Untitled.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JgYDt9qz4T0b",
        "outputId": "10376107-2b45-4616-e870-f4d0e3d9f510"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: opendatasets in /usr/local/lib/python3.10/dist-packages (0.1.22)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from opendatasets) (4.66.2)\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (from opendatasets) (1.5.16)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from opendatasets) (8.1.7)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2024.2.2)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.31.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle->opendatasets) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle->opendatasets) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.6)\n"
          ]
        }
      ],
      "source": [
        "!pip install opendatasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### THE CODE WAS DONE IN GOOGLE COLAB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ohy5Z_-B4q4I",
        "outputId": "d819f732-10fb-493d-bd5f-54c43263bf31"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
            "Your Kaggle username: theraghavjuneja\n",
            "Your Kaggle Key: ··········\n",
            "Downloading new-plant-diseases-dataset.zip to ./new-plant-diseases-dataset\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 2.70G/2.70G [00:39<00:00, 74.1MB/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "import opendatasets as od\n",
        "import pandas\n",
        "od.download(\"https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "525oqL1p61zJ",
        "outputId": "f7bcc3c8-bca3-4817-a853-b49b6e8d62d1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['.config', 'new-plant-diseases-dataset', 'sample_data']"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "os.listdir('/content')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G2unq_cXYoER",
        "outputId": "ffe1c5ae-e87c-49c2-f7b2-5b18d344b70b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['new plant diseases dataset(augmented)',\n",
              " 'New Plant Diseases Dataset(Augmented)',\n",
              " 'test']"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "dataset_path = '/content/new-plant-diseases-dataset'\n",
        "os.listdir(dataset_path)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNxO_qQPIoqZ",
        "outputId": "af7b8e98-7981-4452-b3f4-d0d6f7f021f7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Blueberry___healthy',\n",
              " 'Squash___Powdery_mildew',\n",
              " 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',\n",
              " 'Apple___Cedar_apple_rust',\n",
              " 'Tomato___healthy',\n",
              " 'Tomato___Bacterial_spot',\n",
              " 'Tomato___Septoria_leaf_spot',\n",
              " 'Corn_(maize)___healthy',\n",
              " 'Tomato___Late_blight',\n",
              " 'Potato___Early_blight',\n",
              " 'Grape___Esca_(Black_Measles)',\n",
              " 'Corn_(maize)___Common_rust_',\n",
              " 'Soybean___healthy',\n",
              " 'Apple___healthy',\n",
              " 'Cherry_(including_sour)___Powdery_mildew',\n",
              " 'Potato___healthy',\n",
              " 'Tomato___Early_blight',\n",
              " 'Apple___Black_rot',\n",
              " 'Tomato___Target_Spot',\n",
              " 'Corn_(maize)___Northern_Leaf_Blight',\n",
              " 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',\n",
              " 'Strawberry___Leaf_scorch',\n",
              " 'Potato___Late_blight',\n",
              " 'Peach___Bacterial_spot',\n",
              " 'Strawberry___healthy',\n",
              " 'Orange___Haunglongbing_(Citrus_greening)',\n",
              " 'Tomato___Leaf_Mold',\n",
              " 'Raspberry___healthy',\n",
              " 'Tomato___Tomato_mosaic_virus',\n",
              " 'Grape___Black_rot',\n",
              " 'Pepper,_bell___Bacterial_spot',\n",
              " 'Grape___healthy',\n",
              " 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',\n",
              " 'Peach___healthy',\n",
              " 'Tomato___Spider_mites Two-spotted_spider_mite',\n",
              " 'Pepper,_bell___healthy',\n",
              " 'Cherry_(including_sour)___healthy',\n",
              " 'Apple___Apple_scab']"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "dataset_directory = '/content/new-plant-diseases-dataset/New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)/valid'\n",
        "os.listdir(dataset_directory)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s0kKM-8vZOQX"
      },
      "source": [
        "### I HAVE 3 SORTS OF DATASET TRAIN , TEST AND VALID"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UdvdOiiEZQ9W",
        "outputId": "49389fdb-3d4f-41bf-c4b0-eeadd5ae2395"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Blueberry___healthy',\n",
              " 'Squash___Powdery_mildew',\n",
              " 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',\n",
              " 'Apple___Cedar_apple_rust',\n",
              " 'Tomato___healthy',\n",
              " 'Tomato___Bacterial_spot',\n",
              " 'Tomato___Septoria_leaf_spot',\n",
              " 'Corn_(maize)___healthy',\n",
              " 'Tomato___Late_blight',\n",
              " 'Potato___Early_blight',\n",
              " 'Grape___Esca_(Black_Measles)',\n",
              " 'Corn_(maize)___Common_rust_',\n",
              " 'Soybean___healthy',\n",
              " 'Apple___healthy',\n",
              " 'Cherry_(including_sour)___Powdery_mildew',\n",
              " 'Potato___healthy',\n",
              " 'Tomato___Early_blight',\n",
              " 'Apple___Black_rot',\n",
              " 'Tomato___Target_Spot',\n",
              " 'Corn_(maize)___Northern_Leaf_Blight',\n",
              " 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',\n",
              " 'Strawberry___Leaf_scorch',\n",
              " 'Potato___Late_blight',\n",
              " 'Peach___Bacterial_spot',\n",
              " 'Strawberry___healthy',\n",
              " 'Orange___Haunglongbing_(Citrus_greening)',\n",
              " 'Tomato___Leaf_Mold',\n",
              " 'Raspberry___healthy',\n",
              " 'Tomato___Tomato_mosaic_virus',\n",
              " 'Grape___Black_rot',\n",
              " 'Pepper,_bell___Bacterial_spot',\n",
              " 'Grape___healthy',\n",
              " 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',\n",
              " 'Peach___healthy',\n",
              " 'Tomato___Spider_mites Two-spotted_spider_mite',\n",
              " 'Pepper,_bell___healthy',\n",
              " 'Cherry_(including_sour)___healthy',\n",
              " 'Apple___Apple_scab']"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "validation_set='/content/new-plant-diseases-dataset/New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)/valid'\n",
        "os.listdir(validation_set)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7FAqrXPRZoBX",
        "outputId": "dcae0485-857a-4ec4-d4cc-78bc779d2f44"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')\n",
            "PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "physical_devices = tf.config.experimental.list_physical_devices()\n",
        "for device in physical_devices:\n",
        "    print(device)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QQcZ0BGQZ3Qk",
        "outputId": "0e99a1e7-3f02-4a06-bd73-fa1a38995199"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Hello\n",
            "Hello\n"
          ]
        }
      ],
      "source": [
        "with tf.device(\"/device:GPU:0\"):\n",
        "  print(\"Hello\")\n",
        "with tf.device(\"/device:CPU:0\"):\n",
        "  print(\"Hello\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pwu1cUT8aDRn",
        "outputId": "335acd2d-61d7-4177-d643-2c5b6fa6c763"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Blueberry___healthy',\n",
              " 'Squash___Powdery_mildew',\n",
              " 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',\n",
              " 'Apple___Cedar_apple_rust',\n",
              " 'Tomato___healthy',\n",
              " 'Tomato___Bacterial_spot',\n",
              " 'Tomato___Septoria_leaf_spot',\n",
              " 'Corn_(maize)___healthy',\n",
              " 'Tomato___Late_blight',\n",
              " 'Potato___Early_blight',\n",
              " 'Grape___Esca_(Black_Measles)',\n",
              " 'Corn_(maize)___Common_rust_',\n",
              " 'Soybean___healthy',\n",
              " 'Apple___healthy',\n",
              " 'Cherry_(including_sour)___Powdery_mildew',\n",
              " 'Potato___healthy',\n",
              " 'Tomato___Early_blight',\n",
              " 'Apple___Black_rot',\n",
              " 'Tomato___Target_Spot',\n",
              " 'Corn_(maize)___Northern_Leaf_Blight',\n",
              " 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',\n",
              " 'Strawberry___Leaf_scorch',\n",
              " 'Potato___Late_blight',\n",
              " 'Peach___Bacterial_spot',\n",
              " 'Strawberry___healthy',\n",
              " 'Orange___Haunglongbing_(Citrus_greening)',\n",
              " 'Tomato___Leaf_Mold',\n",
              " 'Raspberry___healthy',\n",
              " 'Tomato___Tomato_mosaic_virus',\n",
              " 'Grape___Black_rot',\n",
              " 'Pepper,_bell___Bacterial_spot',\n",
              " 'Grape___healthy',\n",
              " 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',\n",
              " 'Peach___healthy',\n",
              " 'Tomato___Spider_mites Two-spotted_spider_mite',\n",
              " 'Pepper,_bell___healthy',\n",
              " 'Cherry_(including_sour)___healthy',\n",
              " 'Apple___Apple_scab']"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "validation_set='/content/new-plant-diseases-dataset/New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)/valid'\n",
        "os.listdir(validation_set)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFARiTvYaR2E",
