EE 298 Project 1_Basic_BCNN_for_kinship_detection.ipynb

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  "metadata": {
    "colab": {
      "name": "EE 298_DL_Basic BCNN for kinship detection.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nr1CICYp_h5T",
        "colab_type": "text"
      },
      "source": [
        "# Basic BCNN for Kinship Detection\n",
        "## *Kaggle Competition: Northeastern SMILE Lab - Recognizing Faces in the Wild*\n",
        "\n",
        "###EE 298 Project of:\n",
        "###Jose Lorenzo Capistrano, Eldion Vincent Bartolo and Jasper Ryan Bisnar\n",
        "\n",
        "## Abstract:\n",
        "\n",
        "This Jupyter Notebook is used to predict if two people share a kinship relationship or not based on their facial images.\n",
        "\n",
        "The dataset can be found at\n",
        "https://www.kaggle.com/c/recognizing-faces-in-the-wild\n",
        "and consist of the following:\n",
        "\n",
        "* 10,580 Images:\n",
        "X Individuals, Y Families\n",
        "* 3,598 Image Pairs for Training\n",
        "* 5,310 Image Pairs for Testing\n",
        "* Image size: 224 x 224 pixels\n",
        "\n",
        "This is implemented in Google Colab with GPU runtime\n",
        "## Table of Contents\n",
        "\n",
        "\n",
        "1.   Import data set from Google Drive\n",
        "2.   Show some examples of the dataset\n",
        "3.   Import Necessary Packages\n",
        "4.   Define Functions for Computing distances and Accuracy\n",
        "5.   Define Functions for Local Response Normalization and Batch Normalization\n",
        "6.   Define functions for reading images and csv file for relationships\n",
        "7.   Define the Initial Model\n",
        "8.   Create Checkpoints and Callbacks\n",
        "9.   Create The Bilinear Convolutional Neural Network (BCNN)\n",
        "10.  Train the Network\n",
        "11.  Make Predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prE08hZ2AHAr",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "## Import data set from Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KuoB53k0EmeZ",
        "colab_type": "code",
        "outputId": "40ff0640-9409-493b-f1bb-edacd3ebfe60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "# Connect to google drive with authentication\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "PATH_OF_DATA = '/content/gdrive/\"My Drive\"/SMILE_LAB_dataset'  #change dir to your project folder\n",
        "root_path = '/content/gdrive/My Drive/SMILE_LAB_dataset/'\n",
        "\n",
        "# List all folders\n",
        "!ls {PATH_OF_DATA}"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n",
            " keras_facenet\t\t sub_detect_kinship.csv   train\n",
            " kin-detection\t\t'submission 1.csv'\t  train_relationships.csv\n",
            " Results\t\t'submission 2.csv'\t  train.zip\n",
            " sample_submission.csv\t test\n",
            " saved_models_weights\t test.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7v0nx83yAp0x",
        "colab_type": "text"
      },
      "source": [
        "## Show some examples of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTL-4BcaIi0t",
        "colab_type": "code",
        "outputId": "5f38a530-898f-426b-cad0-d77408c8ae91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.datasets as datasets\n",
        "# Comment 0: define transformation that you wish to apply on image\n",
        "data_transforms = transforms.Compose([\n",
        "                    transforms.CenterCrop(224),\n",
        "                    transforms.Grayscale(num_output_channels=3),\n",
        "                    transforms.ToTensor()])\n",
        "# Comment 1 : Load the datasets with ImageFolder\n",
        "image_datasets = datasets.ImageFolder(root=  root_path +\"/train\", transform=data_transforms)\n",
        "# Comment 2: Using the image datasets and the transforms, define the dataloaders\n",
        "dataloaders = torch.utils.data.DataLoader(image_datasets, batch_size=32, shuffle=True, num_workers=2)\n",
        "\n",
        "# --- Plot all the images in the file\n",
        "import matplotlib.pyplot as plt \n",
        "%matplotlib inline\n",
        "# helper function to un-normalize and display an image\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5  # unnormalize\n",
        "    plt.imshow(np.transpose(img, (1, 2, 0)))  # convert from Tensor image\n",
        "    \n",
        "dataiter = iter(dataloaders)\n",
        "images, labels = dataiter.next()\n",
        "images = images.numpy() # convert images to numpy for display\n",
        "# plot the images in the batch, along with the corresponding labels\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "# display 20 images\n",
        "for idx in np.arange(20):\n",
        "    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n",
        "    imshow(images[idx])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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2g2tfo8GFVU05OBOIPKJ3F5jwfAlg3ejjtkwXVm0itYFCk4BDxIgx5xX2H1eb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goKCgoKCgoKCgoKCgRShB3YKCgoKCgoKCgoKCgoKCgoKCgoKCFqEEdQsKCgoKCgoKCgoKCgoK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\ngeZoj7adGtuc0wLkU95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92tck1HuqwxNlNWnQlg4Kv7mf1z86Oko62Q2G3o99QbPtvK7b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Tc7FX3PiEveZpzlbBj3eJuxFfVEfE6s29uUt5XZXF3ets7+nJw/4D529Lffdizkyv9oo7Rm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\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jyKFo88Q1Hdufmyf3m7xd8fP4WS47l+sIXFCD3v3IqKfLRvV6XR9++GEKBrXbbTWbTb18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\nALpYt/CF5oUpAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1800x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tBfrRBc8A7ET",
        "colab_type": "text"
      },
      "source": [
        "##Import Necessary Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1drkYY9OwoW",
        "colab_type": "code",
        "outputId": "e56afd2f-2a7b-45b8-aee5-3e5fa5b92b89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate\n",
        "from keras.layers.core import Lambda, Flatten, Dense\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers.pooling import MaxPooling2D, AveragePooling2D\n",
        "from keras.models import Model\n",
        "from keras import backend as K\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import keras\n",
        "from keras.models import *\n",
        "from keras.layers import *\n",
        "from keras.preprocessing import *\n",
        "from keras.callbacks import *\n",
        "from keras.optimizers import *\n",
        "from tqdm import tqdm\n",
        "import glob\n",
        "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
        "\n",
        "# from keras.applications.nasnet import preprocess_input, decode_predictions\n",
        "# from keras.applications.densenet import preprocess_input, decode_predictions\n",
        "from keras.preprocessing import image\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import sys\n",
        "\n",
        "import cv2\n",
        "from random import choice, sample\n",
        "import datetime\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping\n",
        "import threading\n",
        "\n",
        "from keras.initializers import glorot_normal\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "from numpy import genfromtxt\n",
        "from keras.layers import Conv2D, ZeroPadding2D, Activation\n",
        "from keras.layers.normalization import BatchNormalization"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E7TACaFSBDxq",
        "colab_type": "text"
      },
      "source": [
        "## Define Functions for Computing distances and Accuracy\n",
        "\n",
        "Mostly used for the construction of BCNN layers\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2h6OqfuO1iv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def outer_product(x):\n",
        "    \"\"\"\n",
        "    calculate outer-products of 2 tensors\n",
        "\n",
        "        args \n",
        "            x\n",
        "                list of 2 tensors\n",
        "                , assuming each of which has shape = (size_minibatch, total_pixels, size_filter)\n",
        "    \"\"\"\n",
        "    return keras.backend.batch_dot(\n",
        "                x[0]\n",
        "                , x[1]\n",
        "                , axes=[1,1]\n",
        "            ) / x[0].get_shape().as_list()[1] \n",
        "\n",
        "def signed_sqrt(x):\n",
        "    \"\"\"\n",
        "    calculate element-wise signed square root\n",
        "\n",
        "        args\n",
        "            x\n",
        "                a tensor\n",
        "    \"\"\"\n",
