Visualizing-convnet-filters-and-heatmaps-of-class-activation.ipynb

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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "q0n5fdXtu3Yk"
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      "outputs": [],
      "source": [
        "#install kaggle\n",
        "!pip install -q kaggle"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the Kaggle Python package is installed using pip, with the -q flag to suppress output messages during installation.\n",
        " Visualizing every channel in every intermediate activation"
      ],
      "metadata": {
        "id": "RNHByACGzNkH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "Ju71gGS_FxoM",
        "outputId": "646a92d0-9039-4155-8b75-15e27852f06b"
      },
      "execution_count": 2,
      "outputs": [
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script>// Copyright 2017 Google LLC\n",
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              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"akmalpaasha\",\"key\":\"625bab25107ad1956d90f0cda810a94d\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the files.upload() function from google.colab is used to prompt the user to upload files to the Google Colab environment. It allows users to select and upload files from their local machine to be used in the Colab notebook."
      ],
      "metadata": {
        "id": "FAnsiWxIzZNi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Kaggle Directory\n",
        "! mkdir ~/.kaggle"
      ],
      "metadata": {
        "id": "hmxMeSU1Gwky"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! cp kaggle.json ~/.kaggle/\n",
        "# copy the kaggle.jasn to created folder"
      ],
      "metadata": {
        "id": "i9mvzvV4HO1I"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cp kaggle.json /content/KaggleDataSets"
      ],
      "metadata": {
        "id": "WOWY2OgvJmfm"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# to grant premission to jasn to act\n",
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "iRbyBSKdKD5N"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# to list all datasets available in kaggle\n",
        "! kaggle datasets list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ehX8v5uuLp9E",
        "outputId": "a794a5ef-6773-46a6-bc00-78ec62a7cbfe"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ref                                                             title                                                size  lastUpdated          downloadCount  voteCount  usabilityRating  \n",
            "--------------------------------------------------------------  --------------------------------------------------  -----  -------------------  -------------  ---------  ---------------  \n",
            "alphiree/cardiovascular-diseases-risk-prediction-dataset        Cardiovascular Diseases Risk Prediction Dataset       5MB  2023-07-03 12:12:19           5796        234  1.0              \n",
            "nelgiriyewithana/countries-of-the-world-2023                    Global Country Information Dataset 2023              23KB  2023-07-08 20:37:33           4604        165  1.0              \n",
            "byomokeshsenapati/spotify-song-attributes                       Spotify Song Attributes                             883KB  2023-07-09 16:00:20           1291         39  1.0              \n",
            "arnavsmayan/netflix-userbase-dataset                            Netflix Userbase Dataset                             25KB  2023-07-04 07:38:41           5754        117  1.0              \n",
            "sumangoda/food-prices                                           Vital Food Costs: A Five-Nation Analysis 2018-2022    8KB  2023-07-16 19:33:29            733         28  1.0              \n",
            "kapturovalexander/covid-in-2023                                 Covid in 2023                                         2MB  2023-07-24 12:51:43            506         26  1.0              \n",
            "floatingcoder/tmdb-20000-movies-dataset                         Tmdb 20000 movies dataset 2023                      468KB  2023-07-17 15:44:29            576         26  1.0              \n",
            "iamsouravbanerjee/data-science-salaries-2023                    Latest Data Science Salaries                         67KB  2023-07-22 07:42:40           2611         86  1.0              \n",
            "rm1000/fortune-500-companies                                    Fortune 500 Companies                               360KB  2023-07-11 01:35:05           1099         34  1.0              \n",
            "harishkumardatalab/housing-price-prediction                     Housing Price Prediction                              5KB  2023-07-07 04:34:24           1844         48  1.0              \n",
            "khushipitroda/stock-market-historical-data-of-top-10-companies  Stock Market: Historical Data of Top 10 Companies   476KB  2023-07-18 10:28:12            746         28  1.0              \n",
            "nathaniellybrand/los-angeles-crime-dataset-2020-present         Los Angeles Crime Dataset (2020 -- Present)          37MB  2023-07-09 16:28:16            568         24  1.0              \n",
            "subhajournal/wine-quality-data-combined                         Wine Quality Data (Combined)                        660KB  2023-07-17 16:51:13            555         27  1.0              \n",
            "ujjwalwadhwa/cars24com-used-cars-dataset                        Cars24.com Used Cars dataset                        131KB  2023-07-16 11:04:03            898         31  1.0              \n",
            "howisusmanali/house-prices-2023-dataset                         House Prices 2023 Dataset                            12MB  2023-07-18 03:07:38            751         27  1.0              \n",
            "sanjanchaudhari/employees-performance-for-hr-analytics          Employee’s Performance for HR Analytics             205KB  2023-07-20 09:08:10            645         32  0.7647059        \n",
            "anshtanwar/pets-facial-expression-dataset                       🐶Pet's Facial Expression Image Dataset😻             138MB  2023-07-24 18:14:34            261         26  1.0              \n",
            "khushipitroda/imdb-top-250-tv-shows                             IMDB Top 250 TV Shows                                35KB  2023-07-18 13:08:14            611         32  1.0              \n",
            "uom190346a/sleep-health-and-lifestyle-dataset                   Sleep Health and Lifestyle Dataset                    3KB  2023-05-26 10:24:31           9318        195  1.0              \n",
            "manishkumar7432698/airline-passangers-booking-data              Airline Customer Holiday Booking Dataset              2MB  2023-07-17 20:19:27           1981         50  1.0              \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The ! at the beginning indicates that the command is executed in the command line or shell within the Colab environment. The command kaggle datasets list is used to list all available datasets on Kaggle, providing users with information about the datasets available for use or exploration."
