Skip to contents

Embedding module

Source: R/nn-sparse.R
nn_embedding.Rd

A simple lookup table that stores embeddings of a fixed dictionary and size. This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.

Usage

nn_embedding(
  num_embeddings,
  embedding_dim,
  padding_idx = NULL,
  max_norm = NULL,
  norm_type = 2,
  scale_grad_by_freq = FALSE,
  sparse = FALSE,
  .weight = NULL
)

Arguments

num_embeddings

(int): size of the dictionary of embeddings

embedding_dim

(int): the size of each embedding vector

padding_idx

(int, optional): If given, pads the output with the embedding vector at padding_idx (initialized to zeros) whenever it encounters the index.

max_norm

(float, optional): If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.

norm_type

(float, optional): The p of the p-norm to compute for the max_norm option. Default 2.

scale_grad_by_freq

(boolean, optional): If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default False.

sparse

(bool, optional): If True, gradient w.r.t. weight matrix will be a sparse tensor.

.weight

(Tensor) embeddings weights (in case you want to set it manually)

See Notes for more details regarding sparse gradients.

Note

Keep in mind that only a limited number of optimizers support sparse gradients: currently it's optim.SGD (CUDA and CPU), optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)

With padding_idx set, the embedding vector at padding_idx is initialized to all zeros. However, note that this vector can be modified afterwards, e.g., using a customized initialization method, and thus changing the vector used to pad the output. The gradient for this vector from nn_embedding is always zero.

Attributes

  • weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim) initialized from \(\mathcal{N}(0, 1)\)

Shape

  • Input: \((*)\), LongTensor of arbitrary shape containing the indices to extract

  • Output: \((*, H)\), where * is the input shape and \(H=\mbox{embedding\_dim}\)

Examples

if (torch_is_installed()) {
# an Embedding module containing 10 tensors of size 3
embedding <- nn_embedding(10, 3)
# a batch of 2 samples of 4 indices each
input <- torch_tensor(rbind(c(1, 2, 4, 5), c(4, 3, 2, 9)), dtype = torch_long())
embedding(input)
# example with padding_idx
embedding <- nn_embedding(10, 3, padding_idx = 1)
input <- torch_tensor(matrix(c(1, 3, 1, 6), nrow = 1), dtype = torch_long())
embedding(input)
}
#> torch_tensor
#> (1,.,.) = 
#>   0.0000  0.0000  0.0000
#>  -1.3306 -1.7775  0.3843
#>   0.0000  0.0000  0.0000
#>  -0.1014  0.5371 -0.2300
#> [ CPUFloatType{1,4,3} ][ grad_fn = <EmbeddingBackward0> ]