IA_Using_Pretrained_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "54a74731-7755-44c5-b334-12c9629f5a26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications import ResNet50,VGG19,VGG16\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9d630442-9737-4d88-bd90-cfe1290dacd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 25565 images belonging to 74 classes.\n",
      "Found 6352 images belonging to 74 classes.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# Set paths and parameters\n",
    "data_path = \"DataSets/AirCrafts\"#'crop'\n",
    "image_size = (224, 224)  # Resize images to match model input size\n",
    "batch_size = 32\n",
    "\n",
    "# Create ImageDataGenerator instance\n",
    "datagen = ImageDataGenerator(rescale=1.0 / 255.0, validation_split=0.2)\n",
    "\n",
    "# Load training and validation sets\n",
    "train_data = datagen.flow_from_directory(\n",
    "    directory=data_path,\n",
    "    target_size=image_size,\n",
    "    batch_size=batch_size,\n",
    "    class_mode='categorical',\n",
    "    subset='training'\n",
    ")\n",
    "\n",
    "val_data = datagen.flow_from_directory(\n",
    "    directory=data_path,\n",
    "    target_size=image_size,\n",
    "    batch_size=batch_size,\n",
    "    class_mode='categorical',\n",
    "    subset='validation'\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f0e0e29-21b9-48d5-9f7d-ba70f6de90a4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "\u001b[1m94765736/94765736\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 0us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\AnnA\\envs\\diat\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m799/799\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1696s\u001b[0m 2s/step - accuracy: 0.0304 - loss: 4.4555 - val_accuracy: 0.0461 - val_loss: 4.0422\n",
      "Epoch 2/10\n",
      "\u001b[1m799/799\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m977s\u001b[0m 1s/step - accuracy: 0.0408 - loss: 4.1774 - val_accuracy: 0.0554 - val_loss: 4.0315\n",
      "Epoch 3/10\n",
      "\u001b[1m799/799\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m979s\u001b[0m 1s/step - accuracy: 0.0447 - loss: 4.1209 - val_accuracy: 0.0529 - val_loss: 4.0260\n",
      "Epoch 4/10\n",
      "\u001b[1m799/799\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m983s\u001b[0m 1s/step - accuracy: 0.0487 - loss: 4.0868 - val_accuracy: 0.0559 - val_loss: 4.0224\n",
      "Epoch 5/10\n",
      "\u001b[1m799/799\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 945ms/step - accuracy: 0.0538 - loss: 4.0519"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 22\u001b[0m\n\u001b[0;32m     19\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39mtf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39moptimizers\u001b[38;5;241m.\u001b[39mAdam(\u001b[38;5;241m1e-5\u001b[39m), loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcategorical_crossentropy\u001b[39m\u001b[38;5;124m'\u001b[39m, metrics\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m     21\u001b[0m \u001b[38;5;66;03m# Initial training\u001b[39;00m\n\u001b[1;32m---> 22\u001b[0m history \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mfit(\n\u001b[0;32m     23\u001b[0m     train_data,\n\u001b[0;32m     24\u001b[0m     validation_data\u001b[38;5;241m=\u001b[39mval_data,\n\u001b[0;32m     25\u001b[0m     epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m\n\u001b[0;32m     26\u001b[0m )\n\u001b[0;32m     28\u001b[0m \u001b[38;5;66;03m# Evaluate on validation data\u001b[39;00m\n\u001b[0;32m     29\u001b[0m val_loss, val_acc \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mevaluate(val_data)\n",
      "File \u001b[1;32mD:\\AnnA\\envs\\diat\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32mD:\\AnnA\\envs\\diat\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:344\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[0;32m    333\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_eval_epoch_iterator\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    334\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_eval_epoch_iterator \u001b[38;5;241m=\u001b[39m TFEpochIterator(\n\u001b[0;32m    335\u001b[0m         x\u001b[38;5;241m=\u001b[39mval_x,\n\u001b[0;32m    336\u001b[0m         y\u001b[38;5;241m=\u001b[39mval_y,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    342\u001b[0m         shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    343\u001b[0m     )\n\u001b[1;32m--> 344\u001b[0m val_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevaluate(\n\u001b[0;32m    345\u001b[0m     x\u001b[38;5;241m=\u001b[39mval_x,\n\u001b[0;32m    346\u001b[0m     y\u001b[38;5;241m=\u001b[39mval_y,\n\u001b[0;32m    347\u001b[0m     sample_weight\u001b[38;5;241m=\u001b[39mval_sample_weight,\n\u001b[0;32m    348\u001b[0m     batch_size\u001b[38;5;241m=\u001b[39mvalidation_batch_size \u001b[38;5;129;01mor\u001b[39;00m