ReversibleEmbedding layer

[source]

ReversibleEmbedding class

keras_nlp.layers.ReversibleEmbedding(
    input_dim,
    output_dim,
    tie_weights=True,
    embeddings_initializer="uniform",
    embeddings_regularizer=None,
    embeddings_constraint=None,
    mask_zero=False,
    reverse_dtype=None,
    **kwargs
)

An embedding layer which can project backwards to the input dim.

This layer is an extension of keras.layers.Embedding for language models. This layer can be called "in reverse" with reverse=True, in which case the layer will linearly project from output_dim back to input_dim.

By default, the reverse projection will use the transpose of the embeddings weights to project to input_dim (weights are "tied"). If tie_weights=False, the model will use a separate, trainable variable for reverse projection.

This layer has no bias terms.

Arguments

• input_dim: Integer. Size of the vocabulary, i.e. maximum integer index + 1.
• output_dim: Integer. Dimension of the dense embedding.
• tie_weights: Boolean, whether or not the matrix for embedding and the matrix for the reverse projection should share the same weights.
• embeddings_initializer: Initializer for the embeddings matrix (see keras.initializers).
• embeddings_regularizer: Regularizer function applied to the embeddings matrix (see keras.regularizers).
• embeddings_constraint: Constraint function applied to the embeddings matrix (see keras.constraints).
• mask_zero: Boolean, whether or not the input value 0 is a special "padding" value that should be masked out.
• reverse_dtype: The dtype for the reverse projection computation. Defaults to the compute_dtype of the layer.
• **kwargs: other keyword arguments passed to keras.layers.Embedding, including name, trainable, dtype etc.

Call arguments

• inputs: The tensor inputs to the layer.
• reverse: Boolean. If True the layer will perform a linear projection from output_dim to input_dim, instead of a normal embedding call. Default to False.

Example

batch_size = 16
vocab_size = 100
hidden_dim = 32
seq_length = 50

# Generate random inputs.
token_ids = np.random.randint(vocab_size, size=(batch_size, seq_length))

embedding = keras_nlp.layers.ReversibleEmbedding(vocab_size, hidden_dim)
# Embed tokens to shape `(batch_size, seq_length, hidden_dim)`.
hidden_states = embedding(token_ids)
# Project hidden states to shape `(batch_size, seq_length, vocab_size)`.
logits = embedding(hidden_states, reverse=True)

References

• Vaswani et al., 2017
• Press and Wolf, 2016