Plant_speci.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/himanshugupta09/Plant_identification/blob/main/Plant_speci.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xoGtYKCxoaxd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a01739c7-002f-4d49-ae4e-4c2c6336d5eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "id": "xoGtYKCxoaxd"
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip list\n",
        "#!pip install timm\n",
        "#!pip install torchvision cuda92"
      ],
      "metadata": {
        "id": "LNcyquovbobP"
      },
      "id": "LNcyquovbobP",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uygIVRhbiK5N"
      },
      "id": "uygIVRhbiK5N",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install arcgis\n",
        "#!pip list\n",
        "%env ARCGIS_ENABLE_TF_BACKEND=1"
      ],
      "metadata": {
        "id": "flJu79iCYNvj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd68d62a-cd36-4bb9-b727-fcffc243d7c9"
      },
      "id": "flJu79iCYNvj",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: ARCGIS_ENABLE_TF_BACKEND=1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f50047b4"
      },
      "outputs": [],
      "source": [
        "%load_ext tensorboard\n",
        "\n",
        "import datetime\n",
        "import numpy as np\n",
        "import os\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm\n",
        "\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.losses import categorical_crossentropy\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\n",
        "\n",
        "import cv2\n",
        "import urllib\n",
        "import requests\n",
        "import PIL.Image\n",
        "import numpy as np"
      ],
      "id": "f50047b4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b7a2e88f"
      },
      "outputs": [],
      "source": [
        "# Define data paths\n",
        "train_data_dir = \"/content/drive/MyDrive/Colab Notebooks/train\"\n",
        "val_data_dir = \"/content/drive/MyDrive/Colab Notebooks/validation\"\n",
        "test_data_dir = \"/content/drive/MyDrive/Colab Notebooks/test\""
      ],
      "id": "b7a2e88f"
    },
    {
      "cell_type": "code",
      "source": [
        "# img = cv2.imread(\"/content/drive/MyDrive/Colab Notebooks/validation/Bellyache bush (Green)/004_20.jpg\",1)\n",
        "# plt.imshow(img)\n",
        "# shp = np.array(img)\n",
        "# print(shp.shape)"
      ],
      "metadata": {
        "id": "jOrjRyMh02r6"
      },
      "id": "jOrjRyMh02r6",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# class AlexNet(Sequential):\n",
        "#     def __init__(self, input_shape, num_classes):\n",
        "#         super().__init__()\n",
        "\n",
        "#         self.add(Conv2D(96, kernel_size=(11,11), strides= 4,\n",
        "#                         padding= 'valid', activation= 'relu',\n",
        "#                         input_shape= input_shape,\n",
        "#                         kernel_initializer= 'he_normal'))\n",
        "#         self.add(MaxPooling2D(pool_size=(3,3), strides= (2,2),\n",
        "#                               padding= 'valid', data_format= None))\n",
        "\n",
        "#         self.add(Conv2D(256, kernel_size=(5,5), strides= 1,\n",
        "#                         padding= 'same', activation= 'relu',\n",
        "#                         kernel_initializer= 'he_normal'))\n",
        "#         self.add(MaxPooling2D(pool_size=(3,3), strides= (2,2),\n",
        "#                               padding= 'valid', data_format= None))\n",
        "\n",
        "#         self.add(Conv2D(384, kernel_size=(3,3), strides= 1,\n",
        "#                         padding= 'same', activation= 'relu',\n",
        "#                         kernel_initializer= 'he_normal'))\n",
        "\n",
        "#         self.add(Conv2D(384, kernel_size=(3,3), strides= 1,\n",
        "#                         padding= 'same', activation= 'relu',\n",
        "#                         kernel_initializer= 'he_normal'))\n",
        "\n",
        "#         self.add(Conv2D(256, kernel_size=(3,3), strides= 1,\n",
        "#                         padding= 'same', activation= 'relu',\n",
        "#                         kernel_initializer= 'he_normal'))\n",
        "\n",
        "#         self.add(MaxPooling2D(pool_size=(3,3), strides= (2,2),\n",
        "#                               padding= 'valid', data_format= None))\n",
        "\n",
        "#         self.add(Flatten())\n",
        "#         self.add(Dense(4096, activation= 'relu'))\n",
        "#         self.add(Dense(4096, activation= 'relu'))\n",
        "#         self.add(Dense(1000, activation= 'relu'))\n",
        "#         self.add(Dense(num_classes, activation= 'softmax'))\n",
        "\n",
        "#         self.compile(optimizer= tf.keras.optimizers.Adam(0.001),\n",
        "#                     loss='categorical_crossentropy',\n",
        "#                     metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "SCiSIk337PE7"
      },
      "id": "SCiSIk337PE7",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_LeFSyhiN027"
      },
      "outputs": [],
      "source": [
        "# train_datagen = ImageDataGenerator(rescale=1.0/255,\n",
        "#                                   zoom_range=0.2,\n",
        "#                                   width_shift_range=0.2,\n",
        "#                                   height_shift_range=0.2,\n",
        "#                                   fill_mode='nearest')\n",
        "# train_generator = train_datagen.flow_from_directory(train_data_dir,\n",
        "#                                                    target_size=(224, 224),\n",
        "#                                                    batch_size=32,\n",
        "#                                                    class_mode='categorical',\n",
        "#                                                    shuffle=True)"
      ],
      "id": "_LeFSyhiN027"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a5622a82",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4b3eff4-2323-4e73-ab6f-22b0ebc68f55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 972 images belonging to 74 classes.\n"
          ]
        }
      ],
      "source": [
        "test_datagen = ImageDataGenerator(rescale=1.0/255,\n",
        "                                  zoom_range=0.2,\n",
        "                                  width_shift_range=0.2,\n",
        "                                  height_shift_range=0.2,\n",
        "                                  fill_mode='nearest')\n",
        "test_generator = test_datagen.flow_from_directory(test_data_dir,\n",
        "                                                   target_size=(224, 224),\n",
        "                                                   batch_size=32,\n",
        "                                                   class_mode='categorical',\n",
        "                                                   shuffle=False)"
      ],
      "id": "a5622a82"
    },
    {
      "cell_type": "code",
      "source": [
        "num_images_to_plot = 2\n",
        "for i in range(num_images_to_plot):\n",
        "    batch = test_generator.next()\n",
        "    image = batch[0][0]  # Take the first image from the batch\n",
        "    plt.subplot(1, num_images_to_plot, i + 1)\n",
        "    plt.imshow(image)\n",
        "    plt.axis('off')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "0Qt7g4lK6qSO",
        "outputId": "314d29c8-dc30-4ac3-b754-dd2c4bebfaa1"
      },
      "id": "0Qt7g4lK6qSO",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, BatchNormalization\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import LearningRateScheduler, TensorBoard\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import datetime\n",
        "from keras.layers import Activation\n",
        "# Define a learning rate schedule\n",
        "initial_lr = 0.001\n",
        "\n",
        "def lr_schedule(epoch):\n",
        "    lr = initial_lr\n",
        "    if epoch > 10:\n",
        "        lr *= 0.1\n",
        "    if epoch > 20:\n",
        "        lr *= 0.1\n",
        "    return lr\n",
        "\n",
        "# Model definition\n",
        "class AlexNet(Sequential):\n",
        "    def __init__(self, input_shape, num_classes):\n",
        "        super().__init__()\n",
        "\n",
        "        self.add(Conv2D(96, kernel_size=(11, 11), strides=4,\n",
        "                        padding='valid', activation='relu',\n",
        "                        input_shape=input_shape,\n",
        "                        kernel_initializer='he_normal'))\n",
        "        self.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2),\n",
        "                              padding='valid', data_format=None))\n",
        "\n",
        "        self.add(Conv2D(256, kernel_size=(5, 5), strides=1,\n",
        "                        padding='same', activation='relu',\n",
        "                        kernel_initializer='he_normal'))\n",
        "        self.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2),\n",
        "                              padding='valid', data_format=None))\n",
        "\n",
        "        self.add(Conv2D(384, kernel_size=(3, 3), strides=1,\n",
        "                        padding='same', activation='relu',\n",
        "                        kernel_initializer='he_normal'))\n",
        "\n",
        "        self.add(Conv2D(384, kernel_size=(3, 3), strides=1,\n",
        "                        padding='same', activation='relu',\n",
        "                        kernel_initializer='he_normal'))\n",
        "\n",
        "        self.add(Conv2D(256, kernel_size=(3, 3), strides=1,\n",
        "                        padding='same', activation='relu',\n",
        "                        kernel_initializer='he_normal'))\n",
        "\n",
        "        self.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2),\n",
