Eyu_Violence_2021.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Eyu Violence 2021.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "tAb77yZ9fzMG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efd9a747-f875-4007-b9fc-012be594d954"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3ZIQtnnuRw2",
        "outputId": "3ba74090-1b26-49b6-a27a-6e41be7cf773"
      },
      "source": [
        "import os\n",
        "import matplotlib\n",
        "%matplotlib\n",
        "from imutils import paths\n",
        "import shutil\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.applications import VGG16\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "from sklearn.metrics import classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using matplotlib backend: agg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pafL7Li0jyXW"
      },
      "source": [
        "ORIG_INPUT_DATASET = \"/content/drive/MyDrive/final data 7800\"\n",
        "BASE_PATH = \"/content/drive/MyDrive/Mini project/dataset\"\n",
        "\n",
        "#define the names of training, validation and testing for directions\n",
        "TRAIN = \"train\"\n",
        "TEST = \"test\"\n",
        "VAL = \"validation\"\n",
        "\n",
        "CLASSES = [\"Violence\", \"Non_violence\"]\n",
        "\n",
        "BATCH_SIZE = 30\n",
        "\n",
        "# initialize the label encoder file path and the output directory to\n",
        "# where the extracted features (in CSV file format) will be stored\n",
        "LE_PATH = os.path.sep.join([\"/content/drive/MyDrive/Mini project/output\", \"le.cpickle\"])\n",
        "BASE_CSV_PATH = \"/content/drive/MyDrive/Mini project/output\"\n",
        "\n",
        "# set the path to the serialized model after training\n",
        "MODEL_PATH = os.path.sep.join([\"/content/drive/MyDrive/Mini project/output\", \"V_pg3.model\"])\n",
        "\n",
        "# define the path to the output training history plots\n",
        "UNFROZEN_PLOT_PATH = os.path.sep.join([\"/content/drive/MyDrive/Mini project/output\", \"unfrozen.png\"])\n",
        "WARMUP_PLOT_PATH = os.path.sep.join([\"/content/drive/MyDrive/Mini project/output\", \"warmup.png\"])"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mscdyW70520-"
      },
      "source": [
        "def plot_training(H, N, plotPath):\n",
        "\t# construct a plot that plots and saves the training history\n",
        "\tplt.style.use(\"ggplot\")\n",
        "\tplt.figure()\n",
        "\tplt.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\")\n",
        "\tplt.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\")\n",
        "\tplt.plot(np.arange(0, N), H.history[\"accuracy\"], label=\"train_acc\")\n",
        "\tplt.plot(np.arange(0, N), H.history[\"val_accuracy\"], label=\"val_acc\")\n",
        "\tplt.title(\"Training Loss and Accuracy\")\n",
        "\tplt.xlabel(\"Epoch #\")\n",
        "\tplt.ylabel(\"Loss/Accuracy\")\n",
        "\tplt.legend(loc=\"lower left\")\n",
        "\tplt.savefig(plotPath)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "yv3DTOBBrDUe",
        "outputId": "c43cf25b-48f7-4994-824c-1fcf293f5c47"
      },
      "source": [
        "# loop over the data splits\n",
        "for split in (TRAIN, TEST, VAL):\n",
        "  # grab all image paths in the current split\n",
        "  print(\"[INFO] processing '{} split'...\".format(split))\n",
        "  p = os.path.sep.join([ORIG_INPUT_DATASET, split])\n",
        "  imagePaths = list(paths.list_images(p))\n",
        "  Eyu = {'V':0, 'NV':1}\n",
        "  # loop over the image paths\n",
        "  for imagePath in imagePaths:\n",
        "    # extract class label from the filename\n",
        "    filename = imagePath.split(os.path.sep)[-1]\n",
        "    label = CLASSES[int(Eyu[filename.split(\"_\")[0]])]\n",
        "    print(label)\n",
        "    # construct the path to the output directory\n",
        "    dirPath = os.path.sep.join([BASE_PATH, split, label])\n",
        "    # if the output directory does not exist, create it\n",
        "    if not os.path.exists(dirPath):\n",
        "      os.makedirs(dirPath)\n",
        "    # construct the path to the output image file and copy it\n",
        "    p = os.path.sep.join([dirPath, filename])\n",
