TensorFlow _Cotton_Leaf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb42642b-7f01-4ff1-b4c6-828053c6d798",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install tensorflow-metal\n",
    "#pip install tensorflow-macos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40aed3f4-06e1-4289-b90f-4566ca05fe3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c69a2da-9570-44bf-9c9e-0ae312fbf0ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "43581e6e-bea2-49b3-8211-bbabee172726",
   "metadata": {},
   "outputs": [],
   "source": [
    "from helper_functions import create_tensorboard_callback, plot_loss_curves, unzip_data, compare_historys, walk_through_dir, pred_and_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e0c2a2cf-7bdc-4efc-9fc8-35df23f92aba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras.layers\n",
    "from keras import layers,models\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras import Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import Callback, EarlyStopping,ModelCheckpoint\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ad611af-9d2b-42fa-a043-38372cd3146b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-09 12:39:26.433434: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M3 Max\n",
      "2024-09-09 12:39:26.433449: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 36.00 GB\n",
      "2024-09-09 12:39:26.433455: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 13.50 GB\n",
      "2024-09-09 12:39:26.433470: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2024-09-09 12:39:26.433480: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    }
   ],
   "source": [
    "pretrained_model = keras.applications.MobileNetV3Large(\n",
    "    input_shape=(224, 224, 3),\n",
    "    include_top=False,\n",
    "    weights='imagenet',\n",
    "    pooling='avg'\n",
    ")\n",
    "\n",
    "pretrained_model.trainable = False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c10da6ab-033d-4889-abc2-b743f324eb2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1710 files belonging to 4 classes.\n",
      "Using 1368 files for training.\n",
      "Using 342 files for validation.\n"
     ]
    }
   ],
   "source": [
    "image_size = (224, 224)\n",
    "batch_size = 128\n",
    "\n",
    "train_ds, val_ds = keras.utils.image_dataset_from_directory(\n",
    "    \"./CottonLeafDiseaseImages\",\n",
    "    validation_split=0.2,\n",
    "    subset=\"both\",\n",
    "    seed=1337,\n",
    "    image_size=image_size,\n",
    "    batch_size=batch_size,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7b023edf-c1c3-4555-a3cd-8ac4194a7677",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_path = \"cotton_classification_model_checkpoint.weights.h5\"\n",
    "checkpoint_callback = ModelCheckpoint(checkpoint_path,\n",
    "                                      save_weights_only=True,\n",
    "                                      monitor=\"val_accuracy\",\n",
    "                                      save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "35911f0e-1ba7-44bb-8791-c4a42235bcb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping(monitor=\"val_loss\", # watch the val loss metric\n",
    "                                                  patience=3,\n",
    "                                                  restore_best_weights=True) # if val loss decreases for 3 epochs in a row, stop training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f7f8a681-1ec1-4570-9053-659319f34a3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving TensorBoard log files to: training_logs/cotton_classification/20240909-123928\n",
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-09 12:39:28.802194: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-09-09 12:39:29.730566: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n",
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      "2024-09-09 12:39:37.835042: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "Epoch 2/100\n",
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      "Epoch 3/100\n",
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      "Epoch 4/100\n"
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      "Epoch 5/100\n"
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      "Epoch 6/100\n",
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      "Epoch 7/100\n"
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      "Epoch 8/100\n"
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      "Epoch 9/100\n",
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      "Epoch 10/100\n"
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      "Epoch 11/100\n"
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      "2024-09-09 12:40:09.913227: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n",
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      "Epoch 12/100\n"
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      "Epoch 13/100\n"
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      "Epoch 14/100\n"
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      "Epoch 15/100\n"
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      "Epoch 16/100\n"
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      "Epoch 17/100\n",
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      "Epoch 18/100\n"
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      "Epoch 19/100\n",
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      "Epoch 20/100\n"
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      "Epoch 21/100\n"
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      "Epoch 22/100\n"
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      "Epoch 23/100\n"
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      "Epoch 24/100\n"
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      "Epoch 25/100\n",
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      "2024-09-09 12:41:56.882045: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "Epoch 27/100\n",
      "\u001b[1m 1/11\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 182ms/step - accuracy: 0.7422 - loss: 0.7385"
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      "2024-09-09 12:41:58.475341: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n",
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      "Epoch 28/100\n"
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      "Epoch 29/100\n"
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      "Epoch 30/100\n"
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      "Epoch 31/100\n"
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      "Epoch 32/100\n"
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      "2024-09-09 12:42:20.015587: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "Epoch 33/100\n"
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      "2024-09-09 12:42:21.248438: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n",
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      "2024-09-09 12:42:22.585235: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n",
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      "Epoch 34/100\n"
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      "Epoch 35/100\n"
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      "Epoch 36/100\n",
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      "Epoch 37/100\n"
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      "Epoch 38/100\n",
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      "2024-09-09 12:42:42.476310: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n"
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      "Epoch 39/100\n"
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      "Epoch 40/100\n"
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      "Epoch 41/100\n"
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      "Epoch 42/100\n"
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      "Epoch 43/100\n"
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      "Epoch 44/100\n"
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      "Epoch 45/100\n"
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      "Epoch 46/100\n"
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      "Epoch 47/100\n"
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      "Epoch 48/100\n"
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      "Epoch 49/100\n"
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      "Epoch 51/100\n"
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      "Epoch 52/100\n"
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      "Epoch 53/100\n"
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      "Epoch 54/100\n"
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      "Epoch 55/100\n"
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      "2024-09-09 12:43:54.501194: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n",
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      "Epoch 56/100\n"
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      "Epoch 57/100\n"
