Project_1_NN_for_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOPJTgWZyy/TQVluz9jH//r",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aaina26/Deep-Learning-Unschool/blob/main/Project_1_NN_for_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ew8_pi_8Ghgw"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf #import lib"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#for loading data\n",
        "mnist=tf.keras.datasets.mnist\n",
        "(xtrain,ytrain),(xtest,ytest)=mnist.load_data()\n",
        "xtrain, xtest=xtrain/255.0,xtest/255.0"
      ],
      "metadata": {
        "id": "jrPlsL7aQj9z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d475e92-457b-44f2-9cbf-69ee6b072723"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"xtrain.shape:\",xtrain.shape)#print shape of xtrain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3x8rj7TsRTRP",
        "outputId": "6dccff09-d4de-4eb2-a57b-6f4ee5e3060c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "xtrain.shape: (60000, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Building the model\n",
        "model=tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(10, activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "QpmnUiYN6-KR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "Slp2Gqpb75O3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training the model\n",
        "t=model.fit(xtrain,ytrain,validation_data=(xtest,ytest),epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "veaDPCf78P3o",
        "outputId": "d07af372-8ff4-4b85-8dc3-5de192572c58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 8s 3ms/step - loss: 0.2945 - accuracy: 0.9138 - val_loss: 0.1405 - val_accuracy: 0.9570\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1401 - accuracy: 0.9576 - val_loss: 0.1071 - val_accuracy: 0.9677\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1070 - accuracy: 0.9674 - val_loss: 0.0907 - val_accuracy: 0.9722\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0871 - accuracy: 0.9729 - val_loss: 0.0820 - val_accuracy: 0.9744\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0755 - accuracy: 