finalAIPhase1.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-1lJK_TPi8F2"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import random, os\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import glob\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k13gSh-pkCp8"
      },
      "source": [
        "فاز یک:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": "OK"
            }
          }
        },
        "id": "rmo62H9mkHZi",
        "outputId": "d762933b-5682-449e-8490-aaf5ab2e340e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7c7bbbd0-9741-4d51-ad6e-19d99337b87e\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7c7bbbd0-9741-4d51-ad6e-19d99337b87e\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving cifar_dataset.zip to cifar_dataset.zip\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "  \n",
        "  \n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nOzVTcWYpPtZ",
        "outputId": "f9c4fa26-bd49-47c8-aad4-526c0e17d509"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "  inflating: /content/des/labels.csv  \n"
          ]
        }
      ],
      "source": [
        "!unzip cifar_dataset.zip -d /content/des"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "9oNFs9q2rI5h",
        "outputId": "e3036aab-63d2-44b7-ad7f-71192bce5000"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "min value of random img 0.0\n",
            "max value of random img 0.9098039\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "random_filename = random.choice([\n",
        "    x for x in os.listdir(\"des/data/\")\n",
        "    if os.path.isfile(os.path.join(\"des/data/\", x))\n",
        "])\n",
        "img = mpimg.imread(\"des/data/\" + random_filename)\n",
        "print(\"min value of random img\", img.min())\n",
        "print(\"max value of random img\", img.max())\n",
        "imgplot = plt.imshow(img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "XuVn2aGJC2oy",
        "outputId": "2233281b-a650-4139-b99d-65dc97311952"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "min value of random img 10\n",
            "max value of random img 221\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAfH0lEQVR4nO2deYxc15Xev1NL791sNptLs7mJiyRSlExJbUGyZEeyI4+8JLKBgWJPYAiBMDQmoyBGZmIIThB7gPzhCWIbRhB4TMfCyBOPbI8twZpAsSwJnlC2PBIpmZtISRQ3cd96Z3dX13LyRxURSrnf7SabXc2Z+/0Agt339H3v1H116lXdr8455u4QQvzjJzPXDggh6oOCXYhEULALkQgKdiESQcEuRCIo2IVIhNxMJpvZAwC+DSAL4H+4+9djf98+P+/dvY1BW0OmxM+DsDxYqOTpnNFK+DwA4G7cRi0Am9UY8T1vZWqbiPg/cKGFOxLxnz6AfIVOacxz/xsy3H8zvloZ4kiFriKQM+7j0GQTtZUnI09jdsg89z2b5Y+5XODnyhS5Gx65rXqWHTCyvrnwAyueGURpeCy4yFcc7GaWBfDfAdwP4BiAbWb2jLvvZXO6exvx1aduDtpWNZyj58qSK3ZgchGd8/cja6mtWGGrCxQitix5cq9rOUPnLM4PUdub4z3U9tNXP0htmYnIM4c8uW1xgU5Zs+Qsta1oG6C2fOSFoDkzGRwfrzTQOZ25MWp77tiN1DZwZD61ZQrhF5fKwrB/ANDZeYHaBg93UlvzSf7cidx7UOgKr6M38he/1oXhtTr0p1vonJm8jb8DwDvuftDdJwH8CMCDMzieEGIWmUmw9wI4esnvx2pjQohrkFnfoDOzzWa23cy2jwxEPtQIIWaVmQT7cQDLL/l9WW3sPbj7Fnfvc/e+9vl8Q0oIMbvMJNi3AVhnZteZWQOAzwF45uq4JYS42lzxbry7l8zsUQDPoSq9Pe7ub8TmNFgJK/L9QdvCDN+JXd8QlqEmnO8UDxe5VBPbRWY77gBwc9v/98YFADAW2WE+WeS7t935UWq76+b91NZf4LJcd1N4J/m2jnfpnJhi0GT8o9eFyOP+xfmw6tKa47vgN7WE1xcA2lZyNeHEEr7GB0cXBMe7Gvnz7cBQeA4ArLuJ+1i4kYdTocx36huJ1NeS52u1vDX83D/bwNdpRjq7uz8L4NmZHEMIUR/0DTohEkHBLkQiKNiFSAQFuxCJoGAXIhFmtBt/uTRaGavzw0HbYIW7MlYJSxDnywvpnK4Gnsywquk8tZ0rtlHbSpKsc7TYRee8PbaE2mKJHw8t2kZtBws8AaglE5ZelkTktRjtmXFqayLJLgAwVgrLcm/182uWiWS9Lcjz68nkNQBY3Ra+1ksa+XrEpNlY0tPeUZ7YVIqkvQ0WmoPjhTKPiaMXwsk/k5E5urMLkQgKdiESQcEuRCIo2IVIBAW7EIlQ1934oUoTnh29IWh79mw4cQIA2nLhHebuRp5I0ts4SG0vD6ymtjdeuJ7ansY9wfGWPl5S699d/yK1bR3i53prgu/s3tdGK39h/2R49/94kZdueurErdTWH6mFt6aLP+53h8LJKcOj4Z1nAHhhkJee8mGedJPv5/ess7eG1ZXbFh0NjgPASJHXkDpR4Ek3n1iwm9rWNZyitv95/kPB8XMFrgzd1XkwOL43z9UT3dmFSAQFuxCJoGAXIhEU7EIkgoJdiERQsAuRCOYea3h0dWlZ1+PXf+uRoG38NZ7M0EByFjL3hevZAUBPRzjhBgBO/XgltS35Fe+Ogly4jthEbzudcvp2LhllebkwXFjBk0KWb+AyzpGj3cHxlk4uyfjr86it8wD3I1Pizx3W9Wqik99f8uP8eE3neC28SgM/ZmFe+JoVW3kbqlIzt0U6dmF8SaSl1CouE5cPhSU2z/Hj5VeEE4OOfPm7mDhwPPgAdGcXIhEU7EIkgoJdiERQsAuRCAp2IRJBwS5EIswo683MDgMYAVAGUHL3vtjflydyGH4rXK+t9/USnZcfDdsOL+cZSKVTvC7c8q08W4vJawBQaQnLaA0DXENb+hJ/XOUmfq5Fr0ck0Z/zx7a2GD7f8OoOOmdkOTVh4MbI/SDiomfDxlIrn1Tu4LXfWromqK2pgctyozvDku7CHRFJ8Rz3sdDBr1mktCEqb/MMNibnuUXW/mBY7s2M8DlXI8X1PnePRI8Q4lpAb+OFSISZBrsD+KWZvWZmm6+GQ0KI2WGmb+PvcffjZrYIwPNm9qa7b730D2ovApsBIDufV0sRQswuM7qzu/vx2v9nADwN4I7A32xx9z5378u2ts7kdEKIGXDFwW5mrWbWfvFnAB8HsOdqOSaEuLrM5G38YgBPm9nF4/y1u/8iNiFbADoOhG0WyaAqk6ymBTt5dtKCl09yRxp46pLnubTi2fD5Ko18Tikir3mG+58lEhoA5Aa51FduDj+25vP8eJUcfxr0b4xkXi3jLZmWLwgX/Pxg1xE650vdv6W2/UVeqPK7p++lth0bw+t/Zi1/DrS18PVd2Mof84Uiz3CM5ZYubw+vVanC78XnxsNSXvY5LkNecbC7+0EAH7jS+UKI+iLpTYhEULALkQgKdiESQcEuRCIo2IVIhLr2eiu3VTD84XD20vA63l+r0hAWLtoPcOkKQ7zAny8NF2WsniuS9UZkOSvyDKqsRYoX5rgtJstNLuAyVLkp/PpdIuMAkC1wYWj+Xu5H+R1eaPNUS9j25PrFdM4rK1dR25GdS6mt4x3uYyNR0Qpr6BRs/Ohhavvi4r+jtrNlnlkY67WXRfj505Xjz+EGUtHzy4280Kru7EIkgoJdiERQsAuRCAp2IRJBwS5EItR1N35p6xD+rO+ZoO3tiSV03qJ8eIfxu4f+GZ1jjTwpoZLjr3Ge5TYrh3dNYzvnmQKvqwZEEmhauK3Yxm1lkt+R4XkwKDdcWbujbDHS/olsJLft49fl7N5l1DZvlJ8rxztbIT9Gdrr38Me8rbiR2rIPcD/u69zHHYlwqLAwOD5UbrnsY01UDlKb7uxCJIKCXYhEULALkQgKdiESQcEuRCIo2IVIhLpKb+5A0cOy0Ufa3qTzvn3s/uB4z8uRZJd5vN0OMlf2Gje+uCk43jAcaV01PEltsdp1LKEFAMpcvaLFzjz2kCP5RDFikh07X5aXd0N2kstaUf9jbaiILFps5b7P288Tmw69eiO1vfSxm6jtX//eL6mtPRtODsuTZJcYkbwr3dmFSAUFuxCJoGAXIhEU7EIkgoJdiERQsAuRCFNKb2b2OIBPAzjj7htrY10AfgxgFYDDAB5y94GpjpU1R3smLDOMVHhdtb3bVwXHrz9/hs6Z7O2kNitxaeVCb1heA4CB3w+3/rFdvBbbshcibZdIWyvgyuvTWSWsQ1XykeNxBTDqRyUiAWZIF6JK5Bln5Vg7LK6vxTLzSkRXjPlRauJ+NJ0eo7a1f82P+f1Vd1HbPcvDmWqTESd7GoeC46WIRjmdO/tfAnjgfWOPAXjR3dcBeLH2uxDiGmbKYK/1W+9/3/CDAJ6o/fwEgM9cZb+EEFeZK/3MvtjdL7ZJPYVqR1chxDXMjDfo3N0R+cKimW02s+1mtn24P1IuRQgxq1xpsJ82sx4AqP1Pd8rcfYu797l7X0dXXb+KL4S4hCsN9mcAPFz7+WEAP7867gghZovpSG9PArgXQLeZHQPwVQBfB/ATM3sEwBEAD03nZBUYJjyskwyWeHG97FhYCvFWLpMVurgek49kqU10cdnls2t3Bsd3dPNCif2HVlBb0wDPavJI9hKT16oTuYlOucKMuJiPTA6LyXwek+XCim3VFkkOK7aGx0uRrDcmGwLA8Fous87/zVFqs20rqa1zdbhi5rwsr6Q5QOLFIxdlymB3988T08emmiuEuHbQN+iESAQFuxCJoGAXIhEU7EIkgoJdiESo67dcsqigNROuOMgKUQLAig8dC44PvNlL5zQO8cy2UqRXWkx2mUeaij3S+2s658t9f0Bty5/j57KIhBbzMVMKTyzxpEJYKaKhRTLbsryWJs0qs9iXKGOKYkyKjByyEimKSY8XkfIGr+f3x3lvdlBbz2+4jLbvE+E+h/9iyTY6p+DhORnjz3vd2YVIBAW7EImgYBciERTsQiSCgl2IRFCwC5EIdZXeSp7F+VK4B9uxyS4671NLdgfHn3iIpDQByP1gHrXlR7m2kilyWe7geHdwfFnD+6t2/T9uuvUwtQ1s5ZlQDUNXVqjSyqzgJH9c2dhL/nik/1okg43Jg6XmK2wsF8uwi8iDubGw/zF5LZZ9N7meS2jj23l/weate6lt39/fEhx//Z/ygqrDREuNFanUnV2IRFCwC5EICnYhEkHBLkQiKNiFSIS67sYXPYuTxXBbpnxke/R4YX5w/J+vCu/SA8AP7r+b2lY9TU00kQQAXj5+XXD8fIGrAoUSX+LRJXw7u3s3zzLxAk92YK2tYo+r0Ml9jORVwO3y2zVdiCSmxHzMTURUgUg7rGwhPC/WTurCIn4P/PDqA9T22vqbqa3nf4dbhwHA8ufD13pP31I659NEoXo+kp2kO7sQiaBgFyIRFOxCJIKCXYhEULALkQgKdiESYTrtnx4H8GkAZ9x9Y23sawD+EMDZ2p99xd2fnepYeStFk0YYWaL/tGS4zPCp28OtmgDgpTdvp7b2o1wCHB9rDI6vbOGP6W+PcTmm8wKXfybnRdpXjfIkmUJ3OCuk1Mhf17OTEVkrIq+Zx+QwaqI0jPDjxWoKRmvXkWd4JvKYY/fA107xVl9F3hkK2Q5eny57ZCA4/uZR3gn9WFe4LuNkJItnOpfkLwE8EBj/lrtvqv2bMtCFEHPLlMHu7lsBXP7tWAhxTTGTz+yPmtkuM3vczMJfcRNCXDNcabB/B8AaAJsAnATwDfaHZrbZzLab2fbRgUjBcyHErHJFwe7up9297O4VAN8DcEfkb7e4e5+797XN55tOQojZ5YqC3cx6Lvn1swD2XB13hBCzxXSktycB3Aug28yOAfgqgHvNbBOqosdhAF+czsnyVsbC3HDQVoxIBp3Zsekc/j20Z3mtsBfvvp7a/G94BlvlfFjWWtQwQueUT/G+S4X5XNbq38gz4nr/DzXh7C3hdWz54Dk6Z3A/r/+XG+c+ViI16MpLw22+MMLf3XW8wI+XG+OSaDlSk+/8xvD52o5yKS/PE9RQ2sq3p1qHI3Le4nD9QgCwUvix2QBfq5MTYSmvWOFrMWWwu/vnA8Pfn2qeEOLaQt+gEyIRFOxCJIKCXYhEULALkQgKdiESoc4FJ3M4RQtO8kyuc6WwzHC8ED4WACyI6Cf/8oZt1Pb4h/8JteUWhuW8IxML6JzON7l0NXgPlwc/tZ5/deHlt/uozYj682/W/R2dc3oVb5V1ZpKnchUqXBo6Nha+Nrt2rqJzYmQKXHobW8T96Lz3VHD8zA6eUbZoG5flimP8/jjWw6/16E1cemvfFW7zFCv2WSK6p0f6ZOnOLkQiKNiFSAQFuxCJoGAXIhEU7EIkgoJdiES4Znq9zYtkqf3VgXC6/PAIzyhbtzQsZwDApxZzWevR+56ntsX5oeD4f3z5M3ROWweXQv7VB35Lbd05nkn33CpqQseBsPZ2qLCQzrm79W1q+4uBe6ltz4keaiufbAmOd73B16PcyLPGrMJtlRw/5h8sfy04vm3eSjpn7/6bqK3Uys+1+XO8FOO3ez9ObevfCkuHsaKduQzJlItU39SdXYhEULALkQgKdiESQcEuRCIo2IVIhLruxpc8g4FieJd2pNxE532490Bw/EyBJ2nkIlkEsXM1ZXi5698Orw2Ot+wPt4UCAL8rvIMPACsaeF243wyvozbjOSFoGA3vxr41yhM/unOj1HZ0mNdcKw7xx93cH76PtJ7mCU8jy3hRu+wkv2atJ3kbsGdO3RIc//crf0Hn/NHGDdS2dCt/Xv3tyfC5AKCzJ1x7EQAml4Sfx56LtMOiu/Ec3dmFSAQFuxCJoGAXIhEU7EIkgoJdiERQsAuRCNNp/7QcwA8ALEa13dMWd/+2mXUB+DGAVai2gHrI3Qdix6rAMF4Jt1BCpN7W4oawbNGWJS2GAAyWwhIfUE3IYWSdO7Krf2lwfKKbz/nMyn3U1l9uo7YLJS5rFZZzqSm3LfzY9vfzGmgTJV7DLZflOl/XUi4rDpTCkt3wCv6UG13BpSY3fs2WHORJQ4dfWh4cP9XL6+7dsOldahv+Tfh4AHDipWXU1nz7eWobXBO+1t7EZcq1LeFEr5h0PJ07ewnAn7j7BgB3AvhjM9sA4DEAL7r7OgAv1n4XQlyjTBns7n7S3V+v/TwCYB+AXgAPAnii9mdPAOB5nkKIOeeyPrOb2SoAtwJ4BcBidz9ZM51C9W2+EOIaZdrBbmZtAH4G4Evu/p4P0e7uQDhr3sw2m9l2M9s+McA/YwshZpdpBbuZ5VEN9B+6+1O14dNm1lOz9wAI7hi4+xZ373P3vqb5fNNJCDG7TBnsZmao9mPf5+7fvMT0DICHaz8/DODnV989IcTVYjpZb3cD+AKA3Wa2ozb2FQBfB/ATM3sEwBEAD011IHfDeDks8zRnuWTAaMxwaWJp4yC1tWS4dDURa2l0Oiwn5XvH6Jz5eW47XuAZZTe2hdsWAUB+PZfDdi/eGBwfm+DvqjYsO0ltC5t4ZmFzlq/jYHfY/x29vXTO/Dy/nkMlLh16lud6de0Ny6JPnb6dzlnbfpbati5dRW15rgAim+Gy4iBJcFyyrJ/OuaEpfM1i0tuUwe7uvwbPnPvYVPOFENcG+gadEImgYBciERTsQiSCgl2IRFCwC5EIdS046TAUK+HsJSbJxWDHAoCuhgvU1pPnstzLA2HpCgBa9oaLHvbcf5TOiRW3HC5xW3eeF4Fc0cwlmZdXhccnz/NWWb038WTFg2Nc8orxe11vBMdvbD1N57REshj/28B91FZq57Li2U1hIWle5LlzYpxnxBVbqQnNZ7i8NvBWF7WVF4UlzN42nlX4wmC4RdVwmWfs6c4uRCIo2IVIBAW7EImgYBciERTsQiSCgl2IRKir9FZ2w2gxLJOMkHEAOD8R1jsy4XoZAIALTaSw5RS8/rs11NZK6kre0BEu/gcApQp/Pc0a97+/xDWe9uwEtbXeQmS53Vz62TXKCyXGilGeGOUSFctIvFDm12XnmXBBTwBoeJtLh6fvoCYsuT2cHXbTPJ7pN0j6EQJAYeM4tTW8zH2MFVRtnRe+ngcHFvDDdYYlxcmIpKg7uxCJoGAXIhEU7EIkgoJdiERQsAuRCHXdjQeAjIW3JVvzvJ4Z29FuyfE57Xm+Yx3DSrye2fimcD251lykDVWR79COFCNJMhFbbzNP5Ll+Qbh+2huDfGd3T38PP14nr8d2ocR31n/x9obgeHsb380uvMoVg0ozVy4+8ABvsbXrTPix7RrgtfDu7D5EbX2rjlDb60dvoLYlN/MEIPbcf/sgvy4fXBJOeMlneH1C3dmFSAQFuxCJoGAXIhEU7EIkgoJdiERQsAuRCFNKb2a2HMAPUG3J7AC2uPu3zexrAP4QwEVt5ivu/mzsWBlzNGTD0sBYgcs4bfmwtNWU5e2CTkbqiA1F5LDW1bzu17J5YdtIpJbcZIUvcSxpIZfhmRNnCrwl0/Bk2Jf8CJeuTrzLZbk1885R28mhDmrLHgz7cSHL16qtn/voD/A6eTe3H6e2Q8NhOa9CmxzFWd7M/Xi1jV+zjkYuBbOknPEVPAmpIxc+XjaSHDYdnb0E4E/c/XUzawfwmpk9X7N9y93/6zSOIYSYY6bT6+0kgJO1n0fMbB8A/o0EIcQ1yWV9ZjezVQBuBfBKbehRM9tlZo+bGW9JKoSYc6Yd7GbWBuBnAL7k7sMAvgNgDYBNqN75v0HmbTaz7Wa2vTBwZV9hFULMnGkFu5nlUQ30H7r7UwDg7qfdvezuFQDfAxCsF+LuW9y9z937GufzzRkhxOwyZbCbmQH4PoB97v7NS8Yv/Zb+ZwHsufruCSGuFtPZjb8bwBcA7DazHbWxrwD4vJltQlWOOwzgi1MdqOKGyXJYbio7f91pIJk8zdkincNq3U3FyAkua701En5nsnY9zwwrlLnMF5PXYrXrcjk+r7clLA8e7eBSU6aFS5htkczCXiJFAsCxcNIbCge5XDe6kpqQneQy1AtnbqS2Dy0KZ7C1RVpNFSJyad4iWWUFvsbvnOZttNZ3nAqOb+zidfLOTbYFx0uROJrObvyvgaAoGdXUhRDXFvoGnRCJoGAXIhEU7EIkgoJdiERQsAuRCHUtOFlxo0UWCyXuykQ5bIu2uiGFLQFgLFIoMdvJpSZ7NyyjvdHDCwNumn+M2k4XuAw1VuE+jpS4rMhaYpW5coUF80eprTnD12NTF39srGjjk8Xb6Zwl80eoLRNplXVikK/jLaRNUizrbbTM13dB/gK1VZq4jyCSMwAUPWzrn+RtqFh2Y1Htn4QQCnYhEkHBLkQiKNiFSAQFuxCJoGAXIhHqKr1lzNFKsqhi0luFZvJEssaInDHVufpWhntoAcCrk9cFx4+9wqt0NX6IZ5R1NYZ7xwFAR6RXXWee90s7NRHO2ivO47LQRJGvR1ukj11c5gmfb2nXMJ2zbh7PHlzWxAs9HmjnGWVM3hyv8PUtO5flYllvd256m9pi0iHLwKs08Hsxy/hUrzchhIJdiFRQsAuRCAp2IRJBwS5EIijYhUiEukpv7kYz1WJyB8t6Y33jpqI5xwtVMskIAG65LpzltaeBZ73t27eM2npX8z5q6+efprZYxlY76Ys3/yZ+rnPneZHNd8fDvdIAoCuSAXa+2BocPzsSHgeAI8e4hLZ2JV+P9ohMOTQZzlQ8CZ4px9YQiMueJy7w/oLFSAHRmMzKaM2GJeyYxKc7uxCJoGAXIhEU7EIkgoJdiERQsAuRCFPuxptZE4CtABprf/9Td/+qmV0H4EcAFgB4DcAX3J0XLEN1x324cPnNHZ3s1Md2zlsibYtiO5axRALGht5w+x4A2HcsnDwDAGe3L6a28u18x/26jn5qY49tded57sexTmp7c2ARtbHWSgBwdiLcnmhhO9/BP3Ka79Qffo2rGr6M75C3tIR31htz/DqPFXnBvvESt/WP8VZfIwO8nty5ofBarVnEFZSOhvAOfqz903Tu7AUAH3X3D6DanvkBM7sTwJ8D+Ja7rwUwAOCRaRxLCDFHTBnsXuVi+dF87Z8D+CiAn9bGnwDwmVnxUAhxVZhuf/ZsrYPrGQDPAzgAYNDdLyZrHwPAk7qFEHPOtILd3cvuvgnAMgB3AOA9ct+HmW02s+1mtr04xD9bCSFml8vajXf3QQC/AnAXgE4zu7jBtwzAcTJni7v3uXtffh7fwBBCzC5TBruZLTSzztrPzQDuB7AP1aD//dqfPQzg57PlpBBi5kwnEaYHwBNmlkX1xeEn7v6/zGwvgB+Z2X8G8DsA35/OCZk01JLnUhmr1RaT0FiNLgDoyPGPE7GWTCyJJxepS9axnkteozsXUNu53VzyKm3gtd/WLwjLgNELHUlC6h/mclihm8tQTdlw7b2lrUN0Tv4Gvo7vvLOEzyNtuQDgwrLwY2tdwGvhlSNJKwWSlAUA1y/gNfTezc+ntqHfhROADmX483v94vB1rkSu5ZTB7u67ANwaGD+I6ud3IcQ/APQNOiESQcEuRCIo2IVIBAW7EImgYBciEcydb+9f9ZOZnQVwpPZrNwCe1lM/5Md7kR/v5R+aHyvdfWHIUNdgf8+Jzba7e9+cnFx+yI8E/dDbeCESQcEuRCLMZbBvmcNzX4r8eC/y4738o/Fjzj6zCyHqi97GC5EIcxLsZvaAmb1lZu+Y2WNz4UPNj8NmttvMdpjZ9jqe93EzO2Nmey4Z6zKz581sf+1/niY1u358zcyO19Zkh5l9sg5+LDezX5nZXjN7w8z+bW28rmsS8aOua2JmTWb2qpntrPnxZ7Xx68zslVrc/NjMGi7rwO5e138AsqiWtVoNoAHATgAb6u1HzZfDALrn4LwfAXAbgD2XjP0XAI/Vfn4MwJ/PkR9fA/CndV6PHgC31X5uB/A2gA31XpOIH3VdEwAGoK32cx7AKwDuBPATAJ+rjf8FgD+6nOPOxZ39DgDvuPtBr5ae/hGAB+fAjznD3bcCeH896AdRLdwJ1KmAJ/Gj7rj7SXd/vfbzCKrFUXpR5zWJ+FFXvMpVL/I6F8HeC+DoJb/PZbFKB/BLM3vNzDbPkQ8XWezuJ2s/nwLAi8rPPo+a2a7a2/xZ/zhxKWa2CtX6Ca9gDtfkfX4AdV6T2SjymvoG3T3ufhuATwD4YzP7yFw7BFRf2YFIB4zZ5TsA1qDaI+AkgG/U68Rm1gbgZwC+5O7vKSVTzzUJ+FH3NfEZFHllzEWwHwew/JLfabHK2cbdj9f+PwPgacxt5Z3TZtYDALX/z8yFE+5+uvZEqwD4Huq0JmaWRzXAfujuT9WG674mIT/mak1q577sIq+MuQj2bQDW1XYWGwB8DsAz9XbCzFrNrP3izwA+DmBPfNas8gyqhTuBOSzgeTG4anwWdVgTMzNUaxjuc/dvXmKq65owP+q9JrNW5LVeO4zv2238JKo7nQcA/Ic58mE1qkrATgBv1NMPAE+i+nawiOpnr0dQ7Zn3IoD9AF4A0DVHfvwVgN0AdqEabD118OMeVN+i7wKwo/bvk/Vek4gfdV0TALegWsR1F6ovLP/pkufsqwDeAfA3ABov57j6Bp0QiZD6Bp0QyaBgFyIRFOxCJIKCXYhEULALkQgKdiESQcEuRCIo2IVIhP8LODASVWtnJEkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import cv2\n",
        "img2 = cv2.imread(\"des/data/\" + random_filename,0)\n",
        "print(\"min value of random img\", img2.min())\n",
        "print(\"max value of random img\", img2.max())\n",
        "imgplot = plt.imshow(img2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rgb2gray(rgb):\n",
        "    return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])"
      ],
      "metadata": {
        "id": "cnXqn9ucQR5_"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {
        "id": "-KroL1ZHIn2Q"
      },
      "outputs": [],
      "source": [
        "from skimage import io\n",
        "# img = io.imread('image.png', as_gray=True)\n",
        "images = [io.imread(file, as_gray=True) for file in glob.glob(\"des/data/*.png\")]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "id": "kZ22EQLcRjKL"
      },
      "outputs": [],
      "source": [
        "x = pd.DataFrame(np.array(images).reshape(10000, -1))\n",
        "y = pd.read_csv('des/labels.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {
        "id": "5NKd-P-JSiwK"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_cat = (y_train[y_train['label'] == 'cat']).sample(1)\n",
        "print(random_cat['label'].iloc[0])\n",
        "img = mpimg.imread(\"des/data/\" + str(random_cat['id'].iloc[0])+\".png\")\n",
        "imgplot = plt.imshow(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "fNnSUwK32fIc",
        "outputId": "ce17d988-34bc-462f-dfec-aec8dad803de"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cat\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_cat = y_train[y_train['label'] == 'horse'].sample(1)\n",
        "print(random_cat['label'].iloc[0])\n",
        "img = mpimg.imread(\"des/data/\" + str(random_cat['id'].iloc[0])+\".png\")\n",
        "imgplot = plt.imshow(img)"
      ],
      "metadata": {
        "id": "oEvK188C4EaV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "310132dc-bbc0-4cc9-95ca-8000519aab8f"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "horse\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y_train[y_train['label'] == 'horse']), len(y_train[y_train['label'] == 'cat'])"
      ],
      "metadata": {
        "id": "Mi8hmO6X4Snd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a9d0f41-c6d7-46cf-aad0-f8f9563faafd"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3446, 3554)"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y_test[y_test['label'] == 'horse']), len(y_test[y_test['label'] == 'cat'])"
      ],
      "metadata": {
        "id": "qYvAK1Iu8hho",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d23198b-ac21-4a01-9f0a-f43cc3f5bef0"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1554, 1446)"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(211)\n",
        "ax.bar(['test', 'train'], [len(y_test[y_test['label'] == 'cat']), len(y_train[y_train['label'] == 'cat'])])\n",
        "plt.title(\"cat\")\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(212)\n",
        "ax.bar(['test', 'train'], [len(y_test[y_test['label'] == 'horse']), len(y_train[y_train['label'] == 'horse'])])\n",
        "plt.title(\"horse\")"
      ],
      "metadata": {
        "id": "VWgihw45B1HW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "outputId": "fd0a6354-0c20-4026-8757-fe9aa1f2ef91"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'horse')"
            ]
          },
          "metadata": {},
          "execution_count": 174
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = y_train.iloc[:, 1]\n",
        "y_test = y_test.iloc[:, 1]"
      ],
      "metadata": {
        "id": "Utdchn98A-cl"
      },
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "VFTcPw1jYz-w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder \n",
        "import random as rand"
      ],
      "metadata": {
        "id": "SusAPVdd3wXY"
      },
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataloader"
      ],
      "metadata": {
        "id": "ndKr7cm74I3S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Dataloader:\n",
        "    '''\n",
        "    This class prepares the dataset for the neural network.\n",
        "    '''\n",
        "    \n",
        "    def __init__(self, data, labels, n_classes, batch_size=None, shuffle=False):\n",
        "        '''\n",
        "        This is the constructor. It gets dataset information and initializes the \n",
        "        Dataloader class fields.\n",
        "            Parameters:\n",
        "                data: features your dataset in pandas.Dataframe format.\n",
        "                labels: labels of your dataset in pandas.Dataframe format.\n",
        "                n_classes: number of classes you have in your dataset.\n",
        "                batch_size: the number of samples that will be propagated through the network.\n",
        "                shuffle: boolean value indicating whether or not the dataset should be shuffled\n",
        "        '''\n",
        "\n",
        "        assert len(data)==len(labels)\n",
        "        self.__n_classes = n_classes\n",
        "        self.__batch_size = batch_size\n",
        "        self.__shuffle = shuffle\n",
        "        self.__data = data\n",
        "        self.__onehot_labels = self.__onehot(labels, self.__n_classes)\n",
        "    \n",
        "    def __onehot(self, labels, n_classes):\n",
        "        '''\n",
        "        This private method gets labels and provides one_hot vectors of labels.\n",
        "        For categorical variables where no such ordinal relationship exists,\n",
        "        the integer encoding is not enough.\n",
        "        In this case, a one-hot encoding can be applied to the integer representation.\n",
        "        This is where the integer encoded variable is removed, and a new binary variable is\n",
        "        added for each unique integer value.\n",
        "        example:\n",
        "            red,    green,    blue\n",
        "            1,      0,        0\n",
        "            0,      1,        0\n",
        "            0,      0,        1\n",
        "                Parameters:\n",
        "                        label: lables of your dataset in pandas.Dataframe format.\n",
        "                        n_classes: number of classes you have in your dataset.\n",
        "                \n",
        "                Returns:\n",
        "                    onehot_vectors: onehot vectors of the labels\n",
        "        '''\n",
        "        # TODO: Implement\n",
        "        encoder = OneHotEncoder(categories = 'auto')\n",
        "        X = encoder.fit_transform(labels.values.reshape(-1,1)).toarray()\n",
        "        onehot_vectors = pd.DataFrame(X)\n",
        "        return onehot_vectors\n",
        "    \n",
        "    def __shuffle_dataset(self):\n",
        "        '''\n",
        "        This private method shuffles your dataset.\n",
        "        It uses data and onehot_labels to shuffle them\n",
        "        symmetrical.\n",
        "        '''\n",
        "        # TODO: Implement\n",
        "        rand = np.random.permutation(len(self.__data))\n",
        "        self.__data = self.__data[rand]\n",
        "        self.__onehot_labels = self.__onehot_labels[rand]\n",
        "    \n",
        "    def __iter__(self):\n",
        "        '''\n",
        "        The __iter__() function returns an iterator for the\n",
        "        given object (array, set, tuple, etc., or custom objects).\n",
        "        This will return your dataset in the batch_size given. This should\n",
        "        be used to provide data for the neural network.\n",
        "        '''\n",
        "        \n",
        "        if self.__shuffle:\n",
        "            self.__shuffle_dataset()\n",
        "            \n",
        "        if self.__batch_size==None:\n",
        "            yield (np.matrix(self.__data), np.matrix(self.__onehot_labels))\n",
        "            return\n",
        "            \n",
        "        for idx in range(0, len(self.__data), self.__batch_size):\n",
        "            yield (np.matrix(self.__data[idx:idx+self.__batch_size]), \n",
        "                   np.matrix(self.__onehot_labels[idx:idx+self.__batch_size]))"
      ],
      "metadata": {
        "id": "YYTmVRce382q"
      },
      "execution_count": 191,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Activation Functions"
      ],
      "metadata": {
        "id": "L3a-YxbI4UgB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Identical:\n",
        "    \n",
        "    \n",
