train_with_random_search.py

n_searches = 20
n_epochs = 15
n_val = 500

for train_size in dataset_size:
    print('Training with subset %1.4f, which is %d images'%(train_size, train_size*total_train))
    
    test_acc[train_size] = []
    test_loss[train_size] = []
    val_acc[train_size] = []
    train_acc[train_size] = []
    
    # Perform random search for that dataset size
    for trial in range(n_searches):
        hyperparam_dict = random_hyperparamters()
        print(hyperparam_dict)
        
        net = Net(hyperparam_dict)
        
        net, loss_list, val_list = train_model(net, trainset_loaders[train_size], valloader, n_val, n_epochs=n_epochs,
                                              lr=hyperparam_dict['lr'], 
                                               momentum=hyperparam_dict['momentum'], 
                                               weight_decay=hyperparam_dict['weight_decay']
                                              )

        test_acc[train_size].append((hyperparam_dict, accuracy))
        test_loss[train_size].append((hyperparam_dict, loss)) 
        val_acc[train_size].append((hyperparam_dict, val_list)) 
        train_acc[train_size].append((hyperparam_dict, loss_list))


        torch.save(net, 'trainset_%d_images_trial%d_val_loss_%1.2f.model'%((train_size*total_train), trial, val_list[-1]))
        torch.save(hyperparam_dict, 'trainset_%d_images_trial%d_val_loss_%1.2f.hparams'%((train_size*total_train), trial, val_list[-1]))