Gradzka_Kazimierczak_NeuralPoker.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dokumentacja projektu NeuralPoker\n",
    "\n",
    "NeuralPoker realizuje sieć neuronową do wyznaczania układu kart oraz prawdopodobieństwa wygranej z daną ręką i etapie rozdania (<i>preflop</i>, <i>flop</i>, <i>turn</i>, <i>river</i>) w pokerze Texas Hold'em. Projekt dostępny pod: https://github.com/kazimierczak-robert/NeuralPoker i kompatybilny z python3.\n",
    "\n",
    "## Zewnętrzne biblioteki\n",
    "W projekcie zawarte w folderze Libraries.\n",
    "\n",
    "### Biblioteka deuces\n",
    "Biblioteka wykorzystana do wyznaczenia układów kart oraz ich sił. Oryginalna postać znajduje się pod: https://github.com/worldveil/deuces i jest kompatybilna z python2.\n",
    "\n",
    "### Biblioteka Monte-Carlo\n",
    "Biblioteka wykorzystana do wyznaczenia prawdopodobieństw wygranej z daną ręką i etapie rozdania metodą Monte Carlo. Oryginalna postać znajduje się pod: https://github.com/Blind0ne/Monte-Carlo. Do działania wymagane jest pobranie biblioteki deuces.\n",
    "\n",
    "## Generowanie zbioru treningowego i testowego\n",
    "Krok wykorzystuje zewnętrze biblioteki opisane w nagłówku [Zewnętrzne biblioteki](## Zewnętrzne biblioteki). Do wygenerowania zbioru testowego i treningowego należy wykorzystać plik <code>SetGenerator.py</code>. Metoda <code>generate_sets</code> przyjmuje jako parametr liczbę wszystkich kart na danym etapie gry <code>all_cards_no</code> (2, 5, 6, 7). Liczebność zbiorów w zależności od parametru:\n",
    "\n",
    "<table class=\"tg\">\n",
    "  <tr>\n",
    "    <th class=\"tg-13j4\">Parametr</th>\n",
    "    <th class=\"tg-13j4\">Zbiór testowy</th>\n",
    "    <th class=\"tg-13j4\">Zbiór treningowy</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-yxcf\">2</td>\n",
    "    <td class=\"tg-yxcf\">133<br></td>\n",
    "    <td class=\"tg-yxcf\">1195</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">5</td>\n",
    "    <td class=\"tg-5q1d\">3979</td>\n",
    "    <td class=\"tg-5q1d\">35803</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">6</td>\n",
    "    <td class=\"tg-mg4r\">3979</td>\n",
    "    <td class=\"tg-mg4r\">35803</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">7</td>\n",
    "    <td class=\"tg-5q1d\">3979</td>\n",
    "    <td class=\"tg-5q1d\">35803</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "Metoda drukuje na standardowe wyjście numer wygenerowanej próbki (treningowej, testowej).\n",
    "\n",
    "Część przykładowego zbioru treningowego dla parametru <code>all_cards_no=2</code>:\n",
    "<code>\n",
    "2,4,2,6,0.20840000000000003\n",
    "1,6,4,13,0.23440000000000005\n",
    "2,6,4,13,0.25\n",
    "3,12,4,12,0.5028\n",
    "1,6,3,6,0.24680000000000002\n",
    "</code>\n",
    "Karta reprezentowana parą liczb: kolor i figura. Dwie pary liczb to karty w ręce. Ostatnia liczba to prawdopodobieństwo wygranej.\n",
    "Część przykładowego zbioru treningowego dla parametru <code>all_cards_no=6</code>:\n",
    "<code>\n",
    "1,5,4,5,1,12,3,7,2,13,4,1,8,0.12680000000000002\n",
    "3,4,4,4,4,5,4,9,2,4,1,4,2,0.9996\n",
    "1,6,4,6,1,8,2,8,2,1,2,2,7,0.28080000000000005\n",
    "1,2,2,5,4,7,3,6,3,13,1,9,9,0.03520000000000001\n",
    "4,4,3,7,4,6,3,9,4,10,1,7,8,0.11599999999999999\n",
    "</code>\n",
    "Karta reprezentowana parą liczb: kolor i figura. Ostatnie dwie liczby to układ (najsilniejszy) i prawdopodobieństwo wygranej. Dwie pierwsze pary to karty w ręce, pozostałe - na stole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from Sets.SetGenerator import SetGenerator\n",
    "all_cards_no = 5\n",
    "generator = SetGenerator()\n",
    "generator.generate_sets(all_cards_no)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wczytywanie zbioru treningowego i testowego\n",
    "Należy zaimportować pliki <code>SetImporter.py</code> i <code>SetGenerator.py</code>. Konstruktor klasy <code>SetImporter</code> jest dwuargumentowy: ścieżka do pliku ze zbiorem i liczba wszystkich kart na danym etapie gry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from Sets.SetImporter import SetImporter\n",
