mnist_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "pELLUNVzJWB6"
      },
      "outputs": [],
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense,Flatten"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train,y_train),(X_test,y_test) = keras.datasets.mnist.load_data()"
      ],
      "metadata": {
        "id": "KRTH93VyMYF7",
        "outputId": "5d899b7b-5a53-44dd-c8e0-52dd4c771eb7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "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": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0hHUKoNMpsZ",
        "outputId": "8d5ce6dc-8815-437f-8d0d-8356ed77ba32"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "skzwnglnMzl6",
        "outputId": "90e745a2-876c-49ae-bf3a-262ebeb8c78f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(X_train[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "jr2MuKsKRH5G",
        "outputId": "36ca1871-36ff-4ea8-f13e-7bb130bfa553"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ec84d6ba8c0>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAa3UlEQVR4nO3df3BU9b3/8dcmJAtosmkIyWZLwIACrUj8lkKai1IsGUI6l+HX7fVX54Lj4EiDt0CtTjoKop1JxRnr6E3xj6tQZ0SUGYEro8yFYMLYBiwIXy7faobkm0q4kKDcm2wIECL53D+4bruSiCfs5p0Nz8fMmSG755Pz9rjDk8NuDj7nnBMAAP0syXoAAMD1iQABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATQ6wH+Kru7m6dPHlSaWlp8vl81uMAADxyzqm9vV2hUEhJSb1f5wy4AJ08eVJ5eXnWYwAArlFTU5NGjRrV6/MDLkBpaWmSpDv0Yw1RivE0AACvvlCXPtC7kd/PexO3AFVWVuq5555Tc3OzCgoK9NJLL2natGlXXfflX7sNUYqG+AgQACSc/73D6NXeRonLhxDefPNNrVq1SmvWrNFHH32kgoIClZSU6PTp0/E4HAAgAcUlQM8//7yWLl2qBx54QN/97nf18ssva/jw4Xr11VfjcTgAQAKKeYAuXryogwcPqri4+K8HSUpScXGxamtrr9i/s7NT4XA4agMADH4xD9Dnn3+uS5cuKScnJ+rxnJwcNTc3X7F/RUWFAoFAZOMTcABwfTD/QdTy8nK1tbVFtqamJuuRAAD9IOafgsvKylJycrJaWlqiHm9paVEwGLxif7/fL7/fH+sxAAADXMyvgFJTUzVlyhRVVVVFHuvu7lZVVZWKiopifTgAQIKKy88BrVq1SosXL9b3v/99TZs2TS+88II6Ojr0wAMPxONwAIAEFJcA3X333frss8+0evVqNTc36/bbb9fOnTuv+GACAOD65XPOOesh/lY4HFYgENBMzeNOCACQgL5wXarWdrW1tSk9Pb3X/cw/BQcAuD4RIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJoZYDwAAXnT8Q6HnNc+uW9+nYz3zj//keY07cLRPx7oecQUEADBBgAAAJmIeoKeeeko+ny9qmzhxYqwPAwBIcHF5D+jWW2/V7t27/3qQIbzVBACIFpcyDBkyRMFgMB7fGgAwSMTlPaBjx44pFApp7Nixuv/++3X8+PFe9+3s7FQ4HI7aAACDX8wDVFhYqI0bN2rnzp1av369Ghsbdeedd6q9vb3H/SsqKhQIBCJbXl5erEcCAAxAMQ9QaWmpfvKTn2jy5MkqKSnRu+++q9bWVr311ls97l9eXq62trbI1tTUFOuRAAADUNw/HZCRkaHx48ervr6+x+f9fr/8fn+8xwAADDBx/zmgs2fPqqGhQbm5ufE+FAAggcQ8QI8++qhqamr0l7/8RX/84x+1YMECJScn69577431oQAACSzmfwV34sQJ3XvvvTpz5oxGjhypO+64Q/v27dPIkSNjfSgAQAKLeYA2b94c6285KJyfN837mhHJntdkvlrreQ2QSE5/3/tf3Dzzl7lxmATXinvBAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAm4v4P0uGykzO8t374uFbvB3rV+xLATJL3G+660ec9r5mV/YnnNZJU5fu7Pq3DN8MVEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAExwN+x+svbvt3he8+zHs+MwCTBwJI8b43nNJz/0fsv32z/8qec1khT603/0aR2+Ga6AAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAAT3Iy0n6T4vrAeARhwhvzruX45zvmG9H45DrzhCggAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMMHNSPug+47bPa+5c+gHsR8ESHA33XCmX46Tt/tSvxwH3nAFBAAwQYAAACY8B2jv3r2aO3euQqGQfD6ftm3bFvW8c06rV69Wbm6uhg0bpuLiYh07dixW8wIABgnPAero6FBBQYEqKyt7fH7dunV68cUX9fLLL2v//v264YYbVFJSogsXLlzzsACAwcPzhxBKS0tVWlra43POOb3wwgt64oknNG/ePEnSa6+9ppycHG3btk333HPPtU0LABg0YvoeUGNjo5qbm1VcXBx5LBAIqLCwULW1tT2u6ezsVDgcjtoAAINfTAPU3NwsScrJyYl6PCcnJ/LcV1VUVCgQCES2vLy8WI4EABigzD8FV15erra2tsjW1NRkPRIAoB/ENEDBYFCS1NLSEvV4S0tL5Lmv8vv9Sk9Pj9oAAINfTAOUn5+vYDCoqqqqyGPhcFj79+9XUVFRLA8FAEhwnj8Fd/bsWdXX10e+bmxs1OHDh5WZmanRo0drxYoV+vWvf61bbrlF+fn5evLJJxUKhTR//vxYzg0ASHCeA3TgwAHdddddka9XrVolSVq8eLE2btyoxx57TB0dHXrooYfU2tqqO+64Qzt37tTQoUNjNzUAIOF5DtDMmTPlnOv1eZ/Pp6efflpPP/30NQ02kH3698M8r8lOHh6HSYCBY8hNoz2v+YfMf4vDJFca1vjffVrHLUzjy/xTcACA6xMBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMeL4bNqQhN7f3y3EufJLRL8cBYqHphRs8r5nu7/a85pXwKM9r1Br2vgZxxxUQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCm5EOYNkHvN+oEYNXctYIz2taFo3v07Ey//GE5zU141/pw5GGel6xvnK+5zXZLX/0vAbxxxUQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCm5EOYOczvf/54IY4zBFL3Xf+H89rXLLP85qmYr/nNZJ0MdTleU1S6iXPa/79zpc8r0nxfhrUfKlv5+HJ/7/A85r/6vZ+89zhSd7PXc7+ds9rnOcV6A9cAQEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJrgZaR90XkjxvKa7D7dD3PCr33pe82/Lb/e8pj89PuJfPa9Jkve7cJ53Fz2vkaSTl7zfHPNfPpvpeU3x7hWe12QcSvW8JvffWzyvkSTfpyc8r/ns42Ge1+Qke7/5q/vTf3heg4GJKyAAgAkCBAAw4TlAe/fu1dy5cxUKheTz+bRt27ao55csWSKfzxe1zZkzJ1bzAgAGCc8B6ujoUEFBgSorK3vdZ86cOTp16lRke+ONN65pSADA4OP5QwilpaUqLS392n38fr+CwWCfhwIADH5xeQ+ourpa2dnZmjBhgpYtW6YzZ870um9nZ6fC4XDUBgAY/GIeoDlz5ui1115TVVWVnn32WdXU1Ki0tFSXevl4a0VFhQKBQGTLy8uL9UgAgAEo5j8HdM8990R+fdttt2ny5MkaN26cqqurNWvWrCv2Ly8v16pVqyJfh8NhIgQA14G4fwx77NixysrKUn19fY/P+/1+paenR20AgMEv7gE6ceKEzpw5o9zc3HgfCgCQQDz/FdzZs2ejrmYaGxt1+PBhZWZmKjMzU2vXrtWiRYsUDAbV0NCgxx57TDfffLNKSkpiOjgAILF5DtCBAwd01113Rb7+8v2bxYsXa/369Tpy5Ih+//vfq7W1VaFQSLNnz9Yzzzwjv98fu6kBAAnP55zzfpfMOAqHwwoEApqpeRri837Tz4GqsaLI85q8qf8Zh0kSz2fvjfK8ZsT/836TS0lK3fmnPq0bbP7z8b/zvOb//vO/eF6z+exIz2tem8CHlAa6L1yXqrVdbW1tX/u+PveCAwCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgImY/5Pc6Fl+ea31CAkrV8etR7juDJ/xWb8c54n3F3leM14fxmESWOAKCABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwwc1IAZgZs91ZjwBDXAEBAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwMsR4AwOCQ7PP+59n/Hp/ieU3wPc9LMEBxBQQAMEGAAAAmPAWooqJCU6dOVVpamrKzszV//nzV1dVF7XPhwgWVlZVpxIgRuvHGG7Vo0SK1tLTEdGgAQOLzFKCamhqVlZVp37592rVrl7q6ujR79mx1dHRE9lm5cqXeeecdbdmyRTU1NTp58qQWLlwY88EBAInN04cQdu7cGfX1xo0blZ2drYMHD2rGjBlqa2vTK6+8ok2bNulHP/qRJGnDhg36zne+o3379ukHP/hB7CYHACS0a3oPqK2tTZKUmZkpSTp48KC6urpUXFwc2WfixIkaPXq0amtre/wenZ2dCofDURsAYPDrc4C6u7u1YsUKTZ8+XZMmTZIkNTc3KzU1VRkZGVH75uTkqLm5ucfvU1FRoUAgENny8vL6OhIAIIH0OUBlZWU6evSoNm/efE0DlJeXq62tLbI1NTVd0/cDACSGPv0g6vLly7Vjxw7t3btXo0aNijweDAZ18eJFtba2Rl0FtbS0KBgM9vi9/H6//H5/X8YAACQwT1dAzjktX75cW7du1Z49e5Sfnx/1/JQpU5SSkqKqqqrIY3V1dTp+/LiKiopiMzEAYFDwdAVUVlamTZs2afv27UpLS4u8rxMIBDRs2DAFAgE9+OCDWrVqlTIzM5Wenq5HHnlERUVFfAIOABDFU4DWr18vSZo5c2bU4xs2bNCSJUskSb/97W+VlJSkRYsWqbOzUyUlJfrd734Xk2EBAIOHpwA55666z9ChQ1VZWanKyso+DwUg8Vxy3d4XcTOw6xr/+wEAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCiT/8iKgDEwrmp56xHgCGugAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAE9yMFEBMJPv48yy84RUDADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJjgZqQArtC5e6TnNZdu747DJBjMuAICAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEz4nHPOeoi/FQ6HFQgENFPzNMSXYj0OAMCjL1yXqrVdbW1tSk9P73U/roAAACYIEADAhKcAVVRUaOrUqUpLS1N2drbmz5+vurq6qH1mzpwpn88XtT388MMxHRoAkPg8BaimpkZlZWXat2+fdu3apa6uLs2ePVsdHR1R+y1dulSnTp2KbOvWrYvp0ACAxOfpX0TduXNn1NcbN25Udna2Dh48qBkzZkQeHz58uILBYGwmBAAMStf0HlBbW5skKTMzM+rx119/XVlZWZo0aZLKy8t17ty5Xr9HZ2enwuFw1AYAGPw8XQH9re7ubq1YsULTp0/XpEmTIo/fd999GjNmjEKhkI4cOaLHH39cdXV1evvtt3v8PhUVFVq7dm1fxwAAJKg+/xzQsmXL9N577+mDDz7QqFGjet1vz549mjVrlurr6zVu3Lgrnu/s7FRnZ2fk63A4rLy8PH4OCAAS1Df9OaA+XQEtX75cO3bs0N69e782PpJUWFgoSb0GyO/3y+/392UMAEAC8xQg55weeeQRbd26VdXV1crPz7/qmsOHD0uScnNz+zQgAGBw8hSgsrIybdq0Sdu3b1daWpqam5slSYFAQMOGDVNDQ4M2bdqkH//4xxoxYoSOHDmilStXasaMGZo8eXJc/gMAAInJ03tAPp+vx8c3bNigJUuWqKmpST/96U919OhRdXR0KC8vTwsWLNATTzzxtX8P+Le4FxwAJLa4vAd0tVbl5eWppqbGy7cEAFynuBccAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMDEEOsBvso5J0n6Ql2SMx4GAODZF+qS9Nffz3sz4ALU3t4uSfpA7xpPAgC4Fu3t7QoEAr0+73NXS1Q/6+7u1smTJ5WWliafzxf1XDgcVl5enpqampSenm40oT3Ow2Wch8s4D5dxHi4bCOfBOaf29naFQiElJfX+Ts+AuwJKSkrSqFGjvnaf9PT06/oF9iXOw2Wch8s4D5dxHi6zPg9fd+XzJT6EAAAwQYAAACYSKkB+v19r1qyR3++3HsUU5+EyzsNlnIfLOA+XJdJ5GHAfQgAAXB8S6goIADB4ECAAgAkCBAAwQYAAACYSJkCVlZW66aabNHToUBUWFurDDz+0HqnfPfXUU/L5fFHbxIkTrceKu71792ru3LkKhULy+Xzatm1b1PPOOa1evVq5ubkaNmyYiouLdezYMZth4+hq52HJkiVXvD7mzJljM2ycVFRUaOrUqUpLS1N2drbmz5+vurq6qH0uXLigsrIyjRgxQjfeeKMWLVqklpYWo4nj45uch5kzZ17xenj44YeNJu5ZQgTozTff1KpVq7RmzRp99NFHKigoUElJiU6fPm09Wr+79dZbderUqcj2wQcfWI8Udx0dHSooKFBlZWWPz69bt04vvviiXn75Ze3fv1833HCDSkpKdOHChX6eNL6udh4kac6cOVGvjzfeeKMfJ4y/mpoalZWVad++fdq1a5e6uro0e/ZsdXR0RPZZuXKl3nnnHW3ZskU1NTU6efKkFi5caDh17H2T8yBJS5cujXo9rFu3zmjiXrgEMG3aNFdWVhb5+tKlSy4UCrmKigrDqfrfmjVrXEFBgfUYpiS5rVu3Rr7u7u52wWDQPffcc5HHWltbnd/vd2+88YbBhP3jq+fBOecWL17s5s2bZzKPldOnTztJrqamxjl3+f99SkqK27JlS2Sfjz/+2ElytbW1VmPG3VfPg3PO/fCHP3Q///nP7Yb6Bgb8FdDFixd18OBBFRcXRx5LSkpScXGxamtrDSezcezYMYVCIY0dO1b333+/jh8/bj2SqcbGRjU3N0e9PgKBgAoLC6/L10d1dbWys7M1YcIELVu2TGfOnLEeKa7a2tokSZmZmZKkgwcPqqurK+r1MHHiRI0ePXpQvx6+eh6+9PrrrysrK0uTJk1SeXm5zp07ZzFerwbczUi/6vPPP9elS5eUk5MT9XhOTo4++eQTo6lsFBYWauPGjZowYYJOnTqltWvX6s4779TRo0eVlpZmPZ6J5uZmSerx9fHlc9eLOXPmaOHChcrPz1dDQ4N+9atfqbS0VLW1tUpOTrYeL+a6u7u1YsUKTZ8+XZMmTZJ0+fWQmpqqjIyMqH0H8+uhp/MgSffdd5/GjBmjUCikI0eO6PHHH1ddXZ3efvttw2mjDfgA4a9KS0sjv548ebIKCws1ZswYvfXWW3rwwQcNJ8NAcM8990R+fdttt2ny5MkaN26cqqurNWvWLMPJ4qOsrExHjx69Lt4H/Tq9nYeHHnoo8uvbbrtNubm5mjVrlhoaGjRu3Lj+HrNHA/6v4LKyspScnHzFp1haWloUDAaNphoYMjIyNH78eNXX11uPYubL1wCvjyuNHTtWWVlZg/L1sXz5cu3YsUPvv/9+1D/fEgwGdfHiRbW2tkbtP1hfD72dh54UFhZK0oB6PQz4AKWmpmrKlCmqqqqKPNbd3a2qqioVFRUZTmbv7NmzamhoUG5urvUoZvLz8xUMBqNeH+FwWPv377/uXx8nTpzQmTNnBtXrwzmn5cuXa+vWrdqzZ4/y8/Ojnp8yZYpSUlKiXg91dXU6fvz4oHo9XO089OTw4cOSNLBeD9afgvgmNm/e7Px+v9u4caP785//7B566CGXkZHhmpubrUfrV7/4xS9cdXW1a2xsdH/4wx9ccXGxy8rKcqdPn7YeLa7a29vdoUOH3KFDh5wk9/zzz7tDhw65Tz/91Dnn3G9+8xuXkZHhtm/f7o4cOeLmzZvn8vPz3fnz540nj62vOw/t7e3u0UcfdbW1ta6xsdHt3r3bfe9733O33HKLu3DhgvXoMbNs2TIXCARcdXW1O3XqVGQ7d+5cZJ+HH37YjR492u3Zs8cdOHDAFRUVuaKiIsOpY+9q56G+vt49/fTT7sCBA66xsdFt377djR071s2YMcN48mgJESDnnHvppZfc6NGjXWpqqps2bZrbt2+f9Uj97u6773a5ubkuNTXVffvb33Z33323q6+vtx4r7t5//30n6Ypt8eLFzrnLH8V+8sknXU5OjvP7/W7WrFmurq7Odug4+LrzcO7cOTd79mw3cuRIl5KS4saMGeOWLl066P6Q1tN/vyS3YcOGyD7nz593P/vZz9y3vvUtN3z4cLdgwQJ36tQpu6Hj4Grn4fjx427GjBkuMzPT+f1+d/PNN7tf/vKXrq2tzXbwr+CfYwAAmBjw7wEBAAYnAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMDE/wB3z3opkp0DGwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train/255\n",
        "X_test = X_test/255"
      ],
      "metadata": {
        "id": "dJmO2WLlRRT1"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1WA9FioRdtB",
        "outputId": "e21d5912-e764-4d97-d3b6-af1dbb4782a9"
      },
      "execution_count": 7,
      "outputs": [
        {
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Flatten(input_shape=(28,28)))\n",
        "model.add(Dense(128,activation='relu'))\n",
        "model.add(Dense(32,activation='relu'))\n",
        "model.add(Dense(10,activation='softmax'))"
      ],
      "metadata": {
        "id": "jr78UU88Rzm6"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ECogPfG5SmNI",
        "outputId": "d8cec026-fa07-46ab-f54e-14931608441c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               100480    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                4128      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                330       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 104938 (409.91 KB)\n",
            "Trainable params: 104938 (409.91 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "0yC51WgjSsLV"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train,y_train,epochs=25,validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L-fkwmIvWT8M",
        "outputId": "ba238f10-9afd-495c-f675-0a5d50c08528"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "1500/1500 [==============================] - 14s 7ms/step - loss: 0.2883 - accuracy: 0.9170 - val_loss: 0.1446 - val_accuracy: 0.9573\n",
            "Epoch 2/25\n",
            "1500/1500 [==============================] - 7s 5ms/step - loss: 0.1191 - accuracy: 0.9635 - val_loss: 0.1222 - val_accuracy: 0.9641\n",