        "    shutil.copy2(imagePath, p)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] processing 'train split'...\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n",
            "Violence\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-0de8915ce3d4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;31m# construct the path to the output image file and copy it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdirPath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m     \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimagePath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/lib/python3.7/shutil.py\u001b[0m in \u001b[0;36mcopy2\u001b[0;34m(src, dst, follow_symlinks)\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m         \u001b[0mdst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m     \u001b[0mcopyfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfollow_symlinks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfollow_symlinks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    267\u001b[0m     \u001b[0mcopystat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfollow_symlinks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfollow_symlinks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/shutil.py\u001b[0m in \u001b[0;36mcopyfile\u001b[0;34m(src, dst, follow_symlinks)\u001b[0m\n\u001b[1;32m    120\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfsrc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfdst\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m                 \u001b[0mcopyfileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfsrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/shutil.py\u001b[0m in \u001b[0;36mcopyfileobj\u001b[0;34m(fsrc, fdst, length)\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0;34m\"\"\"copy data from file-like object fsrc to file-like object fdst\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0mbuf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfsrc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSW1IgvVIn6c",
        "outputId": "d120164c-8241-4e48-a829-816fec37f1ba"
      },
      "source": [
        "print(len(CLASSES))\n",
        "#print(label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LpJyGU1RrDW_",
        "outputId": "36bdbc0f-d1e2-435c-e667-5fdf8385e7f6"
      },
      "source": [
        "# derive the paths to the training, validation, and testing\n",
        "# directories\n",
        "trainPath = os.path.sep.join([BASE_PATH, TRAIN])\n",
        "valPath = os.path.sep.join([BASE_PATH, VAL])\n",
        "testPath = os.path.sep.join([BASE_PATH, TEST])\n",
        "# determine the total number of image paths in training, validation,\n",
        "# and testing directories\n",
        "totalTrain = len(list(paths.list_images(trainPath)))\n",
        "totalVal = len(list(paths.list_images(valPath)))\n",
        "totalTest = len(list(paths.list_images(testPath)))\n",
        "print(totalTrain, totalVal, totalTest)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6000 900 900\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnouhD9_rDZp"
      },
      "source": [
        "# initialize the training data augmentation object\n",
        "trainAug = ImageDataGenerator(\n",
        "\trotation_range=30,\n",
        "\tzoom_range=0.15,\n",
        "\twidth_shift_range=0.2,\n",
        "\theight_shift_range=0.2,\n",
        "\tshear_range=0.15,\n",
        "\thorizontal_flip=True,\n",
        "\tfill_mode=\"nearest\")\n",
        "# initialize the validation/testing data augmentation object (which\n",
        "# we'll be adding mean subtraction to)\n",
        "valAug = ImageDataGenerator()\n",
        "# define the ImageNet mean subtraction (in RGB order) and set the\n",
        "# the mean subtraction value for each of the data augmentation\n",
        "# objects\n",
        "mean = np.array([123.68, 116.779, 103.939], dtype=\"float32\")\n",
        "trainAug.mean = mean\n",
        "valAug.mean = mean"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9R7YgsGBrDcU",
        "outputId": "4abf6867-ccc2-40c8-c2f3-7d11b848cb70"
      },
      "source": [
        "# initialize the training generator\n",
        "trainGen = trainAug.flow_from_directory(\n",
        "\ttrainPath,\n",
        "\tclass_mode=\"categorical\",\n",
        "\ttarget_size=(224, 224),\n",
