HUSE_Tensorflow.ipynb

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  "metadata": {
    "colab": {
      "name": "HUSE-Tensorflow.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMJDqdM2bJn0svb3ZVErfv8",
      "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/guramritpalsaggu/HUSE-Tensorflow/blob/master/HUSE_Tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BLcgxwh1br_c",
        "colab_type": "code",
        "outputId": "83757b1c-c784-4309-d731-64f9c9ece33f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "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&response_type=code&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\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-rfdHoP51Jul",
        "colab_type": "text"
      },
      "source": [
        "Importing all the required dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TaCkQMMJbslf",
        "colab_type": "code",
        "outputId": "be74134c-12db-4ca5-be18-45b1a88cdf11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import cv2 as cv\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "from tensorflow.python.keras.preprocessing import text, sequence\n",
        "from tensorflow.python.keras.layers import Dense,Input\n",
        "from tensorflow.python.keras.layers import Dropout,Embedding\n",
        "from tensorflow.python.keras.models import Sequential\n",
        "from tensorflow.python.keras.layers import Dense, Activation\n",
        "from tensorflow.python.keras import optimizers\n",
        "from tensorflow.python.keras import models\n",
        "from tensorflow.python.keras import layers\n",
        "from tensorflow.keras import models\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.losses import categorical_crossentropy\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras import metrics\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "from tqdm import tqdm\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from IPython.display import Image\n",
        "import glob\n",
        "import numpy as np\n",
        "from sklearn.metrics.pairwise import cosine_distances\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
        "import os\n",
        "import imgaug as aug\n",
        "import imgaug.augmenters as iaa\n",
        "import re\n",
        "import string\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.metrics.pairwise import cosine_distances\n",
        "from tqdm import tqdm"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rj-TKWzFFPRi",
        "colab_type": "text"
      },
      "source": [
        "Loading the training_data.csv as dataframe using Pandas libraray"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkzTzCKAd-Vd",
        "colab_type": "code",
        "outputId": "0634d5e7-7429-4180-a0c5-f0cd39b96580",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/HUSE/training.csv')\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image</th>\n",
              "      <th>name</th>\n",
              "      <th>classes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5da821eb7f3e301b4504bb4a_0.jpg</td>\n",
              "      <td>Marc Jacobs Beauty  Eye-Conic Longwear Eyeshad...</td>\n",
              "      <td>beauty&lt;makeup&lt;eyeshadow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5da81cde7861c2af6a5a88cf_0.jpg</td>\n",
              "      <td>Marc Jacobs Beauty  Eye-Conic Longwear Eyeshad...</td>\n",
              "      <td>beauty&lt;makeup&lt;eyeshadow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5da8219a6504fb65da0050aa_0.jpg</td>\n",
              "      <td>Prada  Wool sweater</td>\n",
              "      <td>clothing&lt;knitwear&lt;fine knit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5da821856504fb65cea703cf_0.jpg</td>\n",
              "      <td>Prada  Printed silk-satin twill straight-leg p...</td>\n",
              "      <td>clothing&lt;pants&lt;straight leg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5da82200b25b035d1d17bec6_0.jpg</td>\n",
              "      <td>Prada  Cropped chain-trimmed printed crepe str...</td>\n",
              "      <td>clothing&lt;pants&lt;straight leg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            image  ...                      classes\n",
              "0  5da821eb7f3e301b4504bb4a_0.jpg  ...      beauty<makeup<eyeshadow\n",
              "1  5da81cde7861c2af6a5a88cf_0.jpg  ...      beauty<makeup<eyeshadow\n",
              "2  5da8219a6504fb65da0050aa_0.jpg  ...  clothing<knitwear<fine knit\n",
              "3  5da821856504fb65cea703cf_0.jpg  ...  clothing<pants<straight leg\n",
              "4  5da82200b25b035d1d17bec6_0.jpg  ...  clothing<pants<straight leg\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Us1cdZhU1Xx_",
        "colab_type": "code",
        "outputId": "162ea276-3b70-4b0b-c83c-eddb9343ca4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(65714, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyCjgOlX16Yz",
        "colab_type": "code",
        "outputId": "9fe99773-a2b8-43d5-c181-3a17a2d5cd49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "df.isnull().any()       #Check for any null values in the dataframe"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "image      False\n",
              "name       False\n",
              "classes    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7RSHFk9GphC",
        "colab_type": "text"
      },
      "source": [
        "Extract the images.zip folder using unzip to move that folder to drive."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZLnO467ebxF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip '/content/drive/My Drive/HUSE/images.zip'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qHYm70q26RX",
