Model_Tester.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>id</th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[{'id': 313576, 'name': 'Hot Tub Time Machine ...</td>\n",
       "      <td>14000000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt2637294</td>\n",
       "      <td>en</td>\n",
       "      <td>Hot Tub Time Machine 2</td>\n",
       "      <td>When Lou, who has become the \"father of the In...</td>\n",
       "      <td>6.575393</td>\n",
       "      <td>...</td>\n",
       "      <td>2/20/15</td>\n",
       "      <td>93.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>The Laws of Space and Time are About to be Vio...</td>\n",
       "      <td>Hot Tub Time Machine 2</td>\n",
       "      <td>[{'id': 4379, 'name': 'time travel'}, {'id': 9...</td>\n",
       "      <td>[{'cast_id': 4, 'character': 'Lou', 'credit_id...</td>\n",
       "      <td>[{'credit_id': '59ac067c92514107af02c8c8', 'de...</td>\n",
       "      <td>12314651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>[{'id': 107674, 'name': 'The Princess Diaries ...</td>\n",
       "      <td>40000000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0368933</td>\n",
       "      <td>en</td>\n",
       "      <td>The Princess Diaries 2: Royal Engagement</td>\n",
       "      <td>Mia Thermopolis is now a college graduate and ...</td>\n",
       "      <td>8.248895</td>\n",
       "      <td>...</td>\n",
       "      <td>8/6/04</td>\n",
       "      <td>113.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>It can take a lifetime to find true love; she'...</td>\n",
       "      <td>The Princess Diaries 2: Royal Engagement</td>\n",
       "      <td>[{'id': 2505, 'name': 'coronation'}, {'id': 42...</td>\n",
       "      <td>[{'cast_id': 1, 'character': 'Mia Thermopolis'...</td>\n",
       "      <td>[{'credit_id': '52fe43fe9251416c7502563d', 'de...</td>\n",
       "      <td>95149435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3300000</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>http://sonyclassics.com/whiplash/</td>\n",
       "      <td>tt2582802</td>\n",
       "      <td>en</td>\n",
       "      <td>Whiplash</td>\n",
       "      <td>Under the direction of a ruthless instructor, ...</td>\n",
       "      <td>64.299990</td>\n",
       "      <td>...</td>\n",
       "      <td>10/10/14</td>\n",
       "      <td>105.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>The road to greatness can take you to the edge.</td>\n",
       "      <td>Whiplash</td>\n",
       "      <td>[{'id': 1416, 'name': 'jazz'}, {'id': 1523, 'n...</td>\n",
       "      <td>[{'cast_id': 5, 'character': 'Andrew Neimann',...</td>\n",
       "      <td>[{'credit_id': '54d5356ec3a3683ba0000039', 'de...</td>\n",
       "      <td>13092000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200000</td>\n",
       "      <td>[{'id': 53, 'name': 'Thriller'}, {'id': 18, 'n...</td>\n",
       "      <td>http://kahaanithefilm.com/</td>\n",
       "      <td>tt1821480</td>\n",
       "      <td>hi</td>\n",
       "      <td>Kahaani</td>\n",
       "      <td>Vidya Bagchi (Vidya Balan) arrives in Kolkata ...</td>\n",
       "      <td>3.174936</td>\n",
       "      <td>...</td>\n",
       "      <td>3/9/12</td>\n",
       "      <td>122.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kahaani</td>\n",
       "      <td>[{'id': 10092, 'name': 'mystery'}, {'id': 1054...</td>\n",
       "      <td>[{'cast_id': 1, 'character': 'Vidya Bagchi', '...</td>\n",
       "      <td>[{'credit_id': '52fe48779251416c9108d6eb', 'de...</td>\n",
       "      <td>16000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 53, 'nam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt1380152</td>\n",
       "      <td>ko</td>\n",
       "      <td>마린보이</td>\n",
       "      <td>Marine Boy is the story of a former national s...</td>\n",
       "      <td>1.148070</td>\n",
       "      <td>...</td>\n",
       "      <td>2/5/09</td>\n",
       "      <td>118.0</td>\n",
       "      <td>[{'iso_639_1': 'ko', 'name': '한국어/조선말'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marine Boy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'cast_id': 3, 'character': 'Chun-soo', 'cred...</td>\n",
       "      <td>[{'credit_id': '52fe464b9251416c75073b43', 'de...</td>\n",
       "      <td>3923970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                              belongs_to_collection    budget  \\\n",
       "0   1  [{'id': 313576, 'name': 'Hot Tub Time Machine ...  14000000   \n",
       "1   2  [{'id': 107674, 'name': 'The Princess Diaries ...  40000000   \n",
       "2   3                                                NaN   3300000   \n",
       "3   4                                                NaN   1200000   \n",
       "4   5                                                NaN         0   \n",
       "\n",
       "                                              genres  \\\n",
       "0                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "1  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
       "2                      [{'id': 18, 'name': 'Drama'}]   \n",
       "3  [{'id': 53, 'name': 'Thriller'}, {'id': 18, 'n...   \n",
       "4  [{'id': 28, 'name': 'Action'}, {'id': 53, 'nam...   \n",
       "\n",
       "                            homepage    imdb_id original_language  \\\n",
       "0                                NaN  tt2637294                en   \n",
       "1                                NaN  tt0368933                en   \n",
       "2  http://sonyclassics.com/whiplash/  tt2582802                en   \n",
       "3         http://kahaanithefilm.com/  tt1821480                hi   \n",
       "4                                NaN  tt1380152                ko   \n",
       "\n",
       "                             original_title  \\\n",
       "0                    Hot Tub Time Machine 2   \n",
       "1  The Princess Diaries 2: Royal Engagement   \n",
       "2                                  Whiplash   \n",
       "3                                   Kahaani   \n",
       "4                                      마린보이   \n",
       "\n",
       "                                            overview  popularity  ...  \\\n",
       "0  When Lou, who has become the \"father of the In...    6.575393  ...   \n",
       "1  Mia Thermopolis is now a college graduate and ...    8.248895  ...   \n",
       "2  Under the direction of a ruthless instructor, ...   64.299990  ...   \n",
       "3  Vidya Bagchi (Vidya Balan) arrives in Kolkata ...    3.174936  ...   \n",
       "4  Marine Boy is the story of a former national s...    1.148070  ...   \n",
       "\n",
       "  release_date runtime                                   spoken_languages  \\\n",
       "0      2/20/15    93.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "1       8/6/04   113.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "2     10/10/14   105.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "3       3/9/12   122.0  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...   \n",
       "4       2/5/09   118.0           [{'iso_639_1': 'ko', 'name': '한국어/조선말'}]   \n",
       "\n",
       "     status                                            tagline  \\\n",
       "0  Released  The Laws of Space and Time are About to be Vio...   \n",
       "1  Released  It can take a lifetime to find true love; she'...   \n",
       "2  Released    The road to greatness can take you to the edge.   \n",
       "3  Released                                                NaN   \n",
       "4  Released                                                NaN   \n",
       "\n",
       "                                      title  \\\n",
       "0                    Hot Tub Time Machine 2   \n",
       "1  The Princess Diaries 2: Royal Engagement   \n",
       "2                                  Whiplash   \n",
       "3                                   Kahaani   \n",
       "4                                Marine Boy   \n",
       "\n",
       "                                            Keywords  \\\n",
       "0  [{'id': 4379, 'name': 'time travel'}, {'id': 9...   \n",
       "1  [{'id': 2505, 'name': 'coronation'}, {'id': 42...   \n",
       "2  [{'id': 1416, 'name': 'jazz'}, {'id': 1523, 'n...   \n",
       "3  [{'id': 10092, 'name': 'mystery'}, {'id': 1054...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                                cast  \\\n",
       "0  [{'cast_id': 4, 'character': 'Lou', 'credit_id...   \n",
       "1  [{'cast_id': 1, 'character': 'Mia Thermopolis'...   \n",
       "2  [{'cast_id': 5, 'character': 'Andrew Neimann',...   \n",
       "3  [{'cast_id': 1, 'character': 'Vidya Bagchi', '...   \n",
       "4  [{'cast_id': 3, 'character': 'Chun-soo', 'cred...   \n",
       "\n",
       "                                                crew   revenue  \n",
       "0  [{'credit_id': '59ac067c92514107af02c8c8', 'de...  12314651  \n",
       "1  [{'credit_id': '52fe43fe9251416c7502563d', 'de...  95149435  \n",
       "2  [{'credit_id': '54d5356ec3a3683ba0000039', 'de...  13092000  \n",
       "3  [{'credit_id': '52fe48779251416c9108d6eb', 'de...  16000000  \n",
       "4  [{'credit_id': '52fe464b9251416c75073b43', 'de...   3923970  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "train_df = pd.read_csv('./data/train.csv')\n",
    "test_df = pd.read_csv('./data/test.csv')\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>title</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hot Tub Time Machine 2</td>\n",
       "      <td>12314651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Princess Diaries 2: Royal Engagement</td>\n",
       "      <td>95149435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Whiplash</td>\n",
       "      <td>13092000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kahaani</td>\n",
       "      <td>16000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Marine Boy</td>\n",
       "      <td>3923970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      title   revenue\n",
       "0                    Hot Tub Time Machine 2  12314651\n",
       "1  The Princess Diaries 2: Royal Engagement  95149435\n",
       "2                                  Whiplash  13092000\n",
       "3                                   Kahaani  16000000\n",
