{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from conversion import CSVToTensor\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model(nn.Module):\n",
    "    def __init__(self) -> None:\n",
    "        super(Model, self).__init__()\n",
    "        self.input_size = 9\n",
    "        self.hidden_size_1 = 9\n",
    "        self.hidden_size_2 = 9\n",
    "        self.hidden_size_3 = 9\n",
    "        self.hidden_size_4 = 9\n",
    "        self.hidden_size_5 = 9\n",
    "        self.hidden_size_6 = 9\n",
    "        self.hidden_size_7 = 9\n",
    "        self.output_size = 9\n",
    "\n",
    "        self.fc1 = nn.Linear(self.input_size, self.hidden_size_1)\n",
    "        self.fc2 = nn.Linear(self.hidden_size_1, self.hidden_size_2)\n",
    "        self.fc3 = nn.Linear(self.hidden_size_2, self.hidden_size_3)\n",
    "        self.fc4 = nn.Linear(self.hidden_size_3, self.hidden_size_4)\n",
    "        self.fc5 = nn.Linear(self.hidden_size_4, self.hidden_size_5)\n",
    "        self.fc6 = nn.Linear(self.hidden_size_5, self.hidden_size_6)\n",
    "        self.fc7 = nn.Linear(self.hidden_size_6, self.hidden_size_7)\n",
    "        self.fc8 = nn.Linear(self.hidden_size_7, self.output_size)\n",
    "\n",
    "        self.loss = nn.CrossEntropyLoss()\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(0.25)\n",
    "\n",
    "        self.optim = None\n",
    "\n",
    "        self.train_data = None\n",
    "        self.test_data = None\n",
    "        self.train_losses = []\n",
    "        self.avg_epoch_losses = []\n",
    "\n",
    "\n",
    "        self.target = None\n",
    "\n",
    "    def initialize_optimizer(self):\n",
    "        self.optim = torch.optim.AdamW(self.parameters())\n",
    "\n",
    "    def forward(self, x):\n",
    "        x1 = self.relu(self.fc1(x))\n",
    "        x2 = self.relu(self.fc2(x1))\n",
    "        x3 = self.relu(self.fc3(x2))\n",
    "        x4 = self.relu(self.fc4(x3))\n",
    "        x5 = self.relu(self.fc5(x4))\n",
    "        x6 = self.relu(self.fc6(x5))\n",
    "        x7 = self.relu(self.fc7(x6))\n",
    "        x8 = self.fc8(x7)\n",
    "        return x8\n",
    "    \n",
    "    def train_model(self, epochs):\n",
    "        for epoch in epochs:\n",
    "            epoch_loss = 0\n",
    "            for src, trg in self.train_data:\n",
    "\n",
    "                self.optim.zero_grad()\n",
    "                output = self.forward(src)\n",
    "                loss = self.loss (output, trg.argmax(dim=1))\n",
    "                self.target = trg\n",
    "                loss.backward()\n",
    "                self.optim.step()\n",
    "                epoch_loss += loss.item()\n",
    "            avg_epoch_loss = epoch_loss / len(self.train_data)\n",
    "            self.avg_epoch_losses.append(avg_epoch_loss)\n",
    "            self.train_losses.append(loss.item())\n",
    "            print(f'Epoch: {epoch}, Loss: {avg_epoch_loss}')\n",
    "            print(f'           Loss.item: {loss.item()}')\n",
    "\n",
    "    def save_model(self, path):\n",
    "        torch.save(self.state_dict(), path)\n",
    "    \n",
    "    def inference(self, input):\n",
    "        return self.forward(input)\n",
    "\n",