        "colab_type": "code",
        "outputId": "684bd311-414b-4d4c-a7d8-264ff78ef056",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "path, dirs, files = next(os.walk(\"/content/netaporter_gb_images\"))\n",
        "file_count = len(files)\n",
        "print(file_count)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "65714\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UOnx9JMbz9bH",
        "colab_type": "text"
      },
      "source": [
        " Hierarchical universal semantic embeddings Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g3tq_3ckA_1U",
        "colab_type": "text"
      },
      "source": [
        "<img src=\"https://drive.google.com/uc?id=16wQ1Z2o2MitrrK9lbNmGFec3gM8_qG8D\" width=\"600\" layout=\"centre\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MLYierj4KIVv",
        "colab_type": "text"
      },
      "source": [
        "####PART 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLwfaXQGDZo8",
        "colab_type": "text"
      },
      "source": [
        "####Image Embeddings Input Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EiS1I7PBJrNU",
        "colab_type": "text"
      },
      "source": [
        "Images are represented to vector embedding space using the img2vec pretrained library. This library uses the ResNet50 model in TensorFlow Keras, pre-trained on Imagenet, to generate image embeddings.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1mPpR3ivLZur",
        "colab_type": "text"
      },
      "source": [
        "Installing the library and its dependencies using pip"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8Mv_grCmdiA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install git+git://github.com/jaredwinick/img2vec-keras.git"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnhQIT--dtTq",
        "colab_type": "text"
      },
      "source": [
        "####Transfer Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JIRut8QeLsyY",
        "colab_type": "text"
      },
      "source": [
        "Importing the Img2Vec library and initializing its object."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Blk16qunmHqK",
        "colab_type": "code",
        "outputId": "37e3c02b-350b-4269-860f-629d9de5be4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "from img2vec_keras import Img2Vec\n",
        "img2vec = Img2Vec()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n",
            "102973440/102967424 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tIo-OTkzMkHO",
        "colab_type": "text"
      },
      "source": [
        "Displaying a Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zux2Yrtdquwu",
        "colab_type": "code",
        "outputId": "0fee4c48-dfde-4006-ae9f-031eec3c3fde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "source": [
        "Image('/content/netaporter_gb_images/5da8219a6504fb65da0050aa_0.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofPF7oxyNWeK",
        "colab_type": "text"
      },
      "source": [
        "Using the glob library to append paths of all the images in the list."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lKPKgv0qvMy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "image_paths = []\n",
        "image_paths.extend(glob.glob('/content/netaporter_gb_images' +  '/*.jpg'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PswVxHBqvKD",
        "colab_type": "code",
        "outputId": "c16a20e4-7d3f-4624-c808-3b838fd9b372",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(image_paths)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "65714"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epuoAbk_db78",
        "colab_type": "text"
      },
      "source": [
        "####Image Augmentation\n",
        "Data augmentation is a powerful technique which helps in almost every case for improving the robustness of a model. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9qRcSgxGdx8Y",
        "colab_type": "text"
      },
      "source": [
        "Augmenters of imgaug library are used to augment images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDIGEeyKaGpK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Augmentation sequence \n",
        "seq = iaa.OneOf([\n",
        "    iaa.Fliplr(), # horizontal flips\n",
        "    iaa.Affine(rotate=20), # roatation\n",
        "    iaa.Multiply((1.2, 1.5))]) #random brightness"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMA1vtAzNoIn",
        "colab_type": "text"
      },
      "source": [
        "Using the img2vec pretrained model to get the vector embeddings of all the images and storing them in dictionary with image path and vector pairs.\n",
        "The image embeddings generated from this model has shape of (1,2048) for each image, but we require embeddings size to be of 512 before passing them to image tower."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MyyCCp8GvRkJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "image_vectors = {}\n",
        "for image_path in image_paths:\n",
        "    # image = seq.augment_image(image_path)    #this can be used to add augmentations appropriately\n",
        "    vector = img2vec.get_vec(image_path)\n",
        "    image_vectors[image_path] = vector"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y6-Tlh-hOorU",
        "colab_type": "text"
      },
      "source": [
        "As we need to reduce the dimensionality of the image embeddings we can using the common dimensionality reduction known as Principle Component Analysis(PCA).\n",
        "\n",
