ImageClassificationwithANN.ipynb

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        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EVRtrYudmYiL",
        "outputId": "ca4bccd6-e282-4993-a685-5394b4bc11ad"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torchvision\n",
        "import keras\n",
        "from keras import backend as K\n",
        "from keras import metrics\n",
        "device = torch.device('cuda' if torch.cuda.is_available else 'cpu')\n",
        "print(device)\n",
        "!nvidia-smi -L"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n",
            "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.\n",
            "\n"
          ]
        }
      ]
    },
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            "29363e55d1fc4d71bdfa263afb534c2f"
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        },
        "id": "FV5rfIZQnZg_",
        "outputId": "37f8bfb6-49cb-4b0f-ce47-93ba14506304"
      },
      "source": [
        "from torchvision import datasets, transforms\n",
        "train = datasets.MNIST('', train=True, download=True,\n",
        "                       transform=transforms.Compose([\n",
        "                           transforms.ToTensor()\n",
        "                       ]))\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b48b3c94c74740358d2d357b1a8db225",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting MNIST/raw/train-images-idx3-ubyte.gz to MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "940d76eb4c38404ea63522208269902c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting MNIST/raw/train-labels-idx1-ubyte.gz to MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "81b192af5b294ea4b3e8c0bdb36c5bab",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting MNIST/raw/t10k-images-idx3-ubyte.gz to MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "98ccf2305b87428b8e7a10c4e89653f4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting MNIST/raw/t10k-labels-idx1-ubyte.gz to MNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Id-TDNgHoU0C"
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qwjfFmhtohKH",
        "outputId": "ecad2e04-5756-4e0d-9fc6-7d70e694e436"
      },
      "source": [
        "train_images.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pIOCYDhol22",
        "outputId": "a969dc97-e997-4c1f-b7d6-6e62c0da5495"
      },
      "source": [
        "len(train_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6rrBnjJotP5",
        "outputId": "3a62e369-a41f-477b-bae5-e438059c5237"
