def __init__(self, n_visible, n_hidden):
super(LearntPrecisionGaussianBinaryRBM, self).__init__()
# data shape
self.n_visible = n_visible
self.n_hidden = n_hidden
# units
self.v = units.LearntPrecisionGaussianUnits(self, name='v') # visibles
self.h = units.BinaryUnits(self, name='h') # hiddens
# parameters
self.Wm = parameters.ProdParameters(self, [self.v, self.h],
theano.shared(
value=self._initial_W(),
name='Wm'),
name='Wm') # weights
self.Wp = parameters.ProdParameters(
self, [self.v.precision_units, self.h],
theano.shared(value=-np.abs(self._initial_W()) / 1000, name='Wp'),
name='Wp') # weights
self.bvm = parameters.BiasParameters(
self,
self.v,
theano.shared(value=self._initial_bias(self.n_visible),
name='bvm'),
name='bvm') # visible bias
self.bvp = parameters.BiasParameters(
self,
self.v.precision_units,
theano.shared(value=self._initial_bias(self.n_visible),
name='bvp'),
name='bvp') # precision bias
self.bh = parameters.BiasParameters(
self,
self.h,
theano.shared(value=self._initial_bias(self.n_hidden), name='bh'),
name='bh') # hidden bias
def __init__(self, n_visible, n_hidden_mean, n_hidden_precision):
super(LearntPrecisionSeparateGaussianBinaryRBM, self).__init__()
# data shape
self.n_visible = n_visible
self.n_hidden_mean = n_hidden_mean
self.n_hidden_precision = n_hidden_precision
# units
self.v = units.LearntPrecisionGaussianUnits(self, name='v') # visibles
self.hm = units.BinaryUnits(self, name='hm') # hiddens for mean
self.hp = units.BinaryUnits(self, name='hp') # hiddens for precision
# parameters
self.Wm = parameters.ProdParameters(
self, [self.v, self.hm],
theano.shared(value=self._initial_W(self.n_visible,
self.n_hidden_mean),
name='Wm'),
name='Wm') # weights
self.Wp = parameters.ProdParameters(
self, [self.v.precision_units, self.hp],
theano.shared(value=-np.abs(
self._initial_W(self.n_visible, self.n_hidden_precision)) /
1000,
name='Wp'),
name='Wp') # weights
self.bvm = parameters.BiasParameters(
self,
self.v,
theano.shared(value=self._initial_bias(self.n_visible),
name='bvm'),
name='bvm') # visible bias
self.bvp = parameters.BiasParameters(
self,
self.v.precision_units,
theano.shared(value=self._initial_bias(self.n_visible),
name='bvp'),
name='bvp') # precision bias
self.bhm = parameters.BiasParameters(
self,
self.hm,
theano.shared(value=self._initial_bias(self.n_hidden_mean),
name='bhm'),
name='bhm') # hidden bias for mean
self.bhp = parameters.BiasParameters(
self,
self.hp,
theano.shared(value=self._initial_bias(self.n_hidden_precision) +
1.0,
name='bhp'),
name='bhp') # hidden bias for precision
def __init__(self, n_visible, n_hidden, n_context):
super(BinaryBinaryCRBM, self).__init__(n_visible, n_hidden)
# data shape
self.n_context = n_context
# units
self.x = units.Units(self, name='x') # context
# parameters
self.A = parameters.ProdParameters(
self, [self.x, self.v],
theano.shared(value=self._initial_A(), name='A'),
name='A') # context-to-visible weights
self.B = parameters.ProdParameters(
self, [self.x, self.h],
theano.shared(value=self._initial_B(), name='B'),
name='B') # context-to-hidden weights
def __init__(self, n_visible, n_hidden):
super(GaussianBinaryRBM, self).__init__()
# data shape
self.n_visible = n_visible
self.n_hidden = n_hidden
# units
self.v = units.GaussianUnits(self, name='v') # visibles
self.h = units.BinaryUnits(self, name='h') # hiddens
# parameters
parameters.FixedBiasParameters(self, self.v.precision_units)
self.W = parameters.ProdParameters(self, [self.v, self.h],
theano.shared(
value=self._initial_W(),
name='W'),
name='W') # weights
self.bv = parameters.BiasParameters(self,
self.v,
theano.shared(
value=self._initial_bv(),
name='bv'),
name='bv') # visible bias
self.bh = parameters.BiasParameters(self,
self.h,
theano.shared(
value=self._initial_bh(),
name='bh'),
name='bh') # hidden bias
def __init__(self, n_visible, n_hidden):
super(TruncExpBinaryRBM, self).__init__()
# data shape
self.n_visible = n_visible
self.n_hidden = n_hidden
# units
self.v = units.TruncatedExponentialUnits(self, name='v') # visibles
self.h = units.BinaryUnits(self, name='h') # hiddens
# parameters
self.W = parameters.ProdParameters(self, [self.v, self.h],
theano.shared(
value=self._initial_W(),
name='W'),
name='W') # weights
self.bv = parameters.BiasParameters(self,
self.v,
theano.shared(
value=self._initial_bv(),
name='bv'),
name='bv') # visible bias
self.bh = parameters.BiasParameters(self,
self.h,
theano.shared(
value=self._initial_bh(),
name='bh'),
name='bh') # hidden bias
