What is the loss function used in CNN?

What is the loss function used in CNN?

The Loss Function is one of the important components of Neural Networks. Loss is nothing but a prediction error of Neural Net. And the method to calculate the loss is called Loss Function. In simple words, the Loss is used to calculate the gradients.

What does the loss function measure?

At its core, a loss function is a measure of how good your prediction model does in terms of being able to predict the expected outcome(or value). We convert the learning problem into an optimization problem, define a loss function and then optimize the algorithm to minimize the loss function.

Is there a CNN based object detection task?

If you are interested in CNN based object detection task, you can find there’s a region proposal network (RPN) in two stage object detection model (RCNN, Fast-RCNN, Faster-RCNN etc.), RPN is used to generate position candidates that may contains a target object, then use a classifier to judge which class the object belongs to.

How to choose loss functions when training neural networks?

Binary Cross-Entropy Hinge Loss Squared Hinge Loss Multi-Class Classification Loss Functions Multi-Class Cross-Entropy Loss Sparse Multiclass Cross-Entropy Loss Kullback Leibler Divergence Loss We will focus on how to choose and implement different loss functions. For more theory on loss functions, see the post:

What is the loss function for binary classification?

In your case you have a binary classification task, therefore your output layer can be the standard sigmoid (where the output represents the probability of a test sample being a face). The loss you would use would be binary cross-entropy.

How to choose loss functions when training deep?

This requires the choice of an error function, conventionally called a loss function, that can be used to estimate the loss of the model so that the weights can Navigation Machine Learning MasteryMaking developers awesome at machine learning Click to Take the FREE Deep Learning Performane Crash-Course Home Main Menu Get Started Blog Topics