notebook0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘/home/codespace/.kaggle’: File exists\n"
     ]
    }
   ],
   "source": [
    "! mkdir ~/.kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "! cp kaggle.json ~/.kaggle/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "! chmod 600 ~/.kaggle/kaggle.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ref                                                       title                                              size  lastUpdated          downloadCount  voteCount  usabilityRating  \n",
      "--------------------------------------------------------  ------------------------------------------------  -----  -------------------  -------------  ---------  ---------------  \n",
      "ruchi798/data-science-job-salaries                        Data Science Job Salaries                           7KB  2022-06-15 08:59:12           8410        278  1.0              \n",
      "surajjha101/bigbasket-entire-product-list-28k-datapoints  BigBasket Entire Product List (~28K datapoints)     6MB  2022-06-22 12:51:18           2943        116  1.0              \n",
      "zusmani/petrolgas-prices-worldwide                        Petrol/Gas Prices Worldwide                        10KB  2022-06-24 01:25:33           2588        122  1.0              \n",
      "ramjasmaurya/1-gb-internet-price                          Internet Prices around 200+ countries in 2022.     22KB  2022-07-17 02:56:12           1494         52  0.9705882        \n",
      "sameepvani/nasa-nearest-earth-objects                     NASA - Nearest Earth Objects                        7MB  2022-06-17 02:32:18           2768        126  1.0              \n",
      "jimschacko/airlines-dataset-to-predict-a-delay            Airlines Dataset to predict a delay                 6MB  2022-06-21 05:45:44           2514         65  1.0              \n",
      "dansbecker/melbourne-housing-snapshot                     Melbourne Housing Snapshot                        451KB  2018-06-05 12:52:24          91508       1114  0.7058824        \n",
      "mukuldeshantri/ecommerce-fashion-dataset                  E-commerce Dataset with 30K Products              546KB  2022-07-08 12:28:18            944         37  1.0              \n",
      "iamsouravbanerjee/nifty500-stocks-dataset                 Stock Market Dataset (NIFTY-500)                   35KB  2022-07-17 11:46:10            892         65  1.0              \n",
      "gabrielsantello/cars-purchase-decision-dataset            Cars - Purchase Decision Dataset                    6KB  2022-07-09 13:18:38            703         28  1.0              \n",
      "datasnaek/youtube-new                                     Trending YouTube Video Statistics                 201MB  2019-06-03 00:56:47         179667       4608  0.7941176        \n",
      "zynicide/wine-reviews                                     Wine Reviews                                       51MB  2017-11-27 17:08:04         163342       3335  0.7941176        \n",
      "datasnaek/chess                                           Chess Game Dataset (Lichess)                        3MB  2017-09-04 03:09:09          30545       1019  0.8235294        \n",
      "rtatman/188-million-us-wildfires                          1.88 Million US Wildfires                         168MB  2020-05-12 21:03:49          20755       1021  0.8235294        \n",
      "residentmario/ramen-ratings                               Ramen Ratings                                      40KB  2018-01-11 16:04:39          34875        793  0.7058824        \n",
      "dansbecker/powerlifting-database                          powerlifting-database                               9MB  2019-04-30 21:07:41           4962         61  0.5882353        \n",
      "jpmiller/publicassistance                                 US Public Food Assistance                         703KB  2020-08-21 16:51:18          16664        396  0.9117647        \n",
      "nasa/kepler-exoplanet-search-results                      Kepler Exoplanet Search Results                     1MB  2017-10-10 18:26:59          10658        665  0.8235294        \n",
      "residentmario/things-on-reddit                            Things on Reddit                                   16MB  2017-10-26 14:10:15           8693        217  0.5882353        \n",
      "nolanbconaway/pitchfork-data                              18,393 Pitchfork Reviews                           33MB  2017-01-13 04:18:10          10600        371  0.7058824        \n"
     ]
    }
   ],
   "source": [
    "! kaggle datasets list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-31 16:11:36.928547: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-07-31 16:11:36.928576: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import os\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout \n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "\n",
    "import cv2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import seaborn as sns\n",
    "\n",
    "from PIL import Image\n",
    "from IPython.display import Image, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil\n",
    "\n",
    "src_root= '/workspaces/icecream-streamlit-combo/inputimages/'\n",
    "dest_train = '/workspaces/icecream-streamlit-combo/projdata/train'\n",
    "dest_test = '/workspaces/icecream-streamlit-combo/projdata/testss'\n",
    "\n",
    "def copy_images_to_work(brand, cnt):\n",
    "    \n",
    "    files = os.listdir(src_root+'/'+brand +'/images')\n",
    "    i=0\n",
    "    for f in files:\n",
    "        \n",
    "        src = src_root+'/'+brand +'/images/'+f\n",
    "        i = i + 1\n",
    "    \n",
    "        if i <= cnt :\n",
    "            \n",
    "            des = dest_train+'/'+brand+'/'+f\n",
    "     \n",
    "            if os.path.isfile(des) != True:\n",
    "                shutil.copyfile(src, des)\n",
    "                print(des+ '-> copied')\n",
    "            else:\n",
    "                print(des+' -> exists')\n",
    "                \n",
    "        else:\n",
    "            \n",
    "            des = dest_test+'/'+brand+'/'+f\n",
    "     \n",
    "            if os.path.isfile(des) != True:\n",
    "                shutil.copyfile(src, des)  \n",
    "                print(des+ '-> copied')\n",
    "            else:\n",