      },
      "source": [
        "test_images.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5-hZq6i1oykD",
        "outputId": "2927521f-fb5e-4494-ba3f-e2b19e51f40d"
      },
      "source": [
        "len(test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Jm7GUIYHozuw",
        "outputId": "50f2a417-a069-4906-ec72-40517b21a782"
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYibLGWMo9k4"
      },
      "source": [
        "train_images = train_images / 255.0\n",
        "\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "id": "psNJT6WNpIZO",
        "outputId": "e5d4e436-0327-4ba1-af10-45154fafa2b1"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i in range(5):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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b7vt2k9L3Tda7N3vyM+el/a7ufM7mHjbPeNMD90E2C3XVRNGDlFqfI0aMiLLXJfdB1vnw4cOTemym4bQhPvSc6/EY7Mf33jS7dZNS69JTNFWPr5fVh3097tNc5n83y0ZXV5ruBdC518MUAPf0THNEi5A+q4N0WS2kz+ogXVaAIikHfgHgEQAfMrO1ZvZ5ANcAONbMVgH4VP1Y9AGkz+ogXVYL6bM6SJfVpUj03DkZRZ/s4bb0Gn5ZkJeR2TzHWZ6BNAs425V9RBtfg81kL7zwQlKPs09zhly/nMwRXf5eHCly8cUXR3nJkiVJPR8p0kmZ9MnL7xMnToyyj2yaPXt2lL0u+Tnys/ff30fsdOKX9rM2keT7AKku2ZzD0YK9TZl0uT2wfr2uu2oS6CTPFM94U9Kee+4Z5VaZ5PqiPjmDe16W7qzoVSA7es6b59gh2rtSMN403wr6oi49RTe69/V47M2LQGZds7xpU6mtlsoILoQQQghRBE2ahBBCCCEKoEmTEEIIIUQBShub2ZN4/5as3bMPPfTQ5Jj9LdjPyNtm2ZbP9ljvG8Hh8twmn5WafXO8XX/o0KFR5nD2r3zlK0m9j3zkIygb3vbN35t14v1VeFf0vGef5w+TFQbbVbJ8ZTjtgSfPpt8Tbeor8Hf1z6RZ9/U+aiKbLH9AIPVZ8fmEuE/n7VzPfYY/4/05Bw58Lxck+zeVPUS9r9JVn6asVAJ5vk/sI8o7Z5QRrTQJIYQQQhRAkyYhhBBCiAKUxjzHS3d5m3FyPV7SK7qEnMfkyZOTY87GzZtF5oW08hK1NwtyaG2WiRBI25u3USlvkMkh02XFm6BYf8wBBxyQHPMGjkVNrUWz1BYlLws8k6cH/y7nhWhXmTyTXF5oek9+Jk8XeRvUtiN5z4N3KOCs30A6ZnKmbw+PmZyZnTPtA9l93evSp3rppMSZwktJnnkubyPyrGsUTf0j85wQQgghRAXQpEkIIYQQogAtW6/Mi4Lq6WXUhx9+ODmeMWNGlOfPnx9lzm4LpJvqcrSNX2bk9vI1/Hfka7Cpzl8vLxqEzUJc76677krqnXzyyZnXKAtZGyfzsj6QRjHycwNSEx9H4/kl46wojqIZpPM2eOVrtKvJbXvIe/ez9OSfK+upaARenqmAj7mPlWDD3paTZ6Jk09rYsWOTsmHDhkWZ+4t/phs3bowym+D8xr78OTYLDho0KKm3bt26zPaKfFauXBll74JQdAPtvPE1qx7/hvKuF2VEK01CCCGEEAXQpEkIIYQQogCaNAkhhBBCFKBlPk1FfT+2bNmSHK9fvz7KbH/l80Dq48P1gNRHhm2z3peIw2QHDx4cZW+TZ18ats/7HdzZrn/UUUdF+Y033kjqzZs3L8ren4BD2tmfZ+HChehrZIX++++clzk7L+NsVr2esMdzm9inJs//o52yfueR94yLpoYomq24K58vmrZApGOVTxXCPkk8ZnKGfyAd/1577bUoex9T9nfy4z3DYzDv0LDvvvsm9ZRaYmtWrFgR5SFDhiRl/Pz5t8zD42FeP+N6/Fv50ksvJfUWLFgQZf7dbBV6U4QQQgghCqBJkxBCCCFEAVpmnnvkkUeS469//etR5s0YebkWyM7+6zdKZfOfXw7mpUBePvSh7rwUOH369CgfccQRST0Of+Vl6LzMppzN+80330zKeFnUmwx5WZQ39i17FtXuwEvxXs9Z4eZ5Zp+u4D/PplEu8xnLxdb0xCa9Rc2yWeY+ryduk3SYbbp68cUXk3pPPvlklEeNGpWUcYZwdnU48MADk3o8jj333HNR9pv88jibB+/kwJuaX3bZZUk9meS2ZtasWVH25nF+J/JMm0VN7Fkb+/r344YbboiyzHNCCCGEEH0ETZqEEEIIIQrQdPNc5zL4pZdempxnE0zehrVZ2bI52zaQmtq82Y3hTSGff/75pOzKK69seA1eLgTSjLRsnps0aVJSj6NLVq1aFWW/mSWbfrypgJc0+Tn5yJC+QNFosrxIS85ay+9Knnkub/k4q8xnx2UTb57Zh1H0XI28TN9ZZre8iLa859qVqEkeE3iz6HYiy3Q1c+bM5HjMmDFR9tn6+dnx2Lr//vsn9Z566qko8/vgo7fYpWHgwIFR9uMnm/U4OziPuQAwevRoiBSOwvY7c/DYVjQqLg/uj/zu+Khzjp4rA1ppEkIIIYQogCZNQgghhBAF0KRJCCGEEKIATfVp2rx5M2655RYAW/sPcbgqh6D6bNneft2J9yVhvwRvG2eb+ttvvx1ltpMDwJQpU6L8y1/+Msonn3xyUm/16tUN2/74448n9ebMmRPlrGyoQOqf5X1pGLY5+3o+NLgvk5XBHUh9IPLCYLP8jth/zNdjHXm/GW/v78SnyBBbwxn0vT6zfCX8+e76h3n98fW8b454D/YrAoDDDjssyl6XPCZ5n1Mmyw8wrw+z76gf69iXKsuvCpBPUyM4dY1P+VA0lUDeuJkFvzv8mwykGcL5PfK/m81imytNZjbUzOaY2ZNmttzMLq2f72dmD5rZqvr/e2/rWqK1SJfVQvqsDtJltZA+q0sR89w7AC4PIYwB8BEAF5vZGABXApgVQhgNYFb9WJQb6bJaSJ/VQbqsFtJnRdmmeS6EsAHAhrr8hpmtALA/gFMAfKJe7RYAcwF8Ne9aO+20UwyN9yYzNsPxstuwYcMy6/Eyv88W269fvyjzxpH+GrzM6zfiZdPPaaedFuVx48Yl9XhJk82HfvmQs1mzWciHXfPGiN7slhVW780XfpPiep0e02UzKbq5c1eWj7PMbP4aeeYh1qVfWs76TE/QV/WZF7rclaX9ouTpOivDe7Mosy7Z/YDTqwCpKZMzcQOpnrkP5/WRvHQzWSY+v7Evm3PYFYN3muhtyqxPhrO2A+kz8mls+Pln7cwBpP22aBoYvvZxxx2X1LvjjjuizC4vrcoOvl2O4GY2AsB4AP8FYGD9xQCAlwAMzPiYKCHSZbWQPquDdFktpM9qUXjSZGa7A5gB4LIQQrKsE2pTxoZ/npnZRWa2yMwWsXO2aB09octm/tUm8pE+q4N0WS2kz+pRKHrOzHZCTfE/DyHcVT+90cwGhRA2mNkgAJsafTaEcCOAGwFgzJgxodMs55dehw4dGmWOQPMvDJu49tlnn4YykC4N+2VdLuPlZb9xLpsK+vfvH2XepBJIl6XZnOijD/he3F5vNmBTgS/jpW1eht5zzz2TekuWLEEjekqXHR0dTbNhFM0wW9ScU9T8kpdNmsvY9MAbKjeDvqjPvIjQrKX9vGzeXcG/K9znePxpJmXVJUen+XGbx1KvVx7veBxjtwoPm4v82Je1qfLIkSOTepz5mz/j/2jfsmVLlNmdo6coqz6ZxYsXZ5bl/fbk9U3WO78Tedn/uf89/fTTST3W4YoVK6JcWvOc1b7ZTwCsCCH8OxXdC6AzJn8KgHt6vnmiJ5Euq4X0WR2ky2ohfVaXIitNHwNwHoAnzKxz+eIqANcAuMPMPg/geQBn9U4TRQ8iXVYL6bM6SJfVQvqsKEWi5+YDyLJ7fLJnmyN6E+myWkif1UG6rBbSZ3VpakbwXXfdFYcffjiANIQfAH72s59FefDgwVE+4IADknqcFoB9kLw9ne2v3obO9nC+ns9MyzZXDmv1Ybds32W7rb8e+2NlpVjw9VgG0nQEbAfmsGBg6+zmZaQrIeVd9W3J8mPK85fKSznA7WDbf1H/q3aG+6p/B/hZ9nToP+vM+1dwX3r22WejPH78+B5tQ1+ExzHf/3hc9P58PO7yuOWfPY+fPC56nxoeJ9etWxfljo6OpN7DDz8cZR6r/XjM/lO94dPUF7j//vuT4wEDBkTZ75bAemOdeV9g7rf8zH09ztbOumZfXX/fJ554osG3aC7ae04IIYQQogCaNAkhhBBCFKCp5jnmqquuSo47zXYAcO2110bZm504VJ9NVz4rLC8j+5QDWaGreVmf80Jr2RSYdz2Gy3zbeYmaw2KBdFmUlzF540wAOPfcc6N83nnnZbajlRTN4M1L+3nZhBkfGp1lmvHmBv+5rPZx2/l6Rc197cz69eszy1gfWekHgOKZw7M2cfZ9k80DbKIQ6S4Hfuzj8XjZsmVJGfdVTonir8HPPs/lgl0peOPgT3/600k9/l3ga/js11kbBbcTbIoG0t8ebybLSsHj6913331RPumkk6K8yy67JPXYnOuzyWfVW758eWa9ZqGVJiGEEEKIAmjSJIQQQghRAE2ahBBCCCEK0HSfpk4fA++jcOKJJzaUZ8+endRjX6g1a9ZE2afIZ58F72fCobB5Ic68yzP7TXRuBdMJ29rZNls0/Jx9doDUx8n73Bx77