adgm.py 文件源码

def gaussian_nll_keepbatch(self, x, mean, ln_var, clip=True):
        if clip:
            clip_min = math.log(0.01)
            clip_max = math.log(10)
            ln_var = F.clip(ln_var, clip_min, clip_max)
        x_prec = F.exp(-ln_var)
        x_diff = x - mean
        x_power = (x_diff * x_diff) * x_prec * 0.5
        # print "nll"
        # print cuda.cupy.amax(x.data), cuda.cupy.amin(x.data)
        # print cuda.cupy.amax(ln_var.data), cuda.cupy.amin(ln_var.data)
        # print cuda.cupy.amax(x_prec.data), cuda.cupy.amin(x_prec.data)
        # print cuda.cupy.amax(x_power.data), cuda.cupy.amin(x_power.data)
        return F.sum((math.log(2.0 * math.pi) + ln_var) * 0.5 + x_power, axis=1)