    "    def plot_losses(self):\n",
    "        plt.figure(figsize=(10, 5))\n",
    "        plt.plot(np.arange(1, len(self.train_losses) + 1), self.train_losses, label='Loss item')\n",
    "        plt.plot(np.arange(1, len(self.avg_epoch_losses) + 1), self.avg_epoch_losses, label='Average Training Loss')\n",
    "        plt.xlabel('Epoch')\n",
    "        plt.ylabel('Loss')\n",
    "        plt.title('Training and Test Loss over Epochs')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prepare the data for training,\n",
    "from torch.utils.data import Dataset\n",
    "\n",
    "path = 'dataset_states.csv'\n",
    "loader = CSVToTensor(path)\n",
    "loader.create_all_tensor()\n",
    "\n",
    "class CustomDataset(Dataset):\n",
    "    def __init__(self, input_tensor, output_tensor):\n",
    "        self.input_tensor = input_tensor\n",
    "        self.output_tensor = output_tensor\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.input_tensor)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        input_data = self.input_tensor[idx]\n",
    "        output_data = self.output_tensor[idx]\n",
    "        return input_data, output_data\n",
    "\n",
    "input_tensor = loader.game_tensor\n",
    "output_tensor = loader.prediction_tensor\n",
    "\n",
    "combined_dataset = CustomDataset(input_tensor, output_tensor)\n",
    "\n",
    "combined_dataloader = DataLoader(combined_dataset, batch_size=28, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model()\n",
    "\n",
    "model.initialize_optimizer()\n",
    "model.train_data = combined_dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Loss: 1.035825204104185\n",
      "           Loss.item: 0.9152618050575256\n",
      "Epoch: 1, Loss: 1.0573974177241325\n",
      "           Loss.item: 1.1231544017791748\n",
      "Epoch: 2, Loss: 1.0846302546560764\n",
      "           Loss.item: 0.7939132452011108\n",
      "Epoch: 3, Loss: 1.1159542202949524\n",
      "           Loss.item: 1.2309269905090332\n",
      "Epoch: 4, Loss: 1.1111208610236645\n",
      "           Loss.item: 1.2248382568359375\n",
      "Epoch: 5, Loss: 1.090789820998907\n",
      "           Loss.item: 1.2458264827728271\n",
      "Epoch: 6, Loss: 1.0429970994591713\n",
      "           Loss.item: 1.24774169921875\n",
      "Epoch: 7, Loss: 1.0323011800646782\n",
      "           Loss.item: 1.083608865737915\n",
      "Epoch: 8, Loss: 1.0345421023666859\n",
      "           Loss.item: 1.252897024154663\n",
      "Epoch: 9, Loss: 1.0391855686903\n",
      "           Loss.item: 1.0108329057693481\n",
      "Epoch: 10, Loss: 1.0506740994751453\n",
      "           Loss.item: 1.2671244144439697\n",
      "Epoch: 11, Loss: 1.035816252231598\n",
      "           Loss.item: 0.9548277258872986\n",
      "Epoch: 12, Loss: 1.0683671683073044\n",
      "           Loss.item: 1.1908386945724487\n",
      "Epoch: 13, Loss: 1.0396330803632736\n",
      "           Loss.item: 1.095572829246521\n",
      "Epoch: 14, Loss: 1.041986133903265\n",
      "           Loss.item: 1.102850079536438\n",
      "Epoch: 15, Loss: 1.0269541144371033\n",
      "           Loss.item: 1.0738226175308228\n",
      "Epoch: 16, Loss: 1.0376965254545212\n",
      "           Loss.item: 1.0702892541885376\n",
