 = GlobalMaxPooling2D()(x2)
# User logs
userlogs_input = Input(shape=(61,7,3), name='userlogs_input')
x3 = Dropout(0.2)(userlogs_input)
x3 = Conv2D(32, kernel_size = (61,1,), padding='valid', activation='relu', strides=1)(x3)
x3 = Conv2D(32, kernel_size = (1,7,), padding='valid', activation='relu', strides=1)(x3)
x3 = GlobalMaxPooling2D()(x3)
# User_logs + Transactions + Members
merged = keras.layers.concatenate([x1,x2,x3]) # Merged layer
out = Dense(2)(merged)
out_2 = Activation('softmax')(out)
model = Model(inputs=[members_input, transactions_input, userlogs_input], outputs=out_2)
model.compile(optimizer="adam", loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
尝试使用LIME：
explainer = lime_image.LimeImageExplainer()
explanation = explainer.explain_instance([members_test[0],transactions_test[0],user_logs_test[0]], model.predict, top_labels=2, hide_color=0, num_samples=1000)
模型摘要：
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
transactions_input (InputLayer) (None, 61, 39, 3) 0
__________________________________________________________________________________________________
userlogs_input (InputLayer) (None, 61, 7, 3) 0
__________________________________________________________________________________________________
members_input (InputLayer) (None, 61, 4, 3) 0
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 61, 39, 3) 0 transactions_input[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 61, 7, 3) 0 userlogs_input[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 61, 4, 3) 0 members_input[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 1, 39, 32) 5888 dropout_2[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 1, 7, 32) 5888 dropout_3[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 1, 1, 32) 23456 dropout_1[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 1, 1, 32) 39968 conv2d_2[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 1, 1, 32) 7200 conv2d_4[0][0]
__________________________________________________________________________________________________
global_max_pooling2d_1 (GlobalM (None, 32) 0 conv2d_1[0][0]
__________________________________________________________________________________________________
global_max_pooling2d_2 (GlobalM (None, 32) 0 conv2d_3[0][0]
__________________________________________________________________________________________________
global_max_pooling2d_3 (GlobalM (None, 32) 0 conv2d_5[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 96) 0 global_max_pooling2d_1[0][0]
global_max_pooling2d_2[0][0]
global_max_pooling2d_3[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 2) 194 concatenate_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 2) 0 dense_1[0][0]
==================================================================================================
因此，我的问题是：有没有人有多输入DNN架构和LIME的经验？有没有我没有看到的解决方法？我可以使用另一种可解释的模型吗？
谢谢。