Linear Regression in TensorFlow with a Real Dataset .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning Objectives:\n",
    "After doing this Notebook exercises, I'll have know how to do the following:\n",
    "\n",
    "Read a .csv file into a pandas DataFrame.\n",
    "Examine a dataset.\n",
    "Experiment with different features in building a model.\n",
    "Tune the model's hyperparameters.\n",
    "\n",
    "\n",
    "# The Dataset\n",
    "The dataset for this exercise is based on 1990 census data from California. The dataset is old but still provides a great opportunity to learn about machine learning programming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import relevant modules\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# The following lines adjust the granularity of reporting. \n",
    "pd.options.display.max_rows = 10\n",
    "pd.options.display.float_format = \"{:.1f}\".format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-114.3</td>\n",
       "      <td>34.2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5612.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>66.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-114.5</td>\n",
       "      <td>34.4</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7650.0</td>\n",
       "      <td>1901.0</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>463.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>80.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-114.6</td>\n",
       "      <td>33.7</td>\n",
       "      <td>17.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>85.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-114.6</td>\n",
       "      <td>33.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1501.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>73.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-114.6</td>\n",
       "      <td>33.6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1454.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>624.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>65.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0     -114.3      34.2                15.0       5612.0          1283.0   \n",
       "1     -114.5      34.4                19.0       7650.0          1901.0   \n",
       "2     -114.6      33.7                17.0        720.0           174.0   \n",
       "3     -114.6      33.6                14.0       1501.0           337.0   \n",
       "4     -114.6      33.6                20.0       1454.0           326.0   \n",
       "\n",
       "   population  households  median_income  median_house_value  \n",
       "0      1015.0       472.0            1.5                66.9  \n",
       "1      1129.0       463.0            1.8                80.1  \n",
       "2       333.0       117.0            1.7                85.7  \n",
       "3       515.0       226.0            3.2                73.4  \n",
       "4       624.0       262.0            1.9                65.5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the dataset from the URL.\n",
    "training_data = pd.read_csv(filepath_or_buffer=\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\")\n",
    "\n",
    "# Scale the label.\n",
    "training_data[\"median_house_value\"] /= 1000.0\n",
    "\n",
    "# Print the first rows of the pandas DataFrame.\n",
    "training_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>17000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.6</td>\n",
       "      <td>35.6</td>\n",
       "      <td>28.6</td>\n",
       "      <td>2643.7</td>\n",
       "      <td>539.4</td>\n",
       "      <td>1429.6</td>\n",
       "      <td>501.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>207.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>12.6</td>\n",
       "      <td>2179.9</td>\n",
       "      <td>421.5</td>\n",
       "      <td>1147.9</td>\n",
       "      <td>384.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.3</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.8</td>\n",
       "      <td>33.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>119.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.5</td>\n",
       "      <td>34.2</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1167.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>180.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.0</td>\n",
       "      <td>37.7</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3151.2</td>\n",
       "      <td>648.2</td>\n",
       "      <td>1721.0</td>\n",
       "      <td>605.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>37937.0</td>\n",
       "      <td>6445.0</td>\n",
       "      <td>35682.0</td>\n",
       "      <td>6082.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "count    17000.0   17000.0             17000.0      17000.0         17000.0   \n",
       "mean      -119.6      35.6                28.6       2643.7           539.4   \n",
       "std          2.0       2.1                12.6       2179.9           421.5   \n",
       "min       -124.3      32.5                 1.0          2.0             1.0   \n",
       "25%       -121.8      33.9                18.0       1462.0           297.0   \n",
       "50%       -118.5      34.2                29.0       2127.0           434.0   \n",
       "75%       -118.0      37.7                37.0       3151.2           648.2   \n",
       "max       -114.3      42.0                52.0      37937.0          6445.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \n",
       "count     17000.0     17000.0        17000.0             17000.0  \n",
       "mean       1429.6       501.2            3.9               207.3  \n",
