semisupervised.py 文件源码

def _decoder(self, z):
        """Define p(x|z) network"""
        if z is None:
            mean = None
            stddev = None
            logits = None
            class_predictions = None
            input_sample = self.epsilon
        else:
            z = tf.reshape(z, [-1, self.flags['hidden_size'] * 2])
            mean, stddev = tf.split(1, 2, z)  # Compute latent variables (z) by calculating mean, stddev
            stddev = tf.sqrt(tf.exp(stddev))
            mlp = Layers(mean)
            mlp.fc(self.flags['num_classes'])
            class_predictions = mlp.get_output()
            logits = tf.nn.softmax(class_predictions)
            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, 64, padding='VALID', stride=2)
        decoder.deconv2d(3, 64, stride=2)
        decoder.deconv2d(5, 32, stride=2)
        decoder.deconv2d(7, 1, activation_fn=tf.nn.tanh, s_value=None)
        return decoder.get_output(), mean, stddev, class_predictions, logits