Bengali_Digit_Recognizer using CNN.ipynb

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    "colab": {
      "name": "Bengali Digit Receognizer1.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nasim-aust/Bangla-Digit-Recognition-using-CNN/blob/master/Bengali_Digit_Receognizer%20using%20CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "d93e43c9-8a48-41c7-93f2-1dbeec38f80e",
        "_uuid": "b2538ad6607dc541ad42195df42b3c70ecdd59a3",
        "id": "asmJRuOT5vvY",
        "colab_type": "text"
      },
      "source": [
        "# Bangla Digit Recognizer "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "8b668dd4-4ea0-4894-bf75-390f6515286f",
        "_uuid": "56ee22f1b808d3ae49db7543cbc439b3ab964426",
        "id": "y6Xk3f-K5vva",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "**Step 1 : Process the data.<br>\n",
        "Step 2 : Design the model.<br>\n",
        "Step 3 : Train the model.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "c386cfd1-3d2e-4bd0-b1bf-2c72698afe96",
        "_uuid": "4cdc8ebf9bc167498cace36993800c819556eb04",
        "id": "tZsTSfYK5vvb",
        "colab_type": "code",
        "colab": {},
        "outputId": "dd55c73e-a500-41ac-bac9-a7a2aa5cccf8"
      },
      "source": [
        "# Importing necessary libraries\n",
        "import numpy as np\n",
        "import os\n",
        "import glob\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import pickle\n",
        "from keras.utils import to_categorical\n",
        "from keras.layers import Dense, Input, Conv2D, Flatten, MaxPooling2D, Activation,Dropout\n",
        "from keras.models import Model\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras import backend as K\n",
        "from __future__ import print_function\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation\n",
        "from keras.utils import to_categorical\n",
        "from keras import backend as k\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "49f11a3e-7149-4877-b2d1-46bcc3876d3b",
        "_uuid": "12815b115dcf40bd1d14daa942fbe18dfc330ebf",
        "id": "XHczvaE25vvj",
        "colab_type": "text"
      },
      "source": [
        "## Step 1: Process the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "db9ec487-5a21-4289-9b9a-f2649e94955a",
        "_uuid": "5e998f2691200a0019ad2e3940d5acee12e628be",
        "id": "QlFAsrlV5vvk",
        "colab_type": "text"
      },
      "source": [
        "Our dataset comes from six different source."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "a42ae61e-565e-48ae-b618-370dc4bd3718",
        "_uuid": "8f087bcd8bb6c49f440bd5bea4769ddd692d291e",
        "id": "tNtw5xWY5vvl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Declaring constants\n",
        "FIG_WIDTH=20 # Width of figure\n",
        "HEIGHT_PER_ROW=3 # Height of each row when showing a figure which consists of multiple rows\n",
        "RESIZE_DIM=32 # The images will be resized to 28x28 pixels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "5dfa2840e7fa2af7ca3ff13dcb92308f41caf244",
        "id": "lpYm-csX5vvp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_dir=os.path.join('..','NumtaDB_with_aug')\n",
        "paths_train_a=glob.glob(os.path.join(data_dir,'training-a','*.png'))\n",
        "paths_train_b=glob.glob(os.path.join(data_dir,'training-b','*.png'))\n",
        "paths_train_e=glob.glob(os.path.join(data_dir,'training-e','*.png'))\n",
        "paths_train_c=glob.glob(os.path.join(data_dir,'training-c','*.png'))\n",
        "paths_train_d=glob.glob(os.path.join(data_dir,'training-d','*.png'))\n",
        "paths_train_all=paths_train_a+paths_train_b+paths_train_c+paths_train_d+paths_train_e\n",
        "\n",
        "paths_test_a=glob.glob(os.path.join(data_dir,'testing-a','*.png'))\n",
        "paths_test_b=glob.glob(os.path.join(data_dir,'testing-b','*.png'))\n",
        "paths_test_e=glob.glob(os.path.join(data_dir,'testing-e','*.png'))\n",
        "paths_test_c=glob.glob(os.path.join(data_dir,'testing-c','*.png'))\n",
        "paths_test_d=glob.glob(os.path.join(data_dir,'testing-d','*.png'))\n",
        "paths_test_f=glob.glob(os.path.join(data_dir,'testing-f','*.png'))+glob.glob(os.path.join(data_dir,'testing-f','*.JPG'))\n",
        "paths_test_auga=glob.glob(os.path.join(data_dir,'testing-auga','*.png'))\n",
        "paths_test_augc=glob.glob(os.path.join(data_dir,'testing-augc','*.png'))\n",
        "paths_test_all=paths_test_a+paths_test_b+paths_test_c+paths_test_d+paths_test_e+paths_test_f+paths_test_auga+paths_test_augc\n",
        "\n",
        "path_label_train_a=os.path.join(data_dir,'training-a.csv')\n",
        "path_label_train_b=os.path.join(data_dir,'training-b.csv')\n",
        "path_label_train_e=os.path.join(data_dir,'training-e.csv')\n",
        "path_label_train_c=os.path.join(data_dir,'training-c.csv')\n",
        "path_label_train_d=os.path.join(data_dir,'training-d.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "45ef7b96-b4cb-46d5-a092-6eb10775cb3e",
        "_uuid": "5aab8d2c6139e67da8cac46a41b02dd3b0c16c1a",
        "id": "Yg6yjUXS5vvq",
        "colab_type": "text"
      },
      "source": [
        "### Image Processing "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "7ee61a9a-b469-4fae-8770-3981bb601a68",
        "_uuid": "21cd71c9e6faa386a2c5829cf3a3e636b338f280",
        "id": "55CiGH6k5vvq",
        "colab_type": "text"
      },
      "source": [
        "We shall write some helper functions to process and visualize the images. We will convert color to gray scale image. For better understanding we will unblur the image and make image sharp by using laplacian filter."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "87d7081d-aa4a-48b9-b1a4-575ceceb2eb3",
        "_uuid": "aa19885280eef5dfed1f8378d668ea7f025a7925",
        "id": "P5HOjz6h5vvr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_key(path):\n",
        "    # seperates the key of an image from the filepath\n",
        "    key=path.split(sep=os.sep)[-1]\n",
        "    return key\n",
        "\n",
        "def get_data(paths_img,path_label=None,resize_dim=None):\n",
        "    '''reads images from the filepaths, resizes them (if given), and returns them in a numpy array\n",
