What Embedding Layer Meaning, Applications & Example
Neural network layer that maps discrete inputs to continuous vectors.
What is an Embedding Layer?
An Embedding Layer in neural networks is a layer that converts categorical data, like words, into dense vectors of fixed size. It is commonly used in natural language processing to transform words or tokens into continuous vector space, capturing semantic relationships among them.
How an Embedding Layer Works
The layer learns embeddings during training, assigning each input (e.g., word or token) to a vector based on context. These vectors are adjusted through backpropagation to capture patterns and relationships, allowing words with similar meanings to have similar embeddings.
Applications of Embedding Layers
- Sentiment Analysis : Converts words in text data into embeddings for more effective analysis of sentiment.
- Machine Translation: Encodes words from one language as embeddings, aiding translation into another language.
- Recommender Systems: Represents users and items as embeddings to find similarities and recommend items accordingly.
Example of an Embedding Layer in Use
In Keras, an Embedding Layer can be set up like this:
from tensorflow.keras.layers import Embedding
embedding_layer = Embedding(input_dim=5000, output_dim=64, input_length=100)
This layer will create 64-dimensional embeddings for a vocabulary size of 5,000, capturing dense representations for each input word in a fixed-length sequence.