[Layer] 'func call' -> 'func callAsFunction'.

README.md

@@ -29,7 +29,7 @@ struct Model: Layer {
     var layer3 = Dense<Float>(inputSize: hiddenSize, outputSize: 3, activation: identity)
 
     @differentiable
-    func call(_ input: Tensor<Float>) -> Tensor<Float> {
+    func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {
         return input.sequenced(through: layer1, layer2, layer3)
     }
 }

Sources/TensorFlow/Layer.swift

@@ -31,7 +31,7 @@ public protocol Layer: Differentiable & KeyPathIterable
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    func call(_ input: Input) -> Output
+    func callAsFunction(_ input: Input) -> Output
 }
 
 public extension Layer {

Sources/TensorFlow/Layers/Convolutional.swift

@@ -59,7 +59,7 @@ public struct Conv1D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer `[batchCount, width, inputChannels]`.
     /// - Returns: The output `[batchCount, newWidth, outputChannels]`.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         let conv2D = input.expandingShape(at: 1).convolved2D(
             withFilter: filter.expandingShape(at: 0), strides: (1, 1, stride, 1), padding: padding)
         return activation(conv2D.squeezingShape(at: 1) + bias)
@@ -177,7 +177,7 @@ public struct Conv2D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return activation(input.convolved2D(withFilter: filter,
                                             strides: (1, strides.0, strides.1, 1),
                                             padding: padding) + bias)
@@ -293,7 +293,7 @@ public struct Conv3D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return activation(input.convolved3D(withFilter: filter,
                                             strides: (1, strides.0, strides.1, strides.2, 1),
                                             padding: padding) + bias)
@@ -411,7 +411,7 @@ public struct TransposedConv2D: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Float>) -> Tensor<Float> {
+    public func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {
         let batchSize = input.shape[0]
         let w = (input.shape[1] - (1 * paddingIndex)) *
           strides.0 + (filter.shape[0] * paddingIndex)

Sources/TensorFlow/Layers/Core.swift

@@ -53,7 +53,7 @@ public struct Dropout<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable(vjp: _vjpApplied(to:))
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         switch Context.local.learningPhase {
         case .training:
             return applyingTraining(to: input)
@@ -92,7 +92,7 @@ public struct Flatten<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         let batchSize = input.shape[0]
         let remaining = input.shape[1..<input.rank].contiguousSize
         return input.reshaped(to: [batchSize, remaining])
@@ -128,7 +128,7 @@ public struct Reshape<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return input.reshaped(toShape: shape)
     }
 }
@@ -163,7 +163,7 @@ public struct Dense<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return activation(matmul(input, weight) + bias)
     }
 }

Sources/TensorFlow/Layers/Normalization.swift

@@ -90,7 +90,7 @@ public struct BatchNorm<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable(vjp: _vjpApplied(to:))
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         switch Context.local.learningPhase {
         case .training:
             return applyingTraining(to: input)
@@ -185,7 +185,7 @@ public struct LayerNorm<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         let mean = input.mean(alongAxes: axis)
         let variance = input.variance(alongAxes: axis)
         let inv = rsqrt(variance + epsilon) * scale

Sources/TensorFlow/Layers/Pooling.swift

@@ -43,7 +43,7 @@ public struct MaxPool1D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return input.expandingShape(at: 1).maxPooled2D(
             kernelSize: (1, 1, poolSize, 1), strides: (1, 1, stride, 1), padding: padding
         ).squeezingShape(at: 1)
@@ -77,7 +77,7 @@ public struct MaxPool2D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return input.maxPooled2D(
             kernelSize: poolSize, strides: strides, padding: padding)
     }
@@ -124,7 +124,7 @@ public struct MaxPool3D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return input.maxPooled3D(kernelSize: poolSize, strides: strides, padding: padding)
     }
 }
@@ -184,7 +184,7 @@ public struct AvgPool1D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return input.expandingShape(at: 1).averagePooled2D(
             kernelSize: (1, 1, poolSize, 1), strides: (1, 1, stride, 1), padding: padding
         ).squeezingShape(at: 1)
@@ -218,7 +218,7 @@ public struct AvgPool2D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return input.averagePooled2D(kernelSize: poolSize, strides: strides, padding: padding)
     }
 }
@@ -264,7 +264,7 @@ public struct AvgPool3D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return input.averagePooled3D(kernelSize: poolSize, strides: strides, padding: padding)
     }
 }
@@ -304,7 +304,7 @@ public struct GlobalAvgPool1D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return input.mean(squeezingAxes: 1)
     }
 }
@@ -320,7 +320,7 @@ public struct GlobalAvgPool2D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return input.mean(squeezingAxes: [1, 2])
     }
 }
@@ -336,7 +336,7 @@ public struct GlobalAvgPool3D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return input.mean(squeezingAxes: [1, 2, 3])
     }
 }
@@ -355,7 +355,7 @@ public struct GlobalMaxPool1D<Scalar: TensorFlowFloatingPoint>: Layer {
     ///     phase.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return input.max(squeezingAxes: 1)
     }
 }
@@ -371,7 +371,7 @@ public struct GlobalMaxPool2D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return input.max(squeezingAxes: [1, 2])
     }
 }
@@ -387,7 +387,7 @@ public struct GlobalMaxPool3D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         return input.max(squeezingAxes: [1, 2, 3])
     }
 }

