Train_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 70295 files belonging to 38 classes.\n"
     ]
    }
   ],
   "source": [
    "training_set = tf.keras.utils.image_dataset_from_directory(\n",
    "    r\"D:\\target\\ml\\PlantDisease\\archive\\New Plant Diseases Dataset(Augmented)\\New Plant Diseases Dataset(Augmented)\\train\",\n",
    "    labels=\"inferred\",\n",
    "    label_mode=\"categorical\",\n",
    "    class_names=None,\n",
    "    color_mode=\"rgb\",\n",
    "    batch_size=32,\n",
    "    image_size=(128, 128),\n",
    "    shuffle=True,\n",
    "    seed=None,\n",
    "    validation_split=None,\n",
    "    subset=None,\n",
    "    interpolation=\"bilinear\",\n",
    "    follow_links=False,\n",
    "    crop_to_aspect_ratio=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 17572 files belonging to 38 classes.\n"
     ]
    }
   ],
   "source": [
    "validation_set = tf.keras.utils.image_dataset_from_directory(\n",
    "    r\"D:\\target\\ml\\PlantDisease\\archive\\New Plant Diseases Dataset(Augmented)\\New Plant Diseases Dataset(Augmented)\\valid\",\n",
    "    labels=\"inferred\",\n",
    "    label_mode=\"categorical\",\n",
    "    class_names=None,\n",
    "    color_mode=\"rgb\",\n",
    "    batch_size=32,\n",
    "    image_size=(128, 128),\n",
    "    shuffle=True,\n",
    "    seed=None,\n",
    "    validation_split=None,\n",
    "    subset=None,\n",
    "    interpolation=\"bilinear\",\n",
    "    follow_links=False,\n",
    "    crop_to_aspect_ratio=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn = tf.keras.models.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,padding='same',activation='relu',input_shape=[128,128,3]))\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,activation='relu'))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Conv2D(filters=64,kernel_size=3,padding='same',activation='relu'))\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=64,kernel_size=3,activation='relu'))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Conv2D(filters=128,kernel_size=3,padding='same',activation='relu'))\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=128,kernel_size=3,activation='relu'))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=256,kernel_size=3,padding='same',activation='relu'))\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=256,kernel_size=3,activation='relu'))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=512,kernel_size=3,padding='same',activation='relu'))\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=512,kernel_size=3,activation='relu'))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cnn.add(tf.keras.layers.Dropout(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dense(units=1500,activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dropout(0.4)) #To avoid overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Output Layer\n",
    "cnn.add(tf.keras.layers.Dense(units=38,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.compile(optimizer=tf.keras.optimizers.Adam(\n",
    "    learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">126</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">126</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">63</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">63</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">63</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">63</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1500</span>)           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">3,073,500</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1500</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">38</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">57,038</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m126\u001b[0m, \u001b[38;5;34m126\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │         \u001b[38;5;34m9,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m63\u001b[0m, \u001b[38;5;34m63\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m63\u001b[0m, \u001b[38;5;34m63\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m36,928\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │       \u001b[38;5;34m147,584\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m295,168\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_18 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m1,180,160\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_19 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_9 