05-feedforward-iris.nb

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Na\[CHacek]ten\[IAcute] knihovny NeuralNetworks\
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Nejd\[RHacek]\[IAcute]ve na\[CHacek]teme knihovnu neuronov\[YAcute]ch s\
\[IAcute]t\[IAcute].\
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Pokud pracujete v Mathematice 8.0, vypn\[EHacek]te je\[SHacek]t\[EHacek] \
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Nastav\[IAcute]me si pracovn\[IAcute] adres\[AAcute]\[RHacek] na ten, kde m\
\[AAcute]me ulo\[ZHacek]en aktu\[AAcute]ln\[IAcute] notebook, na\[CHacek]\
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Jin\[AAcute] mo\[ZHacek]nost je importovat data p\[RHacek]\[IAcute]mo z \
internetu - p\[RHacek]\[IAcute]klad pro UCI datab\[AAcute]zi (stejn\[AAcute] \
data jako p\[RHacek]i na\[CHacek]\[IAcute]t\[AAcute]n\[IAcute] ze souboru, \
jen na\[CHacek]tena p\[RHacek]\[IAcute]mo z internetu.\
\>", "Text",
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V prom\[EHacek]nn\[EAcute] data m\[AAcute]me ulo\[ZHacek]en cel\[YAcute] \
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obsahuje vektor\[URing].\
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Cell["\<\
Je zde 151 vektor\[URing]. Proto\[ZHacek]e ale datov\[YAcute] soubor obsahuje \
pr\[AAcute]zdn\[EAcute] \[RHacek]\[AAcute]dky na konci, nen\[IAcute] to \
matice, ale \"heterogenn\[IAcute]\" seznam. M\[URing]\[ZHacek]eme se pod\
\[IAcute]vat. N\[AAcute]sleduj\[IAcute]c\[IAcute] p\[RHacek]\[IAcute]kaz \
zobraz\[IAcute] 5 posledn\[IAcute]ch z\[AAcute]znam\[URing]:\
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Vid\[IAcute]me, \[ZHacek]e na konci je pr\[AAcute]zdn\[YAcute] \
z\[AAcute]znam. Odstran\[IAcute]me ho a nov\[AAcute] data si ulo\[ZHacek]\
\[IAcute]me do prom\[EHacek]nn\[EAcute] \"data2\" - m\[URing]\[ZHacek]eme si \
p\[RHacek]epsat i p\[URing]vodn\[IAcute] prom\[EHacek]nnou \"data\" - na tom \
v\[IAcute]cem\[EAcute]n\[EHacek] nez\[AAcute]le\[ZHacek]\[IAcute]. \
P\[RHacek]i\[RHacek]azen\[IAcute]m do nov\[EAcute] prom\[EHacek]nn\[EAcute] \
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kdybychom v\[IAcute]ckr\[AAcute]t po sob\[EHacek] p\[RHacek]\[IAcute]kaz \
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\[IAcute] z\[AAcute]znam).\
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Zobraz\[IAcute]me si v\[YAcute]sledek (posledn\[IAcute]ch 10 zaznam\[URing]).\
\
\>", "Text",
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Cell["\<\
Rozd\[EHacek]len\[IAcute] dat na vstupn\[IAcute] a v\[YAcute]stupn\[IAcute] \
vektory\
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Cel\[YAcute] datov\[YAcute] soubor si rozd\[EHacek]l\[IAcute]me na mno\
\[ZHacek]inu vstupn\[IAcute]ch dat a mno\[ZHacek]inu \
v\[YAcute]stupn\[IAcute]ch dat.\
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Cell["\<\
Prvn\[IAcute] 4 polo\[ZHacek]ky jsou vstupn\[IAcute] hodnoty. \
Posledn\[IAcute] (5.) je v\[YAcute]stupn\[IAcute] hodnota. Pro pochopen\
\[IAcute] n\[AAcute]sleduj\[IAcute]c\[IAcute]ho p\[RHacek]\[IAcute]kazu je \
