Day 6 – What is Loss Function in Deep Learning | Loss Function in Machine Learning | Loss Function Types
In this blog, we will cover the concept of a loss function and its significance in artificial neural networks. Loss functions play a crucial role in model training, as they are used by stochastic gradient descent to minimize the error during the training process. We will discuss how loss functions are calculated and their importance in improving the accuracy of the model predictions.
A loss function is a measure of how well a prediction model performs in terms of its ability to predict the expected outcome. In supervised learning, where we have both labels and features, the loss function calculates the error on each input provided by comparing the predicted output with the actual label value.
For example, let’s consider a classification problem where we are predicting whether an image is of a car or an airplane. The label value associated with an airplane is 1, while for a car it is 0. When we pass a new image of an airplane to our model and it outputs a probability of 0.4 (or 40%), the error between the predicted and actual value would be 0.4 – 1, since the actual value for an airplane is 1. Therefore, the difference comes out to be -0.6.
This activity is performed for each input, and the individual values are squared to ensure non-negative values. Squaring the value of -0.6 gives us 0.36. We square each individual value and calculate the average of these squared values to obtain the mean square error loss function.
It’s important to note that there are various other loss functions, such as mean absolute error, mean bias error, hinge loss, cross-entropy, etc. Although the formulas may differ, the underlying idea remains the same for all these functions. The implementation of the loss function may vary based on the algorithm used.
The process of calculating the loss occurs at the end of each epoch during model training. The value of the loss keeps changing since the weights of the model are constantly being updated. The overall goal is to minimize the error and increase the accuracy, so with every epoch, the loss decreases continuously.
In this article, we have discussed the concept of a loss function and its significance in artificial neural networks. Loss functions are used to measure the performance of a prediction model and calculate the error between predicted and actual values. The loss is continuously updated during model training to improve the accuracy. Different loss functions may have different formulas, but the underlying principle remains the same. The goal is to minimize the error and increase the accuracy of the model predictions.
I hope you found this blog has been helpful to you, thanks for reading this article.
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