[0][First_training]DAI_ASSIGNMENT_2_Part_A.ipynb

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    "colab": {
      "name": "DAI_ASSIGNMENT_2_Part_a.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nv4td2SSNVqd"
      },
      "source": [
        "AIM: ​Understand and Perform different types of Adversarial Attacks, Detection and Mitigation.\n",
        "Dataset: D-10 Dataset(​CLICK​ for Dataset) - 10 Class Animal Dataset\n",
        "Reference Paper: ​Adversarial Attacks and Defenses in Deep Learning\n",
        "\n",
        "Dataset Link - https://drive.google.com/file/d/15zl0Ef0G_P_b-9akgIc67gQohU7dUmfy/view\n",
        "\n",
        "Ref Paper - https://doi.org/10.1016/j.eng.2019.12.012"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2Kp2Y4dNU3P"
      },
      "source": [
        " *Part* (A): Take any Deep Model (like VGG16, VGG19, ResNet50, GoogleNet etc.) and train the D-10 Dataset from scratch(no pre-initialised weights) and ​report the accuracy, training-testing loss graph, classification report​(use sklearn library for assistance). ​[ 20 Marks] "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CXprX4jvMCKm",
        "outputId": "2ad6649f-6a22-4ad6-e4d1-9869ba821d8f"
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive') \n",
        "import os\n",
        "%cd '/content/gdrive/My Drive/DAI_ASSIGNMENT_2'"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n",
            "/content/gdrive/My Drive/DAI_ASSIGNMENT_2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "7NmjgL6-Oahj",
        "outputId": "d2180aed-7cd8-4028-8ee8-9ff28fe355f2"
      },
      "source": [
        "# !gdown --id 15zl0Ef0G_P_b-9akgIc67gQohU7dUmfy\n",
        "!pip install kora\n",
        "from kora import drive\n",
        "drive.download_folder('15zl0Ef0G_P_b-9akgIc67gQohU7dUmfy')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting kora\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e7/7c/6847638364fbd192f4672c72bd132d2c3cfe07ad91409ab6321136cc621d/kora-0.9.14-py3-none-any.whl (55kB)\n",
            "\r\u001b[K     |█████▉                          | 10kB 12.9MB/s eta 0:00:01\r\u001b[K     |███████████▊                    | 20kB 18.1MB/s eta 0:00:01\r\u001b[K     |█████████████████▋              | 30kB 9.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████▍        | 40kB 7.8MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▎  | 51kB 4.6MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 61kB 3.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: ipython in /usr/local/lib/python3.6/dist-packages (from kora) (5.5.0)\n",
            "Collecting fastcore\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/17/d8/eb57e62997ae65b379fd5e7cdb9c865a1c7b4b106491c859839f6b6e9b38/fastcore-1.3.18-py3-none-any.whl (53kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 4.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython->kora) (51.1.1)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython->kora) (0.7.5)\n",
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            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.6/dist-packages (from ipython->kora) (4.3.3)\n",
            "Requirement already satisfied: pip in /usr/local/lib/python3.6/dist-packages (from fastcore->kora) (19.3.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from fastcore->kora) (20.8)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython->kora) (0.7.0)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->kora) (1.15.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->kora) (0.2.5)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython->kora) (0.2.0)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->fastcore->kora) (2.4.7)\n",
            "Installing collected packages: fastcore, kora\n",
            "Successfully installed fastcore-1.3.18 kora-0.9.14\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'D-10 Dataset.zip'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnaSJ-FyOxjO",
        "outputId": "bf8cf824-f61b-4399-d8e3-1211e5312e95"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classes.py  Images  processed_images\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HUV1fufPH4E"
      },
      "source": [
        "!unzip \\*.zip\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XzFl2jqPRxv"
      },
      "source": [
        "!cd Images"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aqpo703PQgqo",
        "outputId": "a29db0b4-13b8-428b-a07c-f4c1b661c15a"
      },
      "source": [
        "!pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/DAI_ASSIGNMENT_2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9oUKcNbQiiw",