Sources/TensorFlow/Layers/Recurrent.swift

@@ -62,7 +62,7 @@ public extension RNNCell {
     ///   - previousState: The previous state of the RNN cell.
     /// - Returns: The output.
     @differentiable
-    func call(input: TimeStepInput, state: State) -> RNNCellOutput<TimeStepOutput, State> {
+    func callAsFunction(input: TimeStepInput, state: State) -> RNNCellOutput<TimeStepOutput, State> {
         return self(RNNCellInput(input: input, state: state))
     }
 }
@@ -113,7 +113,7 @@ public struct SimpleRNNCell<Scalar: TensorFlowFloatingPoint>: RNNCell, VectorNum
     /// - Parameter input: The input to the layer.
     /// - Returns: The hidden state.
     @differentiable
-    public func call(_ input: Input) -> Output {
+    public func callAsFunction(_ input: Input) -> Output {
         let concatenatedInput = input.input.concatenated(with: input.state.value, alongAxis: 1)
         let newState = State(tanh(matmul(concatenatedInput, weight) + bias))
         return Output(output: newState, state: newState)
@@ -175,7 +175,7 @@ public struct LSTMCell<Scalar: TensorFlowFloatingPoint>: RNNCell, VectorNumeric
     /// - Parameter input: The input to the layer.
     /// - Returns: The hidden state.
     @differentiable
-    public func call(_ input: Input) -> Output {
+    public func callAsFunction(_ input: Input) -> Output {
         let gateInput = input.input.concatenated(with: input.state.hidden, alongAxis: 1)
 
         let inputGate = sigmoid(matmul(gateInput, inputWeight) + inputBias)
@@ -203,7 +203,7 @@ public struct RNN<Cell: RNNCell>: Layer {
     }
 
     @differentiable(wrt: (self, input), vjp: _vjpCall(_:initialState:))
-    public func call(_ input: [Cell.TimeStepInput],
+    public func callAsFunction(_ input: [Cell.TimeStepInput],
                      initialState: Cell.State) -> [Cell.TimeStepOutput] {
         var currentHiddenState = initialState
         var timeStepOutputs: [Cell.TimeStepOutput] = []
@@ -253,7 +253,7 @@ public struct RNN<Cell: RNNCell>: Layer {
     }
 
     @differentiable(wrt: (self, inputs))
-    public func call(_ inputs: [Cell.TimeStepInput]) -> [Cell.TimeStepOutput] {
+    public func callAsFunction(_ inputs: [Cell.TimeStepInput]) -> [Cell.TimeStepOutput] {
         return self(inputs, initialState: cell.zeroState.withoutDerivative())
     }
 

Sources/TensorFlow/Layers/Upsampling.swift

@@ -29,7 +29,7 @@ public struct UpSampling1D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         let shape = input.shape
         let (batchSize, timesteps, channels) = (shape[0], shape[1], shape[2])
         let scaleOnes = Tensor<Scalar>(ones: [1, 1, size, 1])
@@ -55,7 +55,7 @@ public struct UpSampling2D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         let shape = input.shape
         let (batchSize, height, width, channels) = (shape[0], shape[1], shape[2], shape[3])
         let scaleOnes = Tensor<Scalar>(ones: [1, 1, size, 1, size, 1])
@@ -107,7 +107,7 @@ public struct UpSampling3D<Scalar: TensorFlowFloatingPoint>: Layer {
     /// - Parameter input: The input to the layer.
     /// - Returns: The output.
     @differentiable
-    public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
+    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         var result = repeatingElements(input, alongAxis: 1, count: size)
         result = repeatingElements(result, alongAxis: 2, count: size)
         result = repeatingElements(result, alongAxis: 3, count: size)

Tests/TensorFlowTests/SequentialTests.swift

@@ -24,7 +24,7 @@ final class SequentialTests: XCTestCase {
                                       seed: (0xeffeffe, 0xfffe))
 
             @differentiable
-            func call(_ input: Tensor<Float>) -> Tensor<Float> {
+            func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {
               return input.sequenced(through: dense1, dense2)
             }
         }

Tests/TensorFlowTests/TrivialModelTests.swift

@@ -34,7 +34,7 @@ final class TrivialModelTests: XCTestCase {
                 )
             }
             @differentiable
-            func call(_ input: Tensor<Float>) -> Tensor<Float> {
+            func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {
                 let h1 = l1(input)
                 return l2(h1)
             }