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https://issues.apache.org/jira/browse/FLINK-1807?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14534215#comment-14534215
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ASF GitHub Bot commented on FLINK-1807:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/613#discussion_r29927830
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/optimization/Regularization.scala
---
@@ -0,0 +1,200 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.flink.ml.optimization
+
+import org.apache.flink.ml.math.{Vector => FlinkVector, DenseVector =>
FlinkDenseVector, BLAS}
+import org.apache.flink.ml.math.Breeze._
+
+/** Represents a type of regularization penalty
+ *
+ * Regularization penalties are used to restrict the optimization problem
to solutions with
+ * certain desirable characteristics, such as sparsity for the L1
penalty, or penalizing large
+ * weights for the L2 penalty.
+ *
+ * The regularization term, $R(w)$ is added to the objective function,
$f(w) = L(w) + \lambda R(w)$
+ * where $\lambda$ is the regularization parameter used to tune the
amount of regularization
+ * applied.
+ */
+abstract class Regularization extends Serializable {
+
+ /** Updates the weights by taking a step according to the gradient and
regularization applied
+ *
+ * @param oldWeights The weights to be updated
+ * @param gradient The gradient according to which we will update the
weights
+ * @param effectiveStepSize The effective step size for this iteration
+ * @param regParameter The regularization parameter, $\lambda$.
+ */
+ def takeStep(
+ oldWeights: FlinkVector,
+ gradient: FlinkVector,
+ effectiveStepSize: Double,
+ regParameter: Double) {
+ BLAS.axpy(-effectiveStepSize, gradient, oldWeights)
+ }
+
+ /** Adds the regularization term to the loss value
+ *
+ * @param loss The loss value, before applying regularization.
+ * @param weightVector The current vector of weights.
+ * @param regularizationParameter The regularization parameter,
$\lambda$.
+ * @return The loss value with regularization applied.
+ */
+ def regLoss(loss: Double, weightVector: FlinkVector,
regularizationParameter: Double): Double
+
+}
+
+/** Abstract class for regularization penalties that are differentiable
+ *
+ */
+abstract class DiffRegularization extends Regularization {
+
+ /** Compute the regularized gradient loss for the given data.
+ * The provided cumGradient is updated in place.
+ *
+ * @param loss The loss value without regularization.
+ * @param weightVector The current vector of weights.
+ * @param lossGradient The loss gradient, without regularization.
Updated in-place.
+ * @param regParameter The regularization parameter, $\lambda$.
+ * @return The loss value with regularization applied.
+ */
+ def regularizedLossAndGradient(
+ loss: Double,
+ weightVector: FlinkVector,
+ lossGradient: FlinkVector,
+ regParameter: Double) : Double ={
+ val adjustedLoss = regLoss(loss, weightVector, regParameter)
+ regGradient(weightVector, lossGradient, regParameter)
+
+ adjustedLoss
+ }
+
+ /** Adds the regularization gradient term to the loss gradient. The
gradient is updated in place.
+ *
+ * @param weightVector The current vector of weights
+ * @param lossGradient The loss gradient, without regularization.
Updated in-place.
+ * @param regParameter The regularization parameter, $\lambda$.
+ */
+ def regGradient(
+ weightVector: FlinkVector,
+ lossGradient: FlinkVector,
+ regParameter: Double)
+}
+
+/** Performs no regularization, equivalent to $R(w) = 0$ **/
+class NoRegularization extends Regularization {
+ /** Adds the regularization term to the loss value
+ *
+ * @param loss The loss value, before applying regularization
+ * @param weightVector The current vector of weights
+ * @param regParameter The regularization parameter, $\lambda$
+ * @return The loss value with regularization applied.
+ */
+ override def regLoss(
+ loss: Double,
+ weightVector: FlinkVector,
+ regParameter: Double): Double = {loss}
+}
+
+/** $L_2$ regularization penalty.
+ *
+ * Penalizes large weights, favoring solutions with more small weights
rather than few large ones.
+ *
+ */
+class L2Regularization extends DiffRegularization {
+
+ /** Adds the regularization term to the loss value
+ *
+ * @param loss The loss value, before applying regularization
+ * @param weightVector The current vector of weights
+ * @param regParameter The regularization parameter, $\lambda$
+ * @return The loss value with regularization applied.
+ */
+ override def regLoss(loss: Double, weightVector: FlinkVector,
regParameter: Double)
+ : Double = {
+ val brzVector = weightVector.asBreeze
+ loss + regParameter * (brzVector dot brzVector) / 2
--- End diff --
Breeze. The BLAS object also has a `dot` method.
> Stochastic gradient descent optimizer for ML library
> ----------------------------------------------------
>
> Key: FLINK-1807
> URL: https://issues.apache.org/jira/browse/FLINK-1807
> Project: Flink
> Issue Type: Improvement
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Theodore Vasiloudis
> Labels: ML
>
> Stochastic gradient descent (SGD) is a widely used optimization technique in
> different ML algorithms. Thus, it would be helpful to provide a generalized
> SGD implementation which can be instantiated with the respective gradient
> computation. Such a building block would make the development of future
> algorithms easier.
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