# wordEmbeddingLayer

Word embedding layer for deep learning neural network

## Description

A word embedding layer maps word indices to vectors.

Use a word embedding layer in a deep learning long short-term memory (LSTM) network. An LSTM network is a type of recurrent neural network (RNN) that can learn long-term dependencies between time steps of sequence data. A word embedding layer maps a sequence of word indices to embedding vectors and learns the word embedding during training.

This layer requires Deep Learning Toolbox™.

## Creation

### Syntax

### Description

creates a word embedding layer and specifies the embedding dimension and vocabulary
size.`layer`

= wordEmbeddingLayer(`dimension`

,`numWords`

)

sets optional properties
using one or more name-value pairs. Enclose each property name in single quotes.`layer`

= wordEmbeddingLayer(`dimension`

,`numWords`

,`Name,Value`

)

## Properties

## Examples

## References

[1] Glorot,
Xavier, and Yoshua Bengio. "Understanding the Difficulty of Training Deep Feedforward Neural
Networks." In *Proceedings of the Thirteenth International Conference on Artificial
Intelligence and Statistics*, 249–356. Sardinia, Italy: AISTATS, 2010. https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf

[2] He, Kaiming,
Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Delving Deep into Rectifiers: Surpassing Human-Level
Performance on ImageNet Classification." In *2015 IEEE International Conference on
Computer Vision (ICCV)*, 1026–34. Santiago, Chile: IEEE, 2015. https://doi.org/10.1109/ICCV.2015.123

[3] Saxe, Andrew M., James L. McClelland, and Surya Ganguli. "Exact Solutions to the Nonlinear Dynamics of Learning in Deep Linear Neural Networks.” Preprint, submitted February 19, 2014. https://arxiv.org/abs/1312.6120.

## Extended Capabilities

## Version History

**Introduced in R2018b**

## See Also

`trainNetwork`

(Deep Learning Toolbox) | `doc2sequence`

| `trainWordEmbedding`

| `wordEncoding`

| `lstmLayer`

(Deep Learning Toolbox) | `sequenceInputLayer`

(Deep Learning Toolbox) | `fastTextWordEmbedding`

| `tokenizedDocument`

| `word2vec`