Can a model not converge
Posted: Tue Feb 18, 2025 5:58 am
Parameter stabilization. After each training step, the model updates the weights — the coefficients it uses to calculate the result. If the values of these parameters stop changing significantly between iterations, this means that the model has converged.
Convergence is achieved at the moment when the difference between real and calculated data becomes minimal.
On the graph, convergence looks something like this. It is achieved at the moment when the difference between real and calculated data becomes minimal. Source
Once the function converges, the training algorithm stops to avoid overfitting the model.
There are situations when convergence cannot be peru telegram data achieved for some reason. This is called divergence. With divergence, the values of the objective function do not stop changing - the losses do not reach a minimum.
If a model diverges, no matter how much it is trained, it will never improve its accuracy to a sufficient level. Divergence usually indicates that the model itself, the method, or the training parameters need to be changed.
The model may not converge for various reasons. For example:
because of the characteristics of the data it is trained on - say, it is not scaled or normalized, it contains outliers or noise;
due to incorrectly chosen loss function;
due to inappropriate hyperparameters of the model, such as the training step - if it is too large, the model will diverge.
Convergence is achieved at the moment when the difference between real and calculated data becomes minimal.
On the graph, convergence looks something like this. It is achieved at the moment when the difference between real and calculated data becomes minimal. Source
Once the function converges, the training algorithm stops to avoid overfitting the model.
There are situations when convergence cannot be peru telegram data achieved for some reason. This is called divergence. With divergence, the values of the objective function do not stop changing - the losses do not reach a minimum.
If a model diverges, no matter how much it is trained, it will never improve its accuracy to a sufficient level. Divergence usually indicates that the model itself, the method, or the training parameters need to be changed.
The model may not converge for various reasons. For example:
because of the characteristics of the data it is trained on - say, it is not scaled or normalized, it contains outliers or noise;
due to incorrectly chosen loss function;
due to inappropriate hyperparameters of the model, such as the training step - if it is too large, the model will diverge.