LFRPuSQQ6LcqpTyzcY/D/YnYv35enyc5efir8F4v5ms1AdKObBtuL/4dCD8nPlZer0U9SPjsGmu5/XOvjS8FZJIt7Ly7z37trz22mtJGT9vTiPjfZV4u6nddtst815ZeH8Yvh6/T3xtANiwYUOUP/ShDxW6V9VgnyMAmDt3bpR9n+M+k7ddVJZ/Ut52YXn1eLwYN25c5n2bhVaahBBCCCEKoEmTEEIIIUQBmm6eywrpzmLSpEnJ8cKFCxvWe+qpp5JjXlLm5VoAWLt2bZSHDx8eZW8m89nIRc9SNASfl/Z5B3MgXcrld8u/Z2wS4DLfBj4uujM7o5QD22bixIlRXrlyZVLGJh5elvew6YD1VPQZs2kGSN+JdjXVZPHWW29F2adH8WH8DO92z2OrD/XnsZpTGPB9fT2Wfdh8VmoJ/25weH27cuGFFybHF110UZS9eY5NsT6rO5P1G+9TeXBf5/fj9ddfT+rx8aWXXpp532ahlSYhhBBCiAJo0iSEEEIIUYCWZQTvaQ4++ODcY+bQQw/t7eaIHoSXcf2mj2w248zF3kzGkThFTW15G/FyBCVnPvamgqw2ANtvqq4KbOI5//zzk7I5c+ZEefPmzVH2pho28WRF4QCp3lifI0aMSOqxG4A3QbU7bBIfOXJkUsYmOA+/7xxt5c2uHPk7bdq0KHsz3ic/+cmG1/b9iscL1uWoUaOSesccc0xm29sVzrTud5lg/CbzzKZNmxqe95nD+d3hfurNpjNnzowyu9O0ivYctYUQQgghthNNmoQQQgghCqBJkxBCCCFEASrj0yT6HnnhwMyECROiPHbs2KSMdzTP81VivwfOWOvvmxXK7v2P2I+GfSg4nN7Trj5MHn7G3r9l8uTJDT+zZcuW5Jj9I3g3AK/P/fbbr6FcNJ2B0kQA119/fZR9tmbuV5/5zGeSMvbvY1+UF198ManHflIdHR2F2nT66adnlp155pmFriG2hjNu+5QD8+bNi/KKFSui7Hft+NjHPtbw2pdccklyzL5P/O7wjiBlRKO4EEIIIUQBNGkSQgghhCiAZW1Q2is3M3sZwFsANm+rbhMYgNa3o1ltGB5C2Gfb1YpT1+XzaK/nuC2a0Y4e1yWgvtmiNvSmLtU3U/q6PtU3m9uGTF02ddIEAGa2KIRQzHBd8XaUoQ3dpQzfoQxtKFM7ukpZ2l+GdpShDd2lDN+hDG0oUzu6SlnaX4Z2tLoNMs8JIYQQQhRAkyYhhBBCiAK0YtJ0Ywvu2YgytKMMbeguZfgOZWgDUJ52dJWytL8M7ShDG7pLGb5DGdoAlKcdXaUs7S9DO1rahqb7NAkhhBBC9EVknhNCCCGEKEBTJ01mdoKZPW1mz5jZlU2870/NbJOZLaNz/czsQTNbVf9/715uw1Azm2NmT5rZcjO7tBXt6Cmky+roEpA+q6RP6bI6ugSkz7Lps2mTJjPbAcAPAUwGMAbAOWY2pkm3nwrgBHfuSgCzQgijAcyqH/cm7wC4PIQwBsBHAFxc//7Nbke3kS6ro0tA+kSF9CldVkeXgPSJMuozhNCUfwA+CmAmHf8jgH9s4v1HAFhGx08DGFSXBwF4ulltqd/zHgDHtrod0mV761L6rJY+pcvq6FL6LKc+m2me2x8A79S4tn6uVQwMIWyoyy8BGNisG5vZCADjAfxXK9vRDaTLOhXQJSB9RiqgT+myTgV0CUifkbLoU47gAEJtutqUMEIz2x3ADACXhRBeb1U7qop0WS2kz+ogXVaLdtVnMydN6wAMpeMh9XOtYqOZDacfS+8AAAa/SURBVAKA+v+bevuGZrYTaor/eQjhrla1oweQLqujS0D6rJI+pcvq6BKQPkunz2ZOmh4DMNrMRprZzgDOBnBvE+/vuRfAlLo8BTVbaa9hZgbgJwBWhBD+vVXt6CGky+roEpA+q6RP6bI6ugSkz/Lps8lOXCcCWAngWQBXN/G+vwCwAcBfUbMJfx5Af9S87lcB+A2Afr3chqNRW0JcCmBJ/d+JzW6HdCldSp/V1qd0WR1dSp/l06cyggshhBBCFECO4EIIIYQQBdCkSQghhBCiAJo0CSGEEEIUQJMmIYQQQogCaNIkhBBCCFGASkyazOxUMwtmdnDB+mvMbECD829u5323q37OdS4ws8E9ca2+jpn1N7Ml9X8vmdk6Ot4553MjeDduV/bPZvapjLKtnr2ZnW1mV5vZJ8zsqO59o/ZFumxvzOxvdV0vN7Pfm9nlZlaJ35y+jvpm19mx1Q3oIc4BML/+/z+1uC1d4QIAywCsb3E7Wk4I4RUAhwOAmf1vAG+GEK7t5jW/3ui81XYQvwBbP/vJAL4H4GQAbwJY0J37tyvSZdvzdgihU//7ApgG4INwY7SZ7RhCeKcF7Wtb1De7Tp+f9df3pDkatcRbZ9P5T5jZXDO708yeMrOf17OL8md3MbNfm9mFDa77FTN7zMyWmtn/ybn/d+t/Sc0ys33q5w43s4X1z95tZntnnTezMwB0APh5fZa/S488mApjZmPN7NH681pqZqPrRTuY2U11fTzQ+SzNbGr9OXeuMn7LzH6H2iQ7efb1d+