       "4                                Marine Boy   3923970"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_rev = train_df[['title','revenue']]\n",
    "title_rev.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create_dictionary\n",
    "dictionary = set()\n",
    "def make_dict(x):\n",
    "    global dictionary\n",
    "    x = x.split(\" \")\n",
    "    for i in x:\n",
    "        dictionary.add(i)\n",
    "\n",
    "title_rev['title'].apply(make_dict)\n",
    "\n",
    "def set_to_dict(s):\n",
    "    s = list(s)\n",
    "    dictionary = dict()\n",
    "    for i in range(len(s)):\n",
    "        dictionary[(s[i])] = i\n",
    "    return dictionary\n",
    "dictionary = set_to_dict(dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "word_vec = []\n",
    "def create_word_vec(x):\n",
    "    global word_vec\n",
    "    global dictionary\n",
    "    x = x.split(\" \")\n",
    "    vec = [0.0]*50\n",
    "    for i in range(len(x)):\n",
    "        if x[i] in dictionary:\n",
    "            vec[i]=dictionary[x[i]]\n",
    "    word_vec.append(vec)\n",
    "\n",
    "title_rev['title'].apply(create_word_vec)\n",
    "word_vec = np.array(word_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"title\":list(word_vec)})\n",
    "df['revenue']  = title_rev['revenue']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras import models, layers, regularizers, optimizers\n",
    "def create_embedding_model():\n",
    "    model = models.Sequential()\n",
    "    model.add(layers.Embedding(len(dictionary),30, input_length = 50));\n",
    "    model.add(layers.Flatten())\n",
    "    # model.add(layers.Dense(256,activation = 'relu'))\n",
    "    # model.add(layers.Dropout(0.7))\n",
    "    model.add(layers.Dense(128,activation = 'relu'))\n",
    "    model.add(layers.Dropout(0.5))\n",
    "    model.add(layers.Dense(64,activation = 'relu'))\n",
    "    model.add(layers.Dropout(0.5))\n",
    "    model.add(layers.Dense(1))\n",
    "    model.compile(optimizer='adam',loss='mean_squared_logarithmic_error',metrics=['mean_squared_logarithmic_error'])\n",
    "    return model\n",
    "def perform_k_fold(model_func, data, y_label, epochs, batch_size,x_label = '', k = 10,IsSeries = False):\n",
    "    data = data.sample(frac=1)\n",
    "    train_x = data.drop(y_label,axis = 1)\n",
    "    train_y = data[y_label]\n",
    "    split_value = int(data.shape[0]/k)\n",
    "    split_index = 0\n",
    "    metric_list = []\n",
    "    loss_list = []\n",
    "    for i in range(k):\n",
    "        valid_x = train_x.iloc[split_index:split_value+split_index]\n",
    "        valid_y = train_y.iloc[split_index:split_value+split_index]\n",
    "        tr_x = train_x.drop(valid_x.index)\n",
    "        tr_y = train_y.drop(valid_y.index)\n",
    "        if IsSeries:\n",
    "            valid_x = valid_x[x_label]\n",
    "            tr_x = tr_x[x_label]\n",
    "            valid_x = np.stack(valid_x.to_numpy(),axis = 0)\n",
    "            tr_x = np.stack(tr_x.to_numpy(),axis = 0)\n",
    "       \n",
    "        model = model_func()\n",
    "        model.fit(tr_x,tr_y,epochs=epochs,batch_size = batch_size)\n",
    "        loss, metric = model.evaluate(tr_x,tr_y)\n",
    "        metric_list.append(metric)\n",
    "        loss_list.append(loss)\n",
    "    print(\"Average Loss: \",sum(loss_list)/len(loss_list),\" Average metric\",sum(metric_list)/len(metric_list))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/134\n",
      "2700/2700 [==============================] - 1s 467us/step - loss: 170.4246 - mean_squared_logarithmic_error: 170.4246\n",
      "Epoch 2/134\n",
      "2700/2700 [==============================] - 0s 99us/step - loss: 103.1686 - mean_squared_logarithmic_error: 103.1686\n",
      "Epoch 3/134\n",
      "2700/2700 [==============================] - 0s 99us/step - loss: 80.3570 - mean_squared_logarithmic_error: 80.3570\n",
      "Epoch 4/134\n",
      "2700/2700 [==============================] - 0s 97us/step - loss: 67.3177 - mean_squared_logarithmic_error: 67.3177\n",
      "Epoch 5/134\n",
      "2700/2700 [==============================] - 0s 97us/step - loss: 58.5092 - mean_squared_logarithmic_error: 58.5092\n",
      "Epoch 6/134\n",
      "2700/2700 [==============================] - 0s 96us/step - loss: 51.9100 - mean_squared_logarithmic_error: 51.9100\n",
      "Epoch 7/134\n",
      "2700/2700 [==============================] - 0s 98us/step - loss: 47.0213 - mean_squared_logarithmic_error: 47.0213\n",
      "Epoch 8/134\n",
      "2700/2700 [==============================] - 0s 99us/step - loss: 42.9246 - mean_squared_logarithmic_error: 42.9246\n",
      "Epoch 9/134\n",
      "2700/2700 [==============================] - 0s 100us/step - loss: 39.6201 - mean_squared_logarithmic_error: 39.6201\n",
      "Epoch 10/134\n",
      "2700/2700 [==============================] - 0s 99us/step - loss: 36.9025 - mean_squared_logarithmic_error: 36.9025\n",
      "Epoch 11/134\n",
      "2700/2700 [==============================] - 0s 98us/step - loss: 34.5462 - mean_squared_logarithmic_error: 34.5462\n",
      "Epoch 12/134\n",
      "2700/2700 [==============================] - 0s 97us/step - loss: 32.4740 - mean_squared_logarithmic_error: 32.4740\n",
