pytorch_model.py 文件源码

def forward(self, input, compute_loss=False, avg_loss=True):
        # compute posterior
        en1 = F.softplus(self.en1_fc(input))                            # en1_fc   output
        en2 = F.softplus(self.en2_fc(en1))                              # encoder2 output
        en2 = self.en2_drop(en2)
        posterior_mean   = self.mean_bn  (self.mean_fc  (en2))          # posterior mean
        posterior_logvar = self.logvar_bn(self.logvar_fc(en2))          # posterior log variance
        posterior_var    = posterior_logvar.exp()
        # take sample
        eps = Variable(input.data.new().resize_as_(posterior_mean.data).normal_()) # noise
        z = posterior_mean + posterior_var.sqrt() * eps                 # reparameterization
        p = F.softmax(z)                                                # mixture probability
        p = self.p_drop(p)
        # do reconstruction
        recon = F.softmax(self.decoder_bn(self.decoder(p)))             # reconstructed distribution over vocabulary

        if compute_loss:
            return recon, self.loss(input, recon, posterior_mean, posterior_logvar, posterior_var, avg_loss)
        else:
            return recon