def get_pseudo_likelihood_cost(self, updates):
"""Stochastic approximation to the pseudo-likelihood"""
# index of bit i in expression p(x_i | x_{\i})
bit_i_idx = theano.shared(value=0, name = 'bit_i_idx')
# binarize the input image by rounding to nearest integer
xi = T.iround(self.input)
# calculate free energy for the given bit configuration
fe_xi = self.free_energy(xi)
# flip bit x_i of matrix xi and preserve all other bits x_{\i}
# Equivalent to xi[:,bit_i_idx] = 1-xi[:, bit_i_idx]
# NB: slice(start,stop,step) is the python object used for
# slicing, e.g. to index matrix x as follows: x[start:stop:step]
# In our case, idx_list is a tuple. The first element of the tuple
# describes what slice we want from the first dimension.
# ``slice(None,None,None)`` means that we want all values, equivalent
# to numpy notation ``:``. The second element of the tuple is the
# value bit_i_idx, meaning that we are looking for [:,bit_i_idx].
xi_flip = T.setsubtensor(xi, 1-xi[:, bit_i_idx],
idx_list=(slice(None,None,None),bit_i_idx))
# calculate free energy with bit flipped
fe_xi_flip = self.free_energy(xi_flip)
# equivalent to e^(-FE(x_i)) / (e^(-FE(x_i)) + e^(-FE(x_{\i})))
cost = T.mean(self.n_visible * T.log(T.nnet.sigmoid(fe_xi_flip - fe_xi)))
# increment bit_i_idx % number as part of updates
updates[bit_i_idx] = (bit_i_idx + 1) % self.n_visible
return cost
def get_pseudo_likelihood_cost(self, updates):
''' Stochastic approximation to the pseudo-likelihood'''
# index of bit i in expression p(x_i | x_{\i})
bit_i_idx = theano.shared(value=0, name='bit_i_idx')
# binarize the input image by rounding to nearest integer
xi = T.round(self.input)
# calculate free nergy for the given bit configuration
fe_xi = self.free_energy(xi)
# flip bit x_i of matrix xi and preserve all other bits x_{\i}
# Equivalent to xi[:, bit_i_idx] = 1-xi[:, bit_i_idx], but assigns the result to xi_flip, instead of working in place on xi.
xi_flip = T.setsubtensor(xi[:, bit_i_idx], 1-xi[:,bit_i_idx])
# calculate free energy with bit flipped
fe_xi_flip = self.free_energy(xi_flip)
# equivalent to e^(-FE(x_i)) / (e^(-FE(x_i)) + e^(-FE(x_{\i})))
cost = T.mean(self.n_visible * T.log(T.nnet.sigmoid(fe_xi_flip - fe_xi)))
# increment bit_i_idx % number as part of updates
updates[bit_i_idx] = (bit_i_idx + 1) % self.n_visible
return cost
def get_pseudo_likelihood_cost(self, updates):
"""Stochastic approximation to the pseudo-likelihood"""
# index of bit i in expression p(x_i | x_{\i})
bit_i_idx = theano.shared(value=0, name = 'bit_i_idx')
# binarize the input image by rounding to nearest integer
xi = T.iround(self.input)
# calculate free energy for the given bit configuration
fe_xi = self.free_energy(xi)
# flip bit x_i of matrix xi and preserve all other bits x_{\i}
# Equivalent to xi[:,bit_i_idx] = 1-xi[:, bit_i_idx]
# NB: slice(start,stop,step) is the python object used for
# slicing, e.g. to index matrix x as follows: x[start:stop:step]
xi_flip = T.setsubtensor(xi, 1-xi[:, bit_i_idx],
idx_list=(slice(None,None,None),bit_i_idx))
# calculate free energy with bit flipped
fe_xi_flip = self.free_energy(xi_flip)
# equivalent to e^(-FE(x_i)) / (e^(-FE(x_i)) + e^(-FE(x_{\i})))
cost = self.n_visible * T.log(T.nnet.sigmoid(fe_xi_flip - fe_xi))
# increment bit_i_idx % number as part of updates
updates[bit_i_idx] = (bit_i_idx + 1) % self.n_visible
return cost
def get_pseudo_likelihood_cost(self, updates):
''' Stochastic approximation to the pseudo-likelihood'''
# index of bit i in expression p(x_i | x_{\i})
bit_i_idx = theano.shared(value=0, name='bit_i_idx')
# binarize the input image by rounding to nearest integer
xi = T.round(self.input)
# calculate free nergy for the given bit configuration
fe_xi = self.free_energy(xi)
# flip bit x_i of matrix xi and preserve all other bits x_{\i}
# Equivalent to xi[:, bit_i_idx] = 1-xi[:, bit_i_idx], but assigns the result to xi_flip, instead of working in place on xi.
xi_flip = T.setsubtensor(xi[:, bit_i_idx], 1 - xi[:, bit_i_idx])
# calculate free energy with bit flipped
fe_xi_flip = self.free_energy(xi_flip)
# equivalent to e^(-FE(x_i)) / (e^(-FE(x_i)) + e^(-FE(x_{\i})))
cost = T.mean(self.n_visible *
T.log(T.nnet.sigmoid(fe_xi_flip - fe_xi)))
# increment bit_i_idx % number as part of updates
updates[bit_i_idx] = (bit_i_idx + 1) % self.n_visible
return cost
def get_pseudo_likelihood_cost(self, updates):
"""Stochastic approximation to the pseudo-likelihood"""
# index of bit i in expression p(x_i | x_{\i})
bit_i_idx = theano.shared(value=0, name='bit_i_idx')
# binarize the input image by rounding to nearest integer
xi = T.iround(self.input)
# calculate free energy for the given bit configuration
fe_xi = self.free_energy(xi)
# flip bit x_i of matrix xi and preserve all other bits x_{\i}
# Equivalent to xi[:,bit_i_idx] = 1-xi[:, bit_i_idx]
# NB: slice(start,stop,step) is the python object used for
# slicing, e.g. to index matrix x as follows: x[start:stop:step]
# In our case, idx_list is a tuple. The first element of the tuple
# describes what slice we want from the first dimension.
# ``slice(None,None,None)`` means that we want all values, equivalent
# to numpy notation ``:``. The second element of the tuple is the
# value bit_i_idx, meaning that we are looking for [:,bit_i_idx].
xi_flip = T.setsubtensor(xi,
1 - xi[:, bit_i_idx],
idx_list=(slice(None, None, None), bit_i_idx))
# calculate free energy with bit flipped
fe_xi_flip = self.free_energy(xi_flip)
# equivalent to e^(-FE(x_i)) / (e^(-FE(x_i)) + e^(-FE(x_{\i})))
cost = T.mean(self.n_visible *
T.log(T.nnet.sigmoid(fe_xi_flip - fe_xi)))
# increment bit_i_idx % number as part of updates
updates[bit_i_idx] = (bit_i_idx + 1) % self.n_visible
return cost