    "from Sets.SetGenerator import SetGenerator\n",
    "all_cards_no = 5\n",
    "train_set = SetImporter(\"{}/{}.data\".format(SetGenerator.dir_path, \"training\", all_cards_no), all_cards_no)\n",
    "test_set = SetImporter(\"{}/{}.data\".format(SetGenerator.dir_path, \"test\", all_cards_no), all_cards_no)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model sieci neuronowej\n",
    "W celu wytrenowania modelu i przetestowania go należy wykonać poniższy kod. Parametr metody <code>create</code> oznacza liczbę neuronów warstwy ukrytej."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from Sets.SetImporter import SetImporter\n",
    "from Sets.SetGenerator import SetGenerator\n",
    "from Model.Model import Model\n",
    "\n",
    "all_cards_no = 5\n",
    "train_set = SetImporter(\"{}/poker-hand-{}-{}.data\".format(SetGenerator.dir_path, \"training\", all_cards_no), all_cards_no)\n",
    "test_set = SetImporter(\"{}/poker-hand-{}-{}.data\".format(SetGenerator.dir_path, \"test\", all_cards_no), all_cards_no)\n",
    "\n",
    "model = Model(train_set)\n",
    "model.create(28)\n",
    "model.train()\n",
    "model.test(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Metoda <code>create</code> tworzy model sieci neuronowej z wykorzystaniem biblioteki <code>tensorflow</code>. Sieć składa się z warstwy wejściowej o 17 neuronach (13 figur + 4 kolory), jednej warstwy ukrytej <i>Dense</i>, która wykorzystuje funkcję aktywacji relu. W zależności od wybranego etapu gry (2 lub 5, 6, 7) sieć ma jedną (dla 2) lub 2 warstwy wyjściowe (dla 5, 6, 7). Warstwa wyjściowa z jednym neuronem wyznaczająca prawdopodobieństwo wygranej (regresja) wykorzystuje funkcję aktywacji sigmoid. Warstwa wyjściowa z 10 neuronami wyznaczająca układ kart (klasyfikacja) wykorzytuje funkcję aktywacji softmax. Wykorzystano metryki: acc (klasyfikacja), mse (regresja)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compile(self):\n",
    "    if self.all_cards == 2:\n",
    "        # metrics for regression\n",
    "        self.model.compile(optimizer=tf.train.AdamOptimizer(), loss='mse', metrics=['mse'])\n",
    "    else:\n",
    "        # metrics for classification and regression\n",
    "        self.model.compile(optimizer=tf.train.AdamOptimizer(),\n",
    "                           loss={'output_layer_1': 'sparse_categorical_crossentropy', 'output_layer_2': 'mse'},\n",
    "                           metrics={'output_layer_1': 'acc', 'output_layer_2': 'mse'})\n",
    "\n",
    "def create(self, neurons_in_hidden_layer):\n",
    "    if self.dataset is not None:\n",
    "        self.model = keras.models.Sequential()\n",
    "        # 17 = 13 ranks + 4 suits\n",
    "        input_layer = keras.layers.Input((17*self.dataset.all_cards,), name=\"input_layer\")\n",
    "        hidden_layer = keras.layers.Dense(neurons_in_hidden_layer, activation=tf.nn.relu, name=\"hidden_layer\")(input_layer)\n",
    "        # 1 output in NN\n",
    "        if self.dataset.all_cards == 2:\n",
    "            output_layer = keras.layers.Dense(1, activation=tf.nn.sigmoid, name=\"output_layer\")(hidden_layer)\n",
    "            self.model = keras.models.Model(input_layer, output_layer)\n",
    "        # 2 outputs in NN\n",
    "        else:\n",
    "            output_1 = keras.layers.Dense(10, activation=tf.nn.softmax, name=\"output_layer_1\")(hidden_layer)\n",
    "            output_2 = keras.layers.Dense(1, activation=tf.nn.sigmoid, name=\"output_layer_2\")(hidden_layer)\n",
    "            self.model = keras.models.Model(input_layer, [output_1, output_2])\n",
    "        self.compile()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testowanie konfiguracji\n",
    "Ciemniejszym kolorem oznaczono konfiguracje, dla których uzyskano najlepsze wyniki testów.\n",
    "\n",
    "<style type=\"text/css\">\n",
