conv_vae.py 文件源码

def generative_network(z, zdim):
  """Generative network to parameterize generative model. It takes
  latent variables as input and outputs the likelihood parameters.
  logits = neural_network(z)

  Args:
  z = tensor input
  d = latent variable dimension
  """
  with slim.arg_scope([slim.conv2d_transpose],
                      activation_fn=tf.nn.elu,
                      normalizer_fn=slim.batch_norm,
                      normalizer_params={'scale': True}):
    net = tf.reshape(z, [N_MINIBATCH, 1, 1, zdim])
    net = slim.conv2d_transpose(net, 128, 3, padding='VALID')
    net = slim.conv2d_transpose(net, 64, 5, padding='VALID')
    net = slim.conv2d_transpose(net, 32, 5, stride=2)
    net = slim.conv2d_transpose(net, 1, 5, stride=2, activation_fn=None)
    net = slim.flatten(net)
    #net = slim.nn.sigmoid(net)
    return net