        "Principal Component Analysis (PCA) is an unsupervised technique used to preprocess and reduce the dimensionality of high-dimensional data while explaining the maximum variance and summarize the most information in the data that are mutually uncorrelated."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UIcG4RA_P6Qz",
        "colab_type": "text"
      },
      "source": [
        "We stored the values of the image embeddings in the stack."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvex4qf_qvIh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = np.stack(list(image_vectors.values()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BXrSggNQGAq",
        "colab_type": "text"
      },
      "source": [
        "Initialize the PCA using the sklearn decomposition. n_components parameter is passed which is responsible for number of components to keep and is set to 512 for image embeddings."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-r_ps2UqvFy",
        "colab_type": "code",
        "outputId": "c4f9e77c-6ff5-4735-f25b-f99180f7463a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "pca = PCA(n_components=512)\n",
        "images = pca.fit_transform(X)\n",
        "print('Cumulative explained variation for 512 principal components: {}'.format(np.sum(pca.explained_variance_ratio_)))\n",
        "print(np.shape(images))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cumulative explained variation for 512 principal components: 0.9210123020687525\n",
            "(65714, 512)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXJePYd_CfEi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save('image_embeddings.npy', images)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oq-X1uGxRnuk",
        "colab_type": "text"
      },
      "source": [
        "Final Image embeddings with shape 65714*512 can stored as numpy file."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iEGRzeRrDK8S",
        "colab_type": "text"
      },
      "source": [
        "####Text  Embeddings Input Model "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4hrU-vSn2Vw",
        "colab_type": "text"
      },
      "source": [
        "####Text Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUwtrfTqnFxz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# decontracting all contracted words\n",
        "def decontracted(phrase):\n",
        "    phrase = re.sub(r\"won't\", \"will not\", phrase)\n",
        "    phrase = re.sub(r\"can\\'t\", \"can not\", phrase)\n",
        "    phrase = re.sub(r\"n\\'t\", \" not\", phrase)\n",
        "    phrase = re.sub(r\"\\'re\", \" are\", phrase)\n",
        "    phrase = re.sub(r\"\\'s\", \" is\", phrase)\n",
        "    phrase = re.sub(r\"\\'d\", \" would\", phrase)\n",
        "    phrase = re.sub(r\"\\'ll\", \" will\", phrase)\n",
        "    phrase = re.sub(r\"\\'t\", \" not\", phrase)\n",
        "    phrase = re.sub(r\"\\'ve\", \" have\", phrase)\n",
        "    phrase = re.sub(r\"\\'m\", \" am\", phrase)\n",
        "    return phrase"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-BeWIhby9UMw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Cleaning the texts\n",
        "def preprocessing(phrase):\n",
        "    result = decontracted(str(phrase))      \n",
        "    result = result.strip()             #remove leading and ending spaces\n",
        "    result = result.split()             #Split to tokenize words\n",
        "    ps = PorterStemmer()                #reducing words to their word stem or base\n",
        "    result = [ps.stem(word) for word in result if not word in set(stopwords.words('english'))]  #remove the stopwords\n",
        "    result = ' '.join(result)           #Joining all the tokens \n",
        "    return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDsyMO7x9VLQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "description = df['name'].str.lower()        #Convert text to lowercase\n",
        "sentences = description.map(preprocessing)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yFSqquZ5sHxV",
        "colab_type": "code",
        "outputId": "2d2ad52f-9cea-46d5-b56b-a4a49f44baeb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "sentences"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        marc jacob beauti eye-con longwear eyeshadow p...\n",
              "1        marc jacob beauti eye-con longwear eyeshadow p...\n",
              "2                                       prada wool sweater\n",
              "3           prada print silk-satin twill straight-leg pant\n",
              "4        prada crop chain-trim print crepe straight-leg...\n",
              "                               ...                        \n",
              "65709                         illamasqua lipstick - climax\n",
              "65710       chloé marci medium textured-leath shoulder bag\n",
              "65711    gucci sylvi mini leather-trim calf hair should...\n",
              "65712    marc jacob beauti enamor hi-shin lip lacquer -...\n",
              "65713      halfpenni london luella organza-trim satin gown\n",
              "Name: name, Length: 65714, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XTG5Pfelzrzl",
        "colab_type": "text"
      },
      "source": [
        "####Transfer Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LFhI-Tz5z0az",
        "colab_type": "text"
      },
      "source": [
        "We used BERT pretrained text model for representing text into embedding space. Text embedding created from this part of the model are forwarded into the text tower."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kjs_7JSj2aeJ",
        "colab_type": "text"
      },
      "source": [