      ],
      "metadata": {
        "id": "djqCJrgTzsUM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! kaggle competitions download -c dogs-vs-cats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-dzQnykML8Wc",
        "outputId": "4125a752-5651-40c7-d79c-8b9f6e4524cf"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading dogs-vs-cats.zip to /content\n",
            " 99% 803M/812M [00:04<00:00, 229MB/s]\n",
            "100% 812M/812M [00:04<00:00, 197MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Kaggle API is used to download the dataset for the \"Dogs vs. Cats\" competition in Google Colab. The command fetches the necessary files for the competition, allowing users to access the data and participate in the challenge."
      ],
      "metadata": {
        "id": "HnUC2Zs7zxg5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip dogs-vs-cats.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ir7lWjpgOAvF",
        "outputId": "014bc798-baae-46fa-bfee-a15709ec5e90"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  dogs-vs-cats.zip\n",
            "  inflating: sampleSubmission.csv    \n",
            "  inflating: test1.zip               \n",
            "  inflating: train.zip               \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip train.zip\n",
        "! unzip test1.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z_JFBlBZOJ9K",
        "outputId": "f2e70eda-dc67-4bac-c097-baaa6aac2179"
      },
      "execution_count": 10,
      "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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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os, shutil"
      ],
      "metadata": {
        "id": "OhddW8HHOV6v"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The os and shutil modules are imported, enabling you to perform file and directory operations, such as copying, moving, or deleting files and folders, in Python."
      ],
      "metadata": {
        "id": "PHBNut950PX3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "original_dataset_dir = '/content/train/'"
      ],
      "metadata": {
        "id": "m9LcVfbvXTJ5"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The variable original_dataset_dir is set to the directory path '/content/train/', which represents the location of the original dataset. This directory is likely used as the source from which data is loaded or processed in subsequent operations."
      ],
      "metadata": {
        "id": "KEgK3t9E0a0E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_dir = '/content/UntitledFolder/'\n"
      ],
      "metadata": {
        "id": "S1k7LmkXXb11"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The variable base_dir is set to the directory path '/content/UntitledFolder/', which represents the base directory where a new folder or project will be created. This directory can be used as the root location to organize and store files and data related to a specific task or project."
      ],
      "metadata": {
        "id": "lIAGygff0mXl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dir =os.path.join(base_dir, 'train')\n",
        "os.mkdir(train_dir)"
      ],
      "metadata": {
        "id": "j1NTbeviXj8h"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The variable train_dir is created by joining the base_dir and the folder name 'train' using the os.path.join() function. Then, the os.mkdir() function is used to create a new directory named 'train' within the base_dir directory, where training data can be stored or organized."
      ],
      "metadata": {
        "id": "ds7v2lfZ0zme"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "validation_dir =os.path.join(base_dir, 'validation')\n",
        "os.mkdir(validation_dir)"
      ],
      "metadata": {
        "id": "shYnfnKzYZ6v"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The variable validation_dir is created by joining the base_dir and the folder name 'validation' using the os.path.join() function. Then, the os.mkdir() function is used to create a new directory named 'train' within the base_dir directory, where training data can be stored or organized."
