Python RbmBernoulliTrainer Exemples

Exemple #1

Fichier : rae.py Projet : sapresearch/fuzzy_adventure

class RAE(object):

	def __init__(self, in_dims, out_dims):
		self.dataset = UnsupervisedDataSet(in_dims)
		cfg = RbmGibbsTrainerConfig()
		cfg.maxIter = 5
		self.model = Rbm.fromDims(in_dims, out_dims)
		self.trainer = RbmBernoulliTrainer(self.model, self.dataset, cfg)

	def add_data(self, data):
		for d in data:
			self.dataset.addSample(d)

	def _train(self, iterations):
		for _ in xrange(iterations):
			self.trainer.train()

Exemple #2

from __future__ import print_function

#!/usr/bin/env python
""" Miniscule restricted Boltzmann machine usage example """

__author__ = 'Justin S Bayer, [email protected]'

from pybrain.structure.networks.rbm import Rbm
from pybrain.unsupervised.trainers.rbm import (RbmGibbsTrainerConfig,
                                               RbmBernoulliTrainer)
from pybrain.datasets import UnsupervisedDataSet

ds = UnsupervisedDataSet(6)
ds.addSample([0, 1] * 3)
ds.addSample([1, 0] * 3)

cfg = RbmGibbsTrainerConfig()
cfg.maxIter = 3

rbm = Rbm.fromDims(6, 1)
trainer = RbmBernoulliTrainer(rbm, ds, cfg)
print(rbm.params, rbm.biasParams)
for _ in range(50):
    trainer.train()

print(rbm.params, rbm.biasParams)

Exemple #3

Fichier : rae.py Projet : sapresearch/fuzzy_adventure

	def __init__(self, in_dims, out_dims):
		self.dataset = UnsupervisedDataSet(in_dims)
		cfg = RbmGibbsTrainerConfig()
		cfg.maxIter = 5
		self.model = Rbm.fromDims(in_dims, out_dims)
		self.trainer = RbmBernoulliTrainer(self.model, self.dataset, cfg)

Exemple #4

Fichier : rbm.py Projet : Angeliqe/pybrain

from __future__ import print_function

#!/usr/bin/env python
""" Miniscule restricted Boltzmann machine usage example """

__author__ = 'Justin S Bayer, [email protected]'

from pybrain.structure.networks.rbm import Rbm
from pybrain.unsupervised.trainers.rbm import (RbmGibbsTrainerConfig,
                                               RbmBernoulliTrainer)
from pybrain.datasets import UnsupervisedDataSet


ds = UnsupervisedDataSet(6)
ds.addSample([0, 1] * 3)
ds.addSample([1, 0] * 3)

cfg = RbmGibbsTrainerConfig()
cfg.maxIter = 3

rbm = Rbm.fromDims(6, 1)
trainer = RbmBernoulliTrainer(rbm, ds, cfg)
print(rbm.params, rbm.biasParams)
for _ in range(50):
    trainer.train()

print(rbm.params, rbm.biasParams)