        "Installing the neccessary dependencies for BERT pretrained Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ag-fI8xuRCKe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -U sentence-transformers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9mjTX6wRFf_",
        "colab_type": "code",
        "outputId": "ac11b8be-1840-47e1-d301-97805cc89107",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "!git clone https://github.com/UKPLab/sentence-transformers.git"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'sentence-transformers'...\n",
            "remote: Enumerating objects: 732, done.\u001b[K\n",
            "remote: Total 732 (delta 0), reused 0 (delta 0), pack-reused 732\u001b[K\n",
            "Receiving objects: 100% (732/732), 210.89 KiB | 388.00 KiB/s, done.\n",
            "Resolving deltas: 100% (497/497), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56c0HhTxRFnP",
        "colab_type": "code",
        "outputId": "14855a60-068d-469f-cbd4-4b271fa96fc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd sentence-transformers/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/sentence-transformers\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IwAICjoRFsp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -e ."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UIGGyZtA20Bu",
        "colab_type": "text"
      },
      "source": [
        "First download a pretrained model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AH7IFpcDRFqq",
        "colab_type": "code",
        "outputId": "cdfb5b05-825f-4313-850e-ca4c5c4a2ab7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "model = SentenceTransformer('bert-base-nli-mean-tokens')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 405M/405M [00:45<00:00, 8.96MB/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjiE5j-T248a",
        "colab_type": "text"
      },
      "source": [
        "Providing sentences to the model to make embeddings."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNJE9EEyRCFk",
        "colab_type": "code",
        "outputId": "dd617b9b-4c5e-4b86-cf51-e9b56c805b81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "texts = model.encode(sentences)\n",
        "print(np.shape(texts))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(65714, 768)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rRJfDy2h3Ejj",
        "colab_type": "text"
      },
      "source": [
        "We now have a list of numpy arrays with the embeddings. BERT model make embeddings of dimmesions (1,768) for each sentence."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rvchxLuV3dj4",
        "colab_type": "text"
      },
      "source": [
        "As dimmensions are (1,768) for each sentence and we need 512 salient tokens for our Text Tower so we use PCA dimesnionality reduction method to get top 512 tokens which explain the most variance of the embeddings."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oTWoZ2-wRB_z",
        "colab_type": "code",
        "outputId": "32ee35a5-ccc2-45e7-be8c-d75a99d819c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "pca = PCA(n_components=512)\n",
        "texts = pca.fit_transform(texts)\n",
        "print('Cumulative explained variation for 512 principal components: {}'.format(np.sum(pca.explained_variance_ratio_)))\n",
        "print(np.shape(texts))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cumulative explained variation for 512 principal components: 0.9960515830713841\n",
            "(65714, 512)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YCel7rATUm-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save('/content/text_embeddings.npy', texts) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZZbrSspGKzc",
        "colab_type": "text"
      },
      "source": [
        "####Universal Sentence Encoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hsqn2Znd4QeC",
        "colab_type": "text"
      },
      "source": [
        "We used universal sentence encoder to make embeddings of the classes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iy8V-aUr4gF6",
        "colab_type": "text"
      },
      "source": [
        "Replace < in the class name by space so that sentences generalizes well."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YywOLjLb4oaN",
        "colab_type": "text"
      },
      "source": [
        "Get all the unique classes there are 48 unique classes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CWkXKClGPvE",
        "colab_type": "code",
        "outputId": "84ff84c0-aed7-402a-aa9b-54dd2356e93d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df['classes'] = df['classes'].str.replace('<',' ')\n",
        "all_classes = df['classes'].unique()\n",
        "len(all_classes)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKqCmGX58QaL",
        "colab_type": "text"
      },
      "source": [
        "Downloading the model from tensorflow hub and extracting it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ShvI5rA2G116",
        "colab_type": "code",
        "outputId": "cb9dc2cc-e2e3-4d96-a17d-2ad796f09767",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "!mkdir /content/universal  \n",
        "!curl -L \"https://tfhub.dev/google/universal-sentence-encoder-large/3?tf-hub-format=compressed\" | tar -zxvC /content/universal/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
            "./\n",
            "./tfhub_module.pb\n",
            "./variables/\n",
            "./variables/variables.data-00000-of-00001\n",
            " 97  745M   97  726M    0     0  61.4M      0  0:00:12  0:00:11  0:00:01 59.1M./variables/variables.index\n",
            "./assets/\n",
            "./saved_model.pb\n",