        "                              padding='valid', data_format=None))\n",
        "\n",
        "        self.add(Flatten())\n",
        "        self.add(Dense(512, activation='relu'))\n",
        "        self.add(BatchNormalization())\n",
        "        self.add(Dense(256, activation='relu'))\n",
        "        self.add(BatchNormalization())\n",
        "        self.add(Dense(128, activation='relu'))\n",
        "        self.add(Dense(num_classes, activation='softmax'))\n",
        "\n",
        "        optimizer = Adam(learning_rate=initial_lr)\n",
        "        self.compile(optimizer=optimizer,\n",
        "                     loss='categorical_crossentropy',\n",
        "                     metrics=['accuracy'])\n",
        "\n",
        "# Define data generators\n"
      ],
      "metadata": {
        "id": "yt5QzcNQO7wT"
      },
      "id": "yt5QzcNQO7wT",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# # training parameters\n",
        "# EPOCHS =\n",
        "BATCH_SIZE = 32\n",
        "image_height = 224\n",
        "image_width = 224\n",
        "# train_dir = \"./content/train\"\n",
        "# # valid_dir = \"./content/validation\"\n",
        "# # model_dir = \"./my_model.h5\"\n",
        "\n",
        "train_datagen = ImageDataGenerator(\n",
        "                  rescale=1./255,\n",
        "                  rotation_range=10,\n",
        "                  width_shift_range=0.1,\n",
        "                  height_shift_range=0.1,\n",
        "                  shear_range=0.1,\n",
        "                  zoom_range=0.1)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(train_data_dir,\n",
        "                                                    target_size=(image_height, image_width),\n",
        "                                                    color_mode=\"rgb\",\n",
        "                                                    batch_size=BATCH_SIZE,\n",
        "                                                    seed=1,\n",
        "                                                    shuffle=True,\n",
        "                                                    class_mode=\"categorical\")\n",
        "valid_datagen = ImageDataGenerator(rescale=1.0/255.0)\n",
        "valid_generator = valid_datagen.flow_from_directory(val_data_dir,\n",
        "                                                    target_size=(image_height, image_width),\n",
        "                                                    color_mode=\"rgb\",\n",
        "                                                    batch_size=BATCH_SIZE,\n",
        "                                                    seed=7,\n",
        "                                                    shuffle=True,\n",
        "                                                    class_mode=\"categorical\"\n",
        "                                                    )\n",
        "train_num = train_generator.samples\n",
        "valid_num = valid_generator.samples"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_EgQQXws7xH5",
        "outputId": "8db4bb7a-0b04-4c2f-ea02-9b3f6f9d3238"
      },
      "id": "_EgQQXws7xH5",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 4183 images belonging to 74 classes.\n",
            "Found 866 images belonging to 74 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YyKM9QugMVuD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56e4bb0b-d332-44da-c899-ef669ed3da32"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label Mappings for classes present in the training and validation datasets\n",
            "\n",
            "0 : Amaranthus Green\n",
            "1 : Amaranthus Red\n",
            "2 : Asthma Plant\n",
            "3 : Avaram\n",
            "4 : Balloon vine\n",
            "5 : Bellyache bush (Green)\n",
            "6 : Benghal dayflower\n",
            "7 : Betel Leaves\n",
            "8 : Big Caltrops\n",
            "9 : Black Night Shade\n",
            "10 : Black-Honey Shrub\n",
            "11 : Bristly Wild Grape\n",
            "12 : Butterfly Pea\n",
            "13 : Cape Gooseberry\n",
            "14 : Celery\n",
            "15 : Chinese Spinach\n",
            "16 : Common Wireweed\n",
            "17 : Coriander Leaves\n",
            "18 : Country Mallow\n",
            "19 : Crown flower\n",
            "20 : Curry Leaf\n",
            "21 : Dwarf Copperleaf (Green)\n",
            "22 : Dwarf copperleaf (Red)\n",
            "23 : False Amarnath\n",
            "24 : Fenugreek Leaves\n",
            "25 : Giant Pigweed\n",
            "26 : Gongura\n",
            "27 : Green Chireta\n",
            "28 : Holy Basil\n",
            "29 : Indian CopperLeaf\n",
            "30 : Indian Jujube\n",
            "31 : Indian Sarsaparilla\n",
            "32 : Indian Stinging Nettle\n",
            "33 : Indian Thornapple\n",
            "34 : Indian pennywort\n",
            "35 : Indian wormwood\n",
            "36 : Ivy Gourd\n",
            "37 : Kokilaksha\n",
            "38 : Lagos Spinach\n",
            "39 : Lambs Quarters\n",
            "40 : Land Caltrops (Bindii)\n",
            "41 : Lettuce Tree\n",
            "42 : Madagascar Periwinkle\n",
            "43 : Madras Pea Pumpkin\n",
            "44 : Malabar Catmint\n",
            "45 : Malabar Spinach (Green)\n",
            "46 : Mexican Mint\n",
            "47 : Mexican Prickly Poppy\n",
            "48 : Mint Leaves\n",
            "49 : Mountain Knotgrass\n",
            "50 : Mustard\n",
            "51 : Nalta Jute\n",
            "52 : Night blooming Cereus\n",
            "53 : Palak\n",
            "54 : Panicled Foldwing\n",
            "55 : Prickly Chaff Flower\n",
            "56 : Punarnava\n",
            "57 : Purple Fruited Pea Eggplant\n",
            "58 : Purple Tephrosia\n",
            "59 : Rosary Pea\n",
            "60 : Shaggy button weed\n",
            "61 : Siru Keerai\n",
            "62 : Small Water Clover\n",
            "63 : Spiderwisp\n",
            "64 : Square Stalked Vine\n",
            "65 : Stinking Passionflower\n",
            "66 : Sweet Basil\n",
            "67 : Sweet flag\n",
            "68 : Tinnevelly Senna\n",
            "69 : Trellis Vine\n",
            "70 : Velvet bean\n",
            "71 : Water Spinach\n",
            "72 : coatbuttons\n",
            "73 : heart-leaved moonseed\n"
          ]
        }
      ],
      "source": [
        "labels = {value: key for key, value in train_generator.class_indices.items()}\n",
        "\n",
        "print(\"Label Mappings for classes present in the training and validation datasets\\n\")\n",
        "for key, value in labels.items():\n",
        "    print(f\"{key} : {value}\")"
      ],
      "id": "YyKM9QugMVuD"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e07ce693"
      },
      "outputs": [],
      "source": [
        "# validation_datagen = ImageDataGenerator(rescale=1.0/255)\n",
        "# validation_generator = validation_datagen.flow_from_directory(val_data_dir,\n",
        "#                                                              target_size=(224, 224),\n",
        "#                                                              batch_size=32,\n",
        "#                                                              class_mode='categorical',\n",
        "#                                                              shuffle=False)"
      ],
      "id": "e07ce693"
    },
    {
      "cell_type": "code",
      "source": [
        "# labels = {value: key for key, value in valid_generator.class_indices.items()}\n",
        "\n",
        "# print(\"Label Mappings for classes present in the training and validation datasets\\n\")\n",
        "# for key, value in labels.items():\n",
        "#     print(f\"{key} : {value}\")"
      ],
      "metadata": {
        "id": "lY-NoV-rAq84"
      },
      "id": "lY-NoV-rAq84",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# os.mkdir('./logs')\n",
        "# os.mkdir('./logs/fit')\n",
        "\n",
        "log_dir=\"./logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir)\n",
        "callback_list = [tensorboard_callback]"
      ],
      "metadata": {
        "id": "xkXnPMtAmTk6"
      },
      "id": "xkXnPMtAmTk6",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# os.mkdir('./my_model')\n",
        "model_dir = \"./my_model.h5\"\n"
      ],
      "metadata": {
        "id": "cBG34hqv8_fB"
      },
      "id": "cBG34hqv8_fB",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "NwfVsyg5pK2l"
      },
      "id": "NwfVsyg5pK2l"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7gi6un2IptRE"
      },
      "outputs": [],
      "source": [
        "# model = Sequential([\n",
        "#     Conv2D(filters=256, kernel_size=(5, 5), padding='valid', input_shape=(224, 224, 3)),\n",
        "#     Activation('relu'),\n",
        "#     MaxPooling2D(pool_size=(2, 2)),\n",
        "#     BatchNormalization(),\n",
        "\n",
        "#     Conv2D(filters=128, kernel_size=(3, 3), padding='valid', kernel_regularizer=l2(0.0001)),\n",
        "#     Activation('relu'),\n",
        "#     MaxPooling2D(pool_size=(2, 2)),\n",
        "#     BatchNormalization(),\n",
        "\n",
        "#     Conv2D(filters=64, kernel_size=(3, 3), padding='valid', kernel_regularizer=l2(0.0001)),\n",
        "#     Activation('relu'),\n",
        "#     MaxPooling2D(pool_size=(2, 2)),\n",
        "#     BatchNormalization(),\n",
        "\n",
        "#     Flatten(),\n",
        "\n",
        "#     Dense(units=128, activation='relu'),  # Adjust units to match the number of classes\n",
        "#     Dropout(0.5),\n",
        "#     Dense(units=74, activation='softmax')  # Adjust units to match the number of classes\n",
        "# ])"
      ],
      "id": "7gi6un2IptRE"
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.applications.mobilenet_v2 import MobileNetV2\n",
        "# Load MobileNetV2 base model\n",
        "from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, GlobalAveragePooling2D, AveragePooling2D\n",
