facetask.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2  # For reading images\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "996\n",
      "[19, 21, 39, 92, 123, 159, 172, 182, 187, 199]\n"
     ]
    }
   ],
   "source": [
    "with open(r'/home/runcandel/Desktop/pclubsectask/text.txt', 'r') as file:\n",
    "    lines = file.readlines()\n",
    "lines=lines[1:]\n",
    "for i in range(len(lines)):\n",
    "    lines[i]=lines[i].strip()\n",
    "    lines[i]=lines[i].split(\"}\")\n",
    "    lines[i]=lines[i][:-1]\n",
    "    lines[i]=lines[i][1:3]\n",
    "    if len(lines[i]):\n",
    "        for j in range(2):\n",
    "            lines[i][j]=lines[i][j][lines[i][j].index('0'):]\n",
    "            # print(lines[i][j][lines[i][j].index('0'):])\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "print(len(lines))\n",
    "empty=[]\n",
    "for i in range(len(lines)):\n",
    "    if len(lines[i])==0:\n",
    "        empty.append(i)\n",
    "print(empty)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines[19]=[1,0]\n",
    "lines[21]=[0,1]\n",
    "lines[39]=[0,1]\n",
    "lines[92]=[1,0]\n",
    "lines[123]=[0,1]\n",
    "lines[159]=[0,1]\n",
    "lines[172]=[1,0]\n",
    "lines[182]=[1,0]\n",
    "lines[187]=[1,0]\n",
    "lines[199]=[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=[]\n",
    "for i in range(len(lines)):\n",
    "    y.append(int(lines[i][0]>lines[i][1]))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2  # For reading images\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPool2D, Flatten, Dense\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs=[]\n",
    "folder=r'/home/runcandel/Desktop/pclubsectask/face2'\n",
    "for filename in os.listdir(folder):\n",
    "    img=cv2.imread(os.path.join(folder,filename))\n",
    "    # img=cv2.resize(img,(image_size,image_size))\n",
    "    imgs.append(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "a=list(zip(imgs,y))\n",
    "random.shuffle(a)\n",
    "imgs,y=zip(*a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain=imgs[:800]\n",
    "ytrain=y[:800]\n",
    "xtest=imgs[800:]\n",
    "ytest=y[800:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(224, 224, 3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgs[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 1s/step - accuracy: 0.5123 - loss: 699.0856 - val_accuracy: 0.4250 - val_loss: 5.4918\n",
      "Epoch 2/20\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.5315 - loss: 4.3054 - val_accuracy: 0.5000 - val_loss: 3.9255\n",
      "Epoch 3/20\n",
      "\u001b[1m 4/23\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m23s\u001b[0m 1s/step - accuracy: 0.8034 - loss: 3.6315"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[35], line 16\u001b[0m\n\u001b[1;32m     12\u001b[0m model2\u001b[38;5;241m=\u001b[39mkeras\u001b[38;5;241m.\u001b[39mModel(inputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28minput\u001b[39m,outputs\u001b[38;5;241m=\u001b[39moutput)\n\u001b[1;32m     13\u001b[0m model2\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     14\u001b[0m               loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary_crossentropy\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     15\u001b[0m               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---> 16\u001b[0m history \u001b[38;5;241m=\u001b[39m model2\u001b[38;5;241m.\u001b[39mfit(np\u001b[38;5;241m.\u001b[39marray(xtrain),np\u001b[38;5;241m.\u001b[39marray(ytrain),epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m,validation_split\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m)\n\u001b[1;32m     17\u001b[0m test_loss, test_acc \u001b[38;5;241m=\u001b[39m model2\u001b[38;5;241m.\u001b[39mevaluate(np\u001b[38;5;241m.\u001b[39marray(xtest), np\u001b[38;5;241m.\u001b[39marray(ytest))\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTest accuracy: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtest_acc\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;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[1;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[1;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[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/keras/src/backend/tensorflow/trainer.py:314\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;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[1;32m    312\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[1;32m    313\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 314\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_function(iterator)\n\u001b[1;32m    315\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n\u001b[1;32m    316\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n",
