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/*
* 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 org.apache.spark.ml
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.{Criterion, Module}
import scala.reflect.ClassTag
/**
* Deprecated. Please refer to package com.intel.analytics.bigdl.dlframes.
*
* [[DLEstimator]] helps to train a BigDL Model with the Spark ML Estimator/Transfomer pattern,
* thus Spark users can conveniently fit BigDL into Spark ML pipeline.
*
* [[DLEstimator]] supports feature and label data in the format of
* Array[Double], Array[Float], org.apache.spark.mllib.linalg.{Vector, VectorUDT},
* org.apache.spark.ml.linalg.{Vector, VectorUDT}, Double and Float.
*
* User should specify the feature data dimensions and label data dimensions via the constructor
* parameters featureSize and labelSize respectively. Internally the feature and label data are
* converted to BigDL tensors, to further train a BigDL model efficiently.
*
* For details usage, please refer to examples in package
* com.intel.analytics.bigdl.example.MLPipeline
*
* @param model BigDL module to be optimized
* @param criterion BigDL criterion method
* @param featureSize The size (Tensor dimensions) of the feature data. e.g. an image may be with
* width * height = 28 * 28, featureSize = Array(28, 28).
* @param labelSize The size (Tensor dimensions) of the label data.
*/
@deprecated("`DLEstimator` has been migrated to package `com.intel.analytics.bigdl.dlframes`." +
"org.apache.spark.ml.DLEstimator will be removed in BigDL 0.6.", "0.5.0")
class DLEstimator[T: ClassTag](
@transient override val model: Module[T],
override val criterion : Criterion[T],
featureSize : Array[Int],
override val labelSize : Array[Int],
override val uid: String = "DLEstimator")(implicit ev: TensorNumeric[T])
extends com.intel.analytics.bigdl.dlframes.DLEstimator[T](
model, criterion, featureSize, labelSize) {
override protected def wrapBigDLModel(m: Module[T], featureSize: Array[Int]): DLModel[T] = {
val dlModel = new DLModel[T](m, featureSize)
copyValues(dlModel.setParent(this)).asInstanceOf[DLModel[T]]
}
}
/**
* Deprecated. Please refer to package com.intel.analytics.bigdl.dlframes.
*
* [[DLModel]] helps embed a BigDL model into a Spark Transformer, thus Spark users can
* conveniently merge BigDL into Spark ML pipeline.
* [[DLModel]] supports feature data in the format of
* Array[Double], Array[Float], org.apache.spark.mllib.linalg.{Vector, VectorUDT},
* org.apache.spark.ml.linalg.{Vector, VectorUDT}, Double and Float.
* Internally [[DLModel]] use features column as storage of the feature data, and create
* Tensors according to the constructor parameter featureSize.
*
* [[DLModel]] is compatible with both spark 1.5-plus and 2.0 by extending ML Transformer.
* @param model trainned BigDL models to use in prediction.
* @param featureSize The size (Tensor dimensions) of the feature data. (e.g. an image may be with
* featureSize = 28 * 28).
*/
@deprecated("`DLModel` has been migrated to package `com.intel.analytics.bigdl.dlframes`." +
"This will be removed in BigDL 0.6.", "0.5.0")
class DLModel[T: ClassTag](
@transient override val model: Module[T],
featureSize : Array[Int],
override val uid: String = "DLModel"
)(implicit ev: TensorNumeric[T])
extends com.intel.analytics.bigdl.dlframes.DLModel[T](model, featureSize)
// TODO, add save/load
object DLModel {
}