How would you approach this task?

Submitted by JJ_00ne t3_11cmyf8 in deeplearning

I'm aiming to make a model to create a organization chart for a workplace. There are three key sector and about 15 workers. Each sector require at least one male and one female employee.

I would start by creating a table with all the employees and for each one as features the sex and a percentual about their affinity for each working sector based on their CV.

Starting from this, i'd like to have some clues about the cost function and the activation function to use, and how to set the minimum employees based on their sex (i'm using Keras and Tensorflow, not very skilled yet but i'm following the book Zero to Deep Learning)

2

Comments

You must log in or register to comment.

nibbajenkem t1_ja5568v wrote

Doesnt seem like anything you need deep learning for

0

JJ_00ne OP t1_ja7087f wrote

Yes it's more a way to exercise rather than a real necessity

1

nibbajenkem t1_ja7d93f wrote

What I mean is it doesn't make sense to use deep learning here.

0

usesbinkvideo t1_ja6stdw wrote

Here let me ChatGPT this for you:

Cost Function: Since you want to create an organization chart that meets specific criteria, such as having at least one male and one female employee in each sector, you could use a custom cost function that takes these criteria into account. One option could be to penalize the model heavily for each violation of these criteria. For example, you could add a large penalty to the cost function if a sector does not have at least one male and one female employee.

Activation Function: The choice of activation function depends on the structure of your model and the specific problem you're trying to solve. Since you have a binary classification problem (assigning each worker to a sector), you could use the sigmoid activation function for the output layer to produce a probability score for each sector. The input layer and hidden layers could use the ReLU activation function, which has been shown to work well in many types of neural networks.

Setting Minimum Employees Based on Sex: You mentioned that each sector requires at least one male and one female employee. You could enforce this requirement by adding constraints to the model. For example, you could use a custom constraint that checks the number of male and female employees in each sector after each batch and enforces the requirement that each sector has at least one male and one female employee. This would ensure that your model meets the specific requirements of your problem.

0

JJ_00ne OP t1_ja706gr wrote

Basically it's an "To do what you want to do, do what you want to do"

2