Board_Snapper.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Board Snapper.ipynb",
      "version": "0.3.2",
      "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/Siddhant24/board-snapper/blob/master/Board_Snapper.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "c7hLWYB3z1qd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3b27d0b5-d07c-400a-94d7-71b148ed3050"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import math\n",
        "import os\n",
        "import keras\n",
        "import pandas as pd\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Input, Dense, Conv2D, Conv3D, TimeDistributed, Dropout, Flatten, MaxPooling2D, MaxPool3D, Reshape, LSTM, Bidirectional\n",
        "from keras import regularizers\n",
        "\n",
        "#!rm board-snapper -r\n",
        "# !git clone https://github.com/Siddhant24/board-snapper\n",
        "# !pip install keras-metrics\n",
        "import keras_metrics"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "YolWF-YNz1qk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def load_data(x_folder,y_folder,start=1,end = 50):\n",
        "    x_data=[];\n",
        "    x_paths=[];\n",
        "    y_data=None;\n",
        "    for i in range(start,end):\n",
        "        src='lec'+str(i);\n",
        "        image_folder=x_folder+src;\n",
        "        #for img in sorted(os.listdir(image_folder)):\n",
        "        for j in range(1, len(os.listdir(image_folder)) + 1):\n",
        "            img = 'slide'+str(j)+'.jpg'\n",
        "            image=cv2.resize(cv2.imread(os.path.join(image_folder,img)),(224,224));\n",
        "            x_data.append(image);\n",
        "            x_paths.append(str(os.path.join(image_folder,img)));\n",
        "        print(y_folder+src+'.csv')\n",
        "        label=np.array(np.loadtxt(y_folder+src+'.csv',dtype = int));\n",
        "        y_data = np.concatenate([y_data, label]) if y_data is not None else label;\n",
        "        print(src+\"loaded\");\n",
        "\n",
        "    return np.array(x_data),np.array(y_data),x_paths;"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MnAFpTeUz1qn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def format_data(in_x,in_y,index,timestep,batch_size):\n",
        "    total_samples=in_x.shape[0];\n",
        "    dim=(batch_size,timestep,in_x.shape[1],in_x.shape[2],in_x.shape[3])\n",
        "    \n",
        "    x_train=np.zeros(batch_size*timestep*in_x.shape[1]*in_x.shape[2]*in_x.shape[3]).reshape(*dim);\n",
        "    y_train=np.zeros(batch_size);\n",
        "    \n",
        "    offset=int(timestep/2);\n",
        "    for i in range(dim[0]):\n",
        "        x_train[i]=in_x[i+(index*batch_size):i+(index*batch_size)+timestep];\n",
        "        y_train[i]=in_y[i+(index*batch_size)+offset];\n",
        "    return x_train,y_train;"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KjmlBLN0z1qp",
        "colab_type": "code",
        "outputId": "3fac6163-6e71-4448-f77f-51adaea24e85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        }
      },
      "cell_type": "code",
      "source": [
        "def getmodel(timesteps):\n",
        "    model=Sequential();\n",
        "    model.add(TimeDistributed(Conv2D(32, (3, 3), activation='relu'),input_shape=(timesteps,224, 224, 3)));\n",
        "    model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))));\n",
        "    model.add(TimeDistributed(Conv2D(32, (3, 3),activation='relu',kernel_regularizer = regularizers.l2('0.5'))));\n",
        "    model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))\n",
        "    model.add(TimeDistributed(Conv2D(32, (3, 3),activation='relu',kernel_regularizer = regularizers.l2('0.5'))));\n",
        "    model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))\n",
        "    model.add(TimeDistributed(Conv2D(32, (3, 3),activation='relu',kernel_regularizer = regularizers.l2('0.5'))));\n",
        "    model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))\n",
        "    model.add(TimeDistributed(Conv2D(32, (3, 3),activation='relu',kernel_regularizer = regularizers.l2('0.5'))));\n",
        "    model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))));\n",
        "    model.add(TimeDistributed(Flatten()));\n",
        "    model.add(Bidirectional(LSTM(32,activation='tanh',return_sequences=False, kernel_regularizer=regularizers.l2('0.5')),merge_mode='concat'));\n",
        "    model.add(Dense(1,activation='sigmoid'));\n",
        "    model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy', keras_metrics.precision(), keras_metrics.recall()]);\n",
        "    model.summary();\n",
        "    return model;\n",
        "\n",
        "timesteps=20;\n",
