Functions — github.com/AlanWei/deeplearning-js

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Functions41 in github.com/AlanWei/deeplearning-js

⨍ Functions 41 ◇ Types & classes 2

↓ 14 callersFunctiontranspose

( matrix: number[][], )

src/math/transpose.ts:3

↓ 10 callersFunctionloopTwoMatrix

( left: number[][], right: number[][], func: Function, )

src/util/loopTwoMatrix.ts:3

↓ 6 callersFunctiondot

( left: number[][], right: number[][], )

src/math/dot.ts:1

↓ 6 callersFunctioninitializeParameters

( layers: { size: number, activationFunc?: string, }[], mean: number = 0, variance: number = 1

src/model/initializeParameters.ts:6

↓ 5 callersFunctionsubtract

( left: number[][], right: number[][], )

src/math/subtract.ts:3

↓ 4 callersFunctionforwardPropagation

( x: number[][], parameters: any )

src/model/forwardPropagation.ts:12

↓ 4 callersFunctionmultiply

( left: number[][], right: number[][], )

src/math/multiply.ts:3

↓ 3 callersFunctionadd

( left: number[][], right: number[][], )

src/math/add.ts:3

↓ 3 callersFunctionbatchTrain

( currentBatch: number, totalBatch: number, batchSize: number, input: number[][], output: number[][]

src/model/batchTrain.ts:4

↓ 3 callersFunctioncrossEntropyCost

( yHat: number[][], y: number[][], )

src/costFunction/crossEntropyCost.ts:4

↓ 3 callersFunctionloopMatrix

( matrix: number[][], func: Function, )

src/util/loopMatrix.ts:3

↓ 3 callersFunctionquadraticCost

( yHat: number[][], y: number[][], )

src/costFunction/quadraticCost.ts:4

↓ 3 callersFunctionrandn

( shape: [number, number], mean: number = 0, variance: number = 1, scale: number = 1, )

src/math/randn.ts:3

↓ 3 callersFunctionsigmoid

( z: number[][], )

src/activationFunction/sigmoid.ts:3

↓ 3 callersFunctionsoftmax

( z: number[][], )

src/activationFunction/softmax.ts:21

↓ 3 callersFunctionzeros

( shape: [number, number], )

src/math/zeros.ts:1

↓ 2 callersFunctionbackPropagation

( costFunc: 'quadratic' | 'cross-entropy', forwardResults: { yHat: number[][], caches: Cache[],

src/model/backPropagation.ts:13

↓ 2 callersFunctioncrossEntropyCostBackward

( yHat: number[][], y: number[][], )

src/costFunction/crossEntropyCostBackward.ts:3

↓ 2 callersFunctiondivide

( left: number[][], right: number[][], )

src/math/divide.ts:3

↓ 2 callersFunctionformatLearningRate

( matrix: number[][], learningRate: number, )

src/model/updateParameters.ts:4

↓ 2 callersFunctionformatNumToBool

(output: number[])

demo/logistic.ts:36

↓ 2 callersFunctionlinearBackward

( dZ: number[][], cache: { A: number[][], W: number[][], b: number[][], }, )

src/activationFunction/linearBackward.ts:3

↓ 2 callersFunctionquadraticCostBackward

( yHat: number[][], y: number[][], )

src/costFunction/quadraticCostBackward.ts:3

↓ 2 callersFunctionrelu

( z: number[][], )

src/activationFunction/relu.ts:3

↓ 2 callersFunctionreluBackward

( dA: number[][], cache: number[][], )

src/activationFunction/reluBackward.ts:3

↓ 2 callersFunctionsigmoidBackward

( dA: number[][], cache: number[][], )

src/activationFunction/sigmoidBackward.ts:4

↓ 2 callersFunctionsoftmaxBackward

( dA: number[][], cache: number[][], )

src/activationFunction/softmaxBackward.ts:4

↓ 2 callersFunctiontrain

( input: number[][], output: number[][], parameters: any, costFunc: 'quadratic' | 'cross-entropy', l

src/model/train.ts:9

↓ 2 callersFunctionupdateParameters

( parameters: any, grads: any, learningRate: number, )

src/model/updateParameters.ts:18

↓ 1 callersFunctioncalculateA

(z: number[][])

src/activationFunction/softmax.ts:11

↓ 1 callersFunctionexpZ

(z: number[][])

src/activationFunction/softmax.ts:5

↓ 1 callersFunctionformatDataSet

(dataset: any)

demo/logistic.ts:11

↓ 1 callersFunctionformatDataSet

(dataset: any)

demo/softmax.ts:12

↓ 1 callersFunctionlogistic

( learningRate: number, numOfIterations: number, batchSize: number, callback: any, resolve: any, )

demo/logistic.ts:62

↓ 1 callersFunctionminmax

(values: Array<number>)

src/preprocess/normalization/minmax.ts:3

↓ 1 callersFunctionsoftmax

( learningRate: number, numOfIterations: number, batchSize: number, callback: any, resolve: any, )

demo/softmax.ts:94

↓ 1 callersFunctionzscore

(values: number[])

src/preprocess/normalization/zscore.ts:3

Methodconstructor

( linearCache: { A: number[][], W: number[][], b: number[][], }, activationCache

src/model/Cache.ts:8

FunctionlinearForward

( a: number[][], w: number[][], b: number[][], )

src/activationFunction/linear.ts:3

Functionpredict

( input: number[][], output: number[][], parameters: any, datasetType: string, )

demo/logistic.ts:40

Functionpredict

( input: number[][], output: number[][], parameters: any, datasetType: string, step: number, )

demo/softmax.ts:51