Copy_of_Tomato_Leaf_CNN_Part2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HarikrishnanK9/Tomato_Leaf_Disease_Detection/blob/main/Copy_of_Tomato_Leaf_CNN_Part2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Mounting to Drive"
      ],
      "metadata": {
        "id": "hzPIuT81Buoy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MNVoECygJ7Hj"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load the Dataset"
      ],
      "metadata": {
        "id": "5KuKjWsgB4dD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ub3cCxdMveAq"
      },
      "outputs": [],
      "source": [
        "!cp '/content/drive/MyDrive/Tomato_Leaf/TomatoX.zip' '/content'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3nnK1gdTvhZ2"
      },
      "outputs": [],
      "source": [
        "!unzip TomatoX.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Importing Necessary Libraries"
      ],
      "metadata": {
        "id": "co88b3xGB98R"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4NIFB3_mv02e"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D,MaxPooling2D,Flatten,Dense,Dropout,BatchNormalization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lFTfBBzZ2ROl"
      },
      "outputs": [],
      "source": [
        "input_shape = (256,256,3)\n",
        "num_classes = 11\n",
        "batch_size = 32\n",
        "epochs = 10"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kG4Ovsk0yGVu"
      },
      "source": [
        "# Model1:\n",
        "* 4 hidden layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fCLZ0IHd2Zt2"
      },
      "outputs": [],
      "source": [
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes, activation='softmax'))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NEpVVlFi2ajt"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dvu5pDlH3DPv"
      },
      "outputs": [],
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,              # Normalize pixel values to [0, 1]\n",
        "    rotation_range=40,           # Degree range for random rotations\n",
        "    width_shift_range=0.2,       # Fraction of total width for random horizontal shifts\n",
        "    height_shift_range=0.2,      # Fraction of total height for random vertical shifts\n",
        "    shear_range=0.2,             # Shear intensity (shear angle in radians)\n",
        "    zoom_range=0.2,              # Range for random zoom\n",
        "    horizontal_flip=True,        # Randomly flip inputs horizontally\n",
        "    vertical_flip=True           # Randomly flip inputs vertically\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W1FEBM8E3F2P"
      },
      "outputs": [],
      "source": [
        "test_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k5ayBbBc3IQk",
        "outputId": "f6ce1a25-dee8-4f5c-996e-a68cdbeab6e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 25851 images belonging to 11 classes.\n"
          ]
        }
      ],
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "    '/content/train',\n",
        "        target_size=(256,256),\n",
        "        batch_size=batch_size,\n",
        "        class_mode='categorical')\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hhVykxKA3Nvo",
        "outputId": "732f22e8-5ea1-4c49-f1cd-5f196b5d9a9a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 6683 images belonging to 11 classes.\n"
          ]
        }
      ],
      "source": [
        "validation_generator = test_datagen.flow_from_directory(\n",
        "        '/content/valid',\n",
        "        target_size=(256,256),\n",
        "        batch_size=batch_size,\n",
        "        class_mode='categorical')\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Visualization"
      ],
      "metadata": {
        "id": "FToNe7yHGO9D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_images_to_display = 5\n",
        "category_names = {\n",
        "    0: 'Bacterial_spot',\n",
        "    1: 'Early_blight',\n",
        "    2: 'Late_blight',\n",
        "    3: 'Leaf_Mold',\n",
        "    4: 'Septoria_leaf_spot',\n",
        "    5: 'Spider_mites Two-spotted_spider_mite',\n",
        "    6: 'Target_Spot',\n",
        "    7: 'Tomato_Yellow_Leaf_Curl_Virus',\n",
        "    8: 'Tomato_mosaic_virus',\n",
        "    9: 'healthy',\n",
        "    10: 'powdery_mildew'\n",
        "}\n",
        "\n",
        "# Extract images from the data generator for each category\n",
        "category_images = {}\n",
