burn classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9733771e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import keras\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import statistics\n",
    "import os\n",
    "from sklearn.model_selection  import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D,MaxPooling2D,Dropout,Flatten,Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "adf1ad32",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_images_from_folder(degree1):\n",
    "    images = []\n",
    "    for filename in os.listdir(degree1):\n",
    "        img = cv2.imread(os.path.join(degree1,filename))\n",
    "        if img is not None:\n",
    "            images.append(img)\n",
    "    return images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "06370acd",
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "OpenCV(4.8.1) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\core\\src\\alloc.cpp:73: error: (-4:Insufficient memory) Failed to allocate 1228800 bytes in function 'cv::OutOfMemoryError'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m b\u001b[38;5;241m=\u001b[39mload_images_from_folder(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdegree2\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      3\u001b[0m c\u001b[38;5;241m=\u001b[39mload_images_from_folder(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdegree3\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m a2\u001b[38;5;241m=\u001b[39m\u001b[43mload_images_from_folder\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m1st degree burn\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m b2\u001b[38;5;241m=\u001b[39mload_images_from_folder(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m2nd degree burn\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      6\u001b[0m c2\u001b[38;5;241m=\u001b[39mload_images_from_folder(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m3nd degree burn\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[1;32mIn[2], line 4\u001b[0m, in \u001b[0;36mload_images_from_folder\u001b[1;34m(degree1)\u001b[0m\n\u001b[0;32m      2\u001b[0m images \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m filename \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39mlistdir(degree1):\n\u001b[1;32m----> 4\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdegree1\u001b[49m\u001b[43m,\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m img \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;32m      6\u001b[0m         images\u001b[38;5;241m.\u001b[39mappend(img)\n",
      "\u001b[1;31merror\u001b[0m: OpenCV(4.8.1) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\core\\src\\alloc.cpp:73: error: (-4:Insufficient memory) Failed to allocate 1228800 bytes in function 'cv::OutOfMemoryError'\n"
     ]
    }
   ],
   "source": [
    "a=load_images_from_folder(\"degree1\")\n",
    "b=load_images_from_folder(\"degree2\")\n",
    "c=load_images_from_folder(\"degree3\")\n",
    "a2=load_images_from_folder(\"1st degree burn\")\n",
    "b2=load_images_from_folder(\"2nd degree burn\")\n",
    "c2=load_images_from_folder(\"3nd degree burn\")\n",
    "x=a+b+c+a2+b2+c2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "117d96fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1056\n",
      "978\n",
      "510\n"
     ]
    }
   ],
   "source": [
    "first_degree_y=[]\n",
    "second_degree_y=[]\n",
    "third_degree_y=[]\n",
    "\n",
    "for i in range(1056):\n",
    "    first_degree_y.append(0)\n",
    "for i in range(978):\n",
    "    second_degree_y.append(1)\n",
    "for i in range(510):\n",
    "    third_degree_y.append(2)\n",
    "    \n",
    "print(len(first_degree_y))\n",
    "print(len(second_degree_y))\n",
    "print(len(third_degree_y))\n",
    "\n",
    "y=first_degree_y+second_degree_y+third_degree_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04e845d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ee20c1d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1908\n",
      "1908\n",
      "636\n",
      "636\n"
     ]
    }
   ],
   "source": [
    "print(len(x_train))\n",
    "print(len(y_train))\n",
    "print(len(x_test))\n",
    "print(len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbbe698a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6b789579",
   "metadata": {},
   "outputs": [],
   "source": [
    "z=[]\n",
    "s=[]\n",
    "y=[]\n",
    "for i in x_train+x_test:\n",
    "    z.append(i.shape[0])\n",
    "    s.append(i.shape[1])\n",
    "    y.append(i.shape[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "467f368e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "431.44221698113205\n",
      "468.28891509433964\n",
      "3\n",
      "640\n",
      "640\n",
      "3\n",
      "111\n",
      "153\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "print(statistics.mean(z))\n",
    "print(statistics.mean(s))\n",
    "print(statistics.mean(y))\n",
    "print(statistics.mode(z))\n",
    "print(statistics.mode(s))\n",
    "print(statistics.mode(y))\n",
    "print(min(z))\n",
    "print(min(s))\n",
    "print(min(y))\n",
    "# 421.5776515151515\n",
    "# 459.12689393939394\n",
    "# 3\n",
    "# 640\n",
    "# 640\n",
    "# 3\n",
    "# 117\n",
    "# 153\n",
    "# 3\n",
    "\n",
    "\n",
    "# 469.92638036809814\n",
    "# 508.0051124744376\n",
    "# 3\n",
    "# 640\n",
    "# 640\n",
    "# 3\n",
    "# 116\n",
    "# 159\n",
    "# 3\n",
    "\n",
    "\n",
    "# 378.0686274509804\n",
    "# 411.0980392156863\n",
    "# 3\n",
    "# 640\n",
    "# 640\n",
    "# 3\n",
    "# 111\n",
    "# 168\n",
    "# 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b2ffe92",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_resized=[]\n",
    "x_test_resized=[]\n",
    "for i in x_train:\n",
    "    x_train_resized.append(cv2.resize(i,(420,420)))\n",
    "\n",
    "for i in x_test:\n",
    "    x_test_resized.append(cv2.resize(i,(420,420)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f42abb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1908\n",
      "636\n"
     ]
    }
   ],
   "source": [
    "print(len(x_train_resized))\n",
    "print(len(x_test_resized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff812bda",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_np=np.array(y_train)\n",
    "y_test_np=np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a46347c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_hot=tf.one_hot(y_train_np,3)\n",
    "y_test_hot=tf.one_hot(y_test_np,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4bc40e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1908, 3)\n",
      "(636, 3)\n"
     ]
    }
   ],
   "source": [
    "print(y_train_hot.shape)\n",
    "print(y_test_hot.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e34bfbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_np=np.float32(x_train_resized)\n",
    "x_test_np=np.float32(x_test_resized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee9eb29a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1908, 420, 420, 3)\n",
      "(1908, 3)\n",
      "(636, 420, 420, 3)\n",
      "(636, 3)\n"
     ]
    }
   ],
   "source": [
    "print(x_train_np.shape)\n",
    "print(y_train_hot.shape)\n",
    "print(x_test_np.shape)\n",
    "print(y_test_hot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "db883df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "93a701cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Conv2D(32,(3,3),padding=\"same\",activation=\"relu\"))\n",
    "model.add(Conv2D(32,(3,3),padding=\"same\",activation=\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding=\"same\"))\n",
    "model.add(Conv2D(64,(3,3),padding=\"same\",activation=\"relu\"))\n",
    "model.add(Conv2D(64,(3,3),padding=\"same\",activation=\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding=\"same\"))\n",
    "model.add(Conv2D(128,(3,3),padding=\"same\",activation=\"relu\"))\n",
    "model.add(Conv2D(128,(3,3),padding=\"same\",activation=\"relu\"))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(units=3,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b0e04861",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9cd2197a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "96/96 [==============================] - 583s 6s/step - loss: 30.8204 - accuracy: 0.4313 - val_loss: 0.6080 - val_accuracy: 0.4340\n",
      "Epoch 2/10\n",
      "96/96 [==============================] - 562s 6s/step - loss: 0.5655 - accuracy: 0.5603 - val_loss: 0.5774 - val_accuracy: 0.5896\n",
      "Epoch 3/10\n",
      "96/96 [==============================] - 1994s 21s/step - loss: 0.4762 - accuracy: 0.6688 - val_loss: 0.5570 - val_accuracy: 0.5723\n",
      "Epoch 4/10\n",
      "96/96 [==============================] - 2059s 21s/step - loss: 0.3979 - accuracy: 0.7432 - val_loss: 0.5274 - val_accuracy: 0.6541\n",
      "Epoch 5/10\n",
      "96/96 [==============================] - 1772s 18s/step - loss: 0.3423 - accuracy: 0.7904 - val_loss: 0.5288 - val_accuracy: 0.6714\n",
      "Epoch 6/10\n",
      "96/96 [==============================] - 562s 6s/step - loss: 0.3007 - accuracy: 0.8103 - val_loss: 0.5417 - val_accuracy: 0.7044\n",
      "Epoch 7/10\n",
      "96/96 [==============================] - 559s 6s/step - loss: 0.2651 - accuracy: 0.8422 - val_loss: 0.4911 - val_accuracy: 0.7075\n",
      "Epoch 8/10\n",
      "96/96 [==============================] - 559s 6s/step - loss: 0.2492 - accuracy: 0.8616 - val_loss: 0.4864 - val_accuracy: 0.7186\n",
      "Epoch 9/10\n",
      "96/96 [==============================] - 564s 6s/step - loss: 0.2759 - accuracy: 0.8627 - val_loss: 0.5522 - val_accuracy: 0.7296\n",
      "Epoch 10/10\n",
      "96/96 [==============================] - 1134s 12s/step - loss: 0.2294 - accuracy: 0.8664 - val_loss: 0.6196 - val_accuracy: 0.7563\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x272499c9690>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train_np,y_train_hot,epochs=10,batch_size=20,validation_data=(x_test_np,y_test_hot))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5aabd293",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 420, 420, 32)      896       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 420, 420, 32)      9248      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 210, 210, 32)     0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 210, 210, 64)      18496     \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 210, 210, 64)      36928     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 105, 105, 64)     0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 705600)            0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 3)                 2116803   \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,182,371\n",
      "Trainable params: 2,182,371\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "52da5f2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "96/96 [==============================] - 1985s 21s/step - loss: 0.2296 - accuracy: 0.8810 - val_loss: 0.4986 - val_accuracy: 0.6855\n",
      "Epoch 2/10\n",
      "62/96 [==================>...........] - ETA: 9:21 - loss: 0.2081 - accuracy: 0.8911"
     ]
    },
    {
     "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[21], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_train_np\u001b[49m\u001b[43m,\u001b[49m\u001b[43my_train_hot\u001b[49m\u001b[43m,\u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx_test_np\u001b[49m\u001b[43m,\u001b[49m\u001b[43my_test_hot\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 65\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     66\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     67\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;32m~\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py:1564\u001b[0m, in \u001b[0;36mModel.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, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1556\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[0;32m   1557\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1558\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1561\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m   1562\u001b[0m ):\n\u001b[0;32m   1563\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m-> 1564\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1565\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[0;32m   1566\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\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;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\tensorflow\\python\\eager\\def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    912\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    914\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--> 915\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    917\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    918\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;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\tensorflow\\python\\eager\\def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    944\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    945\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m    946\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[1;32m--> 947\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateless_fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[0;32m    948\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn \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;32m    949\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[0;32m    950\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[0;32m    951\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\tensorflow\\python\\eager\\function.py:2496\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2493\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m   2494\u001b[0m   (graph_function,\n\u001b[0;32m   2495\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2496\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2497\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\tensorflow\\python\\eager\\function.py:1862\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1858\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1859\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   1860\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1861\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1862\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1863\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m   1864\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   1865\u001b[0m     args,\n\u001b[0;32m   1866\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1867\u001b[0m     executing_eagerly)\n\u001b[0;32m   1868\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;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\tensorflow\\python\\eager\\function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    497\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    498\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 499\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    504\u001b[0m \u001b[43m        \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    505\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    506\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m    507\u001b[0m         \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[0;32m    508\u001b[0m         num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    511\u001b[0m         ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[0;32m    512\u001b[0m         cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 54\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     55\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     56\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     57\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": [
    "model.fit(x_train_np,y_train_hot,epochs=10,batch_size=20,validation_data=(x_test_np,y_test_hot))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "244b4b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=model.predict(x_test_np,y_test_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b04c23ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78c97d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save(\"Ai242\",save_format=\"h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12839392",
   "metadata": {},
   "outputs": [],
   "source": [
    "# m=keras.models.load_model(\"Ai242.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f6013ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.predict(x_test_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df514c55",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.evaluate(x_test_np,y_test_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "014ed7ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x_test_np[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c8d88cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x_test_np.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf98560e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# kir=np.array(x_test_np[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51157990",
   "metadata": {},
   "outputs": [],
   "source": [
    "# kir.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fb85648",
   "metadata": {},
   "outputs": [],
   "source": [
    "# kir=kir.reshape([1,420,420,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b476f7ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# kir.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d49b26ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# m.predict(kir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57f75d98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ed2f8c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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