Spatial Representations for Artificial Intelligence - a Python library toolkit for geospatial machine learning focused on creating embeddings for downstream tasks
I am using the hex2vec embedder and upon looking at the training loss, I see that no matter what hyperparameters I use, the first epoch step has a loss larger than 1.0 (e.g. 1.5) and all the next steps have exactly 1.0.
For f-score, the metric gradually increases, so I guess the model is getting better, but the loss stays exactly the same. Can anyone explain this behaviour please?
I am using the hex2vec embedder and upon looking at the training loss, I see that no matter what hyperparameters I use, the first epoch step has a loss larger than 1.0 (e.g. 1.5) and all the next steps have exactly 1.0.
For f-score, the metric gradually increases, so I guess the model is getting better, but the loss stays exactly the same. Can anyone explain this behaviour please?