      "Epoch 13/134\n",
      "2700/2700 [==============================] - 0s 99us/step - loss: 30.5555 - mean_squared_logarithmic_error: 30.5555\n",
      "Epoch 14/134\n",
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      "Epoch 128/134\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2700/2700 [==============================] - 0s 98us/step - loss: 9.2218 - mean_squared_logarithmic_error: 9.2218\n",
      "Epoch 129/134\n",
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      "2700/2700 [==============================] - 0s 146us/step\n",
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      "Epoch 109/134\n",
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      "Epoch 111/134\n",
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      "Epoch 115/134\n",
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      "Epoch 118/134\n",
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      "Epoch 119/134\n",
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      "Epoch 120/134\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2700/2700 [==============================] - 0s 97us/step - loss: 9.4488 - mean_squared_logarithmic_error: 9.4488\n",
      "Epoch 121/134\n",
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      "Epoch 122/134\n",
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      "Epoch 123/134\n",
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      "Epoch 124/134\n",
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      "Epoch 125/134\n",
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      "Epoch 126/134\n",
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      "Epoch 127/134\n",
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      "Epoch 128/134\n",
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      "Epoch 129/134\n",
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      "Epoch 130/134\n",
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      "Epoch 131/134\n",
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      "Epoch 132/134\n",
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      "Epoch 133/134\n",
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      "Epoch 134/134\n",
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      "2700/2700 [==============================] - 0s 158us/step\n",
      "Epoch 1/134\n",
      "2700/2700 [==============================] - 1s 504us/step - loss: 167.8387 - mean_squared_logarithmic_error: 167.8387\n",
      "Epoch 2/134\n",
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      "Epoch 3/134\n",
      "2700/2700 [==============================] - 0s 99us/step - loss: 81.9663 - mean_squared_logarithmic_error: 81.9663\n",
      "Epoch 4/134\n",
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      "Epoch 5/134\n",
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      "Epoch 6/134\n",
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      "Epoch 7/134\n",
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      "Epoch 8/134\n",
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      "Epoch 9/134\n",
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      "Epoch 10/134\n",
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      "Epoch 11/134\n",
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      "Epoch 12/134\n",
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      "Epoch 13/134\n",
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      "Epoch 15/134\n",
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      "Epoch 16/134\n",
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      "Epoch 17/134\n",
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      "Epoch 20/134\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49/134\n",
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      "Epoch 33/134\n",
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      "Epoch 40/134\n",
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      "Epoch 41/134\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2700/2700 [==============================] - 0s 97us/step - loss: 12.8619 - mean_squared_logarithmic_error: 12.8619\n",
      "Epoch 42/134\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 89/134\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2700/2700 [==============================] - 0s 101us/step - loss: 9.6104 - mean_squared_logarithmic_error: 9.6104\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 74/134\n",
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      "Epoch 63/134\n",
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      "Epoch 65/134\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2700/2700 [==============================] - 0s 103us/step - loss: 9.8161 - mean_squared_logarithmic_error: 9.8161\n",
      "Epoch 66/134\n",
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      "Epoch 56/134\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2700/2700 [==============================] - 0s 104us/step - loss: 10.9116 - mean_squared_logarithmic_error: 10.9116\n",
      "Epoch 57/134\n",
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      "2700/2700 [==============================] - 0s 103us/step - loss: 9.4911 - mean_squared_logarithmic_error: 9.4911\n",