      ],
      "metadata": {
        "id": "NCri6b5y0_eF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_dir =os.path.join(base_dir, 'test')\n",
        "os.mkdir(test_dir)"
      ],
      "metadata": {
        "id": "_VWk18ztZpYY"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The variable test_dir is created by joining the base_dir and the folder name 'test' using the os.path.join() function. Then, the os.mkdir() function is used to create a new directory named 'train' within the base_dir directory, where training data can be stored or organized."
      ],
      "metadata": {
        "id": "SgXWGYiS1N_k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_cats_dir =os.path.join(train_dir, 'cats')\n",
        "os.mkdir(train_cats_dir)"
      ],
      "metadata": {
        "id": "4h3a06zXaRdn"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dogs_dir = os.path.join(train_dir, 'dogs')\n",
        "os.mkdir(train_dogs_dir)"
      ],
      "metadata": {
        "id": "PFUI32xUajbb"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "validation_cats_dir =os.path.join(validation_dir, 'cats')\n",
        "os.mkdir(validation_cats_dir)"
      ],
      "metadata": {
        "id": "oAxP5ey5azK9"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "validation_dogs_dir = os.path.join(validation_dir, 'dogs')\n",
        "os.mkdir(validation_dogs_dir)"
      ],
      "metadata": {
        "id": "yQ80nSLObCj2"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_cats_dir = os.path.join(test_dir, 'cats')\n",
        "os.mkdir(test_cats_dir)"
      ],
      "metadata": {
        "id": "ZzRY6ODbbTdX"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dogs_dir = os.path.join(test_dir, 'dogs')\n",
        "os.mkdir(test_dogs_dir)"
      ],
      "metadata": {
        "id": "MnL4r00qbhzk"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]\n",
        "for fname in fnames:\n",
        "  srs= os.path.join(original_dataset_dir, fname)\n",
        "  dst =os.path.join(train_cats_dir, fname)\n",
        "  shutil.copyfile(srs, dst)"
      ],
      "metadata": {
        "id": "hWf6OSF2bzY9"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The code creates a list of 1000 cat image filenames, joins the source and destination paths using os.path.join(), and then copies each cat image from the original dataset to the 'cats' directory in the training directory using shutil.copyfile(). This process organizes and prepares the cat images for machine learning model training."
      ],
      "metadata": {
        "id": "T09kxAO42TrW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fnames = ['cat.{}.jpg'.format(i) for i in range(1000,1500)]\n",
        "for fname in fnames:\n",
        "  srs = os.path.join(original_dataset_dir, fname)\n",
        "  dst = os.path.join(validation_cats_dir, fname)\n",
        "  shutil.copyfile(srs, dst)"
      ],
      "metadata": {
        "id": "dg_ZRGnNdHVA"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here i copies 500 cat images with filenames in the format 'cat.i.jpg', where i ranges from 1000 to 1499, from the original_dataset_dir to the validation_cats_dir."
      ],
      "metadata": {
        "id": "1Mk-MoaQrQTL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
        "for fname in fnames:\n",
        "  srs = os.path.join(original_dataset_dir, fname)\n",
        "  dst = os.path.join(test_cats_dir, fname)\n",
        "  shutil.copyfile(srs, dst)"
      ],
      "metadata": {
        "id": "CLj6IPNYeCvt"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here i am copying 500 cat images with filenames in the format 'cat.i.jpg', where i ranges from 1500 to 1999, from the original_dataset_dir to the test_cats_dir"
      ],
      "metadata": {
        "id": "68jTiT20rUhk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]\n",
        "for fname in fnames:\n",
        "  src = os.path.join(original_dataset_dir, fname)\n",
        "  dst = os.path.join(train_dogs_dir, fname)\n",
        "  shutil.copyfile(src, dst)"
      ],
      "metadata": {
        "id": "yXu5rgZieqH9"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here i am copying 1000 dog images with filenames in the format 'dog.i.jpg', where i ranges from 0 to 999, from the original_dataset_dir to the train_dogs_dir."
      ],
      "metadata": {
        "id": "gMx6ky9brXyK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
        "for fname in fnames:\n",
        "  srs =os.path.join(original_dataset_dir, fname)\n",
        "  dst = os.path.join(validation_dogs_dir, fname)\n",
        "  shutil.copyfile(srs, dst)"
      ],
      "metadata": {
        "id": "X5q8mUdUf45z"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here i am copying 500 dog images with filenames in the format 'dog.i.jpg', where i ranges from 1000 to 1499, from the original_dataset_dir to the validation_dogs_dir."