        "outputId": "bb03c218-decb-4fb0-d05a-a672207c5102"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Classes.py  'D-10 Dataset.zip'   Images\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-64yc7-SRFLh",
        "outputId": "0bbcec29-ea8d-4a43-de72-71e9c3cd540f"
      },
      "source": [
        "%cd '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/'\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/DAI_ASSIGNMENT_2\n",
            " Classes.py  'D-10 Dataset.zip'   Images\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuJ2FjZbRKAh"
      },
      "source": [
        "!mkdir processed_images"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J8nj9wBNROJZ",
        "outputId": "46b605d6-c811-43c2-dbcf-f635d043b543"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Classes.py  'D-10 Dataset.zip'   Images   processed_images\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mGspl9VrQjIe",
        "outputId": "8cfaba4c-14b0-41ed-f626-11487cee6a43"
      },
      "source": [
        "%cd '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/Images'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/DAI_ASSIGNMENT_2/Images\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ynb11DAJQrIZ",
        "outputId": "a988d9df-261d-4213-87d8-4c4b127d1c6e"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cane\t elefante  gallina  mucca   ragno\n",
            "cavallo  farfalla  gatto    pecora  scoiattolo\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QUNy02hQyoY",
        "outputId": "07ec60cf-8146-4d2c-d08b-fe0c00cbe565"
      },
      "source": [
        "!pip install split_folders"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting split_folders\n",
            "  Downloading https://files.pythonhosted.org/packages/b8/5f/3c2b2f7ea5e047c8cdc3bb00ae582c5438fcdbbedcc23b3cc1c2c7aae642/split_folders-0.4.3-py3-none-any.whl\n",
            "Installing collected packages: split-folders\n",
            "Successfully installed split-folders-0.4.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KCRUywlGQ0nl",
        "outputId": "029c50ac-1aca-4c2c-ae24-9bf172031231"
      },
      "source": [
        "import splitfolders\n",
        "folder_dataset = '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/Images'\n",
        "folder_output = '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images'\n",
        "splitfolders.ratio(folder_dataset,folder_output,seed = 100, ratio=(0.6,0.4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Copying files: 26179 files [05:46, 75.48 files/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1XvmUsIKR4lt",
        "outputId": "910e8583-4da6-4127-e2dc-e47483be7ad6"
      },
      "source": [
        "%cd '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images'\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images\n",
            "train  val\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "owkjsRdfThze"
      },
      "source": [
        "!mv val test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-fW3w95T78M",
        "outputId": "d7d02bb1-a4fc-45c3-d12f-59c93afb4199"
      },
      "source": [
        "!ls | wc -l"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "duF5JW6mUkNq",
        "outputId": "69fdb273-a12e-4385-9272-6795e7563632"
      },
      "source": [
        "%cd '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images'\n",
        "!ls\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images\n",
            "final_model.h5\ttest  train\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tOhRCIXpVju3",
        "outputId": "9ae39b43-b78d-492e-ba46-6096e9fcfaca"
      },
      "source": [
        "!find . -maxdepth 2 -type d"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".\n",
            "./train\n",
            "./train/cane\n",
            "./train/cavallo\n",
            "./train/elefante\n",
            "./train/farfalla\n",
            "./train/gallina\n",
            "./train/gatto\n",
            "./train/mucca\n",
            "./train/pecora\n",
            "./train/ragno\n",
            "./train/scoiattolo\n",
            "./test\n",
            "./test/cane\n",
            "./test/cavallo\n",
            "./test/elefante\n",
            "./test/farfalla\n",
            "./test/gallina\n",
            "./test/gatto\n",
            "./test/mucca\n",
            "./test/pecora\n",
            "./test/ragno\n",
            "./test/scoiattolo\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4QHNYBFYWShy",
        "outputId": "a12a6a24-f02c-43f8-d529-ea7bcb121705"
