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I'm currently studying neural network models for image analysis, with the MNIST dataset. I first used only the image to build a first model. Then I created a additionnal variable, which is : 0 when the digit is actually between 0 and 4, and 1 when it's greater or equal than 5.

Therefore, I want to build a model that can take these two informations : the image of the digit, and that additionnal variable I juste created.

I created the two first models, one for the image and one for the exogenous variable, as follow :

import tensorflow as tf
from tensorflow import keras


image_model = keras.models.Sequential()

#First conv layer :
image_model.add( keras.layers.Conv2D( 64, kernel_size=3,
                                               activation=keras.activations.relu,
                                      input_shape=(28, 28, 1) ) )

#Second conv layer :
image_model.add( keras.layers.Conv2D( 32, kernel_size=3, activation=keras.activations.relu ) )

#Flatten layer :
image_model.add( keras.layers.Flatten() )

print( image_model.summary(), '\n' )




info_model = keras.models.Sequential()

info_model.add( keras.layers.Dense( 5, activation=keras.activations.relu, input_shape=(1,) ) )

print( info_model.summary() )

Then I would like to concatenate both final layers, to finally put another dense layer with softmax to predict class probabilities.

I know it's feasible using Keras functionnal API, but how could one do it using tf.keras ?

hellowolrd
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1 Answers1

16

You can easily use Keras' functional API in TF (tested with TF 2.0):

import tensorflow as tf

# Image
input_1 = tf.keras.layers.Input(shape=(28, 28, 1))
conv2d_1 = tf.keras.layers.Conv2D(64, kernel_size=3,
                                  activation=tf.keras.activations.relu)(input_1)

# Second conv layer :
conv2d_2 = tf.keras.layers.Conv2D(32, kernel_size=3,
                                  activation=tf.keras.activations.relu)(conv2d_1)

# Flatten layer :
flatten = tf.keras.layers.Flatten()(conv2d_2)

# The other input
input_2 = tf.keras.layers.Input(shape=(1,))
dense_2 = tf.keras.layers.Dense(5, activation=tf.keras.activations.relu)(input_2)

# Concatenate
concat = tf.keras.layers.Concatenate()([flatten, dense_2])

n_classes = 4
# output layer
output = tf.keras.layers.Dense(units=n_classes,
                               activation=tf.keras.activations.softmax)(concat)

full_model = tf.keras.Model(inputs=[input_1, input_2], outputs=[output])

print(full_model.summary())

Which gives you the model you are looking for.

John Doe
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