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Sukiyaki2

Index

Classes

Variables

Functions

Variables

Buffer

Buffer: any

MatrixCL

MatrixCL: any = null

bn_backward_kernel

bn_backward_kernel: any = null

bn_forward_kernel

bn_forward_kernel: any = null

child_process

child_process: any = require('child_process')

cl_enabled

cl_enabled: boolean = Boolean(Number(process.env['TEST_CL']))

cl_forward_kernel

cl_forward_kernel: any = null

cl_init_random_kernel

cl_init_random_kernel: any = null

cl_max_backward_overlap_kernel

cl_max_backward_overlap_kernel: any = null

cl_max_forward_kernel

cl_max_forward_kernel: any = null

cl_update_random_kernel

cl_update_random_kernel: any = null

col2im_gpu_kernel

col2im_gpu_kernel: any = null

col2im_perm_gpu_kernel

col2im_perm_gpu_kernel: any = null

compress_kernel

compress_kernel: any = null

constant_table

constant_table: Matrix = null

decompress_kernel

decompress_kernel: any = null

find_max_kernel_first

find_max_kernel_first: any = null

find_max_kernel_second

find_max_kernel_second: any = null

fixture_bottom

fixture_bottom: Matrix = load_npy('bottom')

fixture_mean

fixture_mean: Matrix = load_npy('mean')

fixture_top

fixture_top: Matrix = load_npy('top')

forward_bias_kernel

forward_bias_kernel: any = null

fs

fs: any = require('fs')

im2col_gpu_kernel

im2col_gpu_kernel: any = null

im2col_perm_gpu_kernel

im2col_perm_gpu_kernel: any = null

layer_test_cases

layer_test_cases: Array<string> = fs.readdirSync('spec/fixture/layer')

max_pooling_backward_gpu_kernel

max_pooling_backward_gpu_kernel: any = null

mtimes_largek_cl_kernel

mtimes_largek_cl_kernel: any = null

os

os: any = require('os')

process

process: any

require

require: any

update_bias_kernel

update_bias_kernel: any = null

Functions

cl_init_random

  • cl_init_random(sizejsa: number[]): Matrix

cl_max_backward_overlap

  • cl_max_backward_overlap(layer: Pooling2DLayer, h: number, w: number, top_delta: Matrix, top_pos: Matrix): Matrix

cl_max_forward

cl_update_random

  • cl_update_random(rnd: Matrix, dropout_ratio: number): Matrix

classify_node

  • classify_node(netdef: string, mean_file: string, weight: string, dst: string, cl?: boolean): Network

  • Parameters

    • netdef: string
    • mean_file: string
    • weight: string
    • dst: string
    • Default value cl: boolean = false

    Returns Network

col2im_cl

  • col2im_cl(col: Matrix, stride: number[], pad: number[], size: number[]): Matrix

  • Parameters

    • col: Matrix
    • stride: number[]
    • pad: number[]
    • size: number[]

    Returns Matrix

col2im_cl_perm

  • col2im_cl_perm(col: Matrix, stride: number[], pad: number[], size: number[]): Matrix

  • Parameters

    • col: Matrix
    • stride: number[]
    • pad: number[]
    • size: number[]

    Returns Matrix

col2im_cpu

  • col2im_cpu(col: Matrix, stride: number[], pad: number[], size: number[]): Matrix

  • Parameters

    • col: Matrix
    • stride: number[]
    • pad: number[]
    • size: number[]

    Returns Matrix

compress_8bit

  • compress_8bit(weight_mat: Matrix, dst_buf: ArrayBuffer, dst_offset: number, dst_size: number): void

conv_outsize

  • conv_outsize(size: number, k: number, s: number, p: number, cover_all: boolean): number

  • Parameters

    • size: number
    • k: number
    • s: number
    • p: number
    • cover_all: boolean

