Making a choice a loss function
An optimization technique known as stochastic gradient descent is implemented to train deep learning neural networks.
It is essential to repeatedly estimate the error for the model's current condition as part of the optimization approach. In order to change the weights and lower the loss on the comprehensive evaluation, it is necessary to pick an error function, also known as a loss function, that could very well be employed to estimate the loss of the model.
A mapping from inputs to outputs is established by neural network models with examples, and the loss function specified must be acceptable for the specific predictive analysis problems having treated, such as classification or regression. Moreover, the output layer's configuration must be correct for the determined loss function.
Loss and optimizer functions
Loss functions ever provide more than just a static depiction of how nicely your model is functioning; they also establish the foundation for how well your algorithms properly match the data. The lot of machine learning algorithms utilise a loss function of some description during the optimization phase, which incorporates determining the optimal parameters (weights) for your data.
Taking linear regression as a straightforward illustration. Traditional "least squares" regression utilizes MSE to establish the line of best fit, hence the name "least squares"! The MSE is generated for each set of weights that the model tries across all input samples. Through the use of a optimizer procedure such as Gradient Descent, the model then reduces the MSE functions to the absolute bare minimum.
There are multiple independent optimizers, just as there are many kind of loss functions for multiple problems. Although it is within the objective of this post, the loss function and optimizer work collaboratively to provide the the algorithm the greatest feasible fit to your data.
Learn more about loss function in deep learning.
Conclusion
In this article, we discussed about how to choose optimizer , optimizer and loss function in details.