        "for category in category_names:\n",
        "    images, _ = train_generator.next()\n",
        "    category_images[category] = images\n",
        "\n",
        "# Calculate the number of categories\n",
        "num_categories = len(category_names)\n",
        "\n",
        "# Display the images\n",
        "plt.figure(figsize=(10, 2 * num_categories))  # Increase the width of the figure\n",
        "for i in range(num_images_to_display):\n",
        "    for category in category_names:\n",
        "        plt.subplot(num_categories, num_images_to_display, category * num_images_to_display + i + 1)\n",
        "        plt.imshow(category_images[category][i])\n",
        "        plt.title(category_names[category])\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "YmdnplBcE14O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3tXHeCNjQehW",
        "outputId": "b6fa3a86-07a4-4316-a120-07f05edfa745"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "807/807 [==============================] - 520s 628ms/step - loss: 1.6128 - accuracy: 0.4184 - val_loss: 1.2646 - val_accuracy: 0.5718\n",
            "Epoch 2/10\n",
            "807/807 [==============================] - 513s 636ms/step - loss: 1.0304 - accuracy: 0.6413 - val_loss: 0.9987 - val_accuracy: 0.6605\n",
            "Epoch 3/10\n",
            "807/807 [==============================] - 533s 660ms/step - loss: 0.8506 - accuracy: 0.7067 - val_loss: 0.9162 - val_accuracy: 0.7398\n",
            "Epoch 4/10\n",
            "807/807 [==============================] - 509s 631ms/step - loss: 0.7410 - accuracy: 0.7437 - val_loss: 0.9798 - val_accuracy: 0.7336\n",
            "Epoch 5/10\n",
            "807/807 [==============================] - 516s 638ms/step - loss: 0.6687 - accuracy: 0.7686 - val_loss: 0.7077 - val_accuracy: 0.7832\n",
            "Epoch 6/10\n",
            "807/807 [==============================] - 521s 645ms/step - loss: 0.6291 - accuracy: 0.7839 - val_loss: 0.5798 - val_accuracy: 0.8069\n",
            "Epoch 7/10\n",
            "807/807 [==============================] - 521s 645ms/step - loss: 0.5872 - accuracy: 0.7984 - val_loss: 0.5682 - val_accuracy: 0.8086\n",
            "Epoch 8/10\n",
            "807/807 [==============================] - 537s 666ms/step - loss: 0.5658 - accuracy: 0.8074 - val_loss: 0.7522 - val_accuracy: 0.7921\n",
            "Epoch 9/10\n",
            "807/807 [==============================] - 526s 651ms/step - loss: 0.5298 - accuracy: 0.8186 - val_loss: 0.6401 - val_accuracy: 0.7954\n",
            "Epoch 10/10\n",
            "807/807 [==============================] - 501s 620ms/step - loss: 0.5160 - accuracy: 0.8224 - val_loss: 0.3734 - val_accuracy: 0.8748\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(\n",
        "      train_generator,\n",
        "      steps_per_epoch=train_generator.samples // batch_size,\n",
        "      epochs=10,\n",
        "      validation_data=validation_generator,\n",
        "      validation_steps=validation_generator.samples // batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EjYTA9XJ3UvR",
        "outputId": "07c9320f-0557-4fd0-cf8a-fa3ac6afbf28"
      },
      "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"
          ]
        }
      ],
      "source": [
        "# Save the trained model for later use\n",
        "model.save('tomato_leaf_classifier_X_new_new1.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0UruK2Nv-Jhq"
      },
      "outputs": [],
      "source": [
        "!cp '/content/tomato_leaf_classifier_X_new_new1.h5' '/content/drive/MyDrive/Tomato_Leaf'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LoC9i7I93Qud"
      },
      "outputs": [],
      "source": [
        "# !cp '/content/drive/MyDrive/Tomato_Leaf/tomato_leaf_classifier_X.h5' '/content'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dJkfY5NN-i5G"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "def predict_image(image_path): #\n",
        "    img = image.load_img(image_path,target_size = input_shape) #\n",
        "    img = image.img_to_array(img) #converting to array\n",
        "    img = np.expand_dims(img,axis=0)  #expanding dims to match for model\n",
        "    img /=255.0 #normalize\n",
        "    prediction = model.predict(img)\n",
        "    predicted_leaf_index = np.argmax(prediction)\n",
        "\n",
        "    return predicted_leaf_index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rXSiowy3-5TK"
