Auto_Keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import autokeras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Data/classification_testData_shape(395219,82).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395219, 83)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>dti</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>...</th>\n",
       "      <th>cr_year</th>\n",
       "      <th>05113</th>\n",
       "      <th>11650</th>\n",
       "      <th>22690</th>\n",
       "      <th>29597</th>\n",
       "      <th>30723</th>\n",
       "      <th>48052</th>\n",
       "      <th>70466</th>\n",
       "      <th>86630</th>\n",
       "      <th>93700</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.44</td>\n",
       "      <td>329.48</td>\n",
       "      <td>117000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.24</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.99</td>\n",
       "      <td>265.68</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.05</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15600.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.49</td>\n",
       "      <td>506.97</td>\n",
       "      <td>43057.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.79</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.49</td>\n",
       "      <td>220.65</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>24375.0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.27</td>\n",
       "      <td>609.33</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>33.95</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  loan_amnt  term  int_rate  installment  annual_inc  \\\n",
       "0           0    10000.0     0     11.44       329.48    117000.0   \n",
       "1           1     8000.0     0     11.99       265.68     65000.0   \n",
       "2           2    15600.0     0     10.49       506.97     43057.0   \n",
       "3           3     7200.0     0      6.49       220.65     54000.0   \n",
       "4           4    24375.0     1     17.27       609.33     55000.0   \n",
       "\n",
       "   loan_status    dti  open_acc  pub_rec  ...  cr_year  05113  11650  22690  \\\n",
       "0            0  26.24      16.0      0.0  ...     1990      0      0      1   \n",
       "1            0  22.05      17.0      0.0  ...     2004      1      0      0   \n",
       "2            0  12.79      13.0      0.0  ...     2007      1      0      0   \n",
       "3            0   2.60       6.0      0.0  ...     2006      0      0      0   \n",
       "4            1  33.95      13.0      0.0  ...     1999      0      1      0   \n",
       "\n",
       "   29597  30723  48052  70466  86630  93700  \n",
       "0      0      0      0      0      0      0  \n",
       "1      0      0      0      0      0      0  \n",
       "2      0      0      0      0      0      0  \n",
       "3      0      0      0      0      0      0  \n",
       "4      0      0      0      0      0      0  \n",
       "\n",
       "[5 rows x 83 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    317696\n",
       "1     77523\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('loan_status',axis=1)\n",
    "y = df['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(395219, 82) (395219,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = LabelEncoder().fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y,  test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(276653, 82) (276653,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(118566, 82) (118566,)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Reloading Oracle from existing project .\\structured_data_classifier\\oracle.json\n"
     ]
    }
   ],
   "source": [
    "import autokeras as ak\n",
    "search = ak.StructuredDataClassifier(overwrite=False, max_trials=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For regression problems, use StructuredDataRegressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Search: Running Trial #1\n",
      "\n",
      "Hyperparameter    |Value             |Best Value So Far \n",
      "structured_data...|True              |?                 \n",
      "structured_data...|2                 |?                 \n",
      "structured_data...|False             |?                 \n",
      "structured_data...|0                 |?                 \n",
      "structured_data...|32                |?                 \n",
      "structured_data...|32                |?                 \n",
      "classification_...|0                 |?                 \n",
      "optimizer         |adam              |?                 \n",
      "learning_rate     |0.001             |?                 \n",
      "\n",
      "Epoch 1/1000\n",
      "6917/6917 [==============================] - 22s 3ms/step - loss: 0.3044 - accuracy: 0.8765 - val_loss: 0.2615 - val_accuracy: 0.8887\n"
     ]
    },
    {
     "ename": "NotFoundError",
     "evalue": "Failed to create a NewWriteableFile: .\\structured_data_classifier\\trial_6fc679b220ab850d182e01b9b89bd9c7\\checkpoints\\epoch_0\\checkpoint_temp/part-00000-of-00001.data-00000-of-00001.tempstate9793209553172104736 : The system cannot find the path specified.\r\n; No such process [Op:SaveV2]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotFoundError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-308aca8994f3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msearch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\autokeras\\tasks\\structured_data.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, epochs, callbacks, validation_split, validation_data, **kwargs)\u001b[0m\n\u001b[0;32m    313\u001b[0m                 [keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit).\n\u001b[0;32m    314\u001b[0m         \"\"\"\n\u001b[1;32m--> 315\u001b[1;33m         super().fit(\n\u001b[0m\u001b[0;32m    316\u001b[0m             \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    317\u001b[0m             \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\autokeras\\tasks\\structured_data.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, epochs, callbacks, validation_split, validation_data, **kwargs)\u001b[0m\n\u001b[0;32m    132\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_in_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 134\u001b[1;33m         super().fit(\n\u001b[0m\u001b[0;32m    135\u001b[0m             \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m             \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\autokeras\\auto_model.