        "    def __init__(self): pass\n",
        "        \n",
        "    def __val(self, matrix):\n",
        "        \n",
        "        identical_value = np.matrix(matrix, dtype=float)\n",
        "        return identical_value\n",
        "\n",
        "    def derivative(self, matrix):\n",
        "        \n",
        "        temp = np.matrix(matrix, dtype=float)\n",
        "        identical_derivative = np.matrix(np.full(np.shape(temp), 1.))\n",
        "        return identical_derivative\n",
        "    \n",
        "    def __call__(self, matrix):\n",
        "        \n",
        "        return self.__val(matrix)\n",
        "    \n",
        "\n",
        "class Relu:\n",
        "    \n",
        "    \n",
        "    def __init__(self): pass\n",
        "        \n",
        "    \n",
        "    def __val(self, matrix):\n",
        "        \n",
        "        # TODO: Implement\n",
        "        relu_value = np.matrix(matrix, dtype=float)\n",
        "        relu_value[relu_value<0] = 0.0\n",
        "        return relu_value\n",
        "\n",
        "    def derivative(self, matrix):\n",
        "        \n",
        "        # TODO: Implement\n",
        "        relu_derivative = np.matrix(matrix, dtype=float)\n",
        "        relu_derivative[relu_derivative > 0] = 1.\n",
        "        relu_derivative[relu_derivative < 0] = 0.\n",
        "        return relu_derivative\n",
        "    \n",
        "    def __call__(self, matrix):\n",
        "        \n",
        "        return self.__val(matrix)\n",
        "\n",
        "    \n",
        "class LeakyRelu:\n",
        "    \n",
        "    \n",
        "    def __init__(self, negative_slope=0.01):\n",
        "        \n",
        "        self.negative_slope = 0.01\n",
        "    \n",
        "    def __val(self, matrix):\n",
        "        \n",
        "        # TODO: Implement\n",
        "       \n",
        "        leacky_relu_value = np.matrix(matrix, dtype=float)\n",
        "        leacky_relu_value = np.where(leacky_relu_value > 0, leacky_relu_value, 0.1 * leacky_relu_value)\n",
        "        return leacky_relu_value\n",
        "\n",
        "    def derivative(self, matrix):\n",
        "        \n",
        "        # TODO: Implement\n",
        "        leacky_relu_derivative = np.matrix(matrix, dtype=float)\n",
        "        leacky_relu_derivative = np.where(leacky_relu_derivative > 0., 1, 0.1 )\n",
        "        return leacky_relu_derivative\n",
        "    \n",
        "    def __call__(self, matrix):\n",
        "        \n",
        "        return self.__val(matrix)\n",
        "\n",
        "    \n",
        "class Sigmoid:\n",
        "    \n",
        "    \n",
        "    def __init__(self): pass\n",
        "\n",
        "    def __val(self, matrix):\n",
        "        \n",
        "        # TODO: Implement\n",
        "        sigmoid_value = np.negative(matrix, dtype=float)\n",
        "        sigmoid_value = np.matrix(1./(1 + np.exp(sigmoid_value)), dtype = float)\n",
        "        return sigmoid_value\n",
        "\n",
        "    def derivative(self, matrix):\n",
        "        \n",
        "        # TODO: Implement\n",
        "        sigmoid_value = np.negative(matrix, dtype=float)\n",
        "        sigmoid_value = np.matrix(1./(1 + np.exp(sigmoid_value)), dtype = float)\n",
        "        sigmoid_value_minus_one = np.matrix(sigmoid_value + (-1))\n",
        "        sigmoid_derivative = np.multiply(np.squeeze(sigmoid_value), np.squeeze(sigmoid_value_minus_one), out=None) \n",
        "        return sigmoid_derivative\n",
        "    \n",
        "    def __call__(self, matrix):\n",
        "        \n",
        "        return self.__val(matrix)\n",
        "\n",
        "\n",
        "class Softmax:\n",
        "    \n",
        "    def __init__(self): pass\n",
        "        \n",
        "    def __val(self, matrix):\n",
        "        \n",
        "        # TODO: Implement\n",
        "        # print(matrix.shape)\n",
        "        # print(matrix.max(1).shape)\n",
        "        matrix -= np.column_stack((matrix.max(1),  matrix.max(1)))\n",
        "        \n",
        "        softmax_value = np.matrix(np.exp(matrix)/np.column_stack((np.exp(matrix).sum(1), np.exp(matrix).sum(1))), dtype=float)\n",
        "        return softmax_value\n",
        "    \n",
        "    def __call__(self, matrix):\n",
        "        \n",
        "        return self.__val(matrix)\n",
        "    \n",
        "class Tanh:\n",
        "    \n",
        "    def __init__(self): pass\n",
        "      \n",
        "    def __val(self, matrix):\n",
        "        \n",
        "        # TODO: Implement\n",
        "        temp = np.matrix(matrix, dtype=float)\n",
        "        tanh_value = np.tanh(temp)\n",
        "        return tanh_value\n",
        "\n",
        "    def derivative(self, matrix):\n",
        "        \n",
        "        # TODO: Implement\n",
        "        tanh_value = self.__val(matrix)\n",
        "        tanh_derivative = 1 - np.multiply(tanh_value, tanh_value)\n",
        "        return tanh_derivative\n",
        "    \n",
        "    def __call__(self, matrix):\n",
        "        \n",
        "        return self.__val(matrix)"
      ],
      "metadata": {
        "id": "7crMtH1R4XMf"
      },
      "execution_count": 204,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loss Function"
      ],
      "metadata": {
        "id": "U2x7sma84dQJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CrossEntropy: #(with softmax)\n",
        "    \n",
        "    \n",
        "    def __init__(self): pass\n",
        "        \n",
        "\n",
        "    def __val(self, true_val, expected_val):\n",
        "        \n",
        "        assert np.shape(true_val)==np.shape(expected_val)\n",
        "        # TODO: Implement\n",
        "        s = Softmax()(true_val)\n",
        "        # cross_entropy_value = -np.multiply(expected_val, np.ma.log(s)).sum(1)\n",
        "        cross_entropy_value = np.sum(-np.multiply(np.matrix(expected_val, dtype=float),np.log(s)), axis=1)\n",
        "        return cross_entropy_value\n",
        "        \n",
        "    def derivative(self, true_val, expected_val):\n",
        "        \n",
        "        assert np.shape(true_val)==np.shape(expected_val)\n",
        "        # TODO: Implement\n",
        "        s = Softmax()(true_val)\n",
        "        cross_entropy_derivative = s - expected_val\n",
        "        return cross_entropy_derivative\n",
        "    \n",
        "    def __call__(self, true_val, expected_val):\n",
        "        \n",
        "        return self.__val(true_val, expected_val)"
      ],
      "metadata": {
        "id": "kDPu8kX44huk"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Layer"
      ],
      "metadata": {
        "id": "5dyZJA0C4mvn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Layer:\n",
        "    '''\n",
        "    The layer class is used to define neural network layers.\n",
        "    It stores all needed information for each layer, such as neurons count,\n",
        "    weight matrix, bias, the output after applying the activation function, etc.\n",
        "    '''\n",
        "\n",
        "    DEFAULT_LOW, DEFAULT_HIGH, DEFAULT_MEAN, DEFAULT_VAR = 0, 0.05, 0., 1.\n",
        "  \n",
        "    def __init__(self, input_size, output_size, activation=Identical(), initial_weight='uniform', \n",
        "                 **initializing_parameters):\n",
        "        '''\n",
        "        Parameters:\n",
        "            input_size: the size of the input of this layer.\n",
        "            output_size: the size of the output after this layer.\n",
        "            activation: the activation function. It can be initialized to either of the previously defined activation classes.\n",
        "                               default is an Identical activation function.\n",
        "            initial_weight: either normal or uniform. It defines the method for weight initialization.\n",
        "        '''\n",
        "        \n",
        "        assert type(initial_weight)==str, 'Undefined activation function!'\n",
        "        \n",
        "        self.__weight_initializer_dict = {'uniform':self.__uniform_weight, 'normal':self.__normal_weight}\n",
        "        \n",
        "        assert initial_weight in self.__weight_initializer_dict, 'Undefined weight initialization function!'\n",
        "\n",
        "\n",
        "        self.__n_neurons = output_size\n",
        "        weight_initializer = self.__weight_initializer_dict[initial_weight]\n",
        "        self.__weight = weight_initializer(input_size, self.__n_neurons, **initializing_parameters)\n",
        "        self.__bias = weight_initializer(1, self.__n_neurons, **initializing_parameters)\n",
        "        self.__activation = activation\n",
        "        \n",
        "        self.__last_input = None\n",
        "        self.__last_activation_input = None\n",
        "        self.__last_activation_output = None\n",
        "        self.__last_activation_derivative = None\n",
        "        \n",
        "    def forward(self, layer_input):\n",
        "        '''\n",
        "        It calculates the output of this layer for the layer_input argument.\n",
        "        This method also stores __last_input, __last_activation_input, and __last_activation_derivative\n",
        "        for future use in backpropagation.\n",
        "        Parameters:\n",
        "            layer_input: 2d np.matrix representing the input matrix of this layer.\n",
        "        Returns:\n",
        "            Final output of this layer after applying the activation function.\n",
        "        '''\n",
        "        assert np.ndim(layer_input)==2\n",
        "        assert np.size(self.__weight,0) == np.size(layer_input,1)\n",
        "        # TODO: Implement\n",
        "        self.__last_input = layer_input\n",
        "        self.__last_activation_input = np.matmul(layer_input, self.__weight) + self.__bias\n",
        "        self.__last_activation_output = self.__activation(self.__last_activation_input)\n",
        "        self.__last_activation_derivative = self.__activation.derivative(self.__last_activation_input)\n",
        "        return self.__last_activation_output\n",
        "    \n",
        "    def update_weights(self, backprop_tensor, lr):\n",
        "        '''\n",
        "        It updates Layer weights according to the backpropagation matrix and learning rate.\n",
        "        This method updates bias values as well.\n",