      "Epoch 102/134\n",
      "2700/2700 [==============================] - 0s 103us/step - loss: 9.6105 - mean_squared_logarithmic_error: 9.6105\n",
      "Epoch 103/134\n",
      "2700/2700 [==============================] - 0s 102us/step - loss: 9.6255 - mean_squared_logarithmic_error: 9.6255\n",
      "Epoch 104/134\n",
      "2700/2700 [==============================] - 0s 107us/step - loss: 9.5729 - mean_squared_logarithmic_error: 9.5729\n",
      "Epoch 105/134\n",
      "2700/2700 [==============================] - 0s 104us/step - loss: 9.6325 - mean_squared_logarithmic_error: 9.6325\n",
      "Epoch 106/134\n",
      "2700/2700 [==============================] - 0s 105us/step - loss: 9.6295 - mean_squared_logarithmic_error: 9.6295\n",
      "Epoch 107/134\n",
      "2700/2700 [==============================] - 0s 105us/step - loss: 9.5466 - mean_squared_logarithmic_error: 9.5466\n",
      "Epoch 108/134\n",
      "2700/2700 [==============================] - 0s 105us/step - loss: 9.6145 - mean_squared_logarithmic_error: 9.6145\n",
      "Epoch 109/134\n",
      "2700/2700 [==============================] - 0s 104us/step - loss: 9.5653 - mean_squared_logarithmic_error: 9.5653\n",
      "Epoch 110/134\n",
      "2700/2700 [==============================] - 0s 104us/step - loss: 9.6153 - mean_squared_logarithmic_error: 9.6153\n",
      "Epoch 111/134\n",
      "2700/2700 [==============================] - 0s 103us/step - loss: 9.6337 - mean_squared_logarithmic_error: 9.6337\n",
      "Epoch 112/134\n",
      "2700/2700 [==============================] - 0s 103us/step - loss: 9.5682 - mean_squared_logarithmic_error: 9.5682\n",
      "Epoch 113/134\n",
      "2700/2700 [==============================] - 0s 103us/step - loss: 9.5386 - mean_squared_logarithmic_error: 9.5386\n",
      "Epoch 114/134\n",
      "2700/2700 [==============================] - 0s 105us/step - loss: 9.5041 - mean_squared_logarithmic_error: 9.5041\n",
      "Epoch 115/134\n",
      "2700/2700 [==============================] - 0s 106us/step - loss: 9.5136 - mean_squared_logarithmic_error: 9.5136\n",
      "Epoch 116/134\n",
      "2700/2700 [==============================] - 0s 106us/step - loss: 9.5208 - mean_squared_logarithmic_error: 9.5208\n",
      "Epoch 117/134\n",
      "2700/2700 [==============================] - 0s 105us/step - loss: 9.5584 - mean_squared_logarithmic_error: 9.5584\n",
      "Epoch 118/134\n",
      "2700/2700 [==============================] - 0s 104us/step - loss: 9.5018 - mean_squared_logarithmic_error: 9.5018\n",
      "Epoch 119/134\n",
      "2700/2700 [==============================] - 0s 112us/step - loss: 9.5242 - mean_squared_logarithmic_error: 9.5242\n",
      "Epoch 120/134\n",
      "2700/2700 [==============================] - 0s 111us/step - loss: 9.5324 - mean_squared_logarithmic_error: 9.5324\n",
      "Epoch 121/134\n",
      "2700/2700 [==============================] - 0s 112us/step - loss: 9.4641 - mean_squared_logarithmic_error: 9.4641\n",
      "Epoch 122/134\n",
      "2700/2700 [==============================] - 0s 113us/step - loss: 9.4910 - mean_squared_logarithmic_error: 9.4910\n",
      "Epoch 123/134\n",
      "2700/2700 [==============================] - 0s 110us/step - loss: 9.4064 - mean_squared_logarithmic_error: 9.4064\n",
      "Epoch 124/134\n",
      "2700/2700 [==============================] - 0s 109us/step - loss: 9.4348 - mean_squared_logarithmic_error: 9.4348\n",
      "Epoch 125/134\n",
      "2700/2700 [==============================] - 0s 112us/step - loss: 9.4416 - mean_squared_logarithmic_error: 9.4416\n",
      "Epoch 126/134\n",
      "2700/2700 [==============================] - 0s 109us/step - loss: 9.4939 - mean_squared_logarithmic_error: 9.4939\n",
      "Epoch 127/134\n",
      "2700/2700 [==============================] - 0s 112us/step - loss: 9.4504 - mean_squared_logarithmic_error: 9.4504\n",
      "Epoch 128/134\n",
      "2700/2700 [==============================] - 0s 113us/step - loss: 9.3964 - mean_squared_logarithmic_error: 9.3964\n",
      "Epoch 129/134\n",
      "2700/2700 [==============================] - 0s 113us/step - loss: 9.4182 - mean_squared_logarithmic_error: 9.4182\n",
      "Epoch 130/134\n",
      "2700/2700 [==============================] - 0s 106us/step - loss: 9.4127 - mean_squared_logarithmic_error: 9.4127\n",
      "Epoch 131/134\n",
      "2700/2700 [==============================] - 0s 108us/step - loss: 9.4302 - mean_squared_logarithmic_error: 9.4302\n",
      "Epoch 132/134\n",
      "2700/2700 [==============================] - 0s 110us/step - loss: 9.4101 - mean_squared_logarithmic_error: 9.4101\n",
      "Epoch 133/134\n",
      "2700/2700 [==============================] - 0s 109us/step - loss: 9.3837 - mean_squared_logarithmic_error: 9.3837\n",
      "Epoch 134/134\n",
      "2700/2700 [==============================] - 0s 110us/step - loss: 9.3818 - mean_squared_logarithmic_error: 9.3818\n",
      "2700/2700 [==============================] - 1s 231us/step\n",
      "Average Loss:  9.095217164781356  Average metric 9.095217164781356\n"
     ]
    }
   ],