      ],
      "metadata": {
        "id": "RHk9KV5erbow"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
        "for fname in fnames:\n",
        "  srs = os.path.join(original_dataset_dir, fname)\n",
        "  dst = os.path.join(test_dogs_dir, fname)\n",
        "  shutil.copyfile(srs, dst)"
      ],
      "metadata": {
        "id": "tfTmWmt6gaSP"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here i am copying 500 dog images with filenames in the format 'dog.i.jpg', where i ranges from 1500 to 1999, from the original_dataset_dir to the test_dogs_dir."
      ],
      "metadata": {
        "id": "e7YrYUh_rgSX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('total training cats images', len(os.listdir(train_cats_dir)))\n",
        "print('total training dogs images', len(os.listdir(train_dogs_dir)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ZoVpJNEg6Kf",
        "outputId": "a621e3a2-b826-4a73-f12d-46a8ef4be5db"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total training cats images 1000\n",
            "total training dogs images 1000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The total number of training images for both cats and dogs classes in the specified directories."
      ],
      "metadata": {
        "id": "KVBxVaGGrpvh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('total validation cats images', len(os.listdir(validation_cats_dir)))\n",
        "print('total validation dogs images', len(os.listdir(validation_dogs_dir)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mu2OzQbPhSJF",
        "outputId": "0933d35b-bd8b-47c1-d2eb-4bfd72e6fcc4"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total validation cats images 500\n",
            "total validation dogs images 500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The total number of validation images for both cats and dogs classes in the specified directories."
      ],
      "metadata": {
        "id": "FSFvW_D0rq4k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('total test cats images', len(os.listdir(test_cats_dir)))\n",
        "print('total test dogs images', len(os.listdir(test_dogs_dir)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pO5N1boTjnoe",
        "outputId": "a46fc078-585f-4273-ea2d-34106d8d89ad"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total test cats images 500\n",
            "total test dogs images 500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The total number of test images for both cats and dogs classes in the specified directories."
      ],
      "metadata": {
        "id": "u7LVO5p2rt_1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import models\n",
        "from keras import layers"
      ],
      "metadata": {
        "id": "RuY6NYJukQ2u"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model =models.Sequential()\n",
        "model.add(layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (150,150,3)))\n",
        "model.add(layers.MaxPooling2D(2,2))\n",
        "model.add(layers.Conv2D(64, (3,3), activation = 'relu'))\n",
        "model.add(layers.MaxPooling2D(2,2))\n",
        "model.add(layers.Conv2D(128,(3,3), activation = 'relu'))\n",
        "model.add(layers.MaxPooling2D(2,2))\n",
        "model.add(layers.Conv2D(128, (3,3), activation = 'relu'))\n",
        "model.add(layers.MaxPooling2D(2,2))\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(512, activation = 'relu'))\n",
        "model.add(layers.Dense(1, activation = 'sigmoid'))"
      ],
      "metadata": {
        "id": "AWe_7LAzoGk7"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here i defines a sequential model in Keras for image classification. It consists of multiple Conv2D and MaxPooling2D layers, followed by a Flatten layer to convert the 2D feature maps to a 1D vector. It then includes two dense layers with ReLU activation and a final dense layer with sigmoid activation for binary classification (sigmoid outputs a probability between 0 and 1 for binary classification)."
      ],
      "metadata": {
        "id": "CJ92--vYr01A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJEERLXeoaaL",
        "outputId": "25375aae-e6b0-4f43-9b89-b732661411f8"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 148, 148, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 74, 74, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 72, 72, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 36, 36, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 34, 34, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 17, 17, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 15, 15, 128)       147584    \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 7, 7, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 6272)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               3211776   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 513       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,453,121\n",
            "Trainable params: 3,453,121\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The provided summary describes a neural network model for image classification. It consists of four convolutional layers with max-pooling, followed by a flatten layer to convert the 2D feature maps to a 1D vector. The model then includes two dense layers with ReLU activation, and a final dense layer with sigmoid activation for binary classification. The model has a total of 3,453,121 trainable parameters."
      ],
      "metadata": {
        "id": "Px3prsOXr6Ge"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import optimizers"
      ],
      "metadata": {
        "id": "cl4UZKJRpzRR"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy',optimizer=optimizers.RMSprop(learning_rate=1e-4),metrics=['acc'])"
      ],
      "metadata": {
        "id": "GXI_WvRqp-wq"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here i compiles the neural network model using binary cross-entropy as the loss function, RMSprop optimizer with a learning rate of 1e-4, and accuracy as the evaluation metric."