      },
      "outputs": [],
      "source": [
        "img = '/content/train/healthy/000bf685-b305-408b-91f4-37030f8e62db___GH_HL Leaf 308.1.JPG'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FTXVdTqeBeDr",
        "outputId": "e1919ca1-ad4e-462e-90bf-38f9ba8f98bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 361ms/step\n",
            "Predicted species: healthy\n"
          ]
        }
      ],
      "source": [
        "# Dictionary mapping species indices to species names\n",
        "species_mapping = {\n",
        "    0: 'Bacterial_spot',\n",
        "    1: 'Early_blight',\n",
        "    2: 'Late_blight',\n",
        "    3: 'Leaf_Mold',\n",
        "    4: 'Septoria_leaf_spot',\n",
        "    5: 'Spider_mites Two-spotted_spider_mite',\n",
        "    6: 'Target_Spot',\n",
        "    7: 'Tomato_Yellow_Leaf_Curl_Virus',\n",
        "    8: 'Tomato_mosaic_virus',\n",
        "    9: 'healthy',\n",
        "   10: 'powdery_mildew'\n",
        "}\n",
        "\n",
        "# Example usage to identify the species from the predicted index\n",
        "predicted_condition_index = predict_image(img)  # Replace this with the actual predicted index\n",
        "predicted_condition_name = species_mapping.get(predicted_condition_index, 'Unknown Species')\n",
        "\n",
        "print(f'Predicted species: {predicted_condition_name}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "xUT1QFlj5mKU",
        "outputId": "bac71358-fc1e-48a4-967a-50b3cfd2e153"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "\n",
        "# Plotting validation loss\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "\n",
        "plt.title(\"Model Loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "DPbzPev4PF00",
        "outputId": "3a24d332-f862-45f5-8f77-a5e469e2f528"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "# Plotting training accuracy\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "\n",
        "# Plotting validation accuracy\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "\n",
        "plt.title(\"Model Accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1kSj9uhyTPc"
      },
      "source": [
        "# Model2:\n",
        "* vgg16(13 convolutional layers+ 3 fully connected layers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dLPgMwWJ5mMV"
      },
      "outputs": [],
      "source": [
        "model2 = Sequential()\n",
        "\n",
        "# Block 1\n",
        "model2.add(Conv2D(64, (3, 3), input_shape=input_shape, padding='same', activation='relu'))\n",
        "model2.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
        "model2.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "# Block 2\n",
        "model2.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
        "model2.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
        "model2.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "# Block 3\n",
        "model2.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
        "model2.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
        "model2.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
        "model2.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "# Block 4\n",
        "model2.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
        "model2.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
        "model2.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
        "model2.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "# Block 5\n",
        "model2.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
        "model2.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
        "model2.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
        "model2.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "# Fully connected layers\n",
        "model2.add(Flatten())\n",
        "model2.add(Dense(4096, activation='relu'))\n",
        "model2.add(Dense(4096, activation='relu'))\n",
        "model2.add(Dense(11, activation='softmax'))  # Output layer for binary classification\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OeHKikPh5mP8"
      },
      "outputs": [],
      "source": [
        "model2.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K5MJDew2GDaH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9672144f-d8f4-4d3d-8d52-dfa7dfebd71b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "807/807 [==============================] - 709s 847ms/step - loss: 2.3686 - accuracy: 0.1172 - val_loss: 2.3559 - val_accuracy: 0.1188\n",