       "std        1147.9       384.5            1.9               116.0  \n",
       "min           3.0         1.0            0.5                15.0  \n",
       "25%         790.0       282.0            2.6               119.4  \n",
       "50%        1167.0       409.0            3.5               180.4  \n",
       "75%        1721.0       605.2            4.8               265.0  \n",
       "max       35682.0      6082.0           15.0               500.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get statistics on the dataset.\n",
    "training_data.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Identify anomalies in the dataset\n",
    "\n",
    "# The maximum value (max) of several columns seems very\n",
    "# high compared to the other quantiles. For example,\n",
    "# example the total_rooms column. Given the quantile\n",
    "# values (25%, 50%, and 75%), you might expect the \n",
    "# max value of total_rooms to be approximately \n",
    "# 5,000 or possibly 10,000. However, the max value \n",
    "# is actually 37,937.\n",
    "\n",
    "# When you see anomalies in a column, become more careful\n",
    "# about using that column as a feature. That said,\n",
    "# anomalies in potential features sometimes mirror \n",
    "# anomalies in the label, which could make the column \n",
    "# be (or seem to be) a powerful feature.\n",
    "# Also, as you will see later in the course, you \n",
    "# might be able to represent (pre-process) raw data \n",
    "# in order to make columns into useful features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "'return' outside function (<ipython-input-14-1b28cbf06275>, line 19)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-14-1b28cbf06275>\"\u001b[0;36m, line \u001b[0;32m19\u001b[0m\n\u001b[0;31m    return model\u001b[0m\n\u001b[0m                        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m 'return' outside function\n"
     ]
    }
   ],
   "source": [
    "# Define the functions that build and train a model\n",
    "def build_model(my_learning_rate):\n",
    "  \"\"\"Create and compile a simple linear regression model.\"\"\"\n",
    "# Most simple tf.keras models are sequential.\n",
    "  model = tf.keras.models.Sequential()\n",
    "\n",
    "# Describe the topography of the model.\n",
    "# The topography of a simple linear regression model\n",
    "# is a single node in a single layer.\n",
    "  model.add(tf.keras.layers.Dense(units=1, \n",
    "                                  input_shape=(1,)))\n",
    "\n",
    "# Compile the model topography into code that TensorFlow can efficiently\n",
    "# execute. Configure training to minimize the model's mean squared error. \n",
    "  model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=my_learning_rate),\n",
    "                loss=\"mean_squared_error\",\n",
    "                metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
    "\n",
    "return model        \n",
    "\n",
    "\n",
    "def train_model(model, df, feature, label, epochs, batch_size):\n",
    "  \"\"\"Train the model by feeding it data.\"\"\"\n",
    "\n",
    "  # Feed the model the feature and the label.\n",
    "  # The model will train for the specified number of epochs. \n",
    "  history = model.fit(x=df[feature],\n",
    "                      y=df[label],\n",
    "                      batch_size=batch_size,\n",
    "                      epochs=epochs)\n",
    "\n",
    "# Gather the trained model's weight and bias.\n",
    "trained_weight = model.get_weights()[0]\n",
    "trained_bias = model.get_weights()[1]\n",
    "\n",
    "# The list of epochs is stored separately from the rest of history.\n",
    "epochs = history.epoch\n",
    "  \n",
    "# Isolate the error for each epoch.\n",
    "hist = pd.DataFrame(history.history)\n",
    "\n",
    "# To track the progression of training, we're going to take a snapshot\n",
    "# of the model's root mean squared error at each epoch. \n",
    "rmse = hist[\"root_mean_squared_error\"]\n",
    "\n",
    "return trained_weight, trained_bias, epochs, rmse\n",
    "\n",
    "print(\"Defined the create_model and traing_model functions.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defined the plot_the_model and plot_the_loss_curve functions.\n"
     ]
    }
   ],
   "source": [
    "# Define the plotting functions\n",
    "def plot_the_model(trained_weight, trained_bias, feature, label):\n",
    "  \"\"\"Plot the trained model against 200 random training examples.\"\"\"\n",
    "\n",
    "# Label the axes.\n",
    "plt.xlabel(feature)\n",
    "plt.ylabel(label)\n",
    "\n",
    "# Create a scatter plot from 200 random points of the dataset.\n",
    "random_examples = training_data.sample(n=200)\n",
    "plt.scatter(random_examples[feature], random_examples[label])\n",
    "\n",
    "# Create a red line representing the model. The red line starts\n",
    "# at coordinates (x0, y0) and ends at coordinates (x1, y1).\n",
    "x0 = 0\n",
    "y0 = trained_bias\n",
    "x1 = 10000\n",
    "y1 = trained_bias + (trained_weight * x1)\n",
    "plt.plot([x0, x1], [y0, y1], c='r')\n",
    "\n",
    "# Render the scatter plot and the red line.\n",
    "plt.show()\n",
    "\n",
    "\n",
    "def plot_the_loss_curve(epochs, rmse):\n",
    "  \"\"\"Plot a curve of loss vs. epoch.\"\"\"\n",
    "\n",
    "  plt.figure()\n",
    "  plt.xlabel(\"Epoch\")\n",
    "  plt.ylabel(\"Root Mean Squared Error\")\n",
    "\n",
    "  plt.plot(epochs, rmse, label=\"Loss\")\n",
    "  plt.legend()\n",
    "  plt.ylim([rmse.min()*0.97, rmse.max()])\n",
    "  plt.show()  \n",
    "\n",
    "print(\"Defined the plot_the_model and plot_the_loss_curve functions.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "567/567 [==============================] - 0s 571us/step - loss: 221281.5312 - root_mean_squared_error: 470.4057\n",
      "Epoch 2/30\n",
      "567/567 [==============================] - 0s 572us/step - loss: 27972.2109 - root_mean_squared_error: 167.2489\n",
      "Epoch 3/30\n",
      "567/567 [==============================] - 0s 559us/step - loss: 27385.3066 - root_mean_squared_error: 165.4851\n",
      "Epoch 4/30\n",
      "567/567 [==============================] - 0s 562us/step - loss: 26365.7285 - root_mean_squared_error: 162.3753\n",
      "Epoch 5/30\n",
      "567/567 [==============================] - 0s 564us/step - loss: 25592.8027 - root_mean_squared_error: 159.9775\n",
      "Epoch 6/30\n",
      "567/567 [==============================] - 0s 561us/step - loss: 24836.8555 - root_mean_squared_error: 157.5971\n",
      "Epoch 7/30\n",
      "567/567 [==============================] - 0s 564us/step - loss: 24457.1777 - root_mean_squared_error: 156.3879\n",
      "Epoch 8/30\n",
      "567/567 [==============================] - 0s 553us/step - loss: 23714.2949 - root_mean_squared_error: 153.9945\n",
      "Epoch 9/30\n",
      "567/567 [==============================] - 0s 561us/step - loss: 23099.7441 - root_mean_squared_error: 151.9860\n",
      "Epoch 10/30\n",
      "567/567 [==============================] - 0s 560us/step - loss: 22716.3359 - root_mean_squared_error: 150.7194\n",
      "Epoch 11/30\n",
      "567/567 [==============================] - 0s 557us/step - loss: 21903.8203 - root_mean_squared_error: 147.9994\n",
      "Epoch 12/30\n",
      "567/567 [==============================] - 0s 570us/step - loss: 21544.2227 - root_mean_squared_error: 146.7795\n",
      "Epoch 13/30\n",
      "567/567 [==============================] - 0s 558us/step - loss: 21069.9414 - root_mean_squared_error: 145.1549\n",
      "Epoch 14/30\n",
      "567/567 [==============================] - 0s 552us/step - loss: 20294.5059 - root_mean_squared_error: 142.4588\n",
      "Epoch 15/30\n",
      "567/567 [==============================] - 0s 567us/step - loss: 19987.1367 - root_mean_squared_error: 141.3759\n",
      "Epoch 16/30\n",
      "567/567 [==============================] - 0s 566us/step - loss: 19462.1582 - root_mean_squared_error: 139.5068\n",
      "Epoch 17/30\n",
      "567/567 [==============================] - 0s 557us/step - loss: 19152.6777 - root_mean_squared_error: 138.3932\n",
      "Epoch 18/30\n",
      "567/567 [==============================] - 0s 578us/step - loss: 18618.9453 - root_mean_squared_error: 136.4513\n",
      "Epoch 19/30\n",
      "567/567 [==============================] - 0s 565us/step - loss: 18343.5176 - root_mean_squared_error: 135.4382\n",
      "Epoch 20/30\n",
      "567/567 [==============================] - 0s 551us/step - loss: 17976.3926 - root_mean_squared_error: 134.0761\n",
      "Epoch 21/30\n",
      "567/567 [==============================] - 0s 550us/step - loss: 17531.5371 - root_mean_squared_error: 132.4067\n",
      "Epoch 22/30\n",
      "567/567 [==============================] - 0s 539us/step - loss: 17205.8887 - root_mean_squared_error: 131.1712\n",
      "Epoch 23/30\n",
      "567/567 [==============================] - 0s 566us/step - loss: 16794.0195 - root_mean_squared_error: 129.5917\n",
      "Epoch 24/30\n",
      "567/567 [==============================] - 0s 565us/step - loss: 16689.7578 - root_mean_squared_error: 129.1889\n",
      "Epoch 25/30\n",
      "567/567 [==============================] - 0s 548us/step - loss: 16363.4463 - root_mean_squared_error: 127.9197\n",
      "Epoch 26/30\n",
      "567/567 [==============================] - 0s 557us/step - loss: 16196.7510 - root_mean_squared_error: 127.2665\n",
      "Epoch 27/30\n",
      "567/567 [==============================] - 0s 566us/step - loss: 15863.9131 - root_mean_squared_error: 125.9520\n",
      "Epoch 28/30\n",
      "567/567 [==============================] - 0s 560us/step - loss: 15717.3379 - root_mean_squared_error: 125.3688\n",
      "Epoch 29/30\n",
      "567/567 [==============================] - 0s 567us/step - loss: 15506.4932 - root_mean_squared_error: 124.5251\n",
      "Epoch 30/30\n",
      "567/567 [==============================] - 0s 563us/step - loss: 15318.6768 - root_mean_squared_error: 123.7686\n",
      "\n",
      "The learned weight for your model is 0.0317\n",
      "The learned bias for your model is 131.4270\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5+7ZZX9Fh0BiDiEh5YwyfL9oeAba7e1sZ5w0CZ7h7j5mlgIfM7AfA5cDV7n5buKv6/cC14Xm/ux9vZu8GPkt0x3XV5LJJugeGcXfMrJofLSIyb8zYleTu/170+GmZQQGP9ITdVHgUVmW9I5TfDLw9bJ8V9gmvn2lV/nXOZ5MMjzqDI2PV/FgRkXmlnLWSus2sa4pHt5l1zXBuwsw2A7uBHwFPAx3uXuivaSNKGUp43gEQXu8Elk/xnheZ2UYz29je3l7udZYlr5wMIiJlDT5fDXyE6Ie7Ffgw8Pfunnf3plInuvuou68P550CvGyqw8LzVK0DP6jA/Xp33+DuG1paWsqofvm0XpKISHmB4U3u/s/u3u3uXe5+LfAns/kQd+8AHgReAzSbWWFsoxXYGbbbCIvzhdeXAPtm8zmHK5eJkvXo7mcRibNyAsOomZ0buoXqzOxcYHSmk8ysJayxhJnVA78LbAEeAN4RDrsA+F7YvivsE16/390PajFUUmHp7W6tlyQiMVbOrKRzgH8KD4CHQtlMVgE3m1mCKADd7u53m9mTwG1m9vfAr4Ebw/E3Al8zs61ELYV3l38Zc2M877NaDCISY+Xc+byNaMbQrLj7o8Arpih/hmi8YXL5AFESoJrJa4xBRGT6riQz+zMzWxe2zcxuMrNOM3vUzF5ZvSpWTz4bxhg0K0lEYqzUGMNlwLawfTZwMnAc0Q1q/zTNOQtaYyYBKDCISLyVCgwj7l4YhX0rcIu773X3HwONla9a9WWSCdLJOroGNPgsIvFVKjCMmdmqsObRmcCPi16rr2y1aiefUbIeEYm3UoPPHwM2AgngLnd/AsDMfgd4pgp1qwmtsCoicTdtYAhTS9cAeXffX/TSRqq8uF015ZTeU0RiruR01bBm0f5JZb0VrVGN5TJJTVcVkVg7lEQ9i1o+m6JbXUkiEmMKDJPkM0l6tCSGiMRYOUtiYGargTXFx7v7TypVqVqKkvWoxSAi8VVOas9CJrUnObB4nhOl7Vx0cmG6qrK4iUhcldNieDvwEncfrHRl5oN8NsXIWJTFLZtK1Lo6IiJVV84YwzNEaTljQcl6RCTuymkx9AGbzew+YLzV4O6XVqxWNVRI79k9MExLPlPj2oiIVF85geGu8IiFnPI+i0jMlZOP4eZqVGS+KORk0N3PIhJX5cxKWgf8A3ACkC2Uu/txFaxXzYyPMajFICIxVc7g81eAa4ER4I3ALcDXKlmpWspnonF2DT6LSFyVExjq3f0+wNx9u7t/AjijstWqnQNdSbr7WUTiqZzB5wEzqwOeMrNLgOeAlZWtVu00avBZRGKunBbDB4EG4FLgVcB7gAsqWalaSifryCTr1JUkIrFVzqykXwKYmbv7eytfpdrLZ5MafBaR2JqxxWBmrzWzJ4EtYf9kM/vnMs472sweMLMtZvaEmV0Wyj9hZs+Z2ebweEvROVea2VYz+42Zvekwruuw5LMpTVcVkdgqZ4zhi8CbCDe5ufsjZvb6Ms4bAT7k7r8yszywycx+FF672t0/X3ywmZ0AvBt4OXAU8GMze7G7j1JluYzSe4pIfJWVj8Hdd0wqmvHH2t13ufuvwnY3UYtjdYlTzgJuc/dBd/8tsBU4pZz6zbUoi5tmJYlIPJUTGHaY2WmAm1nazP6K0K1ULjNbC7wCeDgUXWJmj5rZTWa2NJStBooDUBtTBBIzu8jMNprZxvb29tlUo2zKySAicVZOYPgAcDHRj3QbsD7sl8XMcsC3gQ+6exfRzXIvCu+zC/jHwqFTnO4HFbhf7+4b3H1DS0tLudWYlXxWXUkiEl/lzEraA5x7KG9uZimioHCru98Z3u+FotdvAO4Ou23A0UWntwI7D+VzD1c+oxaDiMTXtIHBzK4pdeJMy25blP7sRmCLu3+hqHyVu+8Ku38MPB627wK+YWZfIBp8Xgf8YsYrqIBcaDEoi5uIxFGpFsMHiH60byf6y322v5CnA+cBj5nZ5lD2UeBsM1tP1E20DfhzAHd/wsxuJ0ohOgJcXIsZSRBNVx0dcwaGx6hPK4ubiMRLqcCwCngnUb7nEeCbwLfdfX85b+zuDzF1MPl+iXM+BXyqnPevpEJOhu7BYQUGEYmdaQef3X2vu1/n7m8ELgSagSfM7LxqVa5W8krvKSIxVk4+hlcCZwO/B/wA2FTpStXaeBY3BQYRiaFSg8+fBN5KdM/CbcCV7h6LX8p8NsrJoCmrIhJHpVoMfws8A5wcHp8OM3QMcHc/qfLVq43xMQa1GEQkhkoFhmOrVot55sAYg5bFEJH4mTYwuPv2alZkPhnP4qauJBGJobIW0YubRg0+i0iMKTBMIZWoI5uqU7IeEYmlchL1XFZO2WKTy6Q0+CwisVROi2Gq/M4XznE95p0mrbAqIjFV6j6Gs4FzgGPN7K6il/LA3kpXrNZy2SQ9mpUkIjFUarrqfxLlS1jBgZwJAN3Ao5Ws1HyQ09LbIhJTpdZK2u7uD7r7a4H/Imop5IG2ONwBrbzPIhJX5Qw+v5MoL8I7gT8FHjazd1S6YrWWz2rwWUTiacZF9IC/AV7t7rsBzKwF+DFwRyUrVmv5bFJ3PotILJUzK6muEBSCvWWet6AVupLcD0o7LSKyqJXTYvg3M7sX+Jew/y5KJNtZLPLZJGMO/cOjNKTL+c8kIrI4zPiL5+5XmNn/BF5HtLLq9e7+nYrXrMZy2QPLYigwiEiclPuL91NgmChP8y8qV535o7D0dtfACCubalwZEZEqKmdW0p8SBYN3EKtZSVphVUTiqZwWw/8mlrOSQhY3TVkVkZjRrKRpjOd9HtSUVRGJl3J+4P/NzO41swvN7ELgHuAHM51kZkeb2QNmtsXMniisyGpmy8zsR2b2VHheGsrNzK4xs61m9qiZvfJwLuxwFY8xiIjEyYyBwd2vAP4fcBJR7ufr3f2vy3jvEeBD7v4y4DXAxWZ2AvAR4D53XwfcF/YB3gysC4+LgGtneS1zqkldSSISU2XNSnL3O4E7AcwsYWbnuvutM5yzi2gRPty928y2AKuBs4A3hMNuBh4EPhzKb/HojrKfm1mzma0K71N1jZkEoMFnEYmfaVsMZtZkZlea2ZfM7PdDV88lwDNEs5PKZmZrgVcADwNHFH7sw/PKcNhqYEfRaW2hbPJ7XWRmG81sY3t7+2yqMSvJRB31qYSWxRCR2CnVlfQ14CXAY8D/An5ItJDeWe5+VrkfYGY54NvAB929q9ShU5QdtB6Fu1/v7hvcfUNLS0u51TgkOSXrEZEYKtWVdJy7/w8AM/sysAc4xt27y31zM0sRBYVbQ3cUwAuFLiIzWwUUZjy1AUcXnd4K7Cz3syohWkhPgUFE4qVUi2G8D8XdR4HfzjIoGHAjsMXdv1D00l0cSBd6AfC9ovLzQ5fVa4DOWo0vFOSVk0FEYqhUi+FkMyt0/RhQH/YNcHefaaGI04HzgMfMbHMo+yjwGeB2M3s/8CxR9xREC/O9BdgK9AHvne3FzLWcWgwiEkPTBgZ3TxzOG7v7Q0w9bgBw5hTHO3Dx4XzmXMtlkuzp7qt1NUREqmrR38F8OPLZlLqSRCR2FBhKyGWSdGm6qojEjAJDCfmssriJSPwoMJSQzyZxh76h0VpXRUSkahQYSshlwnpJGmcQkRhRYCihkN5Ty2KISJwoMJSQzxQCg1oMIhIfCgwlKL2niMSRAkMJB7qSFBhEJD4UGEoYT++pwCAiMaLAUEI+ZHHrVleSiMSIAkMJajGISBwpMJSQqDMa0sriJiLxosAwg1wmya7OAfb1DmlpDBGJhVL5GARoyWe457Fd3PPYLtKJOlY2ZTiyKcsR4XHkksz49lFL6mldWk9d3XSrjYuIzH8KDDO44fwNbN7RwQtdAzzfNcALnQO80DXIll1dPPCb3Qeto9SQTvCSI/O8bFUTL1vVxAmr8rz0yCYaM/pPLSILg36tZnBUcz1HNddP+Zq70zM4wgtdUbBo29/Hll3dbNnVxd2P7OQbDz87fuya5Q287MimEDDyvPiIPKuX1pNKqDdPROYXBYbDYGbksyny2RTHr8xPeM3d2dk5wJM7u9iy68Dj3iefpzBUkagzWpfWs3Z5I2uXN7B2RSNrlzeyZnkDRy9rUNAQkZpQYKgQM2N1cz2rm+v5vROOGC/vHRzhv57v5pn2Hrbv7WPb3l627e1l0/b9E5beSNRF569Z3jAeLNYub2TtigZalzaQTR1W5lURkWkpMFRZYybJq9Ys5VVrlk4od3f29g6xfW8v2/YUAkYf2/b08t0dz01YlsMMjloSBY01obWxZnkjRy7J0lyfYmlDmnw2qUFwETkkCgzzhJmxIpdhRS7Dq9Ysm/Cau9PRN8y2vb3jrYzC871PPM++3qEp3g+WhCCxpD5Fc0OK5voUzQ1pljWmJwSV5oZ0tS5TRBYABYYFwMxY2phmaWOaVxyz9KDXO/uH2b63lz09g3T0DbO/b5jOviE6+qPtjr4h9vUO8Ux7L/v7hg5aFHBJfSqMbxwIFoXxjqUNKczU8hCJk4oFBjO7CXgrsNvdTwxlnwD+DGgPh33U3b8fXrsSeD8wClzq7vdWqm6LzZL6FCe1Npd9/MDwKDv29bFtbx/b9/by2z1RC2TT9v3c9chOiu/ja0wnWL20fnx2VmHc5KjmelYvreeIfIakBslFFpVKthi+CnwJuGVS+dXu/vniAjM7AXg38HLgKODHZvZid1ey5QrIphKsOyLPuiPyB702ODLKjn394wHjuY5+ntvfz87Ofh7Z0cH+vonLg9QZHNmUZVVzPUsb0ixtSLGsMU1z2C50XRW2mxtSmm0lMs9VLDC4+0/MbG2Zh58F3Obug8BvzWwrcArwswpVT6aRSSY4fmWO41fmpny9b2iEnR0DPNfRz85C0OjoZ1fnAG37+3jsuSH29w0zNDI27Wcc0ZTh+JU51q3M86KVOY5viT5vRS6tbiuReaAWYwyXmNn5wEbgQ+6+H1gN/LzomLZQdhAzuwi4COCYY46pcFVlsoZ0smTggGiwvH94lP19w+zvHWJ/39CEsY5n9/Xx9O4evrVxB71Fd44vqU+FgBG9/7ErGsdbH831KZrqUyQ000qk4qodGK4F/g7w8PyPwPuAqf61T7linbtfD1wPsGHDBq1qNw+ZGQ3pJA3pJKunuWscogCyq3OArbt7okd79PzDJ1/gtl/umOJ9oSmbYmlDiiWFrqow02p5Y5qVTRla8hlW5rOszGdY1pjW+IfIIahqYHD3FwrbZnYDcHfYbQOOLjq0FdhZxapJDZjZ+KD261/cMuG1feGejmiW1RAdfcN09EetjkLZvt4hnm7voaN3eMpkSnUGyxoLwSJ6HLkky3EtjRzfkue4lkatYSUyhar+qzCzVe6+K+z+MfB42L4L+IaZfYFo8Hkd8Itq1k3ml2WN0aB1uQaGR9nTM8ju7kF2dw3S3jNIe9cA7T0H9n/zfDftPYOMjh1oaB61JBuNcxQeLTletDLH8kaNd0h8VXK66r8AbwBWmFkb8HHgDWa2nqibaBvw5wDu/oSZ3Q48CYwAF2tGksxGNpWgdWm0XEgpQyNjPLuv90D3VejCuu0XO+gfPvC/XHNDiiObsjTVp2jKplhSn6KpPhk9Z6Pxjmg7ydLGNEc0ZWnKJhVMZFGwhZx8ZsOGDb5x48ZaV0MWgbExZ2dnP1t39/B0exQ49vQM0tU/TGf/MN0DI3T2D09Yz2qyhnSCVUuyrFpSz5FLsuPbq5ZkOXJJlK+jqV7BQ2rPzDa5+4bpXlcHqwhQV2fjLY43vGT640ZGx+geGKFrYJiu/ihY7Osb4oXOAXZ1DrCrM5q6+9BTe9jdPcDYpL+7knV2oLURWhxLwn7xo7khzaolWY5qrmd5Y1rrXklVKTCIzEIyUTe+PMlMRkbH2N09yK7OAZ4PQWNf7xCdoRXS2T9MV/8wbfv7x/dHJ0cSIJ2oi1oczVGrY1VzFDAK28sa0zRlU1pxV+aMAoNIhSQTdSUTPU3m7vQOjdLZH93/UbhxcGdnPzs7BtjV0c/Dv93H810D0waQfDZJU30qes5Gz4XtpvoUSxujqb1LG9Isz0UD/M31KU3rlQkUGETmCTMjl0mSy0T3f5y4esmUx42MjtHeM8jOjl0UHnkAAAhnSURBVAF2dvTT0TdEV+je6h4Yibq6+ofpHhjmha6B8fLJaWgPfG50c+GyxjTLwhImK/IZjshnWdlUmOqb5YimDMtzGd1kGAMKDCILTDJRFwa16w/K61HK0MgYHX1D7O0dYn9veO4bYm9PdE/Ivr4h9vUMsX1vHxu3759yOfc6g+W5KFgc0ZSlJZehuTFa3r1ws2FhXazCczqp1shCo8AgEhPpZB0rm7KsbMqWdfzQyBh7egZ5oWsguj+ke5DdXQPs7hpkd3c0bvL4c5109A0zNDr92lgN6cR411VLLrrhcPwxab8hrZ+k+UDfgohMKZ0sb4zE3ekbGo3yf/QW7lIP62P1FvKCRC2TXZ0DPPpcJ3t7Bg+asQXRMu9LG6NWRjpRRzpZRypRRyphpBJRWSpRRyoZlTVloyRUS8PKvcvC+EmhrCGd0PTgQ6DAICKHxcxozCRpzJReG6vY6Jizr3eI9u7orvQ94bm9e5D9vUMMjY4xPDrG0MgYw6PO0OgYPYMjE8tGxsbHT6aTTtZFa2vVp6hPJ6lP1ZFNJagPj2w6PKfqwnNiPPPh0nD3/bIYpspVYBCRqkvU2Xj30eEaGR0bX0drX29hba2oxbK/b4iO3mgqcP/wKP3Do+zrHaJ/aJSBkVH6h8YYGB6lb2hkyhZMcX2XNhQFjJAyNxsCTSYEl2wyCi7ZVB2ZZGL89YZ0glwmSUMmSS6dpDGTmNczwRQYRGRBSybqxvOlHyp3Z3jU6R8apWtgeHwwfn9vNDC/vxB0QvnT7T109g8zMDzKwMhYyfwj08kk60JLK0FjOjkeOBrTCerTUVlDOhFWKk7QkElM2F+zrJFjlpdeAuZQKTCISOyZGemkkU7WsaQhxdHLZveDOzYWdXcNDI8yMDzG4Ej0HO2P0jc8Su/gCL2DI/QMjtI3OELP0EgoG6VncIS+oehO+uc7++kdjFo3vYMjDE4TdD7wOy/iI29+6Vxc/kEUGEREDlNdnZGtS1Tk7vPRsSjxVd9gdC9K79AI/UOjc9INNx0FBhGReSxRd+DGx2pZ0Kurmlk7sP0QT18B7JnD6swHi+2aFtv1wOK7psV2PbD4rmmq61nj7i1THQwLPDAcDjPbWGrZ2YVosV3TYrseWHzXtNiuBxbfNR3K9czf+VIiIlITCgwiIjJBnAPD9bWuQAUstmtabNcDi++aFtv1wOK7pllfT2zHGEREZGpxbjGIiMgUFBhERGSCWAYGM/sDM/uNmW01s4/Uuj5zwcy2mdljZrbZzDbWuj6zZWY3mdluM3u8qGyZmf3IzJ4Kz+VnpZkHprmmT5jZc+F72mxmb6llHWfDzI42swfMbIuZPWFml4XyBfk9lbiehfwdZc3sF2b2SLimT4byY83s4fAdfdPMSiYtj90Yg5klgP8Gfg9oA34JnO3uT9a0YofJzLYBG9x9Qd6YY2avB3qAW9z9xFD2f4B97v6ZEMCXuvuHa1nP2Zjmmj4B9Lj752tZt0NhZquAVe7+KzPLA5uAtwMXsgC/pxLX86cs3O/IgEZ37zGzFPAQcBlwOXCnu99mZtcBj7j7tdO9TxxbDKcAW939GXcfAm4DzqpxnWLP3X8C7JtUfBZwc9i+megf7YIxzTUtWO6+y91/Fba7gS3Aahbo91TiehYsj/SE3VR4OHAGcEcon/E7imNgWA3sKNpvY4H/zxA48EMz22RmF9W6MnPkCHffBdE/YmBljeszVy4xs0dDV9OC6HaZzMzWAq8AHmYRfE+TrgcW8HdkZgkz2wzsBn4EPA10uHsho9GMv3lxDAxTpWFaDP1pp7v7K4E3AxeHbgyZf64FXgSsB3YB/1jb6syemeWAbwMfdPeuWtfncE1xPQv6O3L3UXdfD7QS9ZC8bKrDSr1HHANDG3B00X4rsLNGdZkz7r4zPO8GvkP0P8RC90LoBy70B++ucX0Om7u/EP7hjgE3sMC+p9Bv/W3gVne/MxQv2O9pqutZ6N9Rgbt3AA8CrwGazaywPOuMv3lxDAy/BNaFUfo08G7grhrX6bCYWWMYPMPMGoHfBx4vfdaCcBdwQdi+APheDesyJwo/oMEfs4C+pzCweSOwxd2/UPTSgvyeprueBf4dtZhZc9iuB36XaOzkAeAd4bAZv6PYzUoCCNPPvggkgJvc/VM1rtJhMbPjiFoJEOXY+MZCuyYz+xfgDURLBL8AfBz4LnA7cAzwLPBOd18wg7nTXNMbiLooHNgG/Hmhf36+M7PXAf8BPAYU0op9lKhffsF9TyWu52wW7nd0EtHgcoLoD//b3f2q8BtxG7AM+DXwHncfnPZ94hgYRERkenHsShIRkRIUGEREZAIFBhERmUCBQUREJlBgEBGRCRQYREows9GiVTY3z+VqvGa2tnjlVZH5IjnzISKx1h+WFxCJDbUYRA5ByH/x2bD2/S/M7PhQvsbM7gsLsN1nZseE8iPM7DthnfxHzOy08FYJM7shrJ3/w3C3qkhNKTCIlFY/qSvpXUWvdbn7KcCXiO6kJ2zf4u4nAbcC14Tya4B/d/eTgVcCT4TydcD/dfeXAx3An1T4ekRmpDufRUowsx53z01Rvg04w92fCQuxPe/uy81sD1Hyl+FQvsvdV5hZO9BavAxBWOr5R+6+Lux/GEi5+99X/spEpqcWg8ih82m2pztmKsXr1YyicT+ZBxQYRA7du4qefxa2/5NoxV6Ac4lSKwLcB/wFjCdSaapWJUVmS3+diJRWH7JhFfybuxemrGbM7GGiP7DODmWXAjeZ2RVAO/DeUH4ZcL2ZvZ+oZfAXRElgROYdjTGIHIIwxrDB3ffUui4ic01dSSIiMoFaDCIiMoFaDCIiMoECg4iITKDAICIiEygwiIjIBAoMIiIywf8HHjflXReujboAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calling the model functions. The following variables are the hyperparameters.\n",
    "# Use total_rooms as a single feature\n",
    "learning_rate = 0.01\n",
    "epochs = 30\n",
    "batch_size = 30\n",
    "\n",
    "# Specify the feature and the label.\n",
    "my_feature = \"total_rooms\"  # the total number of rooms on a specific city block.\n",
    "my_label=\"median_house_value\" # the median value of a house on a specific city block.\n",
    "# That is, you're going to create a model that predicts house value based \n",
    "# solely on total_rooms.  \n",
    "\n",
    "# Discard any pre-existing version of the model.\n",
    "my_model = None\n",
    "\n",
    "# Invoke the functions.\n",
    "my_model = build_model(learning_rate)\n",
    "weight, bias, epochs, rmse = train_model(my_model, training_data, \n",
    "                                         my_feature, my_label,\n",
    "                                         epochs, batch_size)\n",
    "\n",
    "print(\"\\nThe learned weight for your model is %.4f\" % weight)\n",
    "print(\"The learned bias for your model is %.4f\\n\" % bias )\n",
    "\n",
    "plot_the_model(weight, bias, my_feature, my_label)\n",
    "plot_the_loss_curve(epochs, rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the model to make predictions\n",
    "\n",
    "def predict_house_values(n, feature, label):\n",
    "  \"\"\"Predict house values based on a feature.\"\"\"\n",
    "\n",
    "batch = training_data[feature][10000:10000 + n]\n",
    "predicted_values = my_model.predict_on_batch(x=batch)\n",
    "\n",
    "print(\"feature   label          predicted\")\n",
    "print(\"  value   value          value\")\n",
    "print(\"          in thousand$   in thousand$\")\n",
    "print(\"--------------------------------------\")\n",
    "for i in range(n):\n",
    "print (\"%5.0f %6.0f %15.0f\" % (training_data[feature][10000 + i],\n",
    "                                   training_data[label][10000 + i],\n",
    "                                   predicted_values[i][0] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature   label          predicted\n",
      "  value   value          value\n",
      "          in thousand$   in thousand$\n",
      "--------------------------------------\n",
      " 1960     53             194\n",
      " 3400     92             239\n",
      " 3677     69             248\n",
      " 2202     62             201\n",
      " 2403     80             208\n",
      " 5652    295             311\n",
      " 3318    500             237\n",
      " 2552    342             212\n",
      " 1364    118             175\n",
      " 3468    128             241\n"
     ]
    }
   ],
   "source": [
    "# Predicted Values\n",
    "predict_house_values(10, my_feature, my_label)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Most of the predicted values differ significantly\n",
    "# from the label value, so the trained model probably \n",
    "# doesn't have much predictive power. However, the\n",
    "# first 10 examples might not be representative of \n",
    "# the rest of the examples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "142/142 [==============================] - 0s 592us/step - loss: 549271.0000 - root_mean_squared_error: 741.1282\n",
      "Epoch 2/3\n",
      "142/142 [==============================] - 0s 576us/step - loss: 441103.9688 - root_mean_squared_error: 664.1566\n",
      "Epoch 3/3\n",
      "142/142 [==============================] - 0s 572us/step - loss: 495452.3438 - root_mean_squared_error: 703.8837\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature   label          predicted\n",
      "  value   value          value\n",
      "          in thousand$   in thousand$\n",