pot\[RHacek]eba \"myslet maticov\[EHacek]\" - te\[DHacek] jsou data \
reprezentov\[AAcute]na seznamem, kde ka\[ZHacek]d\[YAcute] prvek je jeden \
vektor vstupu a v\[YAcute]stupu (jeden \[RHacek]\[AAcute]dek datov\[EAcute]ho \
souboru) - pokud tento seznam transponujeme, dostaneme seznam, kde \
ka\[ZHacek]d\[YAcute] prvek bude reprezentovat hodnoty jednotliv\[YAcute]ch \
atribut\[URing] - zam\[EHacek]n\[IAcute]me sloupce a \[RHacek]\[AAcute]dky. V \
transponovan\[EAcute]m seznamu vybereme p\[RHacek]\[IAcute]slu\[SHacek]n\
\[EAcute] sloupce a po transponov\[AAcute]n\[IAcute] zp\[EHacek]t dost\
\[AAcute]v\[AAcute]me zase vektory dat.\
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Cell["\<\
 Proto\[ZHacek]e jako v\[YAcute]stupn\[IAcute] hodnoty \
vyb\[IAcute]r\[AAcute]me data na zadan\[EAcute]m indexu, je pot\[RHacek]eba \
ho napsat do slo\[ZHacek]en\[YAcute]ch z\[AAcute]vorek {5} - syntaktick\
\[AAcute] z\[AAcute]le\[ZHacek]itost. Dalo by se tak\[EAcute] pou\[ZHacek]\
\[IAcute]t \"Take[..., -1]\" - jeden (obecn\[EHacek] n) prvek od konce.\
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Cell["\<\
Vlastn\[IAcute] p\[RHacek]edzpracov\[AAcute]n\[IAcute] - \
p\[RHacek]ek\[OAcute]dov\[AAcute]n\[IAcute] dat\
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Cell["\<\
V datech jsou textov\[EAcute] atributy, kter\[EAcute] je pro pot\[RHacek]eby \
klasifikace dop\[RHacek]ednou s\[IAcute]t\[IAcute] nutn\[EAcute] p\[RHacek]ek\
\[OAcute]dovat. Pou\[ZHacek]ijeme k\[OAcute]d \"1 z N\".\
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Cell["\<\
Proto\[ZHacek]e se n\[AAcute]m nechce zad\[AAcute]vat ru\[CHacek]n\[EHacek] k\
\[OAcute]dovac\[IAcute] tabulku a nav\[IAcute]c chceme m\[IAcute]t univerz\
\[AAcute]ln\[IAcute] \[RHacek]e\[SHacek]en\[IAcute], \
vytvo\[RHacek]\[IAcute]me k\[OAcute]dovac\[IAcute] tabulku automaticky.\
\>", "Text",
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Cell["\<\
Prvn\[IAcute]m krokem je zjistit jak\[YAcute] je obor hodnot \
textov\[EAcute]ho (v\[YAcute]stupn\[IAcute]ho) atributu. Funkc\[IAcute] \
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Data tedy srovn\[AAcute]me funkc\[IAcute] \"Flatten\" a potom se\[RHacek]ad\
\[IAcute]me funkc\[IAcute] \"Sort\". Na tento seznam pou\[ZHacek]ijeme funkci \
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p\[RHacek]\[IAcute]pad\[EHacek] se\[RHacek]azen\[YAcute] seznam \
v\[YAcute]stupn\[IAcute]ch hodnot) rozd\[EHacek]l\[IAcute] na seznamy podle \
funk\[CHacek]n\[IAcute] hodnoty - pokud jsou tedy v datech celkem 3 \
r\[URing]zn\[EAcute] hodnoty, jsou ve v\[YAcute]sledku vytvo\[RHacek]eny 3 \
seznamy - ka\[ZHacek]d\[YAcute] obsahuje prvky s jednou hodnotou.\
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Pro lep\[SHacek]\[IAcute] pochopen\[IAcute] si zkuste vyhodnotit \
n\[AAcute]sleduj\[IAcute]c\[IAcute] p\[RHacek]\[IAcute]kazy (cel\[YAcute] \
postup rozf\[AAcute]zovan\[YAcute] do jednotliv\[YAcute]ch krok\[URing]). \
POZOR - v\[YAcute]stupy mohou b\[YAcute]t dost velik\[EAcute] (zobrazuje se \
