Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "provincial-gallery",
   "metadata": {},
   "source": [
    "# Predicting IKEA's features\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#prep\">Imports and preperations</a></li>\n",
    "<li><a href=\"#model_1\">Model 1</a></li>\n",
    "<li><a href=\"#model_2\">Model 2</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "social-albuquerque",
   "metadata": {},
   "source": [
    "<a id='prep'></a>\n",
    "## Imports and preperations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "intimate-hundred",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from plotnine import *\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import utils\n",
    "from np_utils import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "recreational-bahrain",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>price</th>\n",
       "      <th>old_price</th>\n",
       "      <th>sellable_online</th>\n",
       "      <th>other_colors</th>\n",
       "      <th>depth</th>\n",
       "      <th>height</th>\n",
       "      <th>width</th>\n",
       "      <th>discounted</th>\n",
       "      <th>width_d</th>\n",
       "      <th>height_d</th>\n",
       "      <th>depth_d</th>\n",
       "      <th>discount_amount</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bar furniture</td>\n",
       "      <td>265.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "      <td>46.925926</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5049.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bar furniture</td>\n",
       "      <td>995.0</td>\n",
       "      <td>995.0</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>46.925926</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bar furniture</td>\n",
       "      <td>2095.0</td>\n",
       "      <td>2095.0</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>46.925926</td>\n",
       "      <td>97.571429</td>\n",
       "      <td>52.945946</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bar furniture</td>\n",
       "      <td>69.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>True</td>\n",
       "      <td>Yes</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bar furniture</td>\n",
       "      <td>225.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>190920.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        category   price  old_price  sellable_online other_colors      depth  \\\n",
       "0  Bar furniture   265.0      265.0             True           No  46.925926   \n",
       "1  Bar furniture   995.0      995.0            False           No  46.925926   \n",
       "2  Bar furniture  2095.0     2095.0            False           No  46.925926   \n",
       "3  Bar furniture    69.0       69.0             True          Yes  50.000000   \n",
       "4  Bar furniture   225.0      225.0             True           No  60.000000   \n",
       "\n",
       "       height      width  discounted  width_d  height_d  depth_d  \\\n",
       "0   99.000000  51.000000           0        1         1        0   \n",
       "1  105.000000  80.000000           0        1         1        0   \n",
       "2   97.571429  52.945946           0        0         0        0   \n",
       "3  100.000000  60.000000           0        1         1        1   \n",
       "4   43.000000  74.000000           0        1         1        1   \n",
       "\n",
       "   discount_amount      size  \n",
       "0              0.0    5049.0  \n",
       "1              0.0    8400.0  \n",
       "2              0.0       1.0  \n",
       "3              0.0  300000.0  \n",
       "4              0.0  190920.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('clean_IKEA_dataset.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "strange-supplement",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>price</th>\n",
       "      <th>old_price</th>\n",
       "      <th>sellable_online</th>\n",
       "      <th>other_colors</th>\n",
       "      <th>depth</th>\n",
       "      <th>height</th>\n",
       "      <th>width</th>\n",
       "      <th>discounted</th>\n",
       "      <th>width_d</th>\n",
       "      <th>height_d</th>\n",
       "      <th>depth_d</th>\n",
       "      <th>discount_amount</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bar furniture</td>\n",
       "      <td>0.027343</td>\n",
       "      <td>265.0</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "      <td>0.179398</td>\n",
       "      <td>0.140200</td>\n",
       "      <td>0.119332</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bar furniture</td>\n",
       "      <td>0.103527</td>\n",
       "      <td>995.0</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>0.179398</td>\n",
       "      <td>0.148784</td>\n",
       "      <td>0.188544</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bar furniture</td>\n",
       "      <td>0.218326</td>\n",
       "      <td>2095.0</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>0.179398</td>\n",
       "      <td>0.138157</td>\n",
       "      <td>0.123976</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bar furniture</td>\n",
       "      <td>0.006888</td>\n",
       "      <td>69.0</td>\n",
       "      <td>True</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.191406</td>\n",
       "      <td>0.141631</td>\n",
       "      <td>0.140811</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bar furniture</td>\n",
       "      <td>0.023168</td>\n",
       "      <td>225.0</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "      <td>0.230469</td>\n",
       "      <td>0.060086</td>\n",
       "      <td>0.174224</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        category     price  old_price  sellable_online other_colors     depth  \\\n",
       "0  Bar furniture  0.027343      265.0             True           No  0.179398   \n",
       "1  Bar furniture  0.103527      995.0            False           No  0.179398   \n",
       "2  Bar furniture  0.218326     2095.0            False           No  0.179398   \n",
