Styjun

I am very new to the field of Neural Network. Apologies, if this question is very amateurish.

I am looking to build a neural network model to predict whether a particular image that I am about to post on a social media platform will get a certain engagement rate.

I have around 120 images with historical data about the engagement rate. The following information is available:

1. Images of size 501 px x 501 px


2. Type of image (Exterior photoshoot/Interior photoshoot)


3. Day of posting the image (Sunday/Monday/Tuesday/Wednesday/Thursday/Friday/Saturday)


4. Time of posting the image (18:33, 10:13, 19:36 etc)


5. No. of people who have seen the post (15659, 35754, 25312 etc)


6. Engagement rate (5.22%, 3.12%, 2.63% etc)

I would like the model to predict if a certain image when posted on a particular day and time will give an engagement rate of 3% or more.

As you may have noticed, the input data is images, text (signifying what type or day), time and numbers.

Could you please help me understand how to build a neural network for this problem?

P.S: I am very new to this field. It would be great if you can give a detailed direction how I should proceed to solve this problem.

A neural network has three kinds of neuronal layers:

1. Input layer. It stores the inputs this network will receive. The number of neurons must equal the number of inputs you have;


2. Hidden layer. It uses the inputs that come from the previous layer and it does the necessary calculations so as to obtain a result, which passes to the output layer. More complex problems may require more than one hidden layer. As far as I know, there is not an algorithm to determine the number of neurons in this layer, so I think you determine this number based on trial and error and previous experience;


3. Output layer. It gets the results from the hidden layer and gives it to the user for his personal use. The number of neurons from the output layer equals the number of outputs you have.

According to what you write here, your training database has 6 inputs and one output (the engagement rate). This means that your artificial neural network (ANN) will have 6 neurons on the input layer and one neuron on the output layer.

I not sure if you can pass images as inputs to a neural network. Also, because in theory there are an infinite types of images, I think you should categorize them a bit, each category receiving a number. An example of categorization would be:

• Images with dogs are in category 1;


• Images with hospitals are in category 2, etc.

So, your inputs will look like this:

1. Image category (dogs=1, hospitals=2, etc.);


2. Type of image (Exterior photoshoot=1, interior photoshoot=2);


3. Posting day (Sunday=1, Monday=2, etc.);


4. Time of posting the image;


5. Number of people who have seen the post;


6. Engagement rate.

The number of hidden layers and the number of each neuron from each hidden layer depends on your problem's complexity. Having 120 pictures, I think one hidden layer and 10 neurons on this layer is enough.

The ANN will have one hidden layer (the engagement rate).

Once the database containing the information about the 120 pictures is created (known as training database) is created, the next step is to train the ANN using the database. However, there is some discussion here.

Training an ANN means computing some parameters of the hidden neurons by using an optimization algorithm so as the sum of squared errors is minimum. The training process has some degree of randomness to it. To minimize the effect of the randomness factor and to get as precise estimations as possible, your training database must have:

1. Consistent data;


2. Many records;

I don't know how consistent your data are, but from my experience, a small training database with consistent data beats a huge database with non-consistent ones.

Judging by the problem, I think you should use the default activation function provided by the software you use for ANN handling.

Once you have trained your database, it is time to see how efficient this training was. The software which you use for ANN should provide you with tools to estimate this, tools which should be documented. If training is satisfactory for you, you may begin using it. If it is not, you may either re-train the ANN or use a larger database.