sample z from VAE_GAN

[jiankang1991]

Hi,
I have a question confused about the latent variable z

According to the code in class VAEGAN,

    def _forward(self, batch, test=False):

        encoded, means, ln_vars = self._encode(batch, test=test)
        rec = self._decode(encoded, test=test)
        normer = reduce(lambda x, y: x*y, means.data.shape)
        kl_loss = F.gaussian_kl_divergence(means, ln_vars)/normer

        samp_p = np.random.standard_normal(means.data.shape).astype('float32')
        z_p = chainer.Variable(samp_p)

It means that this z is first sampled according to VAE from:

encoded, means, ln_vars = self._encode(batch, test=test)

    def encode(self, data, test=False):
        x = self.enc(data, test=test)
        mean, ln_var = F.split_axis(x, 2, 1)
        samp = np.random.standard_normal(mean.data.shape).astype('float32')
        samp = Variable(samp)
        if self.flag_gpu:
            samp.to_gpu()
        z = samp * F.exp(0.5*ln_var) + mean

which means that the sampler z is s.t:

then, the sampled z is input to the decoder network to generate images, according to

rec = self._decode(encoded, test=test)

then, another z is sampled by:

        samp_p = np.random.standard_normal(means.data.shape).astype('float32')
        z_p = chainer.Variable(samp_p)

which means it is s.t.

and the generated images is created by:

rec_p = self._decode(z_p)

My question is since the two types of z are sampled from different distributions,
why they are input into the same decoder network?
And which output are the generated images from decoder, since they both created by sampling z according to a distribution ?

This is just my opinion.
Please feel free if I make mistakes.
Thank you.