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Updating convolution docs #246

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Jun 15, 2019
Merged

Updating convolution docs #246

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d6c8177
Updating convolution docs
Shashi456 Jun 15, 2019
85b8616
Changing input to x
Shashi456 Jun 15, 2019
cc80ad1
Making docs more readable
Shashi456 Jun 15, 2019
0e1338a
Updating all convolution and pooling layers:
Shashi456 Jun 15, 2019
f54001c
Add trailing period
Shashi456 Jun 15, 2019
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58 changes: 28 additions & 30 deletions Sources/TensorFlow/Operators/NN.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -384,50 +384,48 @@ func _vjpAvgPool3D<Scalar: TensorFlowFloatingPoint>(
})
}

/// Computes a 2-D convolution using `self` as input, with the specified
/// filter, strides, and padding.
/// Computes a 2-D convolution with the specified input, filter, strides, and padding.
///
/// - Parameters:
/// - x: The input.
/// - input: The input.
/// - filter: The convolution filter.
/// - strides: The strides of the sliding filter for each dimension of the input.
/// - padding: The padding for the operation.
/// - Precondition: `self` must have rank 4.
/// - Precondition: `input` must have rank `4`.
/// - Precondition: `filter` must have rank 4.
@differentiable(wrt: (x, filter), vjp: _vjpConv2D)
@differentiable(wrt: (input, filter), vjp: _vjpConv2D)
public func conv2D<Scalar: TensorFlowFloatingPoint>(
_ x: Tensor<Scalar>,
_ input: Tensor<Scalar>,
filter: Tensor<Scalar>,
strides: (Int, Int, Int, Int),
padding: Padding
) -> Tensor<Scalar> {
return Raw.conv2D(
x,
input,
filter: filter,
strides: [Int32(strides.0), Int32(strides.1), Int32(strides.2), Int32(strides.3)],
padding: padding.raw2,
explicitPaddings: [])
}

/// Computes a 3-D convolution using `self` as input, with the specified
/// filter, strides, and padding.
/// Computes a 3-D convolution with the specified input, filter, strides, and padding.
///
/// - Parameters:
/// - x: The input.
/// - input: The input.
/// - filter: The convolution filter.
/// - strides: The strides of the sliding filter for each dimension of the input.
/// - padding: The padding for the operation.
/// - Precondition: `self` must have rank 5.
/// - Precondition: `input` must have rank `5`.
/// - Precondition: `filter` must have rank 5.
@differentiable(wrt: (x, filter), vjp: _vjpConv3D)
@differentiable(wrt: (input, filter), vjp: _vjpConv3D)
public func conv3D<Scalar: TensorFlowFloatingPoint>(
_ x: Tensor<Scalar>,
_ input: Tensor<Scalar>,
filter: Tensor<Scalar>,
strides: (Int, Int, Int, Int, Int),
padding: Padding
) -> Tensor<Scalar> {
return Raw.conv3D(
x,
input,
filter: filter,
strides: [Int32(strides.0), Int32(strides.1), Int32(strides.2),
Int32(strides.3), Int32(strides.4)],
Expand All @@ -438,19 +436,19 @@ public func conv3D<Scalar: TensorFlowFloatingPoint>(
/// padding.
///
/// - Parameters:
/// - x: The input.
/// - input: The input.
/// - filterSize: The dimensions of the pooling kernel.
/// - strides: The strides of the sliding filter for each dimension of the input.
/// - padding: The padding for the operation.
@differentiable(wrt: x, vjp: _vjpMaxPool2D)
@differentiable(wrt: input, vjp: _vjpMaxPool2D)
public func maxPool2D<Scalar: TensorFlowFloatingPoint>(
_ x: Tensor<Scalar>,
_ input: Tensor<Scalar>,
filterSize: (Int, Int, Int, Int),
strides: (Int, Int, Int, Int),
padding: Padding
) -> Tensor<Scalar> {
return Raw.maxPoolV2(
x,
input,
ksize: Tensor<Int32>([Int32(filterSize.0), Int32(filterSize.1),
Int32(filterSize.2), Int32(filterSize.3)]),
strides: Tensor<Int32>([Int32(strides.0), Int32(strides.1),
Expand All @@ -462,19 +460,19 @@ public func maxPool2D<Scalar: TensorFlowFloatingPoint>(
/// padding.
///
/// - Parameters:
/// - x: The input.
/// - input: The input.
/// - filterSize: The dimensions of the pooling kernel.
/// - strides: The strides of the sliding filter for each dimension of the input.
/// - padding: The padding for the operation.
@differentiable(wrt: x, vjp: _vjpMaxPool3D)
@differentiable(wrt: input, vjp: _vjpMaxPool3D)
public func maxPool3D<Scalar: TensorFlowFloatingPoint>(
_ x: Tensor<Scalar>,
_ input: Tensor<Scalar>,
filterSize: (Int, Int, Int, Int, Int),
strides: (Int, Int, Int, Int, Int),
padding: Padding
) -> Tensor<Scalar> {
return Raw.maxPool3D(
x,
input,
ksize: [Int32(filterSize.0), Int32(filterSize.1),
Int32(filterSize.2), Int32(filterSize.3), Int32(filterSize.4)],
strides: [Int32(strides.0), Int32(strides.1),
Expand All @@ -486,19 +484,19 @@ public func maxPool3D<Scalar: TensorFlowFloatingPoint>(
/// and padding.
///
/// - Parameters:
/// - x: The input.
/// - input: The input.
/// - filterSize: The dimensions of the pooling kernel.
/// - strides: The strides of the sliding filter for each dimension of the input.
/// - padding: The padding for the operation.
@differentiable(wrt: x, vjp: _vjpAvgPool2D)
@differentiable(wrt: input, vjp: _vjpAvgPool2D)
public func avgPool2D<Scalar: TensorFlowFloatingPoint>(
_ x: Tensor<Scalar>,
_ input: Tensor<Scalar>,
filterSize: (Int, Int, Int, Int),
strides: (Int, Int, Int, Int),
padding: Padding
) -> Tensor<Scalar> {
return Raw.avgPool(
value: x,
value: input,
ksize: [Int32(filterSize.0), Int32(filterSize.1),
Int32(filterSize.2), Int32(filterSize.3)],
strides: [Int32(strides.0), Int32(strides.1), Int32(strides.2), Int32(strides.3)],
Expand All @@ -509,19 +507,19 @@ public func avgPool2D<Scalar: TensorFlowFloatingPoint>(
/// and padding.
///
/// - Parameters:
/// - x: The input.
/// - input: The input.
/// - filterSize: The dimensions of the pooling kernel.
/// - strides: The strides of the sliding filter for each dimension of the input.
/// - padding: The padding for the operation.
@differentiable(wrt: x, vjp: _vjpAvgPool3D)
@differentiable(wrt: input, vjp: _vjpAvgPool3D)
public func avgPool3D<Scalar: TensorFlowFloatingPoint>(
_ x: Tensor<Scalar>,
_ input: Tensor<Scalar>,
filterSize: (Int, Int, Int, Int, Int),
strides: (Int, Int, Int, Int, Int),
padding: Padding
) -> Tensor<Scalar> {
return Raw.avgPool3D(
x,
input,
ksize: [Int32(filterSize.0), Int32(filterSize.1),
Int32(filterSize.2), Int32(filterSize.3), Int32(filterSize.4)],
strides: [Int32(strides.0), Int32(strides.1), Int32(strides.2), Int32(strides.3),
Expand Down