RwAFsAfAHAl+tlH6//lTW7fs9ZZjaMrv8jM1tkZivN7KRmP5O+inTZHoQQNgG4CMAlVuMCM7vXzGYDmGVmu5nZT+vvwmIzOwVo/H7U6/4/q61eLTOzz7T0y1UU9c3G9PlJE4BTAPxnCGElgFfM7MNUNh7AZQDGABgF4GNUtjuA+wD8IoRwE1/QzI4DMBrARNQU+2Ez+7sG994NwKIQwlgAD+G9v6BuBfDVEMJhAJ7IOx9CuBPAIgB/H0I4PITwdlceQpvxBQD/t/5XbAdq2WqBms5+WNfHawBOz/j8KyGECSGE27D1sx8P4PchhNUAfgTgu/WyeQC+D+CWuv5+jtpfSZ2MQO19+TSAH5nZB3rw+1YZ6bJNCCE8B2AHAPvWT00AcEYI4b8DuBrA7BDCRADHAPg3M9sNjd+PEwCsDyH8txDCoQD+s8lfpV1Q32xAFSZN5wC4vS7fXj/u5NEQwtoQwruopV8fQWX3APhZCOHWBtc8rv5vMYDfATgYtRfF8y6A6XX5NgBHm9meAPYKITxUP38LgL/LOl/4WwrmEQBXmdlXAQyniebqEMKSuvw4Un0z0zPOA7UB+dcZZR9FzcQAAP+B2gpnJ3eEEN4NIawC8Bxq74zYNtJl+/JgCGFLXT4OwJVmtgTAXAAfADAMjd+PJwAcW1/J+HgI4Q8taHs7oL7ZgD49aTKzfgAmAbjZzNYA+AqAs+pLfwDwZ6r+N6Q+XL8FcALVTS4N4Jv1me/hIYQDQwg/KdAk7UnTC5jZafaek2JHCGEagP8B4G0AvzKzSfWqefpm3sq53XEAHuhCM73u9S40QLpsX8xsFGq67NyRnnVnAE6nMXdYCGFFo/ejblWYgNrk6V/NrKEvjdg+1DeL0acnTQDOAPAfIYThIYQRIYShAFYD+HiBz34dwKsAftigbCaAz1nNXwpmtr/VHBk976u3AQD+J4D59b96XjWzzjacB+ChrPN1+Q0AexRoc1sSQribBtNF9cH3uRDC91BbMTysG5ePz76+Grhj3UkyKauzAO/5zf09gHlUdqaZvc/MDkDNFPx0N9pUWaTL9sRq/p4/AvCD0HjD05kA/qHzj1gzG1//f6v3w2pRWH+sm33+DbUJlOgm6pvF6OuTpnMA3O3OzUBqosvjUgC7mNm3+WQI4QHUlgcfMbMnANyJxpOatwBMtFoI5iQA/1w/PwU1m/xS1HyitnV+Kmr2WTmCF+MsAMvqS/mHouYr1lWmov7sUfur6jdUdh+Azr++Pg7gHwB8tq6/81B7fzp5AcCjqC05fyGE8KdutKmdkC6ryy71570cNV08ACArqOZfAOwEYGm9/r/Uzzd6P8YBeLR+7p8A/Gsvfod2Rn2zAdZ40i9E+2FmNwO4OYSwcDs/NxXA/XWnflECpEshyklf75tVydMkRLcJIfyvVrdB9AzSpRDlpK/3Ta00CSGEEEIUoK/7NAkhhBBCNAVNmoQQQgghCqBJkxBCCCFEATRpEkIIIYQogCZNQgghhBAF0KRJCCGEEKIA/x8E7H96kJ2k9wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0UA72JfpjZ3"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dense(10)\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfNEgb_QplD7"
      },
      "source": [
        "def recall_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    recall = true_positives / (possible_positives + K.epsilon())\n",
        "    return recall\n",
        "\n",
        "def precision_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    return precision\n",
        "\n",
        "def f1_m(y_true, y_pred):\n",
        "    precision = precision_m(y_true, y_pred)\n",
        "    recall = recall_m(y_true, y_pred)\n",
        "    return 2*((precision*recall)/(precision+recall+K.epsilon()))\n",
        "\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy', f1_m, precision_m, recall_m])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2eljZ3cft30z",
        "outputId": "152dcf9b-6c7c-4147-ed54-ca61f50c9f38"
      },
      "source": [
        "history = model.fit(train_images, train_labels,\n",
