(saver, sess, learning_rate, gilbo_step, step, lr, outdir)
Save GILBO model checkpoints, including the current step and lr.

  Args:
    saver: tf.train.Saver.
    sess: tf.Session.
    learning_rate: tf.Variable for the learning rate.
    gilbo_step: tf.Variable for the current training step.
    step: integer for the current step, to be saved in the checkpoint.
    lr: float for the current learning rate, to be saved in the checkpoint.
    outdir: output directory.
Save GILBO model checkpoints, including the current step and lr.
Save GILBO model checkpoints including the current step and lr .
def _save_gilbo(saver, sess, learning_rate, gilbo_step, step, lr, outdir):
  """Save GILBO model checkpoints, including the current step and lr.

  Args:
    saver: tf.train.Saver.
    sess: tf.Session.
    learning_rate: tf.Variable for the learning rate.
    gilbo_step: tf.Variable for the current training step.
    step: integer for the current step, to be saved in the checkpoint.
    lr: float for the current learning rate, to be saved in the checkpoint.
    outdir: output directory.
  """
  # Save the current learning rate and gilbo training step with the checkpoint.
  learning_rate.assign(lr).eval()
  gilbo_step.assign(step).eval()
  filename = os.path.join(outdir, "gilbo_model")
  saver.save(sess, filename, global_step=step)
def _save_gilbo ( saver , sess , learning_rate , gilbo_step , step , lr , outdir ) : # Save the current learning rate and gilbo training step with the checkpoint. learning_rate . assign ( lr ) . eval ( ) gilbo_step . assign ( step ) . eval ( ) filename = os . path . join ( outdir , "gilbo_model" ) saver . save ( sess , filename , global_step = step )