def train_example(dataset=None):
model = GaussianBinaryRBM(nvis=1296,
nhid=61,
irange=0.5,
energy_function_class=grbm_type_1(),
learn_sigma=True,
init_sigma=.4,
init_bias_hid=2.,
mean_vis=False,
sigma_lr_scale=1e-3)
cost = SMD(corruptor=GaussianCorruptor(stdev=0.4))
algorithm = SGD(learning_rate=.1,
batch_size=5,
monitoring_batches=20,
monitoring_dataset=dataset,
cost=cost,
termination_criterion=MonitorBased(prop_decrease=0.01,
N=1))
train = Train(dataset=dataset,
model=model,
save_path="./experiment/training.pkl",
save_freq=10,
algorithm=algorithm,
extensions=[])
train.main_loop()
def get_layer_trainer_sgd_rbm(layer, trainset):
train_algo = SGD(
learning_rate = 1e-1,
batch_size = 5,
#"batches_per_iter" : 2000,
monitoring_batches = 20,
monitoring_dataset = trainset,
cost = SMD(corruptor=GaussianCorruptor(stdev=0.4)),
termination_criterion = EpochCounter(max_epochs=MAX_EPOCHS_UNSUPERVISED),
)
model = layer
extensions = [MonitorBasedLRAdjuster()]
return Train(model = model, algorithm = train_algo,
save_path='grbm.pkl',save_freq=1,
extensions = extensions, dataset = trainset)
def get_layer_trainer_sgd_rbm(layer, trainset):
train_algo = SGD(
learning_rate = 1e-1,
batch_size = 5,
#"batches_per_iter" : 2000,
monitoring_batches = 20,
monitoring_dataset = trainset,
cost = SMD(corruptor=GaussianCorruptor(stdev=0.4)),
termination_criterion = EpochCounter(max_epochs=MAX_EPOCHS),
# another option:
# MonitorBasedTermCrit(prop_decrease=0.01, N=10),
)
model = layer
callbacks = [MonitorBasedLRAdjuster(), ModelSaver()]
return Train(model = model, algorithm = train_algo,
callbacks = callbacks, dataset = trainset)
def __init__(self,
energy_function_class=GRBM_Type_1,
nhid=10,
irange=0.5,
rng=None,
mean_vis=False,
init_sigma=.4,
learn_sigma=True,
sigma_lr_scale=1.,
init_bias_hid=-2.,
min_sigma=.1,
max_sigma=10.,
dataset_adaptor=VectorDataset(),
trainer=SGDTrainer(
SMD(corruptor=GaussianCorruptor(stdev=0.00)))):
# trainer = SGDTrainer(SMD() )):
self.config = {
'energy_function_class': energy_function_class,
'nhid': nhid,
'irange': irange,
'rng': rng,
'mean_vis': mean_vis,
'init_sigma': init_sigma,
'learn_sigma': True,
'sigma_lr_scale': sigma_lr_scale,
'init_bias_hid': init_bias_hid,
'min_sigma': min_sigma,
'max_sigma': max_sigma,
'learn_sigma': learn_sigma
}
self.dataset_adaptor = dataset_adaptor
self.trainer = trainer
return