            "100  745M  100  745M    0     0  61.6M      0  0:00:12  0:00:12 --:--:-- 58.3M\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXCX6Ci5HwQY",
        "colab_type": "code",
        "outputId": "89107ba4-8b3f-4ee2-ffc4-17cdd5bba08d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "#This function is used to create embeddings for classes using universal sentence encoder as given in paper.\n",
        "def embed_useT(module):\n",
        "    with tf.Graph().as_default():\n",
        "        sentences = tf.compat.v1.placeholder(tf.string)\n",
        "        embed = hub.Module(module)\n",
        "        embeddings = embed(sentences)\n",
        "        session = tf.compat.v1.train.MonitoredSession()\n",
        "    return lambda x: session.run(embeddings, {sentences: x})\n",
        "embed_fn = embed_useT('/content/universal')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Graph was finalized.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Graph was finalized.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Running local_init_op.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Running local_init_op.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Done running local_init_op.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Done running local_init_op.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6LYixbw8ZQp",
        "colab_type": "text"
      },
      "source": [
        "Using the above function to represent classes in embedding space using pretrained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RIDJ9v59IVth",
        "colab_type": "code",
        "outputId": "ef42136a-c5a3-47a8-aff4-d0b3238875b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "class_name = all_classes\n",
        "labels_embeddings = embed_fn(class_name)\n",
        "print(np.shape(labels_embeddings))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(48, 512)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SS2wYJS4GLbi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save('/content/class_embeddings.npy',labels_embeddings)\n",
        "np.save('/content/class_name.npy', class_name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "brjUI2v-mjXE",
        "colab_type": "code",
        "outputId": "e0677253-28aa-47ed-85de-2de71ace4760",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        }
      },
      "source": [
        "class_name"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['beauty makeup eyeshadow', 'clothing knitwear fine knit',\n",
              "       'clothing pants straight leg', 'clothing tops blouses',\n",
              "       'shoes sandals flat', 'clothing jumpsuits full length',\n",
              "       'shoes sandals high heel', 'beauty makeup lipstick',\n",
              "       'beauty makeup foundation', 'beauty makeup lipgloss',\n",
              "       'beauty skincare moisturizer', 'shoes pumps mid heel',\n",
              "       'clothing beachwear one-piece', 'shoes sneakers low top',\n",
              "       'clothing dresses gowns', 'clothing dresses midi',\n",
              "       'clothing tops shirts', 'clothing tops tanks and camis',\n",
              "       'clothing jackets blazers', 'clothing tops t-shirts',\n",
              "       'jewelry and watches fashion jewelry necklaces',\n",
              "       'shoes sandals mid heel',\n",
              "       'jewelry and watches fine jewelry necklaces',\n",
              "       'jewelry and watches fashion jewelry earrings',\n",
              "       'jewelry and watches fine jewelry rings', 'beauty skincare serum',\n",
              "       'jewelry and watches fine jewelry earrings',\n",
              "       'bags shoulder bags shoulder bags', 'bags tote bags tote bags',\n",
              "       'clothing pants wide leg', 'clothing tops sweatshirts',\n",
              "       'clothing dresses mini', 'clothing dresses maxi',\n",
              "       'jewelry and watches fine jewelry bracelets',\n",
              "       'clothing coats long', 'clothing dresses knee length',\n",
              "       'accessories wallets cardholders', 'bags shoulder bags cross body',\n",
              "       'shoes boots ankle', 'clothing jackets casual jackets',\n",
              "       'clothing skirts midi',\n",
              "       'jewelry and watches fashion jewelry bracelets',\n",
              "       'clothing knitwear medium knit', 'clothing coats knee length',\n",
              "       'shoes pumps high heel', 'lingerie sleepwear pajamas',\n",
              "       'bags tote bags mini bags', 'clothing beachwear coverups'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YfV3g9Ia8qd5",
        "colab_type": "text"
      },
      "source": [
        "Universal sentence encoder make embeddings of dimmensions (1,512) by default for all each sentence and we can use these embeddnings as it in our model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WC5FHMBTUoo9",
        "colab_type": "text"
      },
      "source": [
        "####Image Tower Model and Text Tower Model. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GC2Sjd9IbOLk",
        "colab_type": "text"
      },
      "source": [
        "This part of the model is build as it is taking input from image and text pretrained model embeddings and parameters used are mentioned in the HUSE paper. The image tower consists of 5 hidden layers of 512 hidden\n",
        "units each and text tower consists of 2 hidden layers of 512 hidden units each with RELU non-linearity and dropout of 0.15 is used between all hidden layers of both towers and the resulting embedding are L2 normalized.\n",
        "\n",
        "Model passes emmbeddings from image tower and text\n",