        "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "# Add custom classification head\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "x = Dropout(0.5)(x)  # Adding dropout for regularization\n",
        "predictions = Dense(74, activation='softmax')(x)\n",
        "epochs  = 25\n",
        "model = Model(inputs=base_model.input, outputs=predictions)\n",
        "batch_size = 32\n",
        "# Freeze the layers of the base model\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=train_generator.samples // batch_size,\n",
        "    epochs=epochs,\n",
        "    validation_data=valid_generator,\n",
        "    validation_steps=valid_generator.samples // batch_size\n",
        ")"
      ],
      "metadata": {
        "id": "20VzcRy27oGy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3cbb4347-9ede-4395-d7b3-2663a4cfcdb9"
      },
      "id": "20VzcRy27oGy",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
            "9406464/9406464 [==============================] - 0s 0us/step\n",
            "Epoch 1/25\n",
            "130/130 [==============================] - 1583s 12s/step - loss: 2.6997 - accuracy: 0.3503 - val_loss: 1.2123 - val_accuracy: 0.7118\n",
            "Epoch 2/25\n",
            "130/130 [==============================] - 190s 1s/step - loss: 1.2560 - accuracy: 0.6519 - val_loss: 0.7745 - val_accuracy: 0.8102\n",
            "Epoch 3/25\n",
            "130/130 [==============================] - 174s 1s/step - loss: 0.8857 - accuracy: 0.7401 - val_loss: 0.6204 - val_accuracy: 0.8368\n",
            "Epoch 4/25\n",
            "130/130 [==============================] - 165s 1s/step - loss: 0.6603 - accuracy: 0.8080 - val_loss: 0.5627 - val_accuracy: 0.8426\n",
            "Epoch 5/25\n",
            "130/130 [==============================] - 163s 1s/step - loss: 0.5647 - accuracy: 0.8338 - val_loss: 0.5006 - val_accuracy: 0.8692\n",
            "Epoch 6/25\n",
            "130/130 [==============================] - 161s 1s/step - loss: 0.4836 - accuracy: 0.8492 - val_loss: 0.4674 - val_accuracy: 0.8762\n",
            "Epoch 7/25\n",
            "130/130 [==============================] - 164s 1s/step - loss: 0.4183 - accuracy: 0.8738 - val_loss: 0.4411 - val_accuracy: 0.8738\n",
            "Epoch 8/25\n",
            "130/130 [==============================] - 163s 1s/step - loss: 0.3771 - accuracy: 0.8863 - val_loss: 0.4119 - val_accuracy: 0.8877\n",
            "Epoch 9/25\n",
            "130/130 [==============================] - 184s 1s/step - loss: 0.3505 - accuracy: 0.8930 - val_loss: 0.4070 - val_accuracy: 0.8935\n",
            "Epoch 10/25\n",
            "130/130 [==============================] - 162s 1s/step - loss: 0.3043 - accuracy: 0.9063 - val_loss: 0.3913 - val_accuracy: 0.8993\n",
            "Epoch 11/25\n",
            "130/130 [==============================] - 162s 1s/step - loss: 0.2825 - accuracy: 0.9094 - val_loss: 0.3852 - val_accuracy: 0.8912\n",
            "Epoch 12/25\n",
            "130/130 [==============================] - 191s 1s/step - loss: 0.2636 - accuracy: 0.9193 - val_loss: 0.3882 - val_accuracy: 0.8819\n",
            "Epoch 13/25\n",
            "130/130 [==============================] - 193s 1s/step - loss: 0.2417 - accuracy: 0.9294 - val_loss: 0.3956 - val_accuracy: 0.8947\n",
            "Epoch 14/25\n",
            "130/130 [==============================] - 169s 1s/step - loss: 0.2287 - accuracy: 0.9287 - val_loss: 0.3459 - val_accuracy: 0.9005\n",
            "Epoch 15/25\n",
            "130/130 [==============================] - 176s 1s/step - loss: 0.2000 - accuracy: 0.9386 - val_loss: 0.3944 - val_accuracy: 0.8854\n",
            "Epoch 16/25\n",
            "130/130 [==============================] - 175s 1s/step - loss: 0.2020 - accuracy: 0.9366 - val_loss: 0.3882 - val_accuracy: 0.8935\n",
            "Epoch 17/25\n",
            "130/130 [==============================] - 171s 1s/step - loss: 0.1915 - accuracy: 0.9400 - val_loss: 0.3720 - val_accuracy: 0.8993\n",
            "Epoch 18/25\n",
            "130/130 [==============================] - 166s 1s/step - loss: 0.1752 - accuracy: 0.9429 - val_loss: 0.3964 - val_accuracy: 0.8993\n",
            "Epoch 19/25\n",
            "130/130 [==============================] - 164s 1s/step - loss: 0.1996 - accuracy: 0.9330 - val_loss: 0.3824 - val_accuracy: 0.8924\n",
            "Epoch 20/25\n",
            "130/130 [==============================] - 164s 1s/step - loss: 0.1664 - accuracy: 0.9434 - val_loss: 0.3869 - val_accuracy: 0.8981\n",
            "Epoch 21/25\n",
            "130/130 [==============================] - 161s 1s/step - loss: 0.1714 - accuracy: 0.9410 - val_loss: 0.3929 - val_accuracy: 0.8912\n",
            "Epoch 22/25\n",
            "130/130 [==============================] - 163s 1s/step - loss: 0.1654 - accuracy: 0.9453 - val_loss: 0.4325 - val_accuracy: 0.8843\n",
            "Epoch 23/25\n",
            "130/130 [==============================] - 161s 1s/step - loss: 0.1619 - accuracy: 0.9453 - val_loss: 0.3787 - val_accuracy: 0.8947\n",
            "Epoch 24/25\n",
            "130/130 [==============================] - 162s 1s/step - loss: 0.1675 - accuracy: 0.9431 - val_loss: 0.4173 - val_accuracy: 0.8958\n",
            "Epoch 25/25\n",
            "130/130 [==============================] - 162s 1s/step - loss: 0.1522 - accuracy: 0.9480 - val_loss: 0.3897 - val_accuracy: 0.8935\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "# Save the model\n",
        "model.save(\"my_model.h5\")\n",
        "\n",
        "# Download the model\n",
        "files.download(\"my_model.h5\")"
      ],
      "metadata": {
        "id": "VF5sgNhslnx3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "cf74e791-db32-4cc1-d42d-1a882068c598"
      },
      "id": "VF5sgNhslnx3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_476c63c3-eede-4ed0-b5b1-85471836cfe4\", \"my_model.h5\", 17867144)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X4wSMMXgptdF"
      },
      "outputs": [],
      "source": [
        "# log_dir=\"./logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
        "# tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir)\n",
        "# callback_list = [tensorboard_callback]\n",
        "\n",
        "# lr_scheduler = LearningRateScheduler(lr_schedule)\n",
        "\n",
        "# # Train the model\n",
        "# model.fit(train_generator,\n",
        "#           epochs=EPOCHS,\n",
        "#           steps_per_epoch=len(train_generator),\n",
        "#           validation_data=valid_generator,\n",
        "#           validation_steps=len(valid_generator),\n",
        "#           callbacks=[tensorboard_callback, lr_scheduler],\n",
        "#           verbose=2)\n"
      ],
      "id": "X4wSMMXgptdF"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6uPX2VCe1FgX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9a31726-49da-4498-ffea-cc1d93fc242e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " Conv1 (Conv2D)              (None, 112, 112, 32)         864       ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " bn_Conv1 (BatchNormalizati  (None, 112, 112, 32)         128       ['Conv1[0][0]']               \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " Conv1_relu (ReLU)           (None, 112, 112, 32)         0         ['bn_Conv1[0][0]']            \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise (D  (None, 112, 112, 32)         288       ['Conv1_relu[0][0]']          \n",
            " epthwiseConv2D)                                                                                  \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise_BN  (None, 112, 112, 32)         128       ['expanded_conv_depthwise[0][0\n",
            "  (BatchNormalization)                                              ]']                           \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise_re  (None, 112, 112, 32)         0         ['expanded_conv_depthwise_BN[0\n",
            " lu (ReLU)                                                          ][0]']                        \n",
            "                                                                                                  \n",
            " expanded_conv_project (Con  (None, 112, 112, 16)         512       ['expanded_conv_depthwise_relu\n",
            " v2D)                                                               [0][0]']                      \n",
            "                                                                                                  \n",
            " expanded_conv_project_BN (  (None, 112, 112, 16)         64        ['expanded_conv_project[0][0]'\n",
            " BatchNormalization)                                                ]                             \n",
            "                                                                                                  \n",
            " block_1_expand (Conv2D)     (None, 112, 112, 96)         1536      ['expanded_conv_project_BN[0][\n",
            "                                                                    0]']                          \n",
            "                                                                                                  \n",
            " block_1_expand_BN (BatchNo  (None, 112, 112, 96)         384       ['block_1_expand[0][0]']      \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_1_expand_relu (ReLU)  (None, 112, 112, 96)         0         ['block_1_expand_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_1_pad (ZeroPadding2D  (None, 113, 113, 96)         0         ['block_1_expand_relu[0][0]'] \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block_1_depthwise (Depthwi  (None, 56, 56, 96)           864       ['block_1_pad[0][0]']         \n",
            " seConv2D)                                                                                        \n",
            "                                                                                                  \n",
