universum_captcha.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "universum-captcha.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abarankab/tinkoff_notebooks/blob/master/universum_captcha.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkkDPZL7Jvqv",
        "colab_type": "text"
      },
      "source": [
        "# Распознаем капчу"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fw7DKxYoPuPv",
        "colab_type": "text"
      },
      "source": [
        "Таски:\n",
        "\n",
        "* Придумать, как это распараллелить\n",
        "* Узнать, каких букв нет\n",
        "* Написать архитектуру\n",
        "* Аугментация\n",
        "* Шедулер для LR\n",
        "* Сделать model.save и обернуть в готовый скриптик"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_B1FgsIBKVNr",
        "colab_type": "text"
      },
      "source": [
        "Конвенции:\n",
        "\n",
        "* скаченные файлы — в папке data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-9xYnl8ZLxG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "arr = [\"requests\", \"csv\", \"tdqm\", \"keras\", \"pickle\", \"cv2\", \"glob\", \"google.colab\", \"numpy\", \"PIL\", \"tensorflow\"]\n",
        "for elem in arr:\n",
        "  os.system(\"pip install \" + elem)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THMfDmr6Nof7",
        "colab_type": "code",
        "outputId": "8dfa5014-bb99-4217-ee10-950825795521",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "!mkdir data\n",
        "!pip install tqdm\n",
        "!pip install opencv-python"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘data’: File exists\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (4.23.4)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.6/dist-packages (3.4.1.15)\n",
            "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from opencv-python) (1.14.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAl_1XToxQCN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import requests\n",
        "import csv\n",
        "from tqdm import trange\n",
        "from keras.utils import to_categorical\n",
        "import keras\n",
        "from keras.preprocessing.image import img_to_array,load_img\n",
        "import numpy as np\n",
        "from keras.models import Sequential, Model, load_model\n",
        "from keras.layers import Dense, Dropout, Flatten, Input, concatenate\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "import keras.backend as K\n",
        "import pickle\n",
        "import cv2\n",
        "import glob, os\n",
        "from google.colab import files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8d20nOwNxPb",
        "colab_type": "code",
        "outputId": "911c005a-59b8-44a9-91f9-2ef5d74fdd86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 4382
        }
      },
      "source": [
        "!ls data"
      ],
      "execution_count": 0,
      "outputs": [
        {
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bpQkOvwzJ3QC",
        "colab_type": "code",
        "outputId": "d87d1f83-2d82-43ec-e1e6-1b2e47da15a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1812
        }
      },
      "source": [
        "\n",
        "fname = 'answers.csv'\n",
        "answers = []\n",
        "for i in trange(100):\n",
        "\tr = requests.get('https://kslweb1.spb.ctf.su/kweb1/captcha/image.php')\n",
        "\tanswer = r.headers['Set-Cookie'].rsplit(' ', 1)[1].split('=')[1] # магия\n",
        "\tanswers += [answer]\n",
        "\twith open('data/%d.jpg' % i, 'wb') as w:\n",
        "\t\tw.write(r.content)\n",
        "\n",
        "with open(fname, 'w', newline='') as file:\n",
        "\twriter = csv.writer(file)\n",
        "\twriter.writerows(answers)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
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            "\u001b[A"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9nfdZCq_doX",
        "colab_type": "code",
        "outputId": "2a9f4b1e-af1d-4056-be7d-5caa6c564fb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(answers)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EbsHEsdNwbGk",
        "colab_type": "code",
        "outputId": "4ec62ce8-690e-423a-f5a2-e6edaa433ea0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data=[]\n",
        "for i in range(100):\n",
        "  img = load_img('data/%d.jpg' % i)\n",
        "  arr = img_to_array(img,  data_format=None) \n",
        "  data.append(arr)\n",
        "X = np.stack(data)\n",
        "X /= 255\n",
        "X.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 60, 100, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9PrgJwhR2SZI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s=''.join(answers)\n",
        "s=set(s)\n",
        "idx2char = list(s)\n",
        "char2idx = {}\n",
        "for i, x in enumerate(s):\n",
        "  char2idx[x] = i\n",
        "with open('dict.pkl','wb') as file:\n",
        "  pickle.dump(idx2char, file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNTBSUSaPkVp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('dict.pkl','rb') as file:\n",
        "  char2idx = pickle.load(file)  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHTXM-IOBODk",
        "colab_type": "code",
        "outputId": "4dc0814a-48c6-435a-ac1b-19a4c14f04ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "answers[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/', '/', '/', '/', '/', '/', '/', '/', '/', '/']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_KLYLoHH9OTT",
        "colab_type": "code",
        "outputId": "f350fafb-1188-4f9c-9e77-3dd45124c0d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35438
        }
      },
      "source": [
        "y = []\n",
        "y = np.array(y)\n",
        "for i, answer in enumerate(answers):\n",
