Embedding_bag

Usage

nnf_embedding_bag(
  input,
  weight,
  offsets = NULL,
  max_norm = NULL,
  norm_type = 2,
  scale_grad_by_freq = FALSE,
  mode = "mean",
  sparse = FALSE,
  per_sample_weights = NULL,
  include_last_offset = FALSE,
  padding_idx = NULL
)

Arguments

input

(LongTensor) Tensor containing bags of indices into the embedding matrix

weight

(Tensor) The embedding matrix with number of rows equal to the maximum possible index + 1, and number of columns equal to the embedding size

offsets

(LongTensor, optional) Only used when input is 1D. offsets determines the starting index position of each bag (sequence) in input.

max_norm

(float, optional) If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm. Note: this will modify weight in-place.

norm_type

(float, optional) The p in 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. Note: this option is not supported when mode="max".

mode

(string, optional) "sum", "mean" or "max". Specifies the way to reduce the bag. Default: 'mean'

sparse

(bool, optional) if TRUE, gradient w.r.t. weight will be a sparse tensor. See Notes under nn_embedding for more details regarding sparse gradients. Note: this option is not supported when mode="max".

per_sample_weights

(Tensor, optional) a tensor of float / double weights, or NULL to indicate all weights should be taken to be 1. If specified, per_sample_weights must have exactly the same shape as input and is treated as having the same offsets, if those are not NULL.

include_last_offset

(bool, optional) if TRUE, the size of offsets is equal to the number of bags + 1.

padding_idx

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