        "\tcolor_mode=\"rgb\",\n",
        "\tshuffle=True,\n",
        "\tbatch_size=BATCH_SIZE)\n",
        "# initialize the validation generator\n",
        "valGen = valAug.flow_from_directory(\n",
        "\tvalPath,\n",
        "\tclass_mode=\"categorical\",\n",
        "\ttarget_size=(224, 224),\n",
        "\tcolor_mode=\"rgb\",\n",
        "\tshuffle=False,\n",
        "\tbatch_size=BATCH_SIZE)\n",
        "# initialize the testing generator\n",
        "testGen = valAug.flow_from_directory(\n",
        "\ttestPath,\n",
        "\tclass_mode=\"categorical\",\n",
        "\ttarget_size=(224, 224),\n",
        "\tcolor_mode=\"rgb\",\n",
        "\tshuffle=False,\n",
        "\tbatch_size=BATCH_SIZE)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 6000 images belonging to 2 classes.\n",
            "Found 900 images belonging to 2 classes.\n",
            "Found 900 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5ctXXprrDfI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f6d08340-744e-4b82-fa20-50fd3ea5928c"
      },
      "source": [
        "# load the VGG16 network, ensuring the head FC layer sets are left\n",
        "# off\n",
        "baseModel = VGG16(weights=\"imagenet\", include_top=False,\n",
        "\tinput_tensor=Input(shape=(224, 224, 3)))\n",
        "# construct the head of the model that will be placed on top of the\n",
        "# the base model\n",
        "headModel = baseModel.output\n",
        "headModel = Flatten(name=\"flatten\")(headModel)\n",
        "headModel = Dense(512, activation=\"relu\")(headModel)\n",
        "headModel = Dropout(0.5)(headModel)\n",
        "headModel = Dense(len(CLASSES), activation=\"softmax\")(headModel)\n",
        "# place the head FC model on top of the base model (this will become\n",
        "# the actual model we will train)\n",
        "model = Model(inputs=baseModel.input, outputs=headModel)\n",
        "\n",
        "from tensorflow.keras import utils\n",
        "utils.plot_model(model, show_shapes=True, show_layer_names=False)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbPlmqMfrDhw"
      },
      "source": [
        "# loop over all layers in the base model and freeze them so they will\n",
        "# *not* be updated during the first training process\n",
        "for layer in baseModel.layers:\n",
        "\tlayer.trainable = False"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGPx7qYZrkhM",
        "outputId": "32c72519-d794-43ec-eedd-da0ec1800dda"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 512)               12845568  \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 2)                 1026      \n",
            "=================================================================\n",
            "Total params: 27,561,282\n",
            "Trainable params: 12,846,594\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saqC12fPmQKx",
        "outputId": "bf02055f-ae70-4cb1-f51b-d9b817b87623"
      },
      "source": [
        "# compile our model (this needs to be done after our setting our\n",
        "# layers to being non-trainable\n",
        "print(\"[INFO] compiling model...\")\n",
        "opt = SGD(lr=1e-4, momentum=0.9)\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
        "\tmetrics=[\"accuracy\"])\n",
        "# train the head of the network for a few epochs (all other layers\n",
        "# are frozen) -- this will allow the new FC layers to start to become\n",
        "# initialized with actual \"learned\" values versus pure random\n",
        "print(\"[INFO] training head...\")\n",
        "H = model.fit(\n",
        "\tx=trainGen,\n",
        "\tsteps_per_epoch=totalTrain // BATCH_SIZE,\n",
        "\tvalidation_data=valGen,\n",
        "\tvalidation_steps=totalVal // BATCH_SIZE,\n",
        "\tepochs=10)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] compiling model...\n",
            "[INFO] training head...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "200/200 [==============================] - 2696s 13s/step - loss: 1.0530 - accuracy: 0.9015 - val_loss: 0.4456 - val_accuracy: 0.9367\n",
            "Epoch 2/10\n",
            "200/200 [==============================] - 103s 515ms/step - loss: 0.3199 - accuracy: 0.9537 - val_loss: 0.2476 - val_accuracy: 0.9556\n",
            "Epoch 3/10\n",
