LeNet_5.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LeNet-5.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "av4RM1mmMkoa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "3e48897b-2fca-4544-a722-05fcb1705347"
      },
      "source": [
        "! nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon Aug 17 16:14:36 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 450.57       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   41C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HeySmfGBNQqu",
        "colab_type": "text"
      },
      "source": [
        "**LeNet-5(1998)**\n",
        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3s6Jpk9/Pnz8vLywmCMJzOVmzuTcmAEPK3sQlycGjGjlBrNEqN5plSeTQr68ajR3l5Dbx9vMHWrVs3Xkq1qtf8/PwCAwMhyqpy7dq158+fl5WVEZW8LR3A3lOIz5bNPtCw4C5TqY5kZaXIZI0UaG3atGns+2T0hjmItZeGW3l5uYFUcwHAW6RRrvLgZJtarYb9+1LKy8vlcjler9hwvhVWbwqFAo8ozR48CBAcThuRCCH0y40GHhKysio2aGVl1aiDTUvz2usgl8vLysrKy8tx4e9bn9y2HJfhQJMygXbjWgMPQHfvVsRXu3bt3oAheLIEw1zdhzmlUmlQwxwANHnNrdt+FU5GdUHBwC5WbCC34LC981qOEznMP0cLCxMe7066NjW1wbacmory87VSqbR9+/Y4q9pIyrsFeu11RMDz58/xvnqLcceWA7UcLYIPfhxo6Zmahg20vLwXgYZTqo09WVKr1SAim+4wBwBNXnPryu63lT1qWqgZDHCpFCzgWpQU4HA4XILoxyTJDh1qsC0fP17x09PTU3c96kaV3aADXgruWIdFwFv8GuzZsoW4jFOZ5+YShEfjBJqHhwcbaI1aW4JlN8RaHYc5PLEEwQ2A5m7EUxLs37qIJEOb+rPdylrOQm5YClAEQRGEv40NQmjrV5yXFpsmJVX8q53iYrRpE8EskudPUVTjVe23QK+9/uTEEBrztTSXcTgcktHc/p06IYS+2vLyQNu1C/3xR10CrSKshgwZohtojV3NBbHWRIc5AGg+mhtC61X3laE15a260EPzj4RKKdDNwsLTyqpMrl2woAE2u2ULevJE4+zs7ObmxuPxcAYOvGY4cWcgsrulTG4rb5ygmEDzsrKSP9fOn99Qgabt2bMnDrRGrS3RizigiQ5zANBMNDecj4CmJwU4HILD4ZEknyTDnZyEFBUTg7788rU2e+ECWrOGgxCaO3euQCDgcrmG052m2fDJJ5/4+vrCfmgagaaT5+aT5OQePYQUFRvbAIEWGfl3oLGTWwi0hkUmk/n6+iYmJsKuAACD09wA0JSkAFNbwiUIAUW1FYmm9uxJcDiLF6M9e+q5zbw8FBRE0rQ2NDT0nXfewVKA7ZYF+xxosbHGBlo7kWhawwQaoVZrQ0JCXF1d2UCDKGtwaJrOz8+Xy+WwKwAANDcAvIYUYIZokkm/ibhcn/btx3XtihAKD0cLFqBXbQRy5Qrq1Yt8/Jh+55135syZIxKJjIyM2Dw37G2gJQcaqgw0Iy53YIcO45nVZOodaL17E48fa1xdXWfPnq0baDC5BQDA0KBgFwAAe9WbYspLBBQl4nJH2dlxEDqQkbF5s/bCBbRhA/Lxefl2ysvRtm1o+XJCoaB79uwZGRlpamoqEon4fL6BX+8+e/bsyZMnZTIZRVEuLi5hYWGWlpaFhYUHDx708vJycXFhXxkTE6NWq8eNG8e0Hs/6/vvv8/LyrK2tp0+ffuHCBSsrq0GDBlXdfnp6+tmzZ8eNG7d9+/bMzEyJRBIWFtarVy/2Bbm5uYcOHbp16xafz3d3dw8LCxMKhbpf7/jx4wUFBRKJ5N133+3Xrx8ctE040LDmpigllzvazg4hdCA9vV6BxlEoND179lyzZg0ONFzEZeALjBcWFh45cuTWrVtFRUV2dnbDhw/HqxEnJCSUlJSEhYXpBkVCQsLw4cNtbW3xUvOXLl1CCA0dOtTe3v7s2bNz586t9iN++OGHrl27lpaWxsXF0TTt4eExbtw4kiTZF5w4cSIpKamwsNDR0TEwMLBz587sU0VFRQcPHvztt99IknR3dw8NDeXz+XDoAsDrA3luAKgMBqakm0sQRhRlzOWK+fwAO7slbm7mAkFKCho4sOJfbCyqacHqvDz03XfIzo5YvBgpFJoRI0Zs3LhRKpWamJgYGRkZ+PXutWvXzps3j6Zpd3d3qVS6c+fOkJAQhUJhaWl5+PDhHTt2sK8sKSlZvXp1fn4+Qujy5cujR4++detW7969y8rKgoKCNm3adObMmWo/IiMjY+vWrRMnTszPzx88eHBBQcHEiRPPnj2Ln71x48aYMWPOnDnj4uJibW39zTffhISElJSU4GfXrFkze/ZsuVzu7u4uk8nCw8O//vprOGKbdKBRBCEgSRxoI21tl7q7v1Kg2dtzmEDTBgQEfPHFF23btjUxMREKhYZfwZWbmzt69Oh9+/ZJJBJHR8dLly5NnDjxxIkTCKGCgoLVq1fn5uayLz506NDOnTulUilN0zNnzoyKiiJJUiKRREVFLVy4cOvWrTV9yu7duyMiIpYvX25tbS2VStevXx8eHo47G9I0/eGHHy5YsKC0tNTR0TEpKWn06NFslXZ2dvaIESP27dtnZWUlFos3bdo0ZsyYoqIiOG4B4PWBPDcAvIDtqMBDSMjlapn7gHtKJBEeHv/744+fHzxITlYnJyOKQl5eyMYGWVsjgaBCGchkKC0NpaTgzWg6duw4ffp0T0/PVgw492bISW6ZTLZv376pU6cuWrQIP+Ls7BwREZGWltarV6+xY8fu3LmzqKjIzMwMIXTq1CmFQhEcHIwQWr16taOj4969e3FCevv27Zs3b679s5ycnDZs2IAQCgsLmzZt2meffebj40OS5CeffGJlZfXf//4Xbyo4ODgkJGTLli2ffvrp5cuX9+3bN2/ePJzSmzt37sqVK7du3Tpo0CBHpiwBaIqBRjLXlHCXRK1W6yyRRHh6nszOTq5roGk7dOgwffp0Dw8PU1PTVq1aGRsbs4FmyObv2LFDqVQeO3YMB9T8+fMHDBgQHx8/fPjwwMDAjRs3njhx4oMPPsDi+Pjx40OHDhUKhQkJCefPn9+0adPw4cMRQtOnTw8KCqr9g/Lz82NjY3ECu3///jNnzjxx4sTIkSPj4+MTExPXrVuH16tftGjRtGnTli5d6ubmZmZm9sknn1AUFRsba2lpiRCaMmVKUFDQF1988fnnn8OhCwCguQGg4dQAh0MgxCMILXMRFld4cwliXNeugzt2vJCfn1ZY+MdffyUnV7OQCp/Pd3Fx8fb29vX1NWEQi8VNopJbKpWeO3eOLeSgaZqiKPwLlr/ffPPN2bNnsc6Oi4vr1atXx44d09PTs7Oz161bx75xypQp27dvr/2zZsyYgX8hSXLChAmzZ89OTU3l8/m5ubm6m3JycvL29o6Pj//000/PnDnD5/OnT5/ObuSjjz46cODAiRMnQHM3adnNJQgt9WIMwtPd8Y6Og21szufl1R5ozs7OONCMjY3FYrGJiYmxsbHu1SRDruRetGjRlClTsODGWFlZlZWVIYTMzMwGDhx45MgRrLmvXbsmk8mwMj558mTHjh2x4MZvwZPhWj7I39+frRjx8fGxtbU9fvz4yJEjjx8/3rFjR7xZHIlz58597733kpOT+/Xrl5qaOn/+fCy4EUKdO3cOCAg4fvx4ZGQkHLcAAJobABpSCiAsu/HIjdCLvmYUJaSoMfb2wzp1eqZS/VFcXFheniKTZRUV2dvbd+/evVOnTs7OzhYWFlhtixiEQiGuLqUoysB1AI/Hi46OTk1Nzc/Pz87O1l093srKys3N7ccffwwODpbJZNeuXYuKikIIPXr0CD+rK4bYoXrhwoWpOut6//TTT+wQzj7YiVkV5f79+7haVHdTCCF81RshlJmZaWlpqVtRasZQWFgIR2zThWDCgUcQiIkOorKBoICiAjt3Ht6pUykTaE/Ky4vKy7OKiu4WF+M5rbOzs7m5OSu12UBrKn2BzMzMsrOz165dm52djcMNIYTruRFC77777syZM9PS0pycnH788UccffjGiTZt2uhuhw2l9PT0tWvXso/PnDnTy8sLIdSxY0fd11taWuLPys7OdnBw0H3K1tYW58Xv37+PQ0/3WSsrK4VCIZPJ4KAFANDcANDwsptEiEMQBEWRBMFlmnYbUVS5Wq2k6VZ8fmuhUKPVPlers4qKunXr5uPjI5FILCwsTE1NjRmMjIz4fD6Px2sSPbmLioqCgoJKS0v9/Pzc3d27du2qVCqnTp3KvmDs2LFLly6VyWRHjhwRCoU42YZFsFKprHablpaWehq6KgqFouIcxKwaWNOz7AfV9CzQDGQ3oTO5NVKrKwJNo2lF022YQNNotUfu3r1bXNylS5d33nnHwsLC3NyczW3rBlqT6FUSHR2Ni7Lc3NyGDh2K67jYZ/v3729paRkfH29ra3vmzBk2DHk83uuHALsR9T8bxNRlBXuSJGGhewAAzQ0AjSK7CaaRMLs+pUCjUVGUUqNRM/+0CImYy+JCodDc3NySwdTU1IgBr8rBigAD1wEnTpzIz8/fu3cvm2xLSEjQfcHw4cNXr16dkJAQHx+Pq0vZNNutW7dwUg13Y8D3ViKEli1bVu1npaens1m0jIwMhFDXrl2x5k5LS2O/AH4lVu22traXLl0qKSkRi8X4qdzcXLlc3qFDBzhWm4fs5pJkRaARBI8kBSSp5HKVNM0GmkarxbEmEoksLCwsLS3NzMxwehsHGnuzRJNoDrhx40YPD49du3axj+Tm5uJMM5a2o0ePPn78eI8ePeRyOS7owrnn5ORkmqbZCWpaWhr71N69e6t+EBuMmPv37+M2QQ4ODunp6bpPZWVl4UDDMZWenu6j0zsmMzNTKBRKpVK9DQIA8MpnvKb4pfv06TNnzhx2AG5sWrduDQeK4fO/P/6IzczU/ZdT2fWidspUKr03xmZmvrjNiyDIyqZmQi7XhMdrxeeb8vnmAoGZQGDE6ACBQNCqErFYbGxszEqBppJ400uYFRUV4TpRNofN5/MDAgL27duXnZ3NloFaWlr6+/t///33uIakpKREN11XE1u2bMHZssLCwu3btzs5OXXu3NnW1tbJyWnPnj3s9evExMRLly6NHTsWIRQYGEjT9P/93//hp2ia/vzzz0mSZGtbgTfJr48f6wXL9TpXHVQN0gK5HLcxqRDcTCeTikDjcnUDzbwy1oyMjNhYw11K+Hx+k+vGTdO07qWbI0eO6FVJ4SKuTZs2eXh4SKVS/OC4ceNKSkrWr1+Pw+fy5cvx8fG1f9Dx48dxMQlOrstkMhxQAQEBMpns+++/x0/J5fLNmzeLxWIvLy+pVOrm5qYbiTdu3Dhz5kxAQAAc+QDw+jTJPHenTp0GDhx4+PDhkrqJqtfhk08+KSkp2bZtGxwrBs78n35S/PPS55c+PjZ1mJhdffhwUXKy3oNBTL0jVt6IwyG0WkKr1RIEl7nSjTst8JiEE4/HE1aCi0q5XC4e/puKCBg1atTOnTvnzJnj4+ND0/T58+f9/PzS09Nzc3PZHHZwcPDhw4fZ6lLMZ599Nm3atNDQUKlUWlRUhJ+qtlCEJTc3d8SIEY6OjlevXkUIffvtt/jxDRs2TJ48eeTIkf369SstLb106ZKHhwe+b9LR0XH58uXr1q27evWqk5PTjRs3ZDJZVFSUXrkq8Gb4+pdfzjJVv39rRAeHPpXSsLapHU1/cPq03oMHR4yQCIV6gcYlCFon0BBzf4VurAkEAqy2cYuSprX2zdixYw8cODBt2jRHR8e0tLTs7GwXFxfdFLKtra2Li0tqauqCBQvYB3v16rVkyZL169fHx8fz+fySkhJHR0fdWyaqYmJiEhQUNHDgwJKSkvPnz7///vs4nEeOHHnu3Ln169efP39eKpVevXq1qKho48aNOI0VGRk5adKkkSNH9u/fX61WJycnOzg4fPzxx3DkA0AL1dzJycm3b99+Mx1De/XqhW/kan5ER0fn5OSEhoba29s3dVt+f/pUQdPrvb09dWqIzQSCOr6XIogzISFUdSM3fghftq5Q3czPF8HDjPckSXK5XCy12dx20/KapaVlXFxcfHx8VlYWbnTo6OiI60rZ17i4uGzcuLFdu3Z6b4yNjcXdFbAc79Kli7RW+bVt27bLly+npaXNnj175MiR7NUqW1vbhISEU6dOpaWlSaXS999/X/fq9qRJk/r06XP27Nn8/PwJEyb4+/uzxeLBwcE+dVlDBQKtgbhZWBjs4DBfZzEjIZdbx0CrkOyDBrnoXDmUVHaqqSbQKgW3XqyxgVb77M5gHffpp5/26tULK11/f/9Ro0ZlZmZeuHBBt3pq0aJF165dGzJkiO4bp0yZ4uPj88svv+A+JPim51o+yNvbe9SoUYmJiWKxeOfOnez8GU9xAwICzp8/L5fLx44dO27cODbYO3bsmMCQlpYmFovXrVvn7++Pd7WJicm8efO6Mgv0QoiBgWBgS9HcjxmqPt6+fXuBQPDgwYPy8vKqz7Zt29bY2LimZ1sgd+/eXbly5YoVK1xcXIKDgxvqcL/z5EmJQmEmEHQxN9d9vEyluvPkCa3R2JmaSnTWF3xYVibicsU83s2CArlK1bFVq7YiEc6KFcjlZgKBSGdEf1peLleprE1M9D40i5mAebdvj9+rx/dM4eMUJyf853WZ7FBGxjpvbzyQ3yosdDAze2lGnFOZTtNWSnNcikr8k8ZOuTWS1ywtLadMmaL7SNXKjZEjR+o9EhoaOmHCBPbxGzduVO2WoAefz9ddZk8XsVgczFDts44M1c6KW2ag5ZWW5pWW8imqm4UFX0d9qjWaO0+eyFUqiVBoZ2qqGzu0RiMRCnNKSmTPnulGaF5pqZDLNdeZo5apVEXl5RKhkP9PXfu0vLxALu8jlVaNwaqRVaZSRVy8uMTNDcd7GlNB4d2+vbjK7YDVBxqjwWuKtTeQ224kx5EkOZJBd0Kru84rbmOie0EJl6BcunRp3bp1bOV3SkqKbhegaunHUO1TPgw1RWK1QSoWi2ta9tJAQqxnz54hISHNT7qBgaC53yYhDLNnz378+LGPj8/cuXMjIyOnTp3atm1bhJBKpdq3b9/x48fxi2NiYuLi4jp27IhPaiqV6siRI7GxsbgqbtWqVRKJZPbs2VU3LpFIVq1ahRAayBAREXH79u1meRCkMrBDy7vvvlu/q/b3iovnnT1758kT/Kdr69b/HjIED7f709PXXb1awtQHUwQR2rXrKg8PPDC/Gx8/oH37WwUFNwsK8BvnvfPOot69EUL+MTEj7ezWeXuzHzH15MmK4WfMGP3cW0GBuUAg5vHO5eUp1OpeUqmugEiRyU4wdY1TnJyuy2Th//ufVCQqUSrxa35/+rSPVPqwrCz18WMzPt+1TRt+rfmzqkP9W7l5q6G89joIhcKoqKiysjJbW9tHjx5t3brVyspKLzkH1OSykJAQGxubemynTKX6188/n6gs1W0rEn01aBCu7rguk/0rOZm9k0E3BtdeuZJbUtJRLI7JzMTP9m3Xbqe/v4jLXX7hQu5ffyUzi/ljvv3tt3//9tvlsDC9WEhlkh2OFha/Pn6cX1rqJJHoTlYLnz8/eveuXK3+ytdXQdPh//vfnSdPwrp1w1/g96dPbcRiksP5iSlNcWnd2rzWK1HVRtRbKSOp6jjc5vJNIpFI4uPjKYoKCwtTq9UnTpy4du0aHqGAvwcChhUrVjg7O7/L0Mwq0MBA0NwGweLFi5OSkr799luhUDhx4sTJkyffvn07JycHPztixIisrKzFixeXlpYGBQWFhITQNB0bG1v7NnNyciIiIlatWpWamnrkyBF2aw3L7du3ExIScNMrXJ74xgYVveuSHA6HHVqcnZ1HjBjh6+tb960paHrC8eNCLjcxOLiLufnNgoIJx48vv3Dh30OGHLt3b9n582Hdui1xcxNS1H9///2zixcRQlGV1zqj79z5wMVlX0CAWqNZeu7c1l9+GWNvb2dqOtLO7lROzipPTzzw55SU/Pr48dr+/at+Ohb6/aKjsaznk+Tyvn0nde+On/2KMWT1pUsFcnlMZqZUJDo4YgQe78tUqnvFxQqaPsj00NBTMHUhIyPj5MmT7HqTOAnXVLz2mmzYsGHz5s1ffvklvizu4+OzaNGialv7YYFuZWXV2PUABui1qgUAui4bNWqUn5/fK21w6blzPz94sH3w4GGdOhXI5TNOnZp95syF8eML5PLw//2vm4XF7uHDbcTi6zLZ7DNnJpw4cXzsWDy//fXxYyGXe2H8eIlQuOPmzY3Xrx/6/fcpTk7vOjjMPXv2ukzGHvZHsrIGdexYVRPfKy5GCM0+cyavtBQ/Eti5c6SXF74YNaxTp62DBs07e/bDn34qkMvvPHmye9gw18pKkpsFBSVKpW6QRnh4vPcqqxqlpaXFxcXhs+UbqC2p3XGjR4/29/d/Y0esl5fXunXrtm7d+uOPP+KrSVFRUTVdFMILXekuu/N2wcOciYlJIw1zVT31GwN7Vhw4cGCjGvjLL78YGxvz+fxGWga1hRsYEBDQ2AaC5n41Ll++rLsi15IlS3r27Mmq5LKyss8//xwv9PXvf//byspq1KhR8fHxKpWqtmRSWRlObBcVFTVehvskgyHsQ7Z0kj3co6Ki9Op3a+F0Ts7DsrLogAB8wbqnRLLU3f02I4X/ffNmF3NzVmG/5+j4+9On0enpi3r3xoN6F3PzJZUXUj9wcTmVk5P6+LGdqWlo164HMzLOPXgwmEkHHs3K4pPkCDu7ajU3SRCbfX092rWTlZVFXLz42cWLEqFwGJOOogjiK19fhVq9PTVVIhTi27Z0xbqdqemeYcPsTE0v5ucvOXdu6smTZ0NCdAtgauEsg0F57aWNsRsKS0vLSIa6vNifwUDOGG/Xa3ouW7NmTd1dViCXJ9y7N++dd/CxLREKV3l67rh582FZ2f47dxQ0/dWgQbjCqo9UutrT84PTp0/n5uIXqzWajQMG4AN7rqvrv3/77Q5T7zHYxkbM4x27d49NlueVllY7uf318WOKIKb06BHapQut1X5/69bmGzeEXC4b3cM6dVrv7Y1vSj44YoTu3DWzqEjM420dNMjTyupecXHExYvLzp+3NjHxtrauo+0nGAzh+MGOW716tXWdv/zrE8iAuwzVNLNl2b9/v+EM0G9xmKvHWFYPDjOAgY1n4Nq1axvVQNDc9Z8k5ebm4g5u7CPXrl3DgpsV6N27d7ezs8uoTG2+RTw8PHr27InvxH/DHa+SGap9ysLCYvjw4YMHD05KStLtI1vbPJi56OzOlPew2prNb83o2VP3xQPat99z+3b6kyf4lsceOvfqSYyM2N9dW7e2MzU9cvcuq7mH29pWWwyaGBxMEgRW8NYmJt8MHtwvOnrP7dvDKi8B55aU3Cws5JNkhWTJzmZru/tIpRfGj2erxj2trNZ7e4cdP3707t3p//zONeHm5ta9e3d26enGznPX4jVzc/OAgIBX8lqL5U16rXaXjRw50t/fPykpaceOHXXZ2i1GJffVCbSeEsnWQYPw3Y0OZma6tzRgOfvro0c4ENqKRLozSXGlbuOTZGDnzgn37n3arx9FELGZmW1FIo/qRrh13t7L3N3ZYu75vXrdKiiI+f33Ze7uOILKVKqDGRkiLldB0z/cudNbKqUqd2xyaCgbpHampi+CNC2t7prb09PT2dkZtwhs7JuVa3ecqanpmDFjhg0b9tNPP3333Xdv8tB9qdo2QBp7mHvpWObn55ecnNx4Z0U/Pz9XV1d2JdQ3P1i3BAOTkpJ2794NmtsgKGaueNbEU+Z+eZYnTGpTKpUagubu16/fxIkTLS0tW7VqhS/cvMmCRb2j3NzcfOzYsaGhof369VMqlU+fPv3555/ruKkSvKZglWjE15H1OhsImW67RXW4mTXYwWHzjRv4/suckpKo6nJvut0PWA3hYG7OXv6+V1w87tgxMY8XHxi45vLl1Zcu6d5SqXc3GJ423Kv1iNKlT58+uFmeubm5sbFxTasqGqbXWixv2Gt6LjM1NcX35Hl6eqpUquLi4prGm6rgqCFrOEuI/ynIsA5+WhloZM2DZZCDw57bt8/l5XlaWZ3Izg7r1o2q7sViHk9v0tu3Xbuz9+8XlZeLuNwylQrXcO8eNiy7uHjJuXO4sgtvSi9IxTxeD4nk91dpP+Xp6Tlp0iQLCwuxWIxb4L/hMyS+zXf8+PFeXl40Tf/11191d1xLpl+/fhMmTJBIJI03zOk5wtLScsyYMfisqFAoGvusOGjQoKlTpxobG2NV+gYOxRZlYHl5ebMZ11riOpQiJg8E3Uv0FMC4ceMGDBhAUZRGo1GpVDWt6V0TeEBV0DR701VOSUnW06fe7dvzSbLg+XPdF8vkcoRQm+p6jFTV3F+mpBy7d+9mYWFbkcizukvwBXL50bt3Pa2sullYsA8q1Gq24cnyCxfEPB4uKcG13V9evz7S1lYiFP76+PHF/PxZzs6swpAzqyLXsc9gU/ca8CbBiu29997DLtNqtWq1uvYKt6pImaiR66zdraDp0zk5vaRSPknm/vWXXmgghGxatXrpZntKJF3MzRPu3VPQdIlSGcz0p6/KD7dvtxWJBuvc+skWZyOEdty8iQV3H6m0j1RaplavvnQp2MHBt0OHh2Vlx+7d8+3QQbeVilylMjf4QEMIGRsbBwUFTZw4kXUczQCHtKFhYWERGBiod1asxyL5YKBBGdicDtEWobn1mgPgCjxcgqJSqSx0hBpC6I3Vwr51LC0tp02bxh7cr5vJaNdue2rq6Zwctt76h9u3o+/c+WXSpL7t2p3KyWGvPiOEEu7eFXG53f6552uS8gOsrRPu3bvz5Al7T6Qeaq026sqVYAeHjZWtr3JKSm4VFrKZ7K2MzsazAlzbfa+4GP+Z+fTpxuvXu5iZsTICd3XwNdR1xRvWaw2FXC5XKBT1u2erqKiIz+cL61Y9D4Hm0ro1nyTP3r/PHqJXHz6ce/ZsdEBA37Ztf37w4GZBQU+JBD919O5dXKNVly2Hdu365fXrT8vLcU1Xta+JTk8vU6kGduiA56hqjeZUTk4Xc3O2RnxYp05sC8IpTk5927bFYa6g6agrV/JKS1d5euJnf3/69GZBwbx33mkhjgPAU2AgxFqLOI+4urq2b9/+wYMHODvo6+ublZX18OFDhNDDhw9dXFw6d+6clZWFEGrXrp1en9SXrqvXdJkzZ04Dbs3b2rpvu3afXbxYplI5Wlhcffjwh9u3p/fsKeJy5/fq9W58/ITjxxf07m3C40XfuXP2/v3lffuK6raURnCXLnj5uppyb21FonHM3Zb4psn7JSUbr183EwhmVbpS76I2RRCsLBhhZ7f111+XXbhQolTampqmyGTrr10baWdX974lTdprr8+FCxc2btyYnp6Oz5tTp07V6/NdCzt27Ni9ezde+NrR0XHZsmV6PYkh0Koi4nI/cHHZfOOGmMfDR/uqy5e7WVi4t23bQyKJTk+fmpgY0a9fB7H46sOH669dG9C+vWfd8ghj7O3XXrny84MH63W6c+oxz9V17tmzM0+dmuXioqTpf9+8ea+4eF9lK3fdyMKw82obsTiwc+fo9HSJUOhtbY2/to1YXMe7JiDW8Mz266+//vHHH/E01cfH57PPPtNduKoWcnNz16xZc+3aNYVCYWlpGRISMnfu3GYztBmap8BAMLBFa26apiMjI5OSkmiaHjhwII/HY+96uXjx4pAhQ1asWHHx4kUul+vm5vbgwQPddbaePHni6enZunXrAwcONNf+3A3FTn//tVevrr58uYy5ZDzX1XWuqytOsx0YMWLtlSuTmJ4DNmLxlz4+QZUCuq1IpHt9mSQIaxMT3fpv3w4dupib25maVrsMBybSy8tMIDiUkbE9NZUiCN8OHT7r168ul61FXO7hUaMiLl5ccu6cWqMR83hTnJw+bo7KrzFITU2dMWOGu7v77t27TUxM4uPj169fT9M0XrO9drZv37558+YZM2b4+/uXlpZu3rw5PDw8Li7upct8APN79RJxud/furU9NZVPkkNsbFZ5elIEIebxDo8ateby5UXJyQqaNhcIpjg5LerTB7/LXCDQWzFKL/TMBYJgB4crf/5ZbWsgdo6q1mo3p6S8Gx+PJfWeYcP61q2fwIYBAyRGRttTUzdev46DNMrLq/b1cQBdFi5cePXq1dmzZzs7O+fn52/cuHHixIlHjx596V2VhYWF7733nkgkWrdunaWl5fnz57/77ruioqKVK1fCXgUA0Nwv4Y8//khKSsIF2TKZLCkpSXcd+PLy8qSkpD/++IN95PLly1lZWQMHDhSJRFeuXDl69Ci7jGVGRsbq1asDAgJcXFwKCgq2b99eXFw8aNAgttp748aNwcHBAoGAW7ekbEtGxLQMi/Ly0q3qxvSRSvFCNlWfOjxqlJ4OuDB+vO4jfJJMrLkN7YvjmCCWuLktcXPDS1dSr3JjdVuR6N/MMi4Fcrmk+VY4NAbR0dFisXjbtm24MsTJySk7O3vnzp2s5k5PT9ddPJL9k6bp77//PiAgYNGiRfip7t27DxgwYM+ePXXsPNjCmd6z5/SePatGU1uR6Bum23fVp5b17au3Eb3Qw21JXvrRY+ztx9jblyiVJIcjepWzIkUQy/r2Xda3LwRaPcjKykpKSlq8eDEbXFKpNDw8/MSJE4GBgQih/Px8qVTKpq7lcnlBQQFeT+Tw4cOFhYX79+/Hf7q5ucnl8gMHDixcuFD8svV3AQBo6Zr7OgMrmvXaj5SUlGzbtk3vLacYqt3abQbdR3Q3mJWV9fnnn8OB8krUso4jv5GvZr7OWA464FVxcXFxcHDQLcV2dHQ8f/48/v3IkSPLly/ftGkTbsudmJi4YMGCqKiowMDAsrKywMBA3WUOxGKxmZlZfn4+7NUmEWivk5+GQKsHarV67Nixuqu147UwccgoFIr33nvP2dl506ZNJEnK5fJp06YVFhbiUU8qlU6YMEF3PT9HR0eapktLS0FzAwBobgAAmgBhYWG6f9I0nZiYyBaHjBo1KikpacGCBZs2bUIILViwwM/PbxSTWxWLxcuWLdN9b1ZWVn5+/ujRo2GvAkBVHB0d9bI/ly9fxheXcNPuZcuWLWCIjIycNWtWenr6f/7zH/xKvJiO7nuTkpL4fL7hLFQJAKC5AQAAXoH169fn5uZ+9dVX+E+SJDdt2oR1AF5VASfhqr5RLpf/61//EovFeiIeAIBqyc/PX79+vaOjY//KJQv8/f1xuF2+fFmtVv/nP//p1atXte9NSEg4c+bMjBkzhHDBAQDeLESzt/Drr782hEWeAaB5s2XLlj179kydOlV3gXeSJIcOHYqbGQ8dOrQmwT1t2rTMzMxt27bVsQkDALRksrOzp02bRpLk//3f/+nGVP/+/W1tbUtKSlwYqn1vYmLikiVLPDw85s+fD3sSAEBzNzDJycmGsN4kADRXaJr+5JNPvvnmmxkzZnz88cd6A/zixYv9GRYvXpyYmKj33vz8/KCgoPT09L1797pBuxgAeBmpqakTJ06Uy+V79+61tbXVm7vm5+fPmzfv6tWrCxYsqLpqz/fff49LvLZt29Zce+ACAGhuAACaJ3K5fOLEiXFxcVFRUWwTEszZs2fxAL+JoVevXgsWLNC96JSamhoSEiKXy/fv31/TdXAAAHQnsZMmTZJKpbGxsVUFN67hnjt37qZNm86cOaMru2maXrly5fr168ePH79p0yaoKgEA0NwAADQxwY1H+p07dwZX6efYqVOnkJAQXMNNkuR3333n5+eHmy3gvoGTJ0+WSCSxsbG6/QQBAKhJcC9YsKB///579+7Vq8Li8/lSqfS7777Dc1dc221tbc0ms5cuXXrgwIHly5d/+umnkOEGgLcF3EMJAEA9+frrr2/cuBEVFdWhQwfdNn9WzMKHtra2uotuCIVC9vZKmqYXLlzI5/MjIyMVCgX7Xj6fDyXdAFAVmUy2dOlSBweHjz/+uIgBP25iYiIWi3Ftt+7rcUEX/j0hISE+Pn7ChAmDBg3SjVNLS8uXrqcDAABobgAA3j779u1DCC1fvlzv8d9//732N54/fz47OxshFBQUpPu4m5vb3r17YccCgB6HDh2Sy+Xp6elDmAW8WObNmzd37tza34vXXd7HoPs43EQBAKC5AQBoGqxatap+b+zUqdO6deuqPi6RSGCvAkBVvLy8OnToUPXxrl27vvS9U6dOrfZx3YpwAABAcwMAYLjoLbRRdzoywA4EgDrSi+ENxykAAA0L3EMJAAAAAAAAAKC5AQAAAAAAAAA0NwAAAAAAAAAAoLkBAAAAAAAA4O3QKPdQcjgc9mdxcTHs5WopLS3V21f4pyGg932uy2Tgr6rcefIE7yVdx711J7Jf4MaNG+CjqqSnpxua19gvA4FWl1gznLMlxFodw013XxnOMAcAzURz47jCbb/+ZIAdXRMWFhYEQRjUaYgd0lgnHszIOJiRAc6qFnNzc4LBEHQbBi8rc5gBHGTIXmMdh10GgfbSs6XeZOmt+w6fISHWmpbjAKBZaW4Oh0MQxLhx48rKyvLy8srLy9VqNU3TCCGtVtvC9zg+75AkSVGUra1tv379KIoyNNmNvydJku+++65cLs/Pz1cqlTRNazQa8CCrk0iS7Nixo5ubG5fLNRAP4m8VEhLy/Plz8FotXnN3d+dyuSRJGoLmxmdLuVyel5enUCjUajW4TNdlBEFwuVw7OzsPDw+SJA1npkQQxPjx4/WGOXCc7oHN5XJtbW09PDwMc5gDgKatudlMG0VRPB4vNDT06dOnpaWl+Hyk0WhgjyOECIKgKEogEJiYmAiFQh6Pxw4kBnJKwqdLiqL4fH5wcHBxcXFZWRkWcDCisPuHx+MJhUKRSCQQCHSdaAheCwoK+uuvv8BrNXlNKBQKBAIsu9+i13TPliEhIU+fPn327JlCocAzJXAZPltyuVyBQNCqVSsjIyMDCTR2fsvlctlh7vnz5zDMVTvMYceB7AYAqvEGfoFAYGxsrNFouFyuUqlUq9Uw8LO7CI+yIpHI2NgYj/0GdTLCwwlWJ2q1msPhCAQClUoF6beqUkB34vTWy0tYr6lUKoIgwGuG7DU2787lco2MjIyNjbVaLZ/Ph2mS3l7icrk8Hs/Y2BjPbw1BuukNczRNc7lcfI0CHFfTMMc6DmQ3AJq7gQd+PIpotVqKorACgIFf73zN5XL5fL5QKDQyMjKQy9x6wwmfz9doNB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ixY3v37t2+ffvWrVvHxsYaGxvfeeed1T9UZ2dnT0/P4cOHz549+/zzz1+6dKm5ubm3t/dZuKD19fUnT5785JNPRkdH0+n0xMREX19fwb/8WpTNZv2wAQDPjnQ6fe3ataampmiLSCqV+vXXX5eeljthdnb2+++/j3Z179ixo+Dfxvu/m5iYGB8fT6fTmzdvjv6sz6ITZmdnb9y40dLSsoaGWur69ethGCYSiUUHf/zxx1gstm3btubm5oJ/SM0NAABPl/3cAACguQEAQHMDAACaGwAANDcAAGhuAABAcwMAgOYGAADNDQAAaG4AANDcAACA5gYAAM0NAACaGwAA0NwAAKC5AQBAcwMAAJobAAA0NwAAaG4AAEBzAwCA5gYAADQ3AABobgAA0NwAAIDmBgAAzQ0AAJobAADQ3AAAoLkBAEBzAwAAmhsAADQ3AACguQEAQHMDAMDa9z8BAAD//2qnKE1cfsCDAAAAAElFTkSuQmCC)\n",
        "\n",
        "**Impact**\n",
        "\n",
        "This architecture has become the standard ‘template’: stacking convolutions and pooling layers, and ending the network with one or more fully-connected layers.\n",
        "\n",
        "[**Official Paper**](http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-BTJ5oNQO28K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense\n",
        "from tensorflow.keras.datasets.mnist import load_data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VH46x3DaSiqd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYBSVbrwNFT1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_model(input_shape):\n",
        "  model = Sequential([\n",
        "      Conv2D(6, (5,5), activation='tanh', padding='same', input_shape=input_shape),\n",
        "      MaxPool2D(),\n",
        "      Conv2D(16, (5,5), activation='tanh',),\n",
        "      MaxPool2D(),\n",
        "      Flatten(),\n",
        "      Dense(120, activation='tanh',),\n",
        "      Dense(84, activation='tanh',),\n",
        "      Dense(10, activation='softmax')\n",
        "  ])\n",
        "\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1bar9a78SwXE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pre_process(data):# shape=(m,width,height,channel=1)\n",
        "  data = data/255.\n",
        "  return data[...,np.newaxis]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K7Zq-54FR-Ln",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(train_X, train_y), (test_X, test_y) = load_data()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fV7_qeUASUp4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_X = pre_process(train_X)\n",
        "test_X = pre_process(test_X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NEy0fcNUmj2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = get_model(train_X[1,:].shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGwuZig5Vgh6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "e104985c-ab55-4616-d34e-df06855a97e0"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 28, 28, 6)         156       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 14, 14, 6)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 10, 10, 16)        2416      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 16)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 400)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 120)               48120     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 84)                10164     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                850       \n",