        "        Parameters:\n",
        "            backprop_tensor: 2d np.matrix passed from the next layer containing gradient values.\n",
        "            lr: learning rate\n",
        "        Returns:\n",
        "            backprop_tensor to be used by the previous layer.\n",
        "        '''\n",
        "        assert np.ndim(backprop_tensor)==2\n",
        "        assert np.size(backprop_tensor,0) == np.size(self.__last_activation_derivative,0)\n",
        "        assert np.size(backprop_tensor,1) == self.__n_neurons\n",
        "        # TODO: Implement\n",
        "        tp = self.__last_input.transpose()\n",
        "        errory = np.multiply(backprop_tensor,self.__last_activation_derivative)\n",
        "        errorw = np.matmul(tp,errory)\n",
        "        backprop_tensor = np.matmul(errory,self.__weight.transpose())\n",
        "        self.__weight -= errorw*lr\n",
        "        bu = np.matmul(np.matrix(np.tile(1, (1, errory.shape[0]))),errory)\n",
        "        self.__bias -= bu*lr\n",
        "        return backprop_tensor\n",
        "\n",
        "    def __uniform_weight(self, dim1, dim2, **initializing_parameters):\n",
        "        '''\n",
        "        Initializes weights as a uniform distribution between low and high values.\n",
        "        It uses default low and high values unless low or high are passed in initializing_parameters.\n",
        "        Parameters:\n",
        "            dim1: the size of the first dimension of weights.\n",
        "            dim2: the size of the second dimension of weights.\n",
        "            initializing_parameters: other initializing parameters; it can include custom low or high values.\n",
        "        Returns:\n",
        "            np.matrix with size (dim1, dim2) initialized using uniformly distributed values.\n",
        "        '''\n",
        "        low, high = self.DEFAULT_LOW, self.DEFAULT_HIGH\n",
        "        if 'low' in initializing_parameters.keys(): low = initializing_parameters['low']\n",
        "        if 'high' in initializing_parameters.keys(): high = initializing_parameters['high']\n",
        "        # TODO: Implement\n",
        "        # weights = [[0]* dim2]*dim1\n",
        "        # for i in range(dim1):\n",
        "        #     for j in range(dim2):\n",
        "        #         weights[i][j] = np.float32(rand.uniform(low, high))\n",
        "        # weights = np.array(weights)\n",
        "        weights = np.random.uniform(low = low, high = high, size = (dim1, dim2))\n",
        "        return weights\n",
        "\n",
        "    def __normal_weight(self, dim1, dim2, **initializing_parameters):\n",
        "        '''\n",
        "        Initializes weights as a normal distribution with mean and var values.\n",
        "        It uses default mean and variance values unless mean or var are passed in initializing_parameters.\n",
        "        Parameters:\n",
        "            dim1: the size of the first dimension of weights.\n",
        "            dim2: the size of the second dimension of weights.\n",
        "            initializing_parameters: other initializing parameters; it can include custom mean or var values.\n",
        "        Returns:\n",
        "            np.matrix with size (dim1, dim2) initialized using normaly distributed values.\n",
        "        ''' \n",
        "        mean, var = self.DEFAULT_MEAN, self.DEFAULT_VAR\n",
        "        if 'mean' in initializing_parameters.keys(): mean = initializing_parameters['mean']\n",
        "        if 'var' in initializing_parameters.keys(): var = initializing_parameters['var']\n",
        "        # TODO: Implement\n",
        "        weights = [[0]* dim2] * dim1\n",
        "        sd = np.sqrt(var)\n",
        "        for i in range(dim1):\n",
        "            for j in range(dim2):\n",
        "                weights[i][j] = np.float32(rand.normal(0.0, sd))\n",
        "        return weights\n",
        "    \n",
        "    @property\n",
        "    def n_neurons(self): return self.__n_neurons\n",
        "    \n",
        "    @property\n",
        "    def weight(self): return self.__weight\n",
        "    \n",
        "    @property\n",
        "    def bias(self): return self.__bias\n",
        "    \n",
        "    @property\n",
        "    def activation(self): return self.__activation"
      ],
      "metadata": {
        "id": "ap-7KH-e4n28"
      },
      "execution_count": 209,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feed Forward Neural Network"
      ],
      "metadata": {
        "id": "H_oX-Y134r5y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class FeedForwardNN:\n",
        "    \n",
        "    def __init__(self, input_shape):\n",
        "        \n",
        "        \n",
        "        self.__input_shape = input_shape\n",
        "        self.__output_shape = None\n",
        "        \n",
        "        self.__layers_list = []\n",
        "        \n",
        "        self.__lr = None\n",
        "        self.__loss = None\n",
        "\n",
        "        \n",
        "    def add_layer(self, n_neurons, activation=Relu(), initial_weight='uniform', **initializing_parameters):\n",
        "        \n",
        "         \n",
        "        assert type(n_neurons)==int, \"Invalid number of neurons for the layer!\"\n",
        "        assert n_neurons>0, \"Invalid number of neurons for the layer!\"\n",
        "        \n",
        "        n_prev_neurons = self.__input_shape if len(self.__layers_list)==0 else self.__layers_list[-1].n_neurons\n",
        "        new_layer = Layer(n_prev_neurons, n_neurons, activation, initial_weight, **initializing_parameters)\n",
        "        self.__layers_list.append(new_layer)\n",
        "        self.__output_shape = self.__layers_list[-1].n_neurons \n",
        "      \n",
        "    \n",
        "    def set_training_param(self, loss=CrossEntropy(), lr=1e-3):\n",
        "        \n",
        "        assert self.__layers_list, \"Uncomplete model!\"\n",
        "        self.__loss = loss\n",
        "        self.__lr = lr\n",
        "    \n",
        "    \n",
        "    def forward(self, network_input):\n",
        "        \n",
        "        assert type(self.__output_shape) != None, \"Model is not compiled!\"\n",
        "        # TODO: Implement\n",
        "        for i in self.__layers_list:\n",
        "            network_output = i.forward(network_input)\n",
        "            network_input = network_output\n",
        "        return network_output\n",
        "    \n",
        "    \n",
        "    def fit(self, epochs, trainloader, testloader=None, print_results=True):\n",
        "        \n",
        "        \n",
        "        assert type(self.__output_shape) != None, \"Model is not compiled!\"\n",
        "        assert type(self.__lr) != None and type(self.__loss) != None, \"Training paramenters are not set!\"\n",
        "\n",
        "        log = {\"train_accuracy\":[], \"train_loss\":[], \"test_accuracy\":[], \"test_loss\":[]}\n",
        "        \n",
        "        for epoch in range(1, epochs+1):\n",
        "            \n",
        "            if print_results: \n",
        "                print('Epoch {}:'.format(epoch)) \n",
        "                \n",
        "            average_accuracy, average_loss = self.__train(trainloader)\n",
        "            log['train_accuracy'].append(average_accuracy)\n",
        "            log['train_loss'].append(average_loss)\n",
        "            if print_results:\n",
        "                print('\\tTrain: Average Accuracy: {}\\tAverage Loss: {}'.format(average_accuracy, average_loss))\n",
        "            \n",
        "            if type(testloader) != type(None):\n",
        "                average_accuracy, average_loss = self.__test(testloader)\n",
        "                log['test_accuracy'].append(average_accuracy)\n",
        "                log['test_loss'].append(average_loss)\n",
        "                if print_results:\n",
        "                    print('\\tTest: Average Accuracy: {}\\tAverage Loss: {}'.format(average_accuracy, average_loss))\n",
        "                    \n",
        "        return log\n",
        "    \n",
        "    \n",
        "    def __train(self, trainloader):\n",
        "        \n",
        "        bach_accuracies, batch_losses = [], []\n",
        "        for x_train, y_train in trainloader:\n",
        "            \n",
        "            batch_accuracy, batch_loss = self.__train_on_batch(x_train, y_train)\n",
        "            bach_accuracies.append(batch_accuracy)\n",
        "            batch_losses.append(batch_loss)\n",
        "        return np.mean(bach_accuracies), np.mean(batch_losses)\n",
        "    \n",
        "    \n",
        "    def __test(self, testloader):\n",
        "        \n",
        "        bach_accuracies, batch_losses = [], []\n",
        "        for x_test, y_test in testloader:\n",
        "            batch_accuracy, batch_loss = self.__test_on_batch(x_test, y_test)\n",
        "            bach_accuracies.append(batch_accuracy)\n",
        "            batch_losses.append(batch_loss)\n",
        "        return np.mean(bach_accuracies), np.mean(batch_losses)\n",
        "\n",
        "    \n",
        "    def __train_on_batch(self, x_batch, y_batch):\n",
        "        \n",
        "        # TODO: Implement\n",
        "        output = self.forward(x_batch)\n",
        "        self.__update_weights(output, y_batch)\n",
        "        batch_accuracy = self.__compute_accuracy(output, y_batch)\n",
        "        batch_average_loss = np.mean(self.__loss.__call__(output, y_batch))\n",
        "        # cross_entropy = CrossEntropy()\n",
        "        # batch_loss = cross_entropy(output, y_batch)\n",
        "        # batch_average_loss = np.sum(batch_loss)/len(output)\n",
        "        return (batch_accuracy, batch_average_loss)\n",
        "        \n",
        "        \n",
        "    def __test_on_batch(self, x_batch, y_batch):\n",
        "        \n",
        "        # TODO: Implement\n",
        "        output = self.forward(x_batch)\n",
        "        batch_accuracy = self.__compute_accuracy(output, y_batch)\n",
        "        batch_average_loss = np.mean(self.__loss.__call__(output, y_batch))\n",
        "        return (batch_accuracy, batch_average_loss)\n",
        "            \n",
        "        \n",
        "    def __get_labels(self, outputs):\n",
        "        \n",
        "        # TODO: Implement\n",
        "        labels = outputs.argmax(axis=1)\n",
