Siamese_Network_Dog_vs_Cat_breeds.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyPC4gW99LyeunqV/7deRlIZ",
      "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/todnewman/coe_training/blob/master/Siamese_Network_Dog_vs_Cat_breeds.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1hJQgpqv2-V"
      },
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, MaxPool2D, Activation, Flatten, Dense, Dropout, MaxPooling2D, SpatialDropout2D, AveragePooling2D, GlobalAveragePooling2D\n",
        "from tensorflow.keras.layers import concatenate, Lambda\n",
        "from tensorflow.keras.layers import LeakyReLU\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras import backend as K\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "import glob\n",
        "import random\n",
        "import cv2\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras.applications import resnet50\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "from tensorflow.keras import optimizers\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1G0JNoUezH-"
      },
      "source": [
        "## Download zip file with dog and cat images from Tod's Google Drive.\n",
        "\n",
        "This is faster than downloading the files due to Google Drive latency issues.\n",
        "\n",
        "Also, download trained model to enable image segmentation prior to training our siamese network.\n",
        "\n",
        "If there is a trained siamese network model on the Google Drive too, grab it also."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADVh5K7sv9Pm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3df62b0-6a66-4681-80ee-8c98729bee8b"
      },
      "source": [
        "%%time\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "%cd /content/drive/'My Drive'/data/animals\n",
        "%cp images.zip /content/images.zip\n",
        "%cp /content/drive/'My Drive'/data/animals/*.h5 /content/.\n",
        "%ls -l /content\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "/content/drive/My Drive/data/animals\n",
            "total 1832248\n",
            "drwx------ 5 root root       4096 Jun 16 15:46 \u001b[0m\u001b[01;34mdrive\u001b[0m/\n",
            "-rw------- 1 root root  788740006 Jun 16 15:46 images.zip\n",
            "drwxr-xr-x 1 root root       4096 Jun  1 13:50 \u001b[01;34msample_data\u001b[0m/\n",
            "-rw------- 1 root root 1011651728 Jun 16 15:47 tod_similarity.h5\n",
            "-rw------- 1 root root   75804992 Jun 16 15:47 tod_unet.h5\n",
            "CPU times: user 963 ms, sys: 170 ms, total: 1.13 s\n",
            "Wall time: 44.3 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lawo42x_fTZ5"
      },
      "source": [
        "## Unzip the Images into the train folder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yT7qn-wnPMv8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cac2b3e-7e4f-422f-8b77-8e117f1508ef"
      },
      "source": [
        "%%time\n",
        "%cd /content\n",
        "!unzip images.zip\n",
        "\n",
        "!rm *.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "CPU times: user 159 ms, sys: 29.8 ms, total: 189 ms\n",
            "Wall time: 6.42 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WNQOZ3kJfkSm"
      },
      "source": [
        "## GPU Info Blocks\n",
        "\n",
        "Probably won't see a high memory session unless you have subscribed to CoLab Pro."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0DucRbkufv3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a78b45e4-698d-4db6-a89e-8eda483b7b10"
      },
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Select the Runtime > \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
        "  print('and then re-execute this cell.')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Jun 16 15:47:17 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   37C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lC61TjVPujCq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bd48941-dc23-4495-f58a-12be41ed28f6"
      },
      "source": [
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
        "\n",
        "if ram_gb < 20:\n",
        "  print('To enable a high-RAM runtime, select the Runtime > \"Change runtime type\"')\n",
        "  print('menu, and then select High-RAM in the Runtime shape dropdown. Then, ')\n",
        "  print('re-execute this cell.')\n",
        "else:\n",
        "  print('You are using a high-RAM runtime!')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your runtime has 27.3 gigabytes of available RAM\n",
            "\n",
            "You are using a high-RAM runtime!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16lYWAVCfwKw"
      },
      "source": [
        "## Create a dictionary of labels based off what's in the train directory\n",
        "\n",
        "We are removing everything after the first period in the file name and the resultant will be the image's label (should be \"dog\" or \"cat\")"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9WcNbiY0YgD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8303a804-0bda-4bf1-d28b-96ae8d6f69cf"
      },
      "source": [
        "all_files = os.listdir('/content/images')\n",
        "lab_arr = []\n",
        "for f in all_files:\n",
        "    label = f.rsplit('_', 1)[0]\n",
        "    lab_arr.append(label)\n",
        "\n",
        "myset = set(lab_arr)\n",
        "lab_arr = list(myset)\n",
        "\n",
        "label_dict = {label:index for index, label in enumerate(lab_arr)}\n",
        "print(label_dict)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Abyssinian': 0, 'Siamese': 1, 'beagle': 2, 'keeshond': 3, 'leonberger': 4, 'english_cocker_spaniel': 5, 'Russian_Blue': 6, 'japanese_chin': 7, 'samoyed': 8, 'shiba_inu': 9, 'saint_bernard': 10, 'Bengal': 11, 'great_pyrenees': 12, 'Ragdoll': 13, 'american_pit_bull_terrier': 14, 'Bombay': 15, 'english_setter': 16, 'pomeranian': 17, 'havanese': 18, 'chihuahua': 19, 'Egyptian_Mau': 20, 'scottish_terrier': 21, 'yorkshire_terrier': 22, 'boxer': 23, 'Maine_Coon': 24, 'pug': 25, 'basset_hound': 26, 'miniature_pinscher': 27, 'wheaten_terrier': 28, 'german_shorthaired': 29, 'staffordshire_bull_terrier': 30, 'British_Shorthair': 31, 'Persian': 32, 'newfoundland': 33, 'Sphynx': 34, 'Birman': 35, 'american_bulldog': 36}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aC-sBeq7gNPd"
      },
      "source": [
        "## Image Generator\n",
        "\n",
        "This does a few things:\n",
        "1.  It takes an array of arrays of image groupings (in this case, [[dog images],[cat images]] and builds two batches.  \n",
        "2.  Each batch has 1/2 of its images matching the associated image in the other batch and the other 1/2 are not matches.  This allows us to train on images that match each other as well as on images that do not match.  The dataset should be balanced this way.\n",
        "3.  Scaling and reshaping occur to each of the two batches separately.\n",
        "4.  Each batch is inferenced against the trained segmentation model to segment the animal from the background.  This seems to work pretty well.\n",
        "5.  the mask and the image are then combined and the background is darkened almost to black.\n",
        "\n",
        "Generators are created at the end for both the training and the test images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvNKBgkqwBnx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d61e8e71-584c-48fb-fe62-541e7f6368b6"
      },
      "source": [
        "def equalize_hist(img):\n",
        "    for c in range(0, 2):\n",
        "       img[:,c] = cv2.equalizeHist(img[:,c])\n",
        "\n",
        "    return img\n",
        "\n",
        "def image_generator(in_groups, batch_size = 16, sz = (256, 256)):\n",
        "    batch_halfsize = batch_size//2\n",
        "    while True:\n",
        "        files, labels, names = [], [], []\n",
        "        batch_x, batch_y = [], []\n",
        "        \n",
        "        out_img_a, out_img_b, out_score = [], [], []\n",
        "        all_groups = list(range(len(in_groups)))\n",
        "        for match_group in [True, False]:\n",
        "            group_idx = np.random.choice(all_groups, size = batch_halfsize)\n",
        "            batch_x += [in_groups[c_idx][np.random.choice(range(in_groups[c_idx].shape[0]))] for c_idx in group_idx]\n",
        "            if match_group:\n",
