[MINOR][DOC] Fix doc style in ml.ann.Layer and MultilayerPerceptronClassifier

## What changes were proposed in this pull request?
1, fix the indentation
2, add a missing param desc

## How was this patch tested?
unit tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12499 from zhengruifeng/fix_doc.

2 files changed, 40 insertions, 40 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
index a5b84116e6..913479f020 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
@@ -88,7 +88,7 @@ private[ann] trait LayerModel extends Serializable {
    * LayerModel implementation and the stack (batch) size
    * Developer is responsible for checking the size of output
    * when writing to it
- *
+   *
    * @param data data
    * @param output output (modified in place)
    */
@@ -100,8 +100,8 @@ private[ann] trait LayerModel extends Serializable {
    * LayerModel implementation and the stack (batch) size
    * Developer is responsible for checking the size of
    * prevDelta when writing to it
-    *
-    * @param delta delta of this layer
+   *
+   * @param delta delta of this layer
    * @param output output of this layer
    * @param prevDelta the previous delta (modified in place)
    */
@@ -185,7 +185,7 @@ private[ann] object AffineLayerModel {
 
   /**
    * Creates a model of Affine layer
- *
+   *
    * @param layer layer properties
    * @param weights vector for weights initialization
    * @param random random number generator
@@ -202,8 +202,8 @@ private[ann] object AffineLayerModel {
    * where a is chosen in a such way that the weight variance corresponds
    * to the points to the maximal curvature of the activation function
    * (which is approximately 2.38 for a standard sigmoid)
-    *
-    * @param numIn number of inputs
+   *
+   * @param numIn number of inputs
    * @param numOut number of outputs
    * @param weights vector for weights initialization
    * @param random random number generator
@@ -354,7 +354,7 @@ private[ann] trait TopologyModel extends Serializable {
   val layerModels: Array[LayerModel]
   /**
    * Forward propagation
- *
+   *
    * @param data input data
    * @return array of outputs for each of the layers
    */
@@ -362,7 +362,7 @@ private[ann] trait TopologyModel extends Serializable {
 
   /**
    * Prediction of the model
- *
+   *
    * @param data input data
    * @return prediction
    */
@@ -370,7 +370,7 @@ private[ann] trait TopologyModel extends Serializable {
 
   /**
    * Computes gradient for the network
- *
+   *
    * @param data input data
    * @param target target output
    * @param cumGradient cumulative gradient
@@ -384,7 +384,7 @@ private[ann] trait TopologyModel extends Serializable {
 /**
  * Feed forward ANN
  *
- * @param layers
+ * @param layers Array of layers
  */
 private[ann] class FeedForwardTopology private(val layers: Array[Layer]) extends Topology {
   override def model(weights: Vector): TopologyModel = FeedForwardModel(this, weights)
@@ -398,7 +398,7 @@ private[ann] class FeedForwardTopology private(val layers: Array[Layer]) extends
 private[ml] object FeedForwardTopology {
   /**
    * Creates a feed forward topology from the array of layers
- *
+   *
    * @param layers array of layers
    * @return feed forward topology
    */
@@ -408,7 +408,7 @@ private[ml] object FeedForwardTopology {
 
   /**
    * Creates a multi-layer perceptron
- *
+   *
    * @param layerSizes sizes of layers including input and output size
    * @param softmaxOnTop wether to use SoftMax or Sigmoid function for an output layer.
    *                Softmax is default
@@ -534,7 +534,7 @@ private[ann] object FeedForwardModel {
 
   /**
    * Creates a model from a topology and weights
- *
+   *
    * @param topology topology
    * @param weights weights
    * @return model
@@ -546,7 +546,7 @@ private[ann] object FeedForwardModel {
 
   /**
    * Creates a model given a topology and seed
- *
+   *
    * @param topology topology
    * @param seed seed for generating the weights
    * @return model
@@ -610,7 +610,7 @@ private[ann] class DataStacker(stackSize: Int, inputSize: Int, outputSize: Int)
 
   /**
    * Stacks the data
- *
+   *
    * @param data RDD of vector pairs
    * @return RDD of double (always zero) and vector that contains the stacked vectors
    */
@@ -643,7 +643,7 @@ private[ann] class DataStacker(stackSize: Int, inputSize: Int, outputSize: Int)
 