       "3  Bar furniture  0.006888       69.0             True          Yes  0.191406   \n",
       "4  Bar furniture  0.023168      225.0             True           No  0.230469   \n",
       "\n",
       "     height     width  discounted  width_d  height_d  depth_d  \\\n",
       "0  0.140200  0.119332           0        1         1        0   \n",
       "1  0.148784  0.188544           0        1         1        0   \n",
       "2  0.138157  0.123976           0        0         0        0   \n",
       "3  0.141631  0.140811           0        1         1        1   \n",
       "4  0.060086  0.174224           0        1         1        1   \n",
       "\n",
       "   discount_amount      size  \n",
       "0              0.0  0.000370  \n",
       "1              0.0  0.000616  \n",
       "2              0.0  0.000000  \n",
       "3              0.0  0.022012  \n",
       "4              0.0  0.014008  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "\n",
    "df[['size', 'width', 'height', 'depth', 'discount_amount','price']] = scaler.fit_transform(df[['size', 'width', 'height', 'depth', 'discount_amount','price']])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "functional-surrey",
   "metadata": {},
   "outputs": [],
   "source": [
    "# encode class values as integers\n",
    "encoder = LabelEncoder()\n",
    "encoded_Y = encoder.fit_transform(df.category)\n",
    "# convert integers to dummy variables (i.e. one hot encoded)\n",
    "dummy_y = utils.to_categorical(encoded_Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "informative-gallery",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train , df_test, dummy_y_train, dummy_y_test = train_test_split(df, dummy_y, shuffle=True, test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "regular-gambling",
   "metadata": {},
   "source": [
    "<a id='model_1'></a>\n",
    "## Model 1: Item's price prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "strange-mother",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_columns = []\n",
    "\n",
    "discount_amount = tf.feature_column.numeric_column(\"discount_amount\")\n",
    "feature_columns.append(discount_amount)\n",
    "\n",
    "size = tf.feature_column.numeric_column(\"size\")\n",
    "feature_columns.append(size)\n",
    "\n",
    "width = tf.feature_column.numeric_column(\"width\")\n",
    "feature_columns.append(width)\n",
    "\n",
    "height = tf.feature_column.numeric_column(\"height\")\n",
    "feature_columns.append(height)\n",
    "\n",
    "depth = tf.feature_column.numeric_column(\"depth\")\n",
    "feature_columns.append(depth)\n",
    "\n",
    "width_d = tf.feature_column.numeric_column(\"width_d\")\n",
    "feature_columns.append(width_d)\n",
    "\n",
    "height_d = tf.feature_column.numeric_column(\"height_d\")\n",
    "feature_columns.append(height_d)\n",
    "\n",
    "depth_d = tf.feature_column.numeric_column(\"depth_d\")\n",
    "feature_columns.append(depth_d)\n",
    "\n",
    "other_colors = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    key='other_colors', vocabulary_list=('Yes', 'No'), default_value=0)\n",
    "feature_columns.append(tf.feature_column.indicator_column(other_colors))\n",
    "\n",
    "category = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    key='category', vocabulary_list=('Bar furniture', 'Beds', 'Bookcases & shelving units',\n",
    "                                     'Cabinets & cupboards', 'Café furniture', 'Chairs',\n",
    "                                     'Chests of drawers & drawer units', \"Children's furniture\",\n",
    "                                     'Nursery furniture', 'Outdoor furniture', 'Room dividers',\n",
    "                                     'Sideboards, buffets & console tables', 'Sofas & armchairs',\n",
    "                                     'Tables & desks', 'Trolleys', 'TV & media furniture', 'Wardrobes'),\n",
    "    default_value=0)\n",
    "feature_columns.append(tf.feature_column.indicator_column(category))\n",
    "\n",
    "my_feature_layer = tf.keras.layers.DenseFeatures(feature_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dynamic-sound",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defined the plot_the_loss_curve function.\n"
     ]
    }
   ],
   "source": [
    "#@title Define the plotting function.\n",
    "\n",
    "def plot_the_loss_curve(epochs, mse):\n",
    "  \"\"\"Plot a curve of loss vs. epoch.\"\"\"\n",
    "\n",
    "  plt.figure()\n",
    "  plt.xlabel(\"Epoch\")\n",
    "  plt.ylabel(\"Mean Squared Error\")\n",
    "\n",
    "  plt.plot(epochs, mse, label=\"Loss\")\n",
    "  plt.legend()\n",
    "  plt.ylim([mse.min()*0.95, mse.max() * 1.03])\n",
    "  plt.show()  \n",
    "\n",
    "print(\"Defined the plot_the_loss_curve function.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aquatic-station",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(my_learning_rate, my_feature_layer):\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",
    "  # Add the layer containing the feature columns to the model.\n",
    "  model.add(my_feature_layer)\n",
    "\n",
    "  # Describe the topography of the model by calling the tf.keras.layers.Dense\n",
    "  # method once for each layer. We've specified the following arguments:\n",
    "  #   * units specifies the number of nodes in this layer.\n",
    "  #   * activation specifies the activation function (Rectified Linear Unit).\n",
    "  #   * name is just a string that can be useful when debugging.\n",
    "\n",
    "  # Define the first hidden layer with 20 nodes.   \n",
    "  model.add(tf.keras.layers.Dense(units=20, \n",
    "                                  activation='relu', \n",
    "                                  kernel_regularizer=tf.keras.regularizers.l2(l=0.0),\n",
    "                                  name='Hidden1'))\n",
    "  \n",
    "  # Define the second hidden layer with 10 nodes. \n",
    "  model.add(tf.keras.layers.Dense(units=10, \n",
    "                                  activation='relu', \n",
    "                                  kernel_regularizer=tf.keras.regularizers.l2(l=0.0),\n",