   "source": [
    "perform_k_fold(create_embedding_model, df, 'revenue',134,32, IsSeries = True, x_label = 'title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model1():\n",
    "    model1 = models.Sequential()\n",
    "    model1.add(layers.Dense(356, activation='relu', input_shape=(3,)))\n",
    "    model1.add(layers.Dropout(0.1))\n",
    "    model1.add(layers.Dense(256, activation='relu'))\n",
    "    model1.add(layers.Dense(10,activation='relu'))\n",
    "    model1.add(layers.Dense(1))\n",
    "    \n",
    "    model1.compile(optimizer=optimizers.rmsprop(lr=1e-5),loss='mse',metrics=['mean_squared_logarithmic_error'])\n",
    "    return model1\n",
    "df = train_df[['revenue','budget','runtime','popularity']]\n",
    "df = df.fillna(0)\n",
    "\n",
    "df = df.apply(np.log)\n",
    "def f(x):\n",
    "    if x<0:\n",
    "        return 0\n",
    "    else:\n",
    "        return x\n",
    "df = df.applymap(f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "perform_k_fold(model1, df, 'revenue', epochs = 40, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "def model2(): \n",
    "    model2 = models.Sequential()\n",
    "    model2.add(layers.Dense(356, activation='relu', input_shape=(2,)))\n",
    "    model2.add(layers.Dropout(0.2))\n",
    "    model2.add(layers.BatchNormalization())\n",
    "    model2.add(layers.Dense(256, activation='relu'))\n",
    "    model2.add(layers.Dropout(0.2))\n",
    "    model2.add(layers.Dense(128, activation='relu'))\n",
    "    model2.add(layers.Dropout(0.2))\n",
    "    model2.add(layers.Dense(10,activation='relu'))\n",
    "    model2.add(layers.Dense(1))\n",
    "\n",
    "    model2.compile(optimizer=optimizers.rmsprop(lr=1e-5),loss='mse',metrics=['mean_squared_logarithmic_error'])\n",
    "    return model2\n",
    "df = train_df[['revenue','crew','cast']]\n",
    "df['revenue'] = df['revenue'].apply(np.log)\n",
    "df['revenue'] = df['revenue'].apply(f)\n",
    "df['crew'] = df['crew'].apply(lambda x: x.count('{') if type(x)==str else 0)\n",
    "df['cast'] = df['cast'].apply(lambda x: x.count('{') if type(x)==str else 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "perform_k_fold(model2, df, 'revenue', epochs = 50, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model3():\n",
    "    model3 = models.Sequential()\n",
    "    model3.add(layers.Dense(356, activation='relu', input_shape=(2,)))\n",
    "    model3.add(layers.Dropout(0.2))\n",
    "    model3.add(layers.Dense(256, activation='relu'))\n",
    "    model3.add(layers.Dense(10,activation='relu'))\n",
    "    model3.add(layers.Dense(1))\n",
    "    model3.compile(optimizer=optimizers.rmsprop(lr=1e-5),loss='mse',metrics=['mean_squared_logarithmic_error'])\n",
    "    return model3\n",
    "def ap(x):\n",
    "    #Convert years (ex: 86 -> 1986, 15 -> 2015)\n",
    "    if type(x) == str:\n",
    "        x = int(x.split('/')[2])\n",
    "        if x<=19:\n",
    "            return 2000+x\n",
    "        else:\n",
    "            return 1900+x\n",
    "    else:\n",
    "        return 0\n",
    "df = pd.DataFrame({\"Month\":train_df['release_date'].apply(lambda x: int(x.split('/')[0]) if type(x)==str else 0 ),'Year':train_df['release_date'].apply(ap),'revenue':train_df['revenue'].apply(np.log).apply(f)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/50\n",
      "2700/2700 [==============================] - 0s 135us/step - loss: 2357.3201 - mean_squared_logarithmic_error: 4.8120\n",
      "Epoch 2/50\n",
      "2700/2700 [==============================] - 0s 55us/step - loss: 959.4692 - mean_squared_logarithmic_error: 3.1172\n",
      "Epoch 3/50\n",
      "2700/2700 [==============================] - 0s 61us/step - loss: 862.4091 - mean_squared_logarithmic_error: 2.9833\n",
      "Epoch 4/50\n",
      "2700/2700 [==============================] - 0s 64us/step - loss: 734.9898 - mean_squared_logarithmic_error: 2.8432\n",
      "Epoch 5/50\n",
      "2700/2700 [==============================] - 0s 62us/step - loss: 635.2096 - mean_squared_logarithmic_error: 2.7818\n",
      "Epoch 6/50\n",
      "2700/2700 [==============================] - 0s 60us/step - loss: 544.3047 - mean_squared_logarithmic_error: 2.5760\n",
      "Epoch 7/50\n",
      "2700/2700 [==============================] - 0s 57us/step - loss: 443.3069 - mean_squared_logarithmic_error: 2.3467\n",
      "Epoch 8/50\n",
      "2700/2700 [==============================] - 0s 53us/step - loss: 203.6607 - mean_squared_logarithmic_error: 1.4938\n",
      "Epoch 9/50\n",
      "2700/2700 [==============================] - 0s 55us/step - loss: 66.6135 - mean_squared_logarithmic_error: 0.5033\n",
      "Epoch 10/50\n",
      "2700/2700 [==============================] - 0s 54us/step - loss: 45.3863 - mean_squared_logarithmic_error: 0.2878\n",
      "Epoch 11/50\n",
      "2700/2700 [==============================] - 0s 59us/step - loss: 35.7113 - mean_squared_logarithmic_error: 0.1995\n",
      "Epoch 12/50\n",
      "2700/2700 [==============================] - 0s 56us/step - loss: 34.6556 - mean_squared_logarithmic_error: 0.1893\n",
      "Epoch 13/50\n",
      "2700/2700 [==============================] - 0s 54us/step - loss: 31.7111 - mean_squared_logarithmic_error: 0.1814\n",