      ],
      "metadata": {
        "id": "IUKFYozCr-X9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "metadata": {
        "id": "8idMispwqfIG"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)"
      ],
      "metadata": {
        "id": "kHjIKBZYrYSG"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The code sets up two image data generators, train_datagen and test_datagen, which are used for data preprocessing and augmentation during training and testing. The rescale parameter is set to 1./255, which means the pixel values of the images will be scaled to the range [0, 1]."
      ],
      "metadata": {
        "id": "nZzCvDfOsFwO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "train_dir,\n",
        "target_size=(150, 150),\n",
        "batch_size=20,\n",
        "class_mode='binary')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dPJKpqHZrtPu",
        "outputId": "b9d4298b-a959-4726-c096-14f85d023989"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2000 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The code creates a train_generator using the flow_from_directory method of the train_datagen. It generates batches of training data from the directory specified by train_dir. The images are resized to a target size of (150, 150), and each batch contains 20 images. The class mode is set to 'binary' because it is a binary classification task (two classes - cats and dogs)."
      ],
      "metadata": {
        "id": "klXufuDYsJMA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "validation_generator = test_datagen.flow_from_directory(validation_dir, target_size=(150,150), batch_size=20, class_mode='binary')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xi4Xc8P5sSoa",
        "outputId": "5463a710-e2d4-40c8-d8cd-3d50b0c40158"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1000 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The code creates a validation_generator using the flow_from_directory method of the test_datagen. It generates batches of validation data from the directory specified by validation_dir. The images are resized to a target size of (150, 150), and each batch contains 20 images. The class mode is set to 'binary' because it is a binary classification task (two classes - cats and dogs) used for validation during model training."
      ],
      "metadata": {
        "id": "MX7KKSvpsNQL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for data_batch, labels_batch in train_generator:\n",
        "  print('Data batch shape', data_batch.shape)\n",
        "  print('Data labels Shape', labels_batch.shape)\n",
        "  break"
      ],
      "metadata": {
        "id": "405z8d7ps9kB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3499f2c5-efbd-4913-f8a3-9677a4134341"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data batch shape (20, 150, 150, 3)\n",
            "Data labels Shape (20,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The provided code is an example of how to use a data generator (train_generator) in a loop to get batches of data and their corresponding labels. During each iteration, it prints the shape of the data batch and the labels batch, allowing you to inspect the size of the batches."
      ],
      "metadata": {
        "id": "fJ0ErKidsvdl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for data_batch, labels_batch in validation_generator:\n",
        "  print('Data batch shape', data_batch.shape)\n",
        "  print('Data labels Shape', labels_batch.shape)\n",
        "  break"
      ],
      "metadata": {
        "id": "ElInYi3svojU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbc1fea7-d832-4838-c4b8-807441e38fcf"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data batch shape (20, 150, 150, 3)\n",
            "Data labels Shape (20,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The provided code is an example of how to use a data generator (validation_generator) in a loop to get batches of data and their corresponding labels. During each iteration, it prints the shape of the data batch and the labels batch, allowing you to inspect the size of the batches."
      ],
      "metadata": {
        "id": "XY4wp1Wxswex"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit_generator(train_generator, steps_per_epoch=100, epochs= 5, validation_data = validation_generator, validation_steps=50 )"
      ],
      "metadata": {
        "id": "bUt6nXMS0-bT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('cats_and_dogs_small_2.h5')"
      ],
      "metadata": {
        "id": "56cefV8g8iAi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "qZX53H0L8uKP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model"
      ],
      "metadata": {
        "id": "XJd7uyBlB0v9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model =load_model('/content/cats_and_dogs_small_2.h5')"
      ],
      "metadata": {
        "id": "7qVWCADmCMZ4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "sRkgeGpwCUOl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img_path = '/content/train/cat.1700.jpg'\n",
        "from keras.preprocessing import image\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "img = tf.keras.preprocessing.image.load_img(img_path, target_size =(150,150))\n",
        "img_tensor = tf.keras.preprocessing.image.img_to_array(img)\n",
        "img_tensor = np.expand_dims(img_tensor, axis=0)\n",
        "img_tensor /=255.\n",
        "print(img_tensor.shape)\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(img_tensor[0])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "GeZTiEg9CbXL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "The provided code loads an image, converts it into a tensor, and visualizes the image using matplotlib after normalizing its pixel values between 0 and 1."