        "lrcn=getmodel(timesteps);\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "time_distributed_1 (TimeDist (None, 20, 222, 222, 32)  896       \n",
            "_________________________________________________________________\n",
            "time_distributed_2 (TimeDist (None, 20, 111, 111, 32)  0         \n",
            "_________________________________________________________________\n",
            "time_distributed_3 (TimeDist (None, 20, 109, 109, 32)  9248      \n",
            "_________________________________________________________________\n",
            "time_distributed_4 (TimeDist (None, 20, 54, 54, 32)    0         \n",
            "_________________________________________________________________\n",
            "time_distributed_5 (TimeDist (None, 20, 52, 52, 32)    9248      \n",
            "_________________________________________________________________\n",
            "time_distributed_6 (TimeDist (None, 20, 26, 26, 32)    0         \n",
            "_________________________________________________________________\n",
            "time_distributed_7 (TimeDist (None, 20, 24, 24, 32)    9248      \n",
            "_________________________________________________________________\n",
            "time_distributed_8 (TimeDist (None, 20, 12, 12, 32)    0         \n",
            "_________________________________________________________________\n",
            "time_distributed_9 (TimeDist (None, 20, 10, 10, 32)    9248      \n",
            "_________________________________________________________________\n",
            "time_distributed_10 (TimeDis (None, 20, 5, 5, 32)      0         \n",
            "_________________________________________________________________\n",
            "time_distributed_11 (TimeDis (None, 20, 800)           0         \n",
            "_________________________________________________________________\n",
            "bidirectional_1 (Bidirection (None, 64)                213248    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 251,201\n",
            "Trainable params: 251,201\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GK7qnlg-z1qt",
        "colab_type": "code",
        "outputId": "2d127a24-f752-43b6-b412-a10f957d71b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1037
        }
      },
      "cell_type": "code",
      "source": [
        "x_folder='board-snapper/slides/';\n",
        "y_folder='board-snapper/labels/';\n",
        "print(\"loading data\");\n",
        "x_data,y_data,x_paths=load_data(x_folder,y_folder, 21, 50);\n",
        "x_data = x_data[...,::-1]; ## BGR to RGB\n",
        "print(\"data loaded\");"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loading data\n",
            "board-snapper/labels/lec21.csv\n",
            "lec21loaded\n",
            "board-snapper/labels/lec22.csv\n",
            "lec22loaded\n",
            "board-snapper/labels/lec23.csv\n",
            "lec23loaded\n",
            "board-snapper/labels/lec24.csv\n",
            "lec24loaded\n",
            "board-snapper/labels/lec25.csv\n",
            "lec25loaded\n",
            "board-snapper/labels/lec26.csv\n",
            "lec26loaded\n",
            "board-snapper/labels/lec27.csv\n",
            "lec27loaded\n",
            "board-snapper/labels/lec28.csv\n",
            "lec28loaded\n",
            "board-snapper/labels/lec29.csv\n",
            "lec29loaded\n",
            "board-snapper/labels/lec30.csv\n",
            "lec30loaded\n",
            "board-snapper/labels/lec31.csv\n",
            "lec31loaded\n",
            "board-snapper/labels/lec32.csv\n",
            "lec32loaded\n",
            "board-snapper/labels/lec33.csv\n",
            "lec33loaded\n",
            "board-snapper/labels/lec34.csv\n",
            "lec34loaded\n",
            "board-snapper/labels/lec35.csv\n",
            "lec35loaded\n",
            "board-snapper/labels/lec36.csv\n",
            "lec36loaded\n",
            "board-snapper/labels/lec37.csv\n",
            "lec37loaded\n",
            "board-snapper/labels/lec38.csv\n",
            "lec38loaded\n",
            "board-snapper/labels/lec39.csv\n",
            "lec39loaded\n",
            "board-snapper/labels/lec40.csv\n",
            "lec40loaded\n",
            "board-snapper/labels/lec41.csv\n",
            "lec41loaded\n",
            "board-snapper/labels/lec42.csv\n",
            "lec42loaded\n",
            "board-snapper/labels/lec43.csv\n",
            "lec43loaded\n",
            "board-snapper/labels/lec44.csv\n",
            "lec44loaded\n",
            "board-snapper/labels/lec45.csv\n",
            "lec45loaded\n",
            "board-snapper/labels/lec46.csv\n",
            "lec46loaded\n",