        "    Args:\n",
        "        paths_img: image filepaths\n",
        "        path_label: pass image label filepaths while processing training data, defaults to None while processing testing data\n",
        "        resize_dim: if given, the image is resized to resize_dim x resize_dim (optional)\n",
        "    Returns:\n",
        "        X: group of images\n",
        "        y: categorical true labels\n",
        "    '''\n",
        "    X=[] # initialize empty list for resized images\n",
        "    for i,path in enumerate(paths_img):\n",
        "        img=cv2.imread(path,cv2.IMREAD_GRAYSCALE) # images loaded in color (BGR)\n",
        "        #img = cv2.bilateralFilter(img,9,75,75)\n",
        "        #img = cv2.medianBlur(img,5)\n",
        "        #img = cv2.fastNlMeansDenoisingColored(img,None,10,10,7,21)\n",
        "        #img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # cnahging colorspace to GRAY\n",
        "        if resize_dim is not None:\n",
        "            img=cv2.resize(img,(resize_dim,resize_dim),interpolation=cv2.INTER_AREA) # resize image to 28x28\n",
        "        #X.append(np.expand_dims(img,axis=2)) # expand image to 28x28x1 and append to the list.\n",
        "        gaussian_3 = cv2.GaussianBlur(img, (9,9), 10.0) #unblur\n",
        "        img = cv2.addWeighted(img, 1.5, gaussian_3, -0.5, 0, img)\n",
        "        kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]]) #filter\n",
        "        img = cv2.filter2D(img, -1, kernel)\n",
        "        #thresh = 200\n",
        "        #maxValue = 255\n",
        "        #th, img = cv2.threshold(img, thresh, maxValue, cv2.THRESH_BINARY);\n",
        "        ret,img = cv2.threshold(img, 128, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n",
        "        X.append(img) # expand image to 28x28x1 and append to the list\n",
        "        # display progress\n",
        "        if i==len(paths_img)-1:\n",
        "            end='\\n'\n",
        "        else: end='\\r'\n",
        "        print('processed {}/{}'.format(i+1,len(paths_img)),end=end)\n",
        "        \n",
        "    X=np.array(X) # tranform list to numpy array\n",
        "    if  path_label is None:\n",
        "        return X\n",
        "    else:\n",
        "        df = pd.read_csv(path_label) # read labels\n",
        "        df=df.set_index('filename') \n",
        "        y_label=[df.loc[get_key(path)]['digit'] for path in  paths_img] # get the labels corresponding to the images\n",
        "        y=to_categorical(y_label,10) # transfrom integer value to categorical variable\n",
        "        return X, y\n",
        "        \n",
        "def imshow_group(X,y,y_pred=None,n_per_row=10,phase='processed'):\n",
        "    '''helper function to visualize a group of images along with their categorical true labels (y) and prediction probabilities.\n",
        "    Args:\n",
        "        X: images\n",
        "        y: categorical true labels\n",
        "        y_pred: predicted class probabilities\n",
        "        n_per_row: number of images per row to be plotted\n",
        "        phase: If the images are plotted after resizing, pass 'processed' to phase argument. \n",
        "            It will plot the image and its true label. If the image is plotted after prediction \n",
        "            phase, pass predicted class probabilities to y_pred and 'prediction' to the phase argument. \n",
        "            It will plot the image, the true label, and it's top 3 predictions with highest probabilities.\n",
        "    '''\n",
        "    n_sample=len(X)\n",
        "    img_dim=X.shape[1]\n",
        "    j=np.ceil(n_sample/n_per_row)\n",
        "    fig=plt.figure(figsize=(FIG_WIDTH,HEIGHT_PER_ROW*j))\n",
        "    for i,img in enumerate(X):\n",
        "        plt.subplot(j,n_per_row,i+1)\n",
        "#         img_sq=np.squeeze(img,axis=2)\n",
        "#         plt.imshow(img_sq,cmap='gray')\n",
        "        plt.imshow(img)\n",
        "        if phase=='processed':\n",
        "            plt.title(np.argmax(y[i]))\n",
        "        if phase=='prediction':\n",
        "            top_n=3 # top 3 predictions with highest probabilities\n",
        "            ind_sorted=np.argsort(y_pred[i])[::-1]\n",
        "            h=img_dim+4\n",
        "            for k in range(top_n):\n",
        "                string='pred: {} ({:.0f}%)\\n'.format(ind_sorted[k],y_pred[i,ind_sorted[k]]*100)\n",
        "                plt.text(img_dim/2, h, string, horizontalalignment='center',verticalalignment='center')\n",
        "                h+=4\n",
        "            if y is not None:\n",
        "                plt.text(img_dim/2, -4, 'true label: {}'.format(np.argmax(y[i])), \n",
        "                         horizontalalignment='center',verticalalignment='center')\n",
        "        plt.axis('off')\n",
        "    plt.show()\n",
        "\n",
        "def create_submission(predictions,keys,path):\n",
        "    result = pd.DataFrame(\n",
        "        predictions,\n",
        "        columns=['label'],\n",
        "        index=keys\n",
        "        )\n",
        "    result.index.name='key'\n",
        "    result.to_csv(path, index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "541cbb22-5319-490a-b6a4-8999db770722",
        "_uuid": "01fee24b540458889ebaa9d86b7646e5264e5b1e",
        "id": "SnwznmaI5vvs",
        "colab_type": "text"
      },
      "source": [
        "Next we are going to use the `get_data()` function to process all the images from dataset "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "e4d65fdcac01f5d367ce863d79d22fbbf9b36b86",
        "id": "Jjdsm8aM5vvs",
        "colab_type": "code",
        "colab": {},
        "outputId": "4f67c5be-b376-4249-f6d8-327249326a6a"
      },
      "source": [
        "X_train_a,y_train_a=get_data(paths_train_a,path_label_train_a,resize_dim=RESIZE_DIM)\n",
        "X_train_b,y_train_b=get_data(paths_train_b,path_label_train_b,resize_dim=RESIZE_DIM)\n",
        "X_train_c,y_train_c=get_data(paths_train_c,path_label_train_c,resize_dim=RESIZE_DIM)\n",