    "                print(des+' -> exists')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/12_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/47_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/2_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/27_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/36_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/22_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/54_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/44_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/51_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/48_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/26_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/40_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/38_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/30_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/28_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/11_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/29_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/55_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/37_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/17_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/56_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/5_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/0_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/42_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/14_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/13_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/15_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/45_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/25_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/43_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/6_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/23_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/7_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/16_bj.png-> copied\n",
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      "/workspaces/icecream-streamlit-combo/projdata/train/bj/31_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/4_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/33_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/46_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/24_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/bj/3_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/bj/19_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/bj/8_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/bj/52_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/bj/21_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/bj/32_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/bj/39_bj.png-> copied\n",
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      "/workspaces/icecream-streamlit-combo/projdata/testss/bj/41_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/bj/34_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/bj/35_bj.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/breyers/65_breyers.png-> copied\n",
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      "/workspaces/icecream-streamlit-combo/projdata/train/breyers/40_breyers.png-> copied\n",
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      "/workspaces/icecream-streamlit-combo/projdata/train/breyers/36_breyers.png-> copied\n",
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      "/workspaces/icecream-streamlit-combo/projdata/testss/breyers/50_breyers.png-> copied\n",
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      "/workspaces/icecream-streamlit-combo/projdata/train/hd/52_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/11_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/41_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/15_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/13_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/60_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/6_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/29_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/65_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/68_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/67_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/54_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/32_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/16_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/24_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/46_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/62_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/51_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/45_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/42_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/hd/17_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/4_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/20_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/33_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/44_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/37_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/48_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/30_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/26_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/0_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/43_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/61_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/12_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/23_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/53_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/19_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/55_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/22_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/57_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/hd/27_hd.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/29_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/20_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/19_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/1_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/39_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/36_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/9_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/6_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/12_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/37_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/4_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/27_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/40_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/22_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/26_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/3_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/16_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/25_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/44_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/17_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