            "board-snapper/labels/lec47.csv\n",
            "lec47loaded\n",
            "board-snapper/labels/lec48.csv\n",
            "lec48loaded\n",
            "board-snapper/labels/lec49.csv\n",
            "lec49loaded\n",
            "data loaded\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5CpB5E4Wz1qx",
        "colab_type": "code",
        "outputId": "9ba7f18b-bb5a-4b68-c7a9-4147e4ceef94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54060
        }
      },
      "cell_type": "code",
      "source": [
        "timesteps=20;\n",
        "lrcn=getmodel(timesteps);\n",
        "total_samples=x_data.shape[0];\n",
        "iter=total_samples-timesteps;\n",
        "batch_size=16;\n",
        "\n",
        "print(\"No. of samples: \", total_samples)\n",
        "print(\"No. of batches: \", int(iter/batch_size))\n",
        "\n",
        "\n",
        "epochs=1;\n",
        "for e in range(epochs):\n",
        "    for i in range(int(iter/batch_size)):\n",
        "        x_train, y_train= format_data(x_data,y_data,i,timesteps,batch_size);\n",
        "        print('training on batch '+str(i)+' for epoch number '+str(e))\n",
        "        loss = lrcn.train_on_batch(x_train,y_train);\n",
        "        print(loss)\n",
        "\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "time_distributed_12 (TimeDis (None, 20, 222, 222, 32)  896       \n",
            "_________________________________________________________________\n",
            "time_distributed_13 (TimeDis (None, 20, 111, 111, 32)  0         \n",
            "_________________________________________________________________\n",
            "time_distributed_14 (TimeDis (None, 20, 109, 109, 32)  9248      \n",
            "_________________________________________________________________\n",
            "time_distributed_15 (TimeDis (None, 20, 54, 54, 32)    0         \n",
            "_________________________________________________________________\n",
            "time_distributed_16 (TimeDis (None, 20, 52, 52, 32)    9248      \n",
            "_________________________________________________________________\n",
            "time_distributed_17 (TimeDis (None, 20, 26, 26, 32)    0         \n",
            "_________________________________________________________________\n",
            "time_distributed_18 (TimeDis (None, 20, 24, 24, 32)    9248      \n",
            "_________________________________________________________________\n",
            "time_distributed_19 (TimeDis (None, 20, 12, 12, 32)    0         \n",
            "_________________________________________________________________\n",
            "time_distributed_20 (TimeDis (None, 20, 10, 10, 32)    9248      \n",
            "_________________________________________________________________\n",
            "time_distributed_21 (TimeDis (None, 20, 5, 5, 32)      0         \n",
            "_________________________________________________________________\n",
            "time_distributed_22 (TimeDis (None, 20, 800)           0         \n",
            "_________________________________________________________________\n",
            "bidirectional_2 (Bidirection (None, 64)                213248    \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 251,201\n",
            "Trainable params: 251,201\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "No. of samples:  25184\n",
            "No. of batches:  1572\n",
            "training on batch 0 for epoch number 0\n",
            "[285.04254, 0.125, 0.0, 0.0]\n",
            "training on batch 1 for epoch number 0\n",
            "[277.71313, 0.125, 0.06666666644444444, 0.9999999500000026]\n",
            "training on batch 2 for epoch number 0\n",
            "[269.4408, 1.0, 0.06666666644444444, 0.9999999500000026]\n",
            "training on batch 3 for epoch number 0\n",
            "[261.03427, 0.9375, 0.06666666644444444, 0.6666666444444451]\n",
            "training on batch 4 for epoch number 0\n",
            "[252.58063, 1.0, 0.06666666644444444, 0.6666666444444451]\n",
            "training on batch 5 for epoch number 0\n",
            "[244.15953, 1.0, 0.06666666644444444, 0.6666666444444451]\n",
            "training on batch 6 for epoch number 0\n",
            "[235.8227, 1.0, 0.06666666644444444, 0.6666666444444451]\n",
            "training on batch 7 for epoch number 0\n",
            "[227.65497, 1.0, 0.06666666644444444, 0.6666666444444451]\n",
            "training on batch 8 for epoch number 0\n",
            "[219.62251, 0.875, 0.06666666644444444, 0.3999999920000002]\n",
            "training on batch 9 for epoch number 0\n",
            "[211.66452, 1.0, 0.06666666644444444, 0.3999999920000002]\n",
            "training on batch 10 for epoch number 0\n",
