portal_captcha.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "portal-captcha.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "LqvMrSJVOu6m",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**upload dataset**"
      ]
    },
    {
      "metadata": {
        "id": "VX7FLmIUIoWA",
        "colab_type": "code",
        "outputId": "e330cce0-4631-45ad-88a9-1c957de64398",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-50c851ee-91bf-4b85-bb41-7e1a83bf89f7\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-50c851ee-91bf-4b85-bb41-7e1a83bf89f7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving captcha.npy to captcha (2).npy\n",
            "User uploaded file \"captcha.npy\" with length 68924880 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_33UHOJSO0JQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**load data**"
      ]
    },
    {
      "metadata": {
        "id": "QhwVHK9ZIqV-",
        "colab_type": "code",
        "outputId": "45e52438-fd4b-460f-c3e8-95222b3eb426",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2034
        }
      },
      "cell_type": "code",
      "source": [
        "from __future__ import print_function\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras import backend as K\n",
        "import numpy as np\n",
        "\n",
        "dataset = np.load(open('captcha (2).npy', 'rb'))\n",
        "# print(type(np.load('captcha.npy')))\n",
        "\n",
        "(x_test, y_test) ,(x_train, y_train)= dataset\n",
        "\n",
        "# (fx_test, fy_test),(fx_train, fy_train) = dataset\n",
        "# fx_train = np.array(fx_train)\n",
        "# x_train = []\n",
        "# fx_test = np.array(fx_test)\n",
        "# x_test = []\n",
        "# for i in range(0,len(fx_train)):\n",
        "#   img = np.zeros((fx_train[i].shape[0],fx_train[i].shape[1],1))\n",
        "#   img[:,:,0] = fx_train[i]\n",
        "#   x_train.append(img)\n",
        "# for i in range(0,len(fx_test)):\n",
        "#   img = np.zeros((fx_test[i].shape[0],fx_test[i].shape[1],1))\n",
        "#   img[:,:,0] = fx_test[i]\n",
        "#   x_test.append(img)\n",
        "\n",
        "\n",
        "# y_train = []\n",
        "# for i in range(0,len(fy_train)):\n",
        "#   if ord(fy_train[i])-97 < 28:\n",
        "#     y_train.append(ord(fy_train[i])-97)\n",
        "#   else:\n",
        "#     y_train.append(24)\n",
        "\n",
        "# y_test = []\n",
        "# for i in range(0,len(fy_test)):\n",
        "#   if ord(fy_test[i])-97 < 28:\n",
        "#     y_test.append(ord(fy_test[i])-97)\n",
        "#   else:\n",
        "#     y_test.append(24)\n",
        "\n",
        "  \n",
        "# x_train = np.array(x_train)\n",
        "# x_test = np.array(x_test)\n",
        "\n",
        "# y_train = np.array(y_train)\n",
        "# y_test = np.array(y_test)\n",
        "\n",
        "print('x_train shape:', x_train.shape)\n",
        "print('y_train shape:', y_train.shape)\n",
        "print('x_test shape:', x_test.shape)\n",
        "print('y_test shape:', y_test.shape)\n",
        "\n",
        "# (x_train, y_train) = dataset\n",
        "\n",
        "# convert class vectors to binary class matrices\n",
        "num_classes = 28\n",
        "# y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "# y_test = keras.utils.to_categorical(y_test, num_classes)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-5eaef60dd7fc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDropout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFlatten\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mConv2D\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMaxPooling2D\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mactivations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mapplications\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/utils/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdata_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mio_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconv_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Globally-importable utils.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/utils/conv_utils.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoves\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0m_BACKEND\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tensorflow'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Using TensorFlow backend.\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mtensorflow_backend\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;31m# Try and load external backend.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mops\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf_ops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmoving_averages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;31m# pylint: disable=g-bad-import-order\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpywrap_tensorflow\u001b[0m  \u001b[0;31m# pylint: disable=unused-import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[0;31m# Ops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstandard_ops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;31m# Namespaces\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/standard_ops.