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1500\u001b[0m)           │     \u001b[38;5;34m3,073,500\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1500\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m38\u001b[0m)             │        \u001b[38;5;34m57,038\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,842,762</span> (29.92 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,842,762\u001b[0m (29.92 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,842,762</span> (29.92 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m7,842,762\u001b[0m (29.92 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1549s\u001b[0m 704ms/step - accuracy: 0.3981 - loss: 2.1354 - val_accuracy: 0.8372 - val_loss: 0.5127\n",
      "Epoch 2/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1522s\u001b[0m 693ms/step - accuracy: 0.8309 - loss: 0.5357 - val_accuracy: 0.9129 - val_loss: 0.2589\n",
      "Epoch 3/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1436s\u001b[0m 654ms/step - accuracy: 0.9012 - loss: 0.3060 - val_accuracy: 0.9225 - val_loss: 0.2458\n",
      "Epoch 4/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1426s\u001b[0m 649ms/step - accuracy: 0.9372 - loss: 0.1962 - val_accuracy: 0.9447 - val_loss: 0.1700\n",
      "Epoch 5/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1429s\u001b[0m 651ms/step - accuracy: 0.9510 - loss: 0.1482 - val_accuracy: 0.9405 - val_loss: 0.1800\n",
      "Epoch 6/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1408s\u001b[0m 641ms/step - accuracy: 0.9637 - loss: 0.1084 - val_accuracy: 0.9557 - val_loss: 0.1432\n",
      "Epoch 7/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1405s\u001b[0m 640ms/step - accuracy: 0.9715 - loss: 0.0886 - val_accuracy: 0.9566 - val_loss: 0.1522\n",
      "Epoch 8/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1413s\u001b[0m 643ms/step - accuracy: 0.9767 - loss: 0.0743 - val_accuracy: 0.9588 - val_loss: 0.1367\n",
      "Epoch 9/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1422s\u001b[0m 647ms/step - accuracy: 0.9777 - loss: 0.0686 - val_accuracy: 0.9673 - val_loss: 0.1127\n",
      "Epoch 10/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1415s\u001b[0m 644ms/step - accuracy: 0.9822 - loss: 0.0573 - val_accuracy: 0.9685 - val_loss: 0.1061\n",
      "Epoch 11/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1421s\u001b[0m 647ms/step - accuracy: 0.9842 - loss: 0.0480 - val_accuracy: 0.9659 - val_loss: 0.1159\n",
      "Epoch 12/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1397s\u001b[0m 636ms/step - accuracy: 0.9863 - loss: 0.0440 - val_accuracy: 0.9551 - val_loss: 0.1659\n",
      "Epoch 13/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1405s\u001b[0m 639ms/step - accuracy: 0.9860 - loss: 0.0416 - val_accuracy: 0.9605 - val_loss: 0.1440\n",
      "Epoch 14/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1424s\u001b[0m 648ms/step - accuracy: 0.9884 - loss: 0.0377 - val_accuracy: 0.9732 - val_loss: 0.1025\n",
      "Epoch 15/15\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1441s\u001b[0m 656ms/step - accuracy: 0.9882 - loss: 0.0396 - val_accuracy: 0.9630 - val_loss: 0.1500\n"
     ]
    }
   ],
   "source": [
    "training_history = cnn.fit(x=training_set,validation_data=validation_set,epochs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m347s\u001b[0m 158ms/step - accuracy: 0.9879 - loss: 0.0399\n",
      "Training accuracy: 0.9890319108963013\n"
     ]
    }
   ],
   "source": [
    "#Training set Accuracy\n",
    "train_loss, train_acc = cnn.evaluate(training_set)\n",
    "print('Training accuracy:', train_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m550/550\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 154ms/step - accuracy: 0.9632 - loss: 0.1513\n",
      "Validation accuracy: 0.9629524350166321\n"
     ]
    }
   ],
   "source": [
    "#Validation set Accuracy\n",
    "val_loss, val_acc = cnn.evaluate(validation_set)\n",
    "print('Validation accuracy:', val_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cnn.save('trained_plant_disease_model.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': [0.5944234728813171,\n",
       "  0.8567607998847961,\n",
       "  0.9121701121330261,\n",
       "  0.9415605664253235,\n",
       "  0.9553169012069702,\n",
       "  0.9650899767875671,\n",
       "  0.9725158214569092,\n",
       "  0.9775944352149963,\n",
       "  0.9794437885284424,\n",
       "  0.9815776348114014,\n",
       "  0.9841524958610535,\n",
       "  0.9859734177589417,\n",
