embedding(input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False)¶
A simple lookup table that looks up embeddings in a fixed dictionary and size.
This module is often used to retrieve word embeddings using indices. The input to the module is a list of indices, and the embedding matrix, and the output is the corresponding word embeddings.
oneflow.nn.Embeddingfor more details.
input (oneflow.LongTensor) – Tensor containing 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
padding_idx (int, optional) – If specified, the entries at
padding_idxdo not contribute to the gradient; therefore, the embedding vector at
padding_idxis not updated during training, i.e. it remains as a fixed “pad”.
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
>>> import oneflow as flow >>> import oneflow.nn.functional as F >>> # a batch of 2 samples of 4 indices each >>> input = flow.tensor([[1,2,4,5],[4,3,2,9]]) >>> # an embedding matrix containing 10 tensors of size 3 >>> embedding_matrix = flow.rand(10, 3) >>> output = F.embedding(input, embedding_matrix) >>> output.shape oneflow.Size([2, 4, 3]) >>> # example with padding_idx >>> input = flow.tensor([[0,2,0,5]]) >>> output = F.embedding(input, embedding_matrix, padding_idx=0) >>> output.shape oneflow.Size([1, 4, 3])