            "200/200 [==============================] - 104s 518ms/step - loss: 0.2399 - accuracy: 0.9613 - val_loss: 0.2622 - val_accuracy: 0.9567\n",
            "Epoch 4/10\n",
            "200/200 [==============================] - 104s 519ms/step - loss: 0.1724 - accuracy: 0.9655 - val_loss: 0.1466 - val_accuracy: 0.9656\n",
            "Epoch 5/10\n",
            "200/200 [==============================] - 103s 516ms/step - loss: 0.1277 - accuracy: 0.9737 - val_loss: 0.1167 - val_accuracy: 0.9722\n",
            "Epoch 6/10\n",
            "200/200 [==============================] - 103s 517ms/step - loss: 0.1314 - accuracy: 0.9705 - val_loss: 0.1427 - val_accuracy: 0.9611\n",
            "Epoch 7/10\n",
            "200/200 [==============================] - 104s 520ms/step - loss: 0.0968 - accuracy: 0.9775 - val_loss: 0.1244 - val_accuracy: 0.9644\n",
            "Epoch 8/10\n",
            "200/200 [==============================] - 104s 518ms/step - loss: 0.0881 - accuracy: 0.9768 - val_loss: 0.1065 - val_accuracy: 0.9700\n",
            "Epoch 9/10\n",
            "200/200 [==============================] - 103s 514ms/step - loss: 0.0866 - accuracy: 0.9788 - val_loss: 0.1133 - val_accuracy: 0.9678\n",
            "Epoch 10/10\n",
            "200/200 [==============================] - 103s 515ms/step - loss: 0.0769 - accuracy: 0.9800 - val_loss: 0.0952 - val_accuracy: 0.9744\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPoK7X6SrDkb",
        "outputId": "e1fd8a35-0a9f-4f1a-b284-d05199ac702f"
      },
      "source": [
        "# reset the testing generator and evaluate the network after\n",
        "# fine-tuning just the network head\n",
        "print(\"[INFO] evaluating after fine-tuning network head...\")\n",
        "testGen.reset()\n",
        "predIdxs = model.predict(x=testGen,\n",
        "\tsteps=(totalTest // BATCH_SIZE) + 1)\n",
        "predIdxs = np.argmax(predIdxs, axis=1)\n",
        "print(classification_report(testGen.classes, predIdxs,\n",
        "\ttarget_names=testGen.class_indices.keys()))\n",
        "plot_training(H, 10, WARMUP_PLOT_PATH)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating after fine-tuning network head...\n",
            "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 31 batches). You may need to use the repeat() function when building your dataset.\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "Non_violence       0.98      0.99      0.98       450\n",
            "    Violence       0.99      0.98      0.98       450\n",
            "\n",
            "    accuracy                           0.98       900\n",
            "   macro avg       0.98      0.98      0.98       900\n",
            "weighted avg       0.98      0.98      0.98       900\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6wGpNc6SrDoD",
        "outputId": "f7449c01-e16c-4ffe-a024-7639f3fb2ebe"
      },
      "source": [
        "# reset our data generators\n",
        "trainGen.reset()\n",
        "valGen.reset()\n",
        "# now that the head FC layers have been trained/initialized, lets\n",
        "# unfreeze the final set of CONV layers and make them trainable\n",
        "for layer in baseModel.layers[15:]:\n",
        "\tlayer.trainable = True\n",
        "# loop over the layers in the model and show which ones are trainable\n",
        "# or not\n",
        "for layer in baseModel.layers:\n",
        "\tprint(\"{}: {}\".format(layer, layer.trainable))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff0a0481b50>: False\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff0a043f850>: False\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff0a047f550>: False\n",
            "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7ff0a0500f10>: False\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff0a03c78d0>: False\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff0a03cb710>: False\n",
            "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7ff0a03d7310>: False\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff0a03d2f50>: False\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff0a03da090>: False\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff0a03e5250>: False\n",