        "        return labels\n",
        "    \n",
        "    \n",
        "    def __compute_accuracy(self, output, expected_output):\n",
        "        \n",
        "        # TODO: Implement\n",
        "        output = np.array(output)\n",
        "        _output = np.argmax(output, axis=1)\n",
        "        expected_output = np.array(expected_output)\n",
        "        _expected_output = np.argmax(expected_output, axis=1)\n",
        "        truePrediced = np.sum(_output == _expected_output)\n",
        "        accuracy = truePrediced/len(_output)*100\n",
        "        return accuracy\n",
        "    \n",
        "    \n",
        "    def __update_weights(self, output, y_train):\n",
        "        \n",
        "        # TODO: Implement\n",
        "        temp = self.__loss.derivative(output, y_train)\n",
        "        for i in reversed(self.__layers_list):\n",
        "            temp = i.update_weights(temp, self.__lr)\n",
        "        return"
      ],
      "metadata": {
        "id": "67jtaN7k4rAR"
      },
      "execution_count": 195,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training Sample Code"
      ],
      "metadata": {
        "id": "Kg9CSj2643Dj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sample code for building and training a model\n",
        "\n",
        "INPUT_SHAPE = 1024\n",
        "LEARNING_RATE = 0.001\n",
        "EPOCHS = 25\n",
        "TRAINLOADER = Dataloader(X_train, y_train, n_classes = 2, batch_size = 32)\n",
        "TESTLOADER = Dataloader(X_test, y_test, n_classes = 2, batch_size = 32)\n",
        "\n",
        "network = FeedForwardNN(INPUT_SHAPE)\n",
        "network.add_layer(200, input_shape=INPUT_SHAPE, activation=Relu(), weight_initializer='uniform')\n",
        "network.add_layer(2, activation=Identical(), weight_initializer='uniform')\n",
        "network.set_training_param(loss=CrossEntropy(), lr=LEARNING_RATE)\n",
        "\n",
        "log = network.fit(EPOCHS, TRAINLOADER, TESTLOADER)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KXB62lmn40IU",
        "outputId": "ea9dc830-31af-41b5-9748-6b85e4915bd7"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: divide by zero encountered in log\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: invalid value encountered in multiply\n",
            "  del sys.path[0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\tTrain: Average Accuracy: 49.980974124809734\tAverage Loss: nan\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6995182992492676\n",
            "Epoch 2:\n",
            "\tTrain: Average Accuracy: 50.90848554033485\tAverage Loss: 0.6963541919092406\n",
            "\tTest: Average Accuracy: 51.684397163120565\tAverage Loss: 0.6942104600909272\n",
            "Epoch 3:\n",
            "\tTrain: Average Accuracy: 50.342465753424655\tAverage Loss: 0.6939364561717202\n",
            "\tTest: Average Accuracy: 51.57358156028369\tAverage Loss: 0.6932307497252439\n",
            "Epoch 4:\n",
            "\tTrain: Average Accuracy: 51.27473363774734\tAverage Loss: 0.6930707506026049\n",
            "\tTest: Average Accuracy: 51.42952127659574\tAverage Loss: 0.6931544329870621\n",
            "Epoch 5:\n",
            "\tTrain: Average Accuracy: 51.77416286149163\tAverage Loss: 0.6925737999656753\n",
            "\tTest: Average Accuracy: 51.628989361702125\tAverage Loss: 0.6931712786171056\n",
            "Epoch 6:\n",
            "\tTrain: Average Accuracy: 52.0880898021309\tAverage Loss: 0.6921800304052655\n",
            "\tTest: Average Accuracy: 51.36303191489362\tAverage Loss: 0.6933225659987731\n",
            "Epoch 7:\n",
            "\tTrain: Average Accuracy: 52.40201674277017\tAverage Loss: 0.69183358914512\n",
            "\tTest: Average Accuracy: 51.263297872340424\tAverage Loss: 0.6934927425123205\n",
            "Epoch 8:\n",
            "\tTrain: Average Accuracy: 52.75875190258753\tAverage Loss: 0.6914687887682662\n",
            "\tTest: Average Accuracy: 51.263297872340424\tAverage Loss: 0.6937449194272678\n",
            "Epoch 9:\n",
            "\tTrain: Average Accuracy: 52.77302130898022\tAverage Loss: 0.6910578008048864\n",
            "\tTest: Average Accuracy: 51.23005319148936\tAverage Loss: 0.6940420101709676\n",
            "Epoch 10:\n",
            "\tTrain: Average Accuracy: 53.144025875190266\tAverage Loss: 0.6906329773157475\n",
            "\tTest: Average Accuracy: 51.59574468085106\tAverage Loss: 0.6943131005308179\n",
            "Epoch 11:\n",
            "\tTrain: Average Accuracy: 53.58637747336378\tAverage Loss: 0.6901997279980554\n",
            "\tTest: Average Accuracy: 51.69547872340426\tAverage Loss: 0.6947788467944473\n",
            "Epoch 12:\n",
            "\tTrain: Average Accuracy: 53.40087519025876\tAverage Loss: 0.6897892787411154\n",
            "\tTest: Average Accuracy: 51.9281914893617\tAverage Loss: 0.6950016625475463\n",
            "Epoch 13:\n",
            "\tTrain: Average Accuracy: 53.843226788432275\tAverage Loss: 0.6891965049174296\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6952380540230269\n",
            "Epoch 14:\n",
            "\tTrain: Average Accuracy: 54.085806697108076\tAverage Loss: 0.6886595037257265\n",
            "\tTest: Average Accuracy: 51.78413120567375\tAverage Loss: 0.6955019272356548\n",
            "Epoch 15:\n",
            "\tTrain: Average Accuracy: 54.18569254185693\tAverage Loss: 0.6881243405946327\n",
            "\tTest: Average Accuracy: 51.55141843971631\tAverage Loss: 0.6964472083626753\n",
            "Epoch 16:\n",
            "\tTrain: Average Accuracy: 54.65658295281583\tAverage Loss: 0.6873980812746342\n",
            "\tTest: Average Accuracy: 51.48492907801418\tAverage Loss: 0.6968665731219681\n",
            "Epoch 17:\n",
            "\tTrain: Average Accuracy: 54.9847792998478\tAverage Loss: 0.6866722892681982\n",
            "\tTest: Average Accuracy: 51.130319148936174\tAverage Loss: 0.6974209982242984\n",
            "Epoch 18:\n",
            "\tTrain: Average Accuracy: 55.156012176560125\tAverage Loss: 0.6858917382943793\n",
            "\tTest: Average Accuracy: 51.329787234042556\tAverage Loss: 0.6987224366701571\n",
            "Epoch 19:\n",
            "\tTrain: Average Accuracy: 55.38432267884323\tAverage Loss: 0.685057024844286\n",
            "\tTest: Average Accuracy: 51.4738475177305\tAverage Loss: 0.6993407604497028\n",
            "Epoch 20:\n",
            "\tTrain: Average Accuracy: 55.79813546423136\tAverage Loss: 0.6840395706733048\n",
            "\tTest: Average Accuracy: 51.64007092198582\tAverage Loss: 0.6995842861647632\n",
            "Epoch 21:\n",
            "\tTrain: Average Accuracy: 56.01217656012177\tAverage Loss: 0.6831405587264268\n",
            "\tTest: Average Accuracy: 51.628989361702125\tAverage Loss: 0.7013120493177948\n",
            "Epoch 22:\n",
            "\tTrain: Average Accuracy: 56.49733637747337\tAverage Loss: 0.6818549000223367\n",
            "\tTest: Average Accuracy: 51.52925531914894\tAverage Loss: 0.701132285222383\n",
            "Epoch 23:\n",
            "\tTrain: Average Accuracy: 56.62576103500761\tAverage Loss: 0.6807083409999596\n",
            "\tTest: Average Accuracy: 51.263297872340424\tAverage Loss: 0.7018160614643831\n",
            "Epoch 24:\n",
            "\tTrain: Average Accuracy: 57.00152207001521\tAverage Loss: 0.6798646056585397\n",
            "\tTest: Average Accuracy: 51.5625\tAverage Loss: 0.7039274542419921\n",
            "Epoch 25:\n",
            "\tTrain: Average Accuracy: 57.35825722983257\tAverage Loss: 0.6783525586531373\n",
            "\tTest: Average Accuracy: 51.496010638297875\tAverage Loss: 0.7045918419299844\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "قسمت سوم تاثیر لرنینگ ریت: \n"
      ],
      "metadata": {
        "id": "0qTZIIS_RjCm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "INPUT_SHAPE = 1024\n",
        "LEARNING_RATE = 0.01\n",
        "EPOCHS = 25\n",
        "TRAINLOADER = Dataloader(X_train, y_train, n_classes = 2, batch_size = 32)\n",
        "TESTLOADER = Dataloader(X_test, y_test, n_classes = 2, batch_size = 32)\n",
        "\n",
        "network = FeedForwardNN(INPUT_SHAPE)\n",
        "network.add_layer(200, input_shape=INPUT_SHAPE, activation=Relu(), weight_initializer='uniform')\n",
        "network.add_layer(2, activation=Identical(), weight_initializer='uniform')\n",
        "network.set_training_param(loss=CrossEntropy(), lr=LEARNING_RATE)\n",
        "\n",
        "log = network.fit(EPOCHS, TRAINLOADER, TESTLOADER, True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DDXJLaUAR1Fe",
        "outputId": "79197001-bc65-4083-bdcf-6d59fc62b167"
      },
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: divide by zero encountered in log\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: invalid value encountered in multiply\n",
            "  del sys.path[0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: nan\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 2:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 3:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 4:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 5:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 6:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 7:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 8:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 9:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 10:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 11:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 12:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 13:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 14:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 15:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 16:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 17:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 18:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 19:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 20:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 21:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 22:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 23:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 24:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n",
            "Epoch 25:\n",
            "\tTrain: Average Accuracy: 49.92389649923896\tAverage Loss: 0.6946608976457601\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.6929690369545634\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "با افزایش لرنینگ ریت دقت کمتر شد و لاس بهبودی نیافت زیرا مقادیر وزن ها واگرا میشوند و یادگیری خاصی صورت نمیگیرد."