        "X_train_d,y_train_d=get_data(paths_train_d,path_label_train_d,resize_dim=RESIZE_DIM)\n",
        "X_train_e,y_train_e=get_data(paths_train_e,path_label_train_e,resize_dim=RESIZE_DIM)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "processed 19702/19702\n",
            "processed 359/359\n",
            "processed 24298/24298\n",
            "processed 10908/10908\n",
            "processed 16778/16778\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "620a40d9-36f8-49fa-b96c-b823999daad0",
        "_uuid": "1d06bb5aaa056f719a918be3206c3f6b2e738e0b",
        "scrolled": true,
        "id": "O2Eu3gYk5vvu",
        "colab_type": "code",
        "colab": {},
        "outputId": "53765bf0-678f-4afd-ac05-4616776db776"
      },
      "source": [
        "X_train_all=np.concatenate((X_train_a,X_train_b,X_train_c,X_train_d,X_train_e),axis=0)\n",
        "y_train_all=np.concatenate((y_train_a,y_train_b,y_train_c,y_train_d,y_train_e),axis=0)\n",
        "X_train_all.shape, y_train_all.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((72045, 32, 32), (72045, 10))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "ed3a982e1b9ac0b91ae9368c546d0d0447cc5fca",
        "id": "TRZVRgXq5vvw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_show_all=X_train_all"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "6a2f1c8a-4bdf-4307-b9ea-7841175be203",
        "_uuid": "08f095d0c3ee5f77e811fa1f8046756544f8cce6",
        "id": "6z5wBRxa5vvx",
        "colab_type": "text"
      },
      "source": [
        "Let's see some samples of the processed data. The digits are more sharp now"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "e0b3d05de0d4a6d683f8bbf9dcadbecd557d8d7c",
        "id": "6YKxQtKc5vvy",
        "colab_type": "code",
        "colab": {},
        "outputId": "c0b4a7c8-6d0a-410c-865b-b2f37f224998"
      },
      "source": [
        "plt.subplot(221)\n",
        "plt.imshow(X_train_all[0], cmap=plt.get_cmap('gray'))\n",
        "plt.subplot(222)\n",
        "plt.imshow(X_train_all[1], cmap=plt.get_cmap('gray'))\n",
        "plt.subplot(223)\n",
        "plt.imshow(X_train_all[2], cmap=plt.get_cmap('gray'))\n",
        "plt.subplot(224)\n",
        "plt.imshow(X_train_all[3], cmap=plt.get_cmap('gray'))\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pkteAJ5N8F/g18OgC65QWwWw3KlVX3DS3mJUlfwX+Abw92EpXx2dYrff92aq6ftlFrIMu12+wej/joazS++6V60EbH0CSl1rcINzq+25Jqz/jMb5vT1mT1Bwbn6TmLKPxbS1hnaug1ffdklZ/xqN734Nv45OkZXOqK6k5gza+JEeS/D7J60mOD7nuISW5OclzSc52lzt6oFt+XZJT3eWOTiXZv+xaNTtzPb5cDzbV7Q4S/QNwmO0Tv18EvlZVrw1SwICSbAAbVfVykk8DZ9i+wsfXgUtV9XD3C7K/qr69xFI1I3M9zlwPOeK7E3i9qv5UVf8CngSODrj+wVTVhap6ubv9HtunQR1g+/2e6B7m5Y7Wg7keYa6HbHwHgD/vuD/xUlbrJMmtwB3AC8CNVXUBtkME3LC8yjQn5nqEuR6y8eUKy9Z6l3KSa4CngQer6t1l16OFMNcjNGTjOw/cvOP+Wl/uJ8lVbIfjiap6plv8Vred5MPtJReXVZ/mxlyPMNdDNr4XgYPdB7lcDXwVODng+geTJGxf0eNsVT2y41sn2b7MEXi5o3VhrkeY66GvzvJl4IfAPuCxqvreYCsfUJK7geeBV4APusUPsb095CngFuBN4L6qurSUIjU35np8ufbMDUnN8cwNSc2x8Ulqjo1PUnNsfJKaY+OT1Bwbn6Tm2PgkNcfGJ6k5/weJoIxStvdxLgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x2501566ec50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "4a5791e2c7d37e6c230dd8ef5fdfb6f7b592cdb6",
        "id": "Hv2YduCx5vv0",
        "colab_type": "text"
      },
      "source": [
        "**Histogram of digit**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "c9d6d00dc0cdda91eb8baaecb33b497c4a216385",
        "id": "7hk9fT_05vv0",
        "colab_type": "code",
        "colab": {},
        "outputId": "280d8711-bc3a-4303-8d0e-562a785f6ce6"
      },
      "source": [
        "imgg=X_train_all[1]\n",
        "hist = cv2.calcHist([imgg],[0],None,[256],[0,256])\n",
        "plt.hist(imgg.ravel(),256,[0,256])\n",
        " \n",
        "# show the plotting graph of an image\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x25015984780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "193edae07f21588132a68d0eed2b1f4646cf8a28",
        "id": "AI5ce1xM5vv2",
        "colab_type": "code",
        "colab": {},
        "outputId": "4710c857-6c79-473b-c711-7edc6331bad6"
      },
      "source": [
        "plt.imshow(X_train_all[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x25015f24b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x25015984c88>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "5930fd41-039e-4385-b753-127a5e3494ce",
        "_uuid": "57a68268499ba8d1d68d6b62d45f6c64a6453d53",
        "id": "XnbJhwPN5vv3",
        "colab_type": "code",
        "colab": {},
        "outputId": "3bb9d7cc-7030-480e-ff4f-89be837e8e72"
      },
      "source": [
        "X_test_a=get_data(paths_test_a,resize_dim=RESIZE_DIM)\n",
        "X_test_b=get_data(paths_test_b,resize_dim=RESIZE_DIM)\n",
        "X_test_c=get_data(paths_test_c,resize_dim=RESIZE_DIM)\n",
        "X_test_d=get_data(paths_test_d,resize_dim=RESIZE_DIM)\n",
        "X_test_e=get_data(paths_test_e,resize_dim=RESIZE_DIM)\n",
        "X_test_f=get_data(paths_test_f,resize_dim=RESIZE_DIM)\n",
        "X_test_auga=get_data(paths_test_auga,resize_dim=RESIZE_DIM)\n",
        "X_test_augc=get_data(paths_test_augc,resize_dim=RESIZE_DIM)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "processed 3489/3489\n",
            "processed 69/69\n",
            "processed 4381/4381\n",
            "processed 1948/1948\n",
            "processed 2970/2970\n",
            "processed 495/495\n",
            "processed 2168/2168\n",
            "processed 2106/2106\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "1b380d90-c66f-42c9-ae97-5103e18a7c7a",
        "_uuid": "a26c04ac67f1c7919dede57707dd11f94d4590cb",
        "scrolled": true,
        "id": "4qtquGrr5vv5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test_all=np.concatenate((X_test_a,X_test_b,X_test_c,X_test_d,X_test_e,X_test_f,X_test_auga,X_test_augc))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "e8ef1f0166e80464b15fc7e23c4be85cc92129ed",
        "id": "KDZmeZZ85vv6",
        "colab_type": "code",
        "colab": {},
        "outputId": "32eaa2b3-4a6f-4a13-b7f4-d4f2ac4c8d7d"
      },
      "source": [
        "X_tshow_all=X_test_all\n",