    "                                  name='Hidden2'))\n",
    "\n",
    "  \n",
    "  # Define the output layer.\n",
    "  model.add(tf.keras.layers.Dense(units=1,  \n",
    "                                  name='Output'))                              \n",
    "  \n",
    "  model.compile(optimizer=tf.keras.optimizers.Adam(lr=my_learning_rate),\n",
    "                loss=\"mean_squared_error\",\n",
    "                metrics=[tf.keras.metrics.MeanSquaredError()])\n",
    "\n",
    "  return model\n",
    "\n",
    "\n",
    "def train_model(model, dataset, epochs, label_name,\n",
    "                batch_size=None):\n",
    "  \"\"\"Train the model by feeding it data.\"\"\"\n",
    "\n",
    "  # Split the dataset into features and label.\n",
    "  features = {name:np.array(value) for name, value in dataset.items()}\n",
    "  label = np.array(features.pop(label_name))\n",
    "  history = model.fit(x=features, y=label, batch_size=batch_size,\n",
    "                      epochs=epochs, shuffle=True) \n",
    "\n",
    "  # The list of epochs is stored separately from the rest of history.\n",
    "  epochs = history.epoch\n",
    "  \n",
    "  # To track the progression of training, gather a snapshot\n",
    "  # of the model's mean squared error at each epoch. \n",
    "  hist = pd.DataFrame(history.history)\n",
    "  mse = hist[\"mean_squared_error\"]\n",
    "\n",
    "  return epochs, mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "regulation-munich",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'category': <tf.Tensor 'ExpandDims:0' shape=(2, 1) dtype=string>, 'old_price': <tf.Tensor 'ExpandDims_7:0' shape=(2, 1) dtype=float32>, 'sellable_online': <tf.Tensor 'ExpandDims_9:0' shape=(2, 1) dtype=bool>, 'other_colors': <tf.Tensor 'ExpandDims_8:0' shape=(2, 1) dtype=string>, 'depth': <tf.Tensor 'ExpandDims_1:0' shape=(2, 1) dtype=float32>, 'height': <tf.Tensor 'ExpandDims_5:0' shape=(2, 1) dtype=float32>, 'width': <tf.Tensor 'ExpandDims_11:0' shape=(2, 1) dtype=float32>, 'discounted': <tf.Tensor 'ExpandDims_4:0' shape=(2, 1) dtype=int64>, 'width_d': <tf.Tensor 'ExpandDims_12:0' shape=(2, 1) dtype=int64>, 'height_d': <tf.Tensor 'ExpandDims_6:0' shape=(2, 1) dtype=int64>, 'depth_d': <tf.Tensor 'ExpandDims_2:0' shape=(2, 1) dtype=int64>, 'discount_amount': <tf.Tensor 'ExpandDims_3:0' shape=(2, 1) dtype=float32>, 'size': <tf.Tensor 'ExpandDims_10:0' shape=(2, 1) dtype=float32>}\n",
      "Consider rewriting this model with the Functional API.\n",
      "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'category': <tf.Tensor 'ExpandDims:0' shape=(2, 1) dtype=string>, 'old_price': <tf.Tensor 'ExpandDims_7:0' shape=(2, 1) dtype=float32>, 'sellable_online': <tf.Tensor 'ExpandDims_9:0' shape=(2, 1) dtype=bool>, 'other_colors': <tf.Tensor 'ExpandDims_8:0' shape=(2, 1) dtype=string>, 'depth': <tf.Tensor 'ExpandDims_1:0' shape=(2, 1) dtype=float32>, 'height': <tf.Tensor 'ExpandDims_5:0' shape=(2, 1) dtype=float32>, 'width': <tf.Tensor 'ExpandDims_11:0' shape=(2, 1) dtype=float32>, 'discounted': <tf.Tensor 'ExpandDims_4:0' shape=(2, 1) dtype=int64>, 'width_d': <tf.Tensor 'ExpandDims_12:0' shape=(2, 1) dtype=int64>, 'height_d': <tf.Tensor 'ExpandDims_6:0' shape=(2, 1) dtype=int64>, 'depth_d': <tf.Tensor 'ExpandDims_2:0' shape=(2, 1) dtype=int64>, 'discount_amount': <tf.Tensor 'ExpandDims_3:0' shape=(2, 1) dtype=float32>, 'size': <tf.Tensor 'ExpandDims_10:0' shape=(2, 1) dtype=float32>}\n",
      "Consider rewriting this model with the Functional API.\n",
      "1289/1289 [==============================] - 2s 934us/step - loss: 0.0156 - mean_squared_error: 0.01560s - loss: 0.021\n",
      "Epoch 2/20\n",
      "1289/1289 [==============================] - 1s 856us/step - loss: 0.0103 - mean_squared_error: 0.0103\n",
      "Epoch 3/20\n",
      "1289/1289 [==============================] - 1s 888us/step - loss: 0.0096 - mean_squared_error: 0.0096\n",
      "Epoch 4/20\n",
      "1289/1289 [==============================] - 1s 846us/step - loss: 0.0093 - mean_squared_error: 0.0093\n",
      "Epoch 5/20\n",
      "1289/1289 [==============================] - 1s 883us/step - loss: 0.0096 - mean_squared_error: 0.0096\n",
      "Epoch 6/20\n",
      "1289/1289 [==============================] - 1s 846us/step - loss: 0.0079 - mean_squared_error: 0.0079\n",
      "Epoch 7/20\n",
      "1289/1289 [==============================] - 1s 967us/step - loss: 0.0087 - mean_squared_error: 0.0087\n",
      "Epoch 8/20\n",
      "1289/1289 [==============================] - 1s 874us/step - loss: 0.0088 - mean_squared_error: 0.0088\n",
      "Epoch 9/20\n",
      "1289/1289 [==============================] - 1s 939us/step - loss: 0.0076 - mean_squared_error: 0.00760s - loss: 0.0075 - mean\n",
      "Epoch 10/20\n",
      "1289/1289 [==============================] - 1s 906us/step - loss: 0.0086 - mean_squared_error: 0.0086\n",
      "Epoch 11/20\n",
      "1289/1289 [==============================] - 1s 1ms/step - loss: 0.0082 - mean_squared_error: 0.0082\n",
      "Epoch 12/20\n",
      "1289/1289 [==============================] - 1s 938us/step - loss: 0.0079 - mean_squared_error: 0.0079\n",
      "Epoch 13/20\n",
      "1289/1289 [==============================] - 1s 853us/step - loss: 0.0064 - mean_squared_error: 0.0064\n",
      "Epoch 14/20\n",
      "1289/1289 [==============================] - 1s 864us/step - loss: 0.0084 - mean_squared_error: 0.0084\n",
      "Epoch 15/20\n",
      "1289/1289 [==============================] - 1s 866us/step - loss: 0.0078 - mean_squared_error: 0.0078\n",
      "Epoch 16/20\n",
      "1289/1289 [==============================] - 1s 869us/step - loss: 0.0072 - mean_squared_error: 0.0072\n",
      "Epoch 17/20\n",
      "1289/1289 [==============================] - 1s 923us/step - loss: 0.0074 - mean_squared_error: 0.0074\n",
      "Epoch 18/20\n",
      "1289/1289 [==============================] - 1s 957us/step - loss: 0.0066 - mean_squared_error: 0.0066\n",
      "Epoch 19/20\n",
      "1289/1289 [==============================] - 1s 891us/step - loss: 0.0076 - mean_squared_error: 0.0076\n",