0.9764 - val_loss: 0.0759 - val_accuracy: 0.9774\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0641 - accuracy: 0.9796 - val_loss: 0.0738 - val_accuracy: 0.9777\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0573 - accuracy: 0.9818 - val_loss: 0.0700 - val_accuracy: 0.9801\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0514 - accuracy: 0.9829 - val_loss: 0.0730 - val_accuracy: 0.9787\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0491 - accuracy: 0.9837 - val_loss: 0.0750 - val_accuracy: 0.9785\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0440 - accuracy: 0.9849 - val_loss: 0.0714 - val_accuracy: 0.9791\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(t.history['loss'],label='loss')\n",
        "plt.plot(t.history['val_loss'],label='val_loss')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "y_IR0uJI-oD3",
        "outputId": "40894cc6-b05e-4d7b-faa8-2449c66cd42f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f72b8571a90>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(t.history['accuracy'],label='accuracy')\n",
        "plt.plot(t.history['val_accuracy'],label='val_accuracy')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "X6dFXY9r_XpA",
        "outputId": "c3e3b788-ba8e-4cde-ea24-973dee4bb24d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f729bc616d0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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zZs3i8OHD1NTUNIyff6Zx86dOncqHH37I8OHDqa2tZfTo0S38ttoJVSdU938BB1Y5XQl11d7QrQV3tfe+Bk9dDZ7aavDUEKF19MQ5h7jh77P6C0prgL3eW0uERTh90eFRkJgOI799svul+wiIjG6Vj2xMU4Iv6ANo5syZLFq0iCNHjjBr1izeeustXC4X69evJzIykvT09NPGmW/K2T7PV0REBB7PyUGpvm58+/vuu4+HHnqIG264gWXLlvHkk09+7Wvfdddd/Pu//zvDhg1jzpw5LaoroDwecO2A/SvhgDfcK13Otthe0G2g058dHoVbIiiqEo5UujlU5ebocaVGnT7dlIQ4UpPi6ZMST/fEOMIivAEd3snp8w2Pcromwpu4NbVveFRQ/HlvQpcFfQvMmjWLuXPnUlhYyPLly3nnnXfo3r07kZGRLF26lAMHDjT/IjjjzTf1vKlTp3LjjTfy0EMPkZSU1DDe/JVXXslLL73Egw8+2NB106NHDwoKCigqKiImJoYPP/yQadOmnfH96se3/+Mf/9iw/kzj5l988cXk5uayYcMGtmzZci5f2fnl8cDRr+DAypOt9hNOdxdxaTDwSkifBP0mURufzpb80oZTHjceLKHG7SEyXBjfJ5FLL0pi0qBkxqYlEBVhoWxCiwV9C4wcOZLy8nJSU1Pp1asXt912G9dffz2jR48