        "                b_group_idx = group_idx\n",
        "                out_score += [1]*batch_halfsize\n",
        "            else:\n",
        "                # anything but the same group\n",
        "                non_group_idx = [np.random.choice([i for i in all_groups if i!=c_idx]) for c_idx in group_idx] \n",
        "                b_group_idx = non_group_idx\n",
        "                out_score += [0]*batch_halfsize\n",
        "                \n",
        "            batch_y += [in_groups[c_idx][np.random.choice(range(in_groups[c_idx].shape[0]))] for c_idx in b_group_idx]\n",
        "        \n",
        "        # Filenames to support generation of images\n",
        "        batch_x = np.array(batch_x) # Filenames for Class X\n",
        "        batch_y = np.array(batch_y) # Filenames for Class Y\n",
        "\n",
        "        raw_x, raw_y = [],[]\n",
        "        \n",
        "        # Go through filenames and grab the associated image and process it.\n",
        "\n",
        "        for f in batch_x: \n",
        "            #preprocess the raw images \n",
        "            raw = Image.open(f'/content/images/{f}')\n",
        "            #Resize and Scale Images using Sklearn's scalers\n",
        "            raw = cv2.resize(np.float32(raw), (224, 224), interpolation=cv2.INTER_AREA)\n",
        "            \n",
        "            #check the number of channels because some of the images are RGBA or GRAY\n",
        "            if len(raw.shape) == 2:              \n",
        "                raw = np.stack((raw,)*3, axis=-1)\n",
        "            else:\n",
        "                raw = raw[:,:,0:3]\n",
        "            \n",
        "            # Get the segmented image from the pre-trained segmenter\n",
        "            #msk = get_mask(raw)\n",
        "            #raw = raw*msk\n",
        "            #raw = equalize_hist(raw)\n",
        "            #raw = gabor(raw)\n",
        "            #raw = cv2.GaussianBlur(raw, (5, 5), 0)\n",
        "            raw_x.append(raw)\n",
        "\n",
        "\n",
        "        for f in batch_y:\n",
        "            #preprocess the raw images \n",
        "            raw = Image.open(f'/content/images/{f}')\n",
        "            raw = cv2.resize(np.float32(raw), (224, 224), interpolation=cv2.INTER_AREA)\n",
        "\n",
        "            #check the number of channels because some of the images are RGBA or GRAY\n",
        "            if len(raw.shape) == 2:             \n",
        "                raw = np.stack((raw,)*3, axis=-1)\n",
        "            else:\n",
        "                raw = raw[:,:,0:3]\n",
        "\n",
        "            # Get the segmented image from the pre-trained segmenter\n",
        "            #msk = get_mask(raw)\n",
        "            #raw = raw*msk\n",
        "            #raw = equalize_hist(raw)\n",
        "            #raw = gabor(raw)\n",
        "            #raw = cv2.GaussianBlur(raw, (5, 5), 0)\n",
        "            raw_y.append(raw)\n",
        "\n",
        "\n",
        "        batch_x = preprocess_input(np.array(raw_x))\n",
        "        batch_y = preprocess_input(np.array(raw_y))\n",
        "\n",
        "        yield np.stack(batch_x), np.stack(batch_y), np.stack(out_score)\n",
        "\n",
        "import keras\n",
        "from random import shuffle\n",
        "\n",
        "file_loc = '/content/images'\n",
        "#all_files = os.listdir('images')\n",
        "all_files = os.listdir(file_loc)\n",
        "shuffle(all_files)\n",
        "\n",
        "split = int(0.7 * len(all_files))\n",
        "\n",
        "#split into training and testing\n",
        "x_train = np.array(all_files[0:split])\n",
        "x_test  = np.array(all_files[split:])\n",
        "\n",
        "#x_train = np.random.choice(x_train, size = 200)\n",
        "#x_test = np.random.choice(x_train, size = 80)\n",
        "\n",
        "y_train = []\n",
        "for f in x_train:\n",
        "    label = f.rsplit('_', 1)[0]\n",
        "    y_train.append(label_dict[label])\n",
        "\n",
        "y_test = []\n",
        "for f in x_test:\n",
        "    label = f.rsplit('_', 1)[0]\n",
        "    y_test.append(label_dict[label])\n",
        "\n",
        "y_train = np.array(y_train)\n",
        "y_test = np.array(y_test)\n",
        "\n",
        "# reorganize by groups\n",
        "train_groups = [x_train[np.where(y_train==i)[0]] for i in np.unique(y_train)]\n",
        "test_groups = [x_test[np.where(y_test==i)[0]] for i in np.unique(y_train)]\n",
        "print('train groups:', [x.shape[0] for x in train_groups])\n",
        "print('test groups:', [x.shape[0] for x in test_groups])\n",
        "\n",
        "train_generator = image_generator(train_groups, batch_size = 16)\n",
        "test_generator  = image_generator(test_groups, batch_size = 16)\n",
        "\n",
        "def gen_random_batch(gen, batch_size):\n",
        "    x, y, score = next(gen, batch_size)\n",
        "    return x,y,score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train groups: [145, 137, 127, 138, 145, 136, 146, 137, 143, 151, 144, 144, 137, 145, 135, 136, 149, 147, 129, 137, 147, 145, 147, 142, 132, 135, 138, 136, 141, 146, 132, 139, 137, 153, 134, 124, 137]\n",
            "test groups: [55, 63, 73, 62, 55, 64, 54, 63, 57, 49, 56, 56, 63, 55, 65, 64, 51, 53, 71, 63, 53, 54, 53, 58, 68, 65, 62, 64, 59, 54, 59, 61, 63, 47, 66, 76, 63]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOiuIHA3t_Ta"
      },
      "source": [
        "## Misc Functions\n",
        "\n",
        "* mean_iou(): Here only because it's a custom loss function and is needed to load the segmentation model using load_model\n",
        "* get_mask(): Function we've created to predict the mask for our raw dog and cat images\n",
        "* t_swish(): Custom activation function that seems to work at least as well as relu.\n",
        "* gabor(): Experimental function.  Goal is to build a function that can apply gabor filter to the first layer of the NN.  In Work. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BymHY7NnUDka",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa5d4279-48db-4a38-f933-5255eea04b6d"
      },
      "source": [
        "saved_seg_model = '/content/tod_unet.h5'\n",
        "\n",
        "def mean_iou(y_true, y_pred):\n",
        "    yt0 = y_true[:,:,:,0]\n",
        "    yp0 = K.cast(y_pred[:,:,:,0] > 0.5, 'float32')\n",
        "    inter = tf.count_nonzero(tf.logical_and(tf.equal(yt0, 1), tf.equal(yp0, 1)))\n",
        "    union = tf.count_nonzero(tf.add(yt0, yp0))\n",
        "    iou = tf.where(tf.equal(union, 0), 1., tf.cast(inter/union, 'float32'))\n",
        "    return iou\n",
        "\n",
        "model_tr = load_model(saved_seg_model, custom_objects={'mean_iou':mean_iou})\n",
        "\n",
        "def get_mask(raw):\n",
        "    # Predict the mask.  Believe that in this approach predict_on_batch might be\n",
        "    #   faster than 'predict'\n",
        "    pred = model_tr.predict_on_batch(np.expand_dims(raw, 0))\n",
        "    #pred = model_tr(np.expand_dims(raw, 0))  # This might be faster...\n",
        "\n",
        "    #mask post-processing \n",
        "    msk = tf.squeeze(pred)\n",
        "    msk = np.stack((msk,)*3, axis=-1)\n",
        "    msk[msk >= 0.5] = 1 \n",
        "    msk[msk < 0.5] = 0.1 # make this zero if you want solid black background\n",
        "    return msk\n",
        "\n",
        "def t_swish(x):\n",
        "    return (K.sigmoid(x) * x * 1.5 -1)\n",
        "\n",
        "import numpy as np\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "sz = (256, 256)\n",
        "\n",
        "def gabor(image) :\n",
        "   \"\"\"\n",
        "   Using Gabor Filters to repair\n",
        "   motion blurriness in an image.\n",
        "   \"\"\"\n",
        "   theta = [0, 45, 90, 135]\n",
        "   filtered_images = []\n",
        "   sharpen_kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])\n",
        "   for th in theta :\n",
        "      kernel = cv2.getGaborKernel(ksize = (3, 3), sigma = 5, lambd = 5, theta = th, \n",
        "               gamma = 0.25)\n",
        "      kernel = tf.expand_dims(kernel, 0)\n",
        "      kernel = tf.expand_dims(kernel, 0)\n",
        "      kernel = tf.cast(kernel, tf.float32)\n",
        "      filtered_images.append(tf.nn.conv2d(image, kernel, strides=[1, 1, 1, 1], \n",
        "                             padding='SAME'))\n",
        "   final_img = tf.math.maximum(tf.math.maximum(filtered_images[0], \n",
        "   filtered_images[1]), tf.math.maximum(filtered_images[2], filtered_images[3]))\n",
        "\n",
        "   #Sharpening the image\n",
        "   sharpen_kernel = tf.expand_dims(sharpen_kernel, 0)\n",
        "   sharpen_kernel = tf.expand_dims(sharpen_kernel, 0)\n",
        "   sharpen_kernel = tf.cast(sharpen_kernel, tf.float32)\n",
        "   return tf.nn.conv2d(final_img, sharpen_kernel, strides=[1, 1, 1, 1], \n",
        "                       padding='SAME')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PKNs6bCyC6EE"
      },
      "source": [
        "## Use a Pretrained ResNet50 as the Feature model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OixANgn-OH_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59365ead-f5a6-446b-909a-de3c9a55ede3"
      },
      "source": [
        "# Two inputs one each - left and right image\n",
        "leaky = LeakyReLU(alpha=0.05)\n",
        "left_input = Input((224,224,3))\n",
        "right_input = Input((224,224,3))\n",
        "\n",
        "convnet = resnet50.ResNet50(weights='imagenet', include_top=False, input_shape=(224,224,3))\n",
        "# Add the final fully connected layers\n",
        "\n",
        "x = convnet.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "#x = Flatten()(x)\n",
        "#x = Dense(1024, activation= 'relu')(x)\n",
        "preds = Dense(18, activation='sigmoid')(x) # Apply sigmoid\n",
        "\n",
        "convnet = Model(inputs=convnet.input, outputs=preds)#Applying above model for both the left and right images\n",
        "\n",
        "encoded_l = convnet(left_input)\n",
        "encoded_r = convnet(right_input)\n",
        "\n",
        "# Concatenate results into a similarity model\n",
        "combined_features = concatenate([encoded_l, encoded_r], name = 'merge_features')\n",
        "combined_features = Dense(500, activation = 'relu')(combined_features)\n",
        "combined_features = BatchNormalization()(combined_features)\n",
        "combined_features = Dropout(0.4)(combined_features)\n",
        "prediction = Dense(1, activation = 'sigmoid')(combined_features)\n",
        "siamese_net = Model(inputs=[left_input,right_input],outputs=prediction, name = 'Siamese_Network')#define the optimizer. Here I have used SGD with nesterov momentum\n",
        "optim = optimizers.SGD(lr=0.005, decay=.01, momentum=0.9, nesterov=True) #compile the network using binary cross entropy loss and the above optimizer\n",
        "#optim = optimizers.Nadam(lr = 0.001)\n",
        "siamese_net.compile(loss=\"binary_crossentropy\",optimizer=optim,metrics=['mean_absolute_error','accuracy'])\n",
        "siamese_net.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94773248/94765736 [==============================] - 0s 0us/step\n",
            "94781440/94765736 [==============================] - 0s 0us/step\n",
            "Model: \"Siamese_Network\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " input_2 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " model (Functional)             (None, 18)           23624594    ['input_1[0][0]',                \n",
            "                                                                  'input_2[0][0]']                \n",
            "                                                                                                  \n",
            " merge_features (Concatenate)   (None, 36)           0           ['model[0][0]',                  \n",
            "                                                                  'model[1][0]']                  \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 500)          18500       ['merge_features[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization (BatchNorm  (None, 500)         2000        ['dense_1[0][0]']                \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 500)          0           ['batch_normalization[0][0]']    \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 1)            501         ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 23,645,595\n",
            "Trainable params: 23,591,475\n",
            "Non-trainable params: 54,120\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgoHPcolNhuC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5079f3fd-388b-4d66-86f3-1614beb60f1b"
      },
      "source": [
        "# Compile our similarity model to setup the optimization process\n",
        "from keras.models import Model, load_model\n",
        "%cd /content/drive/'My Drive'/data/\n",
        "\n",
        "def contrastive_loss(y_true, y_pred):\n",
        "    \n",
        "    '''Contrastive loss from Hadsell-et-al.'06\n",
        "    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n",
        "    '''\n",
        "    y_true = float(y_true)\n",
        "\n",
        "\n",
        "    margin = 1\n",
        "    square_pred = K.square(y_pred)\n",
        "    margin_square = K.square(K.maximum(margin - y_pred, 0))\n",
        "    return K.mean(y_true * square_pred + float(1 - y_true) * margin_square)\n",
        "\n",
        "def build_callbacks():\n",
        "        modelCP = ModelCheckpoint(filepath='siamese_model_breeds.h5', \n",
        "                                  monitor='val_mean_absolute_error', \n",
        "                                  verbose=0, \n",
        "                                  save_best_only=True, \n",
        "                                  period=1)\n",
        "        LR = ReduceLROnPlateau(monitor='val_mean_absolute_error', \n",
        "                        patience=10, \n",
        "                        verbose=1, \n",
        "                        factor=0.2, \n",
        "                        min_lr=0.00001)\n",
        "        callbacks = [modelCP,  LR]\n",
        "        return callbacks\n",
        "\n",
        "    \n",
        "#%cd /content/drive/'My Drive'/data/dogs_and_cats\n",
        "\n",
        "retrain = False\n",
        "\n",
        "if retrain:\n",
        "    siamese_net = load_model('siamese_model_breeds.h5', custom_objects={'contrastive_loss': contrastive_loss, 't_swish':t_swish, 'LeakyReLU': LeakyReLU})    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "amuQgZbgbNo7"
      },
      "source": [
        "\n",
        "def show_model_output(generator, nb_examples = 8):\n",
        "    pv_a, pv_b, score = gen_random_batch(generator, nb_examples)\n",
        "\n",
        "    pred_sim = siamese_net([pv_a, pv_b])\n",
        "    fig, m_axs = plt.subplots(2, pv_a.shape[0], figsize = (30,12))\n",
        "    for c_a, c_b, p_d, a_d, (ax1, ax2) in zip(pv_a, pv_b, pred_sim, score, m_axs.T):\n",
        "        img1 = c_a/np.amax(c_a)\n",
        "        img1 = np.clip(img1, 0, 1)\n",
        "        ax1.imshow(img1)\n",
        "        ax1.set_title('Image A\\n Act: %3.0f%%' % (100*a_d))\n",
        "        ax1.axis('off')\n",
        "        img2 = c_b/np.amax(c_b)\n",
        "        img2 = np.clip(img2, 0, 1)\n",
        "        ax2.imshow(img2)\n",
        "        ax2.set_title('Image B\\n Pred: %3.0f%%' % (100*p_d))\n",
        "        ax2.axis('off')\n",
        "\n",
        "    return fig\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WD0ROa7idkdL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ad236a93-125d-43c7-bfb4-a74c15f71ba0"
      },
      "source": [
        "# make a generator out of the data.  Yield images A and B and score.\n",
        "from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n",
        "train_batch = 16\n",
        "\n",
        "\n",
        "\n",
        "def siam_gen(gen, batch_size = train_batch):\n",
        "    while True:\n",
        "        pv_a, pv_b, score = gen_random_batch(gen, batch_size)\n",
        "        yield [pv_a, pv_b], score\n",
        "\n",
        "steps_per_epoch = 200      \n",
        "# we want a constant validation group to have a frame of reference for model performance\n",
        "valid_a, valid_b, valid_sim = gen_random_batch(test_generator, 16)\n",
        "\n",
        "# Fit the model using Keras' fit_generator.\n",
        "\n",
        "loss_history = siamese_net.fit(siam_gen(train_generator), \n",