batch_size,\n\u001b[0;32m    349\u001b[0m     steps\u001b[38;5;241m=\u001b[39mvalidation_steps,\n\u001b[0;32m    350\u001b[0m     callbacks\u001b[38;5;241m=\u001b[39mcallbacks,\n\u001b[0;32m    351\u001b[0m     return_dict\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    352\u001b[0m     _use_cached_eval_dataset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    353\u001b[0m )\n\u001b[0;32m    354\u001b[0m val_logs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    355\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m name: val \u001b[38;5;28;01mfor\u001b[39;00m name, val \u001b[38;5;129;01min\u001b[39;00m val_logs\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m    356\u001b[0m }\n\u001b[0;32m    357\u001b[0m epoch_logs\u001b[38;5;241m.\u001b[39mupdate(val_logs)\n",
      "File \u001b[1;32mD:\\AnnA\\envs\\diat\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32mD:\\AnnA\\envs\\diat\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:432\u001b[0m, in \u001b[0;36mTensorFlowTrainer.evaluate\u001b[1;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, return_dict, **kwargs)\u001b[0m\n\u001b[0;32m    430\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[0;32m    431\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_test_batch_begin(step)\n\u001b[1;32m--> 432\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtest_function(iterator)\n\u001b[0;32m    433\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_test_batch_end(step, logs)\n\u001b[0;32m    434\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_evaluating:\n",
      "File \u001b[1;32mD:\\AnnA\\envs\\diat\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32mD:\\AnnA\\envs\\diat\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[1;32mD:\\AnnA\\envs\\diat\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[0;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[1;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m tracing_compilation\u001b[38;5;241m.\u001b[39mcall_function(\n\u001b[0;32m    879\u001b[0m     args, kwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_config\n\u001b[0;32m    880\u001b[0m )\n\u001b[0;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[0;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mD:\\AnnA\\envs\\diat\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m function\u001b[38;5;241m.\u001b[39m_call_flat(  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m    140\u001b[0m     flat_inputs, captured_inputs\u001b[38;5;241m=\u001b[39mfunction\u001b[38;5;241m.\u001b[39mcaptured_inputs\n\u001b[0;32m    141\u001b[0m )\n",
      "File \u001b[1;32mD:\\AnnA\\envs\\diat\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[0;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inference_function\u001b[38;5;241m.\u001b[39mcall_preflattened(args)\n\u001b[0;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1324\u001b[0m     args,\n\u001b[0;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1326\u001b[0m     executing_eagerly)\n\u001b[0;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[1;32mD:\\AnnA\\envs\\diat\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcall_flat(\u001b[38;5;241m*\u001b[39margs)\n\u001b[0;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
      "File \u001b[1;32mD:\\AnnA\\envs\\diat\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[0;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mcall_function(\n\u001b[0;32m    252\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname,\n\u001b[0;32m    253\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[0;32m    254\u001b[0m         \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mflat_outputs),\n\u001b[0;32m    255\u001b[0m     )\n\u001b[0;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[0;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[0;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[0;32m    261\u001b[0m     )\n",
      "File \u001b[1;32mD:\\AnnA\\envs\\diat\\Lib\\site-packages\\tensorflow\\python\\eager\\context.py:1552\u001b[0m, in \u001b[0;36mContext.call_function\u001b[1;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[0;32m   1550\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[0;32m   1551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1552\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute(\n\u001b[0;32m   1553\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1554\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   1555\u001b[0m       inputs\u001b[38;5;241m=\u001b[39mtensor_inputs,\n\u001b[0;32m   1556\u001b[0m       attrs\u001b[38;5;241m=\u001b[39mattrs,\n\u001b[0;32m   1557\u001b[0m       ctx\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   1558\u001b[0m   )\n\u001b[0;32m   1559\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1560\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m   1561\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1562\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1566\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[0;32m   1567\u001b[0m   )\n",
