Ejemplo n.º 1
0
def discriminator(x, compress, hparams, name, reuse=None):
    with tf.variable_scope(name, reuse=reuse):
        x = tf.stop_gradient(2 * x) - x  # Reverse gradient.
        if compress:
            x = transformer_vae.compress(x, None, False, hparams, "compress")
        else:
            x = transformer_vae.residual_conv(x, 1, 3, hparams, "compress_rc")
        y = tf.reduce_mean(x, axis=1)
        return tf.tanh(tf.layers.dense(y, 1, name="reduce"))
Ejemplo n.º 2
0
def discriminator(x, compress, hparams, name, reuse=None):
  with tf.variable_scope(name, reuse=reuse):
    x = tf.stop_gradient(2 * x) - x  # Reverse gradient.
    if compress:
      x = transformer_vae.compress(x, None, False, hparams, "compress")
    else:
      x = transformer_vae.residual_conv(x, 1, 3, hparams, "compress_rc")
    y = tf.reduce_mean(x, axis=1)
    return tf.tanh(tf.layers.dense(y, 1, name="reduce"))
Ejemplo n.º 3
0
def generator(x, hparams, name, reuse=False):
    with tf.variable_scope(name, reuse=reuse):
        return transformer_vae.residual_conv(x, 1, 3, hparams, "generator")
Ejemplo n.º 4
0
def generator(x, hparams, name, reuse=False):
  with tf.variable_scope(name, reuse=reuse):
    return transformer_vae.residual_conv(x, 1, 3, hparams, "generator")