mIyODYcOG+fU6Z3reyJEj+elPf0pmZibh4eGMHz+eN954gxdeeIF58+bx6quvEh4ezksvvcTEiRN5/PHHmTBhAqmpqV/73k8++SQzZ84kMTGRqVOnsm/fPoAzjpsPcPPNN7Np0ya/pkFsMx63c5Xn/pVOuB9YefLMjYR+MPQa6DfJCfeEfhRUVLN8l4tlH7vI2r2E8irnoNuIXnHccWk/Lh2UzIT0bmd3cZIxQcTGozdNuu666/jxj3/MlVde2eT2Nvlv4q5zDlruX+GE+sEvodo7MXy3Ad5Qv8y5T+iDx6NsyS/l850FLNtVwJY850ege2wnrhjanclDUpg4MIluXaPOb93GBICNR2/8VlJSwoQJExg7duwZQ/68qauBQxud/vX9KyF3jXNOMzjnj4+a4Q32SyGuNwClx2vJ2u1i6SebWJ7toqiyBhEY3yeBh78xhClDuzOyd5yNsW46NAv682jr1q3cfvvtp6zr1KkTa9asCVBFzUtISCA7O7tt3qyuGvLXnzx4mvsPqPWOA5MyHMbOdlrr/SZBbA/AOWNp55Fylm7IYdlOF+sPHsPtURK6RJI5JKWh5W6tdmNOCpqgV9Wga5WNHj2aTZs2BbqMVnfW3X21JyBvnffA6UrIW+u9AlGgx0i44HveYL8UuiY3PK2yuo5V2482dMkcLnXOMBrZO44fZg7kimEpjOuTSHhLR3Y0poMIiqCPjo6mqKiIpKSkoAv7UKOqFBUVER3dzHnfqs6l70e3OYG+fyXkr3POYZcw52rkjO87B077TjxtwK19hZUs3VnA0l0FrNlbTI3bQ9eocC4fnMKDV6UwZWh3esTZuefG+CMogj4tLY28vDxcLlegSzE4P7xpaWknV9Qcd85fP/KVE+xHtzmnPdZf+SnhzsVBF/8A+l0GfS+BzgmnvGZ1nZs1e4tZuquAZbtc7Ct0BoEamNKV703sx9Rh3clI72anPhpzFoIi6CMjIxuu5jQBpOoMSHXkK1i1yAnzo19B0R4aRiqMinGu9Bx1k9Md02OUc98p9rSXO1RygmW7XHy+s4BVewo5XuOmU0QYEwcmceel6VwxtDt9k0J8GABj2kBQBL0JgOoKKNhxMszrW+r1pzeCM+Z5z1EweubJUE/od8arQOvcHjYcLGHprgKW7ixoGKc9NaEzMy5I44phKUwckEznqBaM+GiMaZYFfUfn8TizENV3t9SHevE+GlrpneKcIB9zszfQRzuDcPkxIFXpiVo+2+EcSM3KdlFWVUdEmJCRnsij1wxj6rDuDOoeY8dejDmPLOg7kqoyKNh+agv96LaT56ojkDTQOVA69paT3S4JfVs8TVxu8XFeX7mfv6w9SGWNm+SYTnxzZE+uGNadywYnExfdwgmtjTFnzYI+VJ0ogX3LnSA/4m2p+84fGh3vBPm4W72BPgq6D3PG2T4HW/NKmb9iLx9tPYwAN4ztzfcuTWdManzLJ7Y2xrQKC/pQ4q6DPZ/D5j/Bzo+cYXglzJlqLvUC5zz1+lZ6fFqrTebs8SjLsguYn7WXL/cWE9spgrsu68+dk9LpFd+5Vd7DGHP2LOhDwZGvYPOfYcs7zrRznbvBhXfAqO9ArzEQeX7CtrrOzf9sPMTLK/ayu6CC3vHR/Oza4cy6qA+x1jVjTLthQR+sKlyw9a9O6/3IVmdiiyHTnL71wd9wxkU/T0qO1/DWmoO8sWo/rvJqRvSK4/lZ47h2TC8iWzr5tTHmvPMr6EVkGvACzpyxr6jqs42298OZEDwFKAa+q6p5InIF4DvZ6DBgtqq+1xrFdzh11bDr707rffenoG5n2rlrfum03rsmnde3zy0+zqtf7OOddbkcr3GTOSSFebMGcOlAu2LZmPas2aAXkXDgReBqIA9YKyLvq+p2n92eAxao6h9FZCrwDHC7qi4FxnlfpxuQA/xfK3+G0KbqjA+z+U/w1d+cq01je8Gl9zqt9+7nf/jmzbklzF+xl79vPUx4mHDD2FTmTu7PsJ5x5/29jTHnzp8W/QQgR1X3AojI2zhTafoG/QjgIe/yUqCpFvt3gL+r6vGzL7cDKcmFLX9xWu9FORDRGYZf54T7gCkQdn4vKvJ4lM93FjB/xV7+sa+Y2OgI5k0eyJ2XptMz3saYMSaY+BP