{
 "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",
    "from collections import Counter\n",
    "import json\n",
    "import os\n",
    "import re\n",
    "import tempfile\n",
    "import numpy as np\n",
    "from os.path import exists\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from PIL import ImageFile\n",
    "import sklearn as sk\n",
    "from sklearn.model_selection import train_test_split, StratifiedShuffleSplit\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,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_regularization(model, regularizer=tf.keras.regularizers.l2(0.0001)):\n",
    "\n",
    "    if not isinstance(regularizer, tf.keras.regularizers.Regularizer):\n",
    "      print(\"Regularizer must be a subclass of tf.keras.regularizers.Regularizer\")\n",
    "      return model\n",
    "\n",
    "    for layer in model.layers:\n",
    "        for attr in ['kernel_regularizer']:\n",
    "            if hasattr(layer, attr):\n",
    "              setattr(layer, attr, regularizer)\n",
    "\n",
    "    # When we change the layers attributes, the change only happens in the model config file\n",
    "    model_json = model.to_json()\n",
    "\n",
    "    # Save the weights before reloading the model.\n",
    "    tmp_weights_path = os.path.join(tempfile.gettempdir(), 'tmp_weights.h5')\n",
    "    model.save_weights(tmp_weights_path)\n",
    "\n",
    "    # load the model from the config\n",
    "    model = tf.keras.models.model_from_json(model_json)\n",
    "    \n",
    "    # Reload the model weights\n",
    "    model.load_weights(tmp_weights_path, by_name=True)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "id": "1057a442",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{source:target} dictionary created @ /tf/training_images\n"
     ]
    }
   ],
   "source": [
    "def dict_pics_jup():\n",
    "    '''\n",
    "    {source:target} dict used to replace source urls with image location as input\n",
    "    '''\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",
    "        \n",
    "    dict_pics = {}\n",
    "    for k in temp_pics_source_list:\n",
    "         patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n",
    "         patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n",
    "         if patt_1 and patt_2 is not None:\n",
    "             tag = patt_1.group() + patt_2.group().lower()\n",
    "             file_name = target_dir + os.sep + tag\n",
    "             dict_pics.update({k:file_name})\n",
    "    print(\"{source:target} dictionary created @ \" + target_dir)\n",
    "    return dict_pics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7a6146e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_pics = dict_pics_jup()\n",
    "\n",
    "with open('women_cat_list.txt') as f:\n",
    "    women_cats = json.load(f)\n",
    "with open('men_cat_list.txt') as f:\n",
    "    men_cats = json.load(f)\n",
    "    \n",
    "with open('temp_pics_source_list.txt') as f:\n",
    "    tempics = json.load(f)\n",
    "# list of image urls that did not get named properly which will be removed from the dataframe\n",
    "drop_row_vals = []\n",
    "for pic in tempics:\n",
    "    try:\n",
    "        dict_pics[pic]\n",
    "    except KeyError:\n",
    "        drop_row_vals.append(pic)\n",
    "\n",
    "df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
    "ddf = df[df.PictureURL.isin(drop_row_vals)==False] # remove improperly named image files\n",
    "df = ddf[ddf.PrimaryCategoryID.isin(men_cats)==False] # removes rows of womens categories\n",
    "\n",
    "blah = pd.Series(df.PictureURL)\n",
    "df = df.drop(labels=['PictureURL'], axis=1)\n",
    "\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",
    "\n",
    "df=df.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'11498': 3913, '11504': 3913, '11505': 3913, '11632': 3913, '15709': 3913, '24087': 3913, '45333': 3913, '53120': 3913, '53548': 3913, '53557': 3913, '55793': 3913, '62107': 3913, '95672': 3913})\n"
     ]
    }
   ],
   "source": [
    "undersample = RandomUnderSampler(sampling_strategy='auto')\n",
    "train, y_under = undersample.fit_resample(df, df['PrimaryCategoryID'])\n",
    "#print(Counter(train['PrimaryCategoryID']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "506aa5cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(train, test_size=0.2, random_state=42)\n",
    "# stratify=train['PrimaryCategoryID']\n",
    "# train['PrimaryCategoryID'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4d72eb90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 32547 validated image filenames belonging to 13 classes.\n",
      "Found 8136 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 12 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=.2,\n",
    "                             #samplewise_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= 90,\n",
    "                             preprocessing_function=tf.keras.applications.vgg16.preprocess_input)\n",