      "Epoch: 17, Loss: 1.0348379462957382\n",
      "           Loss.item: 0.9361099600791931\n",
      "Epoch: 18, Loss: 1.046415776014328\n",
      "           Loss.item: 1.0961010456085205\n",
      "Epoch: 19, Loss: 1.0389952957630157\n",
      "           Loss.item: 1.1051210165023804\n",
      "Epoch: 20, Loss: 1.0363028161227703\n",
      "           Loss.item: 1.1368379592895508\n",
      "Epoch: 21, Loss: 1.0417999140918255\n",
      "           Loss.item: 1.3640046119689941\n",
      "Epoch: 22, Loss: 1.0578102134168148\n",
      "           Loss.item: 1.2626793384552002\n",
      "Epoch: 23, Loss: 1.0550976544618607\n",
      "           Loss.item: 1.029950499534607\n",
      "Epoch: 24, Loss: 1.0664663724601269\n",
      "           Loss.item: 0.9627199769020081\n",
      "Epoch: 25, Loss: 1.0610504522919655\n",
      "           Loss.item: 1.1884706020355225\n",
      "Epoch: 26, Loss: 1.0592405647039413\n",
      "           Loss.item: 1.1647511720657349\n",
      "Epoch: 27, Loss: 1.048243299126625\n",
      "           Loss.item: 1.1287121772766113\n",
      "Epoch: 28, Loss: 1.057954765856266\n",
      "           Loss.item: 1.0848276615142822\n",
      "Epoch: 29, Loss: 1.049967061728239\n",
      "           Loss.item: 1.2820440530776978\n",
      "Epoch: 30, Loss: 1.0372510440647602\n",
      "           Loss.item: 0.9704910516738892\n",
      "Epoch: 31, Loss: 1.0325709395110607\n",
      "           Loss.item: 1.0077580213546753\n",
      "Epoch: 32, Loss: 1.0287923105061054\n",
      "           Loss.item: 1.1531988382339478\n",
      "Epoch: 33, Loss: 1.0265089571475983\n",
      "           Loss.item: 1.1375818252563477\n",
      "Epoch: 34, Loss: 1.0225585214793682\n",
      "           Loss.item: 1.302762508392334\n",
      "Epoch: 35, Loss: 1.0363513827323914\n",
      "           Loss.item: 1.1070747375488281\n",
      "Epoch: 36, Loss: 1.0566767416894436\n",
      "           Loss.item: 0.9857804775238037\n",
      "Epoch: 37, Loss: 1.0980405807495117\n",
      "           Loss.item: 1.0738860368728638\n",
      "Epoch: 38, Loss: 1.0895429216325283\n",
      "           Loss.item: 1.1157019138336182\n",
      "Epoch: 39, Loss: 1.1479238085448742\n",
      "           Loss.item: 0.9941167235374451\n",
      "Epoch: 40, Loss: 1.1842665746808052\n",
      "           Loss.item: 0.9867641925811768\n",
      "Epoch: 41, Loss: 1.1573222316801548\n",
      "           Loss.item: 0.8903546333312988\n",
      "Epoch: 42, Loss: 1.0743254646658897\n",
      "           Loss.item: 1.021823763847351\n",
      "Epoch: 43, Loss: 1.0573531165719032\n",
      "           Loss.item: 1.1530152559280396\n",
      "Epoch: 44, Loss: 1.0501193441450596\n",
      "           Loss.item: 0.9392661452293396\n",
      "Epoch: 45, Loss: 1.0563072226941586\n",
      "           Loss.item: 1.26084303855896\n",
      "Epoch: 46, Loss: 1.0616489797830582\n",
      "           Loss.item: 1.1126567125320435\n",
      "Epoch: 47, Loss: 1.097357276827097\n",
      "           Loss.item: 0.9844733476638794\n",
      "Epoch: 48, Loss: 1.077582262456417\n",
      "           Loss.item: 1.2697186470031738\n",
      "Epoch: 49, Loss: 1.0604580231010914\n",
      "           Loss.item: 0.982615053653717\n",
