why do you do evaluation before training ?

[Starry-lei]

Hi ,thanks for sharing the code! I am wondering why do you do "
else:
ldj = self.log_likelihood(z)
return z, ldj
" in the following code.

if not reverse:

        ldj = z.new_zeros(batch_size, )
        z = z.reshape((batch_size, self.d_in))
        device = z.device
        print("checking z ssssshape", z.shape) # [1, 192])
        print("checking self.training ", self.training) #  False
        
        if self.training:
            t = th.randint(0, self.T, (batch_size, ), device=device).long()
            print("checking t", t)
            # loss mark
            ldj = -self.training_losses(z, t, x_cat, **kwargs)['loss']
            z = z.reshape((batch_size, set_size, hidden_dim))
            return z, ldj
        else:
            ldj = self.log_likelihood(z)

            return z, ldj

    else:
        ldj = self.nll(z)