Concatenate an input before Dense layer. [Keras with TF backend] - tensorflow

Question

So, I need to concatenate an input to the flattened layer before going in the dense layer.
I'm using Keras with TF as backend.
model.add(Flatten())
aux_input = Input(shape=(1, ))
model.add(Concatenate([model, aux_input]))
model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay)))
I have a scenario like this: X_train, y_train, aux_train. The shape of y_train and aux_train is same (1, ). An image has a ground-truth and an aux_input.
How do I add this aux_input to the model while doing model.fit?
As suggested in answers, I changed my model with functional api. However, now, I get the following error.
ValueError: Layer dense_1 was called with an input that isn't a
symbolic tensor. Received type: . Full input:
[]. All
inputs to the layer should be tensors.
Here's the code for that part.
flatten = Flatten()(drop_5)
aux_rand = Input(shape=self.aux_shape)
concat = Concatenate([flatten, aux_input])
fc1 = Dense(512, kernel_regularizer=regularizers.l2(weight_decay))(concat)
Shape of aux input
aux_shape = (1,)
And then calling the model as follow
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
aux_rand = np.random.rand(y_train.shape[0])
model_inst = cifar10vgg()
x_train_input = Input(shape=(32,32,3))
aux_input = Input(shape=(1,))
model = Model(inputs=[x_train_input, aux_input], output=model_inst.build_model())
model.fit(x=[x_train, aux_rand], y=y_train, batch_size=batch_size, steps_per_epoch=x_train.shape[0] // batch_size,
epochs=maxepoches, validation_data=(x_test, y_test),
callbacks=[reduce_lr, tensorboard], verbose=2)
model_inst.build_model() returns Activation('softmax')(fc2) which is the output to be fed into the Model (as far as I understood)

Answer 1

As I see from your code, you implement the model with sequential API which is not a good option in this case. If you have some auxiliary inputs the best way to implement such a feature is to use functional API.
Here is a example from Keras website:
from keras.layers import Input, Embedding, LSTM, Dense
from keras.models import Model
main_input = Input(shape=(100,), dtype='int32', name='main_input')
x = Embedding(output_dim=512, input_dim=10000, input_length=100)(main_input)
lstm_out = LSTM(32)(x)
auxiliary_output = Dense(1, activation='sigmoid', name='aux_output')(lstm_out)
auxiliary_input = Input(shape=(5,), name='aux_input')
x = keras.layers.concatenate([lstm_out, auxiliary_input])
x = Dense(64, activation='relu')(x)
main_output = Dense(1, activation='sigmoid', name='main_output')(x)
model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output, auxiliary_output])
Based on description, I think following code can give you some intuition:
x1 = Input(shape=(32, 32, 3))
flatten1 = Flatten()(x1)
x2 = Input(shape=(244, 244, 3))
vgg = VGG19(weights='imagenet', include_top=False)(x2)
flatten2 = Flatten()(vgg)
concat = Concatenate()([flatten1, flatten2])
d = Dense(10)(concat)
model = Model(inputs=[x1, x2], outputs=[d])
model.compile('adam', 'categorical_crossentropy')
model.fit(x=[x_train1, x_train2],outputs=y_labels)