def initialize_networks(self):
""" Initialize all model networks """
if self.x_dist == 'Gaussian':
self.px_yz = dgm.initGaussNet(self.n_z+self.n_y, self.n_hid, self.n_x, 'px_yz_')
elif self.x_dist == 'Bernoulli':
self.px_yz = dgm.initCatNet(self.n_z+self.n_y, self.n_hid, self.n_x, 'px_yz_')
self.qz_xy = dgm.initGaussNet(self.n_x+self.n_y, self.n_hid, self.n_z, 'qz_xy_')
self.qy_x = dgm.initCatNet(self.n_x, self.n_hid, self.n_y, 'qy_x_') # recognition network
self.py_x = bnn.initTiedBNN(self.qy_x, self.n_hid, self.initVar, 'py_x_', 'Categorical') # discriminator
def initialize_networks(self):
""" Initialize all model networks """
if self.x_dist == 'Gaussian':
self.px_z = dgm.initGaussNet(self.n_z, self.n_hid, self.n_x, 'px_z_')
elif self.x_dist == 'Bernoulli':
self.px_z = dgm.initCatNet(self.n_z, self.n_hid, self.n_x, 'px_y_')
self.pz_c = dgm.initGaussNet(self.n_c, self.n_hid, self.n_z, 'pz_c_')
self.qc_x = dgm.initStatNet(self.n_x, self.n_hid, self.n_e, self.n_c, 'qc_x_')
self.qz_xc = dgm.initGaussNet(self.n_x+self.n_c, self.n_hid, self.n_z, 'qz_xc_')
def initialize_networks(self):
""" Initialize all model networks """
if self.x_dist == 'Gaussian':
self.px_z = dgm.initGaussNet(self.n_z, self.n_hid, self.n_x, 'px_z_')
elif self.x_dist == 'Bernoulli':
self.px_z = dgm.initCatNet(self.n_z, self.n_hid, self.n_x, 'px_z_')
self.qz_x = dgm.initGaussNet(self.n_x, self.n_hid, self.n_z, 'qz_x_')
self.qy_xz = dgm.initCatNet(self.n_x+self.n_z, self.n_hid, self.n_y, 'qy_xz_')
self.py_xz = dgm.initCatNet(self.n_x+self.n_z, self.n_hid, self.n_y, 'py_xz_')
def initialize_networks(self):
""" Initialize all model networks """
if self.x_dist == 'Gaussian':
self.px_yza = dgm.initGaussNet(self.n_y + self.n_z + self.n_a,
self.n_hid, self.n_x, 'px_yza_')
elif self.x_dist == 'Bernoulli':
self.px_yza = dgm.initCatNet(self.n_y + self.n_z + self.n_a,
self.n_hid, self.n_x, 'px_yza_')
self.pa_yz = dgm.initGaussNet(self.n_y + self.n_z, self.n_hid,
self.n_a, 'pa_yz_')
self.qz_xya = dgm.initGaussNet(self.n_x + self.n_y + self.n_a,
self.n_hid, self.n_z, 'qz_xya_')
self.qa_x = dgm.initGaussNet(self.n_x, self.n_hid, self.n_a, 'qa_x_')
self.qy_xa = dgm.initCatNet(self.n_x + self.n_a, self.n_hid, self.n_y,
'qy_xa_')
def initialize_networks(self):
""" Initialize all model networks """
if self.x_dist == 'Gaussian':
self.px_yza = dgm.initGaussNet(self.n_y + self.n_z + self.n_a,
self.n_hid, self.n_x, 'px_yza_')
elif self.x_dist == 'Bernoulli':
self.px_yza = dgm.initCatNet(self.n_y + self.n_z + self.n_a,
self.n_hid, self.n_x, 'px_yza_')
self.pa_yz = dgm.initGaussNet(self.n_y + self.n_z, self.n_hid,
self.n_a, 'pa_yz_')
self.qz_xya = dgm.initGaussNet(self.n_x + self.n_y + self.n_a,
self.n_hid, self.n_z, 'qz_xya_')
self.qa_x = dgm.initGaussNet(self.n_x, self.n_hid, self.n_a, 'qa_x_')
self.pa_x = dgm.initTiedNetwork(self.qa_x, self.n_hid, 'pa_x_',
'Gauss')
self.qy_xa = dgm.initCatNet(self.n_x + self.n_a, self.n_hid, self.n_y,
'qy_xa_')
self.py_xa = bnn.initTiedBNN(self.qy_xa, self.n_hid, self.initVar,
'py_xa_', 'Categorical')