0qUCuz+M84OJG+2wGbsLp3rkRiBWRJFUt8tlnNvCf51Br6KuugB0fOK33fSsAdUZznPQAjPi2M9/peVZV6+a9jfm8vGIve1yVpCZ05mfXDmf2hL7E2ExMxgSl1vqX+zDwOxG5E8gC8gF3/UYR6QWMBj5p6skiMg+YB9C3b99WKilIeDzODEqb/wzb34faSkhMhymPwJhZ0K1txvg5VlnDwi8P8MfV+ymsqGFUahwvzB7Ht0bbAVZjgp0/QZ8P9PF5nOZd10BVD+G06BGRGGCGqpb47HIzsFhVa5t6A1WdD8wHZypBv6sPZoU53lMi/+IMFNYpDkbPgLG3OqM7ttHBzQNFlQ0HWKtqPVwxNIW5kwcwcYAdYDUmVPgT9GuBwSLSHyfgZwO3+u4gIslAsap6gEdxzsDxdYt3fcd24phzQHXzn50x2iUMBk6Fq56EYdeet/Pdm7Lh4DFeztrLx9uOEBEmfHtcKnMnD2BIj9NHmTTGBLdmg15V60TkXpxul3DgNVXdJiJPAetU9X1gCvCMiChO18099c8XkXScvwiWt3r1wcBdCzmfOf3uu/7uTLyRMhyufgpG3wxxvdqsFI9HWbLjKC+v2Mva/ceIi47gh5nOAdbuNomHMSFLznpauPMkIyND161bF+gyzt3hLU7LfetfodIFXZKc4XzH3uJMwtGG3SJVtW7e3ZDHqyv2sbewkrTEznz/sv7cnNGHrnaA1ZiQICLrVTWjqW32r7y1Hd4M/3OP92rVSBjqvVp10NXn9WrVphRX1vDm6gMsWL2fosoaRqfG89tbxnPNqJ5E2AFWYzoMC/rWlLME3rnDGRnyW8/BqBmnzYXaFlSVV7/Yx68+2UV1nYcrh3Vn7uQBXNy/mx1gNaYDsqBvLRvehA8egB4j4Na/tmnfu68TNW4e/dsW3tt0iKuG9+Bfpg1lsB1gNaZDs6A/V6qw7FlY/qxzBs3NC5qcH7Ut5B07zg/eXM/2w2U8/I0h3HPFIGvBG2Ms6M+JuxY+eBA2LYRx34Xrn4fwwAzPu3pPEff8aQO1dR5evSODqcN6BKQOY0z7Y0F/tqrK4K93OBN9ZD7iXMkagNazqvLGqv08/b87SE/qwvzvZTAwpfm5XI0xHYcF/dkoOwxvzXTmX73ht87MTQFQVevmp4u/4t0NeVw1vAf/NWusTfhhjDmNBX1LFeyAhd9xhgu+9R0YfFVAyjhUcoK7F65nS14pD141mPunDrY5WY0xTbKgb4l9K+Dt2yAyGuZ85Fz4FAD/2FfMj95aT1Wth/m3X8g3RvYMSB3GmOBgQe+vrYvgvR9CYn/47iJIaPtRNlWVhV8e4N8+2E7fbl14e96FDOpup04aY76eBX1zVGHlC7DkCWds+NlvQefENi+jus7N4+9t4y/rcpk6rDvPzx5HnPXHG2P8YEH/dTxu+PtPYO0rMPImuPH3ENGpzcs4WlbFD95cz6bcEu6bOogfXzXE+uONMX6zoD+TmuPw7l2w63/h0vvhqn+DsLYfH2b9gWLuXriByuo6fv/dC5g2KjBX3BpjgpcFfVMqC+FPsyB/vTNmzYS5ASnjT2sO8sT7X9E7oTMLv38xQ3taf7wxpuUs6Bsr2gMLZ0D5YZi10JmQu43V1Hl48oNt/GnNQSYPSeG3s8cT38X6440xZ8eC3lfuWvjzLGf5jg+hz0VtXkJBWRU/fGsD6w8c44dTBvLwN4YSbv3xxphzYEFfb8eH8O73IbYXfPddSBrY5iVsPHiMuxeup+xEHb+7dTzXjend5jUYY0KPBT3AmvnO2TWpF8Atf4GYlDYv4Z21ufzsva/oHteJd394KSN6x7V5DcaY0NSxg97jgSWPw6rfwtBvwYxXIapLm5ZQ6/bw8w+3s2D1AS4blMxvbxlPYte2nYnKGBPa/DpfUESmicguEckRkUea2N5PRD4TkS0iskxE0ny29RWR/xORHSKy3TtZeODVVjldNat+Cxfd5Rx4beOQL6yo5raX17Bg9QHmTR7AG3MuspA3xrS6Zlv0IhIOvAhcDeQBa0XkfVXd7rPbc8ACVf2jiEwFngFu925bAPxCVT8VkRjA06qf4GycOOaMWXNgpXN+/KQH2nyI4S15JfzgzfUUV9bwwuxxTB+X2qbvb4zpOPzpupkA5KjqXgAReRuYDvgG/QjgIe/yUuA9774jgAhV/RRAVStaqe6zV3LQGX3y2D6nq2b0d9q8hHfX5/Ho4q2kxDj98aNS49u8BmNMx+FP100qkOvzOM+7ztdm4Cbv8o1ArIgkAUOAEhH5m4hsFJFfef9COIWIzBORdSKyzuVytfxT+OvwZnjlKqg4ArcvbvOQr3V7+LcPtvHPf93MhX0Tef/eSRbyxpjzrrWu6X8YyBSRjUAmkA+4cf5iuNy7/SJgAHBn4yer6nxVzVDVjJSU83TGS84SeP1bEBYJ//QJpF92ft7nDIoqqvneq//g9ZX7mTMpnQXfn0BSTNuPm2OM6Xj86brJB/r4PE7zrmugqofwtui9/fAzVLVERPKATT7dPu8BlwCvtkLt/tvwJnzwAPQYAbf+FeLadryYr/JL+cGb63FVVPPrmWOZcWFa808yxphW4k+Lfi0wWET6i0gUMBt433cHEUkWkfrXehR4zee5CSJS30yfyql9++eXKix9Bt6/FwZkwpy/t3nI/8+mfL7z+1V4VFl090QLeWNMm2u2Ra+qdSJyL/AJEA68pqrbROQpYJ2qvg9MAZ4REQWygHu8z3WLyMPAZyIiwHrg5fPzURpx18IHD8KmhTDuu3D98xDeduPF1Lk9/MfHO3l5xT4mpHfjxdsuICXWumqMMW1PVDXQNZwiIyND161bd24vUlUGf70D9nwOmY/AlEfa9PTJY5U13PfnjXyRU8j3JvbjZ9eOICqi7Yc4NsZ0HCKyXlUzmtoWelfGlh2Gt2ZCwXa44bdwwffa9O0LK6q58b9XcrS0ml/OGMPNF/Vp/knGGHMehVbQF+xwzpGvKoFb34HBV7V5CR9uPkRu8QnenncJlwxIavP3N8aYxkIn6Atz4NVvQmQ0zPkIeo0NSBlZuwvpl9TFQt4Y026ETsdxtwFw4R1w15KAhXx1nZvVe4qYPLjtR780xpgzCZ0WfVgYfOPnAS1h/f5jnKh1M3mIBb0xpv0InRZ9O7B8t4uIMGHiQOu2Mca0Hxb0rSgru5AL+yUS0yl0/lAyxgQ/C/pWUlBexY7DZdZtY4xpdyzoW8mK7EIAMi3ojTHtjAV9K8na7SKpaxQjetlcr8aY9sWCvhV4PMqK3bjUrwcAAA+3SURBVIVcPjiZsLC2nanKGGOaY0HfCrYdKqO4ssb6540x7ZIFfSvI2u3MinW5XShljGmHLOhbwfJsFyN6xdkwxMaYdsmC/hyVV9Wy4cAx67YxxrRbFvTnaPWeIuo8yuQhyYEuxRhjmmRBf46ydrvoEhVORr9ugS7FGGOaZEF/jrKyC5k4IMlmkDLGtFuWTudgf2ElB4uPkznU+ueNMe2XX0EvItNEZJeI5IjII01s7ycin4nIFhFZJiJpPtvcIrLJe3u/NYsPtPrTKm38eWNMe9bsMIsiEg68CFwN5AFrReR9Vd3us9tzwAJV/aOITAWeAW73bjuhquNaue52ISvbRd9uXUhP7hroUowx5oz8adFPAHJUda+q1gBvA9Mb7TMC+Ny7vLSJ7SGnps7jzCZlZ9sYY9o5f4I+Fcj1eZznXedrM3CTd/lGIFZE6mffiBaRdSLypYh8u6k3EJF53n3WuVyuFpQfOOsPHKOyxm3dNsaYdq+1DsY+DGSKyEYgE8gH3N5t/VQ1A7gVeF5EBjZ+sqrOV9UMVc1ISQmO4FyebbNJGWOCgz9TIeUDfXwep3nXNVDVQ3hb9CISA8xQ1RLvtnzv/V4RWQaMB/acc+UBlpXt4oJ+icRGRwa6FGOM+Vr+tOjXAoNFpL+IRAGzgVPOnhGRZBGpf61Hgde86xNFpFP9PsAkwPcgblBylVez/XCZTTJijAkKzQa9qtYB9wKfADuAd1R1m4g8JSI3eHebAuwSkWygB/AL7/rhwDoR2YxzkPbZRmfrBKUVdlqlMSaI+DWLtap+BHzUaN3jPsuLgEVNPG8VMPoca2x3srKd2aRG9rbZpIwx7Z9dGdtC9bNJXWazSRljgoQFfQttP1xGUWWNddsYY4KGBX0LLc/2ziZlF0oZY4KEBX0LZWW7GN4rju6x0YEuxRhj/GJB3wIV1XWsP3DMhj0wxgQVC/oWqJ9NKtP6540xQcSCvgWysl10jgznwvTEQJdijDF+s6BvgazdLiYOTKJTRHigSzHGGL9Z0PvpQFElB4qOM3mw9c8bY4KLBb2fsrynVU628W2MMUHGgt5Py7MLSUvsTH+bTcoYE2Qs6P3gzCZVyOQhKYjYsAfGmOBiQe+HDQdtNiljTPCyoPdDVraL8DDh0kE2m5QxJvhY0Psha7eLC/omEGezSRljgpAFfTMKK6r5Kr/Mum2MMUHLgr4ZX+wuBOy0SmNM8LKgb0ZWtovELpGMSo0PdCnGGHNWLOi/hsejZO0u5LLBKYTbbFLGmCDlV9CLyDQR2SUiOSLySBPb+4nIZyKyRUSWiUhao+1xIpInIr9rrcLbwo4jZRRWVNuwB8aYoNZs0ItIOPAicA0wArhFREY02u05YIGqjgGeAp5ptP3nQNa5l9u2srKtf94YE/z8adFPAHJUda+q1gBvA9Mb7TMC+Ny7vNR3u4hcCPQA/u/cy21bWdkuhvWMpUeczSZljAle/gR9KpDr8zjPu87XZuAm7/KNQKyIJIlIGPBr4OGvewMRmSci60Rkncvl8q/y86yyuo51B4rJtNa8MSbItdbB2IeBTBHZCGQC+YAb+BHwkarmfd2TVXW+qmaoakZKSvsI1i/3FlHrVuu2McYEvQg/9skH+vg8TvOua6Cqh/C26EUkBpihqiUiMhG4XER+BMQAUSJSoaqnHdBtb+pnk8qw2aSMMUHOn6BfCwwWkf44AT8buNV3BxFJBopV1QM8CrwGoKq3+exzJ5ARDCEPkLW7kEsGdLPZpIwxQa/ZrhtVrQPuBT4BdgDvqOo2EXlKRG7w7jYF2CUi2TgHXn9xnuptE7nFx9lXWGndNsaYkOBPix5V/Qj4qNG6x32WFwGLmnmNN4A3WlxhACy32aSMMSHEroxtQla2i9SEzgyw2aSMMSHAgr6RWreHVXuKbDYpY0zIsKBvZMOBY1RU15E5xIY9MMaEBgv6RrJ2188mZUFvjAkNFvSNZGUXMr6PzSZljAkdFvQ+iiqq+epQqZ1tY4wJKRb0Pr7IKUTVTqs0xoQWC3ofy7NdJHSJZLTNJmWMCSEW9F6qyordhVw2KNlmkzLGhBQLeq8dh8txlVdbt40xJuRY0Htl7fYOezDYgt4YE1os6L2ysl0M7RFLz3ibTcoYE1os6IHjNXWs23+MyXY1rDEmBFnQ48wmVeP2WP+8MSYkWdDjXA0bHRnGRendAl2KMca0Ogt6nP75i/snER1ps0kZY0JPhw/63OLj7LXZpIwxIazDB339aZU2LLExJlRZ0Ge76B0fzcCUmECXYowx54VfQS8i00Rkl4jkiMgjTWzvJyKficgWEVkmImk+6zeIyCYR2SYid7f2BzgXtW4Pq3JsNiljTGhrNuhFJBx4EbgGGAHcIiIjGu32HLBAVccATwHPeNcfBiaq6jjgYuAREendWsWfq025JZRX11n/vDEmpPnTop8A5KjqXlWtAd4GpjfaZwTwuXd5af12Va1R1Wrv+k5+vl+bycp2ESYwaaD1zxtjQpc/wZsK5Po8zvOu87UZuMm7fCMQKyJJACLSR0S2eF/jP1T1UOM3EJF5IrJORNa5XK6WfoazlpXtYlyfBOK72GxSxpjQ1Vot7IeBTBHZCGQC+YAbQFVzvV06g4A7RKRH4yer6nxVzVDVjJSUtulGKa6sYUu+zSZljAl9/gR9PtDH53Gad10DVT2kqjep6njgp951JY33Ab4CLj+niluJzSZljOko/An6tcBgEekvIlHAbOB93x1EJFlE6l/rUeA17/o0EensXU4ELgN2tVbx5yIr20V850jGpiUEuhRjjDmvmg16Va0D7gU+AXYA76jqNhF5SkRu8O42BdglItlAD+AX3vXDgTUishlYDjynqltb+TO0mDOblIvLBttsUsaY0Bfhz06q+hHwUaN1j/ssLwIWNfG8T4Ex51hjq9t1tJyjZdVk2iQjxpgOoF2d7thWsrKdM3sut2EPjDEdQAcN+kKG9IihV3znQJdijDHnXYcL+hM1bv6xv9jmhjXGdBgdLui/3FdETZ3NJmWM6Tg6XNBnZbvoFBHGhP42m5QxpmPokEF/8QCbTcoY03F0qKDPLznBHlclkwfb2TbGmI6jQwV9/WmVmdY/b4zpQDpU0C/f5aJXfDSDuttsUsaYjqPDBH2d28PKPYVMHmyzSRljOpYOE/Sbcksor7LZpIwxHU+HCfr62aQuG2QHYo0xHUuHCfrluwsZa7NJGWM6oA4R9Mcqa9iSV2LDHhhjOqQOEfQ2m5QxpiPrEEGfle0iLjqCsWnxgS7FGGPaXMgHvaqS5Z1NKiI85D+uMcacJuSTL/toBUfLqq1/3hjTYYV80NcPe2D988aYjsqvoBeRaSKyS0RyROSRJrb3E5HPRGSLiCwTkTTv+nEislpEtnm3zWrtD9CcrN0uBnWPoXeCzSZljOmYmg16EQkHXgSuAUYAt4jIiEa7PQcsUNUxwFPAM971x4HvqepIYBrwvIgktFbxzTlR42bNPptNyhjTsfnTop8A5KjqXlWtAd4GpjfaZwTwuXd5af12Vc1W1d3e5UNAAdBmqbumYTYpuxrWGNNx+RP0qUCuz+M87zpfm4GbvMs3ArEikuS7g4hMAKKAPY3fQETmicg6EVnncrn8rb1ZWdmFREWEcXH/pOZ3NsaYENVaB2MfBjJFZCOQCeQD7vqNItILeBOYo6qexk9W1fmqmqGqGSkprdfgz9rt4uL+3egcZbNJGWM6rgg/9skH+vg8TvOua+DtlrkJQERigBmqWuJ9HAf8L/BTVf2yNYr2x6GSE+QUVDAro0/zOxtjTAjzp0W/FhgsIv1FJAqYDbzvu4OIJItI/Ws9CrzmXR8FLMY5ULuo9cpunp1WaYwxjmaDXlXrgHuBT4AdwDuquk1EnhKRG7y7TQF2iUg20AP4hXf9zcBk4E4R2eS9jWvtD9GUrN0uesZFM6SHzSZljOnY/Om6QVU/Aj5qtO5xn+VFwGktdlVdCCw8xxpbrM7t4YvdhXxzZE+bTcoY0+GF5JWxm/NKKauqI3OoddsYY0xIBr3NJmWMMSeFZtDvdjEmLYGELlGBLsUYYwIu5IK+9Hgtm3NL7GwbY4zxCrmg/yKnEI9Cpg17YIwxQAgGfVa2i9joCMamtdnYacYY066FVNA3zCY1yGaTMsaYeiGVhjkFFRwurbL+eWOM8RFSQb/chj0wxpjThFTQZ+0uZGBKV1JtNiljjGkQMkFfVetmzd4ia80bY0wjIRP0ZVW1fHNkT64e0SPQpRhjTLvi16BmwaB7bDS/uWV8oMswxph2J2Ra9MYYY5pmQW+MMSHOgt4YY0KcBb0xxoQ4C3pjjAlxFvTGGBPiLOiNMSbEWdAbY0yIE1UNdA2nEBEXcOAcXiIZKGylcoKdfRensu/jVPZ9nBQK30U/VW1yDJh2F/TnSkTWqWpGoOtoD+y7OJV9H6ey7+OkUP8urOvGGGNCnAW9McaEuFAM+vmBLqAdse/iVPZ9nMq+j5NC+rsIuT56Y4wxpwrFFr0xxhgfFvTGGBPiQiboRWSaiOwSkRwReSTQ9QSSiPQRkaUisl1EtonIA4GuKdBEJFxENorIh4GuJdBEJEFEFonIThHZISITA11TIInIj73/Tr4SkT+LSHSga2ptIRH0IhIOvAhcA4wAbhGREYGtKqDqgH9W1RHAJcA9Hfz7AHgA2BHoItqJF4CPVXUYMJYO/L2ISCpwP5ChqqOAcGB2YKtqfSER9MAEIEdV96pqDfA2MD3ANQWMqh5W1Q3e5XKcf8ipga0qcEQkDbgWeCXQtQSaiMQDk4FXAVS1RlVLAltVwEUAnUUkAugCHApwPa0uVII+Fcj1eZxHBw42XyKSDowH1gS2koB6HvgJ4Al0Ie1Af8AFvO7tynpFRLoGuqhAUdV84DngIHAYKFXV/wtsVa0vVILeNEFEYoB3gQdVtSzQ9QSCiFwHFKjq+kDX0k5EABcAL6nqeKAS6LDHtEQkEeev//5Ab6CriHw3sFW1vlAJ+nygj8/jNO+6DktEInFC/i1V/Vug6wmgScANIrIfp0tvqogsDGxJAZUH5Klq/V94i3CCv6O6Ctinqi5VrQX+Blwa4JpaXagE/VpgsIj0F5EonIMp7we4poAREcHpg92hqv8Z6HoCSVUfVdU0VU3H+f/ic1UNuRabv1T1CJArIkO9q64EtgewpEA7CFwiIl28/26uJAQPTkcEuoDWoKp1InIv8AnOUfPXVHVbgMsKpEnA7cBWEdnkXfeYqn4UwJpM+3Ef8Ja3UbQXmBPgegJGVdeIyCJgA87ZahsJweEQbAgEY4wJcaHSdWOMMeYMLOiNMSbEWdAbY0yIs6A3xpgQZ0FvjDEhzoLeGGNCnAW9McaEuP8PFl2JfjZ99fYAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Model Evaluation\n",