        "tower through a shared fully connected layer and the model is trained using softmax cross entropy loss.\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1zb6ks65gSxHTt-Gp-pur_h2elndbme8L\" width=\"400\" layout=\"centre\">\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dTgj2XGUVfW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "inputImage = Input(shape=(512, ))\n",
        "inputText =  Input(shape=(512, ))\n",
        " \n",
        "#constructing the image tower which consists of 5X Dense layers with 512 hidden units with Dropout of 0.15 between them\n",
        "x = Dense(512, 'relu')(inputImage)\n",
        "x = Dropout(0.15)(x)\n",
        "x = Dense(512, 'relu')(x)\n",
        "x = Dropout(0.15)(x)\n",
        "x = Dense(512, 'relu')(x)\n",
        "x = Dropout(0.15)(x)\n",
        "x = Dense(512, 'relu')(x)\n",
        "x = Dropout(0.15)(x)\n",
        "outputImage = Dense(512, 'relu')(x)\n",
        "\n",
        "# constructing the text tower which consists of 2X Dense layers with 512 hidden units with Droupout of 0.15 between them\n",
        "y = Dense(512, 'relu')(inputText)\n",
        "y = Dropout(0.15)(y)\n",
        "outputText = Dense(512, 'relu')(y)\n",
        "\n",
        "\n",
        "outputModel = Dense(48, 'softmax') # same classifier layer for image and text tower\n",
        "\n",
        "# passing the L2 normalized image and text embeddings to the classifier layer \n",
        "outImage = outputModel(tf.keras.backend.l2_normalize(outputImage))\n",
        "outText = outputModel(tf.keras.backend.l2_normalize(outputText))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNsyLJE9fGbr",
        "colab_type": "text"
      },
      "source": [
        "Model takes two inputs image and text and correspondingly two outputs are there."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "we7TyC_cUWNv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Now we'll construct 5 models based on the requirements each model would have different input and output from each other\n",
        "\n",
        "model = Model([inputImage, inputText], [outImage, outText]) # main model with both image and text embeddings as input and class as output\n",
        "\n",
        "# models for creating the image and text embedding in the universal shared space\n",
        "imageEmbeddings = Model(inputImage, outputImage) \n",
        "textEmbeddings = Model(inputText, outputText)\n",
        "\n",
        "# models for obtaining class outout from image or text data\n",
        "imageModel = Model(inputImage, outImage)\n",
        "textModel = Model(inputText, outText)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D1nIWDlkhlrh",
        "colab_type": "text"
      },
      "source": [
        "Now we see the architecure of our model. Type and Number of layers, input ouput shapes and other parameters involved."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-eyYtd3gfUBf",
        "colab_type": "code",
        "outputId": "c240760e-135b-488e-b23b-5af4f149f455",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            [(None, 512)]        0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 512)          262656      input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 512)          0           dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 512)          262656      dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 512)          0           dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 512)          262656      dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 512)          0           dense_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "input_3 (InputLayer)            [(None, 512)]        0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 512)          262656      dropout_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 512)          262656      input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_3 (Dropout)             (None, 512)          0           dense_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_4 (Dropout)             (None, 512)          0           dense_5[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_4 (Dense)                 (None, 512)          262656      dropout_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_6 (Dense)                 (None, 512)          262656      dropout_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Square (TensorFlowO [(None, 512)]        0           dense_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Square_1 (TensorFlo [(None, 512)]        0           dense_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sum (TensorFlowOpLa [(1, 1)]             0           tf_op_layer_Square[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sum_1 (TensorFlowOp [(1, 1)]             0           tf_op_layer_Square_1[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Maximum (TensorFlow [(1, 1)]             0           tf_op_layer_Sum[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Maximum_1 (TensorFl [(1, 1)]             0           tf_op_layer_Sum_1[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Rsqrt (TensorFlowOp [(1, 1)]             0           tf_op_layer_Maximum[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Rsqrt_1 (TensorFlow [(1, 1)]             0           tf_op_layer_Maximum_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul (TensorFlowOpLa [(None, 512)]        0           dense_4[0][0]                    \n",
            "                                                                 tf_op_layer_Rsqrt[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_1 (TensorFlowOp [(None, 512)]        0           dense_6[0][0]                    \n",
