![]() Output Layer Configuration: One node with a sigmoid activation unit.the class that you assign the integer value 1, whereas the other class is assigned the value 0. The problem is framed as predicting the likelihood of an example belonging to class one, e.g. Loss Function: Mean Squared Error (MSE).Ī problem where you classify an example as belonging to one of two classes.Output Layer Configuration: One node with a linear activation unit.179, Deep Learning, 2016 Machine Learning/Deep Learning Problems Regression ProblemĪ problem where you predict a real-value quantity. This means that the cost function is described as the cross-entropy between the training data and the model distribution. Most modern neural networks are trained using maximum likelihood. 156, Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, 1999 The mean squared error is popular for function approximation (regression) problems The cross-entropy error function is often used for classification problems when outputs are interpreted as probabilities of membership in an indicated class. The optimization strategies aim at minimizing the cost function.Ī few basic functions are very commonly used. A cost function, on the other hand, is the average loss over the entire training dataset. It is also sometimes called an error function. , Deep Learning, 2016Ī loss function is for a single training example. When we are minimizing it, we may also call it the cost function, loss function, or error function. Multi-Class Multiple Label:įor this case, we can apply the softmax loss with a little modification.The function we want to minimize or maximize is called the objective function or criterion. It is a Softmax activation plus a Cross-Entropy loss. the number of categories is large to the prediction output becomes overwhelming.Īlso called Softmax Loss.you don’t care at all about other close-enough predictions, when your classes are mutually exclusive, i.e.There are a number of situations to use scce, including: Many categorical models produce scce output because you save space, but lose A LOT of information (for example, in the 2nd example, index 2 was also very close.) I generally prefer cce output for model reliability. In the case of scce, the target index might be, and the model may predict.In the case of cce, the one-hot target might be and the model may predict (probably inaccurate, given that it gives more probability to the first class).Ĭonsider now a classification problem with 3 classes. In the case of scce, the target index may be and the model may predict.In the case of cce, the one-hot target may be and the model may predict (probably right).sparse_categorical_crossentropy ( scce) produces a category index of the most likely matching category.Ĭonsider a classification problem with 5 categories (or classes). ![]() categorical_crossentropy ( cce) produces a one-hot array containing the probable match for each category,.In this case, we can calculate using two different methods: Categorical Cross-Entropy and Sparse Categorical Cross-Entropy. Multi-Label can classify multiples objects in one sample. Multi-Class only classify one object from multiples objects in one sample. Difference between Multi-class and Multi-label. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |