{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e37789ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from bs4 import BeautifulSoup\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import os\n",
    "from time import perf_counter\n",
    "import cv2\n",
    "import tensorflow as tf\n",
    "from random import randint "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f0f6e16",
   "metadata": {},
   "outputs": [],
   "source": [
    "images_path= 'data/images'\n",
    "anno_path= 'data/annotations'\n",
    "size=128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a8bfd197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(478, 400, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20bc162b280>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img= plt.imread(f'data/images/Cars{randint(0,400)}.png')\n",
    "print(img.shape)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f812021c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07b66c3c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bca3feab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(imgDir, annoDir, size=size):\n",
    "    start= perf_counter()\n",
    "    df={\n",
    "    'image':[],\n",
    "    'box':[]\n",
    "    }\n",
    "    images= os.listdir(images_path)   #read file names in directory\n",
    "    for image in images:\n",
    "        plate= annoDir+'/'+image[:-3]+'xml'    \n",
    "        image= imgDir+'/'+image\n",
    "        img2= plt.imread( image ) \n",
    "        #img2= cv2.cvtColor( img2, cv2.COLOR_BGR2GRAY)\n",
    "        img2= img2[:,:,0:3] #remove 4th axis from image  if not gray                         \n",
    "        \n",
    "       \n",
    "        \n",
    "        #img=cv2.resize(img, (int(size), int(size)))\n",
    "        \n",
    "        with open( plate, 'r') as f:\n",
    "            data= f.read()\n",
    "        \n",
    "        Bs = BeautifulSoup(data, \"xml\")\n",
    "        xmax = int (Bs.find('ymax').text)\n",
    "        xmin = int (Bs.find('ymin').text)\n",
    "        ymax = int (Bs.find('xmax').text)\n",
    "        ymin = int (Bs.find('xmin').text)\n",
    " \n",
    "        #\n",
    "        df['image'].append( cv2.resize(img2, (size,size) ) )\n",
    "        #print(xmin,ymin, xmax, ymax)\n",
    "        #img2= cv2.rectangle(img2,(ymin,xmin), (ymax,xmax), 10,0)\n",
    "        x= xmin\n",
    "        while x<xmax:\n",
    "            y=ymin\n",
    "            while y< ymax:\n",
    "                img2[x,y,2]=1\n",
    "                y+=1\n",
    "            x+=1\n",
    "        \n",
    "        df['box'].append(cv2.resize(img2,(size,size)))\n",
    "        \n",
    "    print(perf_counter()-start)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cfa8705",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5f809a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.8605889\n"
     ]
    }
   ],
   "source": [
    "df=get_data(images_path, anno_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f49a1309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20bc1983970>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(df['box'][125])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dc4039a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9038cd96",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78d26b99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d7f36ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "263b9ef2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b730032e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dff=pd.DataFrame(df)\n",
    "x_train= np.array(dff.image[:300].values.tolist()).astype('float32')\n",
    "y_train= np.array(dff.box[:300].values.tolist()).astype('float32')\n",
    "x_test=  np.array(dff.box[300:].values.tolist()).astype('float32')\n",
    "y_test=  np.array(dff.box[300:].values.tolist()).astype('float32')\n",
    "del dff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69185862",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3382755f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebcabb06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d90d8ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPool2D, Dropout, BatchNormalization, Activation, Conv2DTranspose, concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2e76900",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5414495b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(InputImage,dropout=0.25):\n",
    "    \n",
    "    \n",
    "    ################Encoder###########making image smaller(making convnets)\n",
    "    c1=Conv2D( 32, (3,3),kernel_initializer = 'he_normal', padding='same')(InputImage)\n",
    "    c1=BatchNormalization()(c1)\n",
    "    c1=Activation('relu')(c1)\n",
    "    c2=Activation('relu')(c1)\n",
    "    p1=MaxPool2D((2,2))(c1)\n",
    "    p1=Dropout(dropout)(p1)\n",
    "    \n",
    "    c2=Conv2D( 64, (3,3),kernel_initializer = 'he_normal', padding='same')(p1)\n",
    "    c2=BatchNormalization()(c2)\n",
    "    c2=Activation('relu')(c2)\n",
    "    p2=MaxPool2D( (2,2) )(c2)\n",
    "    p2=Dropout(dropout)(p2)\n",
    "    \n",
    "    c3=Conv2D( 128, (3,3),kernel_initializer = 'he_normal', padding='same')(p2)\n",