      },
      "source": [
        "!find . -type f | wc -l"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "^C\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7JTH1l4Whj3",
        "outputId": "e842ab11-698d-413b-8ece-f95acd0b2aa6"
      },
      "source": [
        "!find . -maxdepth 1 -mindepth 1 -type d -exec sh -c 'echo \"{} : $(find \"{}\" -type f | wc -l)\" file\\(s\\)' \\;\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "./train : 15702 file(s)\n",
            "./test : 10477 file(s)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFbzCyqEWrSq"
      },
      "source": [
        "# Yes seems like the split is ok!"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fuJ6mtu7Ww1V"
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd \n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "import os\n",
        "import sys\n",
        "import time\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "from builtins import range, input\n",
        "\n",
        "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten, AveragePooling2D, Dropout, Conv2D, MaxPool2D, BatchNormalization, GlobalAveragePooling2D\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
        "from tensorflow.keras.applications import InceptionV3\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "from tensorflow.keras.applications.resnet50 import ResNet50\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from sklearn.metrics import confusion_matrix, roc_curve\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "import cv2\n",
        "from glob import glob\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from tensorflow.keras.utils import to_categorical\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7vIJqEyz1x2"
      },
      "source": [
        "h,w = (224,224)\n",
        "batch = 32\n",
        "train = '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images/train'\n",
        "test = '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images/test'"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kB91eHgG1C5c",
        "outputId": "3df0df4e-4a56-4826-9ef0-be5b705ea1d1"
      },
      "source": [
        "dataloader = ImageDataGenerator(\n",
        "                                  preprocessing_function = preprocess_input,\n",
        "                                  shear_range = 0.2,\n",
        "                                  zoom_range = 0.2,\n",
        "                                  horizontal_flip = True,\n",
        "                                  )\n",
        "train_loader = dataloader.flow_from_directory(\n",
        "    train, \n",
        "    target_size = (h,w),\n",
        "    batch_size = batch,\n",
        "    class_mode= 'categorical',\n",
        "    subset = 'training'\n",
        ")\n",
        "\n",
        "test_loader = dataloader.flow_from_directory(\n",
        "    test, \n",
        "    target_size = (h,w),\n",
        "    batch_size = 1,\n",
        "    class_mode= 'categorical',\n",
        "    subset = 'training'\n",
        ")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 15702 images belonging to 10 classes.\n",
            "Found 10477 images belonging to 10 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mwR14o7a8x13",
        "outputId": "dddaac69-12e8-4605-af77-a9f88f997b20"
      },
      "source": [
        "print(\"testing example\")\n",
        "xtestex,ytestex = test_loader.next()\n",
        "print(xtestex.shape)\n",
        "print(ytestex.shape)\n",
        "print(\"training example\")\n",
        "xtrainex, ytrainex = train_loader.next()\n",
        "print(xtrainex.shape)\n",
        "print(ytrainex.shape)\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "testing example\n",
            "(1, 224, 224, 3)\n",
            "(1, 10)\n",
            "training example\n",
            "(32, 224, 224, 3)\n",
            "(32, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_7MOs5WBTyY"
      },
      "source": [
        "\n",
        "base_model = ResNet50(weights=None,input_shape=(224,224,3),include_top=False)\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yx3kwJnBFcb2",
        "outputId": "62efd3a3-f0cb-40a7-abf6-7901328078f1"
      },
      "source": [
        "base_model.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"resnet50\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          \n",
            "                                                                 conv2_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           \n",