      "--------------------------------------\n",
      "  393     53             161\n",
      "  618     92             199\n",
      "  863     69             240\n",
      "  471     62             174\n",
      "  483     80             176\n",
      " 1313    295             316\n",
      "  441    500             169\n",
      "  443    342             169\n",
      "  282    118             142\n",
      "  675    128             209\n",
      "  363    187             156\n",
      "  166     80             123\n",
      " 1075    112             276\n",
      "  741     95             220\n",
      "  663     69             207\n"
     ]
    }
   ],
   "source": [
    "# Try a different feature to see if it will have a greater predictive power\n",
    "\n",
    "my_feature = \"total_bedrooms\"   # Replace the ? with population or possibly\n",
    "                   # a different column name.\n",
    "\n",
    "# Experiment with the hyperparameters.\n",
    "learning_rate = 2\n",
    "epochs = 3\n",
    "batch_size = 120\n",
    "\n",
    "# Don't change anything below this line.\n",
    "my_model = build_model(learning_rate)\n",
    "weight, bias, epochs, rmse = train_model(my_model, training_data, \n",
    "                                         my_feature, my_label,\n",
    "                                         epochs, batch_size)\n",
    "plot_the_model(weight, bias, my_feature, my_label)\n",
    "plot_the_loss_curve(epochs, rmse)\n",
    "\n",
    "predict_house_values(15, my_feature, my_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/18\n",
      "5667/5667 [==============================] - 3s 600us/step - loss: 31700.3379 - root_mean_squared_error: 178.0459\n",
      "Epoch 2/18\n",
      "5667/5667 [==============================] - 3s 577us/step - loss: 20372.4922 - root_mean_squared_error: 142.7322 1s - loss: 21079.1309 - root_mean_squ\n",
      "Epoch 3/18\n",
      "5667/5667 [==============================] - 3s 561us/step - loss: 18473.3906 - root_mean_squared_error: 135.9169\n",
      "Epoch 4/18\n",
      "5667/5667 [==============================] - 3s 582us/step - loss: 17998.3574 - root_mean_squared_error: 134.1580\n",
      "Epoch 5/18\n",
      "5667/5667 [==============================] - 3s 554us/step - loss: 18152.0312 - root_mean_squared_error: 134.7295\n",
      "Epoch 6/18\n",
      "5667/5667 [==============================] - 3s 558us/step - loss: 18184.9434 - root_mean_squared_error: 134.8516\n",
      "Epoch 7/18\n",
      "5667/5667 [==============================] - 3s 559us/step - loss: 18141.6250 - root_mean_squared_error: 134.6909\n",
      "Epoch 8/18\n",
      "5667/5667 [==============================] - 3s 554us/step - loss: 17793.6406 - root_mean_squared_error: 133.3928\n",
      "Epoch 9/18\n",
      "5667/5667 [==============================] - 3s 557us/step - loss: 17820.2578 - root_mean_squared_error: 133.4925\n",
      "Epoch 10/18\n",
      "5667/5667 [==============================] - 3s 557us/step - loss: 18259.0879 - root_mean_squared_error: 135.1262\n",
      "Epoch 11/18\n",
      "5667/5667 [==============================] - 3s 568us/step - loss: 18192.5879 - root_mean_squared_error: 134.8799\n",
      "Epoch 12/18\n",
      "5667/5667 [==============================] - 3s 557us/step - loss: 18157.5332 - root_mean_squared_error: 134.7499 1s - loss: 17915.812\n",
      "Epoch 13/18\n",
      "5667/5667 [==============================] - 3s 558us/step - loss: 18052.9219 - root_mean_squared_error: 134.3612\n",
      "Epoch 14/18\n",
      "5667/5667 [==============================] - 3s 576us/step - loss: 18537.0684 - root_mean_squared_error: 136.1509\n",
      "Epoch 15/18\n",
      "5667/5667 [==============================] - 3s 556us/step - loss: 18171.6016 - root_mean_squared_error: 134.8021\n",
      "Epoch 16/18\n",
      "5667/5667 [==============================] - 3s 554us/step - loss: 18267.8633 - root_mean_squared_error: 135.1586\n",
      "Epoch 17/18\n",
      "5667/5667 [==============================] - 3s 563us/step - loss: 18681.7305 - root_mean_squared_error: 136.6811 1s - loss: 19021.3672 - root_mean\n",
      "Epoch 18/18\n",
      "5667/5667 [==============================] - 3s 564us/step - loss: 18163.0352 - root_mean_squared_error: 134.7703\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature   label          predicted\n",
      "  value   value          value\n",
      "          in thousand$   in thousand$\n",
      "--------------------------------------\n",
      " 1286     53             271\n",
      " 1867     92             299\n",
      " 2191     69             314\n",
      " 1052     62             260\n",
      " 1647     80             288\n",
      " 2312    295             320\n",
      " 1604    500             286\n",
      " 1066    342             261\n",
      "  338    118             226\n",
      " 1604    128             286\n"
     ]
    }
   ],
   "source": [
    "# A new feature for more predictive power.\n",
    "\n",
    "my_feature = \"population\" # Pick a feature other than \"total_rooms\"\n",
    "\n",
    "# Possibly, experiment with the hyperparameters.\n",
    "learning_rate = 0.05\n",
    "epochs = 18\n",
    "batch_size = 3\n",
    "\n",
    "# Don't change anything below.\n",
    "my_model = build_model(learning_rate)\n",
    "weight, bias, epochs, rmse = train_model(my_model, training_data, \n",
    "                                         my_feature, my_label,\n",
    "                                         epochs, batch_size)\n",
    "\n",
    "plot_the_model(weight, bias, my_feature, my_label)\n",
    "plot_the_loss_curve(epochs, rmse)\n",
    "\n",
    "predict_house_values(10, my_feature, my_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Training is not entirely deterministic, but population \n",