      "Epoch: 50, Loss: 1.0513021722435951\n",
      "           Loss.item: 1.253225564956665\n",
      "Epoch: 51, Loss: 1.0375463105738163\n",
      "           Loss.item: 1.2587475776672363\n",
      "Epoch: 52, Loss: 1.031319748610258\n",
      "           Loss.item: 0.793746292591095\n",
      "Epoch: 53, Loss: 1.0493962690234184\n",
      "           Loss.item: 1.0012112855911255\n",
      "Epoch: 54, Loss: 1.036032546311617\n",
      "           Loss.item: 0.6033576130867004\n",
      "Epoch: 55, Loss: 1.0275771282613277\n",
      "           Loss.item: 1.098799228668213\n",
      "Epoch: 56, Loss: 1.032669048756361\n",
      "           Loss.item: 1.0529497861862183\n",
      "Epoch: 57, Loss: 1.029368158429861\n",
      "           Loss.item: 1.0606106519699097\n",
      "Epoch: 58, Loss: 1.028975062072277\n",
      "           Loss.item: 0.9984307289123535\n",
      "Epoch: 59, Loss: 1.0393882989883423\n",
      "           Loss.item: 1.1837427616119385\n",
      "Epoch: 60, Loss: 1.0258002057671547\n",
      "           Loss.item: 1.190650224685669\n",
      "Epoch: 61, Loss: 1.0236839912831783\n",
      "           Loss.item: 0.9839427471160889\n",
      "Epoch: 62, Loss: 1.0509137362241745\n",
      "           Loss.item: 1.282866358757019\n",
      "Epoch: 63, Loss: 1.085763793438673\n",
      "           Loss.item: 1.0296906232833862\n",
      "Epoch: 64, Loss: 1.126925002783537\n",
      "           Loss.item: 1.310918927192688\n",
      "Epoch: 65, Loss: 1.1417576968669891\n",
      "           Loss.item: 0.7511917948722839\n",
      "Epoch: 66, Loss: 1.1014165580272675\n",
      "           Loss.item: 1.082553505897522\n",
      "Epoch: 67, Loss: 1.0555853135883808\n",
      "           Loss.item: 1.0409902334213257\n",
      "Epoch: 68, Loss: 1.0566424652934074\n",
      "           Loss.item: 1.0839636325836182\n",
      "Epoch: 69, Loss: 1.035233948379755\n",
      "           Loss.item: 0.939556896686554\n",
      "Epoch: 70, Loss: 1.0231596007943153\n",
      "           Loss.item: 0.6840012073516846\n",
      "Epoch: 71, Loss: 1.0435570627450943\n",
      "           Loss.item: 1.009502649307251\n",
      "Epoch: 72, Loss: 1.0458241701126099\n",
      "           Loss.item: 1.2073050737380981\n",
      "Epoch: 73, Loss: 1.081613164395094\n",
      "           Loss.item: 1.3564718961715698\n",
      "Epoch: 74, Loss: 1.0512792579829693\n",
      "           Loss.item: 1.2051059007644653\n",
      "Epoch: 75, Loss: 1.0252003110945225\n",
      "           Loss.item: 1.1113351583480835\n",
      "Epoch: 76, Loss: 1.0286346524953842\n",
      "           Loss.item: 0.9173478484153748\n",
      "Epoch: 77, Loss: 1.0352487713098526\n",
      "           Loss.item: 1.2343790531158447\n",
      "Epoch: 78, Loss: 1.0257882326841354\n",
      "           Loss.item: 1.0101103782653809\n",
      "Epoch: 79, Loss: 1.0467401258647442\n",
      "           Loss.item: 0.7464591860771179\n",
      "Epoch: 80, Loss: 1.073630876839161\n",
      "           Loss.item: 0.8273550868034363\n",
      "Epoch: 81, Loss: 1.096155546605587\n",
      "           Loss.item: 0.8398966789245605\n",
      "Epoch: 82, Loss: 1.0704758167266846\n",
      "           Loss.item: 0.9116308689117432\n",
      "Epoch: 83, Loss: 1.0645152926445007\n",