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath_grad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom_grad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msparse_grad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mspectral_grad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstate_grad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/sparse_grad.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m \u001b[0;34m@\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRegisterGradient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"SparseReorder\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_SparseReorderGrad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munused_output_indices_grad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_values_grad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m   \"\"\"Gradients for the SparseReorder op.\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, f)\u001b[0m\n\u001b[1;32m   2410\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2411\u001b[0m     \u001b[0;34m\"\"\"Registers the function `f` as gradient function for `op_type`.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2412\u001b[0;31m     \u001b[0m_gradient_registry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_op_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2413\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2414\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/registry.py\u001b[0m in \u001b[0;36mregister\u001b[0;34m(self, candidate, name)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;31m# stack trace is [this_function, Register(), user_function,...]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0;31m# so the user function is #2.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m     \u001b[0mstack\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract_stack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_registry\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0m_TYPE_TAG\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcandidate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_LOCATION_TAG\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstack\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/traceback.py\u001b[0m in \u001b[0;36mextract_stack\u001b[0;34m(f, limit)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_back\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m     \u001b[0mstack\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStackSummary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwalk_stack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m     \u001b[0mstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreverse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mstack\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/traceback.py\u001b[0m in \u001b[0;36mextract\u001b[0;34m(klass, frame_gen, limit, lookup_lines, capture_locals)\u001b[0m\n\u001b[1;32m    358\u001b[0m                 filename, lineno, name, lookup_line=False, locals=f_locals))\n\u001b[1;32m    359\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfnames\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 360\u001b[0;31m             \u001b[0mlinecache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheckcache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    361\u001b[0m         \u001b[0;31m# If immediate lookup was desired, trigger lookups now.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlookup_lines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/compilerop.py\u001b[0m in \u001b[0;36mcheck_linecache_ipython\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \"\"\"\n\u001b[1;32m    140\u001b[0m     \u001b[0;31m# First call the orignal checkcache as intended\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m     \u001b[0mlinecache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_checkcache_ori\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;31m# Then, update back the cache with our data, so that tracebacks related\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m     \u001b[0;31m# to our compiled codes can be produced.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/linecache.py\u001b[0m in \u001b[0;36mcheckcache\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mcontinue\u001b[0m   \u001b[0;31m# no-op for files loaded via a __loader__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0mstat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfullname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "EIqKE4w-WKPM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x_train = x_train.astype('float32')      # This is the way we change the variable type\n",
        "x_test = x_test.astype('float32')\n",
        "x_train /= 255                           # Here we normalize the data between 0 and 1\n",
