conv_vae.py 文件源码

def inference_network(x, xwidth=28, xheight=28, zdim=2):
  """Inference network to parameterize variational model. It takes
  data as input and outputs the variational parameters.
  mu, sigma = neural_network(x)
  """
  with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      activation_fn=tf.nn.elu,
                      normalizer_fn=slim.batch_norm,
                      normalizer_params={'scale': True}):
    net = tf.reshape(x, [N_MINIBATCH, 28, 28, 1])
    net = slim.conv2d(net, 32, 5, stride=2)
    net = slim.conv2d(net, 64, 5, stride=2)
    net = slim.conv2d(net, 128, 5, padding='VALID')
    net = slim.dropout(net, 0.9)
    net = slim.flatten(net)
    params = slim.fully_connected(net, zdim * 2, activation_fn=None)

  mu    = params[:, :zdim]
  #sigma = tf.nn.softplus(params[:, zdim:])
  sigma = params[:, zdim:]
  return mu, sigma


##########################################
# make variational lower bound objective #
##########################################