{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "572dc7fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.image import imread\n",
    "import pandas as pd\n",
    "import json\n",
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "from os.path import exists\n",
    "from PIL import ImageFile\n",
    "#import sklearn as sk\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten, Activation\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "# custom modules\n",
    "import image_faults\n",
    "\n",
    "ImageFile.LOAD_TRUNCATED_IMAGES = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5c72863",
   "metadata": {},
   "outputs": [],
   "source": [
    "# image_faults.faulty_images() # removes faulty images\n",
    "df = pd.read_csv('expanded_class.csv', index_col=[0], low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1057a442",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{source:target} dictionary created @ /tf/training_images\n"
     ]
    }
   ],
   "source": [
    "def dict_pics():\n",
    "    target_dir = os.getcwd() + os.sep + \"training_images\"\n",
    "    with open('temp_pics_source_list.txt') as f:\n",
    "        temp_pics_source_list = json.load(f)\n",
    "    dict_pics = {k:target_dir + os.sep + re.search(r'[^/]+(?=/\\$_|.jpg)', k, re.IGNORECASE).group() + '.jpg' for k in temp_pics_source_list}\n",
    "    print(\"{source:target} dictionary created @ \" + target_dir)\n",
    "    return dict_pics\n",
    "\n",
    "dict_pics = dict_pics()\n",
    "blah = pd.Series(df.PictureURL)\n",
    "df = df.drop(labels=['PictureURL'], axis=1)\n",
    "blah = blah.apply(lambda x: dict_pics[x])\n",
    "df = pd.concat([blah, df],axis=1)\n",
    "df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers\n",
    "# removes non-existent image paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a6146e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
    "\n",
    "df=df.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d72eb90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 6217 validated image filenames belonging to 13 classes.\n",
      "Found 2664 validated image filenames belonging to 13 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py:279: UserWarning: Found 1 invalid image filename(s) in x_col=\"PictureURL\". These filename(s) will be ignored.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "datagen = ImageDataGenerator(rescale=1./255., \n",
    "                             validation_split=.3,\n",
    "                             #featurewise_std_normalization=True,\n",
    "                             #horizontal_flip= True,\n",
    "                             #vertical_flip= True,\n",
    "                             #width_shift_range= 0.2,\n",
    "                             #height_shift_range= 0.2,\n",
    "                             #rotation_range= 180,\n",
    "                             preprocessing_function=tf.keras.applications.vgg16.preprocess_input)\n",
    "train_generator=datagen.flow_from_dataframe(\n",
    "    dataframe=df[:len(df)],\n",
    "    directory='./training_images',\n",
    "    x_col='PictureURL',\n",
    "    y_col='PrimaryCategoryID',\n",
    "    batch_size=32,\n",
    "    seed=42,\n",
    "    shuffle=True,\n",
    "    target_size=(224,224),\n",
    "    subset='training'\n",
    "    )\n",
    "validation_generator=datagen.flow_from_dataframe(\n",
    "    dataframe=df[:len(df)],\n",
    "    directory='./training_images',\n",
    "    x_col='PictureURL',\n",
    "    y_col='PrimaryCategoryID',\n",
    "    batch_size=32,\n",
    "    seed=42,\n",
    "    shuffle=True,\n",
    "    target_size=(224,224),\n",
