Dnn Regression Loss Function. Different loss functions are used for classification problems. In simple words the Loss is used to calculate the gradients.
And gradients are used to update the weights of the Neural Net. In classification we are trying to predict output from set of finite categorical values ie Given large data set of images of hand written digits categorizing them. Contrast this with a classification problem where the aim is to select a class from a list of classes for example where a picture contains an apple or an orange recognizing which fruit is in the picture.
Each point on a chart is the value of a loss function on the last step after learning so far.
Sep 02 2018 Broadly loss functions can be classified into two major categories depending upon the type of learning task we are dealing with Regression losses and Classification losses. The proposed TL detector has a DNN architecture of encoder-decoder with focal regression loss. In order to perform Deep Learning Im currently using a tensorflow -. What I am curious about is the possibility that the weight optimization process in the DNN training could itself eliminate points with a low quality index ie.