        "outputId": "9a40b827-e8b8-467c-ccef-2bd20de15964"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Blueberry___healthy',\n",
              " 'Squash___Powdery_mildew',\n",
              " 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',\n",
              " 'Apple___Cedar_apple_rust',\n",
              " 'Tomato___healthy',\n",
              " 'Tomato___Bacterial_spot',\n",
              " 'Tomato___Septoria_leaf_spot',\n",
              " 'Corn_(maize)___healthy',\n",
              " 'Tomato___Late_blight',\n",
              " 'Potato___Early_blight',\n",
              " 'Grape___Esca_(Black_Measles)',\n",
              " 'Corn_(maize)___Common_rust_',\n",
              " 'Soybean___healthy',\n",
              " 'Apple___healthy',\n",
              " 'Cherry_(including_sour)___Powdery_mildew',\n",
              " 'Potato___healthy',\n",
              " 'Tomato___Early_blight',\n",
              " 'Apple___Black_rot',\n",
              " 'Tomato___Target_Spot',\n",
              " 'Corn_(maize)___Northern_Leaf_Blight',\n",
              " 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',\n",
              " 'Strawberry___Leaf_scorch',\n",
              " 'Potato___Late_blight',\n",
              " 'Peach___Bacterial_spot',\n",
              " 'Strawberry___healthy',\n",
              " 'Orange___Haunglongbing_(Citrus_greening)',\n",
              " 'Tomato___Leaf_Mold',\n",
              " 'Raspberry___healthy',\n",
              " 'Tomato___Tomato_mosaic_virus',\n",
              " 'Grape___Black_rot',\n",
              " 'Pepper,_bell___Bacterial_spot',\n",
              " 'Grape___healthy',\n",
              " 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',\n",
              " 'Peach___healthy',\n",
              " 'Tomato___Spider_mites Two-spotted_spider_mite',\n",
              " 'Pepper,_bell___healthy',\n",
              " 'Cherry_(including_sour)___healthy',\n",
              " 'Apple___Apple_scab']"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "training_set='/content/new-plant-diseases-dataset/New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)/train'\n",
        "os.listdir(training_set)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MiJCuXfsaXzx",
        "outputId": "8a772c1f-24b7-44ba-8397-eb4a5490b7b2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['PotatoEarlyBlight3.JPG',\n",
              " 'AppleCedarRust3.JPG',\n",
              " 'CornCommonRust2.JPG',\n",
              " 'AppleScab3.JPG',\n",
              " 'TomatoHealthy1.JPG',\n",
              " 'AppleCedarRust4.JPG',\n",
              " 'PotatoEarlyBlight1.JPG',\n",
              " 'CornCommonRust1.JPG',\n",
              " 'TomatoHealthy2.JPG',\n",
              " 'AppleCedarRust1.JPG',\n",
              " 'TomatoYellowCurlVirus3.JPG',\n",
              " 'TomatoEarlyBlight1.JPG',\n",
              " 'TomatoYellowCurlVirus2.JPG',\n",
              " 'PotatoHealthy2.JPG',\n",
              " 'TomatoEarlyBlight3.JPG',\n",
              " 'TomatoYellowCurlVirus6.JPG',\n",
              " 'TomatoEarlyBlight6.JPG',\n",
              " 'TomatoHealthy3.JPG',\n",
              " 'TomatoHealthy4.JPG',\n",
              " 'TomatoEarlyBlight4.JPG',\n",
              " 'PotatoEarlyBlight5.JPG',\n",
              " 'PotatoEarlyBlight2.JPG',\n",
              " 'TomatoYellowCurlVirus1.JPG',\n",
              " 'AppleCedarRust2.JPG',\n",
              " 'CornCommonRust3.JPG',\n",
              " 'PotatoEarlyBlight4.JPG',\n",
              " 'AppleScab2.JPG',\n",
              " 'TomatoEarlyBlight5.JPG',\n",
              " 'AppleScab1.JPG',\n",
              " 'TomatoYellowCurlVirus5.JPG',\n",
              " 'TomatoEarlyBlight2.JPG',\n",
              " 'TomatoYellowCurlVirus4.JPG',\n",
              " 'PotatoHealthy1.JPG']"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "testing_set='/content/new-plant-diseases-dataset/test/test'\n",
        "os.listdir(testing_set)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "GqgSCopga0MZ",
        "outputId": "8d1ffb2c-9743-4315-f437-b0d5e8758d45"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "view_image='/content/new-plant-diseases-dataset/New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)/valid/Blueberry___healthy/0b344c16-456c-4085-aa61-6a1c4cb93632___RS_HL 2538.JPG'\n",
        "img=mpimg.imread(view_image)\n",
        "plt.imshow(img)\n",
        "plt.axis('off')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1SLwQqaYbLir"
      },
      "source": [
        "### IN SHORT I HAVE 3 DIRECTORIES TRAIN, TEST AND VALID WHERE I HAVE IMAGED TO VIEW AS SIMPLE AS THAT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fo1ToeMrbQAB",
        "outputId": "c086654a-fe05-4231-f308-b6b903362d18"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 70295 images belonging to 38 classes.\n",
            "Found 17572 images belonging to 38 classes.\n",
            "Epoch 1/10\n",
