Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7621#discussion_r35824469
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
---
@@ -0,0 +1,188 @@
+/*
+ * 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.spark.ml.classification
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.param.shared.{HasTol, HasMaxIter, HasSeed}
+import org.apache.spark.ml.{PredictorParams, PredictionModel, Predictor}
+import org.apache.spark.ml.param.{IntParam, ParamValidators,
IntArrayParam, ParamMap}
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.ml.ann.{FeedForwardTrainer, FeedForwardTopology}
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.sql.DataFrame
+
+/** Params for Multilayer Perceptron. */
+private[ml] trait MultilayerPerceptronParams extends PredictorParams
+with HasSeed with HasMaxIter with HasTol {
+ /**
+ * Layer sizes including input size and output size.
+ * @group param
+ */
+ final val layers: IntArrayParam = new IntArrayParam(this, "layers",
+ "Sizes of layers from input layer to output layer" +
+ " E.g., Array(780, 100, 10) means 780 inputs, " +
+ "one hidden layer with 100 neurons and output layer of 10 neurons.",
+ // TODO: how to check ALSO that all elements are greater than 0?
+ ParamValidators.lengthGt(1)
+ )
+
+ /** @group setParam */
+ def setLayers(value: Array[Int]): this.type = set(layers, value)
+
+ /** @group getParam */
+ final def getLayers: Array[Int] = $(layers)
+
+ /**
+ * Block size for stacking input data in matrices. Speeds up the
computations.
+ * Cannot be more than the size of the dataset.
+ * @group expertParam
+ */
+ final val blockSize: IntParam = new IntParam(this, "blockSize",
+ "Block size for stacking input data in matrices.",
ParamValidators.gt(0))
+
+ /** @group setParam */
+ def setBlockSize(value: Int): this.type = set(blockSize, value)
+
+ /** @group getParam */
+ final def getBlockSize: Int = $(blockSize)
+
+ /**
+ * Set the maximum number of iterations.
+ * Default is 100.
+ * @group setParam
+ */
+ def setMaxIter(value: Int): this.type = set(maxIter, value)
+
+ /**
+ * Set the convergence tolerance of iterations.
+ * Smaller value will lead to higher accuracy with the cost of more
iterations.
+ * Default is 1E-4.
+ * @group setParam
+ */
+ def setTol(value: Double): this.type = set(tol, value)
+
+ /**
+ * Set the seed for weights initialization.
+ * @group setParam
+ */
+ def setSeed(value: Long): this.type = set(seed, value)
+
+ setDefault(maxIter -> 100, tol -> 1e-4, layers -> Array(1, 1), blockSize
-> 1)
--- End diff --
do we want to set the default block size to 1?
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