            "[203.95917, 1.0, 0.06666666644444444, 0.3999999920000002]\n",
            "training on batch 11 for epoch number 0\n",
            "[196.59781, 0.8125, 0.06666666644444444, 0.249999996875]\n",
            "training on batch 12 for epoch number 0\n",
            "[189.2293, 0.875, 0.06666666644444444, 0.19999999800000004]\n",
            "training on batch 13 for epoch number 0\n",
            "[182.00415, 1.0, 0.06666666644444444, 0.19999999800000004]\n",
            "training on batch 14 for epoch number 0\n",
            "[175.11008, 1.0, 0.06666666644444444, 0.19999999800000004]\n",
            "training on batch 15 for epoch number 0\n",
            "[168.40228, 1.0, 0.06666666644444444, 0.19999999800000004]\n",
            "training on batch 16 for epoch number 0\n",
            "[162.03018, 0.875, 0.06666666644444444, 0.1666666652777778]\n",
            "training on batch 17 for epoch number 0\n",
            "[155.66486, 0.9375, 0.06666666644444444, 0.15384615266272192]\n",
            "training on batch 18 for epoch number 0\n",
            "[149.56992, 0.9375, 0.06666666644444444, 0.1428571418367347]\n",
            "training on batch 19 for epoch number 0\n",
            "[143.62686, 0.9375, 0.06666666644444444, 0.13333333244444445]\n",
            "training on batch 20 for epoch number 0\n",
            "[137.82205, 1.0, 0.06666666644444444, 0.13333333244444445]\n",
            "training on batch 21 for epoch number 0\n",
            "[132.29086, 1.0, 0.06666666644444444, 0.13333333244444445]\n",
            "training on batch 22 for epoch number 0\n",
            "[127.15018, 0.875, 0.06666666644444444, 0.11764705813148789]\n",
            "training on batch 23 for epoch number 0\n",
            "[121.976425, 0.875, 0.06666666644444444, 0.10526315734072021]\n",
            "training on batch 24 for epoch number 0\n",
            "[116.77788, 1.0, 0.06666666644444444, 0.10526315734072021]\n",
            "training on batch 25 for epoch number 0\n",
            "[111.97653, 1.0, 0.06666666644444444, 0.10526315734072021]\n",
            "training on batch 26 for epoch number 0\n",
            "[107.3431, 1.0, 0.06666666644444444, 0.10526315734072021]\n",
            "training on batch 27 for epoch number 0\n",
            "[103.35385, 0.75, 0.06666666644444444, 0.0869565213610586]\n",
            "training on batch 28 for epoch number 0\n",
            "[98.57205, 1.0, 0.06666666644444444, 0.0869565213610586]\n",
            "training on batch 29 for epoch number 0\n",
            "[94.545395, 0.9375, 0.06666666644444444, 0.0833333329861111]\n",
            "training on batch 30 for epoch number 0\n",
            "[90.43421, 1.0, 0.06666666644444444, 0.0833333329861111]\n",
            "training on batch 31 for epoch number 0\n",
            "[86.71598, 0.9375, 0.06666666644444444, 0.07999999968]\n",
            "training on batch 32 for epoch number 0\n",
            "[83.02226, 0.9375, 0.06666666644444444, 0.07692307662721894]\n",
            "training on batch 33 for epoch number 0\n",
            "[79.46109, 0.9375, 0.06666666644444444, 0.07407407379972565]\n",
            "training on batch 34 for epoch number 0\n",
            "[76.040016, 0.9375, 0.06666666644444444, 0.07142857117346939]\n",
            "training on batch 35 for epoch number 0\n",
            "[73.042435, 0.8125, 0.06666666644444444, 0.06451612882414151]\n",
            "training on batch 36 for epoch number 0\n",
            "[69.45268, 1.0, 0.06666666644444444, 0.06451612882414151]\n",
            "training on batch 37 for epoch number 0\n",
            "[66.4199, 1.0, 0.06666666644444444, 0.06451612882414151]\n",
            "training on batch 38 for epoch number 0\n",
            "[63.95769, 0.8125, 0.06666666644444444, 0.05882352923875432]\n",
            "training on batch 39 for epoch number 0\n",
            "[60.875114, 0.9375, 0.06666666644444444, 0.05714285697959184]\n",
            "training on batch 40 for epoch number 0\n",
            "[58.03507, 1.0, 0.06666666644444444, 0.05714285697959184]\n",
            "training on batch 41 for epoch number 0\n",
            "[55.466114, 1.0, 0.06666666644444444, 0.05714285697959184]\n",
            "training on batch 42 for epoch number 0\n",
            "[53.002556, 1.0, 0.06666666644444444, 0.05714285697959184]\n",
            "training on batch 43 for epoch number 0\n",
            "[50.638924, 1.0, 0.06666666644444444, 0.05714285697959184]\n",
            "training on batch 44 for epoch number 0\n",
            "[48.542263, 0.9375, 0.06666666644444444, 0.055555555401234566]\n",
            "training on batch 45 for epoch number 0\n",
            "[46.559067, 0.875, 0.06666666644444444, 0.052631578808864266]\n",
