Embedding Layer — это слой нейронной сети, который изучает низкоразмерные векторные представления для высокоразмерных или категориальных входов. Он позволяет моделям обрабатывать категории, токены или сущности эффективнее, чем разреженные one-hot representations.
A special hidden layer that trains on a high-dimensional categorical feature to gradually learn a lower dimension embedding vector. An embedding layer enables a neural network to train far more efficiently than training just on the high-dimensional categorical feature. For example, Earth currently supports about 73,000 tree species. Suppose tree species is a feature in your model, so your model's input layer includes a one-hot vector 73,000 elements long. For example, perhaps would be represented something like this: !An array of 73,000 elements. The first 6,232 elements hold the value 0. The next element holds the value 1. The final 66,767 elements hold the value zero. A 73,000-element array is very long. If you don't add an embedding layer to the model, training is going to be very time consuming due to multiplying 72,999 zeros. Perhaps you pick the embedding layer to consist of 12 dimensions. Consequently, the embedding layer will gradually learn a new embedding vector for each tree species. In certain situations, hashing is a reasonable alternative to an embedding layer. See Embeddings in Machine Learning Crash Course for more information.