            " block_1_depthwise_BN (Batc  (None, 56, 56, 96)           384       ['block_1_depthwise[0][0]']   \n",
            " hNormalization)                                                                                  \n",
            "                                                                                                  \n",
            " block_1_depthwise_relu (Re  (None, 56, 56, 96)           0         ['block_1_depthwise_BN[0][0]']\n",
            " LU)                                                                                              \n",
            "                                                                                                  \n",
            " block_1_project (Conv2D)    (None, 56, 56, 24)           2304      ['block_1_depthwise_relu[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block_1_project_BN (BatchN  (None, 56, 56, 24)           96        ['block_1_project[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_2_expand (Conv2D)     (None, 56, 56, 144)          3456      ['block_1_project_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_2_expand_BN (BatchNo  (None, 56, 56, 144)          576       ['block_2_expand[0][0]']      \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_2_expand_relu (ReLU)  (None, 56, 56, 144)          0         ['block_2_expand_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_2_depthwise (Depthwi  (None, 56, 56, 144)          1296      ['block_2_expand_relu[0][0]'] \n",
            " seConv2D)                                                                                        \n",
            "                                                                                                  \n",
            " block_2_depthwise_BN (Batc  (None, 56, 56, 144)          576       ['block_2_depthwise[0][0]']   \n",
            " hNormalization)                                                                                  \n",
            "                                                                                                  \n",
            " block_2_depthwise_relu (Re  (None, 56, 56, 144)          0         ['block_2_depthwise_BN[0][0]']\n",
            " LU)                                                                                              \n",
            "                                                                                                  \n",
            " block_2_project (Conv2D)    (None, 56, 56, 24)           3456      ['block_2_depthwise_relu[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block_2_project_BN (BatchN  (None, 56, 56, 24)           96        ['block_2_project[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_2_add (Add)           (None, 56, 56, 24)           0         ['block_1_project_BN[0][0]',  \n",
            "                                                                     'block_2_project_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_3_expand (Conv2D)     (None, 56, 56, 144)          3456      ['block_2_add[0][0]']         \n",
            "                                                                                                  \n",
            " block_3_expand_BN (BatchNo  (None, 56, 56, 144)          576       ['block_3_expand[0][0]']      \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_3_expand_relu (ReLU)  (None, 56, 56, 144)          0         ['block_3_expand_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_3_pad (ZeroPadding2D  (None, 57, 57, 144)          0         ['block_3_expand_relu[0][0]'] \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block_3_depthwise (Depthwi  (None, 28, 28, 144)          1296      ['block_3_pad[0][0]']         \n",
            " seConv2D)                                                                                        \n",
            "                                                                                                  \n",
            " block_3_depthwise_BN (Batc  (None, 28, 28, 144)          576       ['block_3_depthwise[0][0]']   \n",
            " hNormalization)                                                                                  \n",
            "                                                                                                  \n",
            " block_3_depthwise_relu (Re  (None, 28, 28, 144)          0         ['block_3_depthwise_BN[0][0]']\n",
            " LU)                                                                                              \n",
            "                                                                                                  \n",
            " block_3_project (Conv2D)    (None, 28, 28, 32)           4608      ['block_3_depthwise_relu[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block_3_project_BN (BatchN  (None, 28, 28, 32)           128       ['block_3_project[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_4_expand (Conv2D)     (None, 28, 28, 192)          6144      ['block_3_project_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_4_expand_BN (BatchNo  (None, 28, 28, 192)          768       ['block_4_expand[0][0]']      \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_4_expand_relu (ReLU)  (None, 28, 28, 192)          0         ['block_4_expand_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_4_depthwise (Depthwi  (None, 28, 28, 192)          1728      ['block_4_expand_relu[0][0]'] \n",
            " seConv2D)                                                                                        \n",
            "                                                                                                  \n",
            " block_4_depthwise_BN (Batc  (None, 28, 28, 192)          768       ['block_4_depthwise[0][0]']   \n",
            " hNormalization)                                                                                  \n",
            "                                                                                                  \n",
            " block_4_depthwise_relu (Re  (None, 28, 28, 192)          0         ['block_4_depthwise_BN[0][0]']\n",
            " LU)                                                                                              \n",
            "                                                                                                  \n",
            " block_4_project (Conv2D)    (None, 28, 28, 32)           6144      ['block_4_depthwise_relu[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block_4_project_BN (BatchN  (None, 28, 28, 32)           128       ['block_4_project[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_4_add (Add)           (None, 28, 28, 32)           0         ['block_3_project_BN[0][0]',  \n",
            "                                                                     'block_4_project_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_5_expand (Conv2D)     (None, 28, 28, 192)          6144      ['block_4_add[0][0]']         \n",
            "                                                                                                  \n",
            " block_5_expand_BN (BatchNo  (None, 28, 28, 192)          768       ['block_5_expand[0][0]']      \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_5_expand_relu (ReLU)  (None, 28, 28, 192)          0         ['block_5_expand_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_5_depthwise (Depthwi  (None, 28, 28, 192)          1728      ['block_5_expand_relu[0][0]'] \n",
            " seConv2D)                                                                                        \n",
            "                                                                                                  \n",
            " block_5_depthwise_BN (Batc  (None, 28, 28, 192)          768       ['block_5_depthwise[0][0]']   \n",
            " hNormalization)                                                                                  \n",
            "                                                                                                  \n",
            " block_5_depthwise_relu (Re  (None, 28, 28, 192)          0         ['block_5_depthwise_BN[0][0]']\n",
            " LU)                                                                                              \n",
            "                                                                                                  \n",
            " block_5_project (Conv2D)    (None, 28, 28, 32)           6144      ['block_5_depthwise_relu[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block_5_project_BN (BatchN  (None, 28, 28, 32)           128       ['block_5_project[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_5_add (Add)           (None, 28, 28, 32)           0         ['block_4_add[0][0]',         \n",
            "                                                                     'block_5_project_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_6_expand (Conv2D)     (None, 28, 28, 192)          6144      ['block_5_add[0][0]']         \n",
            "                                                                                                  \n",
            " block_6_expand_BN (BatchNo  (None, 28, 28, 192)          768       ['block_6_expand[0][0]']      \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_6_expand_relu (ReLU)  (None, 28, 28, 192)          0         ['block_6_expand_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_6_pad (ZeroPadding2D  (None, 29, 29, 192)          0         ['block_6_expand_relu[0][0]'] \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block_6_depthwise (Depthwi  (None, 14, 14, 192)          1728      ['block_6_pad[0][0]']         \n",
            " seConv2D)                                                                                        \n",
            "                                                                                                  \n",