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      "Epoch 58/100\n"
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      "Epoch 59/100\n"
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      "Epoch 60/100\n"
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      "2024-09-09 12:44:17.806675: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "Epoch 61/100\n"
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      "2024-09-09 12:44:19.066592: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "2024-09-09 12:44:19.396374: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n"
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      "Epoch 62/100\n"
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      "Epoch 63/100\n"
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      "Epoch 64/100\n"
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      "Epoch 66/100\n"
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      "2024-09-09 12:44:39.080947: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "2024-09-09 12:44:39.346801: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n"
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      "Epoch 67/100\n",
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      "Epoch 68/100\n",
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      "2024-09-09 12:44:49.325565: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "Epoch 69/100\n"
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      "Epoch 70/100\n"
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      "Epoch 71/100\n",
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      "2024-09-09 12:45:00.136508: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "2024-09-09 12:45:00.373303: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n"
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      "2024-09-09 12:45:02.416698: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "Epoch 72/100\n"
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      "2024-09-09 12:45:03.739272: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n",
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      "2024-09-09 12:45:05.290421: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "Epoch 73/100\n",
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      "Epoch 74/100\n"
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      "Epoch 75/100\n"
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      "Epoch 76/100\n"
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      "2024-09-09 12:45:24.209224: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "Epoch 77/100\n"
     ]
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      "2024-09-09 12:45:25.698689: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n",
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      "2024-09-09 12:45:27.095054: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n"
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      "2024-09-09 12:45:28.868212: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "Epoch 78/100\n"
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      "Epoch 79/100\n"
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      "Epoch 80/100\n"
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      "Epoch 81/100\n"
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      "Epoch 82/100\n"
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      "2024-09-09 12:45:49.819379: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "2024-09-09 12:45:50.342147: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n"
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      "Epoch 83/100\n",
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      "Epoch 84/100\n",
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      "Epoch 85/100\n"
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      "Epoch 86/100\n",
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      "Epoch 87/100\n"
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      "2024-09-09 12:46:14.157603: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n",
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      "2024-09-09 12:46:14.393554: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "2024-09-09 12:46:16.166271: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "Epoch 88/100\n"
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      "2024-09-09 12:46:17.793064: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "Epoch 89/100\n"
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      "Epoch 90/100\n"
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      "Epoch 91/100\n",
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      "Epoch 92/100\n"
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      "2024-09-09 12:46:37.146176: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n"
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      "2024-09-09 12:46:38.528183: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "Epoch 93/100\n"
     ]
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      "2024-09-09 12:46:39.892753: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n",
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      "Epoch 94/100\n",
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      "Epoch 95/100\n"
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      "Epoch 96/100\n"
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      "Epoch 97/100\n"
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      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 360ms/step - accuracy: 0.9536 - loss: 0.2010 - val_accuracy: 0.9591 - val_loss: 0.1891\n",
      "Epoch 98/100\n"
     ]
    },
    {
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      "2024-09-09 12:47:01.554496: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50\n",
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      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 254ms/step - accuracy: 0.9574 - loss: 0.1914"
     ]
    },
    {
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      "2024-09-09 12:47:03.233836: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 356ms/step - accuracy: 0.9572 - loss: 0.1919 - val_accuracy: 0.9591 - val_loss: 0.1867\n",
      "Epoch 99/100\n"
     ]
    },
    {
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      "\u001b[1m 5/11\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 231ms/step - accuracy: 0.9415 - loss: 0.2117"
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      "Epoch 100/100\n"
     ]
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    {
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      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 275ms/step - accuracy: 0.9605 - loss: 0.1841"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-09 12:47:11.769984: W tensorflow/core/lib/png/png_io.cc:89] PNG warning: iCCP: known incorrect sRGB profile\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 443ms/step - accuracy: 0.9601 - loss: 0.1850 - val_accuracy: 0.9591 - val_loss: 0.1822\n"
     ]
    }
   ],
   "source": [
    "resize_and_rescale = tf.keras.Sequential([\n",
    "  layers.Resizing(224,224),\n",
    "  layers.Rescaling(1./255),\n",
    "])\n",
    "\n",
    "\n",
    "inputs = pretrained_model.input\n",
    "x = resize_and_rescale(inputs)\n",
    "\n",
    "x = Dense(256, activation='relu')(pretrained_model.output)\n",
    "x = Dropout(0.2)(x)\n",
    "x = Dense(256, activation='relu')(x)\n",
    "x = Dropout(0.2)(x)\n",
    "\n",
    "outputs = Dense(4, activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=Adam(0.00001),\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "with tf.device('/GPU:0'):\n",
    "    history = model.fit(\n",
    "        train_ds,\n",
    "        #steps_per_epoch=100,\n",
    "        validation_data=val_ds,\n",
    "        #validation_steps=50,\n",
    "        epochs=100,\n",
    "        callbacks=[\n",
    "            early_stopping,\n",
    "            create_tensorboard_callback(\"training_logs\", \n",
    "                                        \"cotton_classification\"),\n",
    "            checkpoint_callback,\n",
    "        ],\n",
    "        verbose=1\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be89bcdd-2510-402b-bcee-0f17f1a2481a",
   "metadata": {},
   "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",
    "accuracy = history.history['accuracy']\n",
    "val_accuracy = history.history['val_accuracy']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(accuracy))\n",
    "plt.plot(epochs, accuracy, 'bo', label='Training accuracy')\n",
    "plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n",
    "\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6ba13725-2af8-4af8-a9df-2ecb48c58f39",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"./models/leaf.cotton.mobilenetv3large.keras\")\n",
    "reconstructed_model = keras.models.load_model(\"./models/leaf.cotton.mobilenetv3large.keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fba478c-5444-4eb2-9169-17feae80cdc2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}