            "                                                                 conv2_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          \n",
            "                                                                 conv3_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           \n",
            "                                                                 conv3_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          \n",
            "                                                                 conv4_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           \n",
            "                                                                 conv4_block6_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          \n",
            "                                                                 conv5_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           \n",
            "==================================================================================================\n",
            "Total params: 23,587,712\n",
            "Trainable params: 23,534,592\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GhfhdaolF5E9",
        "outputId": "9f8af200-0ba5-4e12-ad22-e311498dd693"
      },
      "source": [
        "from IPython.display import HTML, display, clear_output, SVG\n",
        "from keras.utils.vis_utils import model_to_dot\n",
        "\n",
        "SVG(model_to_dot(base_model, show_layer_names=True, show_shapes=True, rankdir='TB', dpi=60).create(prog='dot', format='svg'))\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pt2GAIQ8GUB1"
      },
      "source": [
        "new_model = base_model.output\n",
        "new_model = AveragePooling2D(pool_size=(7, 7))(new_model)\n",
        "new_model = Flatten(name=\"flatten\")(new_model)\n",
        "new_model = Dense(256, activation=\"relu\")(new_model)\n",
        "# new_model = Dropout(0.5)(new_model)\n",
        "new_model = Dense(10, activation=\"softmax\")(new_model)\n",
        "model = Model(inputs=base_model.input, outputs=new_model)\n"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pn39j85dIkyA",
        "outputId": "e2f8672c-9781-4ef2-db98-c782fb30aab3"
      },
      "source": [
        "# import torch\n",
        "# device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "# model = model.to(device)\n",
        "\n",
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))\n",
        "\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AadY5WJ6Jxet",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2469c3e-2960-4bb6-b218-c98447f37233"
      },
      "source": [
        "with tf.device('/device:GPU:0'):\n",
        "  model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])\n",
        "  history = model.fit(train_loader,epochs=20)\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "491/491 [==============================] - 259s 518ms/step - loss: 1.9050 - accuracy: 0.3290\n",
            "Epoch 2/20\n",
            "491/491 [==============================] - 253s 515ms/step - loss: 1.6327 - accuracy: 0.4289\n",
            "Epoch 3/20\n",
            "491/491 [==============================] - 249s 506ms/step - loss: 1.3883 - accuracy: 0.5187\n",
            "Epoch 4/20\n",
            "491/491 [==============================] - 252s 512ms/step - loss: 1.2451 - accuracy: 0.5643\n",
            "Epoch 5/20\n",
            "491/491 [==============================] - 250s 509ms/step - loss: 1.1467 - accuracy: 0.6059\n",
            "Epoch 6/20\n",
            "491/491 [==============================] - 250s 509ms/step - loss: 1.0627 - accuracy: 0.6362\n",
            "Epoch 7/20\n",
            "491/491 [==============================] - 250s 508ms/step - loss: 1.0047 - accuracy: 0.6491\n",
            "Epoch 8/20\n",
            "491/491 [==============================] - 250s 509ms/step - loss: 0.9353 - accuracy: 0.6790\n",
            "Epoch 9/20\n",
            "491/491 [==============================] - 250s 509ms/step - loss: 0.9087 - accuracy: 0.6880\n",
            "Epoch 10/20\n",
            "491/491 [==============================] - 251s 511ms/step - loss: 0.8416 - accuracy: 0.7146\n",
            "Epoch 11/20\n",
            "491/491 [==============================] - 251s 510ms/step - loss: 0.7836 - accuracy: 0.7315\n",
            "Epoch 12/20\n",
            "491/491 [==============================] - 250s 509ms/step - loss: 0.7452 - accuracy: 0.7485\n",
            "Epoch 13/20\n",
            "491/491 [==============================] - 252s 513ms/step - loss: 0.6956 - accuracy: 0.7675\n",
            "Epoch 14/20\n",