            " block_6_depthwise_BN (Batc  (None, 14, 14, 192)          768       ['block_6_depthwise[0][0]']   \n",
            " hNormalization)                                                                                  \n",
            "                                                                                                  \n",
            " block_6_depthwise_relu (Re  (None, 14, 14, 192)          0         ['block_6_depthwise_BN[0][0]']\n",
            " LU)                                                                                              \n",
            "                                                                                                  \n",
            " block_6_project (Conv2D)    (None, 14, 14, 64)           12288     ['block_6_depthwise_relu[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block_6_project_BN (BatchN  (None, 14, 14, 64)           256       ['block_6_project[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_7_expand (Conv2D)     (None, 14, 14, 384)          24576     ['block_6_project_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_7_expand_BN (BatchNo  (None, 14, 14, 384)          1536      ['block_7_expand[0][0]']      \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_7_expand_relu (ReLU)  (None, 14, 14, 384)          0         ['block_7_expand_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_7_depthwise (Depthwi  (None, 14, 14, 384)          3456      ['block_7_expand_relu[0][0]'] \n",
            " seConv2D)                                                                                        \n",
            "                                                                                                  \n",
            " block_7_depthwise_BN (Batc  (None, 14, 14, 384)          1536      ['block_7_depthwise[0][0]']   \n",
            " hNormalization)                                                                                  \n",
            "                                                                                                  \n",
            " block_7_depthwise_relu (Re  (None, 14, 14, 384)          0         ['block_7_depthwise_BN[0][0]']\n",
            " LU)                                                                                              \n",
            "                                                                                                  \n",
            " block_7_project (Conv2D)    (None, 14, 14, 64)           24576     ['block_7_depthwise_relu[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block_7_project_BN (BatchN  (None, 14, 14, 64)           256       ['block_7_project[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_7_add (Add)           (None, 14, 14, 64)           0         ['block_6_project_BN[0][0]',  \n",
            "                                                                     'block_7_project_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_8_expand (Conv2D)     (None, 14, 14, 384)          24576     ['block_7_add[0][0]']         \n",
            "                                                                                                  \n",
            " block_8_expand_BN (BatchNo  (None, 14, 14, 384)          1536      ['block_8_expand[0][0]']      \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_8_expand_relu (ReLU)  (None, 14, 14, 384)          0         ['block_8_expand_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_8_depthwise (Depthwi  (None, 14, 14, 384)          3456      ['block_8_expand_relu[0][0]'] \n",
            " seConv2D)                                                                                        \n",
            "                                                                                                  \n",
            " block_8_depthwise_BN (Batc  (None, 14, 14, 384)          1536      ['block_8_depthwise[0][0]']   \n",
            " hNormalization)                                                                                  \n",
            "                                                                                                  \n",
            " block_8_depthwise_relu (Re  (None, 14, 14, 384)          0         ['block_8_depthwise_BN[0][0]']\n",
            " LU)                                                                                              \n",
            "                                                                                                  \n",
            " block_8_project (Conv2D)    (None, 14, 14, 64)           24576     ['block_8_depthwise_relu[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block_8_project_BN (BatchN  (None, 14, 14, 64)           256       ['block_8_project[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_8_add (Add)           (None, 14, 14, 64)           0         ['block_7_add[0][0]',         \n",
            "                                                                     'block_8_project_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_9_expand (Conv2D)     (None, 14, 14, 384)          24576     ['block_8_add[0][0]']         \n",
            "                                                                                                  \n",
            " block_9_expand_BN (BatchNo  (None, 14, 14, 384)          1536      ['block_9_expand[0][0]']      \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_9_expand_relu (ReLU)  (None, 14, 14, 384)          0         ['block_9_expand_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_9_depthwise (Depthwi  (None, 14, 14, 384)          3456      ['block_9_expand_relu[0][0]'] \n",
            " seConv2D)                                                                                        \n",
            "                                                                                                  \n",
            " block_9_depthwise_BN (Batc  (None, 14, 14, 384)          1536      ['block_9_depthwise[0][0]']   \n",
            " hNormalization)                                                                                  \n",
            "                                                                                                  \n",
            " block_9_depthwise_relu (Re  (None, 14, 14, 384)          0         ['block_9_depthwise_BN[0][0]']\n",
            " LU)                                                                                              \n",
            "                                                                                                  \n",
            " block_9_project (Conv2D)    (None, 14, 14, 64)           24576     ['block_9_depthwise_relu[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block_9_project_BN (BatchN  (None, 14, 14, 64)           256       ['block_9_project[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_9_add (Add)           (None, 14, 14, 64)           0         ['block_8_add[0][0]',         \n",
            "                                                                     'block_9_project_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_10_expand (Conv2D)    (None, 14, 14, 384)          24576     ['block_9_add[0][0]']         \n",
            "                                                                                                  \n",
            " block_10_expand_BN (BatchN  (None, 14, 14, 384)          1536      ['block_10_expand[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_10_expand_relu (ReLU  (None, 14, 14, 384)          0         ['block_10_expand_BN[0][0]']  \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block_10_depthwise (Depthw  (None, 14, 14, 384)          3456      ['block_10_expand_relu[0][0]']\n",
            " iseConv2D)                                                                                       \n",
            "                                                                                                  \n",
            " block_10_depthwise_BN (Bat  (None, 14, 14, 384)          1536      ['block_10_depthwise[0][0]']  \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " block_10_depthwise_relu (R  (None, 14, 14, 384)          0         ['block_10_depthwise_BN[0][0]'\n",
            " eLU)                                                               ]                             \n",
            "                                                                                                  \n",
            " block_10_project (Conv2D)   (None, 14, 14, 96)           36864     ['block_10_depthwise_relu[0][0\n",
            "                                                                    ]']                           \n",
            "                                                                                                  \n",
            " block_10_project_BN (Batch  (None, 14, 14, 96)           384       ['block_10_project[0][0]']    \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " block_11_expand (Conv2D)    (None, 14, 14, 576)          55296     ['block_10_project_BN[0][0]'] \n",
            "                                                                                                  \n",
            " block_11_expand_BN (BatchN  (None, 14, 14, 576)          2304      ['block_11_expand[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_11_expand_relu (ReLU  (None, 14, 14, 576)          0         ['block_11_expand_BN[0][0]']  \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block_11_depthwise (Depthw  (None, 14, 14, 576)          5184      ['block_11_expand_relu[0][0]']\n",
            " iseConv2D)                                                                                       \n",
            "                                                                                                  \n",
            " block_11_depthwise_BN (Bat  (None, 14, 14, 576)          2304      ['block_11_depthwise[0][0]']  \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " block_11_depthwise_relu (R  (None, 14, 14, 576)          0         ['block_11_depthwise_BN[0][0]'\n",
