keras_functional.py

sequence_input = keras.layers.Input((100, 5)) # 100 timesteps, 5 features
image_input = keras.layers.Input((128, 128, 3)) # 128x128 pixels, 3 channels
auxiliary_input = keras.layers.Input((10,)) # Additional vector input

sequence_module = keras.layers.LSTM(128)(sequence_input)
image_module = keras.layers.Conv2D(32, 1)(image_input)
image_features = keras.layers.Flatten()(image_module)

concat = keras.layers.Concatenate()([sequence_module, image_features, auxiliary_input])

classification_output = keras.layers.Dense(1, activation='sigmoid')(concat)
regression_output = keras.layers.Dense(12)(concat)

# Wrap in a model instance, specify lists of inputs and outputs
model = keras.models.Model(inputs=[sequence_input, image_input, auxiliary_input], outputs=[classification_output,
                                                                                           regression_output])