notebook1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-25 03:13:52.360852: 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",
      "2022-07-25 03:13:52.360891: 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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "/workspaces/icecream-streamlit-combo/projdata/train/bj/2_bj.png -> exists\n",
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      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/39_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/36_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/9_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/6_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/12_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/37_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/4_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/27_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/40_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/22_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/26_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/3_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/16_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/25_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/44_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/17_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/15_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/8_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/30_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/2_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/35_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/11_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/21_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/31_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/0_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/5_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/train/talenti/34_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/23_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/38_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/42_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/41_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/28_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/14_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/32_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/13_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/33_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/10_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/7_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/43_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/18_talenti.png -> exists\n",
      "/workspaces/icecream-streamlit-combo/projdata/testss/talenti/24_talenti.png -> exists\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": 4,
   "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": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_test,clas_test = get_data('/workspaces/icecream-streamlit-combo/projdata/testss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_train = img_train / 255.0\n",
    "img_test = img_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-25 03:14:06.863263: 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",
      "2022-07-25 03:14:06.863308: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-07-25 03:14:06.863338: 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",
      "2022-07-25 03:14:06.863885: 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": [
      "6/6 [==============================] - 2s 39ms/step - loss: 3.2477 - accuracy: 0.5172 - val_loss: 2.3761 - val_accuracy: 0.6154\n",
      "Epoch 2/10\n",
      "6/6 [==============================] - 0s 11ms/step - loss: 1.3492 - accuracy: 0.7356 - val_loss: 1.0552 - val_accuracy: 0.5538\n",
      "Epoch 3/10\n",
      "6/6 [==============================] - 0s 10ms/step - loss: 0.5751 - accuracy: 0.8103 - val_loss: 0.8002 - val_accuracy: 0.7538\n",
      "Epoch 4/10\n",
      "6/6 [==============================] - 0s 10ms/step - loss: 0.3169 - accuracy: 0.8908 - val_loss: 0.2316 - val_accuracy: 0.9538\n",
      "Epoch 5/10\n",
      "6/6 [==============================] - 0s 10ms/step - loss: 0.0576 - accuracy: 0.9770 - val_loss: 0.1441 - val_accuracy: 0.9538\n",
      "Epoch 6/10\n",
      "6/6 [==============================] - 0s 10ms/step - loss: 0.0271 - accuracy: 1.0000 - val_loss: 0.1408 - val_accuracy: 0.9692\n",
      "Epoch 7/10\n",
      "6/6 [==============================] - 0s 11ms/step - loss: 0.0184 - accuracy: 0.9943 - val_loss: 0.1173 - val_accuracy: 0.9692\n",
      "Epoch 8/10\n",
      "6/6 [==============================] - 0s 11ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.1283 - val_accuracy: 0.9692\n",
      "Epoch 9/10\n",
      "6/6 [==============================] - 0s 11ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.1577 - val_accuracy: 0.9538\n",
      "Epoch 10/10\n",
      "6/6 [==============================] - 0s 10ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.1693 - val_accuracy: 0.9538\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": 12,
   "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": 13,
   "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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