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, callbacks, validation_split, validation_data, **kwargs)\u001b[0m\n\u001b[0;32m    271\u001b[0m             )\n\u001b[0;32m    272\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 273\u001b[1;33m         self.tuner.search(\n\u001b[0m\u001b[0;32m    274\u001b[0m             \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    275\u001b[0m             \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\autokeras\\engine\\tuner.py\u001b[0m in \u001b[0;36msearch\u001b[1;34m(self, epochs, callbacks, validation_split, **fit_kwargs)\u001b[0m\n\u001b[0;32m    179\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moracle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_space\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 181\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnew_callbacks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    182\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    183\u001b[0m         \u001b[1;31m# Train the best model use validation data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\kerastuner\\engine\\base_tuner.py\u001b[0m in \u001b[0;36msearch\u001b[1;34m(self, *fit_args, **fit_kwargs)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_trial_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_trial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mfit_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_trial_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_search_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\kerastuner\\engine\\tuner.py\u001b[0m in \u001b[0;36mrun_trial\u001b[1;34m(self, trial, *fit_args, **fit_kwargs)\u001b[0m\n\u001b[0;32m    170\u001b[0m         \u001b[0mcopied_fit_kwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'callbacks'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 172\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_build_and_fit_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfit_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopied_fit_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    174\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msave_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrial_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\autokeras\\engine\\tuner.py\u001b[0m in \u001b[0;36m_build_and_fit_model\u001b[1;34m(self, trial, fit_args, fit_kwargs)\u001b[0m\n\u001b[0;32m     99\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madapt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfit_kwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"x\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 101\u001b[1;33m         _, history = utils.fit_with_adaptive_batch_size(\n\u001b[0m\u001b[0;32m    102\u001b[0m             \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhypermodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhypermodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_kwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m         )\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\autokeras\\utils\\utils.py\u001b[0m in \u001b[0;36mfit_with_adaptive_batch_size\u001b[1;34m(model, batch_size, **fit_kwargs)\u001b[0m\n\u001b[0;32m     82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfit_with_adaptive_batch_size\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m     history = run_with_adaptive_batch_size(\n\u001b[0m\u001b[0;32m     85\u001b[0m         \u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_kwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m     )\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\autokeras\\utils\\utils.py\u001b[0m in \u001b[0;36mrun_with_adaptive_batch_size\u001b[1;34m(batch_size, func, **fit_kwargs)\u001b[0m\n\u001b[0;32m     95\u001b[0m     \u001b[1;32mwhile\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     96\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 97\u001b[1;33m             \u001b[0mhistory\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     98\u001b[0m             \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     99\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mResourceExhaustedError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\autokeras\\utils\\utils.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfit_with_adaptive_batch_size\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m     history = run_with_adaptive_batch_size(\n\u001b[1;32m---> 85\u001b[1;33m         \u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_kwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m     )\n\u001b[0;32m     87\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1143\u001b[0m           \u001b[0mepoch_logs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1144\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1145\u001b[1;33m         \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1146\u001b[0m         \u001b[0mtraining_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1147\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36mon_epoch_end\u001b[1;34m(self, epoch, logs)\u001b[0m\n\u001b[0;32m    430\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnumpy_logs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# Only convert once.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m           \u001b[0mnumpy_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numpy_or_python_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m         \u001b[0mcallback\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumpy_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\kerastuner\\engine\\tuner_utils.py\u001b[0m in \u001b[0;36mon_epoch_end\u001b[1;34m(self, epoch, logs)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mon_epoch_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 95\u001b[1;33m         self.tuner.on_epoch_end(\n\u001b[0m\u001b[0;32m     96\u001b[0m             self.trial, self.model, epoch, logs=logs)\n\u001b[0;32m     97\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\kerastuner\\engine\\tuner.py\u001b[0m in \u001b[0;36mon_epoch_end\u001b[1;34m(self, trial, model, epoch, logs)\u001b[0m\n\u001b[0;32m    245\u001b[0m               \u001b[0mthe\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mobjective\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mepoch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m         \"\"\"\n\u001b[1;32m--> 247\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrial_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    248\u001b[0m         \u001b[1;31m# Report intermediate metrics to the `Oracle`.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m         status = self.oracle.update_trial(\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\kerastuner\\engine\\tuner.py\u001b[0m in \u001b[0;36msave_model\u001b[1;34m(self, trial_id, model, step)\u001b[0m\n\u001b[0;32m    174\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msave_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrial_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    175\u001b[0m         \u001b[0mepoch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 176\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_checkpoint_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrial_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    177\u001b[0m         \u001b[1;31m# TODO: save the top epoch checkpoints instead of last ones.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    178\u001b[0m         \u001b[0mepoch_to_delete\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mepoch\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_save_n_checkpoints\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\kerastuner\\engine\\tuner.py\u001b[0m in \u001b[0;36m_checkpoint_model\u001b[1;34m(self, model, trial_id, epoch)\u001b[0m\n\u001b[0;32m    324\u001b[0m         \u001b[0mfname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_checkpoint_fname\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrial_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    325\u001b[0m         \u001b[1;31m# Save in TF format.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 326\u001b[1;33m         \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    327\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mfname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    328\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36msave_weights\u001b[1;34m(self, filepath, overwrite, save_format, options)\u001b[0m\n\u001b[0;32m   2122\u001b[0m              'saved.\\n\\nConsider using a TensorFlow optimizer from `tf.train`.')