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_grbm(structure):
n_input, n_output = structure
config = {
'nvis': n_input,
'nhid': n_output,
"irange": 0.05,
"energy_function_class": GRBM_Type_1,
"learn_sigma": True,
"init_sigma": .4,
"init_bias_hid": -2.,
"mean_vis": False,
"sigma_lr_scale": 1e-3
}
return GaussianBinaryRBM(**config)
required=False, help='Disable plotting of results.')
args = parser.parse_args()
data_rng = numpy.random.RandomState(seed=999)
data = data_rng.normal(size=(500, 20)).astype(theano.config.floatX)
conf = {
'nvis': 20,
'nhid': 30,
'rbm_seed': 1,
'batch_size': 100,
'base_lr': 1e-4,
'anneal_start': 1,
'pcd_steps': 1,
}
rbm = GaussianBinaryRBM(nvis=conf['nvis'], nhid=conf['nhid'],
irange=0.5, energy_function_class = GRBM_Type_1)
rng = numpy.random.RandomState(seed=conf.get('rbm_seed', 42))
sampler = BlockGibbsSampler(rbm, data[0:100], rng,
steps=conf['pcd_steps'])
minibatch = tensor.matrix()
optimizer = SGDOptimizer(rbm, conf['base_lr'], conf['anneal_start'])
updates = training_updates(visible_batch=minibatch, model=rbm,
sampler=sampler, optimizer=optimizer)
proxy_cost = rbm.reconstruction_error(minibatch, rng=sampler.s_rng)
train_fn = theano.function([minibatch], proxy_cost, updates=updates)
vis = tensor.matrix('vis')
free_energy_fn = theano.function([vis], rbm.free_energy_given_v(vis))