{"id":1061,"date":"2021-10-20T08:41:56","date_gmt":"2021-10-20T08:41:56","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2021\/10\/20\/adding-a-custom-attention-layer-to-recurrent-neural-network-in-keras\/"},"modified":"2021-10-20T08:41:56","modified_gmt":"2021-10-20T08:41:56","slug":"adding-a-custom-attention-layer-to-recurrent-neural-network-in-keras","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2021\/10\/20\/adding-a-custom-attention-layer-to-recurrent-neural-network-in-keras\/","title":{"rendered":"Adding A Custom Attention Layer To Recurrent Neural Network In Keras"},"content":{"rendered":"
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Last Updated on October 12, 2021<\/p>\n

Deep learning networks have gained immense popularity in the past few years. The \u2018attention mechanism\u2019 is integrated with the deep learning networks to improve their performance. Adding attention component to the network has shown significant improvement in tasks such as machine translation, image recognition, text summarization and similar applications.<\/p>\n

This tutorial shows how to add a custom attention layer to a network built using a recurrent neural network. We\u2019ll illustrate an end to end application of time series forecasting using a very simple dataset. The tutorial is designed for anyone looking for a basic understanding of how to add user defined layers to a deep learning network and use this simple example to build more complex applications.<\/p>\n

After completing this tutorial, you will know:<\/p>\n