train_CIFAR_models.py

test_acc = {}
val_acc = {}
train_acc = {}
test_loss = {}
for train_size in dataset_size:
    print('Training with subset %1.4f, which is %d images'%(train_size, train_size*total_train))
    net = Net()
    
    # Train model with an early stopping criterion - terminates after 4 epochs of non-improving val loss
    net, loss_list, val_list = train_model(net, trainset_loaders[train_size], valloader, 1000, n_epochs=10)
    test_accuracy, loss = test_model(net, testloader)
    
    print('Accuracy of the network on the 10000 test images: %d %%' % (
        100 * accuracy))
    
    test_acc[train_size] = accuracy
    test_loss[train_size] = loss
    val_acc[train_size] = val_list
    train_acc[train_size] = loss_list
        
    torch.save(net, 'trainset_%1.2f_%d_images.model'%(train_size, train_size*total_train))