EmbeddingLayer

Keras layer that takes in discrete values/categories (one-hot encoded) and embeds (maps) them to continuous values. This embedding is learnt through training.

tokenization_layer.EmbeddingLayer(
    embedding_length, **kwargs
)

Takes in matrix of discrete values (one-hot encoded) and embeds them into continuous values, trained like the rest of the network. Shape of X should be(batch_size, sequence_length, onehot_categories). Note that this is essentially the same as keras.layers.Embedding, except that this version doesn't have to be the first layer in the network (i.e. it has an upstream gradient), unlike the official keras embedding layer.

Parameters

embedding_length ---- int How many values (i.e. dimensions in the vector) each category should be embedded as. The more values the more complex the meaning behind any embedding can be.

**kwargs