            "Epoch 3/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0819 - accuracy: 0.9754 - val_loss: 0.1067 - val_accuracy: 0.9701\n",
            "Epoch 4/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0596 - accuracy: 0.9809 - val_loss: 0.1052 - val_accuracy: 0.9698\n",
            "Epoch 5/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0471 - accuracy: 0.9855 - val_loss: 0.0938 - val_accuracy: 0.9737\n",
            "Epoch 6/25\n",
            "1500/1500 [==============================] - 4s 3ms/step - loss: 0.0395 - accuracy: 0.9877 - val_loss: 0.0993 - val_accuracy: 0.9732\n",
            "Epoch 7/25\n",
            "1500/1500 [==============================] - 5s 4ms/step - loss: 0.0328 - accuracy: 0.9898 - val_loss: 0.1127 - val_accuracy: 0.9721\n",
            "Epoch 8/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0231 - accuracy: 0.9924 - val_loss: 0.1263 - val_accuracy: 0.9690\n",
            "Epoch 9/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0253 - accuracy: 0.9916 - val_loss: 0.1170 - val_accuracy: 0.9726\n",
            "Epoch 10/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0193 - accuracy: 0.9937 - val_loss: 0.1167 - val_accuracy: 0.9753\n",
            "Epoch 11/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0166 - accuracy: 0.9944 - val_loss: 0.1267 - val_accuracy: 0.9715\n",
            "Epoch 12/25\n",
            "1500/1500 [==============================] - 5s 4ms/step - loss: 0.0161 - accuracy: 0.9944 - val_loss: 0.1263 - val_accuracy: 0.9722\n",
            "Epoch 13/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0135 - accuracy: 0.9955 - val_loss: 0.1370 - val_accuracy: 0.9704\n",
            "Epoch 14/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0119 - accuracy: 0.9960 - val_loss: 0.1371 - val_accuracy: 0.9725\n",
            "Epoch 15/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0122 - accuracy: 0.9956 - val_loss: 0.1424 - val_accuracy: 0.9749\n",
            "Epoch 16/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0123 - accuracy: 0.9958 - val_loss: 0.1531 - val_accuracy: 0.9718\n",
            "Epoch 17/25\n",
            "1500/1500 [==============================] - 6s 4ms/step - loss: 0.0089 - accuracy: 0.9970 - val_loss: 0.1586 - val_accuracy: 0.9712\n",
            "Epoch 18/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0121 - accuracy: 0.9959 - val_loss: 0.1470 - val_accuracy: 0.9753\n",
            "Epoch 19/25\n",
            "1500/1500 [==============================] - 4s 3ms/step - loss: 0.0105 - accuracy: 0.9963 - val_loss: 0.1551 - val_accuracy: 0.9728\n",
            "Epoch 20/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0076 - accuracy: 0.9973 - val_loss: 0.1734 - val_accuracy: 0.9732\n",