            "                                                                 tf_op_layer_Rsqrt_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "dense_7 (Dense)                 (None, 48)           24624       tf_op_layer_Mul[0][0]            \n",
            "                                                                 tf_op_layer_Mul_1[0][0]          \n",
            "==================================================================================================\n",
            "Total params: 1,863,216\n",
            "Trainable params: 1,863,216\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cx7oe0lYjdME",
        "colab_type": "text"
      },
      "source": [
        "####Incorporating three losses into the model architecture"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBsgHDfukZuU",
        "colab_type": "text"
      },
      "source": [
        "The cosine distance between two embeddings\n",
        "is treated as edge weight so that two semantically similar classes have a lower edge weight compared to two semantically different classes. This function is used to calculate cosine distance between two embeddings."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VSXqgXb_fZnW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics.pairwise import cosine_distances\n",
        "def dist(embedding1, embedding2):\n",
        "    return cosine_distances(embedding1, embedding2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmvxnUW7mb42",
        "colab_type": "text"
      },
      "source": [
        "####Cross Model Gap Loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AB4AENypuwrF",
        "colab_type": "text"
      },
      "source": [
        "This function calculates the cross modal loss, it returns the cosine distance between the image and text embeddings of the same class.\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1hbhF8VxhcjgR0FI7QZDLPTpvdPy2W3_M\" width=\"400\">"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0HIw16pmZQA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def loss_modal(embeddingsImage, embeddingsText):\n",
        "    distance = dist(embeddingsImage, embeddingsText)\n",
        "    return (1/batch_size) * np.sum(np.diagonal(distance))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UrpAxSD6pbzd",
        "colab_type": "text"
      },
      "source": [
        "####Sementic Similarity Loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LnN1SeXXvCM8",
        "colab_type": "text"
      },
      "source": [
        "This function calculates the semantic loss, it basically calculates the distance between the image embeddings and text embeddings of different classes\n",
        "and then they are compared to the distance between the respective class embeddings and the loss is calculated \n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=19GqY4h7PHo4gTzn7DGls8vvY_DBqrw_w\" width=\"400\">"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UiXKwnjSpCEt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def loss_class(embeddingsImage, embeddingsText, classes, margin):\n",
        "    distanceImage = dist(embeddingsImage, embeddingsImage) \n",
        "    distanceText = dist(embeddingsText, embeddingsText)\n",
        "    distClasses = dist(classes, classes)\n",
        "    \n",
        "    multiplier = np.zeros((batch_size, batch_size))\n",
        "    try:\n",
        "        for i in range(batch_size):\n",
        "            for j in range(i+1, batch_size):\n",
        "                if distClasses[i][j] <= margin:\n",
        "                    multiplier[i][j] = 1\n",
        "    except:\n",
        "        print(distClasses.shape, classes.shape)\n",
        "    \n",
        "    imageLoss = np.multiply(multiplier, (distanceImage - distClasses)**2)  #use np.power() if the tolerance is low\n",
        "    textLoss = np.multiply(multiplier, (distanceText - distClasses)**2)\n",
        "\n",
        "    totalLoss = np.sum(imageLoss + textLoss) * (1/batch_size**2)\n",
        "\n",
        "    return totalLoss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kJPVymZSpgXR",
        "colab_type": "text"
      },
      "source": [
        "####Class Level Similarity Loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fkKJcNPvSN8",
        "colab_type": "text"
      },
      "source": [
        "This function calculates the classification loss, we use simple categorical crossentropy loss for this.\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1ljP2SDj2mX-fZKJSGzfqECaPhh1VrNTJ\" width=\"400\">\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-mgpTuupFCV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def loss_classification(predicted_labels, correct_labels):\n",
        "    return categorical_crossentropy(correct_labels, predicted_labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nK8WwE0byLIF",
        "colab_type": "text"
      },
      "source": [
        "This function is used to train taking input the batch of images and text and their corresponding label embeddings. As in our model as loss and trainable variables are updated at each step we have used the GradientTape() function of Tensorflow. The resources held by a GradientTape are released as soon as GradientTape.gradient() method is called. We used them to optimize loss and trainable variables of our model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xR_gVq_1pJRf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_step(images_batch, text_batch, labels_batch, labels_embeddings):\n",
        "    with tf.GradientTape() as tape:\n",
        "        image_predictions = imageModel(images_batch, training=True)\n",
        "\n",