   /**
    * Unstack the stacked vectors into matrices for batch operations
- *
+   *
    * @param data stacked vector
    * @return pair of matrices holding input and output data and the real stack size
    */
@@ -714,7 +714,7 @@ private[ml] class FeedForwardTrainer(
 
   /**
    * Sets weights
- *
+   *
    * @param value weights
    * @return trainer
    */
@@ -725,7 +725,7 @@ private[ml] class FeedForwardTrainer(
 
   /**
    * Sets the stack size
- *
+   *
    * @param value stack size
    * @return trainer
    */
@@ -737,7 +737,7 @@ private[ml] class FeedForwardTrainer(
 
   /**
    * Sets the SGD optimizer
- *
+   *
    * @return SGD optimizer
    */
   def SGDOptimizer: GradientDescent = {
@@ -748,7 +748,7 @@ private[ml] class FeedForwardTrainer(
 
   /**
    * Sets the LBFGS optimizer
- *
+   *
    * @return LBGS optimizer
    */
   def LBFGSOptimizer: LBFGS = {
@@ -759,7 +759,7 @@ private[ml] class FeedForwardTrainer(
 
   /**
    * Sets the updater
- *
+   *
    * @param value updater
    * @return trainer
    */
@@ -771,7 +771,7 @@ private[ml] class FeedForwardTrainer(
 
   /**
    * Sets the gradient
- *
+   *
    * @param value gradient
    * @return trainer
    */
@@ -801,7 +801,7 @@ private[ml] class FeedForwardTrainer(
 
   /**
    * Trains the ANN
- *
+   *
    * @param data RDD of input and output vector pairs
    * @return model
    */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
index 9ff5252e4f..456e22de91 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -37,8 +37,8 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
   /**
    * Layer sizes including input size and output size.
    * Default: Array(1, 1)
-    *
-    * @group param
+   *
+   * @group param
    */
   final val layers: IntArrayParam = new IntArrayParam(this, "layers",
     "Sizes of layers from input layer to output layer" +
@@ -56,8 +56,8 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
    * a partition then it is adjusted to the size of this data.
    * Recommended size is between 10 and 1000.
    * Default: 128
-    *
-    * @group expertParam
+   *
+   * @group expertParam
    */
   final val blockSize: IntParam = new IntParam(this, "blockSize",
     "Block size for stacking input data in matrices. Data is stacked within partitions." +
@@ -71,7 +71,7 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
   /**
    * Allows setting the solver: minibatch gradient descent (gd) or l-bfgs.
    * l-bfgs is the default one.
- *
+   *
    * @group expertParam
    */
   final val solver: Param[String] = new Param[String](this, "solver",
@@ -84,8 +84,8 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
 
   /**
    * Model weights. Can be returned either after training or after explicit setting
-    *
-    * @group expertParam
+   *
+   * @group expertParam
    */
   final val weights: Param[Vector] = new Param[Vector](this, "weights",
     " Sets the weights of the model ")
@@ -156,8 +156,8 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
   /**
    * Set the maximum number of iterations.
    * Default is 100.
-    *
-    * @group setParam
+   *
+   * @group setParam
    */
   @Since("1.5.0")
   def setMaxIter(value: Int): this.type = set(maxIter, value)
@@ -166,24 +166,24 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
    * 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
+   *
+   * @group setParam
    */
   @Since("1.5.0")
   def setTol(value: Double): this.type = set(tol, value)
 
   /**
    * Set the seed for weights initialization if weights are not set
-    *
-    * @group setParam
+   *
+   * @group setParam
    */
   @Since("1.5.0")
   def setSeed(value: Long): this.type = set(seed, value)
 
   /**
    * Sets the model weights.
-    *
-    * @group expertParam
+   *
+   * @group expertParam
    */
   @Since("2.0.0")
   def setWeights(value: Vector): this.type = set(weights, value)
@@ -232,8 +232,8 @@ object MultilayerPerceptronClassifier
  * :: Experimental ::
  * Classification model based on the Multilayer Perceptron.
  * Each layer has sigmoid activation function, output layer has softmax.
-  *
-  * @param uid uid
+ *
+ * @param uid uid
  * @param layers array of layer sizes including input and output layers
  * @param weights vector of initial weights for the model that consists of the weights of layers
  * @return prediction model