    " typically converges at a slightly higher RMSE than \n",
    " total_rooms.  So, population appears to be about \n",
    " the same or slightly worse at making predictions \n",
    " than total_rooms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/7\n",
      "1134/1134 [==============================] - 1s 648us/step - loss: 16050.7900 - root_mean_squared_error: 126.6917\n",
      "Epoch 2/7\n",
      "1134/1134 [==============================] - 1s 559us/step - loss: 13465.8633 - root_mean_squared_error: 116.0425\n",
      "Epoch 3/7\n",
      "1134/1134 [==============================] - 1s 567us/step - loss: 13466.5215 - root_mean_squared_error: 116.0453\n",
      "Epoch 4/7\n",
      "1134/1134 [==============================] - 1s 560us/step - loss: 13471.7949 - root_mean_squared_error: 116.0681\n",
      "Epoch 5/7\n",
      "1134/1134 [==============================] - 1s 548us/step - loss: 13470.0908 - root_mean_squared_error: 116.0607\n",
      "Epoch 6/7\n",
      "1134/1134 [==============================] - 1s 569us/step - loss: 13470.8584 - root_mean_squared_error: 116.0640\n",
      "Epoch 7/7\n",
      "1134/1134 [==============================] - 1s 565us/step - loss: 13470.6963 - root_mean_squared_error: 116.0633\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature   label          predicted\n",
      "  value   value          value\n",
      "          in thousand$   in thousand$\n",
      "--------------------------------------\n",
      "    1     53             204\n",
      "    1     92             204\n",
      "    1     69             204\n",
      "    1     62             204\n",
      "    1     80             204\n",
      "    1    295             204\n",
      "    1    500             204\n",
      "    1    342             204\n",
      "    1    118             204\n",
      "    1    128             204\n",
      "    1    187             204\n",
      "    1     80             204\n",
      "    1    112             204\n",
      "    1     95             204\n",
      "    1     69             204\n"
     ]
    }
   ],
   "source": [
    "# Define a synthetic feature named rooms_per_person\n",
    "training_data[\"rooms_per_person\"] = 1 # write your code here.\n",
    "\n",
    "# Don't change the next line.\n",
    "my_feature = \"rooms_per_person\"\n",
    "\n",
    "# Assign values to these three hyperparameters.\n",
    "learning_rate = 0.5\n",
    "epochs = 7\n",
    "batch_size = 15\n",
    "\n",
    "# Don't change anything below this line.\n",
    "my_model = build_model(learning_rate)\n",
    "weight, bias, epochs, rmse = train_model(my_model, training_data,\n",
    "                                         my_feature, my_label,\n",
    "                                         epochs, batch_size)\n",
    "\n",
    "plot_the_loss_curve(epochs, rmse)\n",
    "predict_house_values(15, my_feature, my_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/24\n",
      "567/567 [==============================] - 0s 547us/step - loss: 39081.7969 - root_mean_squared_error: 197.6912\n",
      "Epoch 2/24\n",
      "567/567 [==============================] - 0s 582us/step - loss: 19313.0801 - root_mean_squared_error: 138.9715\n",
      "Epoch 3/24\n",
      "567/567 [==============================] - 0s 567us/step - loss: 15117.7373 - root_mean_squared_error: 122.9542\n",
      "Epoch 4/24\n",
      "567/567 [==============================] - 0s 549us/step - loss: 14731.6475 - root_mean_squared_error: 121.3740\n",
      "Epoch 5/24\n",
      "567/567 [==============================] - 0s 567us/step - loss: 14535.4268 - root_mean_squared_error: 120.5630\n",
      "Epoch 6/24\n",
      "567/567 [==============================] - 0s 550us/step - loss: 14421.1240 - root_mean_squared_error: 120.0880\n",
      "Epoch 7/24\n",
      "567/567 [==============================] - 0s 563us/step - loss: 14254.9580 - root_mean_squared_error: 119.3941\n",
      "Epoch 8/24\n",
      "567/567 [==============================] - 0s 569us/step - loss: 14142.9385 - root_mean_squared_error: 118.9241\n",
      "Epoch 9/24\n",
      "567/567 [==============================] - 0s 561us/step - loss: 13999.5459 - root_mean_squared_error: 118.3197\n",
      "Epoch 10/24\n",
      "567/567 [==============================] - 0s 564us/step - loss: 13930.5186 - root_mean_squared_error: 118.0276\n",
      "Epoch 11/24\n",
      "567/567 [==============================] - 0s 570us/step - loss: 13837.9619 - root_mean_squared_error: 117.6349\n",
      "Epoch 12/24\n",
      "567/567 [==============================] - 0s 551us/step - loss: 13758.7646 - root_mean_squared_error: 117.2978\n",
      "Epoch 13/24\n",
      "567/567 [==============================] - 0s 551us/step - loss: 13738.4775 - root_mean_squared_error: 117.2113\n",
      "Epoch 14/24\n",
      "567/567 [==============================] - 0s 559us/step - loss: 13646.8691 - root_mean_squared_error: 116.8198\n",
      "Epoch 15/24\n",
      "567/567 [==============================] - 0s 573us/step - loss: 13615.5264 - root_mean_squared_error: 116.6856\n",
      "Epoch 16/24\n",
      "567/567 [==============================] - 0s 577us/step - loss: 13556.3350 - root_mean_squared_error: 116.4317\n",
      "Epoch 17/24\n",
      "567/567 [==============================] - 0s 568us/step - loss: 13496.6738 - root_mean_squared_error: 116.1752\n",
      "Epoch 18/24\n",
      "567/567 [==============================] - 0s 599us/step - loss: 13467.7959 - root_mean_squared_error: 116.0508\n",
      "Epoch 19/24\n",
      "567/567 [==============================] - 0s 558us/step - loss: 13439.6973 - root_mean_squared_error: 115.9297\n",
