161ae41bebc73c146627169f761e3c4ddf83e5d4,pixyz/losses/losses.py,Divergence,__init__,#Divergence#Any#Any#Any#,237

Before Change

            because these depend on the given distributions and each loss function.

        
        if input_var is not None:
            _input_var = deepcopy(input_var)
        else:
            _input_var = deepcopy(p.input_var)
            if q is not None:
                _input_var += deepcopy(q.input_var)
                _input_var = sorted(set(_input_var), key=_input_var.index)
        super().__init__(_input_var)
        self.p = p
        self.q = q

After Change

    ...     def forward(self, z):
    ...         return {"probs": torch.sigmoid(self.model(z))}
    ...
    >>> p = Generator()
    >>> q = Inference()
    >>> prior = Normal(loc=torch.tensor(0.), scale=torch.tensor(1.),
    ...                var=["z"], features_shape=[64], name="p_{prior}")
    ...
    >>> // Define a loss function (VAE)
    >>> reconst = -p.log_prob().expectation(q)
    >>> kl = KullbackLeibler(q,prior)
    >>> loss_cls = (reconst - kl).mean()
    >>> print(loss_cls)
    mean \\left(- D_{KL} \\left[q(z|x)||p_{prior}(z) \\right] - \\mathbb{E}_{q(z|x)} \\left[\\log p(x|z) \\right] \\right)
    >>> // Evaluate this loss function
    >>> data = torch.randn(1, 128)  // Pseudo data
    >>> loss = loss_cls.eval({"x": data})

Instances