The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems such as machine translation. Attention is an extension to the encoder-decoder model that improves the performance of the approach on longer sequences. Global attention is a simplification of attention that may be easier to implement in declarative deep […]
Search results for "encoder decoder attention"
Implementation Patterns for the Encoder-Decoder RNN Architecture with Attention
The encoder-decoder architecture for recurrent neural networks is proving to be powerful on a host of sequence-to-sequence prediction problems in the field of natural language processing. Attention is a mechanism that addresses a limitation of the encoder-decoder architecture on long sequences, and that in general speeds up the learning and lifts the skill of the […]
How to Develop an Encoder-Decoder Model with Attention in Keras
The encoder-decoder architecture for recurrent neural networks is proving to be powerful on a host of sequence-to-sequence prediction problems in the field of natural language processing such as machine translation and caption generation. Attention is a mechanism that addresses a limitation of the encoder-decoder architecture on long sequences, and that in general speeds up the […]
How Does Attention Work in Encoder-Decoder Recurrent Neural Networks
Attention is a mechanism that was developed to improve the performance of the Encoder-Decoder RNN on machine translation. In this tutorial, you will discover the attention mechanism for the Encoder-Decoder model. After completing this tutorial, you will know: About the Encoder-Decoder model and attention mechanism for machine translation. How to implement the attention mechanism step-by-step. […]
Building Transformer Models with Attention Crash Course. Build a Neural Machine Translator in 12 Days
Transformer is a recent breakthrough in neural machine translation. Natural languages are complicated. A word in one language can be translated into multiple words in another, depending on the context. But what exactly a context is, and how you can teach the computer to understand the context was a big problem to solve. The invention […]
Joining the Transformer Encoder and Decoder Plus Masking
We have arrived at a point where we have implemented and tested the Transformer encoder and decoder separately, and we may now join the two together into a complete model. We will also see how to create padding and look-ahead masks by which we will suppress the input values that will not be considered in […]
Implementing the Transformer Decoder from Scratch in TensorFlow and Keras
There are many similarities between the Transformer encoder and decoder, such as their implementation of multi-head attention, layer normalization, and a fully connected feed-forward network as their final sub-layer. Having implemented the Transformer encoder, we will now go ahead and apply our knowledge in implementing the Transformer decoder as a further step toward implementing the […]
Implementing the Transformer Encoder from Scratch in TensorFlow and Keras
Having seen how to implement the scaled dot-product attention and integrate it within the multi-head attention of the Transformer model, let’s progress one step further toward implementing a complete Transformer model by applying its encoder. Our end goal remains to apply the complete model to Natural Language Processing (NLP). In this tutorial, you will discover how […]
How to Implement Multi-Head Attention from Scratch in TensorFlow and Keras
We have already familiarized ourselves with the theory behind the Transformer model and its attention mechanism. We have already started our journey of implementing a complete model by seeing how to implement the scaled-dot product attention. We shall now progress one step further into our journey by encapsulating the scaled-dot product attention into a multi-head […]
How to Implement Scaled Dot-Product Attention from Scratch in TensorFlow and Keras
Having familiarized ourselves with the theory behind the Transformer model and its attention mechanism, we’ll start our journey of implementing a complete Transformer model by first seeing how to implement the scaled-dot product attention. The scaled dot-product attention is an integral part of the multi-head attention, which, in turn, is an important component of both […]