    ".tg  {border-collapse:collapse;border-spacing:0;}\n",
    ".tg td{font-family:Arial, sans-serif;font-size:14px;padding:10px 12px;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:black;}\n",
    ".tg th{font-family:Arial, sans-serif;font-size:14px;font-weight:normal;padding:10px 12px;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:black;}\n",
    ".tg .tg-13j4{font-weight:bold;font-size:12px;border-color:#efefef;text-align:center;vertical-align:top}\n",
    ".tg .tg-yxcf{font-size:12px;background-color:#efefef;border-color:#efefef;text-align:center;vertical-align:top}\n",
    ".tg .tg-5q1d{font-size:12px;border-color:#efefef;text-align:center;vertical-align:top}\n",
    ".tg .tg-mg4r{font-size:12px;background-color:#efefef;color:#000000;border-color:#efefef;text-align:center;vertical-align:top}\n",
    ".tg .tg-d99v{font-size:12px;background-color:#9b9b9b;color:#000000;border-color:#efefef;text-align:center;vertical-align:top}\n",
    "</style>\n",
    "\n",
    "* <code>all_cards_no</code> = 2\n",
    "\n",
    "<table class=\"tg\">\n",
    "  <tr>\n",
    "    <th class=\"tg-13j4\">neurons_no</th>\n",
    "    <th class=\"tg-13j4\">loss</th>\n",
    "    <th class=\"tg-13j4\">mean_squared_error</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-yxcf\">8</td>\n",
    "    <td class=\"tg-yxcf\">0.0002728728868532926<br></td>\n",
    "    <td class=\"tg-yxcf\">0.0002728728868532926</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">16</td>\n",
    "    <td class=\"tg-5q1d\">0.00019329327915329486</td>\n",
    "    <td class=\"tg-5q1d\">0.00019329327915329486</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">24</td>\n",
    "    <td class=\"tg-mg4r\">0.0002716764865908772</td>\n",
    "    <td class=\"tg-mg4r\">0.0002716764865908772</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-d99v\">28</td>\n",
    "    <td class=\"tg-d99v\">0.00016631107428111136</td>\n",
    "    <td class=\"tg-d99v\">0.00016631107428111136</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">29</td>\n",
    "    <td class=\"tg-mg4r\">0.00021212051797192544</td>\n",
    "    <td class=\"tg-mg4r\">0.00021212051797192544</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">30</td>\n",
    "    <td class=\"tg-5q1d\">0.00021880857821088284</td>\n",
    "    <td class=\"tg-5q1d\">0.00021880857821088284</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">32</td>\n",
    "    <td class=\"tg-mg4r\">0.00019229181634727865</td>\n",
    "    <td class=\"tg-mg4r\">0.00019229181634727865</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "* <code>all_cards_no</code> = 5\n",
    "\n",
    "<table class=\"tg\">\n",
    "  <tr>\n",
    "    <th class=\"tg-13j4\">neurons_no</th>\n",
    "    <th class=\"tg-13j4\">loss</th>\n",
    "    <th class=\"tg-13j4\">output_layer_1_loss</th>\n",
    "    <th class=\"tg-13j4\">output_layer_2_loss</th>\n",
    "    <th class=\"tg-13j4\">output_layer_1_acc</th>\n",
    "    <th class=\"tg-13j4\">output_layer_2_mean_squared_error</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-yxcf\">8</td>\n",
    "    <td class=\"tg-yxcf\">0.8569035530090332</td>\n",
    "    <td class=\"tg-yxcf\">0.8307963013648987</td>\n",
    "    <td class=\"tg-yxcf\">0.026107240468263626</td>\n",
    "    <td class=\"tg-yxcf\">0.6337355375289917</td>\n",
    "    <td class=\"tg-yxcf\">0.026107240468263626</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">16</td>\n",
    "    <td class=\"tg-5q1d\">0.18359050154685974</td>\n",
    "    <td class=\"tg-5q1d\">0.1657908409833908</td>\n",
    "    <td class=\"tg-5q1d\">0.017799654975533485</td>\n",
    "    <td class=\"tg-5q1d\">0.9472096562385559</td>\n",
    "    <td class=\"tg-5q1d\">0.017799654975533485</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">24</td>\n",
    "    <td class=\"tg-mg4r\">0.23562563955783844</td>\n",
    "    <td class=\"tg-mg4r\">0.2202180176973343</td>\n",
    "    <td class=\"tg-mg4r\">0.015407627448439598</td>\n",
