mmd_vae_eval.py 文件源码

def decoder(z, reuse=False):
    with tf.variable_scope('decoder') as vs:
        if reuse:
            vs.reuse_variables()
        fc1 = fc_relu(z, 1024)
        fc2 = fc_relu(fc1, 7*7*128)
        fc2 = tf.reshape(fc2, tf.stack([tf.shape(fc2)[0], 7, 7, 128]))
        conv1 = conv2d_t_relu(fc2, 64, 4, 2)
        mean = tf.contrib.layers.convolution2d_transpose(conv1, 1, 4, 2, activation_fn=tf.sigmoid)
        stddev = tf.contrib.layers.convolution2d_transpose(conv1, 1, 4, 2, activation_fn=tf.sigmoid)
        stddev = tf.maximum(stddev, 0.005)
        return mean, stddev


# Build the computation graph for training