            "Epoch 21/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0101 - accuracy: 0.9965 - val_loss: 0.1634 - val_accuracy: 0.9736\n",
            "Epoch 22/25\n",
            "1500/1500 [==============================] - 5s 4ms/step - loss: 0.0087 - accuracy: 0.9973 - val_loss: 0.1599 - val_accuracy: 0.9749\n",
            "Epoch 23/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0077 - accuracy: 0.9978 - val_loss: 0.1810 - val_accuracy: 0.9734\n",
            "Epoch 24/25\n",
            "1500/1500 [==============================] - 5s 3ms/step - loss: 0.0089 - accuracy: 0.9969 - val_loss: 0.1655 - val_accuracy: 0.9759\n",
            "Epoch 25/25\n",
            "1500/1500 [==============================] - 5s 4ms/step - loss: 0.0079 - accuracy: 0.9976 - val_loss: 0.1884 - val_accuracy: 0.9718\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob = model.predict(X_test)"
      ],
      "metadata": {
        "id": "CazuknkLW_LY",
        "outputId": "83773c35-4f33-462c-b69d-940c877887ee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = y_prob.argmax(axis=1)"
      ],
      "metadata": {
        "id": "5nELQpFrXQIi"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jcyA9XVHXY6o",
        "outputId": "694caece-0bdb-46a7-dd17-ce388fef0e7a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9744"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.plot(history.history['loss'])\n",
        "# plt.plot(history.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "NT2HxBnDXv_V",
        "outputId": "23f51b61-7921-4c5d-a0e6-5e9bdfef9f44"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ec79754b160>]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.plot(history.history['accuracy'])\n",
        "# plt.plot(history.history['val_accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "Ln1DIjDOY8E7",
        "outputId": "843e038e-5d3c-4803-a01a-974988a9eec7"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ec7975f12a0>]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.imshow(X_test[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "F0eTtwmSZGQn",
        "outputId": "bd02da83-f472-4012-adf8-30ee1d8654c3"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ec797403610>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model.predict(X_test[1].reshape(1,28,28)).argmax(axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBKWjIxealMY",
        "outputId": "b81bef72-69fb-43a9-8ad8-f6564b0b9172"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 52ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "pickle.dump(model, open('model.pkl', 'wb'))"
      ],
      "metadata": {
        "id": "onfLjtuZatoe"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xPuqBaQSqMQD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}