      "           Loss.item: 0.9260708689689636\n",
      "Epoch: 84, Loss: 1.0499293580651283\n",
      "           Loss.item: 1.173627495765686\n",
      "Epoch: 85, Loss: 1.0571907721459866\n",
      "           Loss.item: 1.1877286434173584\n",
      "Epoch: 86, Loss: 1.0521412305533886\n",
      "           Loss.item: 1.2185152769088745\n",
      "Epoch: 87, Loss: 1.0416326187551022\n",
      "           Loss.item: 0.7257826924324036\n",
      "Epoch: 88, Loss: 1.0298819281160831\n",
      "           Loss.item: 0.8485149145126343\n",
      "Epoch: 89, Loss: 1.041336253285408\n",
      "           Loss.item: 0.9975781440734863\n",
      "Epoch: 90, Loss: 1.0298775546252728\n",
      "           Loss.item: 0.776104211807251\n",
      "Epoch: 91, Loss: 1.0385399125516415\n",
      "           Loss.item: 1.3210455179214478\n",
      "Epoch: 92, Loss: 1.0399388298392296\n",
      "           Loss.item: 0.9853983521461487\n",
      "Epoch: 93, Loss: 1.0374814458191395\n",
      "           Loss.item: 1.0531173944473267\n",
      "Epoch: 94, Loss: 1.04055854678154\n",
      "           Loss.item: 1.1103209257125854\n",
      "Epoch: 95, Loss: 1.0363882519304752\n",
      "           Loss.item: 1.068872094154358\n",
      "Epoch: 96, Loss: 1.028582639992237\n",
      "           Loss.item: 0.9842924475669861\n",
      "Epoch: 97, Loss: 1.0214046835899353\n",
      "           Loss.item: 0.9041253328323364\n",
      "Epoch: 98, Loss: 1.0362812764942646\n",
      "           Loss.item: 0.8957298994064331\n",
      "Epoch: 99, Loss: 1.0255248360335827\n",
      "           Loss.item: 1.0959115028381348\n",
      "Epoch: 100, Loss: 1.024388324469328\n",
      "           Loss.item: 1.145258903503418\n",
      "Epoch: 101, Loss: 1.015384841710329\n",
      "           Loss.item: 0.8426939845085144\n",
      "Epoch: 102, Loss: 1.0381194092333317\n",
      "           Loss.item: 0.8020358085632324\n",
      "Epoch: 103, Loss: 1.0377642773091793\n",
      "           Loss.item: 1.0998979806900024\n",
      "Epoch: 104, Loss: 1.0270190127193928\n",
      "           Loss.item: 1.1161484718322754\n",
      "Epoch: 105, Loss: 1.0383960045874119\n",
      "           Loss.item: 1.028045415878296\n",
      "Epoch: 106, Loss: 1.0313507243990898\n",
      "           Loss.item: 0.976348876953125\n",
      "Epoch: 107, Loss: 1.0704610049724579\n",
      "           Loss.item: 0.9278178215026855\n",
      "Epoch: 108, Loss: 1.0939825996756554\n",
      "           Loss.item: 1.0137970447540283\n",
      "Epoch: 109, Loss: 1.0479269549250603\n",
      "           Loss.item: 0.7778990268707275\n",
      "Epoch: 110, Loss: 1.0402080193161964\n",
      "           Loss.item: 0.9873312711715698\n",
      "Epoch: 111, Loss: 1.0327975451946259\n",
      "           Loss.item: 0.8353959321975708\n",
      "Epoch: 112, Loss: 1.0498311892151833\n",
      "           Loss.item: 1.0502170324325562\n",
      "Epoch: 113, Loss: 1.0698467120528221\n",
      "           Loss.item: 1.0090914964675903\n",
      "Epoch: 114, Loss: 1.0642726235091686\n",
      "           Loss.item: 1.021988034248352\n",
      "Epoch: 115, Loss: 1.0261698700487614\n",
      "           Loss.item: 1.0348230600357056\n",
      "Epoch: 116, Loss: 1.0147897452116013\n",
