Path: ...!weretis.net!feeder9.news.weretis.net!news.nk.ca!rocksolid2!i2pn2.org!.POSTED!not-for-mail From: melahi_ahmed@yahoo.fr (Ahmed) Newsgroups: comp.lang.forth Subject: Re: Neural networks from scratch in forth Date: Tue, 3 Dec 2024 19:46:33 +0000 Organization: novaBBS Message-ID: <5e90ac201380f1ae57bec4ee4854fe61@www.novabbs.com> References: <06eabe944364625b1eba7ea6e09791ad@www.novabbs.com> <63fe32f186d9d5e4e140cb08ab591fa4@www.novabbs.com> MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8; format=flowed Content-Transfer-Encoding: 8bit Injection-Info: i2pn2.org; logging-data="1126766"; mail-complaints-to="usenet@i2pn2.org"; posting-account="t+/9LUKLIiUqIe6reyFE7me/EcA/Gr17dRXgwnADesE"; User-Agent: Rocksolid Light X-Rslight-Site: $2y$10$jJO9Kd/cYR3rDGPtmB1kLO9A.YSMvIMnL/P5HMCZ1JRvtEiOLuzIO X-Spam-Checker-Version: SpamAssassin 4.0.0 X-Rslight-Posting-User: 5f6b2e70af503e44dad56966aa15d35bdef29623 Bytes: 5630 Lines: 282 Hi again, I did some examples using this framework. Save examples in separate files. Example 01: Fitting data (linear case) \ ------------------Example 01:-------------------- include neural_networks.fs \ data samples \ x1, yd, 10 samples 10 >n_samples create data1 0e f, 0e f, 1e f, 1e f, 2e f, 2e f, 3e f, 3e f, 4e f, 4e f, 5e f, 5e f, 6e f, 6e f, 7e f, 7e f, 8e f, 8e f, 9e f, 9e f, data1 >data \ neuranet 1 2 2 1 2 neuralnet: net1 ' net1 is net net_layers ' dllinear is act_func 1e-2 >eta 0e >beta 1000 >epochs 1e-4 >tol 1 >display_step false >adapt_eta true >init_net learn test \ --------------Example 01 ends here------------------------- Example 02: fitting data (nonlinear case) \ -------------Example 02:----------------------------------- include neural_networks.fs \ data samples \ x1, yd, 10 samples 10 >n_samples create data1 0e f, 0e f, 1e f, 1e f, 2e f, 2e f, 3e f, 3e f, 4e f, 4e f, 5e f, 4e f, 6e f, 4e f, 7e f, 3e f, 8e f, 2e f, 9e f, 1e f, data1 >data \ neuranet 1 5 5 1 2 neuralnet: net1 ' net1 is net net_layers ' dlatan is act_func 1e-2 >eta 0e >beta 1000000 >epochs 1e-2 >tol 100 >display_step false >adapt_eta true >init_net learn test \ --------------Example 02 ends here-------------- Example 03: Approximation of a function: f(x) = sin(x) + cos(x) \ --------------Example 03------------------------ include neural_networks.fs \ data samples sin(x)+cos(x) values for x = 0:0.1:10 : f1() fsincos f+ ; defer f() ' f1() is f() variable n_samples1 10 n_samples1 ! create data1 n_samples1 @ 2* floats allot : data_samples n_samples1 @ 0 do i s>f fdup data1 i 2* floats + f! f() data1 i 2* 1+ floats + f! loop ; data_samples data1 >data n_samples1 @ >n_samples \ neuralnet 1 5 5 5 1 3 neuralnet: net1 ' net1 is net net_layers ' dlatan is act_func 1e-3 >eta 9e-1 >beta 1000000 >epochs 1e-4 >tol 100 >display_step false >adapt_eta true >init_net learn test \ -------------------Example 03 ends here-------------------- Example 04: Work with several nets in the same session \ ------------------Example 04--------------------------- include neural_networks.fs \ data samples sin(x)+cos(x) values for x = 0:1:10 : f1() fsincos f+ ; defer f() ' f1() is f() variable n_samples1 10 n_samples1 ! create data1 n_samples1 @ 2* floats allot : data_samples n_samples1 @ 0 do i s>f fdup data1 i 2* floats + f! f() data1 i 2* 1+ floats + f! loop ; data_samples data1 >data n_samples1 @ >n_samples \ neuralnet \ 2 neural nets in the same session 1 10 1 1 neuralnet: net1 ' net1 is net net_layers ========== REMAINDER OF ARTICLE TRUNCATED ==========