        "                               steps_per_epoch = steps_per_epoch,\n",
        "                               validation_data=([valid_a, valid_b], valid_sim),\n",
        "                               epochs = 13, \n",
        "                               callbacks = build_callbacks(),\n",
        "                               verbose = True)\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "plt.plot(loss_history.history['mean_absolute_error'])\n",
        "plt.plot(loss_history.history['val_mean_absolute_error'])\n",
        "plt.title('model MAE')\n",
        "plt.ylabel('MAE')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(loss_history.history['loss'])\n",
        "plt.plot(loss_history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
            "Epoch 1/13\n",
            "200/200 [==============================] - 67s 236ms/step - loss: 0.7176 - mean_absolute_error: 0.4723 - accuracy: 0.5512 - val_loss: 0.6647 - val_mean_absolute_error: 0.4847 - val_accuracy: 0.6875 - lr: 0.0050\n",
            "Epoch 2/13\n",
            "200/200 [==============================] - 46s 229ms/step - loss: 0.5844 - mean_absolute_error: 0.3997 - accuracy: 0.6825 - val_loss: 0.5963 - val_mean_absolute_error: 0.4340 - val_accuracy: 0.6875 - lr: 0.0050\n",
            "Epoch 3/13\n",
            "200/200 [==============================] - 45s 227ms/step - loss: 0.5562 - mean_absolute_error: 0.3777 - accuracy: 0.7100 - val_loss: 0.5196 - val_mean_absolute_error: 0.3692 - val_accuracy: 0.6875 - lr: 0.0050\n",
            "Epoch 4/13\n",
            "200/200 [==============================] - 45s 225ms/step - loss: 0.5224 - mean_absolute_error: 0.3542 - accuracy: 0.7391 - val_loss: 0.4681 - val_mean_absolute_error: 0.3270 - val_accuracy: 0.8125 - lr: 0.0050\n",
            "Epoch 5/13\n",
            "200/200 [==============================] - 45s 225ms/step - loss: 0.4750 - mean_absolute_error: 0.3238 - accuracy: 0.7806 - val_loss: 0.4208 - val_mean_absolute_error: 0.2979 - val_accuracy: 0.8125 - lr: 0.0050\n",
            "Epoch 6/13\n",
            "200/200 [==============================] - 45s 227ms/step - loss: 0.4730 - mean_absolute_error: 0.3205 - accuracy: 0.7784 - val_loss: 0.3741 - val_mean_absolute_error: 0.2710 - val_accuracy: 0.8125 - lr: 0.0050\n",
            "Epoch 7/13\n",
            "200/200 [==============================] - 45s 223ms/step - loss: 0.4661 - mean_absolute_error: 0.3150 - accuracy: 0.7828 - val_loss: 0.3711 - val_mean_absolute_error: 0.2697 - val_accuracy: 0.8750 - lr: 0.0050\n",
            "Epoch 8/13\n",
            "200/200 [==============================] - 43s 216ms/step - loss: 0.4469 - mean_absolute_error: 0.3071 - accuracy: 0.7881 - val_loss: 0.4087 - val_mean_absolute_error: 0.2852 - val_accuracy: 0.8125 - lr: 0.0050\n",
            "Epoch 9/13\n",
            "200/200 [==============================] - 45s 224ms/step - loss: 0.4337 - mean_absolute_error: 0.2963 - accuracy: 0.8112 - val_loss: 0.3429 - val_mean_absolute_error: 0.2486 - val_accuracy: 0.8750 - lr: 0.0050\n",
            "Epoch 10/13\n",
            "200/200 [==============================] - 43s 214ms/step - loss: 0.4055 - mean_absolute_error: 0.2787 - accuracy: 0.8234 - val_loss: 0.3617 - val_mean_absolute_error: 0.2541 - val_accuracy: 0.8750 - lr: 0.0050\n",
            "Epoch 11/13\n",
            "200/200 [==============================] - 43s 215ms/step - loss: 0.3984 - mean_absolute_error: 0.2724 - accuracy: 0.8284 - val_loss: 0.3739 - val_mean_absolute_error: 0.2536 - val_accuracy: 0.7500 - lr: 0.0050\n",
            "Epoch 12/13\n",
            "200/200 [==============================] - 46s 229ms/step - loss: 0.3962 - mean_absolute_error: 0.2673 - accuracy: 0.8353 - val_loss: 0.3778 - val_mean_absolute_error: 0.2428 - val_accuracy: 0.8750 - lr: 0.0050\n",
            "Epoch 13/13\n",
            "200/200 [==============================] - 46s 229ms/step - loss: 0.3794 - mean_absolute_error: 0.2605 - accuracy: 0.8441 - val_loss: 0.3221 - val_mean_absolute_error: 0.2276 - val_accuracy: 0.8750 - lr: 0.0050\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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km7fJKqVUA9OA8ICokEBuvaA9n208QtpJN4bfABj+GxA7LNP5IpRS/kkDwkPuGGoNvzHzu33u7RDVBgbeCevfgRM+m01VKaWqpAHhIW1ahHJNbYbfALjoAbAHwdJ/eLc4pZSqAw0ID5rqGH7jnVUH3NshMhEG/Rw2zIVjO71bnFJK1ZIGhAdVDL/xxvd73Rt+A2DY/RAQCkv/7t3ilFKqljQgPGzqcGv4jQXuDr8REQ8XTION8yFjm3eLU0qpWtCA8LDhXRzDbyx3c/gNgKHTISgclvzNu8UppVQtaEB4WMXwGzvS81jq7vAbYbEw5B7Y8hEc3eTdApVSyk0aEF5wdR9r+A23H5wDuPBeCI7WVoRSym9oQHhBUICNnw2r5fAboTFWSGz7FA6v926BSinlBg0IL7nlAmv4jVeX16IVMeRuCGkBS57wXmFKKeUmDQgviQoJ5JbB7fh0Qy2G3wiJhqG/gh2LIG2NdwtUSqkaaEB40c+GpSDAG9/vc3+nC+6C0FhY8n/eKksppdyiAeFFbVqEcnWf1sxZdYDsAjeH3wiOhGEzYNdXcGCldwtUSqlqeDUgRGSUiGwXkV0i8nA1290oIkZEBjote8Sx33YRucqbdXrT1BEdOVVcxjsr3Rx+A2DwVAiP11aEUsqnvBYQImIHXgBGAz2AW0Skh4vtIoEZwEqnZT2Am4GewCjgP47jNTo920RzUWdr+I3i0nL3dgoKtwby27ME9n3v1fqUUqoq3mxBDAZ2GWP2GGOKgTnAtS62+yvwd6DQadm1wBxjTJExZi+wy3G8RmnqCMfwGz+5OfwGWEOBRyTCt49bs88ppVQD82ZAtAUOOn1Ocyw7TUTOB9oZYz6r7b6O/aeJSKqIpGZmuvnUsg+McAy/8egnm/lgbZp7Q3AEhlqTCu3/HvYu836RSilVic86qUXEBjwJ/KauxzDGvGKMGWiMGRgfH++54jxMRHh58gC6Jkby63k/MXVWKhk5hTXveP4UiGoL3/6ftiKUUg3OmwFxCGjn9DnJsaxCJNALWCIi+4AhwAJHR3VN+zY6HVqGM/euC/nD2O4s33mMkU8t48N1NbQmAkOsVsTBH2H3Nw1XrFJK4d2AWA10EZEUEQnC6nReULHSGJNtjIkzxiQbY5KBH4FxxphUx3Y3i0iwiKQAXYBVXqy1Qdhtwi+Gd2TRjOF0Tojggbk/MXXWmupbE/0nQ3Q77YtQSjU4rwWEMaYUuA9YDGwF5hljNovIoyIyroZ9NwPzgC3A58C9xhg3Z+Dxfx3jI5h3ujWRycinlvHRukOuWxMBQTDiQTi0BnZ+0fDFKqWaLXF7zgI/N3DgQJOamurrMmptd2YeD773E2sPZDGyRyKPX9+LhMiQszcqK4HnBkBoC5i2FER8U6xSqskRkTXGmIGu1umT1D7WKT6C9+4eyu/HdGfZjkxGPumiNWEPhIsfgiM/wfaFvitWKdWsaED4AbtNmDqiIwtnDKdTfDj3z13PXf9dQ0auU99En4kQ28m6o6nczQfulFKqHjQg/EhFa+J3Y7qxZEcmVz61jI/XO1oT9gC45GFI3wRbF9R8MKWUqicNCD9jtwnTRnRi4fThpMSFM2OO1ZrIzC2CXjdCXFdr1rnyJtNnr5TyUxoQfqpzQgTz7x7KI6Ot1sTIp5by8YajmIsfhsxtsPlDX5eolGriNCD8mN0m3HWx1ZpIbmm1Ju5Z147SuO7WrHNlpb4uUSnVhGlANAKdEyJ4/56hPDy6G9/sOM5Dx8fC8Z2Yje/5ujSlVBOmAdFI2G3C3Rd3YuH0i9jd8hI2l3cg89NHycw+5evSlFJNlAZEI9M5IZL59wzjQN/7SSg9zPNPP8YnPx12b4RYpZSqBQ2IRijAbmP0DT+jML4Pv5T3eeDd1fxy9lqO5RX5ujSlVBOiAdFYiRAy8o8klqfzWp/tfL01g5FPLuXTDbWYlEgppaqhAdGYdRkJSYO45OhbLLx3EO1jw7jvnXU8MHc9OYUlvq5OKdXIaUA0ZiJw6e8gJ43OaR/y/j1DeeCKriz46TBjnllO6r4Tvq5QKdWIaUA0dh0vhfYXwvJ/E1BezIwrujDvrgsRgZteXsFTX+6gtEzHblJK1Z4GRGNX0YrIPQJr3gBgQIcYFk4fznX92/LM1zu56eUVHDie7+NClVKNjQZEU5AyApKHw/InodgKgsiQQJ68qR/P3tKfnRl5jHl2OR+srWGKU6WUcqIB0VRc+js4lQGpr5+1eFzfNiyaMZweraP49byfmD5nPdkF2oGtlKqZBkRT0WGo1R/x3VNQkHXWqqSYMN6dNoQHrzqPhRuPMOaZ5azaqx3YSqnqaUA0JVf8CQqz4cO7zplUyG4T7r20M+/fM5QAu3DzKyv49xfbKdEObKVUFTQgmpI2/eGqv8GOz2H5v11u0q9dCz6bPpzxA5J47ptdjH9pBfuO6XhOSqlzVRsQIhJVzbr2ni9H1dvgqdD7Jvj2cdj1lctNIoID+Mf4vrxw6/nszcxj7LPLeS/1oHZgK6XOUlMLYknFGxH5utK6jzxejao/EbjmaUjoAe//Ak7ur3LTsX1a8/n9I+idFM2D8zdw3zvryM7XDmyllKWmgBCn97HVrFP+JCgcJv7X6oeYNxlKCqrctE2LUGb/YggPjerG4s1HGfXMMlbsPt6AxSql/FVNAWGqeO/qs/InLTvBDS/DkZ/gs/+Bai4f2W3CPZd04sNfDiM00M6tr/3I3z/fRnGpdmAr1ZwF1LA+QUR+jdVaqHiP43O8VytT9XfeaBjxICz7J7QbBAPuqHbz3knRfDr9Ih79ZAsvLtnN97uO8fTEfnSMj2iYepVSfqWmFsSrQCQQ4fS+4vNr3i1NecQlj0Cny2Dhg3BoTY2bhwUF8MSNfXhp0vkcOJHP2Ge/Y86qA9qBrVQzJHX9hy8ig4wxqz1cT50NHDjQpKam+roM/5R/Al6+GEw53LUMwlu6tdvR7EJ+8956vt91nFE9W/G3G3oTEx7k5WKVUg1JRNYYYwa6Wler5yBEpIeI/FVEdgEveqQ65X1hsTBxFpzKhPfvhPIyt3ZrFR3Cf++8gN+N6cbX29IZ9cwyvt91zMvFKqX8RY0BISLJIvKIiGwA/gvcA1xRVeIoP9WmP4z9N+xZAt885vZuNpswbYTVgR0RHMCk11fyt4VbtQNbqWagpgflVgCfYXVm32iMGQDkGmP2NUBtytPOnwznT4HvnoStn9Zq115to/n0V8O5dXB7Xl62hykzV5Grs9Yp1aTV1IJIx+qUTuTMXUtud1qIyCgR2S4iu0TkYRfr7xaRjSKyXkS+E5EejuXJIlLgWL5eRF5y9ztVDUb/w2pNfHQPHNtVq11Dg+w8fn1vnprYl9X7TnDrqys5nlfkpUKVUr5WbUAYY64DegNrgD+LyF4gRkQG13RgEbEDLwCjgR7ALRUB4OQdY0xvY0w/4B/Ak07rdhtj+jled7t/SqpagSFw0yywBcDcSVBc+3GYru+fxKu3D2RnRi4TXlrBoayqH8RTSjVeNfZBGGOyjTFvGGOuBIYA/ws8JSIHa9h1MLDLGLPHGFMMzAGurXTsHKeP4ejDdw2jRXsYPxOObYcFv6r2IbqqXNotgbd/fgHH8ooY/+IP7MrI9UKhSilfqtVdTMaYdGPMc8aYYcBFNWzeFnAOkTTHsrOIyL0ishurBTHdaVWKiKwTkaUiMtzVF4jINBFJFZHUzMzM2pyK6nQpXPYH2PQ+rKzbFbyBybHMvetCSssNE15awfqDWTXvpJRqNKp9DkJEFlS3szFmXDX7jgdGGWN+4fg8GbjAGHNfFdvfClxljJkiIsFAhDHmuIgMwBoYsGelFsdZ9DmIOigvty4z7VwMUz6xJh2qg/3HTzH59VUcyyvilckDuahLnIcLVUp5S32eg7gQSAKWA/8C/l3pVZ1DQDunz0mOZVWZA1wHYIwpMsYcd7xfA+wGutbwfaq2bDa4/kXrktN7d0Du0TodpkPLcObffSHtY8P42ZurWLjxiGfrVEr5RE0B0Qr4HdALeAYYCRwzxiw1xiytYd/VQBcRSRGRIOBm4KwWiYh0cfo4FtjpWB7v6ORGRDoCXYA97p2SqpWQaJg4G4pyrZAoq9utqwlRIcy960L6tWvBve+sZfbKqocZV0o1DjXdxVRmjPncGDMFq4N6F7BERFxeJqq0bylwH7AY2ArMM8ZsFpFHRaTi0tR9IrJZRNYDvwamOJaPADY4ls8H7jbG6CTK