            "491/491 [==============================] - 251s 511ms/step - loss: 0.6588 - accuracy: 0.7805\n",
            "Epoch 15/20\n",
            "491/491 [==============================] - 250s 509ms/step - loss: 0.6393 - accuracy: 0.7828\n",
            "Epoch 16/20\n",
            "491/491 [==============================] - 255s 520ms/step - loss: 0.5934 - accuracy: 0.8054\n",
            "Epoch 17/20\n",
            "491/491 [==============================] - 257s 523ms/step - loss: 0.5712 - accuracy: 0.8037\n",
            "Epoch 18/20\n",
            "491/491 [==============================] - 259s 526ms/step - loss: 0.5310 - accuracy: 0.8189\n",
            "Epoch 19/20\n",
            "491/491 [==============================] - 262s 533ms/step - loss: 0.5104 - accuracy: 0.8259\n",
            "Epoch 20/20\n",
            "491/491 [==============================] - 264s 537ms/step - loss: 0.4678 - accuracy: 0.8441\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1ArrE56a1Hp",
        "outputId": "c51efedf-95f5-49e1-ddcb-0c3cbf535e10"
      },
      "source": [
        "\n",
        "classes = [\n",
        "           'cane',\t 'elefante',  'gallina',  'mucca',   'ragno', 'cavallo',  'farfalla',\n",
        "           'gatto',    'pecora',  'scoiattolo'\n",
        "]\n",
        "\n",
        "model.save('final_model.h5')\n",
        "test_loss, test_accuracy = model.evaluate(test_loader)\n",
        "print(\"test loss:\")\n",
        "print(test_loss)\n",
        "print(\"test accu:\")\n",
        "print(test_accuracy)\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10477/10477 [==============================] - 2586s 247ms/step - loss: 0.7109 - accuracy: 0.7748\n",
            "test loss:\n",
            "0.7108878493309021\n",
            "test accu:\n",
            "0.7748401165008545\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ktDMechbnTDk"
      },
      "source": [
        "# %cd '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images'\n",
        "# model = tf.keras.models.load_model('final_model.h5')"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTQ84y2XfIes"
      },
      "source": [
        "\n",
        "ypred = []\n",
        "yactual = []\n",
        "\n",
        "model = tf.keras.models.load_model('final_model.h5')\n",
        "for iterator in range(len(test_loader)):\n",
        "  xtest,ytest = test_loader.next()\n",
        "  ypred.append(model.predict(xtest))\n",
        "  yactual.append(ytest)\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDUdN4dcxd0Z"
      },
      "source": [
        "Y_pred = [list(train_loader.class_indices.keys())[i.argmax()]for i in ypred]\n",
        "Y_test = [list(train_loader.class_indices.keys())[i.argmax()]for i in yactual]"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_IHM6mxb-Zm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "9e9ede34-bc00-4568-cb4e-0aab71105eff"
      },
      "source": [
        "\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "matrix = confusion_matrix(Y_test, Y_pred)\n",
        "sns.heatmap(matrix.T, square=True, annot=True, fmt='d', cbar=False)\n",
        "plt.xlabel('true label')\n",
        "plt.ylabel('predicted label');"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGuqPlDfgk5_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d460e758-238c-4b63-958a-fe4e27d75526"
      },
      "source": [
        "# Training Metrics\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "print(classification_report(Y_test, Y_pred,\n",
        "                            target_names=classes))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        cane       0.64      0.91      0.75      1945\n",
            "    elefante       0.83      0.70      0.76      1050\n",
            "     gallina       0.80      0.78      0.79       579\n",
            "       mucca       0.79      0.88      0.83       846\n",
            "       ragno       0.93      0.80      0.86      1240\n",
            "     cavallo       0.87      0.40      0.55       668\n",
            "    farfalla       0.81      0.55      0.65       747\n",
            "       gatto       0.68      0.77      0.72       728\n",
            "      pecora       0.90      0.85      0.87      1929\n",
            "  scoiattolo       0.69      0.72      0.71       745\n",
            "\n",
            "    accuracy                           0.77     10477\n",
            "   macro avg       0.79      0.74      0.75     10477\n",
            "weighted avg       0.80      0.77      0.77     10477\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aaG_RC26hC98",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2de7eea2-1e55-4231-9313-2a2a0d4325df"