    "    c3=BatchNormalization()(c3)\n",
    "    c3=Activation('relu')(c3)\n",
    "    \n",
    "    \n",
    "    ################Decoder###########making small image big(img from convnets)\n",
    "    \n",
    "    u1= Conv2DTranspose(64,(3,3), strides=(2,2), padding='same')(c3)\n",
    "    u1= concatenate([u1,c2])\n",
    "    u1= Dropout(dropout)(u1)\n",
    "    c6=Conv2D( 64, (3,3), padding='same')(u1)\n",
    "    c6=BatchNormalization()(c6)\n",
    "    c6=Activation('relu')(c6)\n",
    "    \n",
    "    \n",
    "    u2= Conv2DTranspose(32,(3,3), strides=(2,2), padding='same')(c6)\n",
    "    u2= concatenate([u2,c1])\n",
    "    u2= Dropout(dropout)(u2)\n",
    "    c7=Conv2D( 32, (3,3), padding='same')(u2)\n",
    "    c7=BatchNormalization()(c7)\n",
    "    c7=Activation('relu')(c7)\n",
    "    \n",
    "    output = Conv2D(3,(1,1), activation='sigmoid')(c7)\n",
    "    \n",
    "    m= tf.keras.Model([InputImage], [output])\n",
    "    return m\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "96d5dd0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs= tf.keras.layers.Input((size,size,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "73047d25",
   "metadata": {},
   "outputs": [],
   "source": [
    "my= model(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "803cd428",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 128, 128, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 128, 128, 32) 896         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 128, 128, 32) 128         conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 128, 128, 32) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 64, 64, 32)   0           activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 64, 64, 32)   0           max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 64, 64, 64)   18496       dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 64, 64, 64)   256         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 64, 64, 64)   0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 32, 32, 64)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 32, 32, 64)   0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 32, 32, 128)  73856       dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 32, 32, 128)  512         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 32, 32, 128)  0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose (Conv2DTranspo (None, 64, 64, 64)   73792       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 64, 64, 128)  0           conv2d_transpose[0][0]           \n",
      "                                                                 activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 64, 64, 128)  0           concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 64, 64, 64)   73792       dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 64, 64, 64)   256         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 64, 64, 64)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 128, 128, 32) 18464       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 128, 128, 64) 0           conv2d_transpose_1[0][0]         \n",
      "                                                                 activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 128, 128, 64) 0           concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 128, 128, 32) 18464       dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 128, 128, 32) 128         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 128, 128, 32) 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 128, 128, 3)  99          activation_5[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 279,139\n",
      "Trainable params: 278,499\n",
      "Non-trainable params: 640\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "my.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2678dbd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "my.compile(optimizer = 'Adam', loss = 'binary_crossentropy', metrics = ['accuracy'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c79f6977",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "10/10 [==============================] - 120s 12s/step - loss: 0.6170 - accuracy: 0.3626 - val_loss: 3.4285 - val_accuracy: 0.3598\n",
      "Epoch 2/40\n",
      "10/10 [==============================] - 159s 16s/step - loss: 0.5454 - accuracy: 0.4273 - val_loss: 3.6893 - val_accuracy: 0.3808\n",
      "Epoch 3/40\n",
      "10/10 [==============================] - 85s 9s/step - loss: 0.5272 - accuracy: 0.4790 - val_loss: 2.3057 - val_accuracy: 0.3917\n",
      "Epoch 4/40\n",
      "10/10 [==============================] - 79s 8s/step - loss: 0.5185 - accuracy: 0.5169 - val_loss: 1.4349 - val_accuracy: 0.4053\n",
      "Epoch 5/40\n",
      "10/10 [==============================] - 92s 9s/step - loss: 0.5137 - accuracy: 0.5501 - val_loss: 1.0818 - val_accuracy: 0.4513\n",
      "Epoch 6/40\n",
      "10/10 [==============================] - 94s 9s/step - loss: 0.5101 - accuracy: 0.5716 - val_loss: 0.9405 - val_accuracy: 0.4641\n",
      "Epoch 7/40\n",
      "10/10 [==============================] - 101s 10s/step - loss: 0.5085 - accuracy: 0.5932 - val_loss: 0.7569 - val_accuracy: 0.5156\n",
      "Epoch 8/40\n",
      "10/10 [==============================] - 101s 10s/step - loss: 0.5062 - accuracy: 0.6065 - val_loss: 0.7242 - val_accuracy: 0.5079\n",
      "Epoch 9/40\n",
      "10/10 [==============================] - 89s 9s/step - loss: 0.5059 - accuracy: 0.6271 - val_loss: 0.6386 - val_accuracy: 0.5453\n",
      "Epoch 10/40\n",
      "10/10 [==============================] - 85s 9s/step - loss: 0.5037 - accuracy: 0.6257 - val_loss: 0.5939 - val_accuracy: 0.5683\n",
      "Epoch 11/40\n",
      "10/10 [==============================] - 161s 16s/step - loss: 0.5037 - accuracy: 0.6403 - val_loss: 0.6208 - val_accuracy: 0.5776\n",
      "Epoch 12/40\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-6d17cbbbf79d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mh\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m40\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    106\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    107\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 108\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    109\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m     \u001b[1;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\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   1096\u001b[0m                 batch_size=batch_size):\n\u001b[0;32m   1097\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1098\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1099\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1100\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    778\u001b[0m       \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m         \u001b[0mcompiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"nonXla\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 780\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    781\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    782\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    805\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    806\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 807\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    808\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    809\u001b[0m       \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2827\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2828\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2829\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2830\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2831\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[1;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1841\u001b[0m       \u001b[0;31m`\u001b[0m\u001b[0margs\u001b[0m\u001b[0;31m`\u001b[0m \u001b[1;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \"\"\"\n\u001b[1;32m-> 1843\u001b[1;33m     return self._call_flat(\n\u001b[0m\u001b[0;32m   1844\u001b[0m         [t for t in nest.flatten((args, kwargs), expand_composites=True)\n\u001b[0;32m   1845\u001b[0m          if isinstance(t, (ops.Tensor,\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1921\u001b[0m         and executing_eagerly):\n\u001b[0;32m   1922\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1923\u001b[1;33m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m   1924\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m   1925\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    543\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 545\u001b[1;33m           outputs = execute.execute(\n\u001b[0m\u001b[0;32m    546\u001b[0m               \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    547\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     57\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     61\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "h=my.fit(x_train, y_train, epochs=40, verbose=1, validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f2bed2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.subplot(121)\n",
    "plt.plot(h.history['accuracy'] )\n",
    "plt.plot(h.history['val_accuracy'])\n",
    "plt.legend(['Train Accuracy','Test Accuracy'])\n",
    "plt.subplot(122)\n",
    "plt.plot(h.history['loss'])\n",
    "plt.plot(h.history['val_loss'])\n",
    "plt.legend(['Train Loss','Test Loss'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b8d3ce6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bb43b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=df['image'][311].reshape(-1,128,128,3)\n",
    "b=df['box'][311].reshape(-1,128,128,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "155f02e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcbb5df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "b= randint(0,450)\n",
    "a=df['image'][b].reshape(-1,128,128,3)\n",
    "b=df['box'][b].reshape(-1,128,128,3)\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(131)\n",
    "plt.imshow(a.reshape(128,128,3))\n",
    "plt.subplot(132)\n",
    "plt.imshow(b.reshape(128,128,3))\n",
    "plt.subplot(133)\n",
    "plt.imshow(my.predict(a).reshape(128,128,3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68ab8d44",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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