ap.add_argument("--disable_backprop", default=False) ap.add_argument("--disable_reinforce", default=False) ap.add_argument("--random_glimpse", default=False) args = ap.parse_args() mnist = MiniBatches((MnistDataset()), batch_size=1) model_path = args.model network = get_network(model_path, std=args.variance, disable_reinforce=args.disable_reinforce, random_glimpse=args.random_glimpse) trainer_conf = TrainerConfig() trainer_conf.learning_rate = LearningRateAnnealer.learning_rate( args.learning_rate) trainer_conf.weight_l2 = 0.0001 trainer_conf.hidden_l2 = 0.0001 trainer_conf.method = args.method trainer = FirstGlimpseTrainer(network, network.layers[0], config=trainer_conf) annealer = LearningRateAnnealer(trainer, patience=5) timer = Timer() for _ in trainer.train(mnist.train_set(), mnist.valid_set(), mnist.test_set()): if annealer.invoke(): break
ap.add_argument("--learning_rate", default=0.01) ap.add_argument("--variance", default=0.005) ap.add_argument("--disable_backprop", default=False) ap.add_argument("--disable_reinforce", default=False) ap.add_argument("--random_glimpse", default=False) args = ap.parse_args() mnist = MiniBatches((MnistDataset()), batch_size=1) model_path = args.model network = get_network(model_path, std=args.variance, disable_reinforce=args.disable_reinforce, random_glimpse=args.random_glimpse) trainer_conf = TrainerConfig() trainer_conf.learning_rate = args.learning_rate trainer_conf.weight_l2 = 0.0001 trainer_conf.hidden_l2 = 0.0001 trainer_conf.method = args.method trainer_conf.disable_reinforce=args.disable_reinforce trainer_conf.disable_backprop=args.disable_backprop trainer = AttentionTrainer(network, network.layers[0], config=trainer_conf) trainer_conf.report() timer = Timer() for _ in list(trainer.train(mnist.train_set(), mnist.valid_set(), mnist.test_set())): pass timer.end()
ap.add_argument("--learning_rate", default=0.01) ap.add_argument("--variance", default=0.03) ap.add_argument("--disable_backprop", default=False) ap.add_argument("--disable_reinforce", default=False) ap.add_argument("--random_glimpse", default=False) args = ap.parse_args() mnist = MiniBatches((MnistDataset()), batch_size=1) model_path = args.model network = get_network(model_path, std=args.variance, disable_reinforce=args.disable_reinforce, random_glimpse=args.random_glimpse) trainer_conf = TrainerConfig() trainer_conf.learning_rate = LearningRateAnnealer.learning_rate(args.learning_rate) trainer_conf.weight_l2 = 0.0001 trainer_conf.hidden_l2 = 0.0001 trainer_conf.method = args.method trainer = FirstGlimpseTrainer(network, network.layers[0], config=trainer_conf) annealer = LearningRateAnnealer(trainer, patience=5) timer = Timer() for _ in trainer.train(mnist.train_set(), mnist.valid_set(), mnist.test_set()): if annealer.invoke(): break timer.end() network.save_params(model_path)