        "                    validation_data=(test_images, test_labels), epochs=7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/7\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1404 - accuracy: 0.9472 - f1_m: 1.1723 - precision_m: 0.9161 - recall_m: 1.6396 - val_loss: 0.3832 - val_accuracy: 0.8876 - val_f1_m: 1.1701 - val_precision_m: 0.9163 - val_recall_m: 1.6329\n",
            "Epoch 2/7\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1372 - accuracy: 0.9478 - f1_m: 1.1713 - precision_m: 0.9157 - recall_m: 1.6377 - val_loss: 0.3877 - val_accuracy: 0.8895 - val_f1_m: 1.1794 - val_precision_m: 0.9209 - val_recall_m: 1.6516\n",
            "Epoch 3/7\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1345 - accuracy: 0.9499 - f1_m: 1.1682 - precision_m: 0.9171 - recall_m: 1.6209 - val_loss: 0.4044 - val_accuracy: 0.8838 - val_f1_m: 1.1884 - val_precision_m: 0.9195 - val_recall_m: 1.6947\n",
            "Epoch 4/7\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1312 - accuracy: 0.9495 - f1_m: 1.1680 - precision_m: 0.9191 - recall_m: 1.6137 - val_loss: 0.4045 - val_accuracy: 0.8886 - val_f1_m: 1.1726 - val_precision_m: 0.9207 - val_recall_m: 1.6277\n",
            "Epoch 5/7\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1293 - accuracy: 0.9510 - f1_m: 1.1647 - precision_m: 0.9177 - recall_m: 1.6069 - val_loss: 0.4080 - val_accuracy: 0.8912 - val_f1_m: 1.1685 - val_precision_m: 0.9215 - val_recall_m: 1.6087\n",
            "Epoch 6/7\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.1242 - accuracy: 0.9538 - f1_m: 1.1559 - precision_m: 0.9155 - recall_m: 1.5803 - val_loss: 0.4102 - val_accuracy: 0.8892 - val_f1_m: 1.1756 - val_precision_m: 0.9219 - val_recall_m: 1.6357\n",
            "Epoch 7/7\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1250 - accuracy: 0.9531 - f1_m: 1.1557 - precision_m: 0.9151 - recall_m: 1.5799 - val_loss: 0.4418 - val_accuracy: 0.8889 - val_f1_m: 1.1583 - val_precision_m: 0.9108 - val_recall_m: 1.6036\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "NhdCVSirP009",
        "outputId": "6c9d8878-caea-463f-ee3b-74a6c664823e"
      },
      "source": [
        "history_dict = history.history\n",
        "loss_values = history_dict['loss']\n",
        "val_loss_values = history_dict['val_loss']\n",
        "accuracy = history_dict['accuracy']\n",
        "val_accuracy = history_dict['val_accuracy']\n",
        " \n",
        "epochs = range(1, len(loss_values) + 1)\n",
        "fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n",