        "    return keras.backend.sign(x) * keras.backend.sqrt(keras.backend.abs(x) + 1e-9)\n",
        "\n",
        "def L2_norm(x, axis=-1):\n",
        "    \"\"\"\n",
        "    calculate L2-norm\n",
        "\n",
        "        args \n",
        "            x\n",
        "                a tensor\n",
        "    \"\"\"\n",
        "    return keras.backend.l2_normalize(x, axis=axis)\n",
        "\n",
        "def auc(y_true, y_pred):\n",
        "    return tf.py_func(roc_auc_score, (y_true, y_pred), tf.double)\n",
        "\n",
        "\n",
        "        \n",
        "def euclidean_distance(vects):\n",
        "    x, y = vects\n",
        "    sum_square = K.sum(K.square(x - y), axis=1, keepdims=True)\n",
        "    return K.sqrt(K.maximum(sum_square, K.epsilon()))\n",
        "\n",
        "\n",
        "def eucl_dist_output_shape(shapes):\n",
        "    shape1, shape2 = shapes\n",
        "    return (shape1[0], 1)\n",
        "\n",
        "\n",
        "def contrastive_loss(y_true, y_pred):\n",
        "    '''Contrastive loss from Hadsell-et-al.'06\n",
        "    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n",
        "    '''\n",
        "    margin = 1\n",
        "    sqaure_pred = K.square(y_pred)\n",
        "    margin_square = K.square(K.maximum(margin - y_pred, 0))\n",
        "    return K.mean(y_true * sqaure_pred + (1 - y_true) * margin_square)\n",
        "\n",
        "def create_base_network(input_shape):\n",
        "    new_mod = Model(mod.input,mod.layers[-5].output)\n",
        "    return new_mod\n",
        "\n",
        "def compute_accuracy(y_true, y_pred):\n",
        "    '''Compute classification accuracy with a fixed threshold on distances.\n",
        "    '''\n",
        "    pred = y_pred.ravel() < 0.5\n",
        "    return np.mean(pred == y_true)\n",
        "\n",
        "\n",
        "def accuracy(y_true, y_pred):\n",
        "    '''Compute classification accuracy with a fixed threshold on distances.\n",
        "    '''\n",
        "    return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B65Hl8S2BVVq",
        "colab_type": "text"
      },
      "source": [
        "## Define Functions for Local Response Normalization and Batch Normalization\n",
        "\n",
        "\n",
        "---\n",
        "Normalization compensates for the unbounded nature of certain activation functions such as ReLU, ELU etc.\n",
        "With these activation function, the output layers is not constrained within a bounded range (such as [-1,1] for tanh), rather they can grow as high as the training allows it. To limit the unbounded activation from increasing the output layer values, normalization is used just before the activation function.\n",
        "\n",
        "###Local Response Normalization\n",
        "Local Response Normalization (LRN) was first introduced in AlexNet architecture where the activation function used was ReLU as opposed to the more common tanh and sigmoid at that time. Apart from the reason mentioned above, the reason for using LRN was to **encourage lateral inhibition.** It is a concept in Neurobiology which refers to the capacity of a neuron to reduce the activity of its neighbors.\n",
        "In DNNs, the purpose of this lateral inhibition is to carry out local contrast enhancement so that locally maximum pixels value are used as excitation for next layers.\n",
        "\n",
        "###Batch Normalization:\n",
        "Batch Normalization (BN) is a trainable layer normally used for **addressing the issues of Internal Covariance Shift** (ICF) - Covariance Shift for Hidden layers. ICF arises due to the changing distribution of the hidden neurons/activation. Covariate Shift can be mitigated by making sure that the members within a batch do not belong to same/similar distribution. This can be done by randomly selecting images for batches.\n",
        "\n",
        "\n",
        "**Reference**: https://towardsdatascience.com/difference-between-local-response-normalization-and-batch-normalization-272308c034ac"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wX76AVlPPBr7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def LRN2D(x):\n",
        "    return tf.nn.lrn(x, alpha=1e-4, beta=0.75)\n",
        "\n",
        "def conv2d_bn(\n",
        "  x,\n",
        "  layer=None,\n",
        "  cv1_out=None,\n",
        "  cv1_filter=(1, 1),\n",
        "  cv1_strides=(1, 1),\n",
        "  cv2_out=None,\n",