            "training on batch 46 for epoch number 0\n",
            "[44.13627, 1.0, 0.06666666644444444, 0.052631578808864266]\n",
            "training on batch 47 for epoch number 0\n",
            "[42.141327, 1.0, 0.06666666644444444, 0.052631578808864266]\n",
            "training on batch 48 for epoch number 0\n",
            "[40.75521, 0.8125, 0.06666666644444444, 0.04878048768590125]\n",
            "training on batch 49 for epoch number 0\n",
            "[38.438126, 1.0, 0.06666666644444444, 0.04878048768590125]\n",
            "training on batch 50 for epoch number 0\n",
            "[36.84046, 0.9375, 0.06666666644444444, 0.047619047505668935]\n",
            "training on batch 51 for epoch number 0\n",
            "[34.99829, 1.0, 0.06666666644444444, 0.047619047505668935]\n",
            "training on batch 52 for epoch number 0\n",
            "[33.58069, 0.9375, 0.06666666644444444, 0.046511627798810164]\n",
            "training on batch 53 for epoch number 0\n",
            "[31.859486, 1.0, 0.06666666644444444, 0.046511627798810164]\n",
            "training on batch 54 for epoch number 0\n",
            "[30.391315, 1.0, 0.06666666644444444, 0.046511627798810164]\n",
            "training on batch 55 for epoch number 0\n",
            "[29.650866, 0.8125, 0.06666666644444444, 0.04347826077504726]\n",
            "training on batch 56 for epoch number 0\n",
            "[27.667374, 1.0, 0.06666666644444444, 0.04347826077504726]\n",
            "training on batch 57 for epoch number 0\n",
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            "[0.019475939, 1.0, 0.06666666644444444, 0.002301495972117204]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1W82HPzrz1q2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lrcn.save(\"trained on 21-50\")\n",
        "# x_t,y_t,x_tpaths=load_data(x_folder,y_folder,50,51);\n",
        "#x_test,y_test=format_data(x_t, y_t, 1 ,timesteps, y_t.shape[0] - timesteps - 10);\n",
        "#y_pred=lrcn.predict(x_test,batch_size=batch_size);\n",
        "\n",
        "\n",
        "#np.savetxt('y.csv',y_pred,delimiter=',',fmt='%.6f')\n",
        "#print(y_pred);\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RRSZTPCaz1q6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x_test,y_test=format_data(x_t, y_t, 1 ,timesteps, 500);\n",
        "y_pred=lrcn.predict(x_test,batch_size=batch_size);"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zHKpwyIBEcok",
        "colab_type": "code",
        "outputId": "2d1321f7-7a84-40cf-b4ef-d164dd32cf93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print(y_pred.max())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.050530244\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iEtx5O69Ecw4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6RpOWFszEc0S",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LKtkTFp0Ec3I",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MIsC6HuTz1q9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "for i in range(43):\n",
        "    print(i)\n",
        "    plt.imshow(x_data[y_data==1][i])\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xiqJ_fJEz1rB",
        "colab_type": "code",
        "outputId": "3abe29b7-0e49-408b-ac1a-d3ea9828c8f4",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "np.where(y_data==1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 26,  38,  73, 139, 143, 186, 193, 197, 204, 211, 267, 275, 288,\n",
              "        303, 316, 363, 365, 386, 390, 446, 447, 455, 457, 485, 511, 531,\n",
              "        547, 562, 573, 574, 577, 618, 620, 624, 634, 727, 738, 741, 783,\n",
              "        786, 792, 818, 846]),)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "nkCutjyUz1rF",
        "colab_type": "code",
        "outputId": "4f3428d4-fc9f-4dfa-a994-bbce0df1b4c6",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x12e1158d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "oA0c6fZbz1rI",
        "colab_type": "code",
        "outputId": "3cb64a3b-db7b-4b25-e9c1-9de49522d3e1",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "np.where(y_data==1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  26,   38,   73,  139,  143,  186,  193,  197,  204,  211,  267,\n",
              "         275,  288,  303,  316,  363,  365,  386,  390,  446,  447,  455,\n",
              "         457,  485,  511,  531,  547,  562,  573,  574,  577,  618,  620,\n",
              "         624,  634,  727,  738,  741,  783,  786,  792,  818,  846,  899,\n",
              "         902,  903,  972,  983,  984, 1055, 1059, 1108, 1146, 1149, 1158,\n",
              "        1161, 1257, 1342, 1357, 1359, 1400, 1403, 1419, 1420, 1422, 1436,\n",