            "2196/2196 [==============================] - 130s 56ms/step - loss: 1.0105 - accuracy: 0.6959 - val_loss: 0.4361 - val_accuracy: 0.8621\n",
            "Epoch 2/10\n",
            "2196/2196 [==============================] - 124s 56ms/step - loss: 0.3076 - accuracy: 0.9003 - val_loss: 0.3152 - val_accuracy: 0.8954\n",
            "Epoch 3/10\n",
            "2196/2196 [==============================] - 122s 55ms/step - loss: 0.1940 - accuracy: 0.9355 - val_loss: 0.2241 - val_accuracy: 0.9255\n",
            "Epoch 4/10\n",
            "2196/2196 [==============================] - 122s 55ms/step - loss: 0.1401 - accuracy: 0.9529 - val_loss: 0.2230 - val_accuracy: 0.9280\n",
            "Epoch 5/10\n",
            "2196/2196 [==============================] - 122s 55ms/step - loss: 0.1086 - accuracy: 0.9637 - val_loss: 0.1982 - val_accuracy: 0.9414\n",
            "Epoch 6/10\n",
            "2196/2196 [==============================] - 118s 54ms/step - loss: 0.0934 - accuracy: 0.9693 - val_loss: 0.1941 - val_accuracy: 0.9406\n",
            "Epoch 7/10\n",
            "2196/2196 [==============================] - 120s 55ms/step - loss: 0.0781 - accuracy: 0.9746 - val_loss: 0.3018 - val_accuracy: 0.9218\n",
            "Epoch 8/10\n",
            "2196/2196 [==============================] - 120s 55ms/step - loss: 0.0714 - accuracy: 0.9769 - val_loss: 0.2050 - val_accuracy: 0.9447\n",
            "Epoch 9/10\n",
            "2196/2196 [==============================] - 122s 55ms/step - loss: 0.0696 - accuracy: 0.9780 - val_loss: 0.2929 - val_accuracy: 0.9297\n",
            "Epoch 10/10\n",
            "2196/2196 [==============================] - 123s 56ms/step - loss: 0.0598 - accuracy: 0.9817 - val_loss: 0.2310 - val_accuracy: 0.9431\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# Directory paths\n",
        "train_dir = '/content/new-plant-diseases-dataset/New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)/train'\n",
        "validation_dir = '/content/new-plant-diseases-dataset/New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)/valid'\n",
        "\n",
        "# reducing image size to 150,150\n",
        "batch_size = 32\n",
        "image_size = (150, 150)\n",
        "\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')\n",
        "\n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "    validation_dir,\n",
        "    target_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')\n",
        "\n",
        "# using a sequential model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    #Since number of classes=38\n",
        "    tf.keras.layers.Dense(38, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "\n",
        "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\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XbdHIVIFmwkF"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V2YkAYEdkJQa"
      },
      "outputs": [],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AgheSMVEkx0n"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNKbtHshgrgK",
        "outputId": "04b59f22-6577-47cc-fc23-541213e30fcc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['__class__',\n",
              " '__delattr__',\n",
              " '__dict__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__le__',\n",
              " '__lt__',\n",
              " '__module__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__setattr__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " '__weakref__',\n",
              " '_chief_worker_only',\n",
              " '_implements_predict_batch_hooks',\n",
              " '_implements_test_batch_hooks',\n",
              " '_implements_train_batch_hooks',\n",
              " '_keras_api_names',\n",
              " '_keras_api_names_v1',\n",
              " '_supports_tf_logs',\n",
              " 'epoch',\n",
              " 'history',\n",
              " 'model',\n",
              " 'on_batch_begin',\n",
              " 'on_batch_end',\n",
              " 'on_epoch_begin',\n",
              " 'on_epoch_end',\n",
              " 'on_predict_batch_begin',\n",
              " 'on_predict_batch_end',\n",
              " 'on_predict_begin',\n",
              " 'on_predict_end',\n",
              " 'on_test_batch_begin',\n",
              " 'on_test_batch_end',\n",
              " 'on_test_begin',\n",
              " 'on_test_end',\n",
              " 'on_train_batch_begin',\n",
              " 'on_train_batch_end',\n",
              " 'on_train_begin',\n",
              " 'on_train_end',\n",
              " 'params',\n",
              " 'set_model',\n",
              " 'set_params',\n",
              " 'validation_data']"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dir(history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "NyGLIjONg6xR",
        "outputId": "ff4dab78-42e8-4c59-a87f-3208ec3770b9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FirroSsciiup",
