Keras add a variable into a convolutional neural network's dense layer

cnn_with_other_data.py

"""
Adding a variable into Keras/TensorFlow CNN dense layer

https://stackoverflow.com/questions/42556919/adding-a-variable-into-keras-tensorflow-cnn-dense-layer

              ___________      _________      _________      _________     ________    ______
              | Conv    |     | Max    |     | Conv    |     | Max    |    |       |   |     |
    Image --> | Layer 1 | --> | Pool 1 | --> | Layer 2 | --> | Pool 2 | -->|       |   |     |
              |_________|     |________|     |_________|     |________|    | Dense |   | Out |
                                                                           | Layer |-->|_____|
   Other      ------------------------------------------------------------>|       |
   Data                                                                    |       |
                                                                           |_______|
"""

import keras
from keras.models import Model
from keras.layers import Dense, Merge, Flatten, Input, Conv2D, MaxPooling2D

# this is your image input definition. You have to specify a shape. 
image_input = Input(shape=(32,32,3))
# Some more data input with 10 features (eg.)
other_data_input = Input(shape=(10,))    

# First convolution filled with random parameters for the example
conv1 = Conv2D(32, (3, 3),activation = "tanh")(image_input)
# MaxPool it 
conv1 = MaxPooling2D()(conv1)
# Second Convolution
conv2 = Conv2D(64, (3, 3),activation = "tanh")(conv1)
# MaxPool it
conv2  = MaxPooling2D()(conv2)
# Flatten the output to enable the merge to happen with the other input
first_part_output = Flatten()(conv2)

# Merge the output of the convNet with your added features by concatenation
merged_model = keras.layers.concatenate([first_part_output, other_data_input])

# Predict on the output
predictions = Dense(4, activation='softmax')(merged_model)

# Now create the model
model = Model(inputs=[image_input, other_data_input], outputs=predictions)
# see your model 
model.summary()