        "  cv2_filter=(3, 3),\n",
        "  cv2_strides=(1, 1),\n",
        "  padding=None,\n",
        "):\n",
        "    num = '' if cv2_out == None else '1'\n",
        "    tensor = Conv2D(cv1_out, cv1_filter, strides=cv1_strides, name=layer+'_conv'+num)(x)\n",
        "    tensor = BatchNormalization(axis=3, epsilon=0.00001, name=layer+'_bn'+num)(tensor)\n",
        "    tensor = Activation('relu')(tensor)\n",
        "    if padding == None:\n",
        "        return tensor\n",
        "    tensor = ZeroPadding2D(padding=padding)(tensor)\n",
        "    if cv2_out == None:\n",
        "        return tensor\n",
        "    tensor = Conv2D(cv2_out, cv2_filter, strides=cv2_strides, name=layer+'_conv'+'2')(tensor)\n",
        "    tensor = BatchNormalization(axis=3, epsilon=0.00001, name=layer+'_bn'+'2')(tensor)\n",
        "    tensor = Activation('relu')(tensor)\n",
        "    return tensor"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHJ_b6zCBhqB",
        "colab_type": "text"
      },
      "source": [
        "## Define functions for reading images and csv file for relationships"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TiLSQqW7WxhL",
        "colab_type": "code",
        "outputId": "fc16a943-9488-4dde-c7c8-a75802fc6d12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "ROOT = '/content/gdrive/My Drive/SMILE_LAB_dataset/'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNWeqE2PenyM",
        "colab_type": "code",
        "outputId": "9b70f6fc-4477-43eb-9a5e-eed32d4a0e93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def read_img(im_path):\n",
        "    im1 = image.load_img(im_path, target_size=(96, 96))\n",
        "    im1 = image.img_to_array(im1)\n",
        "    im1 = np.expand_dims(im1, axis=0)\n",
        "    im1 = preprocess_input(im1,mode='tf')\n",
        "    return im1[0]\n",
        "\n",
        "from collections import defaultdict\n",
        "#keeps all photos path in a dictionary\n",
        "allPhotos = defaultdict(list)\n",
        "for family in glob.glob(ROOT+\"train/*\"):\n",
        "    for mem in glob.glob(family+'/*'):\n",
        "        for photo in glob.glob(mem+'/*'):\n",
        "            allPhotos[mem].append(photo)\n",
        "\n",
        "#list of all members with valid photo\n",
        "ppl = list(allPhotos.keys())\n",
        "len(ppl)\n",
        "\n",
        "data = pd.read_csv(ROOT+'train_relationships.csv')\n",
        "data.p1 = data.p1.apply( lambda x: ROOT+'train/'+x )\n",
        "data.p2 = data.p2.apply( lambda x: ROOT+'train/'+x )\n",
        "print(data.shape)\n",
        "data.head()\n",
        "\n",
        "data = data[ ( (data.p1.isin(ppl)) & (data.p2.isin(ppl)) ) ]\n",
        "data = [ ( x[0], x[1]  ) for x in data.values ]\n",
        "len(data)\n",
        "\n",
        "train = [ x for x in data if 'F09' not in x[0]  ]\n",
        "val = [ x for x in data if 'F09' in x[0]  ]\n",
        "len(train), len(val)\n",
        "\n",
        "def getImages(p1,p2):\n",
        "    p1 = read_img(choice(allPhotos[p1]))\n",
        "    p2 = read_img(choice(allPhotos[p2]))\n",
        "    return p1,p2\n",
        "\n",
        "def getMiniBatch(batch_size=16, data=train):\n",
        "    p1 = []; p2 = []; Y = []\n",
        "    batch = sample(data, batch_size//2)\n",
        "    for x in batch:\n",
        "        _p1, _p2 = getImages(*x)\n",
        "        p1.append(_p1);p2.append(_p2);Y.append(1)\n",
        "    while len(Y) < batch_size:\n",
        "        _p1,_p2 = tuple(np.random.choice(ppl,size=2, replace=False))\n",
        "        if (_p1,_p2) not in train+val and (_p2,_p1) not in train+val:\n",
        "            _p1,_p2 = getImages(_p1,_p2)\n",
        "            p1.append(_p1);p2.append(_p2);Y.append(0) \n",
        "    return [np.array(p1),np.array(p2)], np.array(Y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3598, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HnaqB-dcBwjb",
        "colab_type": "text"
      },
      "source": [
        "## Define the Initial Model\n",
        "Model Consists of Seven Conv2D Layers with Batch Normalization and ReLU Activation and Last Layer is Dense"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8iTQhPb7Q11c",
        "colab_type": "code",
        "outputId": "821f7da2-674f-4125-beee-8413ee43b816",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "def create_model():\n",
        "    myInput = Input(shape=(96, 96, 3))\n",
