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All Projects → wangyi-fudan → wymlp

wangyi-fudan / wymlp

Licence: Unlicense License
tiny fast portable real-time deep neural network for regression and classification within 50 LOC.

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Labels

fastiotlibraryreal-timeembeddeddeep-neural-networkscpuportableregressionartificial-intelligencetinyclassificationon-the-flyminimalistic

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View All Similar Projects ➔

Real-Time Intelligence for Every Machine

Tiny fast portable real-time deep neural network for regression and classification within 50 LOC.

Benchmark

Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz Single Thread @ VirtualBox 6.0

-Ofast -mavx2 -mfma

Speed Measure: Sample Per Second

Hidden&Depth float_training float_inference double_training double_inference
16H16L(scalar) 141,958 397,894 140,921 368,760
32H16L 155,054 297,814 96,204 208,723
64H16L 47,253 95,647 24,794 52,018
128H16L 11,469 25,124 4,950 11,696
256H16L 2,356 5,502 1,260 2,725
512H16L 610 1,246 312 648

Code Example:

int	main(void){
	float	x[4]={1,2,3,5},	y[1]={2};
	vector<float>	weight(wymlp<float,4,32,4,1,0>(NULL,NULL,NULL,0,0,0));	//set weight==NULL to return size
	for(size_t	i=0;	i<weight.size();	i++)	weight[i]=3.0*rand()/RAND_MAX-1.5;	
	for(unsigned	i=0;	i<1000000;	i++){	
		x[0]+=0.01;	y[0]+=0.1;	//some "new" data
		wymlp<float,4,32,4,1,0>(weight.data(),	x, y, 0.1,	wygrand(),	0.5);	//	training. set eta>0 to train
		wymlp<float,4,32,4,1,0>(weight.data(),	x, y, -1,	wygrand(),	0.5);	//	prediction. set eta<0 to predict
	}
	return	0;
}

Comments:

0: loss=0: regression; loss=1: logistic; loss=2: softmax

1: dropout<0 lead to size() function

2: eta<0 lead to prediction only.

3: The expected |X[i]|, |Y[i]| should be around 1. Normalize yor input and output first.

4: In practice, it is OK to call model function parallelly with multi-threads, however, they may be slower for small net.

5: The code is portable, however, if Ofast is used on X86, SSE or AVX or even AVX512 will enable very fast code!

6: The default and suggested model is shared hidden-hidden weights. If you want vanilla MLP, define VanillaMLP

MNIST test error of 128H2L with single CPU thread:

0	error=8.240%	eta=0.300	time=2.058s
1	error=6.720%	eta=0.297	time=4.081s
2	error=5.660%	eta=0.294	time=6.112s
3	error=5.200%	eta=0.291	time=8.137s
4	error=4.330%	eta=0.288	time=10.154s
5	error=4.050%	eta=0.285	time=12.184s
6	error=3.740%	eta=0.282	time=14.206s
7	error=3.360%	eta=0.280	time=16.220s
8	error=3.550%	eta=0.277	time=18.274s
9	error=3.170%	eta=0.274	time=20.350s
10	error=2.980%	eta=0.271	time=22.373s
11	error=3.080%	eta=0.269	time=24.452s
12	error=2.670%	eta=0.266	time=26.561s
13	error=2.880%	eta=0.263	time=28.622s
14	error=2.430%	eta=0.261	time=30.712s
15	error=2.660%	eta=0.258	time=32.788s
16	error=2.330%	eta=0.255	time=34.841s
17	error=2.420%	eta=0.253	time=36.871s
18	error=2.470%	eta=0.250	time=38.931s
19	error=2.160%	eta=0.248	time=40.973s
20	error=2.180%	eta=0.245	time=43.015s
21	error=2.220%	eta=0.243	time=45.082s
22	error=2.240%	eta=0.240	time=47.163s
23	error=2.310%	eta=0.238	time=49.222s
24	error=2.100%	eta=0.236	time=51.292s
25	error=2.110%	eta=0.233	time=53.365s
26	error=2.050%	eta=0.231	time=55.427s
27	error=2.060%	eta=0.229	time=57.483s
28	error=2.160%	eta=0.226	time=59.564s
29	error=2.110%	eta=0.224	time=61.624s
30	error=2.060%	eta=0.222	time=63.675s
31	error=2.010%	eta=0.220	time=65.721s
32	error=2.000%	eta=0.217	time=67.772s
33	error=1.960%	eta=0.215	time=69.810s
34	error=1.900%	eta=0.213	time=71.860s
35	error=2.040%	eta=0.211	time=73.899s
36	error=1.960%	eta=0.209	time=75.955s
37	error=1.880%	eta=0.207	time=78.036s
38	error=1.980%	eta=0.205	time=80.074s
39	error=1.840%	eta=0.203	time=82.146s
40	error=1.910%	eta=0.201	time=84.217s
41	error=1.900%	eta=0.199	time=86.276s
42	error=1.870%	eta=0.197	time=88.361s
43	error=1.810%	eta=0.195	time=90.478s
44	error=1.720%	eta=0.193	time=92.610s
45	error=1.860%	eta=0.191	time=94.665s
46	error=1.820%	eta=0.189	time=96.767s
47	error=1.760%	eta=0.187	time=98.826s
48	error=1.870%	eta=0.185	time=100.898s
49	error=2.000%	eta=0.183	time=102.945s
50	error=1.680%	eta=0.182	time=104.979s
51	error=1.750%	eta=0.180	time=107.059s
52	error=1.710%	eta=0.178	time=109.127s
53	error=1.790%	eta=0.176	time=111.144s
54	error=1.870%	eta=0.174	time=113.179s
55	error=1.720%	eta=0.173	time=115.232s
56	error=1.770%	eta=0.171	time=117.281s
57	error=1.690%	eta=0.169	time=119.326s
58	error=1.770%	eta=0.167	time=121.387s
59	error=1.740%	eta=0.166	time=123.436s
60	error=1.740%	eta=0.164	time=125.487s
61	error=1.870%	eta=0.163	time=127.522s
62	error=1.790%	eta=0.161	time=129.578s
63	error=1.730%	eta=0.159	time=131.622s
64	error=1.760%	eta=0.158	time=133.669s
65	error=1.790%	eta=0.156	time=135.743s
66	error=1.640%	eta=0.155	time=137.811s
67	error=1.640%	eta=0.153	time=139.856s
68	error=1.610%	eta=0.151	time=141.894s
69	error=1.600%	eta=0.150	time=143.962s
70	error=1.680%	eta=0.148	time=145.992s
71	error=1.590%	eta=0.147	time=148.026s
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