      ],
      "metadata": {
        "id": "RJOpdxVUtWdR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "layer_outputs = [layer.output for layer in model.layers[:8]]\n",
        "activation_model = models.Model(inputs=model.input, outputs=layer_outputs)"
      ],
      "metadata": {
        "id": "cVyrXJJxC7ar"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The code creates a new model called \"activation_model\" that takes the same input as the original \"model\" but outputs the activations of the first 8 layers as a list. This allows us to extract the intermediate layer outputs for visualization or further analysis."
      ],
      "metadata": {
        "id": "aZLxN_dBtoZp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "activations = activation_model.predict(img_tensor)"
      ],
      "metadata": {
        "id": "4Tn0a4w5C-yg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The code uses the \"activation_model\" to predict the activations of the input image \"img_tensor\" for the first 8 layers of the original model. The activations are stored in the list variable \"activations\"."
      ],
      "metadata": {
        "id": "onzeqJ-xt0Ip"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "first_layer_activation = activations[0]\n",
        "print(first_layer_activation.shape)"
      ],
      "metadata": {
        "id": "TwLdSe5EDM3o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The variable \"first_layer_activation\" contains the activations of the first layer of the model. The shape of these activations is printed, which gives us information about the dimensions of the activations."
      ],
      "metadata": {
        "id": "MiXt804at-cC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.matshow(first_layer_activation[0, :, :, 7], cmap ='viridis')"
      ],
      "metadata": {
        "id": "QEhXhtQADl3M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "plt.matshow(first_layer_activation[0, :, :, 7], cmap ='viridis')"
      ],
      "metadata": {
        "id": "nxxKGnbnuD9b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "layer_names = []\n",
        "for layer in model.layers[:8]:\n",
        "  layer_names.append(layer.name)\n",
        "\n",
        "images_per_row = 16\n",
        "for layer_name, layer_activation in zip (layer_names , activations):\n",
        "  n_features = layer_activation.shape[-1]\n",
        "  size = layer_activation.shape[1]\n",
        "  n_cols = n_features// images_per_row\n",
        "  display_grid = np.zeros((size*n_cols, images_per_row*size))\n",
        "  for col in range(n_cols):\n",
        "    for row in range(images_per_row):\n",
        "      channel_image = layer_activation[0, :,:, col*images_per_row+row]\n",
        "      channel_image -= channel_image.mean()\n",
        "      channel_image /= channel_image.std()\n",
        "      channel_image *= 64\n",
        "      channel_image += 128\n",
        "      channel_image = np.clip(channel_image, 0, 255).astype('uint8')\n",
        "      display_grid[col * size : (col + 1) * size, row * size : (row + 1) * size] = channel_image\n",
        "    scale = 1. / size\n",
        "    plt.figure(figsize=(scale * display_grid.shape[1], scale * display_grid.shape[0]))\n",
        "    plt.title(layer_name)\n",
        "    plt.grid(False)\n",
        "    plt.imshow(display_grid, aspect='auto', cmap='viridis')"
      ],
      "metadata": {
        "id": "IKEqrPiaJJxi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The code creates a grid of visualizations for the activations of the first 8 layers in the model. It iterates through each layer and generates visualizations for each channel in the layer's activation. The visualizations are adjusted to enhance the patterns by normalizing the values and then rescaling the intensity. The resulting grid is displayed for each layer using matplotlib's \"imshow\" function with the \"viridis\" colormap."
      ],
      "metadata": {
        "id": "OA43iQY4uSq7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.applications import VGG16\n",
        "from keras import backend as K\n",
        "tf.compat.v1.disable_eager_execution()"
      ],
      "metadata": {
        "id": "rpb1_-3tLsoW"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = VGG16(weights = 'imagenet', include_top = False)\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "ZZ8WAujuL0Gl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The code initializes a VGG16 model with pre-trained weights from the ImageNet dataset and excludes the fully connected layers at the top for feature extraction purposes. The model can now be used to extract features from input images."
      ],
      "metadata": {
        "id": "I2m4UHYmuanE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "layer_name = 'block3_conv1'\n",
        "filter_index = 0\n",
        "layer_output = model.get_layer(layer_name).output\n",
        "loss = K.mean(layer_output[:,:,:,filter_index])\n"
      ],
      "metadata": {
        "id": "nCvGYwQgNZSS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The code defines a loss function to maximize the activation of a specific filter (filter_index) in a particular layer (block3_conv1) of the VGG16 model. It aims to find an input image that produces a high activation for the specified filter."