    "    <td class=\"tg-mg4r\">0.9678230285644531</td>\n",
    "    <td class=\"tg-mg4r\">0.015407627448439598</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-d99v\">28</td>\n",
    "    <td class=\"tg-d99v\">0.24058467149734497</td>\n",
    "    <td class=\"tg-d99v\">0.2283773422241211</td>\n",
    "    <td class=\"tg-d99v\">0.012207326479256153</td>\n",
    "    <td class=\"tg-d99v\">0.9690799117088318</td>\n",
    "    <td class=\"tg-d99v\">0.012207326479256153</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">29</td>\n",
    "    <td class=\"tg-mg4r\">0.29240182042121887</td>\n",
    "    <td class=\"tg-mg4r\">0.2786564528942108</td>\n",
    "    <td class=\"tg-mg4r\">0.01374536007642746</td>\n",
    "    <td class=\"tg-mg4r\">0.9658119678497314</td>\n",
    "    <td class=\"tg-mg4r\">0.01374536007642746</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">30</td>\n",
    "    <td class=\"tg-5q1d\">0.35394394397735596</td>\n",
    "    <td class=\"tg-5q1d\">0.34037789702415466</td>\n",
    "    <td class=\"tg-5q1d\">0.013566038571298122</td>\n",
    "    <td class=\"tg-5q1d\">0.9615384340286255</td>\n",
    "    <td class=\"tg-5q1d\">0.013566038571298122</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">32</td>\n",
    "    <td class=\"tg-mg4r\">0.39161327481269836</td>\n",
    "    <td class=\"tg-mg4r\">0.37983229756355286</td>\n",
    "    <td class=\"tg-mg4r\">0.011780978180468082</td>\n",
    "    <td class=\"tg-mg4r\">0.9610356688499451</td>\n",
    "    <td class=\"tg-mg4r\">0.011780978180468082</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "* <code>all_cards_no</code> = 6\n",
    "\n",
    "<table class=\"tg\">\n",
    "  <tr>\n",
    "    <th class=\"tg-13j4\">neurons_no</th>\n",
    "    <th class=\"tg-13j4\">loss</th>\n",
    "    <th class=\"tg-13j4\">output_layer_1_loss</th>\n",
    "    <th class=\"tg-13j4\">output_layer_2_loss</th>\n",
    "    <th class=\"tg-13j4\">output_layer_1_acc</th>\n",
    "    <th class=\"tg-13j4\">output_layer_2_mean_squared_error</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-yxcf\">8</td>\n",
    "    <td class=\"tg-yxcf\">1.1386266946792603</td>\n",
    "    <td class=\"tg-yxcf\">1.100320816040039</td>\n",
    "    <td class=\"tg-yxcf\">0.03830586373806</td>\n",
    "    <td class=\"tg-yxcf\">0.5389643311500549</td>\n",
    "    <td class=\"tg-yxcf\">0.03830586373806</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">16</td>\n",
    "    <td class=\"tg-5q1d\">0.5025573968887329</td>\n",
    "    <td class=\"tg-5q1d\">0.4704950153827667</td>\n",
    "    <td class=\"tg-5q1d\">0.0320623554289341</td>\n",
    "    <td class=\"tg-5q1d\">0.8627451062202454</td>\n",
    "    <td class=\"tg-5q1d\">0.0320623554289341</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">24</td>\n",
    "    <td class=\"tg-mg4r\">0.2888332009315491</td>\n",
    "    <td class=\"tg-mg4r\">0.2606068551540375</td>\n",
    "    <td class=\"tg-mg4r\">0.02822634018957615</td>\n",
    "    <td class=\"tg-mg4r\">0.9263448715209961</td>\n",
    "    <td class=\"tg-mg4r\">0.02822634018957615</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">28</td>\n",
    "    <td class=\"tg-5q1d\">0.33630427718162537</td>\n",
    "    <td class=\"tg-5q1d\">0.3104614317417145</td>\n",
    "    <td class=\"tg-5q1d\">0.025842837989330292</td>\n",
    "    <td class=\"tg-5q1d\">0.9281045794487</td>\n",
    "    <td class=\"tg-5q1d\">0.025842837989330292</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">29</td>\n",
    "    <td class=\"tg-mg4r\">0.3946927785873413</td>\n",
    "    <td class=\"tg-mg4r\">0.3673554062843323</td>\n",
    "    <td class=\"tg-mg4r\">0.02733737602829933</td>\n",
    "    <td class=\"tg-mg4r\">0.9263448715209961</td>\n",
    "    <td class=\"tg-mg4r\">0.02733737602829933</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-d99v\">30</td>\n",
    "    <td class=\"tg-d99v\">0.5774040818214417</td>\n",