      "Epoch 14/50\n",
      "2700/2700 [==============================] - 0s 53us/step - loss: 29.0717 - mean_squared_logarithmic_error: 0.1559\n",
      "Epoch 15/50\n",
      "2700/2700 [==============================] - 0s 54us/step - loss: 25.3340 - mean_squared_logarithmic_error: 0.1353\n",
      "Epoch 16/50\n",
      "2700/2700 [==============================] - 0s 62us/step - loss: 20.3141 - mean_squared_logarithmic_error: 0.1201\n",
      "Epoch 17/50\n",
      "2700/2700 [==============================] - 0s 61us/step - loss: 17.2008 - mean_squared_logarithmic_error: 0.0998\n",
      "Epoch 18/50\n",
      "2700/2700 [==============================] - 0s 59us/step - loss: 16.5151 - mean_squared_logarithmic_error: 0.0985\n",
      "Epoch 19/50\n",
      "2700/2700 [==============================] - 0s 63us/step - loss: 16.3520 - mean_squared_logarithmic_error: 0.0982\n",
      "Epoch 20/50\n",
      "2700/2700 [==============================] - 0s 56us/step - loss: 16.1040 - mean_squared_logarithmic_error: 0.0983\n",
      "Epoch 21/50\n",
      "2700/2700 [==============================] - 0s 54us/step - loss: 15.9557 - mean_squared_logarithmic_error: 0.0983\n",
      "Epoch 22/50\n",
      "2700/2700 [==============================] - 0s 54us/step - loss: 14.9463 - mean_squared_logarithmic_error: 0.0906\n",
      "Epoch 23/50\n",
      "2700/2700 [==============================] - 0s 61us/step - loss: 14.4593 - mean_squared_logarithmic_error: 0.0914\n",
      "Epoch 24/50\n",
      "2700/2700 [==============================] - 0s 60us/step - loss: 14.4169 - mean_squared_logarithmic_error: 0.0913\n",
      "Epoch 25/50\n",
      "2700/2700 [==============================] - 0s 61us/step - loss: 14.0606 - mean_squared_logarithmic_error: 0.0874\n",
      "Epoch 26/50\n",
      "2700/2700 [==============================] - 0s 61us/step - loss: 13.7606 - mean_squared_logarithmic_error: 0.0854\n",
      "Epoch 27/50\n",
      "2700/2700 [==============================] - 0s 55us/step - loss: 13.6627 - mean_squared_logarithmic_error: 0.0862\n",
      "Epoch 28/50\n",
      "2700/2700 [==============================] - 0s 54us/step - loss: 12.8525 - mean_squared_logarithmic_error: 0.0843\n",
      "Epoch 29/50\n",
      "2700/2700 [==============================] - 0s 53us/step - loss: 13.4876 - mean_squared_logarithmic_error: 0.0859\n",
      "Epoch 30/50\n",
      "2700/2700 [==============================] - 0s 55us/step - loss: 12.7331 - mean_squared_logarithmic_error: 0.0828\n",
      "Epoch 31/50\n",
      "2700/2700 [==============================] - 0s 59us/step - loss: 12.9831 - mean_squared_logarithmic_error: 0.0832\n",
      "Epoch 32/50\n",
      "2700/2700 [==============================] - 0s 63us/step - loss: 12.5518 - mean_squared_logarithmic_error: 0.0815\n",
      "Epoch 33/50\n",
      "2700/2700 [==============================] - 0s 56us/step - loss: 12.6735 - mean_squared_logarithmic_error: 0.0829\n",
      "Epoch 34/50\n",
      "2700/2700 [==============================] - 0s 63us/step - loss: 12.3493 - mean_squared_logarithmic_error: 0.0809\n",
      "Epoch 35/50\n",
      "2700/2700 [==============================] - 0s 57us/step - loss: 12.5166 - mean_squared_logarithmic_error: 0.0825\n",
      "Epoch 36/50\n",
      "2700/2700 [==============================] - 0s 54us/step - loss: 12.3860 - mean_squared_logarithmic_error: 0.0819\n",
      "Epoch 37/50\n",
      "2700/2700 [==============================] - 0s 54us/step - loss: 11.8286 - mean_squared_logarithmic_error: 0.0790\n",
      "Epoch 38/50\n",
      "2700/2700 [==============================] - 0s 55us/step - loss: 12.2347 - mean_squared_logarithmic_error: 0.0805\n",
      "Epoch 39/50\n",
      "2700/2700 [==============================] - 0s 58us/step - loss: 11.6485 - mean_squared_logarithmic_error: 0.0782\n",
      "Epoch 40/50\n",
      "2700/2700 [==============================] - 0s 55us/step - loss: 11.5215 - mean_squared_logarithmic_error: 0.0771\n",
      "Epoch 41/50\n",
      "2700/2700 [==============================] - 0s 53us/step - loss: 11.8890 - mean_squared_logarithmic_error: 0.0798\n",
      "Epoch 42/50\n",
      "2700/2700 [==============================] - 0s 54us/step - loss: 11.7770 - mean_squared_logarithmic_error: 0.0791\n",
      "Epoch 43/50\n",
      "2700/2700 [==============================] - 0s 54us/step - loss: 11.3764 - mean_squared_logarithmic_error: 0.0775\n",
      "Epoch 44/50\n",
      "2700/2700 [==============================] - 0s 54us/step - loss: 11.1452 - mean_squared_logarithmic_error: 0.0767\n",
      "Epoch 45/50\n",
      "2700/2700 [==============================] - 0s 54us/step - loss: 11.1050 - mean_squared_logarithmic_error: 0.0760\n",
      "Epoch 46/50\n",
      "2700/2700 [==============================] - 0s 62us/step - loss: 11.1320 - mean_squared_logarithmic_error: 0.0767\n",
      "Epoch 47/50\n",
      "2700/2700 [==============================] - 0s 59us/step - loss: 11.0706 - mean_squared_logarithmic_error: 0.0767\n",
      "Epoch 48/50\n",
      "2700/2700 [==============================] - 0s 59us/step - loss: 10.9050 - mean_squared_logarithmic_error: 0.0755\n",