            "=================================================================\n",
            "Total params: 61,706\n",
            "Trainable params: 61,706\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CIw28v4yVtL5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgVktrc5YYLK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "98b575be-bfab-479f-9c71-f234aff6c559"
      },
      "source": [
        "%cd drive/My Drive/Models/LeNet-5 # Connect your drive and upload downloaded model to this directory(or any directory of your choice after changing code hre)\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Models/LeNet-5\n",
            "checkpoint\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "octX08N5Zci4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
        "    'checkpoint', monitor='val_accuracy', verbose=1, save_best_only=True,\n",
        "    )\n",
        "\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_accuracy', patience=5, verbose=0, \n",
        "  )\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9JfRmn5KWkdA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a38b3ab5-b63b-4f33-8fe3-5bdf38e2a7b8"
      },
      "source": [
        "model.fit(train_X, train_y, epochs=30, validation_data=(test_X, test_y), batch_size=8, callbacks=[model_checkpoint, early_stopping])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "7497/7500 [============================>.] - ETA: 0s - loss: 0.1350 - accuracy: 0.9590\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.98440, saving model to checkpoint\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "INFO:tensorflow:Assets written to: checkpoint/assets\n",
            "7500/7500 [==============================] - 64s 9ms/step - loss: 0.1350 - accuracy: 0.9590 - val_loss: 0.0531 - val_accuracy: 0.9844\n",
            "Epoch 2/30\n",
            "7496/7500 [============================>.] - ETA: 0s - loss: 0.0587 - accuracy: 0.9814\n",
            "Epoch 00002: val_accuracy did not improve from 0.98440\n",
            "7500/7500 [==============================] - 65s 9ms/step - loss: 0.0587 - accuracy: 0.9814 - val_loss: 0.0546 - val_accuracy: 0.9833\n",
            "Epoch 3/30\n",
            "7494/7500 [============================>.] - ETA: 0s - loss: 0.0446 - accuracy: 0.9863\n",
            "Epoch 00003: val_accuracy improved from 0.98440 to 0.98500, saving model to checkpoint\n",
            "INFO:tensorflow:Assets written to: checkpoint/assets\n",
            "7500/7500 [==============================] - 64s 9ms/step - loss: 0.0446 - accuracy: 0.9863 - val_loss: 0.0488 - val_accuracy: 0.9850\n",
            "Epoch 4/30\n",
            "7496/7500 [============================>.] - ETA: 0s - loss: 0.0385 - accuracy: 0.9879\n",
            "Epoch 00004: val_accuracy improved from 0.98500 to 0.98610, saving model to checkpoint\n",
            "INFO:tensorflow:Assets written to: checkpoint/assets\n",
            "7500/7500 [==============================] - 65s 9ms/step - loss: 0.0385 - accuracy: 0.9879 - val_loss: 0.0440 - val_accuracy: 0.9861\n",
            "Epoch 5/30\n",
            "7495/7500 [============================>.] - ETA: 0s - loss: 0.0329 - accuracy: 0.9901\n",
            "Epoch 00005: val_accuracy did not improve from 0.98610\n",
            "7500/7500 [==============================] - 62s 8ms/step - loss: 0.0330 - accuracy: 0.9901 - val_loss: 0.0432 - val_accuracy: 0.9861\n",
            "Epoch 6/30\n",
            "7494/7500 [============================>.] - ETA: 0s - loss: 0.0321 - accuracy: 0.9894\n",
            "Epoch 00006: val_accuracy did not improve from 0.98610\n",
            "7500/7500 [==============================] - 62s 8ms/step - loss: 0.0321 - accuracy: 0.9894 - val_loss: 0.0537 - val_accuracy: 0.9843\n",