    Returns number

decompress_8bit

  • decompress_8bit(weight_mat: Matrix, src_buf: ArrayBuffer, src_offset: number, src_size: number): void

get_bn_backward_kernel

  • get_bn_backward_kernel(): any

get_bn_forward_kernel

  • get_bn_forward_kernel(): any

get_cl_forward_kernel

  • get_cl_forward_kernel(): any

get_constant_table

  • get_constant_table(): Matrix

get_forward_bias_kernel

  • get_forward_bias_kernel(): any

get_update_bias_kernel

  • get_update_bias_kernel(): any

im2col_cl

  • im2col_cl(img: Matrix, ksize: number[], stride: number[], pad: number[], pad_val?: number, cover_all?: boolean): Matrix

  • Parameters

    • img: Matrix
    • ksize: number[]
    • stride: number[]
    • pad: number[]
    • Default value pad_val: number = 0
    • Default value cover_all: boolean = false

    Returns Matrix

im2col_cl_perm

  • im2col_cl_perm(img: Matrix, ksize: number[], stride: number[], pad: number[], pad_val?: number, cover_all?: boolean): Matrix

  • Parameters

    • img: Matrix
    • ksize: number[]
    • stride: number[]
    • pad: number[]
    • Default value pad_val: number = 0
    • Default value cover_all: boolean = false

    Returns Matrix

im2col_cl_perm2

  • im2col_cl_perm2(img: Matrix, ksize: number[], stride: number[], pad: number[], pad_val?: number, cover_all?: boolean): Matrix

  • Parameters

    • img: Matrix
    • ksize: number[]
    • stride: number[]
    • pad: number[]
    • Default value pad_val: number = 0
    • Default value cover_all: boolean = false

    Returns Matrix

im2col_cl_permx

  • im2col_cl_permx(img: Matrix, ksize: number[], stride: number[], pad: number[], pad_val?: number, cover_all?: boolean): Matrix

  • Parameters

    • img: Matrix
    • ksize: number[]
    • stride: number[]
    • pad: number[]
    • Default value pad_val: number = 0
    • Default value cover_all: boolean = false

    Returns Matrix

im2col_cpu

  • im2col_cpu(img: Matrix, ksize: number[], stride: number[], pad: number[], pad_val?: number, cover_all?: boolean): Matrix

  • Parameters

    • img: Matrix
    • ksize: number[]
    • stride: number[]
    • pad: number[]
    • Default value pad_val: number = 0
    • Default value cover_all: boolean = false

    Returns Matrix

Export assignment load_layer_case

  • load_layer_case(case_name: string, cl: boolean): any

load_npy

  • load_npy(basedir: string, keys: any[], cl: boolean): Matrix

main

  • main(): void

mtimes_atrans_largek

  • mtimes_atrans_largek(A: Matrix, B: Matrix): Matrix

mtimes_largek_cl

  • mtimes_largek_cl(A: Matrix, B: Matrix): any

mtimes_trans

  • mtimes_trans(A: Matrix, B: Matrix, trans_a: boolean, trans_b: boolean): Matrix

mtimes_trans_cl

  • mtimes_trans_cl(A: Matrix, B: Matrix, trans_a: boolean, trans_b: boolean): Matrix

repeat_scalar

  • repeat_scalar(val: number | number[], length: number): number[]

test_layer_case

  • test_layer_case(case_name: string, done: any, cl: boolean): void

time

  • time(f: BenchBase, n_run?: number, callback: any): void

time_all

  • time_all(f_array: BenchBase[], n_run?: number): void

Export assignment train_imagenet

  • train_imagenet(load_weight?: boolean, cl?: boolean): Network

  • Parameters

    • Default value load_weight: boolean = false
    • Default value cl: boolean = false

    Returns Network

Export assignment train_mnist

  • train_mnist(load_weight?: boolean, cl?: boolean): Network

  • Parameters

    • Default value load_weight: boolean = false
    • Default value cl: boolean = false

    Returns Network

train_node

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