470.470. Siamese Networks

Implement the Siamese Networks algorithm. Siamese Networks is a type of neural network that is used for similarity-based tasks, such as face recognition and anomaly detection. The network consists of two branches, each of which takes an input and produces an output. The outputs of the two branches are then compared using a similarity metric, such as the cosine similarity or the Euclidean distance.

**Example:** Suppose we have two images, and we want to train a Siamese Network to determine whether they are similar or not.

**Constraints:** The network should be able to learn a similarity metric that can distinguish between similar and dissimilar images.

**Note:** This is a simplified version of the Siamese Networks algorithm. In practice, you may need to add more components, such as data augmentation and regularization.