3pLYA8Y9BwdWwBd/rPNhokMDmXXnBVx6XgK//3ATL3y7S8dwUqoRq3EsJkd/wFjgFiAZqxUw0xhT3eWiBqd9EB6w6GFY+SLc+Dr0Hl/nw5SUlfPb+Rv4cN0hfn5RCr8f0x2bTacPUcofVdcHUe1w3yIyC+vy0kLgL8aYTV6oT/mLK/8Kh9dZt74m9LBaFnUQaLfx7wl9iQ4N5PXv9nIyv5i/39iHQLtOga5UY1LTv9hJWNf/ZwA/iEiO45UrIlXeUaQaKXsg3PQWBEdadzcVZtf5UDab8KdrevCbkV35YO0h7nl7DYUl7g0SqJTyDzX1QdiMMZGOV5TTK9IYE9VQRaoGFNkKJrwJJ/fBh/dYt8LWkYjwq8u78NfrevH1tgxuf30VOTp+k1KNhrb51bk6DIUrH4Ptn8H3T9X7cJOHdODZm/uz7uBJJr78I5m5On6TUo2BBoRybcg90OtGa2jw3d/U+3DX9G3Da1MGse/YKSa89AMHT+R7oEillDdpQCjXROCaZyHuPJj/c8iqaeitml3cNZ7ZUy/gZH4JN774A9uP6vhNSvkzDQhVteAImPi29fDcvMlQUljvQ57fPob37r4QEZjw0g+s2a+PtyjlrzQgVPXiOlvDcRxeB4t+65FDdk2MZP7dQ4kND+K211by7fYMjxxXKeVZGhCqZt2vgYsegLVvwdpZHjlku9gw3rt7KJ3iI5j6Viofr/er5y6VUmhAKHdd9kfoeAl89j9Wa8ID4iODeXfaEAZ0iOH+ueuZtWKfR46rlPIMDQjlHpvdGoIjPB7m3g75nuk7iAoJ5K07B3NF90T+9+PNPP3VDh2/SSk/oQGh3BceZ01XmncU3roGcg575LAhgXZevO18xg9I4umvdvLnBZspL9eQUMrXNCBU7SQNgFvmWE9av3YFZGz1yGED7Db+cWMfpg5P4a0V+7l/7nqKS+v+FLdSqv5qHM21sdDRXBvYkQ0wewKUFMDNsyHF5aywtWaM4aWle/j759voFB9O54QIEqNCSIgMJsHxMzEqhMSoEGLCAhHRUWKVqo/qRnPVgFB1l3UA3h4PJ/fCdS/Wa4jwyj5ad4j5a9LIyC0kPaeI7IJzx3AKtAsJkSHERwaTGBVMQmTI6Z8JTp9jwoJ0uHGlqqABobyn4CS8eysc+AFG/hWG/sp6CtvDCkvKyMwtOh0YGTmFpOcWkZ5TSKbjZ0ZuEVn55wZJgE1IiAwmPiqExMhgEqKCSYy0WiGJ0SF0jAunbYtQDRHVLNV5PgilahQaA5M/hA/vgi//CNlpMOpv1l1PHhQSaKddbBjtYsOq3a6qIMnIsZbtP57Pqn0nzgmSsCA7nRMi6JIQSdfECLokWu81OFRzpgGh6i8wBMa/AV8mwYrnIecQ3PgaBIY2eCm1DZLDWQXszjzFjvRcdmXksXxnJu+vTTu9nXNwdEmMsMJDg0M1E3qJSXnWiv/A4t9B0iC4dS6Exfq6olrLzi9hZ0YuO9Lz2JmRy870PHak55LhNEx5RXB0Toiga2KkBodqtLQPQjWszR/BB9OgRTuY9D7EJPu6Io+oCI6dGVZg7HQESHrOmeAIDXS0OBKt4OjiCJCkmFC940r5JQ0I1fD2r4B3b7amMb11HrQ939cVeU12fgm7Mq0WR8Wlqh3pZwdHTFggg5JjGZxivXq0jiJA5+hWfkADQvlG5g54+0bIP25NY9r1Sl9X1KCyC0rYlZHL9qN5rDtwklX7TrD/uDVRUniQnQHJsQxOjmFwSkv6JEUTEujZjn2l3KEBoXwn96j1QF36Zrj6KRgwxdcV+VR6TiGr9p44/dqebk2aFBRgo19SCwanxDIoJZYBHWKICNZ7SJT3aUAo3yrKhXlTYPfXcPFDcBzveOcAABgpSURBVMkjXnlWojE6eaqY1P0nWbX3OKv2nWTToWzKyg12m9CzTdTpy1KDkmOJDQ/ydbmqCdKAUL5XVgKf3A/r34Z+t8E1z1j9E+osp4pKWXvgJKv3nmDl3hOsO5h1ekyqLgkRp/swBqfE0jq64W8jVk2PBoTyD8bAkidg6RPQ6XK46S0IjvR1VX6tqLSMjWnZrHRcklqz/yR5RaUAtIsNZXBySwanWP0YyS3D9E4pVWsaEMq/rJ1ltSYSe8Jt70FkK19X1GiUlpWz7WguK/eeYPXeE6zad4ITp4oBaBUVwqherRjVqxWDkmOx6/MYyg0aEMr/7PzS6pcIawmT5kP8eb6uqFEyxrA7M49Ve0+yZHsGS3dkUlRaTlxEEFf2bMXoXq0Y0rElgXpLraqCBoTyT4fXweyboKwYbnkXOgz1dUWN3qmiUr7dnsGiTUf5dlsG+cVltAgLZGT3REb3bsWwznEEB+jttOoMnwWEiIwCngHswGvGmCcqrb8buBcoA/KAacaYLY51jwA/d6ybboxZXN13aUA0Uif3WUOGZx2AG16Gntf7uqImo7CkjKU7Mvl801G+2pJOblEpkcEBXN49gVG9WnNx13hCgzQsmjufBISI2IEdwEggDVgN3FIRAI5toowxOY7344BfGmNGiUgP4F1gMNAG+Aroaowpq+r7NCAasfwT1lPXB1fBVf8HF/7S1xU1OUWlZfyw6ziLNh3hiy3pZOWXEBpo57JuCYzq1YpLuyXocxfNlK+G+x4M7DLG7HEUMQe4FjgdEBXh4BAOVKTVtcAcY0wRsFdEdjmOt8KL9SpfCYuF2z+GD6bC4kesIcOvfAxset3cU4ID7FzaLYFLuyXweFk5K/ecYNGmIyzenM5nG48QFGDj4q7xjO7Visu7JxIdqrcgK+8GRFvgoNPnNOCCyhuJyL3Ar4Eg4DKnfX+stG9bF/tOA6YBtG/f3iNFKx8JDIUJb1kjwf74gjVk+PUvW0OJK48KtNu4qEscF3WJ49Fre5G67wSLNh1l8eajfLklnUC7MKxzHKN7tWJkj1b6gF4z5s1LTOOBUcaYXzg+TwYuMMbcV8X2twJXGWOmiMjzwI/GmLcd614HFhlj5lf1fXqJqYkwxppT4os/QLshMPFtiIj3dVXNQnm5YX1aFp9vOsqiTUc4eKIAu00Y0jGWUb1ac1XPRBIiNbCbGl/1QVwI/NkYc5Xj8yMAxpi/VbG9DThpjImuvK2ILHYcq8pLTBoQTcymD+CjX1q3wd48G9r083VFzYoxhs2Hc1i06QiLNh1lT+YpRGBQh1imjujIFd0T9KG8JsJXARGA1Ul9OXAIq5P6VmPMZqdtuhhjdjreXwP8yRgzUER6Au9wppP6a6CLdlI3M4fXw5xbrU7sa5+H3uN9XVGzZIxhZ0YeCzce4cN1h9h/PJ+BHWJ4eHQ3BiY3vgmh1Nl8eZvrGOBprNtcZxpjHheRR4FUY8wCEXkGuAIoAU4C91UEiIj8HrgTKAXuN8Ysqu67NCCaqLwMmHc7HFgBFz0Al/3R4/NdK/eVlJUzL/Ugz3y1k4zcIq7onsCDV3XjvFY6ZEpjpQ/KqcattBgWPQhr3oQuV1rzXYdE+7qqZi2/uJQ3vt/HS0t2k1dcyg39k3hgZBeSYqqfC1z5Hw0I1TSsfg0WPQQxKXDLHIjr7OuKmr2Tp4r5z5JdvLViPxiYfGEH7r20s9751IhoQKimY9931iWnslIY/zp0GenrihRwKKuAp7/cwftr0wgPCmDaiI78fHgKYUH68J2/04BQTUvWAXj3VkjfBCP/AkOn6wREfmJHei7/XLydL7ekExcRzIzLO3Pz4PY6WKAf04BQTU/xKes22C0fQe8JMO4562E75RfW7D/B3xdtZ9W+E3RoGcZvrjyPq3u3xqZDkPud6gJCY101TkHhMOFN666mjfNh5ihriI7mqrzMmtrVTwzoEMvcu4Yw846BhATYmf7uOsa98B3Ld2b6ujRVC9qCUI3f9kXw/lSrBTHxv9B+iK8rajglBbB+Nnz/LBRmwR0LoVUvX1d1lrJyw0frDvHklzs4lFXAsM4teWhUN/oktfB1aQq9xKSag4xtMOcWyDoIY/8NA6b4uiLvKsiC1NfhxxfhVCa0HQg5h8GUw8+/gJgOvq7wHEWlZbz94wGe/2YnJ/NLGNu7Nb+5sisd4yN8XVqzpgGhmoeCkzD/Ttj9DQyaCqP+BvYmNipp7lErFFJnQlEOdL7CeoCwwzDI3AYzr4LwBLhzMYS39HW1LuUWlvDqsj289t1eikrLmTioHTMu70JilI7z5AsaEKr5KCuFr/5kDfiXPNwaIdZPf1HWyvHd8MNzsP4dKC+xJlYadj+07nP2dvt/gFnXQaveMGWB1VfjpzJzi3jum528s/IAAXbhzmEp3HVxJx1qvIFpQKjm56c5sGA6RCbCze9YvzAboyM/wXdPW3dr2QKg320w9FfQslPV+2z9FOZNtloXN7/j962o/cdP8e8vdrDgp8NEhwZy76WdmDSkgz5D0UA0IFTzdGgNzLkNCrPh+pegx7W+rsg9xlgPBH73FOz+GoIiYdDPYcg9ENnKvWOkzoRPH7AC5doXGsVzIpsOZfOPxdtZtiMTm0Cn+Ah6tomiZ5vo0z+jw/w77BojDQjVfOUehbmTIG01jPgtXPKI/85UV14OOxZZwZC2GsLjYcgvYeCdEFqHO36+/RssfQIu+jVc8SfP1+slq/ae4LudmWw+nMPmwzkczSk8va5ti9CzQ6NtFK2iQnTo8Xrw1ZSjSvleZCu44zP49New7B/W09fXvwwhUb6u7IyyEtj4nnUp6dh2aNHBuhOr3231e/jvkoch7yh896T1v8MFd3muZi8anBLL4JQzw4gfyytiiyMsNh/OZsvhHL7cmk7F37ax4UH0bBNFD6fgSGkZrg/leYC2IFTzYAysegU+fwTiusAt70JsR9/WVHwK1s6CH56HnDRI7GXdkdTjOrB76G+38jJr7Kptn8H4mdDrBs8c18dOFZWy9ciZ0Nh8OIcd6bmUlFm/z8KC7HRvHUWP1lGnWxxdW0UQHKBDxVeml5iUqrBnCbx3hxUYE96ATpfVtIfn5Z+AVa/Cypeg4IR1i+pFD1idyt64VFJSAP+93uqTmfQ+pIzw/Hf4geLScnZm5LL5cA5bKl5HcsgrKgUgwCZ0Tog43cro0SaKzgkRtAwPataXqDQglHJ2Yq81U13mNrj8f6HjpRAYZl3OOf0z1PO/rLMPwYoXrHktSk5B19FWMLS/wLPf40rBSZg52hqO5GcLz709tokqLzccOJF/Vktj8+EcjuUVnd4mKiSATgkRdIqPoGN8OJ3irfcdWoY1i0EGNSCUqqwoDz68C7Z9WvU2rkIjMNzpfaX1QWEu9gmzZsDb9D78NNd60rn3BBg2AxJ7NNz5ghVQr4+E8lLH09bJDfv9fiQjp5CtR3PZk5nH7sw8dmecYs+xPNJzzgSH3SZ0iA2jY3wEnSqCIyGcjnERxDSh+S40IJRypbwcDq60/rouybcuxZTkV3pfcPb74lPnLqt4X15S9XcFhML5t8OF9/p2GIwMx9PWYS2tkAiP810tfii3sIQ9mVZY7M44xe7MPPZknmLvsVMUl5Wf3i42PIhO8VZYdEoId7Q+ImgXE0pAI2t1aEAo1RDKSqoOl4Se/vNE94EfYda1kNADpnwCwToWUk3Kyg1pJ/PZk2mFhvU6xZ7MPI7lFZ/eLtAuJLcMP+tSVY82UXRJiPDb4NCAUEqdbdtCmHsbdLrcuqPLz5+29mfZ+SXsPpbH7owzobE7M4/9x/MpLbd+v4YE2ujZJpo+SdH0TWpB76Rov7kVVwNCKXWuNW/BJ9Oh7y1w3YuN4mnrxqSkrJz9x/PZdCibDWnZbEjLYtPhbApLrEtVkcEB9GobTZ92jtBoG01STGiD31GlD8oppc41YArkpcO3j0NEojV9q/KYQLuNzgkRdE6I4Lr+bQEoLStnV2YeGw5m81NaFhsPZTPzu72nn9+IDQ+id9to+iZF0yepBX2Soknw4Si3GhBKNWcjHoTcI/D909bT1kPu8XVFTVqA3Ua3VlF0axXFTYPaAdY8GduP5vJTWjYbDlqh8fy3mTiuTtEqKoTeSWdCo3fb6Aa7i0oDQqnmTATG/MuadOjzh63xn3qP93VVzUpwgN3RWmgBQ6w73PKLS9l8OOf0pakNadl8uSX99D7tY8PokxTteLWgV9toIoI9/+tc+yCUUlBSCG/fAAdXwaT50PESX1ekKskuKDmrP2NDWjaHsgoA6NYqks/vr9sT8toHoZSqXmCINXfEG6NhziT42WfQuq+vq1JOokMDGdY5jmGdzzy7ciyviI1p2ZQ4PaPhSf55Y65SquGFtrDGagptAW+Pt4YkUX4tLiKYS7slcGVPN+cJqSUNCKXUGVFtrJAoL7EuOeVl+roi5UN6iUkpdbb48+DWefDWOHhnAkz5tOGeti4vh+wDUJQLYremWbVV/HR+77wswLGt/r3raRoQSqlztRsME960Rr2dNxlumQsBHry1srwcsvZB5nbI2Gr9zNwGx3ZYQ5PUiZwdGja763CpCJ7QGOh6FfQY5/u5QfyUV+9iEpFRwDOAHXjNGPNEpfW/Bn4BlAKZwJ3GmP2OdWXARsemB4wx46r7Lr2LSSkvWPtfWHAf9L7Jmomvtn+ll5fByX2OANh6JhCO7YTSgjPbRbW1Wi7x3a2fYbHWvuWlTj8dL1N+9ueztqlin/IyMJWWZR2EI+ut70/sbc1Z3mOc9f3NiE/uYhIRO/ACMBJIA1aLyAJjzBanzdYBA40x+SJyD/APYKJjXYExpp+36lNKueH8yda0pd88BpGJcOVjrrc7HQTbnFoEFUFwZk5popKsX8ApI5wCoSuERDfI6Zwj6wBs/QS2fAzfPma94s47ExaJvZr1ECTevMQ0GNhljNkDICJzgGuB0wFhjPnWafsfgUlerEcpVRfD/wdy0+GH5yA8Ac4bYwXB6RaB49JQ2Zm5FIhu5wiCiyG+GyR0h7iu/jUXOECL9tYQ7BfeCzlHrPlBtnwMy/9lzWEek2IFRY9roc35zS4svHaJSUTGA6OMMb9wfJ4MXGCMua+K7Z8HjhpjHnN8LgXWY11+esIY85GLfaYB0wDat28/YP/+/V45F6WavfIymP8z65ens+j2VhAkdLOCoKJFEBzpmzo9JS8Ttn8GWxbA3qXWJanodtD9GisskgY3mU5xv39QTkQmAQOBi50WdzDGHBKRjsA3IrLRGLPbeT9jzCvAK2D1QTRYwUo1NzY7XP8KJA+3ZslL6Ga1CBp7EFQlIh4G3GG9Ck7C9kVWWKx+HX78D0S0gu5XW2HRfijY/eJXqcd586wOAe2cPic5lp1FRK4Afg9cbIw53UY1xhxy/NwjIkuA/sDuyvsrpRpIYAgMnurrKhpeaAz0u9V6FebAzi+sltS62bD6NWt2vm5jrbBIHuHZu718zJsBsRroIiIpWMFwM3Cr8wYi0h94GetSVIbT8hgg3xhTJCJxwDCsDmyllPKdkChrMMPe46E4H3Z9ZYXFpg9h7Syrs/28MdB9HHS6zArVRsxrAWGMKRWR+4DFWLe5zjTGbBaRR4FUY8wC4J9ABPCeY5KMittZuwMvi0g51tPeT1S6+0kppXwrKMzRgT3OGuxwzxIrLLYvhJ/ehaAI6zmLAXdYl+YaYQe3juaqlFKeVFYCe5fB1gVWv0XBCesOqGEzrE5um93XFZ5FpxxVSilfKCmEn96xbhE+scd6Ynvor6DvrX5z+am6gGga92kppZQ/CgyBgXfCfalw0ywIaQGfPgBP94Jl/7LukPJjGhBKKeVtNrt1l9PUb6zBD1v3g2/+Ck/2hM9/B9lpvq7QpaZ5865SSvkjEUgZbr2OboIfnoWVL8Gql6H3BBg6HRJ7+LrK07QFoZRSvtCqF9zwCsxYD4OmWndAvXghzL4J9n0PftA/rAGhlFK+1KI9jH4CHtgMl/4BDq2BN8fAa1dYAwmWe2c6UXdoQCillD8Ii4WLH4QHNsHYf0P+MZg7CV4YBGvesu6IamAaEEop5U8CQ2HQL+BXa2H8G9YDd59Mh2f6wPInoSCrwUrRgFBKKX9ks0OvG2DaErh9gTU3xdd/gad6wRd/gJzD3i/B69+glFKq7kSg48Uw+QO4azmcNwpW/Aee7gMf/dKaj8NLNCCUUqqxaN0HbnwNpq+zHsDb9AH85wJ47w6v3PWkz0EopVRjE9MBxvwDLn7IGnK8rNgrgwFqQCilVGMV3hIuechrh9dLTEoppVzSgFBKKeWSBoRSSimXNCCUUkq5pAGhlFLKJQ0IpZRSLmlAKKWUckkDQimllEti/GBSCk8QkUxgfz0OEQcc81A5vtRUzgP0XPxVUzmXpnIeUL9z6WCMiXe1oskERH2JSKoxZqCv66ivpnIeoOfir5rKuTSV8wDvnYteYlJKKeWSBoRSSimXNCDOeMXXBXhIUzkP0HPxV03lXJrKeYCXzkX7IJRSSrmkLQillFIuaUAopZRyqdkHhIiMEpHtIrJLRB72dT11JSLtRORbEdkiIptFZIava6oPEbGLyDoR+dTXtdSHiLQQkfkisk1EtorIhb6uqa5E5AHHf1ubRORdEQnxdU3uEpGZIpIhIpuclsWKyJcistPxM8aXNbqrinP5p+O/sQ0i8qGItPDEdzXrgBARO/ACMBroAdwiIj18W1WdlQK/Mcb0AIYA9zbicwGYAWz1dREe8AzwuTGmG9CXRnpOItIWmA4MNMb0AuzAzb6tqlbeBEZVWvYw8LUxpgvwteNzY/Am557Ll0AvY0wfYAfwiCe+qFkHBDAY2GWM2WOMKQbmANf6uKY6McYcMcasdbzPxfpF1Na3VdWNiCQBY4HXfF1LfYhINDACeB3AGFNsjMnybVX1EgCEikgAEAYc9nE9bjPGLANOVFp8LfCW4/1bwHUNWlQduToXY8wXxphSx8cfgSRPfFdzD4i2wEGnz2k00l+qzkQkGegPrPRtJXX2NPBboNzXhdRTCpAJvOG4XPaaiIT7uqi6MMYcAv4FHACOANnGmC98W1W9JRpjjjjeHwUSfVmMB90JLPLEgZp7QDQ5IhIBvA/cb4zJ8XU9tSUiVwMZxpg1vq7FAwKA84EXjTH9gVM0nssYZ3Fcn78WK/TaAOEiMsm3VXmOse73b/T3/IvI77EuN8/2xPGae0AcAto5fU5yLGuURCQQKxxmG2M+8HU9dTQMGCci+7Au+V0mIm/7tqQ6SwPSjDEVLbn5WIHRGF0B7DXGZBpjSoAPgKE+rqm+0kWkNYDjZ4aP66kXEbkDuBq4zXjoAbfmHhCrgS4ikiIiQVidbgt8XFOdiIhgXeveaox50tf11JUx5hFjTJIxJhnr/49vjDGN8i9VY8xR4KCInOdYdDmwxYcl1ccBYIiIhDn+W7ucRtrh7mQBMMXxfgrwsQ9rqRcRGYV1WXacMSbfU8dt1gHh6NS5D1iM9R/7PGPMZt9WVWfDgMlYf3Gvd7zG+Looxa+A2SKyAegH/J+P66kTRytoPrAW2Ij1u6PRDFUhIu8CK4DzRCRNRH4OPAGMFJGdWC2kJ3xZo7uqOJfngUjgS8e//Zc88l061IZSSilXmnULQimlVNU0IJRSSrmkAaGUUsolDQillFIuaUAopZRySQNCKT8gIpc09pFrVdOjAaGUUsolDQilakFEJonIKsfDSC875q3IE5GnHHMlfC0i8Y5t+4nIj05j9Mc4lncWka9E5CcRWSsinRyHj3CaO2K244llpXxGA0IpN4lId2AiMMwY0w8oA24DwoFUY0xPYCnwJ8cus4CHHGP0b3RaPht4wRjTF2s8o4oRRfsD92PNTdIR6+l4pXwmwNcFKNWIXA4MAFY7/rgPxRrgrRyY69jmbeADx1wQLYwxSx3L3wLeE5FIoK0x5kMAY0whgON4q4wxaY7P64Fk4Dvvn5ZSrmlAKOU+Ad4yxpw1W5eI/LHSdnUdv6bI6X0Z+u9T+ZheYlLKfV8D40UkAU7PadwB69/ReMc2twLfGWOygZMiMtyxfDKw1DHbX5qIXOc4RrCIhDXoWSjlJv0LRSk3GWO2iMgfgC9ExAaUAPdiTQQ02LEuA6ufAqwhpF9yBMAe4GeO5ZOBl0XkUccxJjTgaSjlNh3NVal6EpE8Y0yEr+tQytP0EpNSSimXtAWhlFLKJW1BKKWUckkDQimllEsaEEoppVzSgFBKKeWSBoRSSimX/h/Nn8MpjxeGEAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4yRGhWOS-Ep",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6482e3f0-57f7-4674-9054-e5e1a026df04"
      },
      "source": [
        "%cd /content/drive/'My Drive'/data/\n",
        "saved_model = 'siamese_model_breeds.h5'\n",
        "\n",
        "\n",
        "siamese_net = load_model(saved_model, custom_objects={'contrastive_loss': contrastive_loss, 't_swish': t_swish, 'LeakyReLU': LeakyReLU}) \n",
        "\n",
        "_ = show_model_output(test_generator, 16)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/data\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x864 with 32 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jSXJ8ie5pdJt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8fa64f1-0047-4acf-98ed-a9d98f09902e"
      },
      "source": [
        "tv_a, tv_b, score = gen_random_batch(test_generator, 16)\n",
        "\n",
        "def show_model_val_scores(siamese_net, integ_left, integ_right, integ_target, nb_examples = 16):\n",
        "    newSize = (256,256)\n",
        "    pred_sim = siamese_net.predict_on_batch([integ_left, integ_right])\n",
        "    pred_sim = [val for sublist in pred_sim for val in sublist] \n",
        "    passes = 0\n",
        "    fails = 0\n",
        "    for act, pred in zip(integ_target, pred_sim):\n",
        "        if abs(act-pred) < 0.45: \n",
        "            score = 'pass'\n",
        "            passes+=1\n",
        "        else: \n",
        "            score = 'fail'\n",
        "            fails+=1\n",
        "\n",
        "        print(f'Integration Set: Actual: {act}  Predicted: {pred:2.2}   Score: {score}')\n",
        "    print('**********************************************************************************')\n",
        "    print(f'OVERALL RESULTS: Passes: {passes}  Fails: {fails}')\n",
        "\n",
        "_ = show_model_val_scores(siamese_net, tv_a, tv_b, score,8)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Integration Set: Actual: 1  Predicted: 0.71   Score: pass\n",
            "Integration Set: Actual: 1  Predicted: 0.87   Score: pass\n",
            "Integration Set: Actual: 1  Predicted: 0.79   Score: pass\n",
            "Integration Set: Actual: 1  Predicted: 0.88   Score: pass\n",
            "Integration Set: Actual: 1  Predicted: 0.86   Score: pass\n",
            "Integration Set: Actual: 1  Predicted: 0.81   Score: pass\n",
            "Integration Set: Actual: 1  Predicted: 0.6   Score: pass\n",
            "Integration Set: Actual: 1  Predicted: 0.77   Score: pass\n",
            "Integration Set: Actual: 0  Predicted: 0.016   Score: pass\n",
            "Integration Set: Actual: 0  Predicted: 0.0019   Score: pass\n",
            "Integration Set: Actual: 0  Predicted: 0.043   Score: pass\n",
            "Integration Set: Actual: 0  Predicted: 0.0005   Score: pass\n",
            "Integration Set: Actual: 0  Predicted: 0.0057   Score: pass\n",
            "Integration Set: Actual: 0  Predicted: 0.15   Score: pass\n",
            "Integration Set: Actual: 0  Predicted: 0.18   Score: pass\n",
            "Integration Set: Actual: 0  Predicted: 0.48   Score: fail\n",
            "**********************************************************************************\n",
            "OVERALL RESULTS: Passes: 15  Fails: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6s-X4-IvvUQ"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aszrE1hJtfff"
      },
      "source": [
        "## Backup Modules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "90eGhKd6_KTI"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "depth = 4\n",
        "width = 16\n",
        "activation = t_swish\n",
        "dropout = 0.4\n",
        "\n",
        "x_train, y_train, score = gen_random_batch(train_generator, 8)\n",
        "\n",
        "# Define Input Layer Up front.\n",
        "input_shape = x_train.shape[1:]\n",
        "\n",
        "model.add(Conv2D(filters = width, kernel_size = (7,7), padding = 'Same', \n",
        "                        activation = activation, \n",
        "                        input_shape = input_shape\n",
        "                     ))\n",
        "\n",
        "for layer in range(depth):\n",
        "\n",
        "    model.add(Conv2D(filters = width, kernel_size = (3,3),padding = 'Same', \n",
        "                        activation = activation                        \n",
        "                     ))\n",
        "    model.add(BatchNormalization())\n",
        "    \n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(dropout/2))\n",
        "\n",
        "for layer in range(depth):\n",
        "\n",
        "    model.add(Conv2D(filters = width*2, kernel_size = (3,3),padding = 'Same', \n",
        "                    activation = activation))\n",
        "    model.add(BatchNormalization())\n",
        "\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "model.add(Dropout(dropout))\n",
        "\n",
        "for layer in range(depth):\n",
        "\n",
        "    model.add(Conv2D(filters = width*2, kernel_size = (3,3),padding = 'Same', \n",
        "                    activation = activation))\n",
        "    model.add(BatchNormalization())\n",
        "\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "model.add(Dropout(dropout))\n",
        "\n",
        "for layer in range(depth):\n",
        "\n",
        "    model.add(Conv2D(filters = width*2, kernel_size = (3,3),padding = 'Same', \n",
        "                    activation = activation, activity_regularizer=regularizers.l2(1e-6)))\n",
        "    model.add(BatchNormalization())\n",
        "\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "model.add(Dropout(dropout))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(16))\n",
        "model.add(LeakyReLU(alpha=0.05))\n",
        "model.add(Dropout(dropout))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5f9oxk77npR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "outputId": "80630241-6f0a-490e-a511-17ed0c66e2dd"
      },
      "source": [
        "from keras.models import Model\n",
        "from keras.layers import Input, Conv2D, BatchNormalization, MaxPool2D, Activation, Flatten, Dense, Dropout\n",
        "\n",
        "#x_train, y, nm = next(train_generator)\n",
        "x_train, y_train, score = gen_random_batch(train_generator, 8)\n",
        "\n",
        "# Define Input Layer Up front.\n",
        "img_in = Input(shape = x_train.shape[1:], name = 'FeatureNet_ImageInput')\n",
        "n_layer = img_in\n",
        "\n",
        "# Create our convolutional network in a compressing fashion... \n",
        "# 4 Conv Layers with 8, 16, 16, and 32 filters respectively.\n",
