(sess, model, data_set)
return (total_cost, total_r_cost, total_kl_cost)
Returns the average weighted cost, reconstruction cost and KL cost.
Returns the average weighted cost, reconstruction cost and KL cost.
Returns the average weighted cost reconstruction cost and KL cost .
def evaluate_model(sess, model, data_set):
  """Returns the average weighted cost, reconstruction cost and KL cost."""
  total_cost = 0.0
  total_r_cost = 0.0
  total_kl_cost = 0.0
  for batch in range(data_set.num_batches):
    unused_orig_x, x, s = data_set.get_batch(batch)
    feed = {model.input_data: x, model.sequence_lengths: s}
    (cost, r_cost,
     kl_cost) = sess.run([model.cost, model.r_cost, model.kl_cost], feed)
    total_cost += cost
    total_r_cost += r_cost
    total_kl_cost += kl_cost

  total_cost /= (data_set.num_batches)
  total_r_cost /= (data_set.num_batches)
  total_kl_cost /= (data_set.num_batches)
  return (total_cost, total_r_cost, total_kl_cost)
def evaluate_model ( sess , model , data_set ) : total_cost = 0.0 total_r_cost = 0.0 total_kl_cost = 0.0 for batch in range ( data_set . num_batches ) : unused_orig_x , x , s = data_set . get_batch ( batch ) feed = { model . input_data : x , model . sequence_lengths : s } ( cost , r_cost , kl_cost ) = sess . run ( [ model . cost , model . r_cost , model . kl_cost ] , feed ) total_cost += cost total_r_cost += r_cost total_kl_cost += kl_cost total_cost /= ( data_set . num_batches ) total_r_cost /= ( data_set . num_batches ) total_kl_cost /= ( data_set . num_batches ) return ( total_cost , total_r_cost , total_kl_cost )