        "outputId": "9f3c4817-ca74-4dfb-9392-59b6e39f8adc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['.config', 'new-plant-diseases-dataset', 'sample_data']"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "os.listdir()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQoih9omilLO",
        "outputId": "4c95a472-99f8-48d6-d4fa-f083aef6bfb5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['PotatoEarlyBlight3.JPG',\n",
              " 'AppleCedarRust3.JPG',\n",
              " 'CornCommonRust2.JPG',\n",
              " 'AppleScab3.JPG',\n",
              " 'TomatoHealthy1.JPG',\n",
              " 'AppleCedarRust4.JPG',\n",
              " 'PotatoEarlyBlight1.JPG',\n",
              " 'CornCommonRust1.JPG',\n",
              " 'TomatoHealthy2.JPG',\n",
              " 'AppleCedarRust1.JPG',\n",
              " 'TomatoYellowCurlVirus3.JPG',\n",
              " 'TomatoEarlyBlight1.JPG',\n",
              " 'TomatoYellowCurlVirus2.JPG',\n",
              " 'PotatoHealthy2.JPG',\n",
              " 'TomatoEarlyBlight3.JPG',\n",
              " 'TomatoYellowCurlVirus6.JPG',\n",
              " 'TomatoEarlyBlight6.JPG',\n",
              " 'TomatoHealthy3.JPG',\n",
              " 'TomatoHealthy4.JPG',\n",
              " 'TomatoEarlyBlight4.JPG',\n",
              " 'PotatoEarlyBlight5.JPG',\n",
              " 'PotatoEarlyBlight2.JPG',\n",
              " 'TomatoYellowCurlVirus1.JPG',\n",
              " 'AppleCedarRust2.JPG',\n",
              " 'CornCommonRust3.JPG',\n",
              " 'PotatoEarlyBlight4.JPG',\n",
              " 'AppleScab2.JPG',\n",
              " 'TomatoEarlyBlight5.JPG',\n",
              " 'AppleScab1.JPG',\n",
              " 'TomatoYellowCurlVirus5.JPG',\n",
              " 'TomatoEarlyBlight2.JPG',\n",
              " 'TomatoYellowCurlVirus4.JPG',\n",
              " 'PotatoHealthy1.JPG']"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test='/content/new-plant-diseases-dataset/test/test'\n",
        "os.listdir(test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I1z9QQ5Wh4pR",
        "outputId": "45f3850c-1ac0-4c22-862a-b92c091af320"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 33 images belonging to 1 classes.\n",
            "2/2 [==============================] - 1s 628ms/step - loss: 1677.0747 - accuracy: 0.0606\n",
            "Test Accuracy: 0.06060606241226196\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "testing_set = '/content/new-plant-diseases-dataset/test/'\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "    testing_set,\n",
        "    target_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical',\n",
        "    shuffle=False\n",
        ")\n",
        "\n",
        "# Evaluate the model on the test data\n",
        "evaluation = model.evaluate(test_generator)\n",
        "\n",
        "print(\"Test Accuracy:\", evaluation[1])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cFbDEQUyjMbh",
        "outputId": "584dabbd-9a49-4b68-f585-e62634639f00"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2/2 [==============================] - 0s 7ms/step\n",
            "Predicted: Apple___Cedar_apple_rust, True: Apple___Apple_scab\n",
            "Predicted: Apple___Cedar_apple_rust, True: Apple___Apple_scab\n",
            "Predicted: Apple___Cedar_apple_rust, True: Apple___Apple_scab\n",
            "Predicted: Apple___Cedar_apple_rust, True: Apple___Apple_scab\n",
            "Predicted: Apple___Apple_scab, True: Apple___Apple_scab\n",
            "Predicted: Apple___Apple_scab, True: Apple___Apple_scab\n",
            "Predicted: Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot, True: Apple___Apple_scab\n",
            "Predicted: Corn_(maize)___Common_rust_, True: Apple___Apple_scab\n",
            "Predicted: Corn_(maize)___Common_rust_, True: Apple___Apple_scab\n",
            "Predicted: Corn_(maize)___Common_rust_, True: Apple___Apple_scab\n",
            "Predicted: Potato___Early_blight, True: Apple___Apple_scab\n",
            "Predicted: Potato___Early_blight, True: Apple___Apple_scab\n",
            "Predicted: Soybean___healthy, True: Apple___Apple_scab\n",
            "Predicted: Potato___Early_blight, True: Apple___Apple_scab\n",
            "Predicted: Potato___Early_blight, True: Apple___Apple_scab\n",
            "Predicted: Pepper,_bell___Bacterial_spot, True: Apple___Apple_scab\n",
            "Predicted: Potato___healthy, True: Apple___Apple_scab\n",
            "Predicted: Tomato___Late_blight, True: Apple___Apple_scab\n",
            "Predicted: Tomato___Early_blight, True: Apple___Apple_scab\n",
            "Predicted: Tomato___Spider_mites Two-spotted_spider_mite, True: Apple___Apple_scab\n",
            "Predicted: Tomato___Early_blight, True: Apple___Apple_scab\n",
            "Predicted: Tomato___Early_blight, True: Apple___Apple_scab\n",
            "Predicted: Apple___healthy, True: Apple___Apple_scab\n",
            "Predicted: Tomato___healthy, True: Apple___Apple_scab\n",
            "Predicted: Tomato___healthy, True: Apple___Apple_scab\n",
            "Predicted: Tomato___healthy, True: Apple___Apple_scab\n",
            "Predicted: Tomato___healthy, True: Apple___Apple_scab\n",