      "           Loss.item: 0.9413231611251831\n",
      "Epoch: 117, Loss: 1.0139307081699371\n",
      "           Loss.item: 0.9057963490486145\n",
      "Epoch: 118, Loss: 1.018415529280901\n",
      "           Loss.item: 0.7615081667900085\n",
      "Epoch: 119, Loss: 1.0148726217448711\n",
      "           Loss.item: 0.9957921504974365\n",
      "Epoch: 120, Loss: 1.0417473837733269\n",
      "           Loss.item: 1.1770306825637817\n",
      "Epoch: 121, Loss: 1.0293571576476097\n",
      "           Loss.item: 0.9425175786018372\n",
      "Epoch: 122, Loss: 1.0178682953119278\n",
      "           Loss.item: 0.9819290637969971\n",
      "Epoch: 123, Loss: 1.022982157766819\n",
      "           Loss.item: 1.046703815460205\n",
      "Epoch: 124, Loss: 1.0154258497059345\n",
      "           Loss.item: 0.8533948063850403\n",
      "Epoch: 125, Loss: 1.012107115238905\n",
      "           Loss.item: 0.9210178852081299\n",
      "Epoch: 126, Loss: 1.0140885785222054\n",
      "           Loss.item: 1.1250603199005127\n",
      "Epoch: 127, Loss: 1.0167262703180313\n",
      "           Loss.item: 1.0349258184432983\n",
      "Epoch: 128, Loss: 1.008833408355713\n",
      "           Loss.item: 1.3674254417419434\n",
      "Epoch: 129, Loss: 1.0226391702890396\n",
      "           Loss.item: 1.2108441591262817\n",
      "Epoch: 130, Loss: 1.0220499336719513\n",
      "           Loss.item: 1.164876103401184\n",
      "Epoch: 131, Loss: 1.0304935835301876\n",
      "           Loss.item: 1.0265120267868042\n",
      "Epoch: 132, Loss: 1.0269629955291748\n",
      "           Loss.item: 1.0042810440063477\n",
      "Epoch: 133, Loss: 1.0251267701387405\n",
      "           Loss.item: 0.9722972512245178\n",
      "Epoch: 134, Loss: 1.0243394374847412\n",
      "           Loss.item: 1.3285759687423706\n",
      "Epoch: 135, Loss: 1.017311006784439\n",
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      "           Loss.item: 0.9703627228736877\n",
      "Epoch: 694, Loss: 1.0508269295096397\n",
      "           Loss.item: 1.1480787992477417\n",
      "Epoch: 695, Loss: 0.965581338852644\n",
      "           Loss.item: 0.938060462474823\n",
      "Epoch: 696, Loss: 0.9537656083703041\n",
      "           Loss.item: 1.0312449932098389\n",
      "Epoch: 697, Loss: 0.9579379931092262\n",
      "           Loss.item: 1.187996745109558\n",
      "Epoch: 698, Loss: 0.9640270248055458\n",
      "           Loss.item: 1.0941942930221558\n",
      "Epoch: 699, Loss: 0.9503704011440277\n",
      "           Loss.item: 0.8078204393386841\n"
     ]
    }
   ],
   "source": [
    "model.train_model(range(700))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_max_tensor(output):\n",
    "    max_tensor = torch.zeros_like(output)\n",
    "    max_tensor[0, output.argmax(dim=1)] = 2\n",
    "    return max_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_losses() # for 9->9->9->9->9->9->9->9->9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_losses() # for 9->728->9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 9])\n",
      "tensor([[9.9948e-01, 1.6606e-11, 1.0884e-16, 5.1708e-04, 5.7178e-17, 2.4587e-08,\n",
      "         1.8731e-07, 5.9218e-07, 9.1240e-08]], grad_fn=<SoftmaxBackward0>)\n",