    "    subset='validation'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b70f37f",
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs, labels = next(train_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ed54bf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotImages(images_arr):\n",
    "    fig, axes = plt.subplots(1, 10, figsize=(20,20))\n",
    "    axes = axes.flatten()\n",
    "    for img, ax in zip( images_arr, axes):\n",
    "        ax.imshow(img)\n",
    "        ax.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "85934565",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plotImages(imgs)\n",
    "#print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6322bcad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "physical_devices = tf.config.list_physical_devices('GPU')\n",
    "print(len(physical_devices))\n",
    "tf.config.experimental.set_memory_growth(physical_devices[0], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "07fd25c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# see https://www.kaggle.com/dmitrypukhov/cnn-with-imagedatagenerator-flow-from-dataframe for train/test/val split \n",
    "# example\n",
    "\n",
    "# may need to either create a test dataset from the original dataset or just download a new one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b31af79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "vgg16_model = tf.keras.applications.vgg16.VGG16(weights='imagenet')\n",
    "#weights='imagenet'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fe06f2bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "for layer in vgg16_model.layers[:-1]:\n",
    "    model.add(layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7d3cc82c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in model.layers:\n",
    "    layer.trainable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ea620129",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(units=13, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c774d787",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fd5d1246",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9cd2ba27",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "195/195 - 83s - loss: 2.5928 - accuracy: 0.1139 - val_loss: 2.3657 - val_accuracy: 0.1674 - 83s/epoch - 426ms/step\n",
      "Epoch 2/100\n",
      "195/195 - 77s - loss: 2.1473 - accuracy: 0.2582 - val_loss: 1.9276 - val_accuracy: 0.3281 - 77s/epoch - 394ms/step\n",
      "Epoch 3/100\n",
      "195/195 - 78s - loss: 1.7234 - accuracy: 0.3973 - val_loss: 1.6724 - val_accuracy: 0.4050 - 78s/epoch - 400ms/step\n",
      "Epoch 4/100\n",
      "195/195 - 78s - loss: 1.4692 - accuracy: 0.4843 - val_loss: 1.5583 - val_accuracy: 0.4662 - 78s/epoch - 402ms/step\n",
      "Epoch 5/100\n",
      "195/195 - 79s - loss: 1.2598 - accuracy: 0.5477 - val_loss: 1.5135 - val_accuracy: 0.4944 - 79s/epoch - 403ms/step\n",
      "Epoch 6/100\n",
      "195/195 - 79s - loss: 1.0220 - accuracy: 0.6376 - val_loss: 1.5566 - val_accuracy: 0.4962 - 79s/epoch - 404ms/step\n",
      "Epoch 7/100\n",
      "195/195 - 78s - loss: 0.8021 - accuracy: 0.7084 - val_loss: 1.7647 - val_accuracy: 0.4711 - 78s/epoch - 398ms/step\n",
      "Epoch 8/100\n",
      "195/195 - 78s - loss: 0.5998 - accuracy: 0.7804 - val_loss: 1.8439 - val_accuracy: 0.4869 - 78s/epoch - 400ms/step\n",
      "Epoch 9/100\n",
      "195/195 - 77s - loss: 0.3910 - accuracy: 0.8631 - val_loss: 2.1197 - val_accuracy: 0.4872 - 77s/epoch - 397ms/step\n",
      "Epoch 10/100\n",
      "195/195 - 78s - loss: 0.2600 - accuracy: 0.9094 - val_loss: 2.3586 - val_accuracy: 0.4703 - 78s/epoch - 402ms/step\n",
      "Epoch 11/100\n",