            "Predicted: Tomato___Tomato_Yellow_Leaf_Curl_Virus, True: Apple___Apple_scab\n",
            "Predicted: Tomato___Tomato_Yellow_Leaf_Curl_Virus, True: Apple___Apple_scab\n",
            "Predicted: Tomato___Tomato_Yellow_Leaf_Curl_Virus, True: Apple___Apple_scab\n",
            "Predicted: Tomato___Tomato_Yellow_Leaf_Curl_Virus, True: Apple___Apple_scab\n",
            "Predicted: Tomato___Tomato_Yellow_Leaf_Curl_Virus, True: Apple___Apple_scab\n",
            "Predicted: Tomato___Tomato_Yellow_Leaf_Curl_Virus, True: Apple___Apple_scab\n"
          ]
        }
      ],
      "source": [
        "# import numpy as np\n",
        "\n",
        "# test_generator.reset()\n",
        "\n",
        "\n",
        "# predictions = model.predict(test_generator)\n",
        "\n",
        "# true_labels = test_generator.classes\n",
        "\n",
        "# class_labels = {v: k for k, v in train_generator.class_indices.items()}\n",
        "\n",
        "# for i, prediction in enumerate(predictions):\n",
        "#     predicted_class = np.argmax(prediction)\n",
        "#     true_class = true_labels[i]\n",
        "#     print(f\"Predicted: {class_labels[predicted_class]}, True: {class_labels[true_class]}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8pc-pUTjeHS",
        "outputId": "b266cb7c-ba8a-4c3b-95ad-889684556518"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: 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"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model saved successfully at: /path/to/save/model.h5\n"
          ]
        }
      ],
      "source": [
        "\n",
        "model_save_path = '/path/to/save/model.h5'\n",
        "\n",
        "\n",
        "model.save(model_save_path)\n",
        "\n",
        "print(\"Model saved successfully at:\", model_save_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "bZN5y5ljjt6q",
        "outputId": "6c7ac787-9925-46ac-ee80-2ca83232fdb2"
      },
      "outputs": [
        {
          "data": {
            "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  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_1c5b1b19-f861-491c-8abd-34ebdaeaa1e7\", \"model.h5\", 41724056)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "model_save_path = '/content/model.h5'\n",
        "\n",
        "\n",
        "model.save(model_save_path)\n",
        "\n",
        "files.download(model_save_path)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L4wu1U4EkzRK",
        "outputId": "71231174-0c3c-4e84-9af0-5d925553311e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 148, 148, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 74, 74, 32)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 72, 72, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 36, 36, 64)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 34, 34, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPoolin  (None, 17, 17, 128)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 15, 15, 128)       147584    \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPoolin  (None, 7, 7, 128)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 6272)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               3211776   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 38)                19494     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3472102 (13.25 MB)\n",
            "Trainable params: 3472102 (13.25 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "\n",
        "\n",
        "loaded_model = load_model('/content/model.h5')\n",
        "\n",
        "\n",
        "loaded_model.summary()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABbK7XFClBcP",
        "outputId": "301d0d0e-ed5e-4c0c-cad8-6822ba5518be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2197/2197 [==============================] - 98s 45ms/step - loss: 0.0315 - accuracy: 0.9896\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.031464435160160065, 0.9895867705345154]"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.evaluate(train_generator)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j7VA5bwXmyLW",
        "outputId": "aeb9ec5a-2967-44ee-e4e0-649f990fa5f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "550/550 [==============================] - 24s 44ms/step - loss: 0.2309 - accuracy: 0.9431\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.23090484738349915, 0.9431481957435608]"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.evaluate(validation_generator)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tam6Z5DvnAwm",
        "outputId": "a72d4c66-3f55-4952-8078-9c440025d221"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: 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": [
        "model.save(\"train_model.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rs-8wpFMnXs1",
        "outputId": "43eb92d4-f564-4991-9b5c-7e974fd51386"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'loss': [1.010542392730713,\n",
              "  0.307609885931015,\n",
              "  0.19398264586925507,\n",
              "  0.14014725387096405,\n",
              "  0.10855548828840256,\n",
              "  0.09341824054718018,\n",
              "  0.07808179408311844,\n",
              "  0.07141537964344025,\n",
              "  0.06960716843605042,\n",
              "  0.059834498912096024],\n",
              " 'accuracy': [0.6958569884300232,\n",
              "  0.9002746939659119,\n",
              "  0.9354994893074036,\n",
              "  0.9529339671134949,\n",
              "  0.9637362360954285,\n",
              "  0.9693295359611511,\n",
              "  0.9745954275131226,\n",
              "  0.976858377456665,\n",
              "  0.9780254364013672,\n",
              "  0.9817115664482117],\n",
              " 'val_loss': [0.4361322224140167,\n",
              "  0.3151673972606659,\n",
              "  0.22414042055606842,\n",
              "  0.22295071184635162,\n",
              "  0.19816844165325165,\n",
              "  0.1940544694662094,\n",
              "  0.301788330078125,\n",
              "  0.20501038432121277,\n",
              "  0.29289138317108154,\n",
              "  0.23095403611660004],\n",
              " 'val_accuracy': [0.8621357083320618,\n",
              "  0.8954348564147949,\n",
              "  0.9254895448684692,\n",
              "  0.9279940724372864,\n",
              "  0.9414275884628296,\n",
              "  0.9405737519264221,\n",
              "  0.9218465685844421,\n",
              "  0.9446721076965332,\n",
              "  0.9297017455101013,\n",
              "  0.9431352615356445]}"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "history.history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jil76GORnbpE"
      },
      "outputs": [],
      "source": [
        "#MODEL HISTORY IN JSON"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EdZlwXsmnefI"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "with open(\"training_hist.json\",\"w\") as f:\n",
        "  json.dump(history.history,f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NnJzoTcPoFgN"
      },
      "outputs": [],
      "source": [
        "val_class=os.listdir(validation_set)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vE6AQ9bEpiRU",
        "outputId": "c2e81216-7048-46db-9f36-87c5ff490fc9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'list'>\n"
          ]
        }
      ],
      "source": [
        "print(type(val_class))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uf-FC7AQpQCA",
        "outputId": "5bfe6607-4ff5-4848-e447-0c0b066f10bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 Blueberry___healthy\n",
            "1 Squash___Powdery_mildew\n",
            "2 Grape___Leaf_blight_(Isariopsis_Leaf_Spot)\n",
            "3 Apple___Cedar_apple_rust\n",
            "4 Tomato___healthy\n",
            "5 Tomato___Bacterial_spot\n",
            "6 Tomato___Septoria_leaf_spot\n",
            "7 Corn_(maize)___healthy\n",
            "8 Tomato___Late_blight\n",
            "9 Potato___Early_blight\n",
            "10 Grape___Esca_(Black_Measles)\n",
            "11 Corn_(maize)___Common_rust_\n",
            "12 Soybean___healthy\n",
            "13 Apple___healthy\n",
            "14 Cherry_(including_sour)___Powdery_mildew\n",
            "15 Potato___healthy\n",
            "16 Tomato___Early_blight\n",
            "17 Apple___Black_rot\n",
            "18 Tomato___Target_Spot\n",
            "19 Corn_(maize)___Northern_Leaf_Blight\n",
            "20 Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot\n",
            "21 Strawberry___Leaf_scorch\n",
            "22 Potato___Late_blight\n",
            "23 Peach___Bacterial_spot\n",
            "24 Strawberry___healthy\n",
            "25 Orange___Haunglongbing_(Citrus_greening)\n",
            "26 Tomato___Leaf_Mold\n",
            "27 Raspberry___healthy\n",
            "28 Tomato___Tomato_mosaic_virus\n",
            "29 Grape___Black_rot\n",
            "30 Pepper,_bell___Bacterial_spot\n",
            "31 Grape___healthy\n",
            "32 Tomato___Tomato_Yellow_Leaf_Curl_Virus\n",
            "33 Peach___healthy\n",
            "34 Tomato___Spider_mites Two-spotted_spider_mite\n",
            "35 Pepper,_bell___healthy\n",
            "36 Cherry_(including_sour)___healthy\n",
            "37 Apple___Apple_scab\n"
          ]
        }
      ],
      "source": [
        "for index,cls in enumerate(val_class):\n",
        "  print(index,end=\" \")\n",
        "  print(cls)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JEyym381prmt",
        "outputId": "aee1b8ff-d1ac-4130-ade0-44c646ebf0a5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['PotatoEarlyBlight3.JPG',\n",
              " 'AppleCedarRust3.JPG',\n",
              " 'CornCommonRust2.JPG',\n",
              " 'AppleScab3.JPG',\n",
              " 'TomatoHealthy1.JPG',\n",
              " 'AppleCedarRust4.JPG',\n",
              " 'PotatoEarlyBlight1.JPG',\n",
              " 'CornCommonRust1.JPG',\n",
              " 'TomatoHealthy2.JPG',\n",
              " 'AppleCedarRust1.JPG',\n",
              " 'TomatoYellowCurlVirus3.JPG',\n",
              " 'TomatoEarlyBlight1.JPG',\n",
              " 'TomatoYellowCurlVirus2.JPG',\n",
              " 'PotatoHealthy2.JPG',\n",
              " 'TomatoEarlyBlight3.JPG',\n",
              " 'TomatoYellowCurlVirus6.JPG',\n",
              " 'TomatoEarlyBlight6.JPG',\n",
              " 'TomatoHealthy3.JPG',\n",
              " 'TomatoHealthy4.JPG',\n",
              " 'TomatoEarlyBlight4.JPG',\n",
              " 'PotatoEarlyBlight5.JPG',\n",
              " 'PotatoEarlyBlight2.JPG',\n",
              " 'TomatoYellowCurlVirus1.JPG',\n",
              " 'AppleCedarRust2.JPG',\n",
              " 'CornCommonRust3.JPG',\n",
              " 'PotatoEarlyBlight4.JPG',\n",
              " 'AppleScab2.JPG',\n",
              " 'TomatoEarlyBlight5.JPG',\n",
              " 'AppleScab1.JPG',\n",
              " 'TomatoYellowCurlVirus5.JPG',\n",
              " 'TomatoEarlyBlight2.JPG',\n",
              " 'TomatoYellowCurlVirus4.JPG',\n",
              " 'PotatoHealthy1.JPG']"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "os.listdir(test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_opOl7ppwQU"
      },
      "source": [
        "#### TEST FOLDER HAS SOME RANDOM IMAGES,IT IS NOT HAVING ANY DIRECTORY SO I CAN'T USE IT DIRECTLY"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2u5zkllop2nv",
        "outputId": "40edbe7e-b58f-4994-a9c1-50397793b355"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "32\n",
            "(150, 150)\n"
          ]
        }
      ],
      "source": [
        "print(batch_size)\n",
        "print(image_size)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RU2x0-BlqyQo"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXwczW4HqjRr",
        "outputId": "22dd8458-5628-44f2-c276-6e8c73e5435b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 17572 images belonging to 38 classes.\n"
          ]
        }
      ],
      "source": [
        "test_generator = test_datagen.flow_from_directory(\n",
        "    validation_set,\n",
        "    target_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical',\n",
        "    shuffle=False\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mwm84W7ZrAkO",
        "outputId": "bae0e290-c7ed-4b4f-a712-7f0fc0aba2d8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "550/550 [==============================] - 27s 50ms/step\n"
          ]
        }
      ],
      "source": [
        "predicted_y_value=model.predict(test_generator)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0NjEpDErHv4",
        "outputId": "9e877196-a605-476c-b098-7ec80c25bac4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of rows 17572\n",
            "Number of columns 38\n"
          ]
        }
      ],
      "source": [
        "rows,columns=predicted_y_value.shape\n",
        "print(\"Number of rows\",rows)\n",
        "print(\"Number of columns\",columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lL_AdGLIrQb-",
        "outputId": "2bcb4134-31c5-4f42-f86d-3fa522fa3f80"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[9.9974281e-01, 1.4391385e-11, 3.2077418e-12, ..., 1.3751171e-19,\n",
              "        1.4348906e-14, 8.7318719e-10],\n",
              "       [1.0000000e+00, 9.2911623e-10, 6.3891574e-12, ..., 7.7282755e-26,\n",
              "        1.7419198e-17, 7.4912585e-15],\n",
              "       [1.0000000e+00, 2.7542703e-16, 2.5512807e-16, ..., 6.0317325e-32,\n",
              "        1.7741967e-25, 1.2133360e-17],\n",
              "       ...,\n",
              "       [2.5408198e-08, 5.9570318e-22, 5.4278643e-10, ..., 3.1857117e-15,\n",
              "        1.3648632e-14, 9.8974520e-01],\n",
              "       [2.2279847e-12, 4.1418564e-27, 6.4412112e-15, ..., 2.0972748e-15,\n",
              "        1.4333103e-20, 1.0000000e+00],\n",
              "       [2.9586866e-23, 2.2766441e-23, 4.1150541e-23, ..., 2.1415748e-23,\n",
              "        1.7590306e-18, 9.9998319e-01]], dtype=float32)"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predicted_y_value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VKj74peKrn3E"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rYGpaV_wrlE-"
      },
      "outputs": [],
      "source": [
        "predicted_classes = np.argmax(predicted_y_value, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6jYuvMLrrSI",
        "outputId": "91f0345d-fe19-4754-f417-a6b0fe2608f1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ 0,  0,  0, ..., 37, 37, 37])"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predicted_classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rdNbvMq8v1L5"
      },
      "outputs": [],
      "source": [
        "\n",
        "true_classes = tf.concat([y for x, y in test_generator], axis=0)\n",
        "\n",
        "# Print the true classes\n",
        "print(true_classes)\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}