Sequence Classification with LSTM Recurrent Neural Networks in Python with Keras

 Sequence classification is a predictive modeling problem where you have some sequence of inputs over space or time, and the task is to predict a category for the sequence.

This problem is difficult because the sequences can vary in length, comprise a very large vocabulary of input symbols, and may require the model to learn the long-term context or dependencies between symbols in the input sequence.

In this post, you will discover how you can develop LSTM recurrent neural network models for sequence classification problems in Python using the Keras deep learning library.

After reading this post, you will know:

• How to develop an LSTM model for a sequence classification problem
• How to reduce overfitting in your LSTM models through the use of dropout
• How to combine LSTM models with Convolutional Neural Networks that excel at learning spatial relationshipsSequence classification is a predictive modeling problem where you have some sequence of inputs over space or time, and the task is to predict a category for the sequence.

  This problem is difficult because the sequences can vary in length, comprise a very large vocabulary of input symbols, and may require the model to learn the long-term context or dependencies between symbols in the input sequence.

  In this post, you will discover how you can develop LSTM recurrent neural network models for sequence classification problems in Python using the Keras deep learning library.

  After reading this post, you will know:

  • How to develop an LSTM model for a sequence classification problem
  • How to reduce overfitting in your LSTM models through the use of dropout
  • How to combine LSTM models with Convolutional Neural Networks that excel at learning spatial relationships