      "Epoch 20/20\n",
      "1289/1289 [==============================] - 1s 837us/step - loss: 0.0079 - mean_squared_error: 0.0079\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": [
      "\n",
      " Evaluate the new model against the test set:\n",
      "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'category': <tf.Tensor 'ExpandDims:0' shape=(2, 1) dtype=string>, 'old_price': <tf.Tensor 'ExpandDims_7:0' shape=(2, 1) dtype=float32>, 'sellable_online': <tf.Tensor 'ExpandDims_9:0' shape=(2, 1) dtype=bool>, 'other_colors': <tf.Tensor 'ExpandDims_8:0' shape=(2, 1) dtype=string>, 'depth': <tf.Tensor 'ExpandDims_1:0' shape=(2, 1) dtype=float32>, 'height': <tf.Tensor 'ExpandDims_5:0' shape=(2, 1) dtype=float32>, 'width': <tf.Tensor 'ExpandDims_11:0' shape=(2, 1) dtype=float32>, 'discounted': <tf.Tensor 'ExpandDims_4:0' shape=(2, 1) dtype=int64>, 'width_d': <tf.Tensor 'ExpandDims_12:0' shape=(2, 1) dtype=int64>, 'height_d': <tf.Tensor 'ExpandDims_6:0' shape=(2, 1) dtype=int64>, 'depth_d': <tf.Tensor 'ExpandDims_2:0' shape=(2, 1) dtype=int64>, 'discount_amount': <tf.Tensor 'ExpandDims_3:0' shape=(2, 1) dtype=float32>, 'size': <tf.Tensor 'ExpandDims_10:0' shape=(2, 1) dtype=float32>}\n",
      "Consider rewriting this model with the Functional API.\n",
      "553/553 [==============================] - 1s 780us/step - loss: 0.0080 - mean_squared_error: 0.0080\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.007959499023854733, 0.007959499023854733]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The following variables are the hyperparameters.\n",
    "learning_rate = 0.01\n",
    "epochs = 20\n",
    "batch_size = 2\n",
    "\n",
    "\n",
    "# Specify the label\n",
    "label_name = \"price\"\n",
    "\n",
    "# Establish the model's topography.\n",
    "my_model = create_model(learning_rate, my_feature_layer)\n",
    "\n",
    "# Train the model on the normalized training set. We're passing the entire\n",
    "# normalized training set, but the model will only use the features\n",
    "# defined by the feature_layer.\n",
    "epochs, mse = train_model(my_model, df_train, epochs, \n",
    "                          label_name, batch_size)\n",
    "plot_the_loss_curve(epochs, mse)\n",
    "\n",
    "# After building a model against the training set, test that model\n",
    "# against the test set.\n",
    "test_features = {name:np.array(value) for name, value in df_test.items()}\n",
    "test_label = np.array(test_features.pop(label_name)) # isolate the label\n",
    "print(\"\\n Evaluate the new model against the test set:\")\n",
    "my_model.evaluate(x = test_features, y = test_label, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "understood-transsexual",
   "metadata": {},
   "source": [
    "<a id='model_2'></a>\n",
    "## Model 2: Category classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "australian-logistics",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_columns = []\n",
    "\n",
    "price = tf.feature_column.numeric_column(\"price\")\n",
    "feature_columns.append(price)\n",
    "\n",
    "discount_amount = tf.feature_column.numeric_column(\"discount_amount\")\n",
    "feature_columns.append(discount_amount)\n",
    "\n",
    "size = tf.feature_column.numeric_column(\"size\")\n",
    "feature_columns.append(size)\n",
    "\n",
    "width = tf.feature_column.numeric_column(\"width\")\n",
    "feature_columns.append(width)\n",
    "\n",
    "height = tf.feature_column.numeric_column(\"height\")\n",
    "feature_columns.append(height)\n",
    "\n",
    "depth = tf.feature_column.numeric_column(\"depth\")\n",
    "feature_columns.append(depth)\n",
    "\n",
    "width_d = tf.feature_column.numeric_column(\"width_d\")\n",
    "feature_columns.append(width_d)\n",
    "\n",
    "height_d = tf.feature_column.numeric_column(\"height_d\")\n",
    "feature_columns.append(height_d)\n",
    "\n",
    "depth_d = tf.feature_column.numeric_column(\"depth_d\")\n",
    "feature_columns.append(depth_d)\n",
    "\n",
    "other_colors = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    key='other_colors', vocabulary_list=('Yes', 'No'), default_value=0)\n",
    "feature_columns.append(tf.feature_column.indicator_column(other_colors))\n",
    "\n",
    "\n",
    "my_feature_layer = tf.keras.layers.DenseFeatures(feature_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "sufficient-battery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded the plot_curve function.\n"
     ]
    }
   ],
   "source": [
    "#@title Define the plotting function\n",
    "def plot_curve(epochs, hist, list_of_metrics):\n",
    "  \"\"\"Plot a curve of one or more classification metrics vs. epoch.\"\"\"  \n",
    "  # list_of_metrics should be one of the names shown in:\n",
    "  # https://www.tensorflow.org/tutorials/structured_data/imbalanced_data#define_the_model_and_metrics  \n",
    "\n",
    "  plt.figure()\n",
    "  plt.xlabel(\"Epoch\")\n",
    "  plt.ylabel(\"Value\")\n",
    "\n",
    "  for m in list_of_metrics:\n",
    "    x = hist[m]\n",
    "    plt.plot(epochs[1:], x[1:], label=m)\n",
    "\n",
    "  plt.legend()\n",
    "\n",
    "print(\"Loaded the plot_curve function.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "united-actress",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(my_learning_rate, my_feature_layer):\n",
    "  \"\"\"Create and compile a deep neural net.\"\"\"\n",
    "  \n",
    "  # All models in this course are sequential.\n",
    "  model = tf.keras.models.Sequential()\n",
    "\n",
    "  # The features are stored in a two-dimensional 28X28 array. \n",
    "  # Flatten that two-dimensional array into a a one-dimensional \n",
    "  # 784-element array.\n",
    "  model.add(my_feature_layer)\n",
    "\n",
    "  # Define the first hidden layer.   \n",
    "  model.add(tf.keras.layers.Dense(units=500, activation='relu'))\n",
    "  model.add(tf.keras.layers.Dropout(rate=0.2))\n",
    "\n",
    "  model.add(tf.keras.layers.Dense(units=200, activation='relu'))\n",
    "  model.add(tf.keras.layers.Dropout(rate=0.2))\n",