            " eLU)                                                               ]                             \n",
            "                                                                                                  \n",
            " block_11_project (Conv2D)   (None, 14, 14, 96)           55296     ['block_11_depthwise_relu[0][0\n",
            "                                                                    ]']                           \n",
            "                                                                                                  \n",
            " block_11_project_BN (Batch  (None, 14, 14, 96)           384       ['block_11_project[0][0]']    \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " block_11_add (Add)          (None, 14, 14, 96)           0         ['block_10_project_BN[0][0]', \n",
            "                                                                     'block_11_project_BN[0][0]'] \n",
            "                                                                                                  \n",
            " block_12_expand (Conv2D)    (None, 14, 14, 576)          55296     ['block_11_add[0][0]']        \n",
            "                                                                                                  \n",
            " block_12_expand_BN (BatchN  (None, 14, 14, 576)          2304      ['block_12_expand[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_12_expand_relu (ReLU  (None, 14, 14, 576)          0         ['block_12_expand_BN[0][0]']  \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block_12_depthwise (Depthw  (None, 14, 14, 576)          5184      ['block_12_expand_relu[0][0]']\n",
            " iseConv2D)                                                                                       \n",
            "                                                                                                  \n",
            " block_12_depthwise_BN (Bat  (None, 14, 14, 576)          2304      ['block_12_depthwise[0][0]']  \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " block_12_depthwise_relu (R  (None, 14, 14, 576)          0         ['block_12_depthwise_BN[0][0]'\n",
            " eLU)                                                               ]                             \n",
            "                                                                                                  \n",
            " block_12_project (Conv2D)   (None, 14, 14, 96)           55296     ['block_12_depthwise_relu[0][0\n",
            "                                                                    ]']                           \n",
            "                                                                                                  \n",
            " block_12_project_BN (Batch  (None, 14, 14, 96)           384       ['block_12_project[0][0]']    \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " block_12_add (Add)          (None, 14, 14, 96)           0         ['block_11_add[0][0]',        \n",
            "                                                                     'block_12_project_BN[0][0]'] \n",
            "                                                                                                  \n",
            " block_13_expand (Conv2D)    (None, 14, 14, 576)          55296     ['block_12_add[0][0]']        \n",
            "                                                                                                  \n",
            " block_13_expand_BN (BatchN  (None, 14, 14, 576)          2304      ['block_13_expand[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_13_expand_relu (ReLU  (None, 14, 14, 576)          0         ['block_13_expand_BN[0][0]']  \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block_13_pad (ZeroPadding2  (None, 15, 15, 576)          0         ['block_13_expand_relu[0][0]']\n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block_13_depthwise (Depthw  (None, 7, 7, 576)            5184      ['block_13_pad[0][0]']        \n",
            " iseConv2D)                                                                                       \n",
            "                                                                                                  \n",
            " block_13_depthwise_BN (Bat  (None, 7, 7, 576)            2304      ['block_13_depthwise[0][0]']  \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " block_13_depthwise_relu (R  (None, 7, 7, 576)            0         ['block_13_depthwise_BN[0][0]'\n",
            " eLU)                                                               ]                             \n",
            "                                                                                                  \n",
            " block_13_project (Conv2D)   (None, 7, 7, 160)            92160     ['block_13_depthwise_relu[0][0\n",
            "                                                                    ]']                           \n",
            "                                                                                                  \n",
            " block_13_project_BN (Batch  (None, 7, 7, 160)            640       ['block_13_project[0][0]']    \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " block_14_expand (Conv2D)    (None, 7, 7, 960)            153600    ['block_13_project_BN[0][0]'] \n",
            "                                                                                                  \n",
            " block_14_expand_BN (BatchN  (None, 7, 7, 960)            3840      ['block_14_expand[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_14_expand_relu (ReLU  (None, 7, 7, 960)            0         ['block_14_expand_BN[0][0]']  \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block_14_depthwise (Depthw  (None, 7, 7, 960)            8640      ['block_14_expand_relu[0][0]']\n",
            " iseConv2D)                                                                                       \n",
            "                                                                                                  \n",
            " block_14_depthwise_BN (Bat  (None, 7, 7, 960)            3840      ['block_14_depthwise[0][0]']  \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " block_14_depthwise_relu (R  (None, 7, 7, 960)            0         ['block_14_depthwise_BN[0][0]'\n",
            " eLU)                                                               ]                             \n",
            "                                                                                                  \n",
            " block_14_project (Conv2D)   (None, 7, 7, 160)            153600    ['block_14_depthwise_relu[0][0\n",
            "                                                                    ]']                           \n",
            "                                                                                                  \n",
            " block_14_project_BN (Batch  (None, 7, 7, 160)            640       ['block_14_project[0][0]']    \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " block_14_add (Add)          (None, 7, 7, 160)            0         ['block_13_project_BN[0][0]', \n",
            "                                                                     'block_14_project_BN[0][0]'] \n",
            "                                                                                                  \n",
            " block_15_expand (Conv2D)    (None, 7, 7, 960)            153600    ['block_14_add[0][0]']        \n",
            "                                                                                                  \n",
            " block_15_expand_BN (BatchN  (None, 7, 7, 960)            3840      ['block_15_expand[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_15_expand_relu (ReLU  (None, 7, 7, 960)            0         ['block_15_expand_BN[0][0]']  \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block_15_depthwise (Depthw  (None, 7, 7, 960)            8640      ['block_15_expand_relu[0][0]']\n",
            " iseConv2D)                                                                                       \n",
            "                                                                                                  \n",
            " block_15_depthwise_BN (Bat  (None, 7, 7, 960)            3840      ['block_15_depthwise[0][0]']  \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " block_15_depthwise_relu (R  (None, 7, 7, 960)            0         ['block_15_depthwise_BN[0][0]'\n",
            " eLU)                                                               ]                             \n",
            "                                                                                                  \n",
            " block_15_project (Conv2D)   (None, 7, 7, 160)            153600    ['block_15_depthwise_relu[0][0\n",
            "                                                                    ]']                           \n",
            "                                                                                                  \n",
            " block_15_project_BN (Batch  (None, 7, 7, 160)            640       ['block_15_project[0][0]']    \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " block_15_add (Add)          (None, 7, 7, 160)            0         ['block_14_add[0][0]',        \n",
            "                                                                     'block_15_project_BN[0][0]'] \n",
            "                                                                                                  \n",
            " block_16_expand (Conv2D)    (None, 7, 7, 960)            153600    ['block_15_add[0][0]']        \n",
            "                                                                                                  \n",
