Parameters that affect the process of training an image classification model.
- macOS 10.14+
- Xcode 10.0+
- Create ML
Use this structure to configure the model training process. You can control parameters like the number of iterations used during training, the kinds of augmentation applied to the training data, and the version of the feature extractor. You can also provide validation data through the parameters if you don’t want the model to randomly select validation data from the training data set automatically.
The example below shows how to create a model with the
crop augmentation option that trains for 20 iterations, and that allows the classifier to randomly select validation data from your training data.
Alternatively, you can supply explicit validation data in the form of either a dictionary or a data source, as demonstrated in the discussions of the
init(feature methods respectively.
Provide the resulting
parameters structure to either the
init(training method (if your training data is represented by a data source) or the
init(training method (if your training data is represented by a dictionary) when creating your model.