      "195/195 - 77s - loss: 0.2066 - accuracy: 0.9279 - val_loss: 2.5012 - val_accuracy: 0.4632 - 77s/epoch - 397ms/step\n",
      "Epoch 12/100\n",
      "195/195 - 77s - loss: 0.1266 - accuracy: 0.9571 - val_loss: 2.5961 - val_accuracy: 0.4854 - 77s/epoch - 395ms/step\n",
      "Epoch 13/100\n",
      "195/195 - 77s - loss: 0.1098 - accuracy: 0.9648 - val_loss: 2.9123 - val_accuracy: 0.4602 - 77s/epoch - 395ms/step\n",
      "Epoch 14/100\n",
      "195/195 - 77s - loss: 0.0794 - accuracy: 0.9744 - val_loss: 2.9060 - val_accuracy: 0.4752 - 77s/epoch - 395ms/step\n",
      "Epoch 15/100\n",
      "195/195 - 77s - loss: 0.1061 - accuracy: 0.9656 - val_loss: 2.7269 - val_accuracy: 0.4658 - 77s/epoch - 396ms/step\n",
      "Epoch 16/100\n",
      "195/195 - 77s - loss: 0.0908 - accuracy: 0.9712 - val_loss: 2.9450 - val_accuracy: 0.4621 - 77s/epoch - 396ms/step\n",
      "Epoch 17/100\n",
      "195/195 - 77s - loss: 0.0820 - accuracy: 0.9736 - val_loss: 2.8900 - val_accuracy: 0.4703 - 77s/epoch - 396ms/step\n",
      "Epoch 18/100\n",
      "195/195 - 77s - loss: 0.0827 - accuracy: 0.9722 - val_loss: 2.9304 - val_accuracy: 0.4745 - 77s/epoch - 395ms/step\n",
      "Epoch 19/100\n",
      "195/195 - 77s - loss: 0.0726 - accuracy: 0.9757 - val_loss: 3.2673 - val_accuracy: 0.4580 - 77s/epoch - 393ms/step\n",
      "Epoch 20/100\n",
      "195/195 - 77s - loss: 0.0560 - accuracy: 0.9821 - val_loss: 3.0101 - val_accuracy: 0.4670 - 77s/epoch - 394ms/step\n",
      "Epoch 21/100\n",
      "195/195 - 77s - loss: 0.0355 - accuracy: 0.9907 - val_loss: 3.2575 - val_accuracy: 0.4568 - 77s/epoch - 395ms/step\n",
      "Epoch 22/100\n",
      "195/195 - 78s - loss: 0.0950 - accuracy: 0.9693 - val_loss: 3.0716 - val_accuracy: 0.4568 - 78s/epoch - 399ms/step\n",
      "Epoch 23/100\n",
      "195/195 - 78s - loss: 0.0307 - accuracy: 0.9910 - val_loss: 3.6348 - val_accuracy: 0.4741 - 78s/epoch - 401ms/step\n",
      "Epoch 24/100\n",
      "195/195 - 77s - loss: 0.0315 - accuracy: 0.9903 - val_loss: 3.2422 - val_accuracy: 0.4711 - 77s/epoch - 396ms/step\n",
      "Epoch 25/100\n",
      "195/195 - 78s - loss: 0.0640 - accuracy: 0.9789 - val_loss: 3.5402 - val_accuracy: 0.4298 - 78s/epoch - 402ms/step\n",
      "Epoch 26/100\n",
      "195/195 - 78s - loss: 0.0916 - accuracy: 0.9715 - val_loss: 3.1916 - val_accuracy: 0.4520 - 78s/epoch - 399ms/step\n",
      "Epoch 27/100\n",
      "195/195 - 77s - loss: 0.0634 - accuracy: 0.9799 - val_loss: 3.2234 - val_accuracy: 0.4602 - 77s/epoch - 394ms/step\n",
      "Epoch 28/100\n",
      "195/195 - 78s - loss: 0.0673 - accuracy: 0.9788 - val_loss: 3.4435 - val_accuracy: 0.4647 - 78s/epoch - 400ms/step\n",
      "Epoch 29/100\n",
      "195/195 - 78s - loss: 0.0440 - accuracy: 0.9870 - val_loss: 3.3068 - val_accuracy: 0.4598 - 78s/epoch - 400ms/step\n",
      "Epoch 30/100\n",
      "195/195 - 78s - loss: 0.0165 - accuracy: 0.9950 - val_loss: 3.6233 - val_accuracy: 0.4681 - 78s/epoch - 401ms/step\n",
      "Epoch 31/100\n",
      "195/195 - 79s - loss: 0.0303 - accuracy: 0.9910 - val_loss: 3.7398 - val_accuracy: 0.4572 - 79s/epoch - 403ms/step\n",
      "Epoch 32/100\n",
      "195/195 - 78s - loss: 0.0554 - accuracy: 0.9815 - val_loss: 3.5560 - val_accuracy: 0.4369 - 78s/epoch - 399ms/step\n",
      "Epoch 33/100\n",
      "195/195 - 78s - loss: 0.0556 - accuracy: 0.9829 - val_loss: 3.6555 - val_accuracy: 0.4610 - 78s/epoch - 401ms/step\n",
      "Epoch 34/100\n",
      "195/195 - 78s - loss: 0.0624 - accuracy: 0.9809 - val_loss: 3.3500 - val_accuracy: 0.4617 - 78s/epoch - 401ms/step\n",
      "Epoch 35/100\n",
      "195/195 - 77s - loss: 0.0367 - accuracy: 0.9886 - val_loss: 3.5968 - val_accuracy: 0.4625 - 77s/epoch - 394ms/step\n",
      "Epoch 36/100\n",
