Model Compilation
Explore how to use the compile method in Keras to prepare deep learning models for training. Understand how to select appropriate optimizers like Adam and loss functions such as binary_crossentropy, categorical_crossentropy, or mean_squared_error depending on your task type, and learn how to track model accuracy during training to evaluate progress.
We'll cover the following...
The compile method
After the model architecture is set, the model can be compiled using the compile method. This sets up the network for optimization. It creates an internal function to perform backpropagation efficiently.
Arguments for the compile method
The compile method has two arguments:
1. Optimizer
The optimizer helps specify the learning rate. The learning rate is important since it will help the model quickly find a good set of weights.
📝 Note: There are a few optimizers that automatically tune the learning rate. We do not need to know the details of each optimizer. However, we need to choose an optimizer that serves as a good example for many problems.
Adam (adaptive ...