Java 类名：com.alibaba.alink.operator.batch.tensorflow.TFTableModelPredictBatchOp
Python 类名：TFTableModelPredictBatchOp

功能介绍

使用 TFTableModelTrainBatchOp 或者 TF2TableModelTrainBatchOp 训练产生的模型进行预测。

参数说明

| 名称 | 中文名称 | 描述 | 类型 | 是否必须？ | 取值范围 | 默认值 | | —- | —- | —- | —- | —- | —- | —- |

| outputSchemaStr | Schema | Schema。格式为”colname coltype[, colname2, coltype2[, …]]”，例如 “f0 string, f1 bigint, f2 double” | String | ✓ | | |

| graphDefTag | graph标签 | graph标签 | String | | | “serve” |

| inputSignatureDefs | 输入 SignatureDef | SavedModel 模型的输入 SignatureDef 名，用逗号分隔，需要与输入列一一对应，默认与选择列相同 | String[] | | | null |

| intraOpParallelism | Op 间并发度 | Op 间并发度 | Integer | | | 4 |

| outputSignatureDefs | TF 输出 SignatureDef 名 | 模型的输出 SignatureDef 名，多个输出时用逗号分隔，并且与输出 Schema 一一对应，默认与输出 Schema 中的列名相同 | String[] | | | null |

| reservedCols | 算法保留列名 | 算法保留列 | String[] | | | null |

| selectedCols | 选中的列名数组 | 计算列对应的列名列表 | String[] | | | null |

| signatureDefKey | signature标签 | signature标签 | String | | | “serving_default” |

代码示例

以下代码仅用于示意，可能需要修改部分代码或者配置环境后才能正常运行！

Python 代码

  1. import json
  3. source = RandomTableSourceBatchOp() \
  4.     .setNumRows(100) \
  5.     .setNumCols(10)
  7. colNames = source.getColNames()
  8. source = source.select("*, case when RAND() > 0.5 then 1. else 0. end as label")
  9. label = "label"
  11. userParams = {
  12.     'featureCols': json.dumps(colNames),
  13.     'labelCol': label,
  14.     'batch_size': 16,
  15.     'num_epochs': 1
  16. }
  18. tfTableModelTrainBatchOp = TFTableModelTrainBatchOp() \
  19.     .setUserFiles(["https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/tf_dnn_train.py"]) \
  20.     .setMainScriptFile("https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/tf_dnn_train.py") \
  21.     .setUserParams(json.dumps(userParams)) \
  22.     .linkFrom(source)
  24. tfTableModelPredictBatchOp = TFTableModelPredictBatchOp() \
  25.     .setOutputSchemaStr("logits double") \
  26.     .setOutputSignatureDefs(["logits"]) \
  27.     .setSignatureDefKey("predict") \
  28.     .setSelectedCols(colNames) \
  29.     .linkFrom(tfTableModelTrainBatchOp, source)
  30. tfTableModelPredictBatchOp.print()

Java 代码

  1. import com.alibaba.alink.common.utils.JsonConverter;
  2. import com.alibaba.alink.operator.batch.BatchOperator;
  3. import com.alibaba.alink.operator.batch.source.RandomTableSourceBatchOp;
  4. import com.alibaba.alink.operator.batch.tensorflow.TFTableModelPredictBatchOp;
  5. import com.alibaba.alink.operator.batch.tensorflow.TFTableModelTrainBatchOp;
  6. import org.junit.Test;
  8. import java.util.HashMap;
  9. import java.util.Map;
  11. public class TFTableModelPredictBatchOpTest {
  12.     @Test
  13.     public void testTFTableModelPredictBatchOp() throws Exception {
  14.         BatchOperator<?> source = new RandomTableSourceBatchOp()
  15.             .setNumRows(100L)
  16.             .setNumCols(10);
  18.         String[] colNames = source.getColNames();
  19.         source = source.select("*, case when RAND() > 0.5 then 1. else 0. end as label");
  20.         String label = "label";
  22.         Map <String, Object> userParams = new HashMap <>();
  23.         userParams.put("featureCols", JsonConverter.toJson(colNames));
  24.         userParams.put("labelCol", label);
  25.         userParams.put("batch_size", 16);
  26.         userParams.put("num_epochs", 1);
  28.         TFTableModelTrainBatchOp tfTableModelTrainBatchOp = new TFTableModelTrainBatchOp()
  29.             .setUserFiles(new String[] {"https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/tf_dnn_train.py"})
  30.             .setMainScriptFile("https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/tf_dnn_train.py")
  31.             .setUserParams(JsonConverter.toJson(userParams))
  32.             .linkFrom(source);
  34.         TFTableModelPredictBatchOp tfTableModelPredictBatchOp = new TFTableModelPredictBatchOp()
  35.             .setOutputSchemaStr("logits double")
  36.             .setOutputSignatureDefs(new String[]{"logits"})
  37.             .setSignatureDefKey("predict")
  38.             .setSelectedCols(colNames)
  39.             .linkFrom(tfTableModelTrainBatchOp, source);
  40.         tfTableModelPredictBatchOp.print();
  41.     }
  42. }

运行结果

| col0 | col1 | col2 | col3 | col4 | col5 | col6 | col7 | col8 | col9 | label | logits | | —- | —- | —- | —- | —- | —- | —- | —- | —- | —- | —- | —- |

| 0.7310 | 0.2405 | 0.6374 | 0.5504 | 0.5975 | 0.3332 | 0.3852 | 0.9848 | 0.8792 | 0.9412 | 0 | -0.4253 |

| 0.2750 | 0.1289 | 0.1466 | 0.0232 | 0.5467 | 0.9645 | 0.1045 | 0.6251 | 0.4108 | 0.7763 | 0 | -0.4099 |

| 0.9907 | 0.4872 | 0.7462 | 0.7332 | 0.8173 | 0.8389 | 0.5267 | 0.8993 | 0.1339 | 0.0831 | 0 | -0.3881 |

| 0.9786 | 0.7224 | 0.7150 | 0.1432 | 0.4630 | 0.0045 | 0.0715 | 0.3484 | 0.3388 | 0.8594 | 0 | -0.3044 |

| 0.9715 | 0.8657 | 0.6126 | 0.1790 | 0.2176 | 0.8545 | 0.0097 | 0.6923 | 0.7713 | 0.7127 | 0 | -0.4693 |

| … | … | … | … | … | … | … | … | … | … | … | … |