       "  0.9867416024208069,\n",
       "  0.9893022179603577,\n",
       "  0.9889465570449829],\n",
       " 'loss': [1.3758958578109741,\n",
       "  0.4529331624507904,\n",
       "  0.2715536653995514,\n",
       "  0.17992635071277618,\n",
       "  0.1360170990228653,\n",
       "  0.10427625477313995,\n",
       "  0.08487272262573242,\n",
       "  0.06936278939247131,\n",
       "  0.06351625174283981,\n",
       "  0.05729581415653229,\n",
       "  0.04823645204305649,\n",
       "  0.045325130224227905,\n",
       "  0.04001739248633385,\n",
       "  0.035167843103408813,\n",
       "  0.03525286167860031],\n",
       " 'val_accuracy': [0.8371841311454773,\n",
       "  0.9128727316856384,\n",
       "  0.9224902987480164,\n",
       "  0.9446847438812256,\n",
       "  0.9405303597450256,\n",
       "  0.9556680917739868,\n",
       "  0.9565786719322205,\n",
       "  0.9587981104850769,\n",
       "  0.967277467250824,\n",
       "  0.9684725403785706,\n",
       "  0.9658547639846802,\n",
       "  0.9550990462303162,\n",
       "  0.9605053663253784,\n",
       "  0.9731959700584412,\n",
       "  0.9629524350166321],\n",
       " 'val_loss': [0.5127478241920471,\n",
       "  0.25893256068229675,\n",
       "  0.24582281708717346,\n",
       "  0.16998548805713654,\n",
       "  0.1799532175064087,\n",
       "  0.14322540163993835,\n",
       "  0.15217308700084686,\n",
       "  0.13670092821121216,\n",
       "  0.11273442208766937,\n",
       "  0.10612735152244568,\n",
       "  0.11586169898509979,\n",
       "  0.16590231657028198,\n",
       "  0.14401617646217346,\n",
       "  0.10249309986829758,\n",
       "  0.1499883532524109]}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_history.history #Return Dictionary of history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Recording History in json\n",
    "import json\n",
    "with open('training_hist.json','w') as f:\n",
    "  json.dump(training_history.history,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['accuracy', 'loss', 'val_accuracy', 'val_loss'])\n"
     ]
    }
   ],
   "source": [
    "print(training_history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = [i for i in range(1,16)]\n",
    "plt.plot(epochs,training_history.history['accuracy'],color='red',label='Training Accuracy')\n",
    "plt.plot(epochs,training_history.history['val_accuracy'],color='blue',label='Validation Accuracy')\n",
    "plt.xlabel('No. of Epochs')\n",
    "plt.title('Visualization of Accuracy Result')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_name = validation_set.class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 17572 files belonging to 38 classes.\n"
     ]
    }
   ],
   "source": [
    "test_set = tf.keras.utils.image_dataset_from_directory(\n",
    "    r\"D:\\target\\ml\\PlantDisease\\archive\\New Plant Diseases Dataset(Augmented)\\New Plant Diseases Dataset(Augmented)\\valid\",\n",
    "    labels=\"inferred\",\n",
    "    label_mode=\"categorical\",\n",
    "    class_names=None,\n",
    "    color_mode=\"rgb\",\n",
    "    batch_size=1,\n",
    "    image_size=(128, 128),\n",
    "    shuffle=False,\n",
    "    seed=None,\n",
    "    validation_split=None,\n",
    "    subset=None,\n",
    "    interpolation=\"bilinear\",\n",
    "    follow_links=False,\n",
    "    crop_to_aspect_ratio=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m17572/17572\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m241s\u001b[0m 14ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = cnn.predict(test_set)\n",
    "predicted_categories = tf.argmax(y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_categories = tf.concat([y for x, y in test_set], axis=0)\n",
    "Y_true = tf.argmax(true_categories, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(17572,), dtype=int64, numpy=array([ 0,  0,  0, ..., 37, 37, 37], dtype=int64)>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(17572,), dtype=int64, numpy=array([ 0,  0,  0, ..., 37, 37, 37], dtype=int64)>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "cm = confusion_matrix(Y_true,predicted_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                    precision    recall  f1-score   support\n",
      "\n",
      "                                Apple___Apple_scab       0.98      0.98      0.98       504\n",
      "                                 Apple___Black_rot       0.97      0.99      0.98       497\n",
      "                          Apple___Cedar_apple_rust       0.98      0.99      0.99       440\n",
