def _recog_par_spec(self):
"""Return the specification of the recognition model."""
#n_code_units = self.assumptions.latent_layer_size(self.n_latent)
n_code_units = self.n_latent
spec = mlp.parameters(self.n_inpt, self.n_hiddens_recog, n_code_units)
spec['p_dropout'] = {
'inpt': 1,
'hiddens': [1 for _ in self.n_hiddens_recog],
}
return spec
def _recog_par_spec(self):
"""Return the specification of the recognition model."""
#n_code_units = self.assumptions.latent_layer_size(self.n_latent)
n_code_units = self.n_latent
spec = mlp.parameters(self.n_inpt, self.n_hiddens_recog,
n_code_units)
spec['p_dropout'] = {
'inpt': 1,
'hiddens': [1 for _ in self.n_hiddens_recog],
}
return spec
def _gen_par_spec(self):
"""Return the parameter specification of the generating model."""
n_output = self.assumptions.visible_layer_size(self.n_inpt)
return mlp.parameters(self.n_latent, self.n_hiddens_recog,
n_output)
def _init_pars(self):
spec = mlp.parameters(self.n_inpt, self.n_hiddens, self.n_output)
self.parameters = ParameterSet(**spec)
self.parameters.data[:] = np.random.standard_normal(
self.parameters.data.shape).astype(theano.config.floatX)
def _init_pars(self):
spec = mlp.parameters(self.n_inpt, self.n_hiddens, self.n_output)
self.parameters = ParameterSet(**spec)
self.parameters.data[:] = np.random.standard_normal(
self.parameters.data.shape).astype(theano.config.floatX)
def _gen_par_spec(self):
"""Return the parameter specification of the generating model."""
n_output = self.assumptions.visible_layer_size(self.n_inpt)
return mlp.parameters(self.n_latent, self.n_hiddens_recog, n_output)
