Now that we have trained our model, the next natural step is to use it for inference.

You need two things in order to load weights for a model: the model's record and the model's config. Since parameters in Burn are lazy initialized, no allocation and GPU/CPU kernels are executed by the ModelConfig::init function. The weights are initialized when used for the first time, therefore you can safely use config.init(device).load_record(record) without any meaningful performance cost. Let's create a simple infer method in a new file src/ which we will use to load our trained model.

pub fn infer<B: Backend>(artifact_dir: &str, device: B::Device, item: MnistItem) {
    let config = TrainingConfig::load(format!("{artifact_dir}/config.json"))
        .expect("Config should exist for the model");
    let record = CompactRecorder::new()
        .load(format!("{artifact_dir}/model").into(), &device)
        .expect("Trained model should exist");

    let model = config.model.init::<B>(&device).load_record(record);

    let label = item.label;
    let batcher = MnistBatcher::new(device);
    let batch = batcher.batch(vec![item]);
    let output = model.forward(batch.images);
    let predicted = output.argmax(1).flatten::<1>(0, 1).into_scalar();

    println!("Predicted {} Expected {}", predicted, label);

The first step is to load the configuration of the training to fetch the correct model configuration. Then we can fetch the record using the same recorder as we used during training. Finally we can init the model with the configuration and the record. For simplicity we can use the same batcher used during the training to pass from a MnistItem to a tensor.

By running the infer function, you should see the predictions of your model!

Add the call to infer to the file after the train function call:


The number 42 is the index of the image in the MNIST dataset. You can explore and verify them using this MNIST viewer.