Implementación de Red Neuronal en JavaScript

Red neuronal de ttezel

var funcionSeno = nn(

{

layers: [ 4, 3, 4 ] // Capas y sus neuronas

, iterations: 20000 // Cantidad de iteraciones para entrenar

, errorThresh: 0.0005 // Error

, activation: 'logistic' // Mecanismo de activación

, learningRate: 0.4 // Cuánto aprende

}

) ;

funcionSeno.train(

[

{ input: [ 0.5248588903807104 ], output: [ 0.5010908941521808 ] },

{ input: [ 0 ], output: [ 0 ] },

{ input: [ 0.03929789311951026 ], output: [ 0.03928777911794752 ] },

{ input: [ 0.07391509227454662 ], output: [ 0.07384780553540908 ] },

{ input: [ 0.11062344848178328 ], output: [ 0.1103979598825075 ] },

{ input: [ 0.14104655454866588 ], output: [ 0.14057935309092454 ] },

{ input: [ 0.06176552915712819 ], output: [ 0.06172626426511784 ] },

{ input: [ 0.23915000406559558 ], output: [ 0.2368769073277496 ] },

{ input: [ 0.27090200221864513 ], output: [ 0.267600651550329 ] },

{ input: [ 0.15760037200525404 ], output: [ 0.1569487719674096 ] },

{ input: [ 0.19391102618537845 ], output: [ 0.19269808506017222 ] },

{ input: [ 0.42272064974531537 ], output: [ 0.4102431360805792 ] },

{ input: [ 0.5248469677288086 ], output: [ 0.5010805763172892 ] },

{ input: [ 0.4685300185577944 ], output: [ 0.45157520770441445 ] },

{ input: [ 0.6920387226855382 ], output: [ 0.6381082150316612 ] },

{ input: [ 0.40666140150278807 ], output: [ 0.3955452139761714 ] },

{ input: [ 0.011600911058485508 ], output: [ 0.011600650849602313 ] },

{ input: [ 0.404806485096924 ], output: [ 0.39384089298297537 ] },

{ input: [ 0.13447276877705008 ], output: [ 0.13406785820465852 ] },

{ input: [ 0.22471809106646107 ], output: [ 0.222831550102815 ] }

]

) ;

var probar = funcionSeno.send([ 0.5 ]) ; // => 0.48031129953896595

</script>

CaFeDev