        "print(model.evaluate(xtest,ytest))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1qbyshVy_vmm",
        "outputId": "bfd9d641-58d3-40fa-f6cf-b2515c5c8922"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0714 - accuracy: 0.9791\n",
            "[0.07135886698961258, 0.9790999889373779]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "0QSF_YYAAChu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred=model.predict(xtest).argmax(axis=1)\n",
        "cm=confusion_matrix(ytest,pred)\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8OvthrZSAOgU",
        "outputId": "2e68b02b-6fff-41ff-b4b0-abd6fb344df1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n",
            "[[ 971    0    2    1    0    1    3    1    1    0]\n",
            " [   0 1122    2    2    0    0    2    0    7    0]\n",
            " [   6    1 1007    6    2    1    2    1    6    0]\n",
            " [   0    0    2  997    0    2    0    3    2    4]\n",
            " [   0    0    3    0  955    0    8    3    2   11]\n",
            " [   4    0    0   14    1  861    4    0    7    1]\n",
            " [   2    2    0    1    1    3  945    0    4    0]\n",
            " [   1    4   14    4    2    0    0  995    3    5]\n",
            " [   6    0    5    6    4    1    0    0  951    1]\n",
            " [   2    4    1    6    3    1    0    4    1  987]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "misclassified=np.where(pred!=ytest)[0]\n",
        "i=np.random.choice(misclassified)\n",
        "plt.imshow(xtest[i],cmap='gray')\n",
        "plt.title(\"True labe:%s Predicted:%s\"%(ytest[i],pred[i]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "GFyq29D0Ak1y",
        "outputId": "630f9604-76b0-409b-8811-362684fc62ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'True labe:9 Predicted:1')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pmj4Fsf6CQqI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}