      "Epoch 20/24\n",
      "567/567 [==============================] - 0s 557us/step - loss: 13426.9346 - root_mean_squared_error: 115.8746\n",
      "Epoch 21/24\n",
      "567/567 [==============================] - 0s 577us/step - loss: 13397.0146 - root_mean_squared_error: 115.7455\n",
      "Epoch 22/24\n",
      "567/567 [==============================] - 0s 568us/step - loss: 13357.0342 - root_mean_squared_error: 115.5726\n",
      "Epoch 23/24\n",
      "567/567 [==============================] - 0s 557us/step - loss: 13351.3145 - root_mean_squared_error: 115.5479\n",
      "Epoch 24/24\n",
      "567/567 [==============================] - 0s 561us/step - loss: 13312.3271 - root_mean_squared_error: 115.3791\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x645827440> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "feature   label          predicted\n",
      "  value   value          value\n",
      "          in thousand$   in thousand$\n",
      "--------------------------------------\n",
      "    2     53             190\n",
      "    2     92             201\n",
      "    2     69             196\n",
      "    2     62             212\n",
      "    1     80             187\n",
      "    2    295             225\n",
      "    2    500             211\n",
      "    2    342             223\n",
      "    4    118             286\n",
      "    2    128             214\n",
      "    2    187             224\n",
      "    3     80             234\n",
      "    2    112             225\n",
      "    2     95             219\n",
      "    2     69             211\n"
     ]
    }
   ],
   "source": [
    "# Define a synthetic feature\n",
    "training_data[\"rooms_per_person\"] = training_data[\"total_rooms\"] / training_data[\"population\"]\n",
    "my_feature = \"rooms_per_person\"\n",
    "\n",
    "# Tune the hyperparameters.\n",
    "learning_rate = 0.06\n",
    "epochs = 24\n",
    "batch_size = 30\n",
    "\n",
    "# Don't change anything below this line.\n",
    "my_model = build_model(learning_rate)\n",
    "weight, bias, epochs, mae = train_model(my_model, training_data,\n",
    "                                        my_feature, my_label,\n",
    "                                        epochs, batch_size)\n",
    "\n",
    "plot_the_loss_curve(epochs, mae)\n",
    "predict_house_values(15, my_feature, my_label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>rooms_per_person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rooms_per_person</th>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude                 1.0      -0.9                -0.1          0.0   \n",
       "latitude                 -0.9       1.0                 0.0         -0.0   \n",
       "housing_median_age       -0.1       0.0                 1.0         -0.4   \n",
       "total_rooms               0.0      -0.0                -0.4          1.0   \n",
       "total_bedrooms            0.1      -0.1                -0.3          0.9   \n",
       "population                0.1      -0.1                -0.3          0.9   \n",
       "households                0.1      -0.1                -0.3          0.9   \n",
       "median_income            -0.0      -0.1                -0.1          0.2   \n",
       "median_house_value       -0.0      -0.1                 0.1          0.1   \n",
       "rooms_per_person         -0.1       0.1                -0.1          0.1   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                      0.1         0.1         0.1           -0.0   \n",
       "latitude                      -0.1        -0.1        -0.1           -0.1   \n",
       "housing_median_age            -0.3        -0.3        -0.3           -0.1   \n",
       "total_rooms                    0.9         0.9         0.9            0.2   \n",
       "total_bedrooms                 1.0         0.9         1.0           -0.0   \n",
       "population                     0.9         1.0         0.9           -0.0   \n",
       "households                     1.0         0.9         1.0            0.0   \n",
       "median_income                 -0.0        -0.0         0.0            1.0   \n",
       "median_house_value             0.0        -0.0         0.1            0.7   \n",
       "rooms_per_person               0.0        -0.1        -0.0            0.2   \n",
       "\n",
       "                    median_house_value  rooms_per_person  \n",
       "longitude                         -0.0              -0.1  \n",
       "latitude                          -0.1               0.1  \n",
       "housing_median_age                 0.1              -0.1  \n",
       "total_rooms                        0.1               0.1  \n",
       "total_bedrooms                     0.0               0.0  \n",
       "population                        -0.0              -0.1  \n",
       "households                         0.1              -0.0  \n",
       "median_income                      0.7               0.2  \n",
       "median_house_value                 1.0               0.2  \n",
       "rooms_per_person                   0.2               1.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a correlation matrix.\n",
    "training_data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The median_income correlates 0.7 with the label \n",
    "(median_house_value), so median_income might be a \n",
    "good feature. The other seven potential features\n",
    "all have a correlation relatively close to 0. \n",
    "\n",
    "If time permits, try median_income as the feature\n",
    "and see whether the model improves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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