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Swi$Ͱॻ͔ΕͨϓϩάϥϜΛ
1000ഒͨ͘͠
Yuta Koshizawa @koher
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No content
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͕ΜͬͨΒߦྻͷੵͷܭࢉ͕
1000 ഒ͘ͳͬͨ
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ͦͷͱ͖ʹ Swi% ͍͢͝ͱײͨ͡
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Tensor
let a = Tensor(shape: [2, 3],
elements: [1, 2, 3, 4, 5, 6])
// [[1, 2, 3], [4, 5, 6]]
let b = Tensor(shape: [2, 2, 2],
elements: [1, 2, 3, 4, 5, 6, 7, 8])
// [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]
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ཁૉͷΞΫηε
let a = Tensor(shape: [2, 3],
elements: [1, 2, 3, 4, 5, 6])
// [[1, 2, 3], [4, 5, 6]]
let b = Tensor(shape: [2, 2, 2],
elements: [1, 2, 3, 4, 5, 6, 7, 8])
// [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]
print(a[1, 2]) // 6
print(b[1, 1, 1]) // 8
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Tensor ͷ࣮
public struct Tensor {
public let shape: [Int]
public private(set) var elements: [Float]
}
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subscript ͷ࣮
extension Tensor {
internal func index(indices: [Int]) -> Int {
return zip(shape, indices).reduce(0) { $0 * $1.0 + $1.1 }
}
public subscript(indices: Int...) -> Float {
get {
return elements[index(indices)]
}
set {
elements[index(indices)] = newValue
}
}
}
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matmul
extension Tensor { // Matrix
public func matmul(tensor: Tensor) -> Tensor {
precondition(shape.count == 2)
precondition(tensor.shape.count == 2)
let n = shape[1]
precondition(n == tensor.shape[0])
let numRows = shape[0]
let numCols = tensor.shape[1]
var elements: [Float] = []
elements.reserveCapacity(numCols * numRows)
for r in 0..
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ߦྻʹݶఆ
extension Tensor { // Matrix
public func matmul(tensor: Tensor) -> Tensor {
precondition(shape.count == 2)
precondition(tensor.shape.count == 2)
let n = shape[1]
precondition(n == tensor.shape[0])
let numRows = shape[0]
let numCols = tensor.shape[1]
var elements: [Float] = []
elements.reserveCapacity(numCols * numRows)
for r in 0..
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1.84 ഒߴԽ
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ϧʔϓॱΛೖΕସ͑ͯΩϟογϡͷώοτΛ্
extension Tensor { // Matrix
public func matmul(tensor: Tensor) -> Tensor {
precondition(shape.count == 2)
precondition(tensor.shape.count == 2)
let n = shape[1]
precondition(n == tensor.shape[0])
let numRows = shape[0]
let numCols = tensor.shape[1]
var elements = [Float](count: numCols * numRows, repeatedValue: 0.0)
for r in 0..
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͞Βʹ 4.69 ഒߴԽ
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ϙΠϯλʹॻ͖ม͑
extension Tensor { // Matrix
public func matmul(tensor: Tensor) -> Tensor {
precondition(shape.count == 2)
precondition(tensor.shape.count == 2)
let n = shape[1]
precondition(n == tensor.shape[0])
let numRows = shape[0]
let numCols = tensor.shape[1]
// `Array` ΛϙΠϯλʹม
let leftHead = UnsafeMutablePointer(self.elements)
let rightHead = UnsafeMutablePointer(tensor.elements)
let elements = [Float](count: numCols * numRows, repeatedValue: 0.0)
for r in 0..(elements) + r * numCols
let left = leftHead[r * n + i]
var rightPointer = rightHead + i * numCols
for _ in 0..
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͞Βʹ 2.99 ഒߴԽ
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BLAS ͷར༻
import Accelerate
extension Tensor { // Matrix
public func matmul(tensor: Tensor) -> Tensor {
precondition(shape.count == 2)
precondition(tensor.shape.count == 2)
precondition(shape[1] == tensor.shape[0])
let result = Tensor(shape: [shape[0], tensor.shape[1]],
elements: [Float](count: shape[0] * tensor.shape[1],repeatedValue: 0.0))
let n = Int32(tensor.shape[1])
let k = Int32(shape[1])
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, Int32(shape[0]),
n, k, 1.0, elements, k, tensor.elements, n, 1.0,
UnsafeMutablePointer(result.elements), n)
return result
}
}
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͞Βʹ 39.1 ഒߴԽ
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͋Θͤͯ 1012 ഒߴԽ
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Swi$ Swi$ ͳʢ͍ʣ
ϓϩάϥϜ͕ॻ͚ΔΑ͏ʹ
࡞ΒΕ͍ͯΔʂʂ
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References
6 Accelerate Framework:
h1ps:/
/developer.apple.com/library/tvos/documenta=on/Accelerate/Reference/AccelerateFWRef/index.html
5 BLAS:
h)p:/
/www.netlib.org/blas/
4 "Deep MNIST for Experts":
h5ps:/
/www.tensorflow.org/versions/r0.8/tutorials/mnist/pros/index.html
3 "TensorFlowͰֶशͨ͠ϞσϧΛͬͯiOS/Swi1Ͱ࣮ߦ͢Δ"
h3p:/
/qiita.com/koher/items/2c0bfca4d6e31cde674b
2 TensorSwi,:
h/ps:/
/github.com/qoncept/TensorSwi,
1 Qoncept:
h,p:/
/qoncept.co.jp/