unsupervised.py 文件源码

def _decoder(self, z):
        """ Define p(x|z) network"""
        if z is None:
            mean = None
            stddev = None
            input_sample = self.epsilon
        else:
            z = tf.reshape(z, [-1, self.flags['hidden_size'] * 2])
            print(z.get_shape())
            mean, stddev = tf.split(1, 2, z)
            stddev = tf.sqrt(tf.exp(stddev))
            input_sample = mean + self.epsilon * stddev
        decoder = Layers(tf.expand_dims(tf.expand_dims(input_sample, 1), 1))
        decoder.deconv2d(3, 128, padding='VALID')
        decoder.deconv2d(3, 128, padding='VALID', stride=2)
        decoder.deconv2d(3, 64, stride=2)
        decoder.deconv2d(3, 64, stride=2)
        decoder.deconv2d(5, 1, activation_fn=tf.nn.tanh, s_value=None)
        return decoder.get_output(), mean, stddev