    "\n",
    "  model.add(tf.keras.layers.Dense(units=100, activation='relu'))\n",
    "  model.add(tf.keras.layers.Dropout(rate=0.2))\n",
    "  \n",
    "  model.add(tf.keras.layers.Dense(units=20, activation='relu'))\n",
    "  model.add(tf.keras.layers.Dropout(rate=0.2))\n",
    "\n",
    "\n",
    "  # Output Layer\n",
    "  model.add(tf.keras.layers.Dense(units=17, activation='softmax'))     \n",
    "                           \n",
    "  # Construct the layers into a model that TensorFlow can execute.  \n",
    "  # Notice that the loss function for multi-class classification\n",
    "  # is different than the loss function for binary classification.  \n",
    "  model.compile(optimizer=tf.keras.optimizers.Adam(lr=my_learning_rate),\n",
    "                loss=\"categorical_crossentropy\",\n",
    "                metrics=['accuracy'])\n",
    "  \n",
    "  return model    \n",
    "\n",
    "\n",
    "def train_model(model, dataset, train_label, epochs,\n",
    "                batch_size=None):\n",
    "  \"\"\"Train the model by feeding it data.\"\"\"\n",
    "\n",
    "  # Split the dataset into features and label.\n",
    "  features = {name:np.array(value) for name, value in dataset.items()}\n",
    "  label = np.array(features.pop(label_name))\n",
    "\n",
    "\n",
    "  history = model.fit(x=features, y=train_label, batch_size=batch_size,\n",
    "                      epochs=epochs, shuffle=True) \n",
    "\n",
    "  # The list of epochs is stored separately from the rest of history.\n",
    "  epochs = history.epoch\n",
    "  \n",
    "  # To track the progression of training, gather a snapshot\n",
    "  # of the model's mean squared error at each epoch. \n",
    "  hist = pd.DataFrame(history.history)\n",
    "\n",
    "  return epochs, hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "seventh-outline",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'price': <tf.Tensor 'ExpandDims_8:0' shape=(None, 1) dtype=float32>, 'old_price': <tf.Tensor 'ExpandDims_6:0' shape=(None, 1) dtype=float32>, 'sellable_online': <tf.Tensor 'ExpandDims_9:0' shape=(None, 1) dtype=bool>, 'other_colors': <tf.Tensor 'ExpandDims_7:0' shape=(None, 1) dtype=string>, 'depth': <tf.Tensor 'ExpandDims:0' shape=(None, 1) dtype=float32>, 'height': <tf.Tensor 'ExpandDims_4:0' shape=(None, 1) dtype=float32>, 'width': <tf.Tensor 'ExpandDims_11:0' shape=(None, 1) dtype=float32>, 'discounted': <tf.Tensor 'ExpandDims_3:0' shape=(None, 1) dtype=int64>, 'width_d': <tf.Tensor 'ExpandDims_12:0' shape=(None, 1) dtype=int64>, 'height_d': <tf.Tensor 'ExpandDims_5:0' shape=(None, 1) dtype=int64>, 'depth_d': <tf.Tensor 'ExpandDims_1:0' shape=(None, 1) dtype=int64>, 'discount_amount': <tf.Tensor 'ExpandDims_2:0' shape=(None, 1) dtype=float32>, 'size': <tf.Tensor 'ExpandDims_10:0' shape=(None, 1) dtype=float32>}\n",
      "Consider rewriting this model with the Functional API.\n",
      "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'price': <tf.Tensor 'ExpandDims_8:0' shape=(None, 1) dtype=float32>, 'old_price': <tf.Tensor 'ExpandDims_6:0' shape=(None, 1) dtype=float32>, 'sellable_online': <tf.Tensor 'ExpandDims_9:0' shape=(None, 1) dtype=bool>, 'other_colors': <tf.Tensor 'ExpandDims_7:0' shape=(None, 1) dtype=string>, 'depth': <tf.Tensor 'ExpandDims:0' shape=(None, 1) dtype=float32>, 'height': <tf.Tensor 'ExpandDims_4:0' shape=(None, 1) dtype=float32>, 'width': <tf.Tensor 'ExpandDims_11:0' shape=(None, 1) dtype=float32>, 'discounted': <tf.Tensor 'ExpandDims_3:0' shape=(None, 1) dtype=int64>, 'width_d': <tf.Tensor 'ExpandDims_12:0' shape=(None, 1) dtype=int64>, 'height_d': <tf.Tensor 'ExpandDims_5:0' shape=(None, 1) dtype=int64>, 'depth_d': <tf.Tensor 'ExpandDims_1:0' shape=(None, 1) dtype=int64>, 'discount_amount': <tf.Tensor 'ExpandDims_2:0' shape=(None, 1) dtype=float32>, 'size': <tf.Tensor 'ExpandDims_10:0' shape=(None, 1) dtype=float32>}\n",
      "Consider rewriting this model with the Functional API.\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 2.5498 - accuracy: 0.2031\n",
      "Epoch 2/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 2.0904 - accuracy: 0.3416\n",
      "Epoch 3/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.8639 - accuracy: 0.3924\n",
      "Epoch 4/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.7371 - accuracy: 0.4366\n",
      "Epoch 5/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.5989 - accuracy: 0.4704\n",
      "Epoch 6/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.5333 - accuracy: 0.4976\n",
      "Epoch 7/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.5167 - accuracy: 0.4865\n",
      "Epoch 8/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.4099 - accuracy: 0.5401\n",
      "Epoch 9/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.4340 - accuracy: 0.5275\n",
      "Epoch 10/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.3774 - accuracy: 0.5174\n",
      "Epoch 11/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.3525 - accuracy: 0.5513\n",
      "Epoch 12/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.3937 - accuracy: 0.5151\n",
      "Epoch 13/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.2984 - accuracy: 0.5548\n",
      "Epoch 14/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.3192 - accuracy: 0.5505\n",
      "Epoch 15/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.2540 - accuracy: 0.5599\n",
      "Epoch 16/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.2905 - accuracy: 0.5577\n",
      "Epoch 17/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.2682 - accuracy: 0.5543\n",
      "Epoch 18/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.2179 - accuracy: 0.5801\n",
      "Epoch 19/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.2726 - accuracy: 0.5654\n",
      "Epoch 20/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.2151 - accuracy: 0.5685\n",
      "Epoch 21/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.2313 - accuracy: 0.5724\n",
      "Epoch 22/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.2026 - accuracy: 0.6004\n",
