layers.py 文件源码

def up(self,h):
        h_up = slim.fully_connected(h,self.hps.h_size,activation_fn=tf.nn.relu)

        if self.var_type == 'discrete':
            # q_z
            self.K = K = self.hps.K
            self.N = N = self.hps.N
            h_up = slim.fully_connected(h_up,K*N,activation_fn=None)
            self.logits_q  = tf.reshape(h_up,[-1,K]) # unnormalized logits for N separate K-categorical distributions (shape=(batch_size*N,K))

            h_out = slim.fully_connected(h_up,self.hps.h_size,activation_fn=None)

        elif self.var_type == 'continuous':
            hps =  self.hps
            z_size = hps.z_size
            h_size = hps.h_size

            h_up = slim.fully_connected(h_up,h_size,activation_fn=None)
            h_up = slim.fully_connected(h,z_size*2 + h_size,activation_fn=None)
            self.qz_mean, self.qz_logsd, h_out = split(h_up, 1, [z_size, z_size, h_size])

        if self.hps.resnet:
            return h + 0.2 * h_out
        else:
            return h_out