            " block_16_expand_BN (BatchN  (None, 7, 7, 960)            3840      ['block_16_expand[0][0]']     \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block_16_expand_relu (ReLU  (None, 7, 7, 960)            0         ['block_16_expand_BN[0][0]']  \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block_16_depthwise (Depthw  (None, 7, 7, 960)            8640      ['block_16_expand_relu[0][0]']\n",
            " iseConv2D)                                                                                       \n",
            "                                                                                                  \n",
            " block_16_depthwise_BN (Bat  (None, 7, 7, 960)            3840      ['block_16_depthwise[0][0]']  \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " block_16_depthwise_relu (R  (None, 7, 7, 960)            0         ['block_16_depthwise_BN[0][0]'\n",
            " eLU)                                                               ]                             \n",
            "                                                                                                  \n",
            " block_16_project (Conv2D)   (None, 7, 7, 320)            307200    ['block_16_depthwise_relu[0][0\n",
            "                                                                    ]']                           \n",
            "                                                                                                  \n",
            " block_16_project_BN (Batch  (None, 7, 7, 320)            1280      ['block_16_project[0][0]']    \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " Conv_1 (Conv2D)             (None, 7, 7, 1280)           409600    ['block_16_project_BN[0][0]'] \n",
            "                                                                                                  \n",
            " Conv_1_bn (BatchNormalizat  (None, 7, 7, 1280)           5120      ['Conv_1[0][0]']              \n",
            " ion)                                                                                             \n",
            "                                                                                                  \n",
            " out_relu (ReLU)             (None, 7, 7, 1280)           0         ['Conv_1_bn[0][0]']           \n",
            "                                                                                                  \n",
            " global_average_pooling2d (  (None, 1280)                 0         ['out_relu[0][0]']            \n",
            " GlobalAveragePooling2D)                                                                          \n",
            "                                                                                                  \n",
            " dense (Dense)               (None, 512)                  655872    ['global_average_pooling2d[0][\n",
            "                                                                    0]']                          \n",
            "                                                                                                  \n",
            " dropout (Dropout)           (None, 512)                  0         ['dense[0][0]']               \n",
            "                                                                                                  \n",
            " dense_1 (Dense)             (None, 74)                   37962     ['dropout[0][0]']             \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2951818 (11.26 MB)\n",
            "Trainable params: 693834 (2.65 MB)\n",
            "Non-trainable params: 2257984 (8.61 MB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()\n",
        "\n",
        "# save the whole model\n"
      ],
      "id": "6uPX2VCe1FgX"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cc546deb"
      },
      "outputs": [],
      "source": [
        "# reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1), patience=5)\n",
        "# optimizer = Adam(learning_rate=0.001)\n",
        "# model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n"
      ],
      "id": "cc546deb"
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "1FBneV6MVUTv"
      },
      "id": "1FBneV6MVUTv"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0c4e189d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c5d4d2c-f5db-47d7-903b-175cab344dd5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n",
            "Predicted Label: Sweet flag\n",
            "Confidence: 1.00\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
        "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
        "\n",
        "# Load the trained model\n",
        "model = tf.keras.models.load_model('/content/drive/MyDrive/Colab Notebooks/my_model.h5')\n",
        "\n",
        "# Load and preprocess the image\n",
        "def preprocess_image(image_path):\n",
        "    image = load_img(image_path, target_size=(224, 224))\n",
        "    image_array = img_to_array(image)\n",
        "    image_array = np.expand_dims(image_array, axis=0)\n",
        "    preprocessed_image = preprocess_input(image_array)\n",
        "    return preprocessed_image\n",
        "\n",
        "# Perform prediction\n",
        "def predict_plant(image_path, label_mapping):\n",
        "    preprocessed_image = preprocess_image(image_path)\n",
        "    predictions = model.predict(preprocessed_image)\n",
        "\n",
        "    # Map model's numeric predictions to labels\n",
        "    predicted_label_index = np.argmax(predictions)\n",
        "    predicted_label = label_mapping[predicted_label_index]\n",
        "    confidence = predictions[0][predicted_label_index]\n",
        "\n",
        "    return predicted_label, confidence\n",
        "\n",
        "# Provide the path to the image you want to classify\n",
        "img_path = '/content/drive/MyDrive/Colab Notebooks/train/Sweet flag/045_35.jpg'\n",
        "predicted_label, confidence = predict_plant(img_path, labels)\n",
        "\n",
        "# Print the prediction\n",
        "print(f\"Predicted Label: {predicted_label}\")\n",
        "print(f\"Confidence: {confidence:.2f}\")"
      ],
      "id": "0c4e189d"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6bqT0axmOdRc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17c49f0a-e779-408d-d4be-482560e03293"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "31/31 [==============================] - 305s 10s/step\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Make predictions on new data\n",
        "predictions = model.predict(test_generator)"
      ],
      "id": "6bqT0axmOdRc"
    },
    {
      "cell_type": "code",
      "source": [
        "# /content/drive/MyDrive/Colab Notebooks/train/Sweet flag/045_35.jpg"
      ],
      "metadata": {
        "id": "xk59WGiU7Jzt"
      },
      "id": "xk59WGiU7Jzt",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0603865f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48c1488c-ec63-4ad6-a4a1-fdfb9fc92d0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                             precision    recall  f1-score   support\n",
            "\n",
            "           Amaranthus Green       0.79      0.79      0.79        19\n",
            "             Amaranthus Red       0.80      0.86      0.83        14\n",
            "               Asthma Plant       0.91      0.77      0.83        13\n",
            "                     Avaram       1.00      1.00      1.00        11\n",
            "               Balloon vine       0.92      0.92      0.92        38\n",
            "     Bellyache bush (Green)       0.93      1.00      0.96        13\n",
            "          Benghal dayflower       0.90      0.90      0.90        10\n",
            "               Betel Leaves       0.85      0.85      0.85        20\n",
            "               Big Caltrops       0.67      0.73      0.70        11\n",
            "          Black Night Shade       0.76      0.76      0.76        17\n",
            "          Black-Honey Shrub       0.83      0.88      0.86        17\n",
            "         Bristly Wild Grape       0.83      0.91      0.87        11\n",
            "              Butterfly Pea       1.00      0.89      0.94         9\n",
            "            Cape Gooseberry       0.87      0.68      0.76        19\n",
            "                     Celery       0.81      1.00      0.90        13\n",
            "            Chinese Spinach       0.60      1.00      0.75         9\n",
            "            Common Wireweed       0.90      1.00      0.95         9\n",
            "           Coriander Leaves       0.89      0.89      0.89        18\n",
            "             Country Mallow       1.00      0.91      0.95        11\n",
            "               Crown flower       1.00      1.00      1.00        11\n",
            "                 Curry Leaf       0.87      0.76      0.81        17\n",
            "   Dwarf Copperleaf (Green)       1.00      0.93      0.96        14\n",
            "     Dwarf copperleaf (Red)       0.76      1.00      0.87        13\n",
            "             False Amarnath       0.58      0.88      0.70        16\n",
            "           Fenugreek Leaves       1.00      0.83      0.91        12\n",
            "              Giant Pigweed       0.88      0.94      0.91        16\n",
            "                    Gongura       0.88      0.78      0.82         9\n",
            "              Green Chireta       1.00      0.73      0.84        11\n",
            "                 Holy Basil       0.75      0.67      0.71         9\n",
            "          Indian CopperLeaf       0.83      0.75      0.79        20\n",
            "              Indian Jujube       1.00      0.91      0.95        11\n",
            "        Indian Sarsaparilla       1.00      1.00      1.00        14\n",
            "     Indian Stinging Nettle       0.89      1.00      0.94        17\n",
            "          Indian Thornapple       1.00      1.00      1.00        10\n",