      ],
      "metadata": {
        "id": "iFX6Acuqul0j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n"
      ],
      "metadata": {
        "id": "y4nCLLbDNl2V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grads = K.gradients(loss, model.input)[0]"
      ],
      "metadata": {
        "id": "cv7jvJtCOaKi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The code calculates the gradients of the specified loss function (loss) with respect to the input tensor (model.input) using the Keras backend. These gradients represent the sensitivity of the loss function to changes in the input image pixels, which helps in finding the direction to update the image to maximize the activation of the chosen filter."
      ],
      "metadata": {
        "id": "0DvVUCVow_-a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)"
      ],
      "metadata": {
        "id": "-DXryuh6Xhy4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here i normalize the gradients obtained earlier. It divides each element in the gradients tensor by the square root of the mean of the squared gradients plus a small constant (1e-5) to prevent division by zero. This normalization helps control the scale of the gradients and improves stability during the optimization process."
      ],
      "metadata": {
        "id": "XvZC7yksyRjL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iterate = K.function([model.input], [loss, grads])\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "GIjskyG0yDCw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This line of code creates a Keras function `iterate` that takes the model's input tensor as input and returns a list containing the loss value and the normalized gradients with respect to the input. The `K.function` method is used to convert the model input and the output tensors (loss and grads) into a callable function. This allows you to efficiently compute the loss and gradients for a given input."
      ],
      "metadata": {
        "id": "zvdDeSyxze3U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss_value, grads_value = iterate([np.zeros((1,150,150,3))])"
      ],
      "metadata": {
        "id": "oSFcX1Ecyy_A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "loss_value and grads_value are obtained by calling the iterate function with a dummy input, which is a NumPy array of shape (1, 150, 150, 3) filled with zeros. The iterate function computes the loss and gradients with respect to this dummy input and returns the results in loss_value and grads_value. These values can then be used to visualize the activation and gradients of specific filters in the convolutional layers of the VGG16 model."
      ],
      "metadata": {
        "id": "z761UNZnzqT5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_img_data = np.random.random((1,150,150,3))*20+128."
      ],
      "metadata": {
        "id": "wNvfb_qXzK9Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "'input_img_data` is a random NumPy array of shape `(1, 150, 150, 3)` filled with random values between 128 and 148. This array represents a single input image to be used for visualization of the activation and gradients in the VGG16 model."
      ],
      "metadata": {
        "id": "XioqfrIn0Zcp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "step = 1.\n",
        "for i in range(40):\n",
        "  loss_value, grads_value = iterate([input_img_data])\n",
        "  input_img_data += grads_value*step"
      ],
      "metadata": {
        "id": "PxGjqIwD0QVV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def deprocess_image(x):\n",
        "  x -= x.mean()\n",
        "  x /= (x.std() + 1e-5)\n",
        "  x *= 0.1\n",
        "  x += 0.5\n",
        "  x = np.clip(x, 0, 1)\n",
        "  x *= 255\n",
        "  x = np.clip(x, 0, 255).astype('uint8')\n",
        "  return x"
      ],
      "metadata": {
        "id": "H4FSb5-K08At"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Normalizes the tensor:\n",
        "centers on 0, ensures\n",
        "that std is 0.1\n",
        "Clips to [0, 1] Converts to an RGB array\n"
      ],
      "metadata": {
        "id": "DQcs8bkQ15sY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_pattren(layer_name, filter_index, size = 150):\n",
        "  layer_output = model.get_layer(layer_name).output\n",
        "  loss = K.mean(layer_output[ :, :, :, filter_index])\n",
        "  grads = K.gradients(loss, model.input)[0]\n",
        "  grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)\n",
        "  iterate = K.function([model.input], [loss, grads])\n",
        "  input_img_data = np.random.random((1, size, size, 3)) *20 + 128.\n",
        "  step =1.\n",
        "  for i in range(40):\n",
        "    loss_value, grads_value = iterate(input_img_data)\n",
        "    input_img_data += grads_value*step\n",
        "  img = input_img_data[0]\n",
        "  return deprocess_image(img)\n"
      ],
      "metadata": {
        "id": "FTFLlSvf11Hx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " plt.imshow(generate_pattren('block3_conv1', 0))"
      ],
      "metadata": {
        "id": "7-IyP-FY60l8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "layer_name = 'block3_conv1'\n",
        "size = 64\n",
        "margin = 5\n",
        "results = np.zeros((8*size+ 7*margin, 8*size + 7 * margin, 3))\n",
