com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLogisticRegression.scala Maven / Gradle / Ivy
/*
* Copyright 2016 The BigDL Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.intel.analytics.bigdl.example.MLPipeline
import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Linear, LogSoftMax, Sequential}
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat
import com.intel.analytics.bigdl.utils.Engine
import org.apache.spark.SparkContext
import org.apache.spark.ml.DLClassifier
import org.apache.spark.sql.SQLContext
/**
* Logistic Regression with BigDL layers and DLClassifier
*/
object DLClassifierLogisticRegression {
def main(args: Array[String]): Unit = {
val conf = Engine.createSparkConf()
.setAppName("DLClassifierLogisticRegression")
.setMaster("local[1]")
val sc = new SparkContext(conf)
val sqlContext = SQLContext.getOrCreate(sc)
Engine.init
val model = Sequential().add(Linear(2, 2)).add(LogSoftMax())
val criterion = ClassNLLCriterion()
val estimator = new DLClassifier(model, criterion, Array(2))
.setBatchSize(4)
.setMaxEpoch(10)
val data = sc.parallelize(Seq(
(Array(0.0, 1.0), 1.0),
(Array(1.0, 0.0), 2.0),
(Array(0.0, 1.0), 1.0),
(Array(1.0, 0.0), 2.0)))
val df = sqlContext.createDataFrame(data).toDF("features", "label")
val dlModel = estimator.fit(df)
dlModel.transform(df).show(false)
}
}