      "File \u001b[1;32mD:\\AnnA\\envs\\diat\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications import ResNet50\n",
    "from tensorflow.keras import layers, models\n",
    "import tensorflow as tf\n",
    "\n",
    "# Load base model and freeze layers\n",
    "base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
    "base_model.trainable = False  # Freeze all layers initially\n",
    "\n",
    "# Build model\n",
    "model = models.Sequential([\n",
    "    base_model,\n",
    "    layers.GlobalAveragePooling2D(),\n",
    "    layers.Dense(1024, activation='relu'),\n",
    "    layers.Dropout(0.5),\n",
    "    layers.Dense(len(train_data.class_indices), activation='softmax')  # Output layer for classification\n",
    "])\n",
    "\n",
    "# Compile model with frozen base\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(1e-5), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Initial training\n",
    "history = model.fit(\n",
    "    train_data,\n",
    "    validation_data=val_data,\n",
    "    epochs=10\n",
    ")\n",
    "\n",
    "# Evaluate on validation data\n",
    "val_loss, val_acc = model.evaluate(val_data)\n",
    "print(f\"Validation Accuracy after initial training: {val_acc:.2f}\")\n",
    "\n",
    "# Fine-tuning the last 10 layers of the base model\n",
    "for layer in base_model.layers[:-10]:\n",
    "    layer.trainable = False\n",
    "for layer in base_model.layers[-10:]:\n",
    "    layer.trainable = True\n",
    "\n",
    "# Re-compile the model with a low learning rate for fine-tuning\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(1e-5), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Fine-tuning phase\n",
    "history_fine = model.fit(\n",
    "    train_data,\n",
    "    validation_data=val_data,\n",
    "    epochs=10\n",
    ")\n",
    "\n",
    "# Evaluate on validation data after fine-tuning\n",
    "val_loss_fine, val_acc_fine = model.evaluate(val_data)\n",
    "print(f\"Validation Accuracy after fine-tuning: {val_acc_fine:.2f}\")\n",
    "\n",
    "# Save the model\n",
    "model.save('fighter_jet_classifier.keras', save_format='keras')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5749b4a6-8a77-4c41-b7ab-399f56d0ccd9",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'history' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 26\u001b[0m\n\u001b[0;32m     22\u001b[0m     plt\u001b[38;5;241m.\u001b[39mlegend()\n\u001b[0;32m     24\u001b[0m     plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[1;32m---> 26\u001b[0m plot_training_history(history)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'history' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "# Plot training and validation accuracy and loss\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_training_history(history):\n",
    "    # Plot accuracy\n",
    "    plt.figure(figsize=(14, 5))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "    plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "    plt.title('Model Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "\n",
    "    # Plot loss\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history['loss'], label='Training Loss')\n",
    "    plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "    plt.title('Model Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "plot_training_history(history)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13d04528-6ee0-44ee-8738-4ba9f367ff38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction function for a single image\n",
    "from tensorflow.keras.preprocessing import image\n",
    "import numpy as np\n",
    "\n",
    "def predict_image_class(model, img_path, class_indices):\n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "    img_array = image.img_to_array(img) / 255.0  # Normalize to [0, 1]\n",
    "    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension\n",
    "\n",
    "    predictions = model.predict(img_array)\n",
    "    predicted_class_idx = np.argmax(predictions[0])\n",
    "\n",
    "    # Convert index to class name\n",
    "    class_labels = {v: k for k, v in class_indices.items()}\n",
    "    predicted_class_name = class_labels[predicted_class_idx]\n",
    "\n",
    "    print(f\"Predicted class: {predicted_class_name}\")\n",
    "    return predicted_class_name\n",
    "\n",
    "# Example usage\n",
    "img_path = 'path/to/your/test_image.jpg'  # Replace with your test image path\n",
    "predicted_class = predict_image_class(model, img_path, train_data.class_indices)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd91d6d7-3144-4427-a9fe-de67d6457301",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6a02267-8c33-44f6-aff9-6c9bba330e78",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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