PyroTraining.py

optim = pyro.optim.Adam({"lr": 0.001})
svi = SVI(model, guide, optim, loss=Trace_ELBO())

num_iterations = 50
loss = 0

for j in range(num_iterations):
    loss = 0
    for batch_id, data in enumerate(trainloader):
        # calculate the loss and take a gradient step
        loss += svi.step(data)
    normalizer_train = len(trainloader.dataset)
    total_epoch_loss_train = loss / normalizer_train
    
    correct = 0
    total = 0
    for j, data in enumerate(testloader):
        features = data["features"]
        labels = data["outcomes"]
        predicted = predict(features)
        total += labels.size(0)
        correct += (np.array(predicted) == np.array(labels)).sum().item()
    print("accuracy: {:.3f}".format(100 * correct / total))
    
    print("Epoch ", j, " Loss ", total_epoch_loss_train)