      "Epoch 49/50\n",
      "2700/2700 [==============================] - 0s 60us/step - loss: 10.8659 - mean_squared_logarithmic_error: 0.0754\n",
      "Epoch 50/50\n",
      "2700/2700 [==============================] - 0s 62us/step - loss: 10.8921 - mean_squared_logarithmic_error: 0.0760\n",
      "2700/2700 [==============================] - 0s 33us/step\n",
      "Epoch 1/50\n",
      "2700/2700 [==============================] - 0s 149us/step - loss: 4189.1097 - mean_squared_logarithmic_error: 2.9327\n",
      "Epoch 2/50\n",
      "2700/2700 [==============================] - 0s 63us/step - loss: 913.7956 - mean_squared_logarithmic_error: 3.1321\n",
      "Epoch 3/50\n",
      "2700/2700 [==============================] - 0s 69us/step - loss: 654.6834 - mean_squared_logarithmic_error: 2.7641\n",
      "Epoch 4/50\n",
      "2700/2700 [==============================] - 0s 69us/step - loss: 526.5785 - mean_squared_logarithmic_error: 2.4918\n",
      "Epoch 5/50\n",
      "2700/2700 [==============================] - 0s 67us/step - loss: 480.5628 - mean_squared_logarithmic_error: 2.4326\n",
      "Epoch 6/50\n",
      "2700/2700 [==============================] - 0s 61us/step - loss: 416.7236 - mean_squared_logarithmic_error: 2.2838\n",
      "Epoch 7/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2700/2700 [==============================] - 0s 60us/step - loss: 352.2652 - mean_squared_logarithmic_error: 2.1032\n",
      "Epoch 8/50\n",
      "2700/2700 [==============================] - 0s 59us/step - loss: 316.1058 - mean_squared_logarithmic_error: 2.0031\n",
      "Epoch 9/50\n",
      "2700/2700 [==============================] - 0s 59us/step - loss: 269.8388 - mean_squared_logarithmic_error: 1.8198\n",
      "Epoch 10/50\n",
      "2700/2700 [==============================] - 0s 59us/step - loss: 266.9967 - mean_squared_logarithmic_error: 1.8322\n",
      "Epoch 11/50\n",
      "2700/2700 [==============================] - 0s 59us/step - loss: 224.9665 - mean_squared_logarithmic_error: 1.6350\n",
      "Epoch 12/50\n",
      "2700/2700 [==============================] - 0s 59us/step - loss: 200.8738 - mean_squared_logarithmic_error: 1.4837\n",
      "Epoch 13/50\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
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     "text": [
      "2700/2700 [==============================] - 0s 55us/step - loss: 10.5763 - mean_squared_logarithmic_error: 0.0742\n",
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     ]
    },
    {
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     "text": [
      "2700/2700 [==============================] - 0s 57us/step - loss: 142.2754 - mean_squared_logarithmic_error: 2.0987\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 25/50\n",
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      "Epoch 39/50\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2700/2700 [==============================] - 0s 57us/step - loss: 13.2857 - mean_squared_logarithmic_error: 0.0848\n",
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      "2700/2700 [==============================] - 0s 57us/step - loss: 15.1528 - mean_squared_logarithmic_error: 0.0922\n",
      "Epoch 40/50\n",
      "2700/2700 [==============================] - 0s 57us/step - loss: 14.2687 - mean_squared_logarithmic_error: 0.0878\n",
      "Epoch 41/50\n",
      "2700/2700 [==============================] - 0s 57us/step - loss: 13.8363 - mean_squared_logarithmic_error: 0.0881\n",
      "Epoch 42/50\n",
      "2700/2700 [==============================] - 0s 57us/step - loss: 13.8056 - mean_squared_logarithmic_error: 0.0861\n",
      "Epoch 43/50\n",
      "2700/2700 [==============================] - 0s 58us/step - loss: 14.2936 - mean_squared_logarithmic_error: 0.0911\n",
      "Epoch 44/50\n",
      "2700/2700 [==============================] - 0s 57us/step - loss: 13.2393 - mean_squared_logarithmic_error: 0.0863\n",
      "Epoch 45/50\n",
      "2700/2700 [==============================] - 0s 57us/step - loss: 13.1209 - mean_squared_logarithmic_error: 0.0832\n",
      "Epoch 46/50\n",
      "2700/2700 [==============================] - 0s 57us/step - loss: 12.8031 - mean_squared_logarithmic_error: 0.0838\n",
      "Epoch 47/50\n",
      "2700/2700 [==============================] - 0s 58us/step - loss: 12.8953 - mean_squared_logarithmic_error: 0.0833\n",
      "Epoch 48/50\n",
      "2700/2700 [==============================] - 0s 57us/step - loss: 12.1925 - mean_squared_logarithmic_error: 0.0801\n",
      "Epoch 49/50\n",
      "2700/2700 [==============================] - 0s 58us/step - loss: 12.3100 - mean_squared_logarithmic_error: 0.0819\n",
      "Epoch 50/50\n",
      "2700/2700 [==============================] - 0s 58us/step - loss: 12.2830 - mean_squared_logarithmic_error: 0.0806\n",
      "2700/2700 [==============================] - 0s 81us/step\n",
      "Average Loss:  14.368337583188659  Average metric 0.08552246127746724\n"
     ]
    }
   ],
   "source": [
    "perform_k_fold(model3, df, 'revenue', epochs = 50, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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