python类exp()的实例源码

def gaussian_kl_divergence_keepbatch(self, mean, ln_var):
        var = F.exp(ln_var)
        kld = F.sum(mean * mean + var - ln_var - 1, axis=1) * 0.5
        return kld

def log_pz(self, z, mean, ln_var, test=False):
        if self.type_pz == "gaussianmarg":
            # \int q(z)logp(z)dz = -(J/2)*log2pi - (1/2)*sum_{j=1}^{J} (mu^2 + var)
            # See Appendix B [Auto-Encoding Variational Bayes](http://arxiv.org/abs/1312.6114)
            log_pz = -0.5 * (math.log(2.0 * math.pi) + mean * mean + F.exp(ln_var))
        elif self.type_pz == "gaussian":
            log_pz = -0.5 * math.log(2.0 * math.pi) - 0.5 * z ** 2
        return F.sum(log_pz, axis=1)

    # this will not be used

def gaussian_nll_keepbatch(self, x, mean, ln_var):
        x_prec = F.exp(-ln_var)
        x_diff = x - mean
        x_power = x_diff ** 2 * x_prec * 0.5
        return F.sum((math.log(2.0 * math.pi) + ln_var) * 0.5 + x_power, axis=1)

def gaussian_kl_divergence_keepbatch(self, mean, ln_var):
        var = F.exp(ln_var)
        kld = F.sum(mean ** 2 + var - ln_var - 1, axis=1) * 0.5
        return kld

def normalize_2d(x):
    exp = F.exp(x[0])
    sums = F.sum(F.sum(exp, axis=-1), axis=-1)
    expanded = F.expand_dims(F.expand_dims(sums, axis=-1), axis=-1)
    denominator = F.tile(expanded, (1, 160, 210))
    return exp / denominator