    "    <td class=\"tg-d99v\">0.5508595705032349</td>\n",
    "    <td class=\"tg-d99v\">0.026544496417045593</td>\n",
    "    <td class=\"tg-d99v\">0.9301156401634216</td>\n",
    "    <td class=\"tg-d99v\">0.026544496417045593</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">32</td>\n",
    "    <td class=\"tg-mg4r\">0.44998517632484436</td>\n",
    "    <td class=\"tg-mg4r\">0.4231707453727722</td>\n",
    "    <td class=\"tg-mg4r\">0.02681444026529789</td>\n",
    "    <td class=\"tg-mg4r\">0.9147812724113464</td>\n",
    "    <td class=\"tg-mg4r\">0.02681444026529789</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "\n",
    "* <code>all_cards_no</code> = 7\n",
    "<table class=\"tg\">\n",
    "  <tr>\n",
    "    <th class=\"tg-13j4\">neurons_no</th>\n",
    "    <th class=\"tg-13j4\">loss</th>\n",
    "    <th class=\"tg-13j4\">output_layer_1_loss</th>\n",
    "    <th class=\"tg-13j4\">output_layer_2_loss</th>\n",
    "    <th class=\"tg-13j4\">output_layer_1_acc</th>\n",
    "    <th class=\"tg-13j4\">output_layer_2_mean_squared_error</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-yxcf\">8</td>\n",
    "    <td class=\"tg-yxcf\">1.3301163911819458</td>\n",
    "    <td class=\"tg-yxcf\">1.2595701217651367</td>\n",
    "    <td class=\"tg-yxcf\">0.0705462396144867</td>\n",
    "    <td class=\"tg-yxcf\">0.47561588883399963</td>\n",
    "    <td class=\"tg-yxcf\">0.0705462396144867</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">16</td>\n",
    "    <td class=\"tg-5q1d\">0.9917051196098328</td>\n",
    "    <td class=\"tg-5q1d\">0.928206741809845</td>\n",
    "    <td class=\"tg-5q1d\">0.063498355448246</td>\n",
    "    <td class=\"tg-5q1d\">0.6312217116355896</td>\n",
    "    <td class=\"tg-5q1d\">0.063498355448246</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">24</td>\n",
    "    <td class=\"tg-mg4r\">0.44883644580841064</td>\n",
    "    <td class=\"tg-mg4r\">0.39074623584747314</td>\n",
    "    <td class=\"tg-mg4r\">0.058090221136808395</td>\n",
    "    <td class=\"tg-mg4r\">0.8637506365776062</td>\n",
    "    <td class=\"tg-mg4r\">0.058090221136808395</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">28</td>\n",
    "    <td class=\"tg-5q1d\">0.6418846845626831</td>\n",
    "    <td class=\"tg-5q1d\">0.580848217010498</td>\n",
    "    <td class=\"tg-5q1d\">0.06103646755218506</td>\n",
    "    <td class=\"tg-5q1d\">0.8619909286499023</td>\n",
    "    <td class=\"tg-5q1d\">0.06103646755218506</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">29</td>\n",
    "    <td class=\"tg-mg4r\">0.6029195189476013</td>\n",
    "    <td class=\"tg-mg4r\">0.5420128703117371</td>\n",
    "    <td class=\"tg-mg4r\">0.06090662255883217</td>\n",
    "    <td class=\"tg-mg4r\">0.8501759767532349</td>\n",
    "    <td class=\"tg-mg4r\">0.06090662255883217</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-d99v\">30</td>\n",
    "    <td class=\"tg-d99v\">0.5111713409423828</td>\n",
    "    <td class=\"tg-d99v\">0.4530506730079651</td>\n",
    "    <td class=\"tg-d99v\">0.05812065303325653</td>\n",
    "    <td class=\"tg-d99v\">0.8866264224052429</td>\n",
    "    <td class=\"tg-d99v\">0.05812065303325653</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">32</td>\n",
    "    <td class=\"tg-mg4r\">0.571366548538208</td>\n",
    "    <td class=\"tg-mg4r\">0.5106329917907715</td>\n",
    "    <td class=\"tg-mg4r\">0.060733549296855927</td>\n",
    "    <td class=\"tg-mg4r\">0.8692810535430908</td>\n",
    "    <td class=\"tg-mg4r\">0.060733549296855927</td>\n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Użytkowanie sieci neuronowej"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zapis modelu do plików"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"{}/model-{}\".format(Model.dir_path, all_cards_no))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Odczyt modelu z plików"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load(\"{}/model-{}\".format(Model.dir_path, all_cards_no), all_cards_no)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predykcja wyników\n",