        "leaky = LeakyReLU(alpha=0.05)\n",
        "activation = leaky\n",
        "\n",
        "# Define Input Layer Up front.\n",
        "img_in = Input(shape = x_train.shape[1:], name = 'FeatureNet_ImageInput')\n",
        "n_layer = img_in\n",
        "\n",
        "# Create our convolutional network in a compressing fashion... \n",
        "# 4 Conv Layers with 8, 16, 16, and 32 filters respectively.\n",
        "\n",
        "\n",
        "n_layer = Conv2D(128, kernel_size = (5,5))(n_layer)\n",
        "n_layer = BatchNormalization()(n_layer)\n",
        "n_layer = Activation(activation)(n_layer)\n",
        "n_layer = Conv2D(64, kernel_size = (3,3))(n_layer)\n",
        "n_layer = BatchNormalization()(n_layer)\n",
        "n_layer = Activation(activation)(n_layer)\n",
        "n_layer = MaxPool2D((2,2))(n_layer)\n",
        "n_layer = Conv2D(32, kernel_size = (3,3))(n_layer)\n",
        "n_layer = BatchNormalization()(n_layer)\n",
        "n_layer = Activation(activation)(n_layer)\n",
        "n_layer = Conv2D(16, kernel_size = (3,3))(n_layer)\n",
        "n_layer = BatchNormalization()(n_layer)\n",
        "n_layer = Activation(activation)(n_layer)\n",
        "n_layer = MaxPool2D((2,2))(n_layer)\n",
        "n_layer = Flatten()(n_layer)\n",
        "n_layer = Dense(32)(n_layer)\n",
        "n_layer = Activation('linear')(n_layer)\n",
        "\n",
        "\n",
        "feature_model = Model(inputs = [img_in], outputs = [n_layer], name = 'FeatureGenerationModel')\n",
        "feature_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"FeatureGenerationModel\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "FeatureNet_ImageInput (Input [(None, 256, 256, 3)]     0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 252, 252, 128)     9728      \n",
            "_________________________________________________________________\n",
            "batch_normalization_11 (Batc (None, 252, 252, 128)     512       \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 252, 252, 128)     0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 250, 250, 64)      73792     \n",
            "_________________________________________________________________\n",
            "batch_normalization_12 (Batc (None, 250, 250, 64)      256       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 250, 250, 64)      0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 125, 125, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 123, 123, 32)      18464     \n",
            "_________________________________________________________________\n",
            "batch_normalization_13 (Batc (None, 123, 123, 32)      128       \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 123, 123, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 121, 121, 16)      4624      \n",
            "_________________________________________________________________\n",
            "batch_normalization_14 (Batc (None, 121, 121, 16)      64        \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 121, 121, 16)      0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 60, 60, 16)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 57600)             0         \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 32)                1843232   \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 32)                0         \n",
            "=================================================================\n",
            "Total params: 1,950,800\n",
            "Trainable params: 1,950,320\n",
            "Non-trainable params: 480\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzex954GsfCN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42993edd-6645-48a2-99c5-8acf67986c9d"
      },
      "source": [
        "from keras.layers import concatenate, Lambda\n",
        "\n",
        "def euclidean_distance(vects):\n",
        "    x, y = vects\n",
        "    return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True))\n",
        "\n",
        "def eucl_dist_output_shape(shapes):\n",
        "    shape1, shape2 = shapes\n",
        "    return (shape1[0], 1)\n",
        "\n",
        "\n",
        "# Our Similarity Model will Have Dual Inputs, one for each image\n",
        "img_a_in = Input(shape = x_train.shape[1:], name = 'ImageA_Input')\n",
        "img_b_in = Input(shape = x_train.shape[1:], name = 'ImageB_Input')\n",
        "\n",
        "# Run each Input layer through the feature model\n",
        "#img_a_feat = feature_model(img_a_in)\n",
        "#img_b_feat = feature_model(img_b_in)\n",
        "img_a_feat = model(img_a_in)\n",
        "img_b_feat = model(img_b_in)\n",
        "\n",
        "# Getting the L1 Distance between the 2 encodings\n",
        "L1_layer = Lambda(lambda tensor:K.abs(tensor[0] - tensor[1]))\n",
        "#distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([img_a_feat, img_b_feat]])\n",
        "distance = Lambda(lambda tensor:K.sqrt(K.sum(K.square(tensor[0] - tensor[1]), axis=1, keepdims=True)) )\n",
        "\n",
        "# Add the distance function to the network\n",
        "L1_distance = L1_layer([img_a_feat, img_b_feat])\n",
        "Euclidean_distance = distance([img_a_feat, img_b_feat])\n",
        "\n",
        "#prediction = Dense(1,activation='sigmoid')(L1_distance)\n",
        "prediction = Dense(1,activation='sigmoid')(Euclidean_distance)\n",
        "similarity_model = Model(inputs=[img_a_in, img_b_in],outputs=prediction)\n",
        "similarity_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "ImageA_Input (InputLayer)       [(None, 256, 256, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "ImageB_Input (InputLayer)       [(None, 256, 256, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "FeatureGenerationModel (Functio (None, 32)           24120480    ImageA_Input[0][0]               \n",
            "                                                                 ImageB_Input[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "lambda_1 (Lambda)               (None, 1)            0           FeatureGenerationModel[2][0]     \n",
            "                                                                 FeatureGenerationModel[3][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 1)            2           lambda_1[0][0]                   \n",
            "==================================================================================================\n",
            "Total params: 24,120,482\n",
            "Trainable params: 532,770\n",
            "Non-trainable params: 23,587,712\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BCUtoVBAfPa"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fVCf5FwoQH8"
      },
      "source": [
        "#\n",
        "#  Densenet Class\n",
        "#  Usage:  densenet = DenseNet((28,28,1), nb_classes=10, dropout_rate=0.3, depth=8)\n",
        "#\n",
        "leaky = LeakyReLU(alpha=0.05)\n",
        "\n",
        "class DenseNet:\n",
        "    def __init__(self, input_shape=None, dense_blocks=3, dense_layers=-1, growth_rate=6, nb_classes=None,\n",
        "                 dropout_rate=None, bottleneck=False, compression=1.0, weight_decay=1e-4, depth=20):\n",
        "\n",
        "        # Checks\n",
        "        if nb_classes == None:\n",
        "            raise Exception( 'Please define number of classes (e.g. num_classes=10). This is required for final softmax.')\n",
        "\n",
        "        if compression <= 0.0 or compression > 1.0:\n",
        "            raise Exception('Compression have to be a value between 0.0 and 1.0.')\n",
        "\n",
        "        if type(dense_layers) is list:\n",
        "            if len(dense_layers) != dense_blocks:\n",
        "                raise AssertionError('Number of dense blocks have to be same length to specified layers')\n",
        "        elif dense_layers == -1:\n",
        "            dense_layers = int((depth - 4) / 3)\n",
        "            if bottleneck:\n",
        "                dense_layers = int(dense_layers / 2)\n",
        "            dense_layers = [dense_layers for _ in range(dense_blocks)]\n",
        "        else:\n",
        "            dense_layers = [dense_layers for _ in range(dense_blocks)]\n",
        "\n",
        "        self.dense_blocks = dense_blocks\n",
        "        self.dense_layers = dense_layers\n",
        "        self.input_shape = input_shape\n",