        "\n",
        "    x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)\n",
        "    x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)\n",
        "    x = BatchNormalization(axis=3, epsilon=0.00001, name='bn1')(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = ZeroPadding2D(padding=(1, 1))(x)\n",
        "    x = MaxPooling2D(pool_size=3, strides=2)(x)\n",
        "    x = Lambda(LRN2D, name='lrn_1')(x)\n",
        "    x = Conv2D(64, (1, 1), name='conv2')(x)\n",
        "    x = BatchNormalization(axis=3, epsilon=0.00001, name='bn2')(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = ZeroPadding2D(padding=(1, 1))(x)\n",
        "    x = Conv2D(192, (3, 3), name='conv3')(x)\n",
        "    x = BatchNormalization(axis=3, epsilon=0.00001, name='bn3')(x)\n",
        "    x = Activation('relu')(x)\n",
        "    #ADDITIONAL LAYERS - START\n",
        "    x = ZeroPadding2D(padding=(1, 1))(x)\n",
        "    x = MaxPooling2D(pool_size=3, strides=2)(x)\n",
        "    x = Lambda(LRN2D, name='lrn_2')(x)\n",
        "    x = Conv2D(64, (1, 1), name='conv4')(x)\n",
        "    x = BatchNormalization(axis=3, epsilon=0.00001, name='bn4')(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = ZeroPadding2D(padding=(1, 1))(x)\n",
        "    x = Conv2D(192, (3, 3), name='conv5')(x)\n",
        "    x = BatchNormalization(axis=3, epsilon=0.00001, name='bn5')(x)\n",
        "    x = Activation('relu')(x)\n",
        "    #ADDITIONAL LAYERS - MID\n",
        "    x = ZeroPadding2D(padding=(1, 1))(x)\n",
        "    x = MaxPooling2D(pool_size=3, strides=2)(x)\n",
        "    x = Lambda(LRN2D, name='lrn_3')(x)\n",
        "    x = Conv2D(64, (1, 1), name='conv6')(x)\n",
        "    x = BatchNormalization(axis=3, epsilon=0.00001, name='bn6')(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = ZeroPadding2D(padding=(1, 1))(x)\n",
        "    x = Conv2D(192, (3, 3), name='conv7')(x)\n",
        "    x = BatchNormalization(axis=3, epsilon=0.00001, name='bn7')(x)\n",
        "    x = Activation('relu')(x)\n",
        "     #ADDITIONAL LAYERS - END\n",
        "    x = Lambda(LRN2D, name='lrn_4')(x)\n",
        "    x = ZeroPadding2D(padding=(1, 1))(x)\n",
        "    x = MaxPooling2D(pool_size=3, strides=2)(x)\n",
        "\n",
        "    reshape_layer = Flatten()(x)\n",
        "    dense_layer = Dense(128, name='dense_layer')(reshape_layer)\n",
        "    norm_layer = Lambda(lambda  x: K.l2_normalize(x, axis=1), name='norm_layer')(dense_layer)\n",
        "\n",
        "    return Model(inputs=[myInput], outputs=norm_layer)\n",
        "\n",
        "mod = create_model()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2041: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J4zMOh4gDH_q",
        "colab_type": "text"
      },
      "source": [
        "## Create Checkpoints and Callbacks\n",
        "\n",
        "---\n",
        "\n",
        "For Callbacks Monitor Early Stopping and Reduction of Learning Rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znA9GsqvSSxI",
        "colab_type": "code",
        "outputId": "5a966be5-8b40-4f76-b3cb-773fdf3cffee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ModelCheckpoint('model_best_checkpoint.h5', save_best_only=True,\n",
        "                                    save_weights_only=True, monitor='val_auc', mode='max', verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.ModelCheckpoint at 0x7f71aa9659e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21DhsnURS_LM",
        "colab_type": "code",
        "outputId": "a532f549-bd2e-4a62-a269-98eaf07279e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss',\n",
        "                           patience=8,\n",
        "                           verbose=1,\n",
        "                           min_delta=1e-4),\n",
        "             keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n",
        "                               factor=0.1,\n",
        "                               patience=4,\n",
        "                               verbose=1,\n",
        "                               epsilon=1e-4),\n",
        "            keras.callbacks.ModelCheckpoint('model_best_checkpoint.h5',save_best_only=True,\n",
        "                                            verbose=0, save_weights_only=True,monitor='val_auc',mode='max'),\n",
