Add comments to DLRM initializers to explain the hyperparameters.

Author: jordannad

Models/Recommendation/DLRM.swift

@@ -46,12 +46,21 @@ public struct DLRM: Module {
     @noDerivative public let nDense: Int
     @noDerivative public let interaction: InteractionType
 
+    /// Randomly initialize a DLRM model from the given hyperparameters.
+    ///
+    /// - Parameters:
+    ///    - nDense: The number of continuous or dense inputs for each example.
+    ///    - mSpa: The "width" of all embedding tables.
+    ///    - lnEmb: Defines the "heights" of each of each embedding table.
+    ///    - lnBot: The size of the hidden layers in the bottom MLP.
+    ///    - lnTop: The size of the hidden layers in the top MLP.
+    ///    - interaction: The type of interactions between the hidden  features.
     public init(nDense: Int, mSpa: Int, lnEmb: [Int], lnBot: [Int], lnTop: [Int],
                 interaction: InteractionType = .concatenate) {
         self.nDense = nDense
         mlpBottom = MLP(dims: [nDense] + lnBot)
         let topInput = lnEmb.count * mSpa + lnBot.last!
-        mlpTop = MLP(dims: [topInput] + lnTop + [1])
+        mlpTop = MLP(dims: [topInput] + lnTop + [1], sigmoidLastLayer: true)
         latentFactors = lnEmb.map { Embedding(vocabularySize: $0, embeddingSize: mSpa) }
         self.interaction = interaction
     }

Models/Recommendation/MLP.swift

@@ -17,6 +17,13 @@ import TensorFlow
 /// MLP is a multi-layer perceptron and is used as a component of the DLRM model
 public struct MLP: Layer {
     public var blocks: [Dense<Float>] = []
+
+    /// Randomly initializes a new multilayer perceptron from the given hyperparameters.
+    ///
+    /// - Parameter dims: Dims represents the size of the input, hidden layers, and output of the
+    ///   multi-layer perceptron.
+    /// - Parameter sigmoidLastLayer: if `true`, use a `sigmoid` activation function for the last layer,
+    ///   `relu` otherwise.
     init(dims: [Int], sigmoidLastLayer: Bool = false) {
         for i in 0..<(dims.count-1) {
             if sigmoidLastLayer && i == dims.count - 2 {