Python Keras：如何在输入子集上连接

我正在使用Keras和Theano训练神经网络，其中输入的格式如下：

[
   [Situation features],
   [Option 1 features],
   [Option 2 features],
]

我想训练一个模型来预测每个选项的选择频率，让模型学习如何对每个选项进行评分，以及情况如何使评分差异变得更重要或更不重要

我的模型看起来像：

option_inputs = [Input(shape=(NUM_FEATURES,), name='situation_input'),
                  Input(shape=(NUM_FEATURES,), name='option_input_0'),
                  Input(shape=(NUM_FEATURES,), name='option_input_1')]
situation_input_processing = Dense(5, activation='relu', name='situation_input_processing')
option_input_processing = Dense(20, activation='relu', name='option_input_processing')
diversity_neuron = Dense(1, activation='softplus', name='diversity_neuron')
scoring_neuron = Dense(1, activation='linear', name='scoring_neuron')

diversity_output = diversity_neuron(situation_input_processing(journey_inputs[0]))
scoring_outputs = [scoring_neuron(option_input_processing(option_input)) for option_input in option_inputs[1:2]]

logit_outputs = [Multiply()([diversity_output, scoring_output]) for scoring_output in scoring_outputs]
probability_outputs = Activation('softmax')(keras.layers.concatenate(logit_outputs, axis=-1))

model = Model(inputs=option_inputs, outputs=probability_outputs)

option_inputs = [Input(shape=(NUM_FEATURES,), name='option_input_0'),
                  Input(shape=(NUM_FEATURES,), name='option_input_1')]
situation_input_filtering = Dense(NUM_SITUATION_FEATURES, activation='linear', name='situation_input_filtering')
situation_input_filtering.trainable = False
situation_input_processing = Dense(5, activation='relu', name='situation_input_processing')
option_input_processing = Dense(20, activation='relu', name='option_input_processing')
diversity_neuron = Dense(1, activation='sigmoid', name='diversity_neuron')
scoring_neuron = Dense(1, activation='linear', name='scoring_neuron')

diversity_outputs = [diversity_neuron(situation_input_processing(situation_input_filtering(option_input))) for
                     option_input in option_inputs]
scoring_outputs = [scoring_neuron(option_input_processing(option_input)) for option_input in option_inputs]

logit_outputs = [Multiply()([diversity_output, scoring_output]) for diversity_output, scoring_output in
                 zip(diversity_outputs, scoring_outputs)]
combined = keras.layers.concatenate(logit_outputs, axis=-1)
probability_outputs = Activation('softmax')(combined)

model = Model(inputs=option_inputs, outputs=probability_outputs)
model.compile(optimizer='adadelta', loss='categorical_crossentropy', metrics=['accuracy'])

mask_weights = np.zeros([NUM_FEATURES, NUM_SITUATION_FEATURES])
for i in xrange(NUM_situation_FEATURES):
    mask_weights[i, i] = 1.0

for layer in model.layers:
    if layer.name == 'situation_input_filtering':
        layer.set_weights([mask_weights, np.zeros(NUM_SITUATION_FEATURES)])

当尝试获取概率输出时，我得到错误：

ValueError:

应在输入列表上调用连接层

触发此错误的原因似乎是，
logit\u输出
不是通过所有3个输入功能集合（其中只有2个）迭代生成的

你知道如何解决这个问题吗

一旦对模型进行了训练，我想观察
多样性\神经元
和
评分\神经元
的输出，以了解如何推断任意数量选项的评分，并了解驱动多样性的因素
 为了解决这个问题，我做了以下更改：

我在每个选项的开头都包含了情境特征
功能列表
我已经添加了一层，可以过滤的情况
来自选项输入的功能。这是通过手动设置来实现的
不可训练层的重量
然后我可以遍历所有
网络任何路径中的输入
最后的代码如下所示：

option_inputs = [Input(shape=(NUM_FEATURES,), name='situation_input'),
                  Input(shape=(NUM_FEATURES,), name='option_input_0'),
                  Input(shape=(NUM_FEATURES,), name='option_input_1')]
situation_input_processing = Dense(5, activation='relu', name='situation_input_processing')
option_input_processing = Dense(20, activation='relu', name='option_input_processing')
diversity_neuron = Dense(1, activation='softplus', name='diversity_neuron')
scoring_neuron = Dense(1, activation='linear', name='scoring_neuron')

diversity_output = diversity_neuron(situation_input_processing(journey_inputs[0]))
scoring_outputs = [scoring_neuron(option_input_processing(option_input)) for option_input in option_inputs[1:2]]

logit_outputs = [Multiply()([diversity_output, scoring_output]) for scoring_output in scoring_outputs]
probability_outputs = Activation('softmax')(keras.layers.concatenate(logit_outputs, axis=-1))

model = Model(inputs=option_inputs, outputs=probability_outputs)

option_inputs = [Input(shape=(NUM_FEATURES,), name='option_input_0'),
                  Input(shape=(NUM_FEATURES,), name='option_input_1')]
situation_input_filtering = Dense(NUM_SITUATION_FEATURES, activation='linear', name='situation_input_filtering')
situation_input_filtering.trainable = False
situation_input_processing = Dense(5, activation='relu', name='situation_input_processing')
option_input_processing = Dense(20, activation='relu', name='option_input_processing')
diversity_neuron = Dense(1, activation='sigmoid', name='diversity_neuron')
scoring_neuron = Dense(1, activation='linear', name='scoring_neuron')

diversity_outputs = [diversity_neuron(situation_input_processing(situation_input_filtering(option_input))) for
                     option_input in option_inputs]
scoring_outputs = [scoring_neuron(option_input_processing(option_input)) for option_input in option_inputs]

logit_outputs = [Multiply()([diversity_output, scoring_output]) for diversity_output, scoring_output in
                 zip(diversity_outputs, scoring_outputs)]
combined = keras.layers.concatenate(logit_outputs, axis=-1)
probability_outputs = Activation('softmax')(combined)

model = Model(inputs=option_inputs, outputs=probability_outputs)
model.compile(optimizer='adadelta', loss='categorical_crossentropy', metrics=['accuracy'])

mask_weights = np.zeros([NUM_FEATURES, NUM_SITUATION_FEATURES])
for i in xrange(NUM_situation_FEATURES):
    mask_weights[i, i] = 1.0

for layer in model.layers:
    if layer.name == 'situation_input_filtering':
        layer.set_weights([mask_weights, np.zeros(NUM_SITUATION_FEATURES)])

尝试：
logit\u outputs=[Multiply（[diversity\u output，scoring\u output]）在scoring\u outputs中为scoring\u outputs输入分数]