        "  raw_array = []\n",
        "  for c in answer:\n",
        "    print(c, answer)\n",
        "    raw_array.append(to_categorical(char2idx[c], num_classes = 30))\n",
        "  y = np.stack(raw_array)\n",
        "  print(y.shape)\n",
        "y = y.reshape(-1,120)\n",
        "y_test = y[:201]\n",
        "y_train = y[201:]\n",
        "X_train = X[201:]\n",
        "X_test = X[:201]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/ /\n",
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            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
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            "/ /\n",
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            "/ /\n",
            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "/ /\n",
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            "/ /\n",
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            "/ /\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
            "/ /\n",
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            "/ /\n",
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            "/ /\n",
            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "/ /\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
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            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
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            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "(1, 30)\n",
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            "/ /\n",
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            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n",
            "/ /\n",
            "(1, 30)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-e320489976bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m   \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m120\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m201\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m201\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 30 into shape (120)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SCyww3EgBCOI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kO5oboCAlNM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7LaoiuJ-bWt",
        "colab_type": "code",
        "outputId": "2ff603c8-9efe-45a6-f036-665122bda32e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(799, 60, 100, 3)\n",
            "(49, 120)\n",
            "(201, 60, 100, 3)\n",
            "(201, 120)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fuT2CE-FHHiA",
        "colab_type": "code",
        "outputId": "8b737d33-c7f9-42c5-fa60-f3a254922a82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "CNN = Sequential([\n",
        "    Conv2D(32, (3, 3), activation='relu', input_shape=(60, 100, 3)),\n",
        "    Conv2D(64, (3, 3), activation='relu'),\n",
        "    MaxPooling2D(pool_size=(2, 2)),\n",
        "   # Conv2D(128, (3, 3), activation='relu'),\n",
        "    Flatten(),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(128, activation='relu'),\n",
        "])\n",
        "\n",
        "x = Input((60, 100, 3))\n",
        "h = CNN(x)\n",
        "\n",
        "outputs = [None]*4\n",
        "for i in range(4):\n",
        "  outputs[i] = Dense(30, activation='softmax')(h)\n",
        "  \n",
        "output = concatenate(outputs)\n",
        "\n",
        "model = Model(x, output)\n",
        "\n",
        "model.summary()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 60, 100, 3)   0                                            \n",
            "__________________________________________________________________________________________________\n",
            "sequential_1 (Sequential)       (None, 128)          11046080    input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 30)           3870        sequential_1[1][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dense_4 (Dense)                 (None, 30)           3870        sequential_1[1][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 30)           3870        sequential_1[1][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dense_6 (Dense)                 (None, 30)           3870        sequential_1[1][0]               \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 120)          0           dense_3[0][0]                    \n",
            "                                                                 dense_4[0][0]                    \n",
            "                                                                 dense_5[0][0]                    \n",
            "                                                                 dense_6[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 11,061,560\n",
            "Trainable params: 11,061,560\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YRXt93aPBpx",
        "colab_type": "code",
        "outputId": "ef3efa00-58df-4281-e6b5-c63b6aaf6a34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        }
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['mae', 'acc'])  # metrics=['mae', 'acc'])         metrics=[metrics.mae, metrics.categorical_accuracy])\n",
        "\n",
        "model.fit(X_train, y_train, batch_size=16, epochs=100, validation_split = 0, validation_data=(X_test, y_test), shuffle = False)\n",
