def forward(self, x, logdet, dsparams, mollify=0.0, delta=nn_.delta):
ndim = self.num_ds_dim
a_ = self.act_a(dsparams[:, :, 0 * ndim:1 * ndim])
b_ = self.act_b(dsparams[:, :, 1 * ndim:2 * ndim])
w = self.act_w(dsparams[:, :, 2 * ndim:3 * ndim])
a = a_ * (1 - mollify) + 1.0 * mollify
b = b_ * (1 - mollify) + 0.0 * mollify
pre_sigm = a * x[:, :, None] + b
sigm = torch.sigmoid(pre_sigm)
x_pre = torch.sum(w * sigm, dim=2)
x_pre_clipped = x_pre * (1 - delta) + delta * 0.5
x_ = log(x_pre_clipped) - log(1 - x_pre_clipped)
xnew = x_
logj = F.log_softmax(dsparams[:,:,2*ndim:3*ndim], dim=2) + \
nn_.logsigmoid(pre_sigm) + \
nn_.logsigmoid(-pre_sigm) + log(a)
logj = utils.log_sum_exp(logj, 2).sum(2)
logdet_ = logj + np.log(1-delta) - \
(log(x_pre_clipped) + log(-x_pre_clipped+1))
logdet = logdet_.sum(1) + logdet
return xnew, logdet
def forward(self, inputs):
if len(inputs) == 2:
input, logdet = inputs
elif len(inputs) == 3:
input, logdet, context = inputs
else:
raise (Exception('inputs length not correct'))
output = log(input) - log(1 - input)
logdet -= sum_from_one(log(input) + log(-input + 1))
if len(inputs) == 2:
return output, logdet
elif len(inputs) == 3:
return output, logdet, context
else:
raise (Exception('inputs length not correct'))
def forward(self, x, logdet, dsparams):
inv = np.log(np.exp(1 - nn_.delta) - 1)
ndim = self.hidden_dim
pre_u = self.u_[None, None, :, :] + dsparams[:, :,
-self.in_dim:][:, :,
None, :]
pre_w = self.w_[None, None, :, :] + dsparams[:, :, 2 * ndim:3 *
ndim][:, :, None, :]
a = self.act_a(dsparams[:, :, 0 * ndim:1 * ndim] + inv)
b = self.act_b(dsparams[:, :, 1 * ndim:2 * ndim])
w = self.act_w(pre_w)
u = self.act_u(pre_u)
pre_sigm = torch.sum(u * a[:, :, :, None] * x[:, :, None, :], 3) + b
sigm = torch.sigmoid(pre_sigm)
x_pre = torch.sum(w * sigm[:, :, None, :], dim=3)
x_pre_clipped = x_pre * (1 - nn_.delta) + nn_.delta * 0.5
x_ = log(x_pre_clipped) - log(1 - x_pre_clipped)
xnew = x_
logj = F.log_softmax(pre_w, dim=3) + \
nn_.logsigmoid(pre_sigm[:,:,None,:]) + \
nn_.logsigmoid(-pre_sigm[:,:,None,:]) + log(a[:,:,None,:])
# n, d, d2, dh
logj = logj[:, :, :, :, None] + F.log_softmax(pre_u, dim=3)[:, :,
None, :, :]
# n, d, d2, dh, d1
logj = utils.log_sum_exp(logj, 3).sum(3)
# n, d, d2, d1
logdet_ = logj + np.log(1-nn_.delta) - \
(log(x_pre_clipped) + log(-x_pre_clipped+1))[:,:,:,None]
logdet = utils.log_sum_exp(
logdet_[:, :, :, :, None] + logdet[:, :, None, :, :], 3).sum(3)
# n, d, d2, d1, d0 -> n, d, d2, d0
return xnew, logdet
