layers.py 文件源码

def forward(self,z):
        if not self.ar:
            mu,log_sigma = self._get_mu_and_sigma(z)
        else:
            # permute z
            z = tf.reshape(z,[-1]+[1]*self.hps.z_size)
            perm = np.random.permutation(self.hps.z_size)+1
            z = tf.transpose(z,np.append([0],perm))
            z = tf.reshape(z,[-1,self.hps.z_size])
            mu,log_sigma = ar_layer(z,self.hps,n_hidden=self.n_hidden)
        log_sigma = tf.clip_by_value(log_sigma,-5,5)
        if not self.hps.ignore_sigma_flow:
            y = z * tf.exp(log_sigma) + mu
            log_det = -1 * log_sigma
        else:
            y = z + mu
            log_det = 0.0
        return y,log_det