Keras Embedding Layer Masking

In Keras, the embedding layer is a crucial component for dealing with text data. One important feature of the embedding layer is masking, which allows the model to ignore padded tokens during training. This is particularly useful when working with variable-length sequences, such as in natural language processing tasks.

When creating an embedding layer in Keras, one of the parameters that needs to be specified is the input_dim, which represents the size of the vocabulary. However, it is commonly recommended to set the input_dim to |vocabulary| + 2.

But why does input_dim need to be set as |vocabulary| + 2? The addition of 2 is due to the special tokens that are often used in natural language processing tasks. These tokens are typically used for padding and for representing out-of-vocabulary words.

By setting input_dim as |vocabulary| + 2, we are ensuring that the embedding layer can properly handle these special tokens and effectively learn the embeddings for each word in the vocabulary while ignoring the padding tokens during training.

Overall, masking in the embedding layer is a powerful feature that can greatly improve the performance of models when working with text data. By setting the input_dim to |vocabulary| + 2, we are ensuring that the embedding layer can effectively handle special tokens and learn meaningful representations for words in the vocabulary.