KerasSequential分类预测 (KerasSequentialClassifierPredictBatchOp)

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

source = CsvSourceBatchOp() \
    .setFilePath("https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/random_tensor.csv") \
    .setSchemaStr("tensor string, label int")

source = ToTensorBatchOp() \
    .setSelectedCol("tensor") \
    .setTensorDataType("DOUBLE") \
    .setTensorShape([200, 3]) \
    .linkFrom(source)

trainBatchOp = KerasSequentialClassifierTrainBatchOp() \
    .setTensorCol("tensor") \
    .setLabelCol("label") \
    .setLayers([
        "Conv1D(256, 5, padding='same', activation='relu')",
        "Conv1D(128, 5, padding='same', activation='relu')",
        "Dropout(0.1)",
        "MaxPooling1D(pool_size=8)",
        "Conv1D(128, 5, padding='same', activation='relu')",
        "Conv1D(128, 5, padding='same', activation='relu')",
        "Flatten()"
    ]) \
    .setOptimizer("Adam()") \
    .setNumEpochs(1) \
    .linkFrom(source)

predictBatchOp = KerasSequentialClassifierPredictBatchOp() \
    .setPredictionCol("pred") \
    .setPredictionDetailCol("pred_detail") \
    .setReservedCols(["label"]) \
    .linkFrom(trainBatchOp, source)
predictBatchOp.lazyPrint(10)
BatchOperator.execute()

Java 代码

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.classification.KerasSequentialClassifierPredictBatchOp;
import com.alibaba.alink.operator.batch.classification.KerasSequentialClassifierTrainBatchOp;
import com.alibaba.alink.operator.batch.dataproc.ToTensorBatchOp;
import com.alibaba.alink.operator.batch.source.CsvSourceBatchOp;
import org.junit.Test;

public class KerasSequentialClassifierPredictBatchOpTest {

	@Test
	public void testKerasSequentialClassifierPredictBatchOp() throws Exception {
		BatchOperator<?> source = new CsvSourceBatchOp()
			.setFilePath("https://alink-release.oss-cn-beijing.aliyuncs.com/data-files/random_tensor.csv")
			.setSchemaStr("tensor string, label int");

		source = new ToTensorBatchOp()
			.setSelectedCol("tensor")
			.setTensorDataType("DOUBLE")
			.setTensorShape(200, 3)
			.linkFrom(source);

		KerasSequentialClassifierTrainBatchOp trainBatchOp = new KerasSequentialClassifierTrainBatchOp()
			.setTensorCol("tensor")
			.setLabelCol("label")
			.setLayers(new String[] {
				"Conv1D(256, 5, padding='same', activation='relu')",
				"Conv1D(128, 5, padding='same', activation='relu')",
				"Dropout(0.1)",
				"MaxPooling1D(pool_size=8)",
				"Conv1D(128, 5, padding='same', activation='relu')",
				"Conv1D(128, 5, padding='same', activation='relu')",
				"Flatten()"
			})
			.setOptimizer("Adam()")
			.setNumEpochs(1)
			.linkFrom(source);

		KerasSequentialClassifierPredictBatchOp predictBatchOp = new KerasSequentialClassifierPredictBatchOp()
			.setPredictionCol("pred")
			.setPredictionDetailCol("pred_detail")
			.setReservedCols("label")
			.linkFrom(trainBatchOp, source);
		predictBatchOp.lazyPrint(10);
		BatchOperator.execute();
	}
}

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}