(block_id, progress)
return tf.maximum(0.0,
                    tf.minimum(progress - (block_id - 2), block_id - progress))
Returns the block output parameter for the generator network.

  The generator has N blocks with `block_id` = 1,2,...,N. Each block
  block_id outputs a fake data output(block_id). The generator output is a
  linear combination of all block outputs, i.e.
  SUM_block_id(output(block_id) * alpha(block_id, progress)) where
  alpha(block_id, progress) = _generator_alpha(block_id, progress). Note it
  garantees that SUM_block_id(alpha(block_id, progress)) = 1 for any progress.

  With a fixed block_id, the plot of alpha(block_id, progress) against progress
  is a 'triangle' with its peak at (block_id - 1, 1).

  Args:
    block_id: An integer of generator block id.
    progress: A scalar float `Tensor` of training progress.

  Returns:
    A scalar float `Tensor` of block output parameter.
Returns the block output parameter for the generator network.
Returns the block output parameter for the generator network .
def _generator_alpha(block_id, progress):
  """Returns the block output parameter for the generator network.

  The generator has N blocks with `block_id` = 1,2,...,N. Each block
  block_id outputs a fake data output(block_id). The generator output is a
  linear combination of all block outputs, i.e.
  SUM_block_id(output(block_id) * alpha(block_id, progress)) where
  alpha(block_id, progress) = _generator_alpha(block_id, progress). Note it
  garantees that SUM_block_id(alpha(block_id, progress)) = 1 for any progress.

  With a fixed block_id, the plot of alpha(block_id, progress) against progress
  is a 'triangle' with its peak at (block_id - 1, 1).

  Args:
    block_id: An integer of generator block id.
    progress: A scalar float `Tensor` of training progress.

  Returns:
    A scalar float `Tensor` of block output parameter.
  """
  return tf.maximum(0.0,
                    tf.minimum(progress - (block_id - 2), block_id - progress))
def _generator_alpha ( block_id , progress ) : return tf . maximum ( 0.0 , tf . minimum ( progress - ( block_id - 2 ) , block_id - progress ) )