def _batch_data(batch): images = float_tensor(batch[0].float()) bsize = len(images) return m( images=images, x1=float_tensor(batch[3].float()), x2=float_tensor(batch[4].float()), id_labels=init(batch[1]), pose_labels=init(batch[2]), fake_pose_labels=long_tensor(np.random.randint(args.Np, size=bsize)), ones=ones(bsize), zeros=zeros(bsize), noise=float_tensor(np.random.uniform(-1., 1., (bsize, args.Nz))))
def blank_loglikes(n): a = ones((NUM_CHARS, n)) * 0.1 a[0, :] = 0.9 a /= sqrt(square(a).sum(axis=0)) return log(a)
def uniform_loglikes(n): return log(ones((NUM_CHARS, n)) / float(NUM_CHARS))
def bprop(self): logger.debug('%s backprop' % str(self)) # TODO This can be merged / sped up self.grad = ones(self.pred[0].out.shape) self.full_grad = self.grad
def bprop(self): logger.debug("%s backprop" % str(self)) # TODO This can be merged / sped up self.grad = ones(self.pred[0].out.shape) self.full_grad = self.grad