Sequence Classification with LSTM Recurrent Neural Networks 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.

What makes this problem difficult is that the sequences can vary in length, be comprised of 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.

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

By the end 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?

Source Code

Report

Next: Understanding Stateful LSTM Recurrent Neural Networks with Keras