Update README with Sequential API

README.md

@@ -23,22 +23,16 @@ import TensorFlow
 
 let hiddenSize: Int = 10
 
-struct Model: Layer {
-    var layer1 = Dense<Float>(inputSize: 4, outputSize: hiddenSize, activation: relu)
-    var layer2 = Dense<Float>(inputSize: hiddenSize, outputSize: hiddenSize, activation: relu)
-    var layer3 = Dense<Float>(inputSize: hiddenSize, outputSize: 3, activation: identity)
-
-    @differentiable
-    func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {
-        return input.sequenced(through: layer1, layer2, layer3)
-    }
+var classifier = Sequential {
+    Dense<Float>(inputSize: 4, outputSize: hiddenSize, activation: relu)
+    Dense<Float>(inputSize: hiddenSize, outputSize: hiddenSize, activation: relu)
+    Dense<Float>(inputSize: hiddenSize, outputSize: 3, activation: identity)
 }
 ```
 
 #### Initialize a model and an optimizer
 
 ```swift
-var classifier = Model()
 let optimizer = SGD(for: classifier, learningRate: 0.02)
 Context.local.learningPhase = .training
 let x: Tensor<Float> = ...

Sources/TensorFlow/Layers/Dropout.swift

@@ -46,24 +46,14 @@ public struct Dropout<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
 
     /// Creates a dropout layer.
     ///
-    /// - Parameter probability: The drop probability.
+    /// - Parameter probability: The probability of a node dropping out.
     /// - Precondition: probability must be a value between 0 and 1 (inclusive). 
     public init(probability: Double) {
         precondition(0...1 ~= probability,
             "Probability must be a value between 0 and 1 (inclusive) but is \(probability)")
         self.probability = probability
     }
 
-    @differentiable
-    private func applyingTraining(to input: Tensor<Scalar>) -> Tensor<Scalar> {
-        return input._droppingOut(probability: probability)
-    }
-
-    @differentiable
-    private func applyingInference(to input: Tensor<Scalar>) -> Tensor<Scalar> {
-        return input
-    }
-
     /// Returns the output obtained from applying the layer to the given input.
     ///
     /// - Parameter input: The input to the layer.
@@ -72,9 +62,9 @@ public struct Dropout<Scalar: TensorFlowFloatingPoint>: ParameterlessLayer {
     public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         switch Context.local.learningPhase {
         case .training:
-            return applyingTraining(to: input)
+            return input._droppingOut(probability: probability)
         case .inference:
-            return applyingInference(to: input)
+            return input
         }
     }
 }