Tune hyperparameters
Submit a hyperparameter tuning job for your training model.
Before you begin
Hyperparameter tuning is run on a train model, to learn more about creating a training model, see Create a training model.
About this task
When tuning hyperparameters, IBM Spectrum Conductor Deep Learning Impact takes advantage of IBM Spectrum Conductor to launch multiple parallel searches for the optimal hyperparameters when training your model. As a result, a new tuned model is created that contains the most optimal hyperparameters which maximize your models accuracy.
During a hyperparameter tuning job on a Caffe training model, IBM Spectrum Conductor Deep Learning Impact automatically passes the hyperparameters suggested by the hyperparameter tuning optimizer to the solver.prototxt.
lDuring a hyperparameter tuning job on a TensorFlow training model, after you import the hyperparameter tuning packages into your model, IBM Spectrum Conductor Deep Learning Impact uses the learning rate operator and optimizer provided.