      },
      "source": [
        "print(accuracy_score(Y_test,Y_pred)*100)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "77.45537844802901\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7vx6_GfGMHQ"
      },
      "source": [
        "train_losses = history.history['loss'] # train erros\n",
        "train_accuracies = history.history['accuracy']\n",
        "# history.history"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "nFojbu1sGMEr",
        "outputId": "3baa56d3-9cf1-411e-fa2d-76778f59277c"
      },
      "source": [
        "plt.plot(train_losses)\n",
        "plt.title(\"Training Loss Curve\")\n",
        "plt.xlabel('Loss')\n",
        "plt.ylabel('EPOCH')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'EPOCH')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "DTDLG9fvGMCi",
        "outputId": "b2ef7697-1329-4c55-ab5a-ee77e766d00f"
      },
      "source": [
        "plt.plot(train_accuracies)\n",
        "plt.title(\"Train Accuracies Curve\")\n",
        "plt.xlabel('Accuracy')\n",
        "plt.ylabel('EPOCH')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'EPOCH')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7UCqltgfZ0h"
      },
      "source": [
        "testing_losses = []\n",
        "for i in range(len(test_loader)):\n",
        "  stepss = 500*(i+1)\n",
        "  loss,accu = model.evaluate(test_loader,steps=stepss)\n",
        "  testing_losses.append(loss)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ljc3dS3vKz7"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "cn7ms7javPfE",
        "outputId": "3f2ad383-22bd-46cb-a8c0-2c36a8e4f2e6"
      },
      "source": [
        "plt.plot(testing_losses)\n",
        "plt.title(\"Testing Loss Curve\")\n",
        "plt.xlabel('EPOCH')\n",
        "plt.ylabel('Loss values')"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Loss values')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59mc1fEkGMAd"
      },
      "source": [
        "# # https://www.tensorflow.org/api_docs/python/tf/keras/Model#evaluate\n",
        "# test_losses = []\n",
        "# test_accuracies = []\n",
        "# for i in range(len(test_loader)):\n",
        "#   test_loss, test_accuracy = model.evaluate(test_loader[i][0],test_loader[i][1], batch_size=32)\n",
        "#   test_losses.append(test_loss)\n",
        "#   test_accuracies.append(test_accuracy)\n",
        "# # test_loss, test_accuracy = model.evaluate(test_loader[i][0],test_loader[i][1])"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PE6o8ZVQi1Lw"
      },
      "source": [
        "# ref: https://www.pluralsight.com/guides/data-visualization-deep-learning-model-using-matplotlib"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gufKh8Sm2_Hr"
      },
      "source": [
        "# loss_train = history.history['train_loss']\n",
        "# loss_val = history.history['val_loss']\n",
        "# epochs = range(1,20)\n",
        "# plt.plot(epochs, loss_train, 'g', label='Training loss')\n",
        "# plt.plot(epochs, loss_val, 'b', label='Test loss')\n",
        "# plt.title('Training and Testing loss')\n",
        "# plt.xlabel('Epochs')\n",
        "# plt.ylabel('Loss')\n",
        "# plt.legend()\n",
        "# plt.show()"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWIkvwAI_PeK"
      },
      "source": [
        "# # https://machinelearningmastery.com/display-deep-learning-model-training-history-in-keras/\n",
        "# # https://machinelearningmastery.com/display-deep-learning-model-training-history-in-keras/\n",
        "# # People tend to consider test and validation sets same sometimes. However, \n",
        "# # trainset = the datasets on which model learns the parameters and weights\n",
        "# # val set = model finds the best hyperparameters\n",
        "# # testset = completely unseen data\n",
        "\n",
        "# # Since we have taken here only 2 sets from the start thus it is ok to assume val set and test  set as same!\n",
        "# loss_train = history.history['acc']\n",
        "# loss_val = history.history['val_acc']\n",
        "# epochs = range(1,11)\n",
        "# plt.plot(epochs, loss_train, 'g', label='Training accuracy')\n",
        "# plt.plot(epochs, loss_val, 'b', label='Testing/Val accuracy')\n",
        "# plt.title('Training and Testing/val accuracy')\n",
        "# plt.xlabel('Epochs')\n",
        "# plt.ylabel('Accuracy')\n",
        "# plt.legend()\n",
        "# plt.show()"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYSD7VDR_d5g"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}