      "0\n",
      "tensor([[2., 0., 0., 0., 0., 0., 0., 0., 0.]])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_424322/3980566399.py:34: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  self.x2 = F.softmax(self.fc2(self.x1))\n"
     ]
    }
   ],
   "source": [
    "input = torch.tensor([[0,1,1,0,2,0,0,0,0]], dtype=torch.float32)\n",
    "\n",
    "output = model.inference(input)\n",
    "\n",
    "print(output.shape)\n",
    "print(output)\n",
    "print(output.argmax(dim=1).item())\n",
    "\n",
    "max_output = get_max_tensor(output)\n",
    "print(max_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([27, 9])\n",
      "torch.Size([27, 729])\n",
      "torch.Size([27, 9])\n",
      "torch.Size([27, 9])\n",
      "tensor([[9.9937e-01, 6.3111e-04, 1.9531e-08, 2.7194e-14, 6.9854e-22, 1.5216e-10,\n",
      "         2.8058e-13, 2.1871e-10, 9.4791e-09],\n",
      "        [1.0002e-04, 2.7582e-28, 1.4718e-13, 1.7997e-09, 7.6573e-10, 1.8049e-15,\n",
      "         9.9990e-01, 1.5432e-15, 2.9874e-12],\n",
      "        [1.7479e-01, 6.2029e-26, 5.8647e-13, 3.4043e-01, 4.8478e-01, 3.4507e-19,\n",
      "         1.1280e-18, 4.9722e-14, 5.8799e-12],\n",
      "        [2.5361e-04, 7.5946e-23, 3.6425e-13, 2.6651e-04, 3.1559e-19, 1.3607e-05,\n",
      "         3.7033e-11, 2.0794e-08, 9.9947e-01],\n",
      "        [3.0786e-20, 8.9839e-05, 9.9991e-01, 7.2398e-08, 3.2949e-14, 9.7942e-25,\n",
      "         2.5318e-09, 7.1753e-21, 1.3631e-16],\n",
      "        [9.9906e-01, 2.0813e-25, 2.6293e-17, 8.2989e-05, 9.1269e-06, 2.0505e-04,\n",
      "         8.0913e-18, 1.1268e-09, 6.4638e-04],\n",
      "        [1.1255e-23, 9.9929e-01, 3.3739e-19, 1.9856e-16, 1.1636e-26, 8.7244e-15,\n",
      "         7.1195e-04, 3.3724e-20, 4.1140e-10],\n",
      "        [2.8911e-05, 1.3650e-06, 2.5163e-03, 6.5631e-18, 5.9959e-16, 9.9704e-01,\n",
      "         5.4976e-07, 4.5021e-06, 4.0412e-04],\n",
      "        [4.9475e-10, 4.2534e-14, 1.9212e-04, 2.3187e-19, 2.3389e-24, 9.9981e-01,\n",
      "         1.2381e-10, 2.4613e-11, 6.9494e-15],\n",
      "        [2.2989e-22, 9.9841e-01, 5.7903e-13, 2.8730e-12, 5.0020e-20, 6.5759e-08,\n",
      "         5.1297e-11, 3.3419e-11, 1.5945e-03],\n",
      "        [9.9999e-01, 4.7761e-20, 5.8537e-06, 2.5902e-16, 2.7415e-09, 1.3995e-07,\n",
      "         7.1686e-20, 6.1387e-08, 3.3184e-07],\n",
      "        [2.4649e-15, 2.8321e-22, 9.9993e-01, 6.8171e-05, 2.5976e-20, 5.5131e-15,\n",
      "         1.6951e-17, 4.0039e-09, 5.7697e-09],\n",
      "        [2.1240e-04, 1.1985e-28, 2.8901e-23, 1.4249e-15, 9.9979e-01, 1.0684e-08,\n",
      "         1.1610e-23, 2.2217e-17, 3.4762e-09],\n",
      "        [1.0000e+00, 4.4238e-24, 1.0068e-07, 2.0783e-12, 3.0429e-23, 8.5294e-15,\n",
      "         5.2237e-11, 2.4209e-09, 8.0898e-16],\n",
      "        [5.2454e-23, 1.4917e-13, 1.0182e-17, 6.7274e-17, 3.3791e-21, 4.6535e-13,\n",
      "         1.0000e+00, 8.4337e-08, 3.8244e-08],\n",
      "        [1.4076e-23, 9.9833e-01, 2.3086e-05, 1.6892e-12, 8.8285e-23, 1.4811e-14,\n",
      "         4.4916e-12, 1.4507e-10, 1.6459e-03],\n",
      "        [3.8436e-16, 2.5303e-05, 2.1670e-09, 1.8117e-09, 2.3307e-09, 9.9996e-01,\n",
      "         1.1962e-05, 1.1197e-19, 6.7530e-10],\n",
