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()):
ap.add_argument("--method", default="momentum")
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("--method", default="MOMENTUM")
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()