    "train_generator=datagen.flow_from_dataframe(\n",
    "    dataframe=train[:len(train)],\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=(244,244),\n",
    "    subset='training'\n",
    "    )\n",
    "validation_generator=datagen.flow_from_dataframe(\n",
    "    dataframe=train[:len(train)], # is using train right?\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=(244,244),\n",
    "    subset='validation'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7b70f37f",
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs, labels = next(train_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "id": "85934565",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plotImages(imgs)\n",
    "#print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 14,
   "id": "b31af79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = tf.keras.applications.vgg19.VGG19(weights='imagenet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fe06f2bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "for layer in base_model.layers[:-1]:\n",
    "    model.add(layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7d3cc82c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in model.layers:\n",
    "    layer.trainable = True\n",
    "    \n",
    "#model.add(Dropout(.5))\n",
    "#model.add(Dense(64, activation='softmax'))\n",
    "# model.add(Dropout(.25))\n",
    "\n",
    "model.add(Dense(units=7, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c774d787",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 25088)             0         \n",
      "                                                                 \n",
      " fc1 (Dense)                 (None, 4096)              102764544 \n",
      "                                                                 \n",
      " fc2 (Dense)                 (None, 4096)              16781312  \n",
      "                                                                 \n",
      " dense (Dense)               (None, 13)                53261     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 134,313,805\n",
      "Trainable params: 134,313,805\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#model = add_regularization(model)\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fd5d1246",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(learning_rate=.0001), loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "# sparse_categorical_crossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9cd2ba27",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1018/1018 [==============================] - 391s 379ms/step - loss: 2.4329 - accuracy: 0.1571 - val_loss: 2.1902 - val_accuracy: 0.2525\n",
      "Epoch 2/30\n",
      "1018/1018 [==============================] - 379s 373ms/step - loss: 2.0578 - accuracy: 0.2960 - val_loss: 1.9160 - val_accuracy: 0.3330\n",
      "Epoch 3/30\n",
      "1018/1018 [==============================] - 381s 374ms/step - loss: 1.8221 - accuracy: 0.3681 - val_loss: 1.8588 - val_accuracy: 0.3535\n",
      "Epoch 4/30\n",
      "1018/1018 [==============================] - 383s 376ms/step - loss: 1.6406 - accuracy: 0.4272 - val_loss: 1.7819 - val_accuracy: 0.4028\n",
      "Epoch 5/30\n",
      "1018/1018 [==============================] - 383s 376ms/step - loss: 1.4577 - accuracy: 0.4920 - val_loss: 1.7216 - val_accuracy: 0.4158\n",
      "Epoch 6/30\n",
      "1018/1018 [==============================] - 379s 372ms/step - loss: 1.2528 - accuracy: 0.5607 - val_loss: 1.7924 - val_accuracy: 0.4140\n",
      "Epoch 7/30\n",
      "1018/1018 [==============================] - 378s 371ms/step - loss: 1.0030 - accuracy: 0.6469 - val_loss: 1.8017 - val_accuracy: 0.4303\n",
      "Epoch 8/30\n",
      "1018/1018 [==============================] - 379s 372ms/step - loss: 0.7405 - accuracy: 0.7420 - val_loss: 1.9863 - val_accuracy: 0.4453\n",
      "Epoch 9/30\n",
      "1018/1018 [==============================] - 379s 372ms/step - loss: 0.4704 - accuracy: 0.8354 - val_loss: 2.3988 - val_accuracy: 0.4263\n",
      "Epoch 10/30\n",
      "1018/1018 [==============================] - 379s 372ms/step - loss: 0.3059 - accuracy: 0.8944 - val_loss: 2.7526 - val_accuracy: 0.4303\n",
      "Epoch 11/30\n",
      "1018/1018 [==============================] - 377s 371ms/step - loss: 0.2160 - accuracy: 0.9278 - val_loss: 3.0618 - val_accuracy: 0.4250\n",
      "Epoch 12/30\n",
      " 437/1018 [===========>..................] - ETA: 2:53 - loss: 0.1370 - accuracy: 0.9536"
     ]
    },
    {
     "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-20-4cd4443bbf2a>\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;36m30\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=30,\n",
    "          verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63f791af",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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