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Improve API documentation. #114

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Apr 24, 2019
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Improve API documentation. #114

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71 changes: 23 additions & 48 deletions Sources/DeepLearning/Layer.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -31,8 +31,7 @@ public protocol Layer: Differentiable & KeyPathIterable

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
func call(_ input: Input) -> Output
Expand Down Expand Up @@ -216,8 +215,7 @@ public struct Dense<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand Down Expand Up @@ -325,8 +323,7 @@ public struct Conv1D<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer `[batchCount, width, inputChannels]`.
/// - 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> {
Expand Down Expand Up @@ -444,8 +441,7 @@ public struct Conv2D<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand Down Expand Up @@ -563,8 +559,7 @@ public struct TransposedConv2D: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Float>) -> Tensor<Float> {
Expand Down Expand Up @@ -718,8 +713,7 @@ public struct BatchNorm<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable(vjp: _vjpApplied(to:))
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand Down Expand Up @@ -796,8 +790,7 @@ public struct MaxPool1D<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand Down Expand Up @@ -843,8 +836,7 @@ public struct MaxPool2D<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand Down Expand Up @@ -881,8 +873,7 @@ public struct AvgPool1D<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand Down Expand Up @@ -928,8 +919,7 @@ public struct AvgPool2D<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand All @@ -946,8 +936,7 @@ public struct GlobalAvgPool1D<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand All @@ -963,8 +952,7 @@ public struct GlobalAvgPool2D<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand All @@ -980,8 +968,7 @@ public struct GlobalAvgPool3D<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand Down Expand Up @@ -1035,8 +1022,7 @@ public struct LayerNorm<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand Down Expand Up @@ -1085,8 +1071,7 @@ public struct Dropout<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable(vjp: _vjpApplied(to:))
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand Down Expand Up @@ -1129,8 +1114,7 @@ public struct UpSampling1D<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand All @@ -1156,8 +1140,7 @@ public struct UpSampling2D<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand All @@ -1179,8 +1162,7 @@ public struct Flatten<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand Down Expand Up @@ -1216,8 +1198,7 @@ public struct Reshape<Scalar: TensorFlowFloatingPoint>: Layer {

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - Parameter input: The input to the layer.
/// - Returns: The output.
@differentiable
public func call(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
Expand Down Expand Up @@ -1280,7 +1261,7 @@ public extension RNNCell {
}
}

/// A Simple RNN Cell.
/// A simple RNN cell.
public struct SimpleRNNCell<Scalar: TensorFlowFloatingPoint>: RNNCell, VectorNumeric {
public var weight: Tensor<Scalar>
public var bias: Tensor<Scalar>
Expand Down Expand Up @@ -1316,10 +1297,7 @@ public struct SimpleRNNCell<Scalar: TensorFlowFloatingPoint>: RNNCell, VectorNum

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - context: The contextual information for the layer application, e.g. the current learning
/// phase.
/// - Parameter input: The input to the layer.
/// - Returns: The hidden state.
@differentiable
public func call(_ input: Input) -> Output {
Expand All @@ -1329,7 +1307,7 @@ public struct SimpleRNNCell<Scalar: TensorFlowFloatingPoint>: RNNCell, VectorNum
}
}

/// An LSTM Cell.
/// An LSTM cell.
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Shouldn't this be A LSTM cell ?

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@rxwei rxwei Apr 24, 2019 •
edited
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Since people tend to pronounce the acronym directly, the pronunciation starts with a vowel, so "An" would be most appropriate.

public struct LSTMCell<Scalar: TensorFlowFloatingPoint>: RNNCell, VectorNumeric {
public var inputWeight, updateWeight, forgetWeight, outputWeight: Tensor<Scalar>
public var inputBias, updateBias, forgetBias, outputBias: Tensor<Scalar>
Expand Down Expand Up @@ -1381,10 +1359,7 @@ public struct LSTMCell<Scalar: TensorFlowFloatingPoint>: RNNCell, VectorNumeric

/// Returns the output obtained from applying the layer to the given input.
///
/// - Parameters:
/// - input: The input to the layer.
/// - context: The contextual information for the layer application, e.g. the current learning
/// phase.
/// - Parameter input: The input to the layer.
/// - Returns: The hidden state.
@differentiable
public func call(_ input: Input) -> Output {
Expand Down