            "Epoch 7/30\n",
            "7494/7500 [============================>.] - ETA: 0s - loss: 0.0276 - accuracy: 0.9911\n",
            "Epoch 00007: val_accuracy did not improve from 0.98610\n",
            "7500/7500 [==============================] - 63s 8ms/step - loss: 0.0276 - accuracy: 0.9911 - val_loss: 0.0483 - val_accuracy: 0.9850\n",
            "Epoch 8/30\n",
            "7496/7500 [============================>.] - ETA: 0s - loss: 0.0244 - accuracy: 0.9920\n",
            "Epoch 00008: val_accuracy improved from 0.98610 to 0.98690, saving model to checkpoint\n",
            "INFO:tensorflow:Assets written to: checkpoint/assets\n",
            "7500/7500 [==============================] - 63s 8ms/step - loss: 0.0244 - accuracy: 0.9920 - val_loss: 0.0470 - val_accuracy: 0.9869\n",
            "Epoch 9/30\n",
            "7499/7500 [============================>.] - ETA: 0s - loss: 0.0238 - accuracy: 0.9922\n",
            "Epoch 00009: val_accuracy did not improve from 0.98690\n",
            "7500/7500 [==============================] - 68s 9ms/step - loss: 0.0238 - accuracy: 0.9922 - val_loss: 0.0526 - val_accuracy: 0.9865\n",
            "Epoch 10/30\n",
            "7497/7500 [============================>.] - ETA: 0s - loss: 0.0207 - accuracy: 0.9933\n",
            "Epoch 00010: val_accuracy did not improve from 0.98690\n",
            "7500/7500 [==============================] - 66s 9ms/step - loss: 0.0207 - accuracy: 0.9933 - val_loss: 0.0526 - val_accuracy: 0.9857\n",
            "Epoch 11/30\n",
            "7495/7500 [============================>.] - ETA: 0s - loss: 0.0208 - accuracy: 0.9932\n",
            "Epoch 00011: val_accuracy did not improve from 0.98690\n",
            "7500/7500 [==============================] - 66s 9ms/step - loss: 0.0209 - accuracy: 0.9932 - val_loss: 0.0614 - val_accuracy: 0.9840\n",
            "Epoch 12/30\n",
            "7495/7500 [============================>.] - ETA: 0s - loss: 0.0202 - accuracy: 0.9935\n",
            "Epoch 00012: val_accuracy did not improve from 0.98690\n",
            "7500/7500 [==============================] - 66s 9ms/step - loss: 0.0202 - accuracy: 0.9935 - val_loss: 0.0556 - val_accuracy: 0.9846\n",
            "Epoch 13/30\n",
            "7500/7500 [==============================] - ETA: 0s - loss: 0.0189 - accuracy: 0.9938\n",
            "Epoch 00013: val_accuracy did not improve from 0.98690\n",
            "7500/7500 [==============================] - 69s 9ms/step - loss: 0.0189 - accuracy: 0.9938 - val_loss: 0.0562 - val_accuracy: 0.9868\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f1c150dd710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGmL1V_H-GOc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def decode_output(predict):\n",
        "  return np.argmax(predict)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrw4QsRG96_y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.load_model('checkpoint')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HyUFSC3Y8srk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "539fd112-47e7-47e6-e3cf-4a3d9d4274c8"
      },
      "source": [
        "i=11\n",
        "img = test_X[i]\n",
        "\n",
        "plt.imshow(img[:,:,0])\n",
        "\n",
        "print(decode_output(model.predict(img[np.newaxis,...])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z8fxL58V-wNa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "layers = model.layers\n",
        "input = model.inputs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5E0VS5rJ_nW3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras import Model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LzQKnBhD-9FB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "layers_outputs = [layer.output for layer in layers]\n",