        "        ]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1335: UserWarning: `epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n",
            "  warnings.warn('`epsilon` argument is deprecated and '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FtjWONSZDSEK",
        "colab_type": "text"
      },
      "source": [
        "## Create The Bilinear Convolutional Neural Network (BCNN)\n",
        "\n",
        "###BCNNs\n",
        "These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs belong to the class of orderless texture representations but unlike prior work they can be trained in an end-to-end manner.\n",
        "\n",
        "A recognition architecture that consists of two feature extractors whose outputs are multiplied using outer product at each location of the image and pooled to obtain an image descriptor.\n",
        "\n",
        "Related to the two stream hypothesis of visual processing in the human brain.  The ventral stream (or, “what pathway”) is involved with object identification and recognition. The dorsal stream (or, “where pathway”) is involved with processing the object’s spatial location relative to the viewer\n",
        "\n",
        "Available at: https://github.com/tkhs3/BCNN_keras.git\n",
        "\n",
        "\n",
        "<img src=\"http://vis-www.cs.umass.edu/bcnn/docs/teaser-bcnn.png\" width=\"800\">\n",
        "\n",
        "**Reference:** http://vis-www.cs.umass.edu/bcnn/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxGTDXvvTD49",
        "colab_type": "code",
        "outputId": "c8cc704e-51a7-4a29-f292-dfd99aff7998",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "input_shape = (96,96,3)\n",
        "\n",
        "input_a = Input(shape=input_shape)\n",
        "input_b = Input(shape=input_shape)\n",
        "\n",
        "base_network = create_base_network(input_shape)\n",
        "\n",
        "# because we re-use the same instance `base_network`,\n",
        "# the weights of the network\n",
        "# will be shared across the two branches\n",
        "processed_a = base_network(input_a)\n",
        "processed_b = base_network(input_b)\n",
        "\n",
        "\n",
        "# extract features from detector\n",
        "x_detector = processed_a\n",
        "shape_detector = processed_a.shape\n",
        "\n",
        "# extract features from extractor , same with detector for symmetry DxD model\n",
        "shape_extractor = processed_b.shape\n",
        "x_extractor = processed_b\n",
        "\n",
        "# rehape to (minibatch_size, total_pixels, filter_size)\n",
        "x_detector = keras.layers.Reshape([shape_detector[1] * shape_detector[2] , shape_detector[-1]])(x_detector)\n",
        "x_extractor = keras.layers.Reshape([shape_extractor[1] * shape_extractor[2] , shape_extractor[-1]])(x_extractor)\n",
        "\n",
        "# outer products of features, output shape=(minibatch_size, filter_size_detector*filter_size_extractor)\n",
        "x = keras.layers.Lambda(outer_product)([x_detector, x_extractor])\n",
        "\n",
        "# rehape to (minibatch_size, filter_size_detector*filter_size_extractor)\n",
        "x = keras.layers.Reshape([shape_detector[-1]*shape_extractor[-1]])(x)\n",
        "# signed square-root \n",
        "\n",
        "x = keras.layers.Lambda(signed_sqrt)(x)\n",
        "# L2 normalization\n",
        "x = keras.layers.Lambda(L2_norm)(x)\n",
        "\n",
        "### \n",
        "### attach FC-Layer\n",
        "###\n",
        "\n",
        "x = Dense(100, activation=\"relu\")(x)\n",
        "x = Dropout(0.01)(x)\n",
        "\n",
        "out = Dense(units=1,kernel_regularizer=keras.regularizers.l2(1e-8),kernel_initializer='glorot_normal',activation=\"sigmoid\")(x)\n",
        "\n",
        "\n",
        "\n",
        "model = Model([input_a, input_b], out)\n",
        "\n",
        "model.compile(loss=\"binary_crossentropy\", metrics=['acc',auc], optimizer=Adam(0.001))\n",
        "\n",
        "\n",
        "# model.load_weights(\"/home/datumx/data_science_experiments/detect_kinship/logs/kin_relation_2019_05_28_21_53_51966472/siamese_kins_detection_13-val_loss_0.4720-val_acc_0.7680.h5\")\n",
        "# train\n",
        "\n",
        "\n",
        "print(model.summary())\n",
        "# train_batches =batch_generator(data_train,batch_size=8)\n",
        "# valid_batches =batch_generator(data_valid,batch_size=8)\n",
        "\n",
        "def Generator(batch_size, data ):\n",
        "    while True:\n",