      "File \u001b[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;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[1;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[1;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[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;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[1;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[0;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[1;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[1;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[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;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[1;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[1;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[0;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m tracing_compilation\u001b[38;5;241m.\u001b[39mcall_function(\n\u001b[1;32m    879\u001b[0m     args, kwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_config\n\u001b[1;32m    880\u001b[0m )\n\u001b[1;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[1;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[1;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[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;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[1;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[0;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[1;32m    140\u001b[0m     flat_inputs, captured_inputs\u001b[38;5;241m=\u001b[39mfunction\u001b[38;5;241m.\u001b[39mcaptured_inputs\n\u001b[1;32m    141\u001b[0m )\n",
      "File \u001b[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;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[1;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;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[1;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[1;32m   1324\u001b[0m     args,\n\u001b[1;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1326\u001b[0m     executing_eagerly)\n\u001b[1;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[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;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[1;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[0;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[1;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[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;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[0;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[1;32m    252\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname,\n\u001b[1;32m    253\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;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[1;32m    255\u001b[0m     )\n\u001b[1;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[1;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;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[1;32m    261\u001b[0m     )\n",
      "File \u001b[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/tensorflow/python/eager/context.py:1500\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1498\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1499\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[0;32m-> 1500\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute(\n\u001b[1;32m   1501\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[1;32m   1502\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   1503\u001b[0m       inputs\u001b[38;5;241m=\u001b[39mtensor_inputs,\n\u001b[1;32m   1504\u001b[0m       attrs\u001b[38;5;241m=\u001b[39mattrs,\n\u001b[1;32m   1505\u001b[0m       ctx\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1506\u001b[0m   )\n\u001b[1;32m   1507\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1508\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1509\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[1;32m   1510\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1514\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1515\u001b[0m   )\n",
      "File \u001b[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;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[1;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[1;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[1;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[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "input = keras.layers.Input(shape=(224,224,3))\n",
    "x = Conv2D(40, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=keras.regularizers.l2(0.05))(input)\n",
    "x = MaxPool2D(pool_size=(2, 2))(x)\n",
    "x = Conv2D(25, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=keras.regularizers.l2(0.05))(x)\n",
    "x = MaxPool2D(pool_size=(2, 2))(x)\n",
    "x = Conv2D(18, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=keras.regularizers.l2(0.05))(x)\n",
    "x = Conv2D(12, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=keras.regularizers.l2(0.05))(x)\n",
    "x = Flatten()(x)\n",
    "x = Dense(1024, activation='relu')(x)\n",
    "x = Dense(256, activation='relu')(x)\n",
    "output = Dense(1,activation='sigmoid')(x)\n",
    "model2=keras.Model(inputs=input,outputs=output)\n",
    "model2.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "history = model2.fit(np.array(xtrain),np.array(ytrain),epochs=20,validation_split=0.1)\n",
    "test_loss, test_acc = model2.evaluate(np.array(xtest), np.array(ytest))\n",
    "print(f'Test accuracy: {test_acc:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 208ms/step\n"
     ]
    }
   ],
   "source": [
    "predicts=model2.predict(np.array(xtest))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[47.937973]\n",
      "[99.9529]\n",
      "[39.18288]\n",
      "[90.80864]\n",
      "[87.036]\n",
      "[67.82363]\n",
      "[5.0842286e-30]\n",
      "[0.19099887]\n",
      "[0.05607489]\n",
      "[1.7266135]\n",
      "[66.802795]\n",
      "[1.6273888]\n",
      "[0.54984975]\n",
      "[1.5697065e-11]\n",
      "[100.]\n",
      "[2.2327938]\n",
      "[7.153364e-13]\n",
      "[99.98816]\n",
      "[99.84219]\n",
      "[2.6435882e-06]\n",
      "[0.04972972]\n",
      "[77.72112]\n",
      "[9.128333]\n",
      "[2.1709142]\n",
      "[1.5325073]\n",
      "[0.00116635]\n",
      "[81.785484]\n",
      "[0.01734611]\n",
      "[0.85646284]\n",
      "[96.65619]\n",
      "[0.03214957]\n",
      "[0.00229253]\n",
      "[0.00779616]\n",
      "[99.920105]\n",
      "[100.]\n",
      "[7.5746735e-05]\n",
      "[29.296339]\n",
      "[4.3295048e-11]\n",
      "[99.99971]\n",
      "[5.101097]\n",
      "[9.547508]\n",
      "[56.68106]\n",
      "[4.4020557]\n",
      "[99.624405]\n",
      "[99.48108]\n",
      "[100.]\n",
      "[98.105034]\n",
      "[100.]\n",
      "[70.74176]\n",
      "[99.967674]\n",
      "[1.4716074]\n",
      "[84.94661]\n",
      "[0.08095273]\n",
      "[98.825325]\n",
      "[99.79861]\n",
      "[99.991806]\n",
      "[8.798223e-08]\n",
      "[78.86886]\n",
      "[80.50139]\n",
      "[29.443832]\n",
      "[66.301506]\n",
      "[11.128135]\n",
      "[82.901634]\n",
      "[100.]\n",
      "[0.00186746]\n",
      "[100.]\n",
      "[5.831737e-10]\n",
      "[20.672916]\n",
      "[72.21831]\n",
      "[4.1731637e-06]\n",
      "[65.935936]\n",
      "[100.]\n",
      "[15.063606]\n",
      "[100.]\n",
      "[99.99913]\n",
      "[9.337246]\n",
      "[1.5163407e-05]\n",
      "[13.289528]\n",
      "[99.99618]\n",
      "[39.08028]\n",
      "[5.702352e-10]\n",
      "[82.51251]\n",
      "[92.81108]\n",
      "[4.9945517]\n",
      "[0.01437647]\n",
      "[0.2831276]\n",
      "[1.7037739]\n",
      "[99.91376]\n",
      "[0.00475778]\n",
      "[9.06541e-05]\n",
      "[99.96652]\n",
      "[88.429276]\n",
      "[100.]\n",
      "[64.12186]\n",
      "[90.143166]\n",
      "[86.70792]\n",
      "[26.372381]\n",
      "[91.32819]\n",
      "[90.92577]\n",
      "[0.04604534]\n",
      "[3.8737183]\n",
      "[10.887536]\n",
      "[99.627335]\n",
      "[5.657207e-07]\n",
      "[42.130836]\n",
      "[100.]\n",
      "[0.13763236]\n",
      "[6.0854616]\n",
      "[94.794014]\n",
      "[4.767971e-08]\n",
      "[97.80735]\n",
      "[1.0529007]\n",
      "[99.66264]\n",
      "[97.172745]\n",
      "[1.9644696e-17]\n",
      "[11.707363]\n",
      "[2.5256526]\n",
      "[5.3038588]\n",
      "[25.716785]\n",
      "[80.34655]\n",
      "[0.70429164]\n",
      "[1.9437816]\n",
      "[99.798454]\n",
      "[100.]\n",
      "[79.36179]\n",
      "[2.8072564e-05]\n",
      "[59.221764]\n",
      "[100.]\n",
      "[99.963165]\n",
      "[99.99996]\n",
      "[100.]\n",
      "[5.0340655e-06]\n",
      "[92.48234]\n",
      "[5.019659e-08]\n",
      "[0.53379893]\n",
      "[1.0137868e-06]\n",
      "[8.519601]\n",
      "[99.99995]\n",
      "[0.00149012]\n",
      "[92.22229]\n",
      "[29.315584]\n",
      "[0.00037484]\n",
      "[18.343248]\n",
      "[1.6202267e-07]\n",
      "[99.07864]\n",
      "[99.718056]\n",
      "[14.192261]\n",
      "[0.6390007]\n",
      "[91.57893]\n",
      "[99.99939]\n",
      "[95.627365]\n",
      "[100.]\n",
      "[99.99884]\n",
      "[99.95299]\n",
      "[31.748045]\n",
      "[36.42994]\n",
      "[22.241901]\n",
      "[82.29915]\n",
      "[97.76029]\n",
      "[98.58926]\n",
      "[82.82673]\n",
      "[5.0423633e-08]\n",
      "[99.959915]\n",
      "[100.]\n",
      "[74.55845]\n",
      "[7.5918015e-21]\n",
      "[9.653321]\n",
      "[82.51548]\n",
      "[99.24519]\n",
      "[13.358916]\n",
      "[1.0504133]\n",
      "[35.275757]\n",
      "[99.999985]\n",
      "[0.00012363]\n",
      "[4.6565986]\n",
      "[0.28370842]\n",
      "[98.49548]\n",
      "[99.963165]\n",
      "[87.86061]\n",
      "[1.5306503e-11]\n",
      "[9.195555]\n",
      "[82.76907]\n",
      "[99.99999]\n",
      "[100.]\n",
      "[0.00357507]\n",
      "[1.5077103]\n",
      "[43.85484]\n",
      "[100.]\n",
      "[99.28941]\n",
      "[3.5698283]\n",
      "[62.551105]\n",
      "[0.33139706]\n",
      "[9.624851e-05]\n",
      "[100.]\n",
      "[0.17562565]\n",
      "[5.9842776e-08]\n"
     ]
    }
   ],
   "source": [
    "for i in predicts:\n",
    "    print(i*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "print(ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'layer_utils' from 'keras.utils' (/home/runcandel/anaconda3/envs/yolo7/lib/python3.12/site-packages/keras/api/utils/__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[39], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m  Model\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Flatten, Dense, Input\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras_vggface\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvggface\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m VGGFace\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m#custom parameters\u001b[39;00m\n\u001b[1;32m      7\u001b[0m nb_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/keras_vggface/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras_vggface\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvggface\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m VGGFace\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras_vggface\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversion\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__\n",
      "File \u001b[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/keras_vggface/vggface.py:9\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;124;03m'''VGGFace models for Keras.\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \n\u001b[1;32m      3\u001b[0m \u001b[38;5;124;03m# Reference:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      6\u001b[0m \n\u001b[1;32m      7\u001b[0m \u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m__future__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m print_function\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras_vggface\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RESNET50, VGG16, SENET50\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mVGGFace\u001b[39m(include_top\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, model\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvgg16\u001b[39m\u001b[38;5;124m'\u001b[39m, weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvggface\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     13\u001b[0m             input_tensor\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, input_shape\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     14\u001b[0m             pooling\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     15\u001b[0m             classes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m     16\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Instantiates the VGGFace architectures.\u001b[39;00m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;124;03m    Optionally loads weights pre-trained\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;124;03m    on VGGFace datasets. Note that when using TensorFlow,\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;124;03m            or invalid input shape.\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/yolo7/lib/python3.12/site-packages/keras_vggface/models.py:16\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Flatten, Dense, Input, GlobalAveragePooling2D, \\\n\u001b[1;32m     13\u001b[0m     GlobalMaxPooling2D, Activation, Conv2D, MaxPooling2D, BatchNormalization, \\\n\u001b[1;32m     14\u001b[0m     AveragePooling2D, Reshape, Permute, multiply\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras_applications\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimagenet_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _obtain_input_shape\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m layer_utils\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_file\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend \u001b[38;5;28;01mas\u001b[39;00m K\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'layer_utils' from 'keras.utils' (/home/runcandel/anaconda3/envs/yolo7/lib/python3.12/site-packages/keras/api/utils/__init__.py)"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "from keras import  Model\n",
    "from keras.layers import Flatten, Dense, Input\n",
    "from keras_vggface.vggface import VGGFace\n",
    "\n",
    "#custom parameters\n",
    "nb_class = 1\n",
    "\n",
    "vgg_model = VGGFace(include_top=False, input_shape=(224, 224, 3))\n",
    "last_layer = vgg_model.get_layer('avg_pool').output\n",
    "x = Flatten(name='flatten')(last_layer)\n",
    "out = Dense(nb_class, activation='sigmoid', name='classifier')(x)\n",
    "custom_vgg_model = Model(vgg_model.input, out)\n",
    "\n",
    "# Train your model as usual.\n",
    "custom_vgg_model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "history = custom_vgg_model.fit(np.array(xtrain),np.array(ytrain),epochs=20,validation_split=0.1)\n",
    "test_loss, test_acc = custom_vgg_model.evaluate(np.array(xtest), np.array(ytest))\n",
    "print(f'Test accuracy: {test_acc:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'inception_resnet_v1'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[40], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01minception_resnet_v1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m      2\u001b[0m model \u001b[38;5;241m=\u001b[39m InceptionResNetV1()\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'inception_resnet_v1'"
     ]
    }
   ],
   "source": [
    "from inception_resnet_v1 import *\n",
    "model = InceptionResNetV1()"
   ]
  }
 ],
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