In life we have to process inputs of different types; images, text, numbers, etc. and neural networks can too.

For example, let’s suppose we want to build a network that predicts house prices using location data, attributes, and images. The process would look something like this:

Figure 1. Neural network data flow

We will work with a simplified example. We will input an image with either an X or an I, and numerical data with either a 0 or a 1. The outputs are shown in the following table:

Figure 2. Input-Output table

So, if we add an image with an X and the number 0 we get an output of 1.

Before starting to build our network, we import the required libraries:

import numpy as np
import random
import tensorflow as tf
from tensorflow import keras

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Building our data

Let’s start by building our input data. We will create the two 3x3, black and white images.

#0 is black, 255 is white.
X_image = [[0,255,0],[255,0,255],[0,255,0]]
I_image = [[255,0,255],[255,0,255],[255,0,255]]

Figure 3. X and I, increased 100x in size.

We will now create our data.

#keras #deep-learning #deep learning