The 5 Step Life-Cycle for Long Short-Term Memory Models in Keras
Last Updated on August 27, 2020
Deep learning neural networks are very easy to create and evaluate in Python with Keras, but you must follow a strict model life-cycle.
In this post, you will discover the step-by-step life-cycle for creating, training, and evaluating Long Short-Term Memory (LSTM) Recurrent Neural Networks in Keras and how to make predictions with a trained model.
After reading this post, you will know:
- How to define, compile, fit, and evaluate an LSTM in Keras.
- How to select standard defaults for regression and classification sequence prediction problems.
- How to tie it all together to develop and run your first LSTM recurrent neural network in Keras.
Kick-start your project with my new book Long Short-Term Memory Networks With Python, including step-by-step tutorials and the Python source code files for all examples.
Let’s get started.
- Update June/2017: Fixed typo in input resizing example.
The 5 Step Life-Cycle for Long Short-Term Memory Models in Keras
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