        "y_pred = model.predict(X)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-ef61407dc11e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m               metrics=['mae', 'acc'])  # metrics=['mae', 'acc'])         metrics=[metrics.mae, metrics.categorical_accuracy])\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\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/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1628\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1629\u001b[0m             \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1630\u001b[0;31m             batch_size=batch_size)\n\u001b[0m\u001b[1;32m   1631\u001b[0m         \u001b[0;31m# Prepare validation data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1632\u001b[0m         \u001b[0mdo_validation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[1;32m   1488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1489\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcheck_array_lengths\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1490\u001b[0;31m             \u001b[0m_check_array_lengths\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1491\u001b[0m         _check_loss_and_target_compatibility(y,\n\u001b[1;32m   1492\u001b[0m                                              \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_feed_loss_fns\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/engine/training.py\u001b[0m in \u001b[0;36m_check_array_lengths\u001b[0;34m(inputs, targets, weights)\u001b[0m\n\u001b[1;32m    218\u001b[0m                          \u001b[0;34m'the same number of samples as target arrays. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m                          \u001b[0;34m'Found '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' input samples '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m                          'and ' + str(list(set_y)[0]) + ' target samples.')\n\u001b[0m\u001b[1;32m    221\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset_w\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m         raise ValueError('All sample_weight arrays should have '\n",
            "\u001b[0;31mValueError\u001b[0m: Input arrays should have the same number of samples as target arrays. Found 799 input samples and 49 target samples."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuCyOxag-GIG",
        "colab_type": "code",
        "outputId": "85ab8792-75ae-4efe-869b-75a4905d0c59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(49, 120)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mzySjgOn9HE8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.fit?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1K_1zqhzoKX",
        "colab_type": "code",
        "outputId": "5d266a10-44cb-4a00-d447-f5005484f819",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(449, 120)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X5-bM2scynv2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "K.mean?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "caXMcvVFVrbk",
        "colab_type": "code",
        "outputId": "7f13a20d-64a9-465b-9506-7a253421720c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "'''\n",
        "\n",
        "test_fname = 'test.csv'\n",
        "test_answers = []\n",
        "for i in range(200):\n",
        "\tr = requests.get('https://kslweb1.spb.ctf.su/kweb1/captcha/image.php')\n",
        "\ttest_answer = r.headers['Set-Cookie'].rsplit(' ', 1)[1].split('=')[1] # магия\n",
        "\ttest_answers += [test_answer]\n",
        "\twith open('test_data/%d.jpg' % i, 'wb') as w:\n",
        "\t\tw.write(r.content)\n",
        "    \n",
        "print(test_answers)\n",
        "\n",
        "with open(test_fname, 'w', newline='') as file:\n",
        "\twriter = csv.writer(file)\n",
        "\twriter.writerows(test_answers)\n",
        "  \n",
        "  \n",
        "test_data=[]\n",
        "for i in range(200):\n",
        "  img = load_img('test_data/%d.jpg' % i)\n",
        "  arr = img_to_array(img,  data_format=None) \n",
        "  test_data.append(arr)\n",
        "tX = np.stack(test_data)\n",
        "tX /= 255\n",
        "tX.shape\n",
        "\n",
        "ts=''.join(test_answers)\n",
        "ts=set(s)\n",
        "tidx2char = list(ts)\n",
        "tchar2idx = {}\n",
        "for i, x in enumerate(ts):\n",
        "  tchar2idx[x] = i\n",
        "with open('tdict.pkl','wb') as file:\n",
        "  pickle.dump(tchar2idx, file)\n",
        "\n",
        "with open('tdict.pkl','rb') as file:\n",
        "  tchar2idx = pickle.load(file)  \n",
        "  \n",
        "ty = []\n",
        "ty = np.array(ty)\n",
        "for i, answer in enumerate(test_answers):\n",
        "  word = test_answers[i]\n",
        "  raw_array = []\n",
        "  raw_array = np.array(raw_array)\n",
        "  for c in word:\n",
        "    raw_array = np.append(raw_array, to_categorical(tchar2idx[c], num_classes = 30))\n",
        "  ty = np.concatenate((ty, raw_array), axis = 0)\n",
        "ty = ty.reshape(-1,120)\n",
        "  \n",
        "model.evaluate(tX, ty)'''"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\n\\ntest_fname = 'test.csv'\\ntest_answers = []\\nfor i in range(200):\\n\\tr = requests.get('https://kslweb1.spb.ctf.su/kweb1/captcha/image.php')\\n\\ttest_answer = r.headers['Set-Cookie'].rsplit(' ', 1)[1].split('=')[1] # магия\\n\\ttest_answers += [test_answer]\\n\\twith open('test_data/%d.jpg' % i, 'wb') as w:\\n\\t\\tw.write(r.content)\\n    \\nprint(test_answers)\\n\\nwith open(test_fname, 'w', newline='') as file:\\n\\twriter = csv.writer(file)\\n\\twriter.writerows(test_answers)\\n  \\n  \\ntest_data=[]\\nfor i in range(200):\\n  img = load_img('test_data/%d.jpg' % i)\\n  arr = img_to_array(img,  data_format=None) \\n  test_data.append(arr)\\ntX = np.stack(test_data)\\ntX /= 255\\ntX.shape\\n\\nts=''.join(test_answers)\\nts=set(s)\\ntidx2char = list(ts)\\ntchar2idx = {}\\nfor i, x in enumerate(ts):\\n  tchar2idx[x] = i\\nwith open('tdict.pkl','wb') as file:\\n  pickle.dump(tchar2idx, file)\\n\\nwith open('tdict.pkl','rb') as file:\\n  tchar2idx = pickle.load(file)  \\n  \\nty = []\\nty = np.array(ty)\\nfor i, answer in enumerate(test_answers):\\n  word = test_answers[i]\\n  raw_array = []\\n  raw_array = np.array(raw_array)\\n  for c in word:\\n    raw_array = np.append(raw_array, to_categorical(tchar2idx[c], num_classes = 30))\\n  ty = np.concatenate((ty, raw_array), axis = 0)\\nty = ty.reshape(-1,120)\\n  \\nmodel.evaluate(tX, ty)\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRWvTQuO0pfp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load(imagename):\n",