        "\n",
        "feature_extractor = Model(inputs=input, outputs=layers_outputs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTwpASSoA_2d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        },
        "outputId": "24cb581e-56cc-4854-d025-db4776331dcf"
      },
      "source": [
        "tf.keras.utils.plot_model(feature_extractor, 'LeNet.png', show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M9tcUvmDAmGX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = feature_extractor(img[np.newaxis,...])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5SWOtgtwA48w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 897
        },
        "outputId": "b3996f30-cbff-481b-f5ee-fabcb9871aa9"
      },
      "source": [
        "#layer 1(conv2d layer)\n",
        "# o/p=(None,28,28,6)\n",
        "\n",
        "l1 = features[0]\n",
        "print(f'\\n{l1.shape}')\n",
        "imgs = l1[0,:,:,:]\n",
        "plt.figure(figsize=(15,15))\n",
        "\n",
        "for n in range(6):\n",
        "    ax = plt.subplot(3,2, n+1)\n",
        "    plt.imshow(imgs[:,:,n])\n",
        "    plt.axis('off')\n",
        "    plt.title(f'Filter-{n+1}')\n",
        "plt.subplots_adjust(wspace=0.01, hspace=0.1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "(1, 28, 28, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T_HpaN51CHlV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 897
        },
        "outputId": "ee1eecff-934e-4101-eb42-b8ae39b32c87"
      },
      "source": [
        "#layer 2(max_pooling2d)\n",
        "#o/p=(None,14,14,6)\n",
        "\n",
        "l2 = features[1]\n",
        "print(f'\\n{l2.shape}')\n",
        "imgs = l2[0,:,:,:]\n",
        "plt.figure(figsize=(15,15))\n",
        "\n",
        "for n in range(6):\n",
        "    ax = plt.subplot(3,2, n+1)\n",
        "    plt.imshow(imgs[:,:,n])\n",
        "    plt.axis('off')\n",
        "    plt.title(f'Filter-{n+1}')\n",
        "plt.subplots_adjust(wspace=0.01, hspace=0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "(1, 14, 14, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0KVYZibKDxEt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 897
        },
        "outputId": "9442cd92-62ad-49a3-85ce-c48a01a39548"
      },
      "source": [
        "#layer 3(conv2d_1)\n",
        "#o/p=(None,10,10,16)\n",
        "\n",
        "l3 = features[2]\n",
        "print(f'\\n{l3.shape}')\n",
        "imgs = l3[0,:,:,:]\n",
        "plt.figure(figsize=(15,15))\n",
        "\n",
        "for n in range(16):\n",
        "    ax = plt.subplot(4,4, n+1)\n",
        "    plt.imshow(imgs[:,:,n])\n",
        "    plt.axis('off')\n",
        "    plt.title(f'Filter-{n+1}')\n",
        "plt.subplots_adjust(wspace=0.01, hspace=0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "(1, 10, 10, 16)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-HI7v3DEa3P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 897
        },
        "outputId": "c159eb93-601c-4e65-f1fb-cdf74d8e1885"
      },
      "source": [
        "#layer 4(max_pooling2d_1)\n",
        "#o/p=(None,5,5,16)\n",
        "\n",
        "l4 = features[3]\n",
        "print(f'\\n{l4.shape}')\n",
        "imgs = l4[0,:,:,:]\n",
        "plt.figure(figsize=(15,15))\n",
        "\n",
        "for n in range(16):\n",
        "    ax = plt.subplot(4,4, n+1)\n",
        "    plt.imshow(imgs[:,:,n])\n",
        "    plt.axis('off')\n",
        "    plt.title(f'Filter-{n+1}')\n",
        "plt.subplots_adjust(wspace=0.01, hspace=0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "(1, 5, 5, 16)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}