        "        self.growth_rate = growth_rate\n",
        "        self.weight_decay = weight_decay\n",
        "        self.dropout_rate = dropout_rate\n",
        "        self.bottleneck = bottleneck\n",
        "        self.compression = compression\n",
        "        self.nb_classes = nb_classes\n",
        "        \n",
        "        \n",
        "    def build_model(self):\n",
        "        img_input = Input(shape=self.input_shape, name='img_input')\n",
        "        nb_channels = self.growth_rate\n",
        "        \n",
        "        x = Conv2D(2*self.growth_rate, (3,3), \n",
        "                   padding='same', strides = (1,1), \n",
        "                   kernel_regularizer=keras.regularizers.l2(self.weight_decay))(img_input)\n",
        "        \n",
        "        for block in range(self.dense_blocks-1):\n",
        "            x, nb_channels = self.dense_block(x, self.dense_layers[block], nb_channels, self.growth_rate,\n",
        "                                              self.dropout_rate, self.bottleneck, self.weight_decay)\n",
        "            \n",
        "            x = self.transition_layer(x, nb_channels, self.dropout_rate, self.compression, self.weight_decay)\n",
        "            nb_channels = int(nb_channels*self.compression)\n",
        "            \n",
        "        x, nb_channels = self.dense_block(x, self.dense_layers[-1], nb_channels, self.growth_rate, self.dropout_rate, self.weight_decay)\n",
        "        \n",
        "        x = BatchNormalization()(x)\n",
        "        x = Activation(leaky)(x)\n",
        "        x = GlobalAveragePooling2D()(x)\n",
        "        #prediction = Dense(self.nb_classes, activation='softmax')(x)\n",
        "        prediction = Dense(16, activation='sigmoid')(x)\n",
        "        return Model(inputs=img_input, outputs=prediction, name='densenet')\n",
        "        \n",
        "    def dense_block(self, x, nb_layers, nb_channels, growth_rate, dropout_rate=None, bottleneck=False, weight_decay=1e-4):\n",
        "        for i in range(nb_layers):\n",
        "            cb = self.convolution_block(x, growth_rate, dropout_rate, bottleneck)\n",
        "            nb_channels += growth_rate\n",
        "            x = concatenate([cb,x])\n",
        "            \n",
        "        return x, nb_channels\n",
        "    \n",
        "    def convolution_block(self, x, nb_channels, dropout_rate=None, bottleneck=False, weight_decay=1e-4):       \n",
        "\n",
        "        # Bottleneck\n",
        "        if bottleneck:\n",
        "            bottleneckWidth = 4\n",
        "            x = BatchNormalization()(x)\n",
        "            x = Activation(leaky)(x)\n",
        "            x = Conv2D(nb_channels * bottleneckWidth, (1, 1),\n",
        "                                     kernel_regularizer=keras.regularizers.l2(weight_decay))(x)\n",
        "            # Dropout\n",
        "            if dropout_rate:\n",
        "                x = Dropout(dropout_rate)(x)\n",
        "\n",
        "        # Standard (BN-ReLU-Conv)\n",
        "        x = BatchNormalization()(x)\n",
        "        x = Activation(leaky)(x)\n",
        "        x = Conv2D(nb_channels, (3, 3), padding='same')(x)\n",
        "\n",
        "        # Dropout\n",
        "        if dropout_rate:\n",
        "            x = Dropout(dropout_rate)(x)\n",
        "\n",
        "        return x\n",
        "\n",
        "    def transition_layer(self, x, nb_channels, dropout_rate=None, compression=1.0, weight_decay=1e-4):\n",
        "        x = BatchNormalization()(x)\n",
        "        x = Activation(leaky)(x)\n",
        "        x = Conv2D(int(nb_channels * compression), (1, 1), padding='same',\n",
        "                                 kernel_regularizer=keras.regularizers.l2(weight_decay))(x)\n",
        "\n",
        "        # Adding dropout\n",
        "        if dropout_rate:\n",
        "            x = Dropout(dropout_rate)(x)\n",
        "        x = SpatialDropout2D(0.4, data_format=None)(x)\n",
        "        x = AveragePooling2D((2, 2), strides=(2, 2))(x)\n",
        "        return x\n",
        "    \n",
        "densenet = DenseNet((256,256,3), nb_classes=2, growth_rate = 20, dropout_rate=0.05, depth=10, \n",
        "                     weight_decay=1e-3)\n",
        "\n",
        "model = densenet.build_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6TUQ_XcBHlk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "outputId": "c4fae862-d5d8-4ceb-dadd-e7b9e5e6e591"
      },
      "source": [
        "from keras.layers import concatenate, Lambda\n",
        "\n",
        "x_train, y_train, score = gen_random_batch(train_generator, 32 )\n",
        "\n",
        "# Our Similarity Model will Have Dual Inputs, one for each image\n",
        "img_a_in = Input(shape = x_train.shape[1:], name = 'ImageA_Input')\n",
        "img_b_in = Input(shape = x_train.shape[1:], name = 'ImageB_Input')\n",
        "\n",
        "# Run each Input layer through the feature model\n",
        "img_a_feat = feature_model(img_a_in)\n",
        "img_b_feat = feature_model(img_b_in)\n",
        "#img_a_feat = model(img_a_in)\n",
        "#img_b_feat = model(img_b_in)\n",
        "\n",
        "# Concatenate results into a similarity model\n",
        "combined_features = concatenate([img_a_feat, img_b_feat], name = 'merge_features')\n",
        "combined_features = Dense(3000, activation = leaky)(combined_features)\n",
        "combined_features = BatchNormalization()(combined_features)\n",
        "combined_features = Dense(3000, activation = leaky)(combined_features)\n",
        "combined_features = BatchNormalization()(combined_features)\n",
        "combined_features = Dense(500, activation = leaky)(combined_features)\n",
        "combined_features = BatchNormalization()(combined_features)\n",
        "ombined_features = Dense(500, activation = leaky)(combined_features)\n",
        "combined_features = BatchNormalization()(combined_features)\n",
        "combined_features = Dropout(0.4)(combined_features)\n",
        "combined_features = Dense(1, activation = 'sigmoid')(combined_features)\n",
        "similarity_model = Model(inputs = [img_a_in, img_b_in], outputs = [combined_features], name = 'Similarity_Model')\n",
        "similarity_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"Similarity_Model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "ImageA_Input (InputLayer)       [(None, 256, 256, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "ImageB_Input (InputLayer)       [(None, 256, 256, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "FeatureGenerationModel (Functio (None, 32)           1950800     ImageA_Input[0][0]               \n",
            "                                                                 ImageB_Input[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "merge_features (Concatenate)    (None, 64)           0           FeatureGenerationModel[0][0]     \n",
            "                                                                 FeatureGenerationModel[1][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dense_23 (Dense)                (None, 3000)         195000      merge_features[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 3000)         12000       dense_23[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_24 (Dense)                (None, 3000)         9003000     batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 3000)         12000       dense_24[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_25 (Dense)                (None, 500)          1500500     batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 500)          2000        dense_25[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 500)          2000        batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dropout_11 (Dropout)            (None, 500)          0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dense_27 (Dense)                (None, 1)            501         dropout_11[0][0]                 \n",
            "==================================================================================================\n",
            "Total params: 12,677,801\n",
            "Trainable params: 12,663,321\n",
            "Non-trainable params: 14,480\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}