        "  img = img_to_array(load_img(imagename, target_size=(60, 100, 3)))\n",
        "  return img\n",
        "\n",
        "\n",
        "X = np.array([load(file) for file in glob.glob(\"data/*.jpg\")])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PhKQIMUWHDSw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def model_predict(model, img):\n",
        "  result = np.split(model.predict(img), 4)\n",
        "  captcha = ''\n",
        "  for i in range(4):\n",
        "    captcha += idx2char[np.argmax(result[i])]\n",
        "  return captcha"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zhBLJ863HFQg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('model.h5')               \n",
        "model = load_model('model.h5')       "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xIVYWyn6LNM3",
        "colab_type": "code",
        "outputId": "b5a54370-4a96-4665-cfc8-f8e4283828d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/models.py:282: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
            "  warnings.warn('No training configuration found in save file: '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.engine.training.Model at 0x7ff45f626048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOH7sZDJL1Ii",
        "colab_type": "code",
        "outputId": "811a0440-5d40-467d-a218-9b9e2408a37f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "source": [
        "files.download(\"dict.pkl\")\n",
        "files.download(\"model.h5\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------\n",
            "Exception happened during processing of request from ('::ffff:127.0.0.1', 42794, 0, 0)\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 317, in _handle_request_noblock\n",
            "    self.process_request(request, client_address)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 348, in process_request\n",
            "    self.finish_request(request, client_address)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 361, in finish_request\n",
            "    self.RequestHandlerClass(request, client_address, self)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 696, in __init__\n",
            "    self.handle()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 418, in handle\n",
            "    self.handle_one_request()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 406, in handle_one_request\n",
            "    method()\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 639, in do_GET\n",
            "    self.copyfile(f, self.wfile)\n",
            "  File \"/usr/lib/python3.6/http/server.py\", line 800, in copyfile\n",
            "    shutil.copyfileobj(source, outputfile)\n",
            "  File \"/usr/lib/python3.6/shutil.py\", line 82, in copyfileobj\n",
            "    fdst.write(buf)\n",
            "  File \"/usr/lib/python3.6/socketserver.py\", line 775, in write\n",
            "    self._sock.sendall(b)\n",
            "ConnectionResetError: [Errno 104] Connection reset by peer\n",
            "----------------------------------------\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pSD_mfB-RX1A",
        "colab_type": "code",
        "outputId": "4db1cc73-c580-4f3d-89fd-2a7a5be60264",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "def predict(imgname):\n",
        "  model = load_model('model.h5')\n",
        "  with open('dict.pkl','rb') as file:\n",
        "    idx2char = pickle.load(file)\n",
        "  x = img_to_array(load_img(imgname), data_format = None)\n",
        "  x = x.reshape((1, 60, 100, 3))\n",
        "  y = np.split(model.predict(x).reshape((120,)), 4)\n",
        "  captcha = ''\n",
        "  for i in range(4):\n",
        "    captcha += idx2char[np.argmax(y[i])]\n",
        "  return captcha\n",
        "predict('data/0.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'7t74'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5dGcQc42NwC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "counter = 0\n",
        "for i in range(y.shape[0]):\n",
        "    imgname = 'data/' + str(i) + '.jpg'\n",
        "    if predict(imgname) == answers[i]:\n",
        "        print('Ura ', predict(imgname), answers[i])\n",
        "        counter += 1\n",
        "print(counter/y.shape[0])    \n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JV2zyJHzYnt1",
        "colab_type": "code",
        "outputId": "d75badcd-3898-4db0-8638-ff0409d359e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "answers[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'7t74'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "POOQbXQ5Xo3s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'''def acc_pred(y, y_pred):\n",
        "    np_y = K.get\n",
        "    counter = []\n",
        "    for i in range(y.shape[0]):\n",
        "        flag = False\n",
        "        y_eval = np.split(y[i], 4)\n",
        "        y_pred_eval = np.split(y_pred[i], 4)\n",
        "        for i in range(4):\n",
        "            if y_eval[i][argmax(y_pred[i])] == 1:\n",
        "                flag = True\n",
        "            else:\n",
        "                flag = False\n",
        "                break\n",
        "        if flag:\n",
        "            counter.append(1)\n",
        "        else:\n",
        "            counter.append(0)        \n",
        "    return K.mean(counter)'''"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}