The neural network never reaches to minimum gradient

Hi,

I am using neural network for solving a dynamic economic model. The problem is that the neural network doesn't reach to minimum gradient even after many iterations (more than 122 iterations). It stops mostly because of validation checks or, but this happens too rarely, due to maximum epoch reach. What could I change to increase the performance of my neural network?\

This is how it looks like.

Answer 1

[Parag]

Hi,

The goal in training a neural network is not to achieve a "minimum gradient" but rather to minimize the loss function (or objective function), which measures how well the network's predictions match the target values. Here's how gradients fit into this process:

1. Objective: The primary objective is to minimize the loss function, which quantifies the error between the predicted outputs and the actual targets. A lower loss indicates better performance of the network.
2. Role of Gradients: Gradients are used as a tool to achieve this objective. They provide the direction and rate of change of the loss function with respect to the network's parameters (weights and biases).

You can check this documentation for more detail

https://www.mathworks.com/matlabcentral/fileexchange/102709-neural-network-optimization