        "X_tshow_all.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17626, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "050db40f873909fb7eaddc593742560b81bb104f",
        "id": "IEXF5bG85vv9",
        "colab_type": "text"
      },
      "source": [
        "We will now convert image to 1 color channel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "3a924ec72e3e201ab7fc7a7b14c4285625cc7788",
        "id": "7-h2lXrV5vv9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_all = X_train_all.reshape(X_train_all.shape[0],32, 32,1).astype('float32')\n",
        "X_test_all = X_test_all.reshape(X_test_all.shape[0],32, 32,1).astype('float32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "b6e46f3c07abdc22612a4996ea626f050a9b84d9",
        "id": "7hH5Aihy5vv_",
        "colab_type": "code",
        "colab": {},
        "outputId": "5ba02db3-078f-49f7-ea2e-be04f868ddc3"
      },
      "source": [
        "X_train_all.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(72045, 32, 32, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "a75a4c52d3ebc246ac7ed01da04a47e4c0c6816e",
        "id": "CD80F_yk5vwC",
        "colab_type": "text"
      },
      "source": [
        "**Normalize data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "f35d6a704e6d1015a2e65a5ca8f66e1aef8afb9b",
        "id": "HOJig0sU5vwD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_all = X_train_all/255\n",
        "X_test_all=X_test_all/255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "d32f9092-0d61-4a31-940a-c47bd4a7ab70",
        "_uuid": "100fe511f70f708fe5a9a4a20a423d5cc5fc5d31",
        "id": "AcRNimMg5vwF",
        "colab_type": "text"
      },
      "source": [
        "Next, we are going to randomly choose 80% of the training data and use it to train our neural network. The remaining 20% images are going to be our validation data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "45550d4b-7b11-4277-b968-3cbc74161537",
        "_uuid": "ec1470e959cf087a27d9ca3d349f2f5dd8687528",
        "id": "nmBOV3uD5vwF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "indices=list(range(len(X_train_all)))\n",
        "np.random.seed(42)\n",
        "np.random.shuffle(indices)\n",
        "\n",
        "ind=int(len(indices)*0.80)\n",
        "# train data\n",
        "X_train=X_train_all[indices[:ind]] \n",
        "y_train=y_train_all[indices[:ind]]\n",
        "# validation data\n",
        "X_val=X_train_all[indices[-(len(indices)-ind):]] \n",
        "y_val=y_train_all[indices[-(len(indices)-ind):]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "32382aae-4756-457e-af68-5fd94603ddfb",
        "_uuid": "ba5d4e25fb97b4afcb84f85c8788da6244afadaf",
        "id": "qQFsPI3S5vwG",
        "colab_type": "text"
      },
      "source": [
        "## Step 2: Design the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "8f3f9cf5-17d0-4b3a-b544-84bee00bf280",
        "_uuid": "52b0f9b0fabc92f87bcd85ca6e04c93df7343ae4",
        "id": "nJXUqiF05vwH",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "9748e26b1a1cbda489a438723277d7a93abcb3c8",
        "id": "m5Ff27HH5vwH",
        "colab_type": "code",
        "colab": {},
        "outputId": "1cbf5d94-d5f2-47f0-d869-bbeaee260c75"
      },
      "source": [
        "def my_model(img_size=32,channels=1):\n",
        "    model = Sequential()\n",
        "    input_shape = (img_size,img_size,channels)\n",
        "    model.add(Conv2D(32, (5, 5), input_shape=input_shape,activation='relu', padding='same'))\n",
        "    model.add(Conv2D(32, (5, 5), activation='relu', padding='same'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    model.add(Conv2D(128, (3, 3),activation='relu',padding='same'))\n",
        "    model.add(Conv2D(128, (3, 3),activation='relu',padding='same'))\n",
        "    \n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    model.add(Conv2D(256, (3, 3),activation='relu',padding='same'))\n",
        "    model.add(Conv2D(256, (3, 3),activation='relu',padding='same'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(64))\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(Dropout(0.2))\n",
        "    model.add(Dense(10))\n",
        "    model.add(Activation('softmax'))\n",
        "    \n",
        "    model.compile(loss='categorical_crossentropy', metrics=['accuracy'],optimizer='adam')\n",
        "    # UNCOMMENT THIS TO VIEW THE ARCHITECTURE\n",
        "    #model.summary()\n",
        "    \n",
        "    return model\n",
        "model=my_model()\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        832       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        25632     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 128)       36992     \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 256)         295168    \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 256)         590080    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                262208    \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                650       \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 1,359,146\n",
            "Trainable params: 1,359,146\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "ee9607d4-40d3-449d-948d-ca743a6f9d77",
        "_uuid": "0b0adac9af387df7adc0cf26954802305849396e",
        "id": "HKEyRXhG5vwJ",
        "colab_type": "text"
      },
      "source": [
        "## Step 3: Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "7b15bdc9-1794-4eda-9d48-f544be3e382c",
        "_uuid": "f220f61fae1fae8c1cfb2f843300b489937b0090",
        "scrolled": true,
        "id": "g-4HMu905vwJ",
        "colab_type": "code",
        "colab": {},
        "outputId": "207ef042-b859-4b5e-da34-99dae0885688"
      },
      "source": [
        "path_model='model_filter.h5' # save model at this location after each epoch\n",
        "K.tensorflow_backend.clear_session() # destroys the current graph and builds a new one\n",
        "model=my_model() # create the model\n",
        "K.set_value(model.optimizer.lr,1e-3) # set the learning rate\n",
        "# fit the model\n",
        "h=model.fit(x=X_train,     \n",
        "            y=y_train, \n",
        "            batch_size=64, \n",
