def __call__(self, var):
dot_output = BF.dot(self.w, var, reduce=False) + self.b
output = var + self.u*BF.sigmoid(dot_output)
d_sigmoid = lambda x: BF.sigmoid(x)*(1. - BF.sigmoid(x))
psy = d_sigmoid(dot_output)*self.w
log_det = -BF.log(BF.abs(1. + BF.dot(self.u, psy)) + self.shift)
return DeterministicVariable(output,
log_determinant=log_det,
name="PlanarFlow {}".format(var.name))
def __init__(self, scale, jitter=0.):
self.scale = var2link(scale)
covariance = lambda x, y: BF.exp(-BF.abs(x - y) /
(scale)) + BF.delta(x, y) * jitter
super().__init__(covariance=covariance)
def __call__(self, var):
output = BF.matmul(self.tri_matrix, var)
log_det = -BF.sum(BF.log(BF.abs(self.tri_matrix[:, self.diag_indices[0], self.diag_indices[1]]) + self.shift), axis=1)
return DeterministicVariable(output,
log_determinant=log_det,
name="L {}".format(var.name))
def __call__(self, var):
return DeterministicVariable(self.s*var,
log_determinant=-BF.log(BF.abs(DeterministicVariable(self.s,
learnable=self.learnable,
name="{}_scale".format(var.name)))),
name="{}*{}".format(self.s, var.name))