cel\[YAcute] soubor dat).\
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Te\[DHacek] m\[AAcute]me tolik seznam\[URing], kolik je \
r\[URing]zn\[YAcute]ch hodnot v\[YAcute]stup\[URing] - z \
ka\[ZHacek]d\[EAcute]ho tohoto seznamu tedy vezmeme prvn\[IAcute] prvek jako \
reprezentanta. Ud\[EHacek]l\[AAcute]me to namapovan\[IAcute]m funkce \
\"First\" (vrac\[IAcute] prvn\[IAcute] prvek zadan\[EAcute]ho seznamu).\
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V prom\[EHacek]nn\[EAcute] \"outVal\" te\[DHacek] m\[AAcute]me obor hodnot v\
\[YAcute]stupu.\
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Vytvo\[RHacek]\[IAcute]me p\[RHacek]evodn\[IAcute] tabulku. MapIndexed \
funguje stejn\[EHacek] jako Map, ale v druh\[EAcute]m parametru (#2) p\
\[RHacek]ed\[AAcute]v\[AAcute] index prvku, na kter\[YAcute] je funkce aktu\
\[AAcute]ln\[EHacek] mapov\[AAcute]na. SparseArray slou\[ZHacek]\[IAcute] k \
vytv\[AAcute]\[RHacek]en\[IAcute] tzv. \[RHacek]\[IAcute]dk\[YAcute]ch matic \
- zde ji pou\[ZHacek]ijeme jako n\[AAcute]stroj pro jednoduch\[EAcute] vytvo\
\[RHacek]en\[IAcute] v\[YAcute]sledn\[EAcute]ho vektoru. Chceme toti\[ZHacek] \
nulov\[YAcute] vektor s jedni\[CHacek]kou pouze na jedn\[EAcute] pozici.\
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Vid\[IAcute]me, \[ZHacek]e to je p\[RHacek]esn\[EHacek] to, co chceme, tak\
\[ZHacek]e te\[DHacek] aplikujeme tento p\[RHacek]\[IAcute]kaz na \
cel\[YAcute] seznam v\[YAcute]stupn\[IAcute]ch hodnot, \
ulo\[ZHacek]en\[YAcute]ch v prom\[EHacek]nn\[EAcute] \"outVal\".\
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Prom\[EHacek]nn\[AAcute] \"encode\" te\[DHacek] obsahuje seznam tzv. p\
\[RHacek]episovac\[IAcute]ch pravidel - vid\[IAcute]me, \[ZHacek]e \
ka\[ZHacek]d\[EAcute] textov\[EAcute] hodnot\[EHacek] je \
p\[RHacek]i\[RHacek]azen jeden vektor.\
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Pomoc\[IAcute] konstrukce \"/.\" m\[URing]\[ZHacek]eme tato \
p\[RHacek]episovac\[IAcute] pravidla aplikovat na p\[URing]vodn\[IAcute] data \
a z\[IAcute]sk\[AAcute]me tak zak\[OAcute]dovan\[AAcute] data.\
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P\[RHacek]\[IAcute]klad pro pochopen\[IAcute] (znak \[SHacek]ipky se zad\
\[AAcute]v\[AAcute] jako \"poml\[CHacek]ka\" a \
\"v\[EHacek]t\[SHacek]\[IAcute]tko\" tj. \"-\" a \">\"):\
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Cell["\<\
Te\[DHacek] u\[ZHacek] m\[URing]\[ZHacek]eme kone\[CHacek]n\[EHacek] zak\
\[OAcute]dovat na\[SHacek]e data.\
\>", "Text",
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Cell["\<\
Zji\[SHacek]t\[EHacek]n\[IAcute], zda jsou n\[EHacek]kter\[EAcute] \
v\[YAcute]stupn\[IAcute] hodnoty textov\[EAcute] - pokud ano, provede se p\
\[RHacek]ek\[OAcute]dov\[AAcute]ni do \"1 z N\".\
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Cell["Jak to vypad\[AAcute]? Koukneme se :", "Text",
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Cell["\<\
Jednoduch\[EAcute] zpracov\[AAcute]n\[IAcute] dat neuronovou s\[IAcute]t\
\[IAcute]\