        "            epochs=10, \n",
        "            verbose=1, \n",
        "            validation_data=(X_val,y_val),\n",
        "            shuffle=True,\n",
        "            callbacks=[\n",
        "                ModelCheckpoint(filepath=path_model),\n",
        "            ]\n",
        "            )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 57636 samples, validate on 14409 samples\n",
            "Epoch 1/100\n",
            "57636/57636 [==============================] - 316s 5ms/step - loss: 0.6478 - acc: 0.7713 - val_loss: 0.1537 - val_acc: 0.9563\n",
            "Epoch 2/100\n",
            "57636/57636 [==============================] - 320s 6ms/step - loss: 0.1647 - acc: 0.9514 - val_loss: 0.1006 - val_acc: 0.9699\n",
            "Epoch 3/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.1058 - acc: 0.9698 - val_loss: 0.0848 - val_acc: 0.9766\n",
            "Epoch 4/100\n",
            "57636/57636 [==============================] - 316s 5ms/step - loss: 0.0744 - acc: 0.9790 - val_loss: 0.0789 - val_acc: 0.9784\n",
            "Epoch 5/100\n",
            "57636/57636 [==============================] - 312s 5ms/step - loss: 0.0620 - acc: 0.9818 - val_loss: 0.0769 - val_acc: 0.9792\n",
            "Epoch 6/100\n",
            "57636/57636 [==============================] - 313s 5ms/step - loss: 0.0510 - acc: 0.9859 - val_loss: 0.0674 - val_acc: 0.9804\n",
            "Epoch 7/100\n",
            "57636/57636 [==============================] - 332s 6ms/step - loss: 0.0407 - acc: 0.9884 - val_loss: 0.0711 - val_acc: 0.9805\n",
            "Epoch 8/100\n",
            "57636/57636 [==============================] - 319s 6ms/step - loss: 0.0351 - acc: 0.9900 - val_loss: 0.0731 - val_acc: 0.9813\n",
            "Epoch 9/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0306 - acc: 0.9909 - val_loss: 0.0750 - val_acc: 0.9811\n",
            "Epoch 10/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0282 - acc: 0.9919 - val_loss: 0.0703 - val_acc: 0.9847\n",
            "Epoch 11/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0232 - acc: 0.9936 - val_loss: 0.0664 - val_acc: 0.9838\n",
            "Epoch 12/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0223 - acc: 0.9930 - val_loss: 0.0809 - val_acc: 0.9822\n",
            "Epoch 13/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0226 - acc: 0.9938 - val_loss: 0.0967 - val_acc: 0.9811\n",
            "Epoch 14/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0226 - acc: 0.9935 - val_loss: 0.0779 - val_acc: 0.9840\n",
            "Epoch 15/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0179 - acc: 0.9947 - val_loss: 0.0757 - val_acc: 0.9829\n",
            "Epoch 16/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0180 - acc: 0.9945 - val_loss: 0.0730 - val_acc: 0.9842\n",
            "Epoch 17/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0185 - acc: 0.9948 - val_loss: 0.0747 - val_acc: 0.9841\n",
            "Epoch 18/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0157 - acc: 0.9953 - val_loss: 0.0737 - val_acc: 0.9847\n",
            "Epoch 19/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0146 - acc: 0.9960 - val_loss: 0.0864 - val_acc: 0.9824\n",
            "Epoch 20/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0134 - acc: 0.9962 - val_loss: 0.0875 - val_acc: 0.9842\n",
            "Epoch 21/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0139 - acc: 0.9960 - val_loss: 0.0955 - val_acc: 0.9834\n",
            "Epoch 22/100\n",
            "57636/57636 [==============================] - 313s 5ms/step - loss: 0.0146 - acc: 0.9957 - val_loss: 0.0843 - val_acc: 0.9838\n",
            "Epoch 23/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0120 - acc: 0.9964 - val_loss: 0.0939 - val_acc: 0.9849\n",
            "Epoch 24/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0103 - acc: 0.9973 - val_loss: 0.0883 - val_acc: 0.9844\n",
            "Epoch 25/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0117 - acc: 0.9967 - val_loss: 0.0970 - val_acc: 0.9824\n",
            "Epoch 26/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0153 - acc: 0.9955 - val_loss: 0.1030 - val_acc: 0.9826\n",
            "Epoch 27/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0139 - acc: 0.9960 - val_loss: 0.0888 - val_acc: 0.9846\n",
            "Epoch 28/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0064 - acc: 0.9982 - val_loss: 0.0929 - val_acc: 0.9858\n",
            "Epoch 29/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0092 - acc: 0.9977 - val_loss: 0.0858 - val_acc: 0.9838\n",
            "Epoch 30/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0140 - acc: 0.9963 - val_loss: 0.0811 - val_acc: 0.9842\n",
            "Epoch 31/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0122 - acc: 0.9970 - val_loss: 0.0945 - val_acc: 0.9838\n",
            "Epoch 32/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0091 - acc: 0.9978 - val_loss: 0.0984 - val_acc: 0.9858\n",
            "Epoch 33/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0058 - acc: 0.9986 - val_loss: 0.1054 - val_acc: 0.9845\n",
            "Epoch 34/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0126 - acc: 0.9968 - val_loss: 0.0965 - val_acc: 0.9850\n",
            "Epoch 35/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0109 - acc: 0.9972 - val_loss: 0.1086 - val_acc: 0.9831\n",
            "Epoch 36/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0108 - acc: 0.9971 - val_loss: 0.1043 - val_acc: 0.9842\n",
            "Epoch 37/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0080 - acc: 0.9980 - val_loss: 0.1223 - val_acc: 0.9811\n",
            "Epoch 38/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0135 - acc: 0.9967 - val_loss: 0.1045 - val_acc: 0.9856\n",
            "Epoch 39/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0079 - acc: 0.9979 - val_loss: 0.1239 - val_acc: 0.9828\n",
            "Epoch 40/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0111 - acc: 0.9974 - val_loss: 0.1165 - val_acc: 0.9842\n",
            "Epoch 41/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0113 - acc: 0.9971 - val_loss: 0.1020 - val_acc: 0.9848\n",
