def _report_benchmark(self, stats, wall_time_sec, top_1_max=None, top_1_min=None, log_steps=None, total_batch_size=None, warmup=1):
    "Report benchmark results by writing to local protobuf file.\n\n    Args:\n      stats: dict returned from keras models with known entries.\n      wall_time_sec: the during of the benchmark execution in seconds\n      top_1_max: highest passing level for top_1 accuracy.\n      top_1_min: lowest passing level for top_1 accuracy.\n      log_steps: How often the log was created for stats['step_timestamp_log'].\n      total_batch_size: Global batch-size.\n      warmup: number of entries in stats['step_timestamp_log'] to ignore.\n    "
    metrics = []
    if ('accuracy_top_1' in stats):
        metrics.append({'name': 'accuracy_top_1', 'value': stats['accuracy_top_1'], 'min_value': top_1_min, 'max_value': top_1_max})
        metrics.append({'name': 'top_1_train_accuracy', 'value': stats['training_accuracy_top_1']})
    if (warmup and ('step_timestamp_log' in stats) and (len(stats['step_timestamp_log']) > warmup)):
        time_log = stats['step_timestamp_log']
        elapsed = (time_log[(- 1)].timestamp - time_log[warmup].timestamp)
        num_examples = ((total_batch_size * log_steps) * ((len(time_log) - warmup) - 1))
        examples_per_sec = (num_examples / elapsed)
        metrics.append({'name': 'exp_per_second', 'value': examples_per_sec})
    if ('avg_exp_per_second' in stats):
        metrics.append({'name': 'avg_exp_per_second', 'value': stats['avg_exp_per_second']})
    self.report_benchmark(iters=(- 1), wall_time=wall_time_sec, metrics=metrics)
Report benchmark results by writing to local protobuf file.

Args:
  stats: dict returned from keras models with known entries.
  wall_time_sec: the during of the benchmark execution in seconds
  top_1_max: highest passing level for top_1 accuracy.
  top_1_min: lowest passing level for top_1 accuracy.
  log_steps: How often the log was created for stats['step_timestamp_log'].
  total_batch_size: Global batch-size.
  warmup: number of entries in stats['step_timestamp_log'] to ignore.
def _report_benchmark(self, stats, wall_time_sec, top_1_max=None, top_1_min=None, log_steps=None, total_batch_size=None, warmup=1):
    "Report benchmark results by writing to local protobuf file.\n\n    Args:\n      stats: dict returned from keras models with known entries.\n      wall_time_sec: the during of the benchmark execution in seconds\n      top_1_max: highest passing level for top_1 accuracy.\n      top_1_min: lowest passing level for top_1 accuracy.\n      log_steps: How often the log was created for stats['step_timestamp_log'].\n      total_batch_size: Global batch-size.\n      warmup: number of entries in stats['step_timestamp_log'] to ignore.\n    "
    metrics = []
    if ('accuracy_top_1' in stats):
        metrics.append({'name': 'accuracy_top_1', 'value': stats['accuracy_top_1'], 'min_value': top_1_min, 'max_value': top_1_max})
        metrics.append({'name': 'top_1_train_accuracy', 'value': stats['training_accuracy_top_1']})
    if (warmup and ('step_timestamp_log' in stats) and (len(stats['step_timestamp_log']) > warmup)):
        time_log = stats['step_timestamp_log']
        elapsed = (time_log[(- 1)].timestamp - time_log[warmup].timestamp)
        num_examples = ((total_batch_size * log_steps) * ((len(time_log) - warmup) - 1))
        examples_per_sec = (num_examples / elapsed)
        metrics.append({'name': 'exp_per_second', 'value': examples_per_sec})
    if ('avg_exp_per_second' in stats):
        metrics.append({'name': 'avg_exp_per_second', 'value': stats['avg_exp_per_second']})
    self.report_benchmark(iters=(- 1), wall_time=wall_time_sec, metrics=metrics)