    "Układy oznaczane są numerem. Karty reprezentowane są za pomocą konkatenacji znaków: figura i kolor.\n",
    "<div>\n",
    "<div style=\"margin-top:10px\">\n",
    "<table style=\"float:left; margin-right:40px\" class=\"tg\">\n",
    "  <tr>\n",
    "    <th class=\"tg-13j4\">Numer układu<br></th>\n",
    "    <th class=\"tg-13j4\">Układ<br></th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-yxcf\">0</td>\n",
    "    <td class=\"tg-yxcf\">Royal Straight Flush</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">1<br></td>\n",
    "    <td class=\"tg-5q1d\">Straight Flush</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">2</td>\n",
    "    <td class=\"tg-mg4r\">Four of a Kind</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">3</td>\n",
    "    <td class=\"tg-5q1d\">Full House</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-yxcf\">4</td>\n",
    "    <td class=\"tg-yxcf\">Flush</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">5</td>\n",
    "    <td class=\"tg-5q1d\">Straight</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">6</td>\n",
    "    <td class=\"tg-mg4r\">Three of a Kind</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">7</td>\n",
    "    <td class=\"tg-5q1d\">Two Pair</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-yxcf\">8</td>\n",
    "    <td class=\"tg-yxcf\">Pair</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">9</td>\n",
    "    <td class=\"tg-5q1d\">High Card</td>\n",
    "  </tr>\n",
    "</table>    \n",
    "</div>\n",
    "<div>\n",
    "<table  style=\"float:left; margin-right:40px\" class=\"tg\">\n",
    "  <tr>\n",
    "    <th class=\"tg-13j4\">Figura</th>\n",
    "    <th class=\"tg-13j4\">Znak</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-yxcf\">2,3,...,9</td>\n",
    "    <td class=\"tg-yxcf\">2,3,...,9</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">10<br></td>\n",
    "    <td class=\"tg-5q1d\">10</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">Jack</td>\n",
    "    <td class=\"tg-mg4r\">J</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">Queen</td>\n",
    "    <td class=\"tg-5q1d\">Q</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-mg4r\">King</td>\n",
    "    <td class=\"tg-mg4r\">K</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">Ace</td>\n",
    "    <td class=\"tg-5q1d\">A</td>\n",
    "  </tr>\n",
    "</table>\n",
    "    </div>\n",
    "<div>\n",
    "<table class=\"tg\" style=\"float:left\">\n",
    "  <tr>\n",
    "    <th class=\"tg-13j4\">Kolor</th>\n",
    "    <th class=\"tg-13j4\">Znak</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-yxcf\">Spades</td>\n",
    "    <td class=\"tg-yxcf\">s</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">Hearts</td>\n",
    "    <td class=\"tg-5q1d\">h</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-yxcf\">Diamonds</td>\n",
    "    <td class=\"tg-yxcf\">d</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-5q1d\">Clubs</td>\n",
    "    <td class=\"tg-5q1d\">c</td>\n",
    "  </tr>\n",
    "</table>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predykcja dla etapu gry z 2 kartami w ręce zwraca prawdopodobieństwo wygranej w rozdaniu. Dla innych etapów dodatkowo zwracany jest numer układu kart (najsilniejszego)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(model.predict(['Qs', 'Qh', 'Qd', 'Kc', 'Kh']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Przykładowy scenariusz użytkowania\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 0.89730614)\n"
     ]
    }
   ],
   "source": [
    "from Model.Model import Model\n",
    "\n",
    "all_cards_no = 5\n",
    "model = Model()\n",
    "model.load(\"{}/model-{}\".format(Model.dir_path, all_cards_no), all_cards_no)\n",
    "print(model.predict(['Qs', 'Qh', 'Qd', 'Kc', 'Kh']))"
   ]
  }
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