/15_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/8_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/30_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/2_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/35_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/11_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/21_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/31_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/0_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/5_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/34_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/23_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/38_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/42_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/41_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/28_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/14_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/32_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/13_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/33_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/10_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/7_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/43_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/18_talenti.png-> copied\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/24_talenti.png-> copied\n"
     ]
    }
   ],
   "source": [
    "copy_images_to_work('bj',41)\n",
    "copy_images_to_work('breyers',51)\n",
    "copy_images_to_work('hd',51)\n",
    "copy_images_to_work('talenti',31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "LABELS = ['bj','breyers','hd','talenti']\n",
    "IMG_SIZE = 32\n",
    "\n",
    "IMG_HEIGHT = IMG_SIZE\n",
    "IMG_WIDTH  = IMG_SIZE\n",
    "\n",
    "import cv2\n",
    "\n",
    "import cv2\n",
    "def get_data(data_dir):\n",
    "    data = []\n",
    "    clas = []\n",
    "    for label in LABELS: \n",
    "        path = os.path.join(data_dir, label)\n",
    "      \n",
    "        class_num = LABELS.index(label)\n",
    "        for img in os.listdir(path):\n",
    "            try:\n",
    "                img_arr = cv2.imread(os.path.join(path, img))[...,::-1] #convert BGR to RGB format\n",
    "                resized_arr = cv2.resize(img_arr, (IMG_HEIGHT, IMG_WIDTH)) # Reshaping images to preferred size\n",
    "                data.append(resized_arr)\n",
    "                clas.append(class_num)\n",
    "            except Exception as e:\n",
    "                print(e)\n",
    "    #return np.array(data,dtype = \"object\")\n",
    "    return data,clas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "libpng warning: iCCP: known incorrect sRGB profile\n"
     ]
    }
   ],
   "source": [
    "img_train,clas_train = get_data('/workspaces/icecream-streamlit-combo/projdata/train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_test,clas_test = get_data('/workspaces/icecream-streamlit-combo/projdata/testss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data = np.asarray(img_train[10])\n",
    "print(data.shape)\n",
    "imgplot = plt.imshow(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_train  = np.asarray(img_train)\n",
    "clas_train = np.asarray(clas_train)\n",
    "img_test   = np.asarray(img_test)\n",
    "clas_test  = np.asarray(clas_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_train = img_train / 255.0\n",
    "img_test = img_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "print(img_train.dtype)\n",
    "print(img_test.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-31 16:16:20.126948: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/python/3.10.4/lib/python3.10/site-packages/cv2/../../lib64:\n",
      "2022-07-31 16:16:20.126978: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-07-31 16:16:20.126999: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (codespaces-3b58c1): /proc/driver/nvidia/version does not exist\n",
      "2022-07-31 16:16:20.127380: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "6/6 [==============================] - 1s 33ms/step - loss: 2.5071 - accuracy: 0.3908 - val_loss: 1.8944 - val_accuracy: 0.5385\n",
      "Epoch 2/10\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 0.7104 - accuracy: 0.7816 - val_loss: 0.4730 - val_accuracy: 0.8154\n",
      "Epoch 3/10\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 0.2279 - accuracy: 0.8966 - val_loss: 0.2734 - val_accuracy: 0.8923\n",
      "Epoch 4/10\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 0.1149 - accuracy: 0.9540 - val_loss: 0.2593 - val_accuracy: 0.9385\n",
      "Epoch 5/10\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 0.0261 - accuracy: 0.9885 - val_loss: 0.2758 - val_accuracy: 0.9385\n",
      "Epoch 6/10\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 0.0217 - accuracy: 0.9943 - val_loss: 0.2295 - val_accuracy: 0.9385\n",
      "Epoch 7/10\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.2168 - val_accuracy: 0.9385\n",
      "Epoch 8/10\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.2174 - val_accuracy: 0.9385\n",
      "Epoch 9/10\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.2122 - val_accuracy: 0.9385\n",
      "Epoch 10/10\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.2077 - val_accuracy: 0.9385\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def make_model():\n",
    "      model = keras.Sequential([\n",
    "         keras.layers.Flatten(input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)),\n",
    "         keras.layers.Dense(512, activation='relu'),\n",
    "         keras.layers.Dense(4)\n",
    "      ])\n",
    "      model.compile(optimizer='adam',\n",
    "                   loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "                   metrics=['accuracy'])\n",
    "      return model\n",
    "\n",
    "model=make_model()\n",
    "#history = model.fit(istrain, isclas, epochs=10)\n",
    "history = model.fit(img_train,clas_train,epochs = 10 , validation_data = (img_test, clas_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs_range = range(10)\n",
    "\n",
    "plt.figure(figsize=(15, 15))\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(epochs_range, loss, label='Training Loss')\n",
    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /workspaces/icecream-streamlit-combo/modelz/icecream/assets\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "modelname = '/workspaces/icecream-streamlit-combo/modelz/icecream'\n",
    "#model.save(modelname,save_format='h5')\n",
    "model.save(modelname)\n",
    "print('done')"
   ]
  }
 ],
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