An encoder for learning embeddings via minimizing the magnet loss. In the embedding space each class forms its own cluster. See "METRIC LEARNING WITH ADAPTIVE DENSITY DISCRIMINATION" by Rippel et al. The codes from the following repos are used:
- https://github.com/pumpikano/tf-magnet-loss
- https://github.com/nwojke/cosine_metric_learning/blob/master/losses.py
Batch Losses
plot_smooth(batch_losses)
Initial Embeddings of the MNIST dataset
plot_embedding(initial_reps[:500], y_train[:500])
Final Embeddings
plot_embedding(final_reps[:500], y_train[:500])
