Life-Cycle for Neural Network Models in Keras

Deep learning neural networks are very easy to create and evaluate in Python with Keras, but you must follow a strict model life-cycle.

Let us discover the step-by-step life-cycle for creating, training and evaluating deep learning neural networks in Keras and how to make predictions with a trained model.

By the end you will know:

• How to define, compile, fit and evaluate a deep learning neural network in Keras.
• How to select standard defaults for regression and classification predictive modeling problems.
• How to tie it all together to develop and run your first Multilayer Perceptron network in Keras.

Overview

Below is an overview of the 5 steps in the neural network model life-cycle in Keras that we are going to look at.

• Define Network.
• Compile Network.
• Fit Network.
• Evaluate Network.
• Make Predictions.

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