获得可重现结果的问题，设置种子 Tensorflow 对象检测 API

如何解决获得可重现结果的问题，设置种子 Tensorflow 对象检测 API

我在 tensorflow v1.12 中使用对象检测 API。我在获得可重现的结果时遇到了麻烦 - 每次运行我的代码时，我都会得到不同的结果。有没有办法在训练/预测级别设置随机种子？我试图在 model_main.py 中设置种子，但没有帮助。

def main(unused_argv):
    tf.random.set_random_seed(1234)
    flags.mark_flag_as_required('model_dir')
    flags.mark_flag_as_required('pipeline_config_path')
    config = tf.estimator.runconfig(model_dir=FLAGS.model_dir,tf_random_seed=1234)

我的pipeline.config 供参考：

model {
  faster_rcnn {
    num_classes: 1
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 600
        max_dimension: 1024
      }
    }
    feature_extractor {
      type: "faster_rcnn_resnet101"
      first_stage_features_stride: 16
    }
    first_stage_anchor_generator {
      grid_anchor_generator {
        height_stride: 16
        width_stride: 16
        scales: 0.25
        scales: 0.5
        scales: 1.0
        scales: 2.0
        aspect_ratios: 0.5
        aspect_ratios: 1.0
        aspect_ratios: 2.0
      }
    }
    first_stage_Box_predictor_conv_hyperparams {
      op: CONV
      regularizer {
        l2_regularizer {
          weight: 0.0
        }
      }
      initializer {
        truncated_normal_initializer {
          stddev: 0.0099999998
        }
      }
    }
    first_stage_nms_score_threshold: 0.0
    first_stage_nms_IoU_threshold: 0.69999999
    first_stage_max_proposals: 100
    first_stage_localization_loss_weight: 2.0
    first_stage_objectness_loss_weight: 1.0
    initial_crop_size: 14
    maxpool_kernel_size: 2
    maxpool_stride: 2
    second_stage_Box_predictor {
      mask_rcnn_Box_predictor {
        fc_hyperparams {
          op: FC
          regularizer {
            l2_regularizer {
              weight: 0.0
            }
          }
          initializer {
            variance_scaling_initializer {
              factor: 1.0
              uniform: true
              mode: FAN_AVG
            }
          }
        }
        use_dropout: false
        dropout_keep_probability: 1.0
      }
    }
    second_stage_post_processing {
      batch_non_max_suppression {
        score_threshold: 0.30000001
        IoU_threshold: 0.60000002
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: softmax
    }
    second_stage_localization_loss_weight: 2.0
    second_stage_classification_loss_weight: 1.0
  }
}
train_config {
  batch_size: 1
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  optimizer {
    momentum_optimizer {
      learning_rate {
        manual_step_learning_rate {
          initial_learning_rate: 0.00030000001
          schedule {
            step: 814096
            learning_rate: 2.9999999e-05
          }
        }
      }
      momentum_optimizer_value: 0.89999998
    }
    use_moving_average: false
  }
  gradient_clipping_by_norm: 10.0
  fine_tune_checkpoint: "/home/kombajn/tensorflow/models/research/object_detection/2020_20_11/model_to_send/model.ckpt"
  from_detection_checkpoint: false
  num_steps: 30000
}
train_input_reader {
  label_map_path: "/home/kombajn/tensorflow/models/research/object_detection/2020_22_12_test/data/pack.pbtxt"
  tf_record_input_reader {
    input_path: "/home/kombajn/tensorflow/models/research/object_detection/2020_22_12_test/data/train.record"
  }
}
eval_config {
  num_examples: 100
  use_moving_averages: false
}
eval_input_reader {
  label_map_path: "/home/kombajn/tensorflow/models/research/object_detection/2020_22_12_test/data/pack.pbtxt"
  shuffle: false
  num_readers: 1
  tf_record_input_reader {
    input_path: "/home/kombajn/tensorflow/models/research/object_detection/2020_22_12_test/data/eval.record"
  }
}

解决方法

在 tensorflow 中获得可重复的结果是一个非常困难的问题。如果您在 Stack Overflow 上搜索，您会发现有关此问题的许多问题。最重要的是，您必须追踪并播种在您的模型中或在您生成数据管道的方式中运行的每个随机源。这不是一件容易的事。例如权重初始化、混洗数据的生成器、dropout 层等。