      "Epoch 23/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1770 - accuracy: 0.5996\n",
      "Epoch 24/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1817 - accuracy: 0.5896\n",
      "Epoch 25/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.2276 - accuracy: 0.5690\n",
      "Epoch 26/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1679 - accuracy: 0.6027\n",
      "Epoch 27/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1587 - accuracy: 0.5961: 0s - loss: 1.1584 - accuracy: 0.59\n",
      "Epoch 28/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1410 - accuracy: 0.6219\n",
      "Epoch 29/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1417 - accuracy: 0.6048\n",
      "Epoch 30/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1384 - accuracy: 0.6110\n",
      "Epoch 31/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1158 - accuracy: 0.6234\n",
      "Epoch 32/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1455 - accuracy: 0.6052\n",
      "Epoch 33/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1216 - accuracy: 0.5997\n",
      "Epoch 34/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1359 - accuracy: 0.6222\n",
      "Epoch 35/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1266 - accuracy: 0.6090\n",
      "Epoch 36/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 1.1619 - accuracy: 0.5969\n",
      "Epoch 37/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 1.0821 - accuracy: 0.6278\n",
      "Epoch 38/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1052 - accuracy: 0.6182: 0s - loss: 1.1041 - accuracy: 0.\n",
      "Epoch 39/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1271 - accuracy: 0.5978\n",
      "Epoch 40/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0289 - accuracy: 0.6459\n",
      "Epoch 41/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1059 - accuracy: 0.6149\n",
      "Epoch 42/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1175 - accuracy: 0.6209\n",
      "Epoch 43/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0518 - accuracy: 0.6341\n",
      "Epoch 44/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1400 - accuracy: 0.5988\n",
      "Epoch 45/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0949 - accuracy: 0.6299\n",
      "Epoch 46/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 1.0951 - accuracy: 0.6156\n",
      "Epoch 47/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1578 - accuracy: 0.6087\n",
      "Epoch 48/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0042 - accuracy: 0.6625: 0s -\n",
      "Epoch 49/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.1160 - accuracy: 0.6384\n",
      "Epoch 50/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0787 - accuracy: 0.6241\n",
      "Epoch 51/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9998 - accuracy: 0.6513\n",
      "Epoch 52/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0198 - accuracy: 0.6393\n",
      "Epoch 53/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0552 - accuracy: 0.6124\n",
      "Epoch 54/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0323 - accuracy: 0.6242\n",
      "Epoch 55/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9981 - accuracy: 0.6559: 0s - l\n",
      "Epoch 56/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0367 - accuracy: 0.6489\n",
      "Epoch 57/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0127 - accuracy: 0.6619\n",
      "Epoch 58/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0842 - accuracy: 0.6221\n",
      "Epoch 59/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0302 - accuracy: 0.6348\n",
      "Epoch 60/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0137 - accuracy: 0.6570\n",
      "Epoch 61/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0520 - accuracy: 0.6306\n",
      "Epoch 62/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0087 - accuracy: 0.6538: 0s -\n",
      "Epoch 63/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0815 - accuracy: 0.6410\n",
      "Epoch 64/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0080 - accuracy: 0.6507\n",
      "Epoch 65/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0188 - accuracy: 0.6374: 0s - los\n",
      "Epoch 66/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0095 - accuracy: 0.6636\n",
      "Epoch 67/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0239 - accuracy: 0.6437\n",
      "Epoch 68/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0202 - accuracy: 0.6575\n",
      "Epoch 69/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9619 - accuracy: 0.6592\n",
      "Epoch 70/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9831 - accuracy: 0.6614\n",
      "Epoch 71/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0273 - accuracy: 0.6526\n",
      "Epoch 72/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0384 - accuracy: 0.6424\n",
      "Epoch 73/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9665 - accuracy: 0.6554\n",
      "Epoch 74/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0002 - accuracy: 0.6469\n",
      "Epoch 75/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9485 - accuracy: 0.6659\n",
      "Epoch 76/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0096 - accuracy: 0.6444\n",
      "Epoch 77/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9576 - accuracy: 0.6676\n",
      "Epoch 78/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9818 - accuracy: 0.6569: 0s - l\n",
      "Epoch 79/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9581 - accuracy: 0.6500\n",
      "Epoch 80/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9742 - accuracy: 0.6625\n",
      "Epoch 81/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9313 - accuracy: 0.6700\n",
      "Epoch 82/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9009 - accuracy: 0.6649\n",
      "Epoch 83/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9637 - accuracy: 0.6620\n",
      "Epoch 84/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9481 - accuracy: 0.6626\n",