        "        yield getMiniBatch(batch_size=batch_size, data=data)\n",
        "\n",
        "train_gen = Generator(batch_size=16,data=train)\n",
        "val_gen = Generator(batch_size=16,data=val)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4479: The name tf.truncated_normal is deprecated. Please use tf.random.truncated_normal instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From <ipython-input-4-513fdfe268fa>:37: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "tf.py_func is deprecated in TF V2. Instead, there are two\n",
            "    options available in V2.\n",
            "    - tf.py_function takes a python function which manipulates tf eager\n",
            "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
            "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
            "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
            "    being differentiable using a gradient tape.\n",
            "    - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
            "    (it is not differentiable, and manipulates numpy arrays). It drops the\n",
            "    stateful argument making all functions stateful.\n",
            "    \n",
            "Model: \"model_3\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            (None, 96, 96, 3)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_3 (InputLayer)            (None, 96, 96, 3)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "model_2 (Model)                 (None, 8, 8, 192)    374016      input_2[0][0]                    \n",
            "                                                                 input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "reshape_1 (Reshape)             (None, Dimension(64) 0           model_2[1][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "reshape_2 (Reshape)             (None, Dimension(64) 0           model_2[2][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_1 (Lambda)               (None, 192, 192)     0           reshape_1[0][0]                  \n",
            "                                                                 reshape_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "reshape_3 (Reshape)             (None, Dimension(368 0           lambda_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "lambda_2 (Lambda)               (None, 36864)        0           reshape_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_3 (Lambda)               (None, 36864)        0           lambda_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 100)          3686500     lambda_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 100)          0           dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 1)            101         dropout_1[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 4,060,617\n",
            "Trainable params: 4,058,953\n",
            "Non-trainable params: 1,664\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lI1Ehb2DDwxK",
        "colab_type": "text"
      },
      "source": [
        "## Train the Network\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "The Network is trained for 100 epochs but it early stopped at the 11th epoch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWNfBqj3UvXU",
        "colab_type": "code",
        "outputId": "f6d3b7a3-167b-4d1f-9ae0-f69cadf2e5d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "source": [
        "model.fit_generator(train_gen,steps_per_epoch=200,use_multiprocessing=True,\n",
        "          epochs=100,validation_data=val_gen,validation_steps=100,callbacks=callbacks)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "Epoch 1/100\n",
            "200/200 [==============================] - 35s 175ms/step - loss: 0.6817 - acc: 0.5484 - auc: 0.5755 - val_loss: 0.6616 - val_acc: 0.5925 - val_auc: 0.6383\n",
            "Epoch 2/100\n",
            "200/200 [==============================] - 31s 154ms/step - loss: 0.6722 - acc: 0.5644 - auc: 0.6145 - val_loss: 0.6649 - val_acc: 0.5913 - val_auc: 0.6259\n",
            "Epoch 3/100\n",
            "200/200 [==============================] - 32s 158ms/step - loss: 0.6667 - acc: 0.5800 - auc: 0.6181 - val_loss: 0.6456 - val_acc: 0.6275 - val_auc: 0.6647\n",
            "Epoch 4/100\n",
            "200/200 [==============================] - 32s 158ms/step - loss: 0.6567 - acc: 0.5891 - auc: 0.6487 - val_loss: 0.8409 - val_acc: 0.5387 - val_auc: 0.5311\n",
