Keras Layer Concatenation

Question

I'm trying to see how I can create a model in Keras with multiple Embedding Layers and other inputs. Here's how my model is structured(E=Embedding Layer, [....]=Input Layer):

E   E [V V V]
\   |  /
 \  | /
  Dense
    |
  Dense

Here is my code so far:

model_a = Sequential()
model_a.add(Embedding(...))

model_b = Sequential()
model_b.add(Embedding(...))

model_c = Sequential()
model_c.add(Embedding(...))

model_values = Sequential()
model_values.add(Input(...))

classification_model = Sequential()
classification_layers = [
    Concatenate([model_a,model_b,model_c, model_values]),
    Dense(...),
    Dense(...),
    Dense(2, activation='softmax')
]
for layer in classification_layers:
    classification_model.add(layer)

classification_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
classification_model.fit(train_data,one_hot_labels, epochs=1, validation_split=0.2)

However I get the following error:

ValueError: A `Concatenate` layer should be called on a list of at least 2 inputs

I am at a loss at what I'm doing wrong here. Here's the a little more detail for the error log:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-37-d5ab23b17e9d> in <module>()
----> 1 classification_model.fit(train_data,one_hot_labels, epochs=1, validation_split=0.2)

/usr/local/lib/python3.5/dist-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
    953             sample_weight=sample_weight,
    954             class_weight=class_weight,
--> 955             batch_size=batch_size)
    956         # Prepare validation data.
    957         do_validation = False

/usr/local/lib/python3.5/dist-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    674             # to match the value shapes.
    675             if not self.inputs:
--> 676                 self._set_inputs(x)
    677 
    678         if y is not None:

/usr/local/lib/python3.5/dist-packages/keras/engine/training.py in _set_inputs(self, inputs, outputs, training)
    574                 assert len(inputs) == 1
    575                 inputs = inputs[0]
--> 576             self.build(input_shape=(None,) + inputs.shape[1:])
    577             return
    578 

/usr/local/lib/python3.5/dist-packages/keras/engine/sequential.py in build(self, input_shape)
    225             self.inputs = [x]
    226             for layer in self._layers:
--> 227                 x = layer(x)
    228             self.outputs = [x]
    229 

/usr/local/lib/python3.5/dist-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
    430                                          '`layer.build(batch_input_shape)`')
    431                 if len(input_shapes) == 1:
--> 432                     self.build(input_shapes[0])
    433                 else:
    434                     self.build(input_shapes)

/usr/local/lib/python3.5/dist-packages/keras/layers/merge.py in build(self, input_shape)
    339         # Used purely for shape validation.
    340         if not isinstance(input_shape, list) or len(input_shape) < 2:
--> 341             raise ValueError('A `Concatenate` layer should be called '
    342                              'on a list of at least 2 inputs')
    343         if all([shape is None for shape in input_shape]):

ValueError: A `Concatenate` layer should be called on a list of at least 2 inputs

Answer 1

input1 = Input(input_shape=...)
input2 = Input(...)
input3 = Input(...)
values = Input(...)

out1 = Embedding(...)(input1)
out2 = Embedding(...)(input2)   
out3 = Embedding(...)(input3)

#make sure values has a shape compatible with the embedding outputs.
#usually it should have shape (equal_samples, equal_length, features)   
joinedInput = Concatenate()([out1,out2,out3,values])

out = Dense(...)(joinedInput)
out = Dense(...)(out)
out = Dense(2, activation='softmax')(out)

model = Model([input1,input2,input3,values], out)