Java 类名：com.alibaba.alink.operator.batch.classification.KerasSequentialClassifierPredictBatchOp
Python 类名：KerasSequentialClassifierPredictBatchOp

功能介绍

与 KerasSequential分类训练组件对应的预测组件。

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 | | —- | —- | —- | —- | —- | —- | —- |

| predictionCol | 预测结果列名 | 预测结果列名 | String | ✓ | | |

| inferBatchSize | 推理数据批大小 | 推理数据批大小 | Integer | | | 256 |

| predictionDetailCol | 预测详细信息列名 | 预测详细信息列名 | String | | | |

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

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. source = CsvSourceBatchOp() \
  2.     .setFilePath("https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/random_tensor.csv") \
  3.     .setSchemaStr("tensor string, label int")
  5. source = ToTensorBatchOp() \
  6.     .setSelectedCol("tensor") \
  7.     .setTensorDataType("DOUBLE") \
  8.     .setTensorShape([200, 3]) \
  9.     .linkFrom(source)
  11. trainBatchOp = KerasSequentialClassifierTrainBatchOp() \
  12.     .setTensorCol("tensor") \
  13.     .setLabelCol("label") \
  14.     .setLayers([
  15.         "Conv1D(256, 5, padding='same', activation='relu')",
  16.         "Conv1D(128, 5, padding='same', activation='relu')",
  17.         "Dropout(0.1)",
  18.         "MaxPooling1D(pool_size=8)",
  19.         "Conv1D(128, 5, padding='same', activation='relu')",
  20.         "Conv1D(128, 5, padding='same', activation='relu')",
  21.         "Flatten()"
  22.     ]) \
  23.     .setOptimizer("Adam()") \
  24.     .setNumEpochs(1) \
  25.     .linkFrom(source)
  27. predictBatchOp = KerasSequentialClassifierPredictBatchOp() \
  28.     .setPredictionCol("pred") \
  29.     .setPredictionDetailCol("pred_detail") \
  30.     .setReservedCols(["label"]) \
  31.     .linkFrom(trainBatchOp, source)
  32. predictBatchOp.lazyPrint(10)
  33. BatchOperator.execute()

Java 代码

  1. import com.alibaba.alink.operator.batch.BatchOperator;
  2. import com.alibaba.alink.operator.batch.classification.KerasSequentialClassifierPredictBatchOp;
  3. import com.alibaba.alink.operator.batch.classification.KerasSequentialClassifierTrainBatchOp;
  4. import com.alibaba.alink.operator.batch.dataproc.ToTensorBatchOp;
  5. import com.alibaba.alink.operator.batch.source.CsvSourceBatchOp;
  6. import org.junit.Test;
  8. public class KerasSequentialClassifierPredictBatchOpTest {
  10.     @Test
  11.     public void testKerasSequentialClassifierPredictBatchOp() throws Exception {
  12.         BatchOperator<?> source = new CsvSourceBatchOp()
  13.             .setFilePath("https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/random_tensor.csv")
  14.             .setSchemaStr("tensor string, label int");
  16.         source = new ToTensorBatchOp()
  17.             .setSelectedCol("tensor")
  18.             .setTensorDataType("DOUBLE")
  19.             .setTensorShape(200, 3)
  20.             .linkFrom(source);
  22.         KerasSequentialClassifierTrainBatchOp trainBatchOp = new KerasSequentialClassifierTrainBatchOp()
  23.             .setTensorCol("tensor")
  24.             .setLabelCol("label")
  25.             .setLayers(new String[] {
  26.                 "Conv1D(256, 5, padding='same', activation='relu')",
  27.                 "Conv1D(128, 5, padding='same', activation='relu')",
  28.                 "Dropout(0.1)",
  29.                 "MaxPooling1D(pool_size=8)",
  30.                 "Conv1D(128, 5, padding='same', activation='relu')",
  31.                 "Conv1D(128, 5, padding='same', activation='relu')",
  32.                 "Flatten()"
  33.             })
  34.             .setOptimizer("Adam()")
  35.             .setNumEpochs(1)
  36.             .linkFrom(source);
  38.         KerasSequentialClassifierPredictBatchOp predictBatchOp = new KerasSequentialClassifierPredictBatchOp()
  39.             .setPredictionCol("pred")
  40.             .setPredictionDetailCol("pred_detail")
  41.             .setReservedCols("label")
  42.             .linkFrom(trainBatchOp, source);
  43.         predictBatchOp.lazyPrint(10);
  44.         BatchOperator.execute();
  45.     }
  46. }

运行结果

| label | pred | pred_detail | | —- | —- | —- |

| 0 | 0 | {“0”:0.636155836712713,”1”:0.36384416328728697} |

| 1 | 0 | {“0”:0.6334926095655181,”1”:0.3665073904344819} |

| 1 | 0 | {“0”:0.6381823204965642,”1”:0.3618176795034358} |

| 1 | 0 | {“0”:0.6376416296248051,”1”:0.362358370375195} |

| 1 | 0 | {“0”:0.6345856985385896,”1”:0.36541430146141035} |

| 1 | 0 | {“0”:0.6357593109428179,”1”:0.364240689057182} |

| 0 | 0 | {“0”:0.6404387449594703,”1”:0.3595612550405296} |

| 1 | 0 | {“0”:0.6372702905549685,”1”:0.36272970944503136} |

| 0 | 0 | {“0”:0.635502012172225,”1”:0.36449798782777487} |

| 0 | 0 | {“0”:0.6262401788033837,”1”:0.37375982119661644} |