        "        text_predictions = textModel(text_batch, training=True)\n",
        "\n",
        "        image_embeddings = imageEmbeddings(images_batch, training=True)\n",
        "        image_embeddings = tf.keras.backend.l2_normalize(image_embeddings)\n",
        "        image_embeddings = np.array(image_embeddings)\n",
        "\n",
        "        text_embeddings = textEmbeddings(text_batch, training=True)\n",
        "        text_embeddings = tf.keras.backend.l2_normalize(text_embeddings)\n",
        "        text_embeddings = text_embeddings.numpy()\n",
        "\n",
        "        # calcualting all the necessary kind of losses\n",
        "        classification_loss = loss_classification(image_predictions, labels_batch) + loss_classification(text_predictions, labels_batch)\n",
        "        class_loss = loss_class(image_embeddings, text_embeddings, labels_embeddings, margin)\n",
        "        modal_loss = loss_modal(image_embeddings, text_embeddings)\n",
        "\n",
        "        total_loss = alpha * classification_loss + beta * class_loss + gamma * modal_loss\n",
        "\n",
        "        history.append(total_loss.numpy().mean())\n",
        "\n",
        "        # calcualting grads and optimizing the trainable params.\n",
        "        optimizer = optimizers.RMSprop(lr=1e-3, decay=0.9)\n",
        "        grads = tape.gradient(total_loss, model.trainable_variables)\n",
        "        optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
        "        \n",
        "        #calculating the accuracy\n",
        "        predictions = model([images_batch, text_batch])\n",
        "        predictions = np.array(predictions)\n",
        "        avg_predictions = predictions[0] + predictions[1]\n",
        "        correct_labels = np.array([np.argmax(i) for i in labels_batch])\n",
        "        preds = np.array([np.argmax(i) for i in avg_predictions])\n",
        "        print(accuracy_score(correct_labels, preds), f1_score(correct_labels, preds, average='weighted'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rMjuZqZwF6QF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#loading intial embeddings obtained by pre trained networks\n",
        "images = np.load('/content/image_embeddings.npy')\n",
        "texts = np.load('/content/text_embeddings.npy')\n",
        "labels_embeddings = np.load('/content/class_embeddings.npy')\n",
        "class_name = np.load('/content//class_name.npy', allow_pickle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISEvSTti7n95",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_labels_map = {}\n",
        "inv_class_labels_map = {}\n",
        "for i in range(0, 48):\n",
        "    class_labels_map[i] = class_name[i]\n",
        "    inv_class_labels_map[class_name[i]] = i"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5-3jioh7oEi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def repair(x):\n",
        "    x = x.replace('<', ' ')\n",
        "    return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJwy1Ea57oH9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#one hot encoding labels for training\n",
        "labels = df['classes'].map(repair)\n",
        "labels_num = labels.map(inv_class_labels_map)\n",
        "labels = to_categorical(labels_num, 48)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xPZpnz7-nVyl",
        "colab_type": "code",
        "outputId": "e0ffffab-e2eb-445c-c483-d1bcf5236869",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "labels_num"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         0\n",
              "1         0\n",
              "2         1\n",
              "3         2\n",
              "4         2\n",
              "         ..\n",
              "65709     7\n",
              "65710    28\n",
              "65711    27\n",
              "65712     9\n",
              "65713    14\n",
              "Name: classes, Length: 65714, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MTsxhPo-fJj",
        "colab_type": "text"
      },
      "source": [
        "We are now going to train our model and all the parameters required to train the model is defined before hand. We have trained thr model with batch size of 32 and for 1 epoch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jwfTY2wV7s0C",
        "colab_type": "code",
        "outputId": "9e9eba07-97b1-4f6e-b584-663f60fc3073",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#training\n",
        "history = []\n",
        "batch_size = 32\n",
        "margin = 0.5\n",
        "alpha, beta, gamma = 1, 1, 1\n",
        "EPOCHS = 1\n",
        "for epoch in tqdm(range(EPOCHS)):\n",
        "    for i in tqdm(range(batch_size,len(images), batch_size)):\n",
        "        images_batch = images[i-32:i]\n",
        "        texts_batch = texts[i-32:i]\n",
        "        labels_batch = labels[i-32:i]\n",
        "        labels_num_batch = labels_num[i-32:i]\n",
        "        labels_embeddings_batch = []\n",
        "        for label in labels_num_batch:\n",
        "            labels_embeddings_batch.append(labels_embeddings[label])\n",
        "        labels_embeddings_batch = np.array(labels_embeddings_batch)\n",
        "        assert(images_batch.shape == (32, 512))       # Check on inputs shape\n",
        "        assert(texts_batch.shape == (32, 512))\n",
        "        assert(labels_batch.shape == (32, 48))\n",
        "        assert(labels_embeddings_batch.shape == (32, 512))\n",
        "        train_step(images_batch, texts_batch, labels_batch, labels_embeddings_batch)"
      ],
      "execution_count": 0,
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}