parameters: (discriminator_gen_outputs)
return_statement: return loss
docstring: Original generator loss for GANs.
  
    L = -log(sigmoid(D(G(z))))
  
    See `Generative Adversarial Nets` (https://arxiv.org/abs/1406.2661)
    for more details.
  
    Args:
      discriminator_gen_outputs: Discriminator output on generated data. Expected
        to be in the range of (-inf, inf).
  
    Returns:
      A scalar loss Tensor.
docstring_summary: Original generator loss for GANs.
docstring_tokens: Original generator loss for GANs .
function: def generator_loss(discriminator_gen_outputs):
    """Original generator loss for GANs.
  
    L = -log(sigmoid(D(G(z))))
  
    See `Generative Adversarial Nets` (https://arxiv.org/abs/1406.2661)
    for more details.
  
    Args:
      discriminator_gen_outputs: Discriminator output on generated data. Expected
        to be in the range of (-inf, inf).
  
    Returns:
      A scalar loss Tensor.
    """
    loss = tf.losses.sigmoid_cross_entropy(
        tf.ones_like(discriminator_gen_outputs), discriminator_gen_outputs)
    tf.contrib.summary.scalar('generator_loss', loss)
    return loss
function_tokens: def generator_loss ( discriminator_gen_outputs ) : loss = tf . losses . sigmoid_cross_entropy ( tf . ones_like ( discriminator_gen_outputs ) , discriminator_gen_outputs ) tf . contrib . summary . scalar ( 'generator_loss' , loss ) return loss