tf keras中图神经网络多输入模型的误差

Question

我正在使用Spektral训练一个带有辅助输入层的Graph神经网络。我正在拼接这些层。该模型可以完美地编译。但是，当将数据拟合到模型中时，我得到了以下错误。

ValueError: No data provided for "input_10". Need data for each key in: ['input_10', 'input_12']

代码如下所示

X_in = Input(shape=(1375, 3))
A_in = Input(tensor=sp_matrix_to_sp_tensor(adj_mat))

Feat_input = Input(shape=(55,8))

Feat_layer = Bidirectional(LSTM(32, return_sequences=True,),name='lstm_input')(Feat_input)
Feat_layer = Dense(512,activation='relu')(Feat_layer)
Feat_layer = Flatten()(Feat_layer)

graph_conv = GraphConvSkip(64, activation='relu',kernel_regularizer=l2(l2_reg),name='graph_input')([X_in, A_in])
graph_conv = Dropout(0.5)(graph_conv)

graph_conv = ChebConv(32, activation='relu', kernel_regularizer=l2(l2_reg)([graph_conv,A_in])

graph_conv = Dropout(0.5)(graph_conv)

graph_conv = GraphConvSkip(64, activation='relu', kernel_regularizer=l2(l2_reg)([graph_conv,A_in])
graph_conv = Dropout(0.5)(graph_conv)
graph_conv = ChebConv(32, activation='relu', kernel_regularizer=l2(l2_reg))([graph_conv, A_in])

flatten = Flatten()(graph_conv)

concatenated = concatenate([flatten, Feat_layer])

fc = Dense(512, activation='relu')(concatenated)
fc = Dense(256, activation='relu')(FC)
output = Dense(n_out, activation='softmax')(FC)

model = Model(inputs={'graph_input':[X_in, A_in], 'lstm_input':Feat_input}, outputs=output)

optimizer = RMSprop(lr=learning_rate)

model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['acc'])
model.summary()
history = model.fit({'graph_input': [X_train], 'lstm_input': x_train_feat }, y_train, batch_size=28, epochs=250,steps_per_epoch=10)

Answer 1

在这里，当您定义Model时，您已经为graph_input - X_in和A_in定义了two inputs。

model = Model(inputs={'graph_input':[X_in, A_in], 'lstm_input':Feat_input}, outputs=output)

但是在调用model.fit时，您只传递了graph_input .i.e的one input。X_train和另一个输入丢失。这就是它抛出错误的原因。

history = model.fit({'graph_input': [X_train], 'lstm_input': x_train_feat }, y_train, batch_size=28, epochs=250,steps_per_epoch=10)

请传递graph_input的Second input，错误应该会被修复。

希望这能回答你的问题。快乐学习。