        "x_test /= 255                            # Here we normalize the data between 0 and 1\n",
        "\n",
        "letters = set()\n",
        "for i in range(0,len(fy_train)):\n",
        "   letters.add(fy_train[i])\n",
        "\n",
        "num_classes = 28\n",
        "\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "E4fs2EEOO3-v",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**build neural network and train the model**"
      ]
    },
    {
      "metadata": {
        "id": "TUvScH3fIdyT",
        "colab_type": "code",
        "outputId": "6d479989-3352-4209-9113-16bf64966ebe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        }
      },
      "cell_type": "code",
      "source": [
        "# Build the neural network!\n",
        "model = Sequential()\n",
        "\n",
        "# First convolutional layer with max pooling\n",
        "model.add(Conv2D(20, (5, 5), padding=\"same\", input_shape=x_train[0], activation=\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "\n",
        "# Second convolutional layer with max pooling\n",
        "model.add(Conv2D(50, (5, 5), padding=\"same\", activation=\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "\n",
        "# Hidden layer with 500 nodes\n",
        "model.add(Flatten())\n",
        "model.add(Dense(500, activation=\"relu\"))\n",
        "\n",
        "# Output layer with 32 nodes (one for each possible letter/number we predict)\n",
        "model.add(Dense(num_classes, activation=\"softmax\"))\n",
        "\n",
        "# Ask Keras to build the TensorFlow model behind the scenes\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "\n",
        "# Train the neural network\n",
        "model.fit(x_train, y_train, validation_data=(x_test, y_test), batch_size=32, epochs=20, verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 9729 samples, validate on 406 samples\n",
            "Epoch 1/20\n",
            "9729/9729 [==============================] - 28s 3ms/step - loss: 1.0104 - acc: 0.7225 - val_loss: 0.3675 - val_acc: 0.8966\n",
            "Epoch 2/20\n",
            "9729/9729 [==============================] - 28s 3ms/step - loss: 0.3207 - acc: 0.9213 - val_loss: 0.3018 - val_acc: 0.9113\n",
            "Epoch 3/20\n",
            "9729/9729 [==============================] - 28s 3ms/step - loss: 0.2107 - acc: 0.9523 - val_loss: 0.2593 - val_acc: 0.9163\n",
            "Epoch 4/20\n",
            "9729/9729 [==============================] - 28s 3ms/step - loss: 0.1480 - acc: 0.9669 - val_loss: 0.2463 - val_acc: 0.9236\n",
            "Epoch 5/20\n",
            "9729/9729 [==============================] - 28s 3ms/step - loss: 0.0974 - acc: 0.9796 - val_loss: 0.2983 - val_acc: 0.9212\n",
            "Epoch 6/20\n",
            "9729/9729 [==============================] - 27s 3ms/step - loss: 0.0786 - acc: 0.9845 - val_loss: 0.2811 - val_acc: 0.9138\n",
            "Epoch 7/20\n",
            "9729/9729 [==============================] - 28s 3ms/step - loss: 0.0766 - acc: 0.9857 - val_loss: 0.2606 - val_acc: 0.9236\n",
            "Epoch 8/20\n",
            "9729/9729 [==============================] - 27s 3ms/step - loss: 0.0620 - acc: 0.9894 - val_loss: 0.2700 - val_acc: 0.9261\n",
            "Epoch 9/20\n",
            "9729/9729 [==============================] - 27s 3ms/step - loss: 0.0474 - acc: 0.9905 - val_loss: 0.2603 - val_acc: 0.9360\n",
            "Epoch 10/20\n",
            "9729/9729 [==============================] - 27s 3ms/step - loss: 0.0566 - acc: 0.9891 - val_loss: 0.3066 - val_acc: 0.9335\n",
            "Epoch 11/20\n",
            "9729/9729 [==============================] - 27s 3ms/step - loss: 0.0498 - acc: 0.9909 - val_loss: 0.3530 - val_acc: 0.9212\n",
            "Epoch 12/20\n",
            "9729/9729 [==============================] - 27s 3ms/step - loss: 0.0427 - acc: 0.9930 - val_loss: 0.3440 - val_acc: 0.9187\n",
            "Epoch 13/20\n",
            "9729/9729 [==============================] - 28s 3ms/step - loss: 0.0486 - acc: 0.9898 - val_loss: 0.3457 - val_acc: 0.9261\n",
            "Epoch 14/20\n",
            "9729/9729 [==============================] - 28s 3ms/step - loss: 0.0457 - acc: 0.9913 - val_loss: 0.3476 - val_acc: 0.9163\n",
            "Epoch 15/20\n",
            "9729/9729 [==============================] - 27s 3ms/step - loss: 0.0461 - acc: 0.9912 - val_loss: 0.3101 - val_acc: 0.9236\n",
            "Epoch 16/20\n",
            "9729/9729 [==============================] - 27s 3ms/step - loss: 0.0386 - acc: 0.9939 - val_loss: 0.3818 - val_acc: 0.9187\n",
            "Epoch 17/20\n",
            "9729/9729 [==============================] - 27s 3ms/step - loss: 0.0332 - acc: 0.9953 - val_loss: 0.3597 - val_acc: 0.9335\n",
            "Epoch 18/20\n",
            "9729/9729 [==============================] - 27s 3ms/step - loss: 0.0344 - acc: 0.9957 - val_loss: 0.3696 - val_acc: 0.9261\n",
            "Epoch 19/20\n",
            "9729/9729 [==============================] - 27s 3ms/step - loss: 0.0305 - acc: 0.9960 - val_loss: 0.3520 - val_acc: 0.9310\n",
            "Epoch 20/20\n",