      "195/195 - 78s - loss: 0.0301 - accuracy: 0.9912 - val_loss: 3.5188 - val_accuracy: 0.4643 - 78s/epoch - 399ms/step\n",
      "Epoch 37/100\n",
      "195/195 - 79s - loss: 0.0491 - accuracy: 0.9850 - val_loss: 3.3079 - val_accuracy: 0.4471 - 79s/epoch - 403ms/step\n",
      "Epoch 38/100\n",
      "195/195 - 78s - loss: 0.0577 - accuracy: 0.9821 - val_loss: 3.4042 - val_accuracy: 0.4381 - 78s/epoch - 400ms/step\n",
      "Epoch 39/100\n",
      "195/195 - 78s - loss: 0.0431 - accuracy: 0.9878 - val_loss: 3.4389 - val_accuracy: 0.4550 - 78s/epoch - 399ms/step\n",
      "Epoch 40/100\n",
      "195/195 - 77s - loss: 0.0218 - accuracy: 0.9940 - val_loss: 3.1989 - val_accuracy: 0.4854 - 77s/epoch - 397ms/step\n",
      "Epoch 41/100\n",
      "195/195 - 77s - loss: 0.0296 - accuracy: 0.9921 - val_loss: 3.2759 - val_accuracy: 0.4651 - 77s/epoch - 394ms/step\n",
      "Epoch 42/100\n",
      "195/195 - 78s - loss: 0.0176 - accuracy: 0.9947 - val_loss: 3.2391 - val_accuracy: 0.4745 - 78s/epoch - 398ms/step\n",
      "Epoch 43/100\n",
      "195/195 - 79s - loss: 0.0099 - accuracy: 0.9965 - val_loss: 3.5696 - val_accuracy: 0.4553 - 79s/epoch - 405ms/step\n",
      "Epoch 44/100\n",
      "195/195 - 78s - loss: 0.0516 - accuracy: 0.9852 - val_loss: 3.3857 - val_accuracy: 0.4482 - 78s/epoch - 402ms/step\n",
      "Epoch 45/100\n",
      "195/195 - 79s - loss: 0.0412 - accuracy: 0.9860 - val_loss: 3.3717 - val_accuracy: 0.4580 - 79s/epoch - 404ms/step\n",
      "Epoch 46/100\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-1a4715fb06ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m model.fit(x=train_generator,\n\u001b[0m\u001b[1;32m      2\u001b[0m           \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m           \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m           \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m           \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;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[1;32m   1214\u001b[0m                 _r=1):\n\u001b[1;32m   1215\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1217\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1218\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 910\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    940\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    941\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    943\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3128\u001b[0m       (graph_function,\n\u001b[1;32m   3129\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 3130\u001b[0;31m     return graph_function._call_flat(\n\u001b[0m\u001b[1;32m   3131\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m   3132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1957\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1958\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1959\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1960\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1961\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    599\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     56\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     59\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     60\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(x=train_generator,\n",
    "          steps_per_epoch=len(train_generator),\n",
    "          validation_data=validation_generator,\n",
    "          validation_steps=len(validation_generator),\n",
    "          epochs=100,\n",
    "          verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63f791af",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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