evaluate.py 文件源码

def evaluate_model(sess, target_cross_entropy_losses, target_cross_entropy_loss_weights, global_step, summary_writer, summary_op):
  """Computes perplexity-per-word over the evaluation dataset.

  Summaries and perplexity-per-word are written out to the eval directory.

  Args:
    sess: Session object.
    model: Instance of ShowAndTellModel; the model to evaluate.
    global_step: Integer; global step of the model checkpoint.
    summary_writer: Instance of SummaryWriter.
    summary_op: Op for generating model summaries.
  """
  # Log model summaries on a single batch.
  summary_str = sess.run(summary_op)
  summary_writer.add_summary(summary_str, global_step)

  # Compute perplexity over the entire dataset.
  num_eval_batches = int(
      math.ceil(num_eval_examples / batch_size))

  start_time = time.time()
  sum_losses = 0.
  sum_weights = 0.
  for i in xrange(num_eval_batches):
    cross_entropy_losses, weights = sess.run([
        target_cross_entropy_losses,
        target_cross_entropy_loss_weights
    ])
    sum_losses += np.sum(cross_entropy_losses * weights)
    sum_weights += np.sum(weights)
    if not i % 100:
      tf.logging.info("Computed losses for %d of %d batches.", i + 1,
                      num_eval_batches)
  eval_time = time.time() - start_time

  perplexity = math.exp(sum_losses / sum_weights)
  tf.logging.info("Perplexity = %f (%.2g sec)", perplexity, eval_time)

  # Log perplexity to the SummaryWriter.
  summary = tf.Summary()
  value = summary.value.add()
  value.simple_value = perplexity
  value.tag = "Perplexity"
  summary_writer.add_summary(summary, global_step)

  # Write the Events file to the eval directory.
  summary_writer.flush()
  tf.logging.info("Finished processing evaluation at global step %d.",
                  global_step)