            "Epoch 2/10\n",
            "807/807 [==============================] - 661s 819ms/step - loss: 2.3607 - accuracy: 0.1160 - val_loss: 2.3577 - val_accuracy: 0.1187\n",
            "Epoch 3/10\n",
            "807/807 [==============================] - 668s 828ms/step - loss: 2.3602 - accuracy: 0.1178 - val_loss: 2.3566 - val_accuracy: 0.1185\n",
            "Epoch 4/10\n",
            "807/807 [==============================] - 660s 818ms/step - loss: 2.3601 - accuracy: 0.1172 - val_loss: 2.3559 - val_accuracy: 0.1187\n",
            "Epoch 5/10\n",
            "807/807 [==============================] - 670s 830ms/step - loss: 2.3600 - accuracy: 0.1213 - val_loss: 2.3555 - val_accuracy: 0.1184\n",
            "Epoch 6/10\n",
            "807/807 [==============================] - 674s 835ms/step - loss: 2.3596 - accuracy: 0.1201 - val_loss: 2.3563 - val_accuracy: 0.1206\n",
            "Epoch 7/10\n",
            "807/807 [==============================] - 672s 833ms/step - loss: 2.3597 - accuracy: 0.1202 - val_loss: 2.3576 - val_accuracy: 0.1182\n",
            "Epoch 8/10\n",
            "807/807 [==============================] - 660s 818ms/step - loss: 2.3600 - accuracy: 0.1187 - val_loss: 2.3556 - val_accuracy: 0.1179\n",
            "Epoch 9/10\n",
            "807/807 [==============================] - 664s 823ms/step - loss: 2.3595 - accuracy: 0.1194 - val_loss: 2.3547 - val_accuracy: 0.1182\n",
            "Epoch 10/10\n",
            "807/807 [==============================] - 670s 830ms/step - loss: 2.3595 - accuracy: 0.1191 - val_loss: 2.3554 - val_accuracy: 0.1185\n"
          ]
        }
      ],
      "source": [
        "\n",
        "history2 = model2.fit(\n",
        "      train_generator,\n",
        "      steps_per_epoch=train_generator.samples // batch_size,\n",
        "      epochs=10,\n",
        "      validation_data=validation_generator,\n",
        "      validation_steps=validation_generator.samples // batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "eBgS2M3LlXtq",
        "outputId": "81807daf-f358-4689-85fe-a66a11cb7d9d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history2.history['loss'], label='Training Loss')\n",
        "\n",
        "# Plotting validation loss\n",
        "plt.plot(history2.history['val_loss'], label='Validation Loss')\n",
        "\n",
        "plt.title(\"Model Loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "3Lk0QZo0lXxG",
        "outputId": "b10fe36f-01a0-4d3e-eca1-5c06682d525b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "# Plotting training accuracy\n",
        "plt.plot(history2.history['accuracy'], label='Training Accuracy')\n",
        "\n",
        "# Plotting validation accuracy\n",
        "plt.plot(history2.history['val_accuracy'], label='Validation Accuracy')\n",
        "\n",
        "plt.title(\"Model Accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model3"
      ],
      "metadata": {
        "id": "VX_P3Rx8HPHQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Modifying 1st model by Implementing Batch Normalization and Global Average Pooling"
      ],
      "metadata": {
        "id": "pmOddIHoJ4P7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import GlobalAveragePooling2D, BatchNormalization\n",
        "\n",
        "model3 = Sequential()\n",
        "model3.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))\n",
        "model3.add(BatchNormalization())\n",
        "model3.add(MaxPooling2D((2, 2)))\n",
        "model3.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model3.add(BatchNormalization())\n",
        "model3.add(MaxPooling2D((2, 2)))\n",
        "model3.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model3.add(BatchNormalization())\n",
        "model3.add(MaxPooling2D((2, 2)))\n",
        "model3.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model3.add(BatchNormalization())\n",
        "model3.add(MaxPooling2D((2, 2)))\n",
        "model3.add(GlobalAveragePooling2D())\n",
        "model3.add(Dense(512, activation='relu'))\n",
        "model3.add(BatchNormalization())\n",
        "model3.add(Dropout(0.5))\n",
        "model3.add(Dense(num_classes, activation='softmax'))\n"
      ],
      "metadata": {