            "Epoch 42/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0066 - acc: 0.9983 - val_loss: 0.1109 - val_acc: 0.9853\n",
            "Epoch 43/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0082 - acc: 0.9981 - val_loss: 0.1111 - val_acc: 0.9833\n",
            "Epoch 44/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0108 - acc: 0.9976 - val_loss: 0.1166 - val_acc: 0.9825\n",
            "Epoch 45/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0109 - acc: 0.9972 - val_loss: 0.1241 - val_acc: 0.9842\n",
            "Epoch 46/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0119 - acc: 0.9971 - val_loss: 0.1126 - val_acc: 0.9848\n",
            "Epoch 47/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0111 - acc: 0.9977 - val_loss: 0.0921 - val_acc: 0.9849\n",
            "Epoch 48/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0069 - acc: 0.9982 - val_loss: 0.1179 - val_acc: 0.9836\n",
            "Epoch 49/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0096 - acc: 0.9979 - val_loss: 0.1122 - val_acc: 0.9842\n",
            "Epoch 50/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0113 - acc: 0.9977 - val_loss: 0.0994 - val_acc: 0.9854\n",
            "Epoch 51/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0045 - acc: 0.9990 - val_loss: 0.1187 - val_acc: 0.9845\n",
            "Epoch 52/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0121 - acc: 0.9972 - val_loss: 0.1199 - val_acc: 0.9838\n",
            "Epoch 53/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0109 - acc: 0.9976 - val_loss: 0.1114 - val_acc: 0.9840\n",
            "Epoch 54/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0083 - acc: 0.9980 - val_loss: 0.1158 - val_acc: 0.9845\n",
            "Epoch 55/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0051 - acc: 0.9988 - val_loss: 0.1288 - val_acc: 0.9829\n",
            "Epoch 56/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0122 - acc: 0.9971 - val_loss: 0.1215 - val_acc: 0.9840\n",
            "Epoch 57/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0106 - acc: 0.9975 - val_loss: 0.1167 - val_acc: 0.9847\n",
            "Epoch 58/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0088 - acc: 0.9979 - val_loss: 0.1157 - val_acc: 0.9849\n",
            "Epoch 59/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0083 - acc: 0.9980 - val_loss: 0.1274 - val_acc: 0.9844\n",
            "Epoch 60/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0072 - acc: 0.9985 - val_loss: 0.1237 - val_acc: 0.9843\n",
            "Epoch 61/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0066 - acc: 0.9983 - val_loss: 0.1275 - val_acc: 0.9847\n",
            "Epoch 62/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0096 - acc: 0.9978 - val_loss: 0.1189 - val_acc: 0.9856\n",
            "Epoch 63/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0090 - acc: 0.9980 - val_loss: 0.1163 - val_acc: 0.9849\n",
            "Epoch 64/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0130 - acc: 0.9974 - val_loss: 0.1425 - val_acc: 0.9831\n",
            "Epoch 65/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0128 - acc: 0.9975 - val_loss: 0.1281 - val_acc: 0.9846\n",
            "Epoch 66/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0055 - acc: 0.9989 - val_loss: 0.1436 - val_acc: 0.9840\n",
            "Epoch 67/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0111 - acc: 0.9976 - val_loss: 0.1288 - val_acc: 0.9852\n",
            "Epoch 68/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0055 - acc: 0.9987 - val_loss: 0.1250 - val_acc: 0.9861\n",
            "Epoch 69/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0069 - acc: 0.9986 - val_loss: 0.1196 - val_acc: 0.9851\n",
            "Epoch 70/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0053 - acc: 0.9989 - val_loss: 0.1507 - val_acc: 0.9822\n",
            "Epoch 71/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0085 - acc: 0.9982 - val_loss: 0.1329 - val_acc: 0.9864\n",
            "Epoch 72/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0159 - acc: 0.9968 - val_loss: 0.1223 - val_acc: 0.9852\n",
            "Epoch 73/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0092 - acc: 0.9981 - val_loss: 0.1239 - val_acc: 0.9837\n",
            "Epoch 74/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0118 - acc: 0.9975 - val_loss: 0.1110 - val_acc: 0.9851\n",
            "Epoch 75/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0084 - acc: 0.9984 - val_loss: 0.1319 - val_acc: 0.9844\n",
            "Epoch 76/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0100 - acc: 0.9980 - val_loss: 0.1285 - val_acc: 0.9845\n",
            "Epoch 77/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0062 - acc: 0.9988 - val_loss: 0.1535 - val_acc: 0.9839\n",
            "Epoch 78/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0083 - acc: 0.9986 - val_loss: 0.1462 - val_acc: 0.9844\n",
            "Epoch 79/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0126 - acc: 0.9974 - val_loss: 0.1264 - val_acc: 0.9845\n",
            "Epoch 80/100\n",
            "57636/57636 [==============================] - 311s 5ms/step - loss: 0.0095 - acc: 0.9981 - val_loss: 0.1407 - val_acc: 0.9846\n",
            "Epoch 81/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0083 - acc: 0.9981 - val_loss: 0.1495 - val_acc: 0.9846\n",
            "Epoch 82/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0116 - acc: 0.9979 - val_loss: 0.1359 - val_acc: 0.9847\n",
            "Epoch 83/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0116 - acc: 0.9978 - val_loss: 0.1285 - val_acc: 0.9853\n",
            "Epoch 84/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0145 - acc: 0.9978 - val_loss: 0.1623 - val_acc: 0.9832\n",
            "Epoch 85/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0144 - acc: 0.9973 - val_loss: 0.1384 - val_acc: 0.9844\n",
            "Epoch 86/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0097 - acc: 0.9979 - val_loss: 0.1294 - val_acc: 0.9858\n",