      "Epoch 85/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9669 - accuracy: 0.6655\n",
      "Epoch 86/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9776 - accuracy: 0.6510\n",
      "Epoch 87/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9510 - accuracy: 0.6556\n",
      "Epoch 88/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9718 - accuracy: 0.6580\n",
      "Epoch 89/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 0.9917 - accuracy: 0.6659\n",
      "Epoch 90/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9879 - accuracy: 0.6545: 1s - - ETA: 0s - loss: 1.0015 - accu - ETA: 0s - loss: 0.9924 - accuracy\n",
      "Epoch 91/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9931 - accuracy: 0.6590\n",
      "Epoch 92/200\n",
      "860/860 [==============================] - 2s 3ms/step - loss: 1.0085 - accuracy: 0.6478\n",
      "Epoch 93/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9823 - accuracy: 0.6553\n",
      "Epoch 94/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9576 - accuracy: 0.6563\n",
      "Epoch 95/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9659 - accuracy: 0.6580\n",
      "Epoch 96/200\n",
      "860/860 [==============================] - 2s 3ms/step - loss: 0.9698 - accuracy: 0.6629\n",
      "Epoch 97/200\n",
      "860/860 [==============================] - 2s 3ms/step - loss: 0.9268 - accuracy: 0.6665\n",
      "Epoch 98/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9264 - accuracy: 0.6627: \n",
      "Epoch 99/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9614 - accuracy: 0.6654\n",
      "Epoch 100/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9911 - accuracy: 0.6453\n",
      "Epoch 101/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9760 - accuracy: 0.6542\n",
      "Epoch 102/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9465 - accuracy: 0.6606\n",
      "Epoch 103/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9234 - accuracy: 0.6745\n",
      "Epoch 104/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9291 - accuracy: 0.6626\n",
      "Epoch 105/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9523 - accuracy: 0.6850\n",
      "Epoch 106/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9693 - accuracy: 0.6757\n",
      "Epoch 107/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 0.9358 - accuracy: 0.6638\n",
      "Epoch 108/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 0.9116 - accuracy: 0.6770\n",
      "Epoch 109/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 0.9155 - accuracy: 0.6734\n",
      "Epoch 110/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9777 - accuracy: 0.6671\n",
      "Epoch 111/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9205 - accuracy: 0.6762\n",
      "Epoch 112/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8988 - accuracy: 0.6887\n",
      "Epoch 113/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9307 - accuracy: 0.6667\n",
      "Epoch 114/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9298 - accuracy: 0.6657: 0s - los\n",
      "Epoch 115/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9420 - accuracy: 0.6529\n",
      "Epoch 116/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9853 - accuracy: 0.6659\n",
      "Epoch 117/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9754 - accuracy: 0.6712\n",
      "Epoch 118/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8901 - accuracy: 0.6937\n",
      "Epoch 119/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9543 - accuracy: 0.6612: 0s - loss: 0.947\n",
      "Epoch 120/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8726 - accuracy: 0.6821\n",
      "Epoch 121/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9138 - accuracy: 0.6805\n",
      "Epoch 122/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9032 - accuracy: 0.6792\n",
      "Epoch 123/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8853 - accuracy: 0.6982\n",
      "Epoch 124/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9296 - accuracy: 0.6654\n",
      "Epoch 125/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8816 - accuracy: 0.6901\n",
      "Epoch 126/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9714 - accuracy: 0.6768\n",
      "Epoch 127/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9265 - accuracy: 0.6809\n",
      "Epoch 128/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9248 - accuracy: 0.6839\n",
      "Epoch 129/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8951 - accuracy: 0.6950\n",
      "Epoch 130/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9508 - accuracy: 0.6670\n",
      "Epoch 131/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9262 - accuracy: 0.6738\n",
      "Epoch 132/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8714 - accuracy: 0.6823\n",
      "Epoch 133/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9336 - accuracy: 0.6760\n",
      "Epoch 134/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 1.0358 - accuracy: 0.6507\n",
      "Epoch 135/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8650 - accuracy: 0.6860\n",
      "Epoch 136/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9244 - accuracy: 0.6871\n",
      "Epoch 137/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 0.9038 - accuracy: 0.6809\n",
      "Epoch 138/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 0.8476 - accuracy: 0.7088\n",
      "Epoch 139/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 0.9163 - accuracy: 0.6767\n",
      "Epoch 140/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 0.8930 - accuracy: 0.6820\n",
      "Epoch 141/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 0.8911 - accuracy: 0.6788\n",
      "Epoch 142/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9069 - accuracy: 0.6676\n",
      "Epoch 143/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9203 - accuracy: 0.6822\n",
      "Epoch 144/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8977 - accuracy: 0.6856\n",
      "Epoch 145/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8778 - accuracy: 0.6785\n",