      "        [3.4956e-09, 3.4041e-04, 1.9794e-07, 9.9966e-01, 3.3471e-23, 6.1475e-10,\n",
      "         2.7900e-07, 3.8294e-09, 1.4489e-16],\n",
      "        [9.9985e-01, 4.7857e-17, 2.2025e-09, 4.0603e-13, 6.5598e-22, 2.6748e-08,\n",
      "         1.4119e-04, 5.3719e-06, 4.6289e-15],\n",
      "        [4.2920e-22, 2.1428e-19, 1.0863e-09, 1.6659e-11, 1.3148e-16, 3.3962e-13,\n",
      "         7.4767e-10, 9.9390e-01, 6.1039e-03],\n",
      "        [1.3828e-07, 6.7368e-15, 1.1628e-15, 1.2645e-18, 8.3825e-06, 9.1330e-10,\n",
      "         1.0745e-04, 9.9988e-01, 1.5372e-11],\n",
      "        [7.2831e-05, 9.8738e-17, 9.2304e-10, 9.9993e-01, 3.7969e-18, 3.2297e-23,\n",
      "         2.3196e-23, 3.4696e-11, 2.1680e-11],\n",
      "        [6.0659e-21, 3.7115e-27, 2.2259e-08, 3.1784e-23, 1.1522e-10, 3.0536e-13,\n",
      "         9.9884e-01, 4.5485e-07, 1.1601e-03],\n",
      "        [6.7364e-10, 1.2138e-10, 1.1532e-23, 1.0901e-20, 5.3245e-05, 7.3751e-14,\n",
      "         5.7173e-04, 4.7552e-04, 9.9890e-01],\n",
      "        [2.5760e-01, 2.9696e-23, 2.8728e-08, 1.5809e-09, 7.4127e-01, 1.1314e-03,\n",
      "         2.0229e-18, 7.2747e-12, 3.8861e-08],\n",
      "        [6.3310e-21, 2.0155e-24, 1.0000e+00, 1.0385e-15, 6.2695e-15, 2.0401e-26,\n",
      "         6.3182e-15, 2.8841e-06, 1.1408e-13],\n",
      "        [6.4278e-18, 1.0000e+00, 1.0776e-14, 1.9244e-18, 1.2993e-07, 5.7629e-10,\n",
      "         2.2357e-20, 2.6028e-12, 2.9579e-19]], grad_fn=<SoftmaxBackward0>)\n",
      "tensor([[2., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 2., 0., 0.],\n",
      "        [0., 0., 2., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 2.],\n",
      "        [0., 0., 2., 0., 0., 0., 0., 0., 0.],\n",
      "        [2., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 2., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 2., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 2., 0., 0., 0.],\n",
      "        [0., 2., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [2., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 2., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 2., 0., 0., 0., 0.],\n",
      "        [2., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 2., 0., 0.],\n",
      "        [0., 2., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 2., 0., 0., 0.],\n",
      "        [0., 0., 0., 2., 0., 0., 0., 0., 0.],\n",
      "        [2., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 2., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 2., 0.],\n",
      "        [0., 0., 0., 2., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 2., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 2.],\n",
      "        [0., 0., 2., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 2., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 2., 0., 0., 0., 0., 0., 0., 0.]])\n"
     ]
    }
   ],
   "source": [
    "print(model.x0.shape)\n",
    "print(model.x1.shape)\n",
    "print(model.x2.shape)\n",
    "print(model.target.shape)\n",
    "print(model.x2)\n",
    "print(model.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_model('model_weights.pth')"
   ]
  }
 ],
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