      ],
      "metadata": {
        "id": "F4Rb2X97TH_i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "INPUT_SHAPE = 1024\n",
        "LEARNING_RATE = 0.0001\n",
        "EPOCHS = 25\n",
        "TRAINLOADER = Dataloader(X_train, y_train, n_classes = 2, batch_size = 32)\n",
        "TESTLOADER = Dataloader(X_test, y_test, n_classes = 2, batch_size = 32)\n",
        "\n",
        "network = FeedForwardNN(INPUT_SHAPE)\n",
        "network.add_layer(200, input_shape=INPUT_SHAPE, activation=Relu(), weight_initializer='uniform')\n",
        "network.add_layer(2, activation=Identical(), weight_initializer='uniform')\n",
        "network.set_training_param(loss=CrossEntropy(), lr=LEARNING_RATE)\n",
        "\n",
        "log = network.fit(EPOCHS, TRAINLOADER, TESTLOADER, True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zQLwQRAkTXW0",
        "outputId": "e0d18bb8-4c3c-4ec7-b1d8-c8e7c7cf55c1"
      },
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\n",
            "\tTrain: Average Accuracy: 49.9476788432268\tAverage Loss: 20.52480079640831\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 27.83139379233007\n",
            "Epoch 2:\n",
            "\tTrain: Average Accuracy: 50.50418569254186\tAverage Loss: 15.832199871118126\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 22.245031779815747\n",
            "Epoch 3:\n",
            "\tTrain: Average Accuracy: 50.18074581430745\tAverage Loss: 12.548161236304079\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 1.279587212662533\n",
            "Epoch 4:\n",
            "\tTrain: Average Accuracy: 50.35197869101978\tAverage Loss: 9.448129515239819\n",
            "\tTest: Average Accuracy: 48.24911347517731\tAverage Loss: 5.614732407593026\n",
            "Epoch 5:\n",
            "\tTrain: Average Accuracy: 50.646879756468806\tAverage Loss: 7.311761091343842\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 9.512705254580444\n",
            "Epoch 6:\n",
            "\tTrain: Average Accuracy: 50.618340943683414\tAverage Loss: 5.693924616599017\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 7.097131383906906\n",
            "Epoch 7:\n",
            "\tTrain: Average Accuracy: 50.466133942161335\tAverage Loss: 4.399418278440866\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 1.773241407315528\n",
            "Epoch 8:\n",
            "\tTrain: Average Accuracy: 50.35197869101978\tAverage Loss: 3.442670726467418\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 1.0192175478101115\n",
            "Epoch 9:\n",
            "\tTrain: Average Accuracy: 50.632610350076106\tAverage Loss: 2.6495796315449094\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 2.835696144447164\n",
            "Epoch 10:\n",
            "\tTrain: Average Accuracy: 50.36149162861492\tAverage Loss: 2.1032409723932686\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 2.1574384362610113\n",
            "Epoch 11:\n",
            "\tTrain: Average Accuracy: 50.80384322678844\tAverage Loss: 1.6281768591787058\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 1.2919078429894788\n",
            "Epoch 12:\n",
            "\tTrain: Average Accuracy: 51.68854642313547\tAverage Loss: 1.29167195587391\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 1.161294339873809\n",
            "Epoch 13:\n",
            "\tTrain: Average Accuracy: 51.12252663622526\tAverage Loss: 1.1296250274613309\n",
            "\tTest: Average Accuracy: 48.24911347517731\tAverage Loss: 0.7545570326828651\n",
            "Epoch 14:\n",
            "\tTrain: Average Accuracy: 50.80859969558599\tAverage Loss: 1.0027504621593786\n",
            "\tTest: Average Accuracy: 48.21586879432625\tAverage Loss: 0.7136023720062564\n",
            "Epoch 15:\n",
            "\tTrain: Average Accuracy: 51.15106544901065\tAverage Loss: 0.9123902632396008\n",
            "\tTest: Average Accuracy: 48.138297872340424\tAverage Loss: 0.7012101163085422\n",
            "Epoch 16:\n",
            "\tTrain: Average Accuracy: 50.83713850837138\tAverage Loss: 0.8546315685591062\n",
            "\tTest: Average Accuracy: 47.949911347517734\tAverage Loss: 0.6967426262085983\n",
            "Epoch 17:\n",
            "\tTrain: Average Accuracy: 50.922754946727544\tAverage Loss: 0.8181433776098166\n",
            "\tTest: Average Accuracy: 49.013741134751776\tAverage Loss: 0.6945678957555949\n",
            "Epoch 18:\n",
            "\tTrain: Average Accuracy: 50.780060882800605\tAverage Loss: 0.7941520301014628\n",
            "\tTest: Average Accuracy: 51.163563829787236\tAverage Loss: 0.6936611552663883\n",
            "Epoch 19:\n",
            "\tTrain: Average Accuracy: 50.780060882800605\tAverage Loss: 0.7769052423198078\n",
            "\tTest: Average Accuracy: 51.2854609929078\tAverage Loss: 0.6935359491677785\n",
            "Epoch 20:\n",
            "\tTrain: Average Accuracy: 50.75152207001521\tAverage Loss: 0.7642420458450498\n",
            "\tTest: Average Accuracy: 51.45168439716312\tAverage Loss: 0.6938690131583519\n",
            "Epoch 21:\n",
            "\tTrain: Average Accuracy: 50.66590563165905\tAverage Loss: 0.7547811523212693\n",
            "\tTest: Average Accuracy: 51.81737588652482\tAverage Loss: 0.6944577313793286\n",
            "Epoch 22:\n",
            "\tTrain: Average Accuracy: 50.83713850837138\tAverage Loss: 0.7475454560843894\n",
            "\tTest: Average Accuracy: 51.98359929078014\tAverage Loss: 0.6951799935625257\n",
            "Epoch 23:\n",
            "\tTrain: Average Accuracy: 50.66590563165905\tAverage Loss: 0.7418672767246425\n",
            "\tTest: Average Accuracy: 51.71764184397163\tAverage Loss: 0.6959617582255081\n",
            "Epoch 24:\n",
            "\tTrain: Average Accuracy: 50.56601978691019\tAverage Loss: 0.737298653859073\n",
            "\tTest: Average Accuracy: 51.850620567375884\tAverage Loss: 0.6967569365318838\n",
            "Epoch 25:\n",
            "\tTrain: Average Accuracy: 50.43759512937594\tAverage Loss: 0.7335382599867553\n",
            "\tTest: Average Accuracy: 51.95035460992907\tAverage Loss: 0.6975361785180462\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "با کاهش لرنینگ ریت دقت کمتر شد و لاس بهبودی نیافت زیرا سرعت یادگیری افت میکند.\n",
        "با این حساب بهترین لرنینگ ریت همان 0.001 است."
      ],
      "metadata": {
        "id": "Jj4VKsVGTo10"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "قسمت چهارم. تاثیر اکتیویشن فانکشن"
      ],
      "metadata": {
        "id": "zLutrkZpT-EK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "INPUT_SHAPE = 1024\n",
        "LEARNING_RATE = 0.001\n",
        "EPOCHS = 25\n",
        "TRAINLOADER = Dataloader(X_train, y_train, n_classes = 2, batch_size = 32)\n",
        "TESTLOADER = Dataloader(X_test, y_test, n_classes = 2, batch_size = 32)\n",
        "\n",
        "network = FeedForwardNN(INPUT_SHAPE)\n",
        "network.add_layer(200, input_shape=INPUT_SHAPE, activation=Sigmoid(), weight_initializer='uniform')\n",
        "network.add_layer(2, activation=Identical(), weight_initializer='uniform')\n",
        "network.set_training_param(loss=CrossEntropy(), lr=LEARNING_RATE)\n",
        "\n",
        "log = network.fit(EPOCHS, TRAINLOADER, TESTLOADER, True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w68PEYgrULoV",
        "outputId": "341460f3-9243-4424-b9aa-62bb881c8b19"
      },
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\n",
            "\tTrain: Average Accuracy: 51.407914764079145\tAverage Loss: 1.2327070833891471\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037891950480863\n",
            "Epoch 2:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.233269345171558\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037891606877237\n",
            "Epoch 3:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332692828590395\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037891263614543\n",
            "Epoch 4:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.233269220310372\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037890920692161\n",
            "Epoch 5:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332691575267656\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037890578109465\n",
            "Epoch 6:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332690945094178\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037890235865835\n",
            "Epoch 7:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332690312595316\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037889893960656\n",
            "Epoch 8:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332689677783026\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037889552393304\n",
            "Epoch 9:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332689040669198\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037889211163165\n",
            "Epoch 10:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.233268840126579\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037888870269626\n",
            "Epoch 11:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332687759584589\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037888529712069\n",
            "Epoch 12:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.23326871156375\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037888189489879\n",
            "Epoch 13:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.233268646943621\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037887849602451\n",
            "Epoch 14:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.23326858209925\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037887510049167\n",
            "Epoch 15:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.233268517031811\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.703788717082942\n",
            "Epoch 16:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332684517424666\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037886831942602\n",
            "Epoch 17:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.233268386232385\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.703788649338811\n",
            "Epoch 18:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332683205027195\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037886155165333\n",
            "Epoch 19:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332682545546358\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037885817273667\n",
            "Epoch 20:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332681883892807\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037885479712509\n",
            "Epoch 21:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332681220078021\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037885142481262\n",
            "Epoch 22:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332680554113546\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037884805579316\n",
            "Epoch 23:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332679886010738\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037884469006077\n",
            "Epoch 24:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2332679215780982\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037884132760949\n",
            "Epoch 25:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.233267854343566\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.703788379684333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "INPUT_SHAPE = 1024\n",
        "LEARNING_RATE = 0.001\n",
        "EPOCHS = 25\n",
        "TRAINLOADER = Dataloader(X_train, y_train, n_classes = 2, batch_size = 32)\n",
        "TESTLOADER = Dataloader(X_test, y_test, n_classes = 2, batch_size = 32)\n",
        "\n",
        "network = FeedForwardNN(INPUT_SHAPE)\n",
        "network.add_layer(200, input_shape=INPUT_SHAPE, activation=Tanh(), weight_initializer='normal')\n",
        "network.add_layer(2, activation=Identical(), weight_initializer='uniform')\n",
        "network.set_training_param(loss=CrossEntropy(), lr=LEARNING_RATE)\n",
        "\n",
        "log = network.fit(EPOCHS, TRAINLOADER, TESTLOADER, True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7nNEs0HAUioC",
        "outputId": "05d52753-3960-4d05-f53f-ff946a3bb97b"
      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\n",
            "\tTrain: Average Accuracy: 50.89421613394216\tAverage Loss: 1.2677679164236373\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037905649028744\n",
            "Epoch 2:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334764427215006\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037905535690592\n",
            "Epoch 3:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334764773801132\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037905422330004\n",
            "Epoch 4:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.233476512053466\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037905308946891\n",
            "Epoch 5:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.233476546741615\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037905195541176\n",
            "Epoch 6:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334765814446018\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037905082112768\n",
            "Epoch 7:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334766161625028\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037904968661591\n",
            "Epoch 8:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334766508953412\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037904855187557\n",
            "Epoch 9:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334766856431865\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037904741690582\n",
            "Epoch 10:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334767204060877\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037904628170583\n",
            "Epoch 11:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334767551840884\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037904514627477\n",
            "Epoch 12:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334767899772519\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037904401061178\n",
            "Epoch 13:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334768247856254\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037904287471606\n",
            "Epoch 14:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334768596092627\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037904173858673\n",
            "Epoch 15:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.23347689444821\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037904060222298\n",
            "Epoch 16:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334769293025232\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037903946562396\n",
            "Epoch 17:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334769641722672\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037903832878881\n",
            "Epoch 18:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334769990574725\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.703790371917167\n",
            "Epoch 19:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334770339582135\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037903605440679\n",
            "Epoch 20:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334770688745254\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037903491685827\n",
            "Epoch 21:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.233477103806473\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037903377907025\n",
            "Epoch 22:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334771387540957\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037903264104193\n",
            "Epoch 23:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334771737174561\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037903150277237\n",
            "Epoch 24:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334772086966086\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037903036426086\n",
            "Epoch 25:\n",
            "\tTrain: Average Accuracy: 51.35083713850837\tAverage Loss: 1.2334772436916068\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7037902922550647\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " این توابع به مقادیر بسیار کم یا بسیار زیادی ورودی واکنش درخوری نشان نمیدهد و شیب را از بین میبرد\n",
        " \n",
        " که به این مساله\n",
        "\n",
        " vanishing gradient\n",
        " \n",
        " نیز گفته می شود."