              "        1437, 1508, 1570, 1573, 1645, 1670, 1672, 1676, 1689, 1719, 1763,\n",
              "        1809, 1811, 1860, 1864, 1938, 1970, 1973, 2037, 2040, 2060, 2109,\n",
              "        2115, 2176, 2181, 2267, 2269, 2389, 2391, 2393, 2408, 2411, 2414,\n",
              "        2420, 2492, 2496, 2548, 2561, 2635, 2699, 2702, 2705, 2709, 2719,\n",
              "        2736, 2749, 2761, 2791, 2809, 2823, 2832, 2843, 2852, 2854, 2887,\n",
              "        2899, 2931, 2943, 2961, 2971, 2984, 2988, 2993, 2999, 3014, 3017,\n",
              "        3041, 3053, 3066, 3095, 3105, 3159, 3191, 3220, 3221, 3225, 3231,\n",
              "        3241, 3242, 3252, 3264, 3272, 3292, 3300, 3308, 3322, 3343, 3381,\n",
              "        3401, 3405, 3414, 3423, 3425, 3426, 3430, 3442, 3448, 3458, 3488,\n",
              "        3512, 3523, 3527, 3578, 3588, 3610, 3620, 3686, 3698, 3705, 3708,\n",
              "        3746, 3754, 3757, 3787, 3788, 3826, 3883, 3893, 3899, 3955, 4002,\n",
              "        4074, 4160, 4217, 4218, 4238, 4321, 4391, 4440, 4490]),)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "H0LdIL7Hz1rL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nHTz71aMz1rN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "os.listdir('board-snapper/labels')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7Fc2yzf5z1rU",
        "colab_type": "code",
        "outputId": "5d79ebd4-b89c-4766-c769-bd8e2fa279f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "np.zeros((1,2,3,4,5)).shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 2, 3, 4, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "Gfl7ArMhq9eR",
        "colab_type": "code",
        "outputId": "907adc66-c502-46db-bd49-cd40289c9e7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "os.listdir('board-snapper')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['convLstmbatch.py',\n",
              " 'labels',\n",
              " '.ipynb_checkpoints',\n",
              " '.DS_Store',\n",
              " 'Board Snapper.ipynb',\n",
              " 'slides',\n",
              " '.git']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "IiiI5Eykq9hT",
        "colab_type": "code",
        "outputId": "744c1d9a-d103-47d2-885e-3c1c550036a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65
        }
      },
      "cell_type": "code",
      "source": [
        "label=np.array(pd.read_csv(('')));"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "board-snapper  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Y8DXZJEOq9kB",
        "colab_type": "code",
        "outputId": "c33951a6-dd06-4f9c-9b8e-c40c910edc87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 16711
        }
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "np.array(pd.read_csv(\"board-snapper/labels/lec26.csv\"))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
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      "metadata": {
        "id": "WRv13ck1q9m3",
        "colab_type": "code",
        "colab": {}
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      "cell_type": "code",
      "source": [
        ""
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      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UBQCBCXFq9p2",
        "colab_type": "code",
        "colab": {}
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      "cell_type": "code",
      "source": [
        ""
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      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Md4EXZu0q9si",
        "colab_type": "code",
        "outputId": "9d91348d-29ac-46b5-b665-e869dc180b2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "y_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(847,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "_C-DHRU5z1rX",
        "colab_type": "code",
        "outputId": "f156ee92-487d-48db-a01c-e8839125fa3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
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      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'board-snapper'...\n",
            "remote: Enumerating objects: 39594, done.\u001b[K\n",
            "remote: Counting objects: 100% (39594/39594), done.\u001b[K\n",
            "remote: Compressing objects: 100% (39533/39533), done.\u001b[K\n",
            "remote: Total 39594 (delta 61), reused 39594 (delta 61), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (39594/39594), 1.05 GiB | 13.83 MiB/s, done.\n",
            "Resolving deltas: 100% (61/61), done.\n",
            "Checking out files: 100% (40976/40976), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "q3Qom2JIzva2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}