\>", "Section",
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Cell["\<\
Inicializace s\[IAcute]t\[EHacek] - zad\[AAcute]me tr\[EAcute]novac\[IAcute] \
mno\[ZHacek]inu, po\[CHacek]et neuron\[URing], p\[RHacek]\[IAcute]padn\
\[EHacek] m\[URing]\[ZHacek]eme zadat i aktiva\[CHacek]n\[IAcute] funkci \
neuronu. Po\[CHacek]et neuron\[URing] se zad\[AAcute]v\[AAcute] jako seznam, \
ka\[ZHacek]d\[YAcute] prvek (\[CHacek]\[IAcute]slo) seznamu odpov\[IAcute]d\
\[AAcute] po\[CHacek]tu neuron\[URing] v jedn\[EAcute] vrstv\[EHacek]. {5} \
znamen\[AAcute] jedna skryt\[AAcute] vrstva s p\[EHacek]ti neurony. {4,3} \
znamen\[AAcute] dv\[EHacek] skryt\[EAcute] vrstvy, jedna se \[CHacek]ty\
\[RHacek]mi neurony a druh\[AAcute] se t\[RHacek]emi neurony.\
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Vytvo\[RHacek]enou s\[IAcute]\[THacek] si ulo\[ZHacek]\[IAcute]me do prom\
\[EHacek]nn\[EAcute] \"net\".\
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M\[URing]\[ZHacek]eme si nechat zobrazit podrobn\[EHacek]j\[SHacek]\[IAcute] \
informace o vytvo\[RHacek]en\[EAcute] s\[IAcute]ti.\
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Te\[DHacek] s\[IAcute]\[THacek] natr\[EAcute]nujeme pomoc\[IAcute] funkce \
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Funkce NeuralFit vyprodukuje nau\[CHacek]enou s\[IAcute]\[THacek] a \
z\[AAcute]znam o pr\[URing]b\[EHacek]hu u\[CHacek]en\[IAcute] - ob\[EHacek] \
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M\[AAcute]me s\[IAcute]\[THacek] nau\[CHacek]enou a m\[URing]\[ZHacek]eme se \
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\[YAcute]stupy, proto n\[AAcute]sleduj\[IAcute]c\[IAcute] \
p\[RHacek]\[IAcute]kaz vyprodukuje 3 grafy. Grafy zobrazuj\[IAcute] \
v\[YAcute]voj dob\[RHacek]e a \[SHacek]patn\[EHacek] klasifikovan\[YAcute]ch \
instanc\[IAcute] jednotliv\[YAcute]ch t\[RHacek]\[IAcute]d, pro \
ka\[ZHacek]dou t\[RHacek]\[IAcute]du je jeden graf.\
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M\[URing]\[ZHacek]eme se pod\[IAcute]vat na klasifikaci pomoc\[IAcute] \
\"data/model\" diagramu. Graf je interaktivn\[IAcute] - pomoc\[IAcute] my\
\[SHacek]i ho m\[URing]zeme ot\[AAcute]\[CHacek]et, posouvat (Shift + my\
\[SHacek]) a zoomovat (Ctrl + my\[SHacek]). \[CapitalCHacek]\[IAcute]m v\
\[IAcute]ce prvk\[URing] je na diagon\[AAcute]le, t\[IAcute]m l\[EAcute]pe.\
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}, Open  ]],

Cell["\<\
S\[IAcute]\[THacek] je mo\[ZHacek]n\[EAcute] si nechat symbolicky vyhodnotit \
- pro v\[EHacek]t\[SHacek]\[IAcute] s\[IAcute]t\[EHacek] je to ale u\[ZHacek] \
zcela nep\[RHacek]ehledn\[EAcute], nicm\[EAcute]n\[EHacek] aktiva\[CHacek]n\
\[IAcute] funkce tam zcela jist\[EHacek] pozn\[AAcute]te.\
\>", "Text",
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Cell["\<\
S\[IAcute]\[THacek] funguje v Mathematice jako klasick\[AAcute] funkce, tak\
\[ZHacek]e ji m\[URing]\[ZHacek]eme d\[AAcute]vat parametry - v\[CHacek]etn\
\[EHacek] symbol\[URing]:\
\>", "Text",
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