      ],
      "metadata": {
        "id": "WJSp1jNXoiuE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "INPUT_SHAPE = 1024\n",
        "LEARNING_RATE = 0.001\n",
        "EPOCHS = 25\n",
        "TRAINLOADER = Dataloader(X_train, y_train, n_classes = 2, batch_size = 32)\n",
        "TESTLOADER = Dataloader(X_test, y_test, n_classes = 2, batch_size = 32)\n",
        "\n",
        "network = FeedForwardNN(INPUT_SHAPE)\n",
        "network.add_layer(200, input_shape=INPUT_SHAPE, activation=LeakyRelu(), weight_initializer='uniform')\n",
        "network.add_layer(2, activation=Identical(), weight_initializer='uniform')\n",
        "network.set_training_param(loss=CrossEntropy(), lr=LEARNING_RATE)\n",
        "\n",
        "log = network.fit(EPOCHS, TRAINLOADER, TESTLOADER, True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F--cMlD2Viu6",
        "outputId": "f909d7dc-0ccc-4a21-a3d0-c2aa2255f004"
      },
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: divide by zero encountered in log\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: invalid value encountered in multiply\n",
            "  del sys.path[0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\tTrain: Average Accuracy: 50.26636225266362\tAverage Loss: nan\n",
            "\tTest: Average Accuracy: 51.75088652482269\tAverage Loss: 0.7001748680778034\n",
            "Epoch 2:\n",
            "\tTrain: Average Accuracy: 50.48040334855403\tAverage Loss: 0.6962670922545975\n",
            "\tTest: Average Accuracy: 51.684397163120565\tAverage Loss: 0.6945881198701939\n",
            "Epoch 3:\n",
            "\tTrain: Average Accuracy: 50.932267884322684\tAverage Loss: 0.6939118989251211\n",
            "\tTest: Average Accuracy: 51.4738475177305\tAverage Loss: 0.6934028408998376\n",
            "Epoch 4:\n",
            "\tTrain: Average Accuracy: 51.560121765601224\tAverage Loss: 0.6930342492318414\n",
            "\tTest: Average Accuracy: 51.329787234042556\tAverage Loss: 0.6932179340423938\n",
            "Epoch 5:\n",
            "\tTrain: Average Accuracy: 52.202245053272456\tAverage Loss: 0.6925294340503956\n",
            "\tTest: Average Accuracy: 51.36303191489362\tAverage Loss: 0.6932525480078378\n",
            "Epoch 6:\n",
            "\tTrain: Average Accuracy: 52.416286149162865\tAverage Loss: 0.6921545006156639\n",
            "\tTest: Average Accuracy: 51.72872340425532\tAverage Loss: 0.6934410020010229\n",
            "Epoch 7:\n",
            "\tTrain: Average Accuracy: 52.71594368340944\tAverage Loss: 0.691799319614088\n",
            "\tTest: Average Accuracy: 51.70656028368795\tAverage Loss: 0.6936502108166931\n",
            "Epoch 8:\n",
            "\tTrain: Average Accuracy: 52.78729071537291\tAverage Loss: 0.6914183329712988\n",
            "\tTest: Average Accuracy: 51.606826241134755\tAverage Loss: 0.6939873520154352\n",
            "Epoch 9:\n",
            "\tTrain: Average Accuracy: 52.88717656012177\tAverage Loss: 0.6910578538973046\n",
            "\tTest: Average Accuracy: 51.73980496453901\tAverage Loss: 0.6942279912009938\n",
            "Epoch 10:\n",
            "\tTrain: Average Accuracy: 53.129756468797574\tAverage Loss: 0.6906493630441878\n",
            "\tTest: Average Accuracy: 51.54033687943263\tAverage Loss: 0.6945967877737209\n",
            "Epoch 11:\n",
            "\tTrain: Average Accuracy: 53.25818112633182\tAverage Loss: 0.6902317516200778\n",
            "\tTest: Average Accuracy: 51.54033687943263\tAverage Loss: 0.694948243778725\n",
            "Epoch 12:\n",
            "\tTrain: Average Accuracy: 53.40087519025876\tAverage Loss: 0.6899189086226635\n",
            "\tTest: Average Accuracy: 51.54033687943263\tAverage Loss: 0.6952499274526259\n",
            "Epoch 13:\n",
            "\tTrain: Average Accuracy: 53.32952815829529\tAverage Loss: 0.6894040745132116\n",
            "\tTest: Average Accuracy: 51.70656028368795\tAverage Loss: 0.6959276983168018\n",
            "Epoch 14:\n",
            "\tTrain: Average Accuracy: 53.757610350076106\tAverage Loss: 0.688892580939034\n",
            "\tTest: Average Accuracy: 51.52925531914894\tAverage Loss: 0.6964284970794419\n",
            "Epoch 15:\n",
            "\tTrain: Average Accuracy: 54.071537290715376\tAverage Loss: 0.6884055471981754\n",
            "\tTest: Average Accuracy: 51.73980496453901\tAverage Loss: 0.6970062201600037\n",
            "Epoch 16:\n",
            "\tTrain: Average Accuracy: 54.385464231354646\tAverage Loss: 0.6877994061007293\n",
            "\tTest: Average Accuracy: 51.67331560283688\tAverage Loss: 0.697669009759037\n",
            "Epoch 17:\n",
            "\tTrain: Average Accuracy: 54.66133942161339\tAverage Loss: 0.6872479187571788\n",
            "\tTest: Average Accuracy: 51.828457446808514\tAverage Loss: 0.6987463608072292\n",
            "Epoch 18:\n",
            "\tTrain: Average Accuracy: 55.146499238964985\tAverage Loss: 0.6865486826402619\n",
            "\tTest: Average Accuracy: 51.76196808510638\tAverage Loss: 0.6993669274831142\n",
            "Epoch 19:\n",
            "\tTrain: Average Accuracy: 55.24638508371385\tAverage Loss: 0.685900517243091\n",
            "\tTest: Average Accuracy: 51.684397163120565\tAverage Loss: 0.7005643001895347\n",
            "Epoch 20:\n",
            "\tTrain: Average Accuracy: 55.631659056316586\tAverage Loss: 0.6851410495645197\n",
            "\tTest: Average Accuracy: 51.78413120567375\tAverage Loss: 0.7019823466257514\n",
            "Epoch 21:\n",
            "\tTrain: Average Accuracy: 55.80289193302892\tAverage Loss: 0.6844998719808548\n",
            "\tTest: Average Accuracy: 51.61790780141844\tAverage Loss: 0.7023928947386208\n",
            "Epoch 22:\n",
            "\tTrain: Average Accuracy: 55.87423896499239\tAverage Loss: 0.6836592414793902\n",
            "\tTest: Average Accuracy: 51.61790780141844\tAverage Loss: 0.7036020100154314\n",
            "Epoch 23:\n",
            "\tTrain: Average Accuracy: 55.917047184170464\tAverage Loss: 0.6826737051185007\n",
            "\tTest: Average Accuracy: 51.518173758865245\tAverage Loss: 0.7047157781565824\n",
            "Epoch 24:\n",
            "\tTrain: Average Accuracy: 56.159627092846264\tAverage Loss: 0.6818480199540743\n",
            "\tTest: Average Accuracy: 51.518173758865245\tAverage Loss: 0.7059946030957692\n",
            "Epoch 25:\n",
            "\tTrain: Average Accuracy: 56.45928462709284\tAverage Loss: 0.6809748724370539\n",
            "\tTest: Average Accuracy: 51.48492907801418\tAverage Loss: 0.706721998686557\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "چون این تابع برای مقادیر منفی  شیب کوچکی دارد به هنگام بک پروپگیت یادگیری انجام میشود."
