Why does the output layer of the CNN for identifying dogs vs cats have only 2 nodes?
The output layer of a Convolutional Neural Network (CNN) for identifying dogs vs cats typically has only 2 nodes due to the binary nature of the classification task. In this specific case, the goal is to determine whether an input image belongs to the "dog" class or the "cat" class. As a result, the output layer needs to have a representation for each of these two classes, hence the requirement for 2 nodes.
Each node in the output layer corresponds to one class label, and the activation value of the node represents the network's confidence in the presence of that particular class. In this scenario, the first node could represent the "dog" class, and the second node could represent the "cat" class. The activation values of these nodes can be interpreted as the probabilities of the input image belonging to each class.
By having only 2 nodes in the output layer, the CNN is able to provide a clear decision boundary between the two classes. The network learns to assign higher activation values to the node that corresponds to the correct class, indicating a higher confidence in that classification. For instance, if the network predicts a dog image, the activation value of the first node would be higher than the activation value of the second node, and vice versa for a cat image.
It is worth noting that the choice of 2 nodes in the output layer is not arbitrary but is based on the specific problem being solved. In cases where there are more than two classes, the number of nodes in the output layer would be equal to the number of classes. For example, if we were to classify images into three classes (e.g., dog, cat, bird), the output layer would consist of three nodes, each representing one class.
Having more nodes in the output layer than necessary would not provide any additional benefit in this binary classification task. It would only introduce unnecessary complexity and computational overhead. Therefore, keeping the output layer concise with 2 nodes allows for a more efficient and focused representation of the classification problem at hand.
The output layer of a CNN for identifying dogs vs cats has only 2 nodes to represent the binary classification task. Each node corresponds to one class label, and the activation values of these nodes indicate the network's confidence in the presence of each class. This design choice ensures a clear decision boundary between the two classes and avoids unnecessary complexity.
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