Chapter 4: Relation and Matching Networks Using TensorFlow

1. A relation network consists of two important functions: the embedding function, denoted by , and the relation function, denoted by . 
2. Once we have the feature vectors of the support set, , and query set,  , we combine them using an operator, . Here,  can be any combination operator; we use concatenation as an operator to combine the feature vectors of the support set and the query set—that is, .

3. The relation function, , will generate a relation score ranging from 0 to 1, representing the similarity between samples in the support set, , and samples in the query set, .

4. Our loss function can be represented as follows:

    

5. In matching networks, we use two embedding...