        "\n",
        "# Plot the model accuracy vs Epochs\n",
        "\n",
        "ax[0].plot(epochs, accuracy, 'bo', label='Training accuracy')\n",
        "ax[0].plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n",
        "ax[0].set_title('Training & Validation Accuracy', fontsize=16)\n",
        "ax[0].set_xlabel('Epochs', fontsize=16)\n",
        "ax[0].set_ylabel('Accuracy', fontsize=16)\n",
        "ax[0].legend()\n",
        "\n",
        "# Plot the loss vs Epochs\n",
        "\n",
        "ax[1].plot(epochs, loss_values, 'bo', label='Training loss')\n",
        "ax[1].plot(epochs, val_loss_values, 'b', label='Validation loss')\n",
        "ax[1].set_title('Training & Validation Loss', fontsize=16)\n",
        "ax[1].set_xlabel('Epochs', fontsize=16)\n",
        "ax[1].set_ylabel('Loss', fontsize=16)\n",
        "ax[1].legend()\n",
        "\n",
        "#The difference between training accuracy and validation(testing) accuracy is due to the model over-fitting on the training data\n",
        "#The model started overfitting after the 10th epoch so early stopping could prevent it from overfitting "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd1fd837bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y_S-tGw-u0NU",
        "outputId": "879acb88-b5da-4dd0-a0d6-995bdf123977"
      },
      "source": [
        "test_loss, test_acc, *is_anything_else_being_returned = model.evaluate(test_images, test_labels)\n",
        "\n",
        "print('Test accuracy:', test_acc)\n",
        "print('Test loss:', test_loss)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4418 - accuracy: 0.8889 - f1_m: 1.1583 - precision_m: 0.9108 - recall_m: 1.6036\n",
            "Test accuracy: 0.8888999819755554\n",
            "Test loss: 0.44175538420677185\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6D1h42VvSfQ"
      },
      "source": [
        "predictions = model.predict(test_images)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CER7ytJGvVjk",
        "outputId": "53d18ff1-8ae5-4ff2-a489-8a60187481b8"
      },
      "source": [
        "predictions[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-24.778694 , -30.211304 , -30.488497 , -42.02908  , -35.97228  ,\n",
              "       -15.304914 , -24.248812 ,  -2.5439212, -25.9451   ,   5.1701827],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CK24P15Tvcjb",
        "outputId": "f9925e51-04b8-4c87-fae0-88d77b135acf"
      },
      "source": [
        "predictions[0].argmax()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K1PkyM5zvdyU",