      "                                   Apple___healthy       0.98      0.96      0.97       502\n",
      "                               Blueberry___healthy       0.98      0.99      0.98       454\n",
      "          Cherry_(including_sour)___Powdery_mildew       0.97      0.99      0.98       421\n",
      "                 Cherry_(including_sour)___healthy       0.96      1.00      0.98       456\n",
      "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot       0.97      0.86      0.91       410\n",
      "                       Corn_(maize)___Common_rust_       1.00      0.99      0.99       477\n",
      "               Corn_(maize)___Northern_Leaf_Blight       0.89      0.99      0.94       477\n",
      "                            Corn_(maize)___healthy       1.00      1.00      1.00       465\n",
      "                                 Grape___Black_rot       0.97      0.98      0.97       472\n",
      "                      Grape___Esca_(Black_Measles)       1.00      0.98      0.99       480\n",
      "        Grape___Leaf_blight_(Isariopsis_Leaf_Spot)       0.97      1.00      0.99       430\n",
      "                                   Grape___healthy       0.99      1.00      0.99       423\n",
      "          Orange___Haunglongbing_(Citrus_greening)       0.99      0.98      0.99       503\n",
      "                            Peach___Bacterial_spot       0.97      0.97      0.97       459\n",
      "                                   Peach___healthy       0.99      0.98      0.98       432\n",
      "                     Pepper,_bell___Bacterial_spot       1.00      0.83      0.91       478\n",
      "                            Pepper,_bell___healthy       1.00      0.91      0.95       497\n",
      "                             Potato___Early_blight       0.99      0.95      0.97       485\n",
      "                              Potato___Late_blight       0.91      0.98      0.95       485\n",
      "                                  Potato___healthy       0.98      0.97      0.98       456\n",
      "                               Raspberry___healthy       0.98      1.00      0.99       445\n",
      "                                 Soybean___healthy       0.96      0.99      0.97       505\n",
      "                           Squash___Powdery_mildew       0.92      1.00      0.96       434\n",
      "                          Strawberry___Leaf_scorch       0.95      0.99      0.97       444\n",
      "                              Strawberry___healthy       0.99      0.99      0.99       456\n",
      "                           Tomato___Bacterial_spot       0.90      0.98      0.94       425\n",
      "                             Tomato___Early_blight       0.94      0.85      0.89       480\n",
      "                              Tomato___Late_blight       0.96      0.89      0.92       463\n",
      "                                Tomato___Leaf_Mold       1.00      0.91      0.95       470\n",
      "                       Tomato___Septoria_leaf_spot       0.86      0.90      0.88       436\n",
      "     Tomato___Spider_mites Two-spotted_spider_mite       0.95      0.94      0.95       435\n",
      "                              Tomato___Target_Spot       0.85      0.97      0.90       457\n",
      "            Tomato___Tomato_Yellow_Leaf_Curl_Virus       0.99      0.99      0.99       490\n",
      "                      Tomato___Tomato_mosaic_virus       0.95      0.99      0.97       448\n",
      "                                  Tomato___healthy       0.99      0.97      0.98       481\n",
      "\n",
      "                                          accuracy                           0.96     17572\n",
      "                                         macro avg       0.96      0.96      0.96     17572\n",
      "                                      weighted avg       0.96      0.96      0.96     17572\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Precision Recall Fscore\n",
    "print(classification_report(Y_true,predicted_categories,target_names=class_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 4000x4000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40, 40))\n",
    "sns.heatmap(cm,annot=True,annot_kws={\"size\": 10})\n",
    "\n",
    "plt.xlabel('Predicted Class',fontsize = 20)\n",
    "plt.ylabel('Actual Class',fontsize = 20)\n",
    "plt.title('Plant Disease Prediction Confusion Matrix',fontsize = 25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}