            "9729/9729 [==============================] - 27s 3ms/step - loss: 0.0377 - acc: 0.9937 - val_loss: 0.4924 - val_acc: 0.9113\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7efed6862fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "metadata": {
        "id": "rwToen16eb5U",
        "colab_type": "code",
        "outputId": "e56d4098-cd23-4277-86e0-1bc6235c48b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "cell_type": "code",
      "source": [
        "model.summary()\n",
        "\n",
        "score = model.evaluate(x_test, y_test, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_19 (Conv2D)           (None, 30, 27, 20)        520       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_19 (MaxPooling (None, 15, 13, 20)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 15, 13, 50)        25050     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_20 (MaxPooling (None, 7, 6, 50)          0         \n",
            "_________________________________________________________________\n",
            "flatten_10 (Flatten)         (None, 2100)              0         \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 500)               1050500   \n",
            "_________________________________________________________________\n",
            "dense_20 (Dense)             (None, 28)                14028     \n",
            "=================================================================\n",
            "Total params: 1,090,098\n",
            "Trainable params: 1,090,098\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Test loss: 0.4924178986713804\n",
            "Test accuracy: 0.9113300507291785\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0S2Y6r_Ke4Mh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping\n",
        "# Save the model according to the conditions  \n",
        "checkpoint = ModelCheckpoint(\"vgg16_1.h5\", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)\n",
        "early = EarlyStopping(monitor='val_acc', min_delta=0, patience=10, verbose=1, mode='auto')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "C4H5fGEDhGJN",
        "colab_type": "code",
        "outputId": "f755e333-2394-4f2b-d9a8-68bc2f62741e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "model_json = model.to_json()\n",
        "with open(\"model.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "# serialize weights to HDF5\n",
        "model.save_weights(\"model.h5\")\n",
        "print(\"Saved model to disk\")\n",
        "files.download('model.json')\n",
        "files.download('model.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saved model to disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MqzKaJXNhY6-",
        "colab_type": "code",
        "outputId": "116fddaa-a2dd-453a-bc90-ad7340c0ed10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.models import model_from_json\n",
        "\n",
        "# load json and create model\n",
        "json_file = open('model.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "# load weights into new model\n",
        "loaded_model.load_weights(\"model.h5\")\n",
        "print(\"Loaded model from disk\")\n",
        " \n",
        "# evaluate loaded model on test data\n",
        "# loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
        "# score = loaded_model.evaluate(X, Y, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded model from disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GLSlLnQOkT9N",
        "colab_type": "code",
        "outputId": "c23a423f-6bbc-48fd-da17-7c821e8914c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "cell_type": "code",
      "source": [
        "#how to use model\n",
        "prediction = list(model.predict(np.array([x_test[1],x_test[2]])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([9.9995923e-01, 6.9134400e-11, 1.6254357e-12, 8.4139683e-12,\n",
            "       5.7138919e-11, 7.9623802e-10, 3.0462056e-11, 3.3133727e-08,\n",
            "       7.5919464e-18, 1.6129251e-08, 2.7561074e-07, 2.3430437e-12,\n",
            "       3.5505590e-10, 2.0173392e-08, 8.2865555e-12, 4.8882352e-15,\n",
            "       1.3984727e-17, 2.5177800e-07, 2.0167015e-07, 3.0228732e-12,\n",
            "       2.3081584e-15, 7.2169071e-15, 2.0031009e-10, 3.9699567e-05,\n",
            "       2.9178175e-16, 2.0419066e-07, 3.2268231e-16, 5.6638934e-18],\n",
            "      dtype=float32), array([9.9999976e-01, 2.1129650e-12, 3.4115687e-16, 1.1989429e-11,\n",
            "       7.1839702e-13, 4.7650655e-08, 9.0255011e-15, 1.5326564e-10,\n",
            "       8.8396404e-20, 1.5979893e-11, 1.1734593e-07, 2.7225646e-17,\n",
            "       6.8331434e-12, 3.4125716e-08, 7.6571446e-14, 3.0323679e-16,\n",
            "       3.7532356e-19, 1.9176367e-09, 4.1691372e-08, 2.2645080e-12,\n",
            "       1.2903279e-18, 2.5425334e-18, 3.2423721e-12, 2.4592572e-09,\n",
            "       5.8188708e-19, 2.5319830e-11, 3.7692784e-18, 1.5960973e-19],\n",
            "      dtype=float32)]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}