        "outputId": "cd919d8e-e1b8-4609-8922-1a2b4be215f0"
      },
      "source": [
        "test_labels[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "id": "6ovFefF6vm2v",
        "outputId": "3137c5dc-b57f-4abe-f3bf-d2dd94b0cc94"
      },
      "source": [
        "# Installing helper utilities needed\n",
        "!git clone https://github.com/liady/ssp19ai_utils.git\n",
        "!git -C ssp19ai_utils pull\n",
        "import ssp19ai_utils.utils as utils\n",
        "import importlib\n",
        "importlib.reload(utils)\n",
        "\n",
        "# Show the 1st image:\n",
        "index = 1\n",
        "utils.plot_image_and_prob(predictions, test_labels, test_images, i=index, big=True, class_names=class_names)\n",
        "\n",
        "# Show the 15th image:\n",
        "index = 15\n",
        "utils.plot_image_and_prob(predictions, test_labels, test_images, i=index)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'ssp19ai_utils' already exists and is not an empty directory.\n",
            "Already up to date.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N/Dap8mb6vCpvrrX5bT+U9AcpabokpaTWlDR3tJ9Eft9z8///eVUK9y95dEjSTfmfb5Y+9DPp1yR9I8vUL6lOlZ9xB6Xz7ln3SLrU2ZRx85qmpKRK0dudbQjlQtizfkiW6Scp6V9V+ZnvLVU+WlnfvyMl/bOkF/ObvpV/XFOWaVdKapT0Tpbp3fy2jSlpoaStKUmSulT5+fDM0Ps+V0r6kqS/UOWN9uOU9IMs0x+mpBZVPnLOkDSY/zLmE/lvZ/8t/5m1X9IXs0xnTy60QtKa/OfKXkl/dM7jXCxpcZZVfkkj6R8kvSTpuKTPnmfTnpG0VNI+4zUaT6/ptZK25b90mzD4b4PhSkk/J+k7WaZfrfa2fBQpaY2k9Vmm/672toymC/VjMIYh37s9fvY/igjg1YlWVIk9KxAGe1YgCMoKBEFZgSAoKxAEZQWC+D/KiTDz9ND+4QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "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 432x216 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "_3V7E8gH16vf",
        "outputId": "1c916477-6560-49db-b441-5d61948144d0"
      },
      "source": [
        "# Plot the first X test images, their predicted label, and the true label\n",
        "# Color correct predictions in blue, incorrect predictions in red\n",
        "utils.plot_multi_images_prob(predictions, test_labels, test_images)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "rYaxDvUN2BOM",
        "outputId": "61d21d17-d01f-4a62-bfde-c2d1407297e3"
      },
      "source": [
        "# Plotting the confusion matrix \n",
        "predicted_classes = utils.label_with_highest_prob(predictions)\n",
        "utils.plot_confusion_matrix(y_pred=predicted_classes, y_true=test_labels, classes=np.array(class_names))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1feae4990>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}