            "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7ff0a03e9a10>: False\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff0a03da550>: False\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff0a03ee990>: False\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff0a03fb0d0>: False\n",
            "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7ff0a03f2ed0>: False\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff0a0400b90>: True\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff0a0386210>: True\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff0a0386590>: True\n",
            "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7ff0a0400510>: True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JWDed-MFtm3Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a45e393-1a8e-487e-c1d1-a754b1ff8f34"
      },
      "source": [
        "# for the changes to the model to take affect we need to recompile\n",
        "# the model, this time using SGD with a *very* small learning rate\n",
        "print(\"[INFO] re-compiling model...\")\n",
        "opt = SGD(lr=1e-4, momentum=0.9)\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
        "\tmetrics=[\"accuracy\"])\n",
        "# train the model again, this time fine-tuning *both* the final set\n",
        "# of CONV layers along with our set of FC layers\n",
        "H = model.fit(\n",
        "\tx=trainGen,\n",
        "\tsteps_per_epoch=totalTrain // BATCH_SIZE,\n",
        "\tvalidation_data=valGen,\n",
        "\tvalidation_steps=totalVal // BATCH_SIZE,\n",
        "\tepochs=5)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] re-compiling model...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "200/200 [==============================] - 106s 520ms/step - loss: 0.0702 - accuracy: 0.9810 - val_loss: 0.0676 - val_accuracy: 0.9811\n",
            "Epoch 2/5\n",
            "200/200 [==============================] - 105s 523ms/step - loss: 0.0509 - accuracy: 0.9877 - val_loss: 0.0555 - val_accuracy: 0.9889\n",
            "Epoch 3/5\n",
            "200/200 [==============================] - 105s 523ms/step - loss: 0.0330 - accuracy: 0.9895 - val_loss: 0.0501 - val_accuracy: 0.9867\n",
            "Epoch 4/5\n",
            "200/200 [==============================] - 104s 521ms/step - loss: 0.0295 - accuracy: 0.9925 - val_loss: 0.0415 - val_accuracy: 0.9911\n",
            "Epoch 5/5\n",
            "200/200 [==============================] - 105s 523ms/step - loss: 0.0263 - accuracy: 0.9933 - val_loss: 0.0575 - val_accuracy: 0.9856\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lExE8pm3ttx1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97277685-262e-4d4f-899f-2f658ef2cd7d"
      },
      "source": [
        "# reset the testing generator and then use our trained model to\n",
        "# make predictions on the data\n",
        "print(\"[INFO] evaluating after fine-tuning network...\")\n",
        "testGen.reset()\n",
        "predIdxs = model.predict(x=testGen,\n",
        "\tsteps=(totalTest // BATCH_SIZE) + 1)\n",
        "predIdxs = np.argmax(predIdxs, axis=1)\n",
        "print(classification_report(testGen.classes, predIdxs,\n",
        "\ttarget_names=testGen.class_indices.keys()))\n",
        "plot_training(H, 5, UNFROZEN_PLOT_PATH)\n",
        "# serialize the model to disk\n",
        "print(\"[INFO] serializing network...\")\n",
        "model.save(MODEL_PATH, save_format=\"h5\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] evaluating after fine-tuning network...\n",
            "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 31 batches). You may need to use the repeat() function when building your dataset.\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "Non_violence       0.98      1.00      0.99       450\n",
            "    Violence       1.00      0.98      0.99       450\n",
            "\n",
            "    accuracy                           0.99       900\n",
            "   macro avg       0.99      0.99      0.99       900\n",
            "weighted avg       0.99      0.99      0.99       900\n",
            "\n",
            "[INFO] serializing network...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pd0YslTNjZGF"
      },
      "source": [
        "model.save(\"/content/drive/MyDrive/Mini project/output/vmodel.h5\")"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04khIiVTN_Xb"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}