      "Epoch 146/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9112 - accuracy: 0.6825\n",
      "Epoch 147/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 0.9654 - accuracy: 0.6736\n",
      "Epoch 148/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8410 - accuracy: 0.6908\n",
      "Epoch 149/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8749 - accuracy: 0.6984\n",
      "Epoch 150/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9171 - accuracy: 0.6542\n",
      "Epoch 151/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8566 - accuracy: 0.6956\n",
      "Epoch 152/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9220 - accuracy: 0.6746: 0s - los\n",
      "Epoch 153/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8917 - accuracy: 0.6795\n",
      "Epoch 154/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8569 - accuracy: 0.6994\n",
      "Epoch 155/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8783 - accuracy: 0.6861\n",
      "Epoch 156/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8080 - accuracy: 0.7042\n",
      "Epoch 157/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8995 - accuracy: 0.6809\n",
      "Epoch 158/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8764 - accuracy: 0.6864\n",
      "Epoch 159/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 0.8731 - accuracy: 0.6803\n",
      "Epoch 160/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9077 - accuracy: 0.6892\n",
      "Epoch 161/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8564 - accuracy: 0.6956\n",
      "Epoch 162/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9659 - accuracy: 0.6862\n",
      "Epoch 163/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8606 - accuracy: 0.6976\n",
      "Epoch 164/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8609 - accuracy: 0.7037\n",
      "Epoch 165/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9197 - accuracy: 0.6583\n",
      "Epoch 166/200\n",
      "860/860 [==============================] - 1s 2ms/step - loss: 0.8419 - accuracy: 0.6871\n",
      "Epoch 167/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8785 - accuracy: 0.6877\n",
      "Epoch 168/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9062 - accuracy: 0.6766\n",
      "Epoch 169/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8737 - accuracy: 0.6966\n",
      "Epoch 170/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8029 - accuracy: 0.7135\n",
      "Epoch 171/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8775 - accuracy: 0.6956\n",
      "Epoch 172/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8629 - accuracy: 0.6828\n",
      "Epoch 173/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8952 - accuracy: 0.6953\n",
      "Epoch 174/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8402 - accuracy: 0.6813\n",
      "Epoch 175/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9226 - accuracy: 0.6804: 1s - loss: 0 - ETA: 0s - los\n",
      "Epoch 176/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8639 - accuracy: 0.6976\n",
      "Epoch 177/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9310 - accuracy: 0.6801\n",
      "Epoch 178/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8860 - accuracy: 0.6928\n",
      "Epoch 179/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9101 - accuracy: 0.6713\n",
      "Epoch 180/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8486 - accuracy: 0.7053\n",
      "Epoch 181/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9248 - accuracy: 0.6722\n",
      "Epoch 182/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8853 - accuracy: 0.6945\n",
      "Epoch 183/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8554 - accuracy: 0.6997\n",
      "Epoch 184/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8521 - accuracy: 0.6996\n",
      "Epoch 185/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8943 - accuracy: 0.6656\n",
      "Epoch 186/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8722 - accuracy: 0.6897\n",
      "Epoch 187/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8132 - accuracy: 0.7156\n",
      "Epoch 188/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8911 - accuracy: 0.6861\n",
      "Epoch 189/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8471 - accuracy: 0.6959\n",
      "Epoch 190/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8700 - accuracy: 0.6943\n",
      "Epoch 191/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8464 - accuracy: 0.7048\n",
      "Epoch 192/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8472 - accuracy: 0.6925\n",
      "Epoch 193/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9001 - accuracy: 0.6857\n",
      "Epoch 194/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8667 - accuracy: 0.7011\n",
      "Epoch 195/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.9556 - accuracy: 0.6710\n",
      "Epoch 196/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8610 - accuracy: 0.6993\n",
      "Epoch 197/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8698 - accuracy: 0.6981\n",
      "Epoch 198/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8578 - accuracy: 0.7089\n",
      "Epoch 199/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.8860 - accuracy: 0.6944\n",
      "Epoch 200/200\n",
      "860/860 [==============================] - 2s 2ms/step - loss: 0.7965 - accuracy: 0.7084\n",
      "\n",
      " Evaluate the new model against the test set:\n",
      "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'category': <tf.Tensor 'ExpandDims:0' shape=(None, 1) dtype=string>, 'price': <tf.Tensor 'ExpandDims_9:0' shape=(None, 1) dtype=float32>, 'old_price': <tf.Tensor 'ExpandDims_7:0' shape=(None, 1) dtype=float32>, 'sellable_online': <tf.Tensor 'ExpandDims_10:0' shape=(None, 1) dtype=bool>, 'other_colors': <tf.Tensor 'ExpandDims_8:0' shape=(None, 1) dtype=string>, 'depth': <tf.Tensor 'ExpandDims_1:0' shape=(None, 1) dtype=float32>, 'height': <tf.Tensor 'ExpandDims_5:0' shape=(None, 1) dtype=float32>, 'width': <tf.Tensor 'ExpