CNN training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "262c8ee3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import tensorflow\n",
    "from tensorflow import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv2D, Flatten, MaxPool2D\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dab73538",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(64, kernel_size=3, activation='relu', input_shape=(500,500,1)))\n",
    "model.add(MaxPool2D( pool_size=(2, 2), strides=None, padding='valid', data_format=None))\n",
    "model.add(Conv2D(64, kernel_size=3, activation='relu'))\n",
    "model.add(MaxPool2D( pool_size=(2, 2), strides=None, padding='valid', data_format=None))\n",
    "model.add(Conv2D(32, kernel_size=3, activation='relu'))\n",
    "model.add(MaxPool2D( pool_size=(2, 2), strides=None, padding='valid', data_format=None))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(100, activation='sigmoid'))\n",
    "model.add(Dense(50, activation='sigmoid'))\n",
    "model.add(Dense(20, activation='sigmoid'))\n",
    "model.add(Dense(4, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "815c684c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7240ebfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6607"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "path = r\".\\extracted_data\" \n",
    "# os.chdir(path)\n",
    "DATA_PATHS =list(os.walk(path)) \n",
    "len(DATA_PATHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4cd000e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(file ):\n",
    "  f = open(file,encoding=\"utf8\")\n",
    "  data= json.load(f)\n",
    "  f.close()\n",
    "  return data\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e7ba769",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150693\n",
      "150693\n",
      "'.\\\\extracted_data\\\\acl17\\\\103\\\\slide(1)_img(1).jpg'\n",
      "('0.1552734375', '0.4027777777777778', '0.6201171875', '0.546875')\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "images =[]\n",
    "sizes = []\n",
    "for dir, subdirs, files in os.walk(path):\n",
    "    #print(dir,'\\n=====\\n', subdirs, '\\n======\\n', files )\n",
    "    for file in files:\n",
    "        if file.endswith('.json'):\n",
    "            json_path= '\\\\'.join([dir , file])\n",
    "            data =  get_data(json_path)\n",
    "            keys = data.keys()\n",
    "#             print(keys)\n",
    "            for image in keys :\n",
    "#                 print(image)\n",
    "                dimensions = data[image]\n",
    "                image_full_path = '\\\\'.join([dir, image])\n",
    "                images.append(image_full_path)\n",
    "                sizes.append((dimensions['startx'], dimensions['starty'], dimensions['width'], dimensions['hight']))\n",
    "                \n",
    "#                 break\n",
    "#         break\n",
    "#     break\n",
    "            \n",
    "pprint(len(images))\n",
    "pprint(len(sizes))\n",
    "pprint(images[0])\n",
    "pprint(sizes[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3d9c6d05",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('.\\\\extracted_data\\\\acl17\\\\103\\\\slide(1)_img(1).jpg', ('0.1552734375', '0.4027777777777778', '0.6201171875', '0.546875'))\n",
      "('.\\\\extracted_data\\\\cvpr20\\\\1121\\\\slide(7)_img(1).jpg', ('0.7216796875', '0.6736111111111112', '0.1552734375', '0.2795138888888889'))\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "random.seed(0)\n",
    "data = list(zip(images,sizes))\n",
    "print(data[0])\n",
    "random.shuffle(data)\n",
    "print(data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "35309b6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[:100000]\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac6e968e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "be59e43a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shapes \n",
      " train im = (800,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\numpy\\core\\_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-4b237febde92>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     26\u001b[0m     \u001b[0mtest_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m     \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34mf\"shapes \\n train im = {train_images.shape}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_images\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_images\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\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   1106\u001b[0m          \u001b[0mtraining_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRespectCompiledTrainableState\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   1107\u001b[0m       \u001b[1;31m# Creates a `tf.data.Dataset` and handles batch and epoch iteration.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1108\u001b[1;33m       data_handler = data_adapter.get_data_handler(\n\u001b[0m\u001b[0;32m   1109\u001b[0m           \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1110\u001b[0m           \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36mget_data_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1346\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"model\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"_cluster_coordinator\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\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   1347\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_ClusterCoordinatorDataHandler\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[1;32m-> 1348\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0mDataHandler\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   1349\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1350\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution, distribute)\u001b[0m\n\u001b[0;32m   1136\u001b[0m     \u001b[0madapter_cls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mselect_data_adapter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1137\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_verify_data_adapter_compatibility\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madapter_cls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1138\u001b[1;33m     self._adapter = adapter_cls(\n\u001b[0m\u001b[0;32m   1139\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1140\u001b[0m         \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)\u001b[0m\n\u001b[0;32m    229\u001b[0m                **kwargs):\n\u001b[0;32m    230\u001b[0m     \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mTensorLikeDataAdapter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\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[1;32m--> 231\u001b[1;33m     \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_tensorlike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weights\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[0;32m    232\u001b[0m     sample_weight_modes = broadcast_sample_weight_modes(\n\u001b[0;32m    233\u001b[0m         sample_weights, sample_weight_modes)\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36m_process_tensorlike\u001b[1;34m(inputs)\u001b[0m\n\u001b[0;32m   1028\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1029\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1030\u001b[1;33m   \u001b[0minputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_convert_numpy_and_scipy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\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   1031\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__internal__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlist_to_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1032\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tensorflow\\python\\util\\nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[1;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[0;32m    865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    866\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 867\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\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[0;32m    868\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    869\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tensorflow\\python\\util\\nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    866\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 867\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\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[0;32m    868\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    869\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36m_convert_numpy_and_scipy\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1023\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0missubclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloating\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   1024\u001b[0m         \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbackend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloatx\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-> 1025\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\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   1026\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0m_is_scipy_sparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\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   1027\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0m_scipy_sparse_to_sparse_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    204\u001b[0m     \u001b[1;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\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[1;32m--> 206\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mtarget\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    207\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\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    208\u001b[0m       \u001b[1;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor_v2_with_dispatch\u001b[1;34m(value, dtype, dtype_hint, name)\u001b[0m\n\u001b[0;32m   1428\u001b[0m     \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mIf\u001b[0m \u001b[0mthe\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;31m`\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0ma\u001b[0m \u001b[0mtensor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mof\u001b[0m \u001b[0mgiven\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;31m`\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgraph\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1429\u001b[0m   \"\"\"\n\u001b[1;32m-> 1430\u001b[1;33m   return convert_to_tensor_v2(\n\u001b[0m\u001b[0;32m   1431\u001b[0m       value, dtype=dtype, dtype_hint=dtype_hint, name=name)\n\u001b[0;32m   1432\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor_v2\u001b[1;34m(value, dtype, dtype_hint, name)\u001b[0m\n\u001b[0;32m   1434\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mconvert_to_tensor_v2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype_hint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\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   1435\u001b[0m   \u001b[1;34m\"\"\"Converts the given `value` to a `Tensor`.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1436\u001b[1;33m   return convert_to_tensor(\n\u001b[0m\u001b[0;32m   1437\u001b[0m       \u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1438\u001b[0m       \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tensorflow\\python\\profiler\\trace.py\u001b[0m in \u001b[0;36mwrapped\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    161\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mTrace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrace_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mtrace_kwargs\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    162\u001b[0m           \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\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[1;32m--> 163\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\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    164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[1;34m(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)\u001b[0m\n\u001b[0;32m   1564\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1565\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mret\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-> 1566\u001b[1;33m       \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mas_ref\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   1567\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1568\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tensorflow\\python\\framework\\tensor_conversion_registry.py\u001b[0m in \u001b[0;36m_default_conversion_function\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m     50\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_default_conversion_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mas_ref\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     51\u001b[0m   \u001b[1;32mdel\u001b[0m \u001b[0mas_ref\u001b[0m  \u001b[1;31m# Unused.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 52\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0mconstant_op\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconstant\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\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[0m\n\u001b[0m\u001b[0;32m     53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py\u001b[0m in \u001b[0;36mconstant\u001b[1;34m(value, dtype, shape, name)\u001b[0m\n\u001b[0;32m    262\u001b[0m     \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcalled\u001b[0m \u001b[0mon\u001b[0m \u001b[0ma\u001b[0m \u001b[0msymbolic\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    263\u001b[0m   \"\"\"\n\u001b[1;32m--> 264\u001b[1;33m   return _constant_impl(value, dtype, shape, name, verify_shape=False,\n\u001b[0m\u001b[0;32m    265\u001b[0m                         allow_broadcast=True)\n\u001b[0;32m    266\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py\u001b[0m in \u001b[0;36m_constant_impl\u001b[1;34m(value, dtype, shape, name, verify_shape, allow_broadcast)\u001b[0m\n\u001b[0;32m    274\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"tf.constant\"\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    275\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 276\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverify_shape\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    277\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m   \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_graph\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;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py\u001b[0m in \u001b[0;36m_constant_eager_impl\u001b[1;34m(ctx, value, dtype, shape, verify_shape)\u001b[0m\n\u001b[0;32m    299\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverify_shape\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    300\u001b[0m   \u001b[1;34m\"\"\"Implementation of eager constant.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 301\u001b[1;33m   \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_to_eager_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\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    302\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mshape\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[0;32m    303\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\master\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[1;34m(value, ctx, dtype)\u001b[0m\n\u001b[0;32m     96\u001b[0m       \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\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---> 98\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\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     99\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray)."
     ]
    }
   ],
   "source": [
    "index_sample_of_data = int(0.01 * len(data))\n",
    "for i in range(0 , len(data),index_sample_of_data ):\n",
    "    _set =data[i : i+ index_sample_of_data]\n",
    "    index = int(0.8 * len(_set))\n",
    "    train_set = _set[:index]\n",
    "    test_set = _set[index:]\n",
    "    train_images = []\n",
    "    train_labels = []\n",
    "    test_images = []\n",
    "    test_labels = []\n",
    "    count =0\n",
    "    for image_path, size in train_set: \n",
    "        count+=1\n",
    "        if count%1000 == 0: print(f\"loaded train images count = {count}..\")\n",
    "        train_images.append(plt.imread(image_path))\n",
    "        train_labels.append(size)\n",
    "    count = 0\n",
    "    for image_path, size in test_set:\n",
    "        count+=1\n",
    "        if count%1000 == 0: print(f\"test count = {count}..\")\n",
    "        test_images.append(plt.imread(image_path))\n",
    "        test_labels.append(size)\n",
    "    train_images = np.asarray(train_images)\n",
    "    train_labels = np.asarray(train_labels)\n",
    "    test_images = np.asarray(test_images)\n",
    "    test_labels = np.asarray(test_labels)\n",
    "    print (f\"shapes \\n train im = {train_images.shape, train_labels.shape}\")\n",
    "    model.fit(train_images, train_labels, validation_data=(test_images, test_labels), epochs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e11782f",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Num GPUs Available: \", len(tensorflow.config.list_physical_devices('CPU')))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b060a5c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = int(0.8 * len(data))\n",
    "train_set = data[:index]\n",
    "test_set = data[index:]"
   ]
  }
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