        "id": "9XrQ0-oDHgsc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model3.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "PiZTBfYcHhmX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jNhIxPLsyr5B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44257b32-61bd-4430-b12c-1b8c72eec4e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "807/807 [==============================] - 523s 643ms/step - loss: 1.3611 - accuracy: 0.5711 - val_loss: 1.4624 - val_accuracy: 0.5580\n",
            "Epoch 2/20\n",
            "807/807 [==============================] - 506s 627ms/step - loss: 0.8499 - accuracy: 0.7130 - val_loss: 1.3000 - val_accuracy: 0.6190\n",
            "Epoch 3/20\n",
            "807/807 [==============================] - 524s 650ms/step - loss: 0.7180 - accuracy: 0.7567 - val_loss: 1.6054 - val_accuracy: 0.5328\n",
            "Epoch 4/20\n",
            "807/807 [==============================] - 504s 625ms/step - loss: 0.6058 - accuracy: 0.7935 - val_loss: 1.9553 - val_accuracy: 0.6187\n",
            "Epoch 5/20\n",
            "807/807 [==============================] - 503s 624ms/step - loss: 0.5444 - accuracy: 0.8144 - val_loss: 1.2181 - val_accuracy: 0.6264\n",
            "Epoch 6/20\n",
            "807/807 [==============================] - 509s 631ms/step - loss: 0.4934 - accuracy: 0.8342 - val_loss: 1.6089 - val_accuracy: 0.6224\n",
            "Epoch 7/20\n",
            "807/807 [==============================] - 508s 629ms/step - loss: 0.4499 - accuracy: 0.8462 - val_loss: 0.9894 - val_accuracy: 0.7063\n",
            "Epoch 8/20\n",
            "807/807 [==============================] - 520s 645ms/step - loss: 0.4157 - accuracy: 0.8604 - val_loss: 0.4907 - val_accuracy: 0.8519\n",
            "Epoch 9/20\n",
            "807/807 [==============================] - 503s 624ms/step - loss: 0.3991 - accuracy: 0.8653 - val_loss: 0.5298 - val_accuracy: 0.8382\n",
            "Epoch 10/20\n",
            "807/807 [==============================] - 496s 614ms/step - loss: 0.3580 - accuracy: 0.8781 - val_loss: 0.4924 - val_accuracy: 0.8445\n",
            "Epoch 11/20\n",
            "807/807 [==============================] - 490s 607ms/step - loss: 0.3378 - accuracy: 0.8837 - val_loss: 0.5232 - val_accuracy: 0.8418\n",
            "Epoch 12/20\n",
            "807/807 [==============================] - 489s 606ms/step - loss: 0.3271 - accuracy: 0.8885 - val_loss: 0.4723 - val_accuracy: 0.8543\n",
            "Epoch 13/20\n",
            "807/807 [==============================] - 492s 609ms/step - loss: 0.3146 - accuracy: 0.8926 - val_loss: 0.9497 - val_accuracy: 0.7704\n",
            "Epoch 14/20\n",
            "439/807 [===============>..............] - ETA: 3:31 - loss: 0.3102 - accuracy: 0.8948"
          ]
        }
      ],
      "source": [
        "from keras.callbacks import EarlyStopping\n",
        "\n",
        "# Define the EarlyStopping callback\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
        "\n",
        "history3 = model3.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=train_generator.samples // batch_size,\n",
        "    epochs=20,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=validation_generator.samples // batch_size,\n",
        "    callbacks=[early_stopping])  # Pass the EarlyStopping callback to the 'callbacks' parameter\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model3.save('tomato_leaf_classifier_X_new_new2.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5VoinAEHz7i",
        "outputId": "de21af1f-6a4a-4230-9b42-b2a63678b1e8"
      },
      "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",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp '/content/tomato_leaf_classifier_X_new_new2.h5' '/content/drive/MyDrive/Tomato_Leaf'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-VeQBD8Hklj-",
        "outputId": "58ee261c-b9d6-4095-94c3-5db10984ec1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cp: cannot create regular file '/content/drive/MyDrive/Tomato_Leaf': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from keras.layers import  BatchNormalization\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "\n",
        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "\n",
        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(num_classes, activation='softmax'))\n"
      ],
      "metadata": {
        "id": "8IFNPxEco4F5"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1AHURhE-Mk2VeVkXM2ldfWbKMzrW3wRzS",
      "authorship_tag": "ABX9TyO/1xKtRR5/YjuM/8TXaWGu",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}