      ],
      "metadata": {
        "id": "9Ta1Frddp63C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "INPUT_SHAPE = 1024\n",
        "LEARNING_RATE = 0.001\n",
        "EPOCHS = 25\n",
        "TRAINLOADER = Dataloader(X_train, y_train, n_classes = 2, batch_size = 16)\n",
        "TESTLOADER = Dataloader(X_test, y_test, n_classes = 2, batch_size = 16)\n",
        "\n",
        "network = FeedForwardNN(INPUT_SHAPE)\n",
        "network.add_layer(200, input_shape=INPUT_SHAPE, activation=LeakyRelu(), weight_initializer='uniform')\n",
        "network.add_layer(2, activation=Identical(), weight_initializer='uniform')\n",
        "network.set_training_param(loss=CrossEntropy(), lr=LEARNING_RATE)\n",
        "\n",
        "log = network.fit(EPOCHS, TRAINLOADER, TESTLOADER, True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GtTEuQR6qHv_",
        "outputId": "f56ee4d5-48c5-4deb-8603-9e99eeb9ec8f"
      },
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: divide by zero encountered in log\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: invalid value encountered in multiply\n",
            "  del sys.path[0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\tTrain: Average Accuracy: 50.52796803652968\tAverage Loss: nan\n",
            "\tTest: Average Accuracy: 51.795212765957444\tAverage Loss: 0.7038060611527935\n",
            "Epoch 2:\n",
            "\tTrain: Average Accuracy: 50.94178082191781\tAverage Loss: 0.7002131976637403\n",
            "\tTest: Average Accuracy: 51.795212765957444\tAverage Loss: 0.6990974113037585\n",
            "Epoch 3:\n",
            "\tTrain: Average Accuracy: 51.08447488584475\tAverage Loss: 0.6952150259027623\n",
            "\tTest: Average Accuracy: 51.69547872340426\tAverage Loss: 0.6951615008265325\n",
            "Epoch 4:\n",
            "\tTrain: Average Accuracy: 51.09874429223744\tAverage Loss: 0.6936331437065485\n",
            "\tTest: Average Accuracy: 51.828457446808514\tAverage Loss: 0.693785455482477\n",
            "Epoch 5:\n",
            "\tTrain: Average Accuracy: 51.55536529680365\tAverage Loss: 0.6929019800236341\n",
            "\tTest: Average Accuracy: 51.795212765957444\tAverage Loss: 0.6934145620972518\n",
            "Epoch 6:\n",
            "\tTrain: Average Accuracy: 51.72659817351598\tAverage Loss: 0.692402572127422\n",
            "\tTest: Average Accuracy: 51.994680851063826\tAverage Loss: 0.6934668908495198\n",
            "Epoch 7:\n",
            "\tTrain: Average Accuracy: 52.04052511415525\tAverage Loss: 0.6919955730399904\n",
            "\tTest: Average Accuracy: 51.994680851063826\tAverage Loss: 0.6936216366381119\n",
            "Epoch 8:\n",
            "\tTrain: Average Accuracy: 52.42579908675799\tAverage Loss: 0.6916088322575372\n",
            "\tTest: Average Accuracy: 51.52925531914894\tAverage Loss: 0.6938415521614064\n",
            "Epoch 9:\n",
            "\tTrain: Average Accuracy: 52.59703196347032\tAverage Loss: 0.6912313848459084\n",
            "\tTest: Average Accuracy: 52.12765957446808\tAverage Loss: 0.6941130857540089\n",
            "Epoch 10:\n",
            "\tTrain: Average Accuracy: 52.75399543378995\tAverage Loss: 0.690860428057418\n",
            "\tTest: Average Accuracy: 52.027925531914896\tAverage Loss: 0.6943130667137878\n",
            "Epoch 11:\n",
            "\tTrain: Average Accuracy: 53.167808219178085\tAverage Loss: 0.6904512343278232\n",
            "\tTest: Average Accuracy: 52.160904255319146\tAverage Loss: 0.6946664333944935\n",
            "Epoch 12:\n",
            "\tTrain: Average Accuracy: 53.29623287671233\tAverage Loss: 0.6900037913757217\n",
            "\tTest: Average Accuracy: 51.9281914893617\tAverage Loss: 0.6950453160701984\n",
            "Epoch 13:\n",
            "\tTrain: Average Accuracy: 53.538812785388124\tAverage Loss: 0.6895604541999443\n",
            "\tTest: Average Accuracy: 51.89494680851064\tAverage Loss: 0.6955872731057984\n",
            "Epoch 14:\n",
            "\tTrain: Average Accuracy: 53.75285388127854\tAverage Loss: 0.6890249269914335\n",
            "\tTest: Average Accuracy: 51.994680851063826\tAverage Loss: 0.6960336698759545\n",
            "Epoch 15:\n",
            "\tTrain: Average Accuracy: 53.92408675799087\tAverage Loss: 0.6885253695939177\n",
            "\tTest: Average Accuracy: 52.12765957446808\tAverage Loss: 0.6966275127273124\n",
            "Epoch 16:\n",
            "\tTrain: Average Accuracy: 54.29509132420091\tAverage Loss: 0.687885253832611\n",
            "\tTest: Average Accuracy: 52.194148936170215\tAverage Loss: 0.6966376692676508\n",
            "Epoch 17:\n",
            "\tTrain: Average Accuracy: 54.3236301369863\tAverage Loss: 0.6872933751620675\n",
            "\tTest: Average Accuracy: 51.861702127659576\tAverage Loss: 0.6976537966643113\n",
            "Epoch 18:\n",
            "\tTrain: Average Accuracy: 54.92294520547945\tAverage Loss: 0.68648864653602\n",
            "\tTest: Average Accuracy: 51.961436170212764\tAverage Loss: 0.6979927966597992\n",
            "Epoch 19:\n",
            "\tTrain: Average Accuracy: 55.279680365296805\tAverage Loss: 0.6856589162291412\n",
            "\tTest: Average Accuracy: 51.994680851063826\tAverage Loss: 0.6987616190012039\n",
            "Epoch 20:\n",
            "\tTrain: Average Accuracy: 55.46518264840183\tAverage Loss: 0.6847286103081937\n",
            "\tTest: Average Accuracy: 52.09441489361702\tAverage Loss: 0.6994207273700113\n",
            "Epoch 21:\n",
            "\tTrain: Average Accuracy: 55.45091324200913\tAverage Loss: 0.6839631393565135\n",
            "\tTest: Average Accuracy: 51.76196808510638\tAverage Loss: 0.6998452343669909\n",
            "Epoch 22:\n",
            "\tTrain: Average Accuracy: 55.821917808219176\tAverage Loss: 0.6830913537371511\n",
            "\tTest: Average Accuracy: 51.795212765957444\tAverage Loss: 0.7006755899202922\n",
            "Epoch 23:\n",
            "\tTrain: Average Accuracy: 55.92180365296804\tAverage Loss: 0.6820449218911837\n",
            "\tTest: Average Accuracy: 51.69547872340426\tAverage Loss: 0.7021675285759229\n",
            "Epoch 24:\n",
            "\tTrain: Average Accuracy: 56.17865296803653\tAverage Loss: 0.6809874240199563\n",
            "\tTest: Average Accuracy: 51.52925531914894\tAverage Loss: 0.7031109878051932\n",
            "Epoch 25:\n",
            "\tTrain: Average Accuracy: 56.207191780821915\tAverage Loss: 0.6797481703224071\n",
            "\tTest: Average Accuracy: 51.662234042553195\tAverage Loss: 0.7044219266853242\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "INPUT_SHAPE = 1024\n",
        "LEARNING_RATE = 0.001\n",
        "EPOCHS = 25\n",
        "TRAINLOADER = Dataloader(X_train, y_train, n_classes = 2, batch_size = 256)\n",
        "TESTLOADER = Dataloader(X_test, y_test, n_classes = 2, batch_size = 256)\n",
        "\n",
        "network = FeedForwardNN(INPUT_SHAPE)\n",
        "network.add_layer(200, input_shape=INPUT_SHAPE, activation=LeakyRelu(), weight_initializer='uniform')\n",
        "network.add_layer(2, activation=Identical(), weight_initializer='uniform')\n",
        "network.set_training_param(loss=CrossEntropy(), lr=LEARNING_RATE)\n",
        "\n",
        "log = network.fit(EPOCHS, TRAINLOADER, TESTLOADER, True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFWtMhtpr6QV",
        "outputId": "f98f04f8-1c04-4e3e-a3be-1f637034df82"
      },
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: divide by zero encountered in log\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: invalid value encountered in multiply\n",
            "  del sys.path[0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\tTrain: Average Accuracy: 49.24031047077922\tAverage Loss: nan\n",
            "\tTest: Average Accuracy: 48.2804008152174\tAverage Loss: 12.085565266894507\n",
            "Epoch 2:\n",
            "\tTrain: Average Accuracy: 50.09004667207792\tAverage Loss: 28.57715244195313\n",
            "\tTest: Average Accuracy: 51.7195991847826\tAverage Loss: 0.8071812773013868\n",
            "Epoch 3:\n",
            "\tTrain: Average Accuracy: 51.26192167207792\tAverage Loss: 3.6408591269460877\n",
            "\tTest: Average Accuracy: 48.24784873188406\tAverage Loss: 0.7885425902731042\n",
            "Epoch 4:\n",
            "\tTrain: Average Accuracy: 48.20667613636363\tAverage Loss: 0.8950136675003326\n",
            "\tTest: Average Accuracy: 51.7195991847826\tAverage Loss: 0.6928317627834278\n",
            "Epoch 5:\n",
            "\tTrain: Average Accuracy: 48.639153814935064\tAverage Loss: 0.713462746088967\n",
            "\tTest: Average Accuracy: 48.312952898550726\tAverage Loss: 0.6934014427556631\n",
            "Epoch 6:\n",
            "\tTrain: Average Accuracy: 48.443841314935064\tAverage Loss: 0.7069699644607157\n",
            "\tTest: Average Accuracy: 48.2804008152174\tAverage Loss: 0.6936263059663249\n",
            "Epoch 7:\n",
            "\tTrain: Average Accuracy: 48.80656452922078\tAverage Loss: 0.7039334705287389\n",
            "\tTest: Average Accuracy: 48.27332427536232\tAverage Loss: 0.6937260845886656\n",
            "Epoch 8:\n",
            "\tTrain: Average Accuracy: 48.51359577922078\tAverage Loss: 0.7021083923262635\n",
            "\tTest: Average Accuracy: 48.05819746376812\tAverage Loss: 0.693772091777923\n",
            "Epoch 9:\n",
            "\tTrain: Average Accuracy: 48.6531047077922\tAverage Loss: 0.7008661569723087\n",
            "\tTest: Average Accuracy: 48.38937952898551\tAverage Loss: 0.6937897082226999\n",
            "Epoch 10:\n",
            "\tTrain: Average Accuracy: 48.49964488636363\tAverage Loss: 0.6999535489164842\n",
            "\tTest: Average Accuracy: 48.89039855072463\tAverage Loss: 0.6937898379580117\n",
            "Epoch 11:\n",
            "\tTrain: Average Accuracy: 48.51359577922078\tAverage Loss: 0.6992472338158912\n",
            "\tTest: Average Accuracy: 49.17062952898551\tAverage Loss: 0.6937790731660227\n",
            "Epoch 12:\n",
            "\tTrain: Average Accuracy: 48.80656452922078\tAverage Loss: 0.6986797388152521\n",
            "\tTest: Average Accuracy: 49.58673007246377\tAverage Loss: 0.6937612841188597\n",
            "Epoch 13:\n",
            "\tTrain: Average Accuracy: 49.18323863636363\tAverage Loss: 0.6982097240112136\n",
            "\tTest: Average Accuracy: 49.88677536231884\tAverage Loss: 0.6937382295247652\n",
            "Epoch 14:\n",
            "\tTrain: Average Accuracy: 49.0437297077922\tAverage Loss: 0.6978124840347737\n",
            "\tTest: Average Accuracy: 49.796195652173914\tAverage Loss: 0.6937131448867683\n",
            "Epoch 15:\n",
            "\tTrain: Average Accuracy: 49.0437297077922\tAverage Loss: 0.6974725326118555\n",
            "\tTest: Average Accuracy: 49.946218297101446\tAverage Loss: 0.6936873643393637\n",
            "Epoch 16:\n",
            "\tTrain: Average Accuracy: 49.05768060064935\tAverage Loss: 0.6971758228326298\n",
            "\tTest: Average Accuracy: 49.52304121376812\tAverage Loss: 0.6936611508028446\n",
            "Epoch 17:\n",
            "\tTrain: Average Accuracy: 49.0437297077922\tAverage Loss: 0.696913889698613\n",
            "\tTest: Average Accuracy: 49.58106884057971\tAverage Loss: 0.6936350369647514\n",
            "Epoch 18:\n",
            "\tTrain: Average Accuracy: 49.01582792207792\tAverage Loss: 0.696681422439883\n",
            "\tTest: Average Accuracy: 49.470674818840585\tAverage Loss: 0.6936093420895695\n",
            "Epoch 19:\n",
            "\tTrain: Average Accuracy: 49.21114042207792\tAverage Loss: 0.696471874428909\n",
            "\tTest: Average Accuracy: 49.796195652173914\tAverage Loss: 0.6935847810532044\n",
            "Epoch 20:\n",
            "\tTrain: Average Accuracy: 49.40645292207792\tAverage Loss: 0.6962839434367194\n",
            "\tTest: Average Accuracy: 49.83582427536232\tAverage Loss: 0.69356134341536\n",
            "Epoch 21:\n",
            "\tTrain: Average Accuracy: 49.462256493506494\tAverage Loss: 0.6961131687976295\n",
            "\tTest: Average Accuracy: 49.777796648550726\tAverage Loss: 0.6935393238664519\n",
            "Epoch 22:\n",
            "\tTrain: Average Accuracy: 49.4343547077922\tAverage Loss: 0.6959570804739331\n",
            "\tTest: Average Accuracy: 50.01839900362319\tAverage Loss: 0.6935192727577247\n",
            "Epoch 23:\n",
            "\tTrain: Average Accuracy: 49.39250202922078\tAverage Loss: 0.6958138243260005\n",
            "\tTest: Average Accuracy: 49.960371376811594\tAverage Loss: 0.6934997773641224\n",
            "Epoch 24:\n",
            "\tTrain: Average Accuracy: 49.420403814935064\tAverage Loss: 0.6956816484724992\n",
            "\tTest: Average Accuracy: 49.960371376811594\tAverage Loss: 0.6934842238762436\n",
            "Epoch 25:\n",
            "\tTrain: Average Accuracy: 49.40645292207792\tAverage Loss: 0.6955601663862997\n",
            "\tTest: Average Accuracy: 50.01273777173913\tAverage Loss: 0.6934719748606795\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "batch_size 32\n",
        "\n",
        "از همه بهتر است.چون همگرایی سریع تری رقم میزند. \n",
        "\n",
        "اگر خیلی کوچک در نظر بگیریم اصلا بهینه نیست ویادگیری خیلی کند میشود."
      ],
      "metadata": {
        "id": "-1BYSyKxsTc4"
      }
    }
  ],
  "metadata": {
    "colab": {
      "name": "finalAIPhase1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}