            "Epoch 5/100\n",
            "200/200 [==============================] - 32s 160ms/step - loss: 0.6730 - acc: 0.5709 - auc: 0.6076 - val_loss: 0.6627 - val_acc: 0.5913 - val_auc: 0.6459\n",
            "Epoch 6/100\n",
            "200/200 [==============================] - 31s 157ms/step - loss: 0.6668 - acc: 0.5797 - auc: 0.6312 - val_loss: 0.6704 - val_acc: 0.5887 - val_auc: 0.6284\n",
            "Epoch 7/100\n",
            "200/200 [==============================] - 32s 158ms/step - loss: 0.6805 - acc: 0.5519 - auc: 0.5852 - val_loss: 0.6584 - val_acc: 0.6088 - val_auc: 0.6567\n",
            "\n",
            "Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "Epoch 8/100\n",
            "200/200 [==============================] - 31s 157ms/step - loss: 0.6651 - acc: 0.5884 - auc: 0.6345 - val_loss: 0.6572 - val_acc: 0.5988 - val_auc: 0.6484\n",
            "Epoch 9/100\n",
            "200/200 [==============================] - 32s 158ms/step - loss: 0.6543 - acc: 0.6019 - auc: 0.6490 - val_loss: 0.6509 - val_acc: 0.6119 - val_auc: 0.6587\n",
            "Epoch 10/100\n",
            "200/200 [==============================] - 31s 156ms/step - loss: 0.6541 - acc: 0.6066 - auc: 0.6555 - val_loss: 0.6463 - val_acc: 0.6112 - val_auc: 0.6614\n",
            "Epoch 11/100\n",
            "200/200 [==============================] - 32s 158ms/step - loss: 0.6525 - acc: 0.6103 - auc: 0.6535 - val_loss: 0.6541 - val_acc: 0.6081 - val_auc: 0.6619\n",
            "\n",
            "Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "Epoch 00011: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f71aa9864a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qi2tyi11EYeU",
        "colab_type": "text"
      },
      "source": [
        "## Make Predictions\n",
        "\n",
        "---\n",
        "\n",
        "Upon submission in Kaggle it achieved the following scores:\n",
        "\n",
        "|Model                                        |Kaggle Score:  \n",
        "----------------------------------------------|--------------------------\n",
        "|Basic CNN                                    | 0.642\n",
        "|Basic CNN + additional TWO conv2D layers     | 0.662\n",
        "|Basic CNN + additional FOUR conv2D layers    | 0.608\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yluF8D6Sy59B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_path = ROOT+\"test/\"\n",
        "\n",
        "model.load_weights('model_best_checkpoint.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2HW_CHnnn5v4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def chunker(seq, size=32):\n",
        "    return (seq[pos:pos + size] for pos in range(0, len(seq), size))\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "submission = pd.read_csv(ROOT+'sample_submission.csv')\n",
        "\n",
        "predictions = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "feHqMhNVzFJO",
        "colab_type": "code",
        "outputId": "cf3e2819-7396-44d2-f097-aab3442ee57e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "for batch in tqdm(chunker(submission.img_pair.values)):\n",
        "    X1 = [x.split(\"-\")[0] for x in batch]\n",
        "    X1 = np.array([read_img(test_path + x) for x in X1])\n",
        "\n",
        "    X2 = [x.split(\"-\")[1] for x in batch]\n",
        "    X2 = np.array([read_img(test_path + x) for x in X2])\n",
        "\n",
        "    pred = model.predict([X1, X2]).ravel().tolist()\n",
        "    predictions += pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "166it [00:34,  3.44it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RcIQYdTXzNSr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "submission['is_related'] = predictions\n",
        "\n",
        "#submission.to_csv(\"sub_detect_kinship_joel_model01.csv\", index=False) # INITIAL MODEL\n",
        "#!cp sub_detect_kinship_joel_model01.csv /content/gdrive/\"My Drive\"/DLP1\n",
        "\n",
        "#submission.to_csv(\"sub_detect_kinship_joel_model02.csv\", index=False) # TWO CONV2D LAYERS ADDED\n",
        "#!cp sub_detect_kinship_joel_model02.csv /content/gdrive/\"My Drive\"/DLP1\n",
        "\n",
        "submission.to_csv(\"sub_detect_kinship_joel_model03.csv\", index=False) # FOUR CONV2D LAYERS ADDED\n",
        "!cp sub_detect_kinship_joel_model03.csv /content/gdrive/\"My Drive\"/DLP1\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}