\n\u001b[0;32m   2123\u001b[0m             % (optimizer,))\n\u001b[1;32m-> 2124\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_trackable_saver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2125\u001b[0m       \u001b[1;31m# Record this checkpoint so it's visible from tf.train.latest_checkpoint.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2126\u001b[0m       checkpoint_management.update_checkpoint_state_internal(\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\util.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, file_prefix, checkpoint_number, session, options)\u001b[0m\n\u001b[0;32m   1214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1215\u001b[0m     \u001b[0mfile_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecursive_create_dir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdirname\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_prefix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1216\u001b[1;33m     save_path, new_feed_additions = self._save_cached_when_graph_building(\n\u001b[0m\u001b[0;32m   1217\u001b[0m         file_prefix_tensor, object_graph_tensor, options)\n\u001b[0;32m   1218\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnew_feed_additions\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\util.py\u001b[0m in \u001b[0;36m_save_cached_when_graph_building\u001b[1;34m(self, file_prefix, object_graph_tensor, options)\u001b[0m\n\u001b[0;32m   1160\u001b[0m         or context.executing_eagerly() or ops.inside_function()):\n\u001b[0;32m   1161\u001b[0m       \u001b[0msaver\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunctional_saver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMultiDeviceSaver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnamed_saveable_objects\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1162\u001b[1;33m       \u001b[0msave_op\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msaver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_prefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1163\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"/cpu:0\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1164\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontrol_dependencies\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msave_op\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saving\\functional_saver.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, file_prefix, options)\u001b[0m\n\u001b[0;32m    298\u001b[0m       \u001b[0mtf_function_save\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    299\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 300\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0msave_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    301\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mrestore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile_prefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saving\\functional_saver.py\u001b[0m in \u001b[0;36msave_fn\u001b[1;34m()\u001b[0m\n\u001b[0;32m    272\u001b[0m           \u001b[1;31m# initial read operations should be placed on the SaveableObject's\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    273\u001b[0m           \u001b[1;31m# device.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 274\u001b[1;33m           \u001b[0msharded_saves\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msaver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshard_prefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    276\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontrol_dependencies\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msharded_saves\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saving\\functional_saver.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, file_prefix, options)\u001b[0m\n\u001b[0;32m     81\u001b[0m     \u001b[0msave_device\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_io_device\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m\"cpu:0\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msave_device\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mio_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_v2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_prefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtensor_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtensor_slices\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mrestore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile_prefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gen_io_ops.py\u001b[0m in \u001b[0;36msave_v2\u001b[1;34m(prefix, tensor_names, shape_and_slices, tensors, name)\u001b[0m\n\u001b[0;32m   1711\u001b[0m       \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1712\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1713\u001b[1;33m       return save_v2_eager_fallback(\n\u001b[0m\u001b[0;32m   1714\u001b[0m           \u001b[0mprefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtensor_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape_and_slices\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1715\u001b[0m           ctx=_ctx)\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gen_io_ops.py\u001b[0m in \u001b[0;36msave_v2_eager_fallback\u001b[1;34m(prefix, tensor_names, shape_and_slices, tensors, name, ctx)\u001b[0m\n\u001b[0;32m   1732\u001b[0m   \u001b[0m_inputs_flat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mprefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtensor_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape_and_slices\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1733\u001b[0m   \u001b[0m_attrs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"dtypes\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_attr_dtypes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1734\u001b[1;33m   _result = _execute.execute(b\"SaveV2\", 0, inputs=_inputs_flat, attrs=_attrs,\n\u001b[0m\u001b[0;32m   1735\u001b[0m                              ctx=ctx, name=name)\n\u001b[0;32m   1736\u001b[0m   \u001b[0m_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     57\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     61\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNotFoundError\u001b[0m: Failed to create a NewWriteableFile: .\\structured_data_classifier\\trial_6fc679b220ab850d182e01b9b89bd9c7\\checkpoints\\epoch_0\\checkpoint_temp/part-00000-of-00001.data-00000-of-00001.tempstate9793209553172104736 : The system cannot find the path specified.\r\n; No such process [Op:SaveV2]"
     ]
    }
   ],
   "source": [
    "search.fit(x=X_train, y=y_train, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss, acc = search.evaluate(X_test, y_test, verbose=0) #classification\n",
    "#mae, _ = search.evaluate(X_test, y_test, verbose=0) #regression\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predictions = search.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix, plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(classification_report())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = search.export_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = search.export_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('auto_keras_model_classification.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Classification/Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* search = ak.ImageClassifier(max_trials=3)\n",
    "* search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text Classification/Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* search = ak.TextClassifier(max_trials=3)\n",
    "* search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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