            "Epoch 87/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0076 - acc: 0.9985 - val_loss: 0.1388 - val_acc: 0.9861\n",
            "Epoch 88/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0109 - acc: 0.9982 - val_loss: 0.1781 - val_acc: 0.9818\n",
            "Epoch 89/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0141 - acc: 0.9974 - val_loss: 0.1412 - val_acc: 0.9860\n",
            "Epoch 90/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0114 - acc: 0.9980 - val_loss: 0.1395 - val_acc: 0.9851\n",
            "Epoch 91/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0118 - acc: 0.9980 - val_loss: 0.1360 - val_acc: 0.9856\n",
            "Epoch 92/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0069 - acc: 0.9986 - val_loss: 0.1513 - val_acc: 0.9837\n",
            "Epoch 93/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0113 - acc: 0.9977 - val_loss: 0.1415 - val_acc: 0.9830\n",
            "Epoch 94/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0068 - acc: 0.9986 - val_loss: 0.1622 - val_acc: 0.9841\n",
            "Epoch 95/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0079 - acc: 0.9986 - val_loss: 0.1519 - val_acc: 0.9847\n",
            "Epoch 96/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0116 - acc: 0.9981 - val_loss: 0.1611 - val_acc: 0.9827\n",
            "Epoch 97/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0233 - acc: 0.9964 - val_loss: 0.1583 - val_acc: 0.9833\n",
            "Epoch 98/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0117 - acc: 0.9980 - val_loss: 0.1435 - val_acc: 0.9846\n",
            "Epoch 99/100\n",
            "57636/57636 [==============================] - 310s 5ms/step - loss: 0.0072 - acc: 0.9989 - val_loss: 0.1863 - val_acc: 0.9824\n",
            "Epoch 100/100\n",
            "57636/57636 [==============================] - 309s 5ms/step - loss: 0.0182 - acc: 0.9975 - val_loss: 0.2646 - val_acc: 0.9774\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "1862ff7c-a071-4015-bb48-f9670a3112db",
        "_uuid": "d89525da13f845e3417890b968fb5d7753f4e54f",
        "id": "Z61Vbph95vwM",
        "colab_type": "text"
      },
      "source": [
        "# After 30 epochs we have a decent training and valiadation accuracy on dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "85658a4d-697d-46ef-91cc-e87a8c4c2f89",
        "_uuid": "97b8d77f4c3a692f939ffa94a466dfd257834d5a",
        "id": "et0fg71f5vwM",
        "colab_type": "text"
      },
      "source": [
        "## Result Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "6b4b108e-4a11-489c-b068-55fecad1ea67",
        "_uuid": "8913f144fc15314e1a98cead1048635bfd758fd0",
        "id": "_apnLg345vwM",
        "colab_type": "text"
      },
      "source": [
        "Let's observe the images by our model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "383c8574-c022-4a52-a3d9-f6c7edabc44d",
        "_uuid": "0b019de716cc8c0ab602d2600b590d83e310fb1f",
        "id": "2EDsSP3H5vwN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions_prob=model.predict(X_test_all) # get predictions for all the test data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "d0993d24-b26d-4934-84a0-ac145f4e8594",
        "_uuid": "4822a937ea504052db7b326595866e7a276e0a2a",
        "id": "WWcx9YXO5vwO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_sample=200\n",
        "np.random.seed(42)\n",
        "ind=np.random.randint(0,len(X_test_all), size=n_sample)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "9f4260e9-e5e6-4b5e-8565-d5bed1bbc359",
        "_uuid": "2b6ec0f33275de9e2b2a4b0bd12b202f8d067551",
        "id": "3QL41zbG5vwP",
        "colab_type": "code",
        "colab": {},
        "outputId": "00ab69a1-33df-4e9c-a324-aa5c0ef587d6"
      },
      "source": [
        "imshow_group(X=X_tshow_all[ind],y=None,y_pred=predictions_prob[ind], phase='prediction')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x250186ca198>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "5ac67cd3-11ac-4bb1-9d7a-f60417f45849",
        "_uuid": "6653b48712048170c2f12148c6dfe4988660c02f",
        "id": "37Li23it5vwQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels=[np.argmax(pred) for pred in predictions_prob]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "2dd35d48dc06d1532d165756677e990c1fe8eaa3",
        "id": "wS0NiOwO5vwR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "keys=[get_key(path) for path in paths_test_all ]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "8fbcfb14dbfe0de631f40ec36bb3e007fdd67ea9",
        "id": "hLnSIOGX5vwT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "create_submission(predictions=labels,keys=keys,path='submission7.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "886f58583f4c1e063e7dd2f040a3bef4247f65f9",
        "id": "bB3Co9KP5vwU",
        "colab_type": "code",
        "colab": {},
        "outputId": "e5b9983e-81b6-4d3e-af17-6a69bd9a0af2"
      },
      "source": [
        "prediction = pd.read_csv('submission7.csv')\n",
        "prediction.head(100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>key</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a00000.png</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a00001.png</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a00002.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a00003.png</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>a00004.png</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>a00005.png</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>a00006.png</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>a00007.png</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>a00008.png</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>a00009.png</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>a00010.png</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>a00011.png</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>a00012.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>a00013.png</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>a00014.png</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>a00015.png</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>a00016.