        "for i in range(8):\n",
        "  for j in range(8):\n",
        "    filter_img = generate_pattren(layer_name, i + (j*8), size =size)\n",
        "    horizontal_start = i* size + i * margin\n",
        "    horizontal_end = horizontal_start + size\n",
        "    vertical_start = j * size + j * margin\n",
        "    vertical_end = vertical_start + size\n",
        "    results[horizontal_start: horizontal_end,vertical_start: vertical_end, :] = filter_img\n",
        "plt.figure(figsize=(20, 20))\n",
        "plt.imshow(results)"
      ],
      "metadata": {
        "id": "Bap0Pk157kzL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "p-ax7uMKZnhr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generating a grid of all filter response patterns in a layer"
      ],
      "metadata": {
        "id": "INLD0Lv6Zn0L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.applications.vgg16 import VGG16\n",
        "model = VGG16(weights='imagenet')"
      ],
      "metadata": {
        "id": "ttKgpPe1ZNil"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing import image\n",
        "from keras.applications.vgg16 import preprocess_input, decode_predictions\n",
        "import numpy as np\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "aMToQQZwZpYN"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_path = '/content/ElephantImage/Elephant-0-jpg'\n",
        "img = tf.keras.preprocessing.image.load_img(input_path, target_size = (224, 224))\n",
        "x = tf.keras.preprocessing.image.img_to_array(img)\n",
        "x = np.expand_dims(x, axis=0)\n",
        "x = preprocess_input(x)\n",
        "print(x.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K6QXYXb0bZDp",
        "outputId": "e6d82c6b-4300-416e-f4ac-1fac9a4be7fd"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 224, 224, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicts = model.predict(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HoXn6WUsb9v1",
        "outputId": "3c43693d-9eaa-4689-f2eb-e75dfb9d9931"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/engine/training_v1.py:2359: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
            "  updates=self.state_updates,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('predications', decode_predictions(predicts, top = 3)[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J9pZJNJxdPDc",
        "outputId": "d8a3bd63-6cab-4ff8-b776-5026588d6451"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predications [('n02504458', 'African_elephant', 0.84132445), ('n01871265', 'tusker', 0.14088209), ('n02504013', 'Indian_elephant', 0.017427128)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(predicts[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnraPMeEdRf3",
        "outputId": "a6498e26-88bf-4ef0-f25a-778d4c18c081"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "386"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "african_elephant_output = model.output[:, 386]"
      ],
      "metadata": {
        "id": "vAXAMN5vdd3U"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "last_conve_layer = model.get_layer('block5_conv3')"
      ],
      "metadata": {
        "id": "NpFTBE4TgO2P"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grads = K.gradients(african_elephant_output, last_conve_layer.output)[0]"
      ],
      "metadata": {
        "id": "EpmtUzUwgfKv"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pooled_grads = K.mean(grads, axis =(0,1,2))"
      ],
      "metadata": {
        "id": "pqtPDRQFghlE"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iterate = K.function([model.input],[pooled_grads, last_conve_layer.output[0]])\n",
        "pooled_grads_value, conv_layer_output_value = iterate([x])\n",
        "for i in range(512):\n",
        "  conv_layer_output_value[:,:,i] *= pooled_grads_value[i]\n",
        "heatmap = np.mean(conv_layer_output_value, axis = -1)"
      ],
      "metadata": {
        "id": "HbcdoPqRjVLr"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heatmap = np.maximum(heatmap, 0)"
      ],
      "metadata": {
        "id": "Xt6j15TSkszp"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heatmap /= np.max(heatmap)\n",
        "import matplotlib.pyplot as plt\n",
        "plt.matshow(heatmap)"
      ],
      "metadata": {
        "id": "_Prifw8rmwWi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "img = cv2.imread(input_path)"
      ],
      "metadata": {
        "id": "1Vnd2PiNnR6p"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heatmap = cv2.resize(heatmap , (img.shape[1], img.shape[0]))"
      ],
      "metadata": {
        "id": "LzrYyiV3o7Tc"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heatmap = np.uint8(255*heatmap)"
      ],
      "metadata": {
        "id": "qq-gCeyupirJ"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)"
      ],
      "metadata": {
        "id": "2C2C7Jz-p6SQ"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "superimposed_img = heatmap*0.4+img"
      ],
      "metadata": {
        "id": "RjsRzHK7qeWn"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv2.imwrite('/content/ElephantImage/Elephant-0.jpg', superimposed_img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97HqUe7TqqEN",
        "outputId": "382f99a8-f348-436b-de32-7504c14791f5"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    }
  ]
}