            "           Indian pennywort       0.83      0.91      0.87        11\n",
            "            Indian wormwood       0.91      0.91      0.91        11\n",
            "                  Ivy Gourd       1.00      1.00      1.00         9\n",
            "                 Kokilaksha       0.89      0.80      0.84        10\n",
            "              Lagos Spinach       0.80      0.86      0.83        14\n",
            "             Lambs Quarters       1.00      0.82      0.90        11\n",
            "     Land Caltrops (Bindii)       1.00      0.90      0.95        10\n",
            "               Lettuce Tree       0.85      1.00      0.92        11\n",
            "      Madagascar Periwinkle       1.00      0.89      0.94         9\n",
            "         Madras Pea Pumpkin       0.90      1.00      0.95         9\n",
            "            Malabar Catmint       1.00      0.82      0.90        11\n",
            "    Malabar Spinach (Green)       0.94      0.88      0.91        17\n",
            "               Mexican Mint       0.89      1.00      0.94        17\n",
            "      Mexican Prickly Poppy       1.00      0.89      0.94         9\n",
            "                Mint Leaves       0.74      1.00      0.85        20\n",
            "         Mountain Knotgrass       1.00      0.54      0.70        13\n",
            "                    Mustard       1.00      0.67      0.80        12\n",
            "                 Nalta Jute       1.00      0.83      0.91        12\n",
            "      Night blooming Cereus       1.00      0.78      0.88         9\n",
            "                      Palak       0.65      0.93      0.76        14\n",
            "          Panicled Foldwing       1.00      0.64      0.78        11\n",
            "       Prickly Chaff Flower       1.00      0.56      0.71         9\n",
            "                  Punarnava       0.50      0.58      0.54        12\n",
            "Purple Fruited Pea Eggplant       0.91      0.91      0.91        11\n",
            "           Purple Tephrosia       0.83      1.00      0.91        10\n",
            "                 Rosary Pea       1.00      1.00      1.00        11\n",
            "         Shaggy button weed       0.73      0.73      0.73        11\n",
            "                Siru Keerai       0.58      0.64      0.61        11\n",
            "         Small Water Clover       1.00      1.00      1.00        10\n",
            "                 Spiderwisp       0.90      1.00      0.95         9\n",
            "        Square Stalked Vine       0.95      0.76      0.84        25\n",
            "     Stinking Passionflower       0.92      1.00      0.96        11\n",
            "                Sweet Basil       0.86      1.00      0.93        19\n",
            "                 Sweet flag       0.77      0.83      0.80        12\n",
            "           Tinnevelly Senna       1.00      0.89      0.94         9\n",
            "               Trellis Vine       0.91      1.00      0.95        10\n",
            "                Velvet bean       1.00      0.83      0.91        12\n",
            "              Water Spinach       0.82      1.00      0.90         9\n",
            "                coatbuttons       1.00      1.00      1.00         9\n",
            "      heart-leaved moonseed       1.00      0.87      0.93        30\n",
            "\n",
            "                   accuracy                           0.87       972\n",
            "                  macro avg       0.89      0.87      0.87       972\n",
            "               weighted avg       0.89      0.87      0.87       972\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Assuming test_generator is correctly set up with class labels,\n",
        "# you can calculate classification metrics like accuracy, precision, recall, and F1-score.\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "class_labels = list(test_generator.class_indices.keys())\n",
        "y_true = test_generator.classes\n",
        "y_pred = predictions.argmax(axis=-1)\n",
        "\n",
        "print(classification_report(y_true, y_pred, target_names=class_labels))"
      ],
      "id": "0603865f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dna9XY5tMGVJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b61c6f47-30c7-43a2-facb-7d0f829961d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 66ms/step\n",
            "Predicted class: Curry Leaf\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from keras.preprocessing import image\n",
        "\n",
        "\n",
        "\n",
        "# Preprocess the image\n",
        "image_path = \"/content/drive/MyDrive/Colab Notebooks/train/Amaranthus Green/20200306_091854.jpg\"\n",
        "img = image.load_img(image_path, target_size=(224, 224,3))\n",
        "x = image.img_to_array(img)\n",
        "x = np.expand_dims(x, axis=0)\n",
        "\n",
        "# Make a prediction\n",
        "prediction = model.predict(x)\n",
        "\n",
        "# Get the predicted class\n",
        "predicted_class = np.argmax(prediction)\n",
        "\n",
        "# Display the predicted class\n",
        "print('Predicted class:', labels[predicted_class])"
      ],
      "id": "dna9XY5tMGVJ"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "coiYDbR9MuCW"
      },
      "outputs": [],
      "source": [
        "# from PIL import Image\n",
        "\n",
        "# img_p = \"/content/drive/MyDrive/Colab Notebooks/validation/Amaranthus Green/20200306_091846.jpg\"\n",
        "# # Open the image file.\n",
        "# img = Image.open(img_p)\n",
        "\n",
        "# # Get the dimensions of the image.\n",
        "# width, height = img.size\n",
        "\n",
        "# print(f\"Image Dimensions: {width} x {height} pixels\")"
      ],
      "id": "coiYDbR9MuCW"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JgqhqLnD4bqh"
      },
      "outputs": [],
      "source": [
        "# labels = {value: key for key, value in test_generator.class_indices.items()}\n",
        "\n",
        "# print(\"Label Mappings for classes present in the training and validation datasets\\n\")\n",
        "# for key, value in labels.items():\n",
        "#     print(f\"{key} : {value}\")"
      ],
      "id": "JgqhqLnD4bqh"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "#predictions = model.predict_generator(test_generator, steps=len(test_generator), verbose=1)\n",
        "predicted_classes = np.argmax(predictions, axis=1)\n",
        "true_classes = test_generator.classes\n",
        "\n",
        "# Generate the confusion matrix\n",
        "cnf_matrix = confusion_matrix(true_classes, predicted_classes)\n",
        "plt.figure(figsize=(25,25))\n",
        "plt.imshow(cnf_matrix, interpolation='nearest', cmap='Blues')\n",
        "plt.colorbar()\n",
        "plt.title(\"Confusion Matrix (MobileNet Plant Classification)\")\n",
        "plt.xlabel(\"Predicted Class\")\n",
        "plt.ylabel(\"True Class\")\n",
        "\n",
        "class_labels = test_generator.class_indices.keys()\n",
        "\n",
        "plt.xticks(np.arange(len(class_labels)), class_labels, rotation=45, ha=\"right\")\n",
        "plt.yticks(np.arange(len(class_labels)), class_labels)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Print classification metrics\n",
        "print(\"Accuracy:\", accuracy_score(true_classes, predicted_classes))\n",
        "print(\"Precision:\", precision_score(true_classes, predicted_classes, average=\"weighted\"))\n",
        "print(\"Recall:\", recall_score(true_classes, predicted_classes, average=\"weighted\"))\n",
        "print(\"F1-Score:\", f1_score(true_classes, predicted_classes, average=\"weighted\"))"
      ],
      "metadata": {
        "id": "sHDGlK6sA2dW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a3bc7854-d398-419f-b456-d611c43a3c96"
      },
      "id": "sHDGlK6sA2dW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x2500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8683127572016461\n",
            "Precision: 0.8826985406863188\n",
            "Recall: 0.8683127572016461\n",
            "F1-Score: 0.8688616382083478\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# # Load MobileNetV2 base model\n",
        "# base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "# # Add custom classification head\n",
        "# x = base_model.output\n",
        "# x = GlobalAveragePooling2D()(x)\n",
        "# x = Dense(512, activation='relu')(x)\n",
        "# x = Dropout(0.5)(x)  # Adding dropout for regularization\n",
        "# predictions = Dense(num_classes, activation='softmax')(x)\n",
        "\n",
        "# model = Model(inputs=base_model.input, outputs=predictions)\n",
        "\n",
        "# # Freeze the layers of the base model\n",
        "# for layer in base_model.layers:\n",
        "#     layer.trainable = False\n",
        "\n",
        "# # Compile the model\n",
        "# model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "#               loss='categorical_crossentropy',\n",
        "#               metrics=['accuracy'])\n",
        "\n",
        "# # Train the model\n",
        "# history = model.fit(\n",
        "#     train_generator,\n",
        "#     steps_per_epoch=train_generator.samples // batch_size,\n",
        "#     epochs=epochs,\n",
        "#     validation_data=validation_generator,\n",
        "#     validation_steps=validation_generator.samples // batch_size\n",
        "# )"
      ],
      "metadata": {
        "id": "cYjG42GpBFs7"
      },
      "id": "cYjG42GpBFs7",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot training & validation accuracy values\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "sz4hKsuS8I5Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "outputId": "de82bf67-1f2f-406b-fa22-44a7029ab706"
      },
      "id": "sz4hKsuS8I5Y",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XEgXWxxKScEW"
      },
      "id": "XEgXWxxKScEW",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}