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>a00017.png</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>a00018.png</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>a00019.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>a00020.png</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>a00021.png</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>a00022.png</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>a00023.png</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>a00024.png</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>a00025.png</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>a00026.png</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>a00027.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>a00028.png</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>a00029.png</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>a00070.png</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>a00071.png</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>a00072.png</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>a00073.png</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>a00074.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>a00075.png</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>a00076.png</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>a00077.png</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>a00078.png</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>a00079.png</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>a00080.png</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>a00081.png</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>a00082.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>a00083.png</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>a00084.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>a00085.png</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>a00086.png</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>a00087.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>a00088.png</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>a00089.png</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>a00090.png</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>a00091.png</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>a00092.png</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>a00093.png</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>a00094.png</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>a00095.png</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>a00096.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>a00097.png</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>a00098.png</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>a00099.png</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           key  label\n",
              "0   a00000.png      4\n",
              "1   a00001.png      9\n",
              "2   a00002.png      3\n",
              "3   a00003.png      0\n",
              "4   a00004.png      4\n",
              "5   a00005.png      6\n",
              "6   a00006.png      4\n",
              "7   a00007.png      5\n",
              "8   a00008.png      2\n",
              "9   a00009.png      4\n",
              "10  a00010.png      2\n",
              "11  a00011.png      5\n",
              "12  a00012.png      3\n",
              "13  a00013.png      7\n",
              "14  a00014.png      5\n",
              "15  a00015.png      9\n",
              "16  a00016.png      3\n",
              "17  a00017.png      5\n",
              "18  a00018.png      1\n",
              "19  a00019.png      3\n",
              "20  a00020.png      5\n",
              "21  a00021.png      4\n",
              "22  a00022.png      7\n",
              "23  a00023.png      5\n",
              "24  a00024.png      7\n",
              "25  a00025.png      8\n",
              "26  a00026.png      1\n",
              "27  a00027.png      3\n",
              "28  a00028.png      8\n",
              "29  a00029.png      7\n",
              "..         ...    ...\n",
              "70  a00070.png      6\n",
              "71  a00071.png      1\n",
              "72  a00072.png      7\n",
              "73  a00073.png      7\n",
              "74  a00074.png      3\n",
              "75  a00075.png      0\n",
              "76  a00076.png      6\n",
              "77  a00077.png      8\n",
              "78  a00078.png      6\n",
              "79  a00079.png      8\n",
              "80  a00080.png      4\n",
              "81  a00081.png      7\n",
              "82  a00082.png      3\n",
              "83  a00083.png      9\n",
              "84  a00084.png      3\n",
              "85  a00085.png      8\n",
              "86  a00086.png      2\n",
              "87  a00087.png      3\n",
              "88  a00088.png      8\n",
              "89  a00089.png      0\n",
              "90  a00090.png      5\n",
              "91  a00091.png      7\n",
              "92  a00092.png      0\n",
              "93  a00093.png      9\n",
              "94  a00094.png      5\n",
              "95  a00095.png      7\n",
              "96  a00096.png      3\n",
              "97  a00097.png      9\n",
              "98  a00098.png      6\n",
              "99  a00099.png      7\n",
              "\n",
              "[100 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "51e7528de57416eff84e8fa131124d138dc99ea4",
        "id": "FIC6MSaI5vwV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#df=pd.read_csv(path_label_train_c,na_filter=False)\n",
        "#df.head(100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "f1d1ee1fb15f6bba61fd94d4492524fbb32967a0",
        "id": "NVkc6BOv5vwW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#y=np.array(df.iloc[:,3:4])\n",
        "#print(y)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "c68432b931be3671f4c7b501ac1ff4339bbaa745",
        "id": "wO3t5jF_5vwY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "2f3cef391776026ced9f273990934402ce275c92",
        "id": "ZdSrUJ905vwZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}