[神经网络]一步一步使用Mobile-Net完成视觉识别（四）

1.环境配置

2.数据集获取

3.训练集获取

4.训练

5.调用测试训练结果

6.代码讲解

　　本文是第四篇，下载预训练模型并训练自己的数据集。

前面我们配置好了labelmap，下面我们开始下载训练好的模型。

http://download.tensorflow.org/models/object_detection/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz

下载下来解压，然后我们配置下pipeline文件

需要改动的地方有

num_classes：这个是我们的分类数，我们只有red和blue就填2

batch_size:这里我填的是2，batch_size过大，每次放入内存中训练的数据就会越多，如果你的内存不够大且数据量比较小，就填小点，我的是8G内存，图片也不过一两千张。

initial_learning_rate：学习速率，可以不修改。

fine_tune_checkpoint：输入我们下载的模型的ckpt文件的绝对路径

label_map_path：配置好的labelmap的绝对路径

tf_record_input_reader的input_path：填之前生成好的tfrecord文件的绝对路径

我的配置为以下文件：

model {

  ssd {

    num_classes: 2

    image_resizer {

      fixed_shape_resizer {

        height: 300

        width: 300

      }

    }

    feature_extractor {

      type: "ssd_mobilenet_v2"

      depth_multiplier: 1.0

      min_depth: 16

      conv_hyperparams {

        regularizer {

          l2_regularizer {

            weight: 3.99999989895e-05

          }

        }

        initializer {

          truncated_normal_initializer {

            mean: 0.0

            stddev: 0.0299999993294

          }

        }

        activation: RELU_6

        batch_norm {

          decay: 0.999700009823

          center: true

          scale: true

          epsilon: 0.0010000000475

          train: true

        }

      }

      use_depthwise: true

    }

    box_coder {

      faster_rcnn_box_coder {

        y_scale: 10.0

        x_scale: 10.0

        height_scale: 5.0

        width_scale: 5.0

      }

    }

    matcher {

      argmax_matcher {

        matched_threshold: 0.5

        unmatched_threshold: 0.5

        ignore_thresholds: false

        negatives_lower_than_unmatched: true

        force_match_for_each_row: true

      }

    }

    similarity_calculator {

      iou_similarity {

      }

    }

    box_predictor {

      convolutional_box_predictor {

        conv_hyperparams {

          regularizer {

            l2_regularizer {

              weight: 3.99999989895e-05

            }

          }

          initializer {

            truncated_normal_initializer {

              mean: 0.0

              stddev: 0.0299999993294

            }

          }

          activation: RELU_6

          batch_norm {

            decay: 0.999700009823

            center: true

            scale: true

            epsilon: 0.0010000000475

            train: true

          }

        }

        min_depth: 0

        max_depth: 0

        num_layers_before_predictor: 0

        use_dropout: false

        dropout_keep_probability: 0.800000011921

        kernel_size: 3

        box_code_size: 4

        apply_sigmoid_to_scores: false

        use_depthwise: true

      }

    }

    anchor_generator {

      ssd_anchor_generator {

        num_layers: 6

        min_scale: 0.20000000298

        max_scale: 0.949999988079

        aspect_ratios: 1.0

        aspect_ratios: 2.0

        aspect_ratios: 0.5

        aspect_ratios: 3.0

        aspect_ratios: 0.333299994469

      }

    }

    post_processing {

      batch_non_max_suppression {

        score_threshold: 0.300000011921

        iou_threshold: 0.600000023842

        max_detections_per_class: 100

        max_total_detections: 100

      }

      score_converter: SIGMOID

    }

    normalize_loss_by_num_matches: true

    loss {

      localization_loss {

        weighted_smooth_l1 {

        }

      }

      classification_loss {

        weighted_sigmoid {

        }

      }

      hard_example_miner {

        num_hard_examples: 3000

        iou_threshold: 0.990000009537

        loss_type: CLASSIFICATION

        max_negatives_per_positive: 3

        min_negatives_per_image: 3

      }

      classification_weight: 1.0

      localization_weight: 1.0

    }

  }

}

train_config {

  batch_size: 2

  data_augmentation_options {

    random_horizontal_flip {

    }

  }

  data_augmentation_options {

    ssd_random_crop {

    }

  }

  optimizer {

    rms_prop_optimizer {

      learning_rate {

        exponential_decay_learning_rate {

          initial_learning_rate: 0.00400000018999

          decay_steps: 800720

          decay_factor: 0.949999988079

        }

      }

      momentum_optimizer_value: 0.899999976158

      decay: 0.899999976158

      epsilon: 1.0

    }

  }

  fine_tune_checkpoint: "/home/xueaoru/models/research/ssdlite_mobilenet_v2_coco_2018_05_09/model.ckpt"

  num_steps: 200000

  fine_tune_checkpoint_type: "detection"

}

train_input_reader {

  label_map_path: "/home/xueaoru/models/research/car_label_map.pbtxt"

  tf_record_input_reader {

    input_path: "/home/xueaoru/models/research/train.record"

  }

}

eval_config {

  num_examples: 60

  max_evals: 10

  use_moving_averages: false

}

eval_input_reader {

  label_map_path: "/home/xueaoru/models/research/car_label_map.pbtxt"

  shuffle: true

  num_readers: 1

  tf_record_input_reader {

    input_path: "/home/xueaoru/models/research/test.record"

  }

}

在models/research目录下执行以下命令：

python object_detection/model_main.py \

    --pipeline_config_path=/home/xueaoru/models/research/ssdlite_mobilenet_v2_coco_2018_05_09/pipeline.config \

    --num_train_steps=200000 \

    --sample_1_of_n_eval_examples=25 \

    --alsologtostderr \

    --model_dir=/home/xueaoru/models/research/car_data

其中pipeline_config_path为之前配置好的pipeline的绝对路径

num_train_steps为训练步数

sample_1_of_n_eval_examples为每多少个验证数据抽样一次

alsologtostderr输出std错误信息

model_dir输出训练过程中的数据的存放文件夹

执行完以上命令之后，基本上训练就开始了，我们只需要通过tensorboard来看看训练效果就可以了

tensorboard --logdir car_data

打开输出的地址：

就可以看到训练效果啦

等到差不多收敛了，我们就可以输出我们的模型了

命令行输入以下命令：

python object_detection/export_inference_graph.py --input_type image_tensor --pipeline_config_path /home/xueaoru/models/research/ssdlite_mobilenet_v2_coco_2018_05_09/pipeline.config --trained_checkpoint_prefix /home/xueaoru/models/research/car_data/model.ckpt-87564 --output_directory /home/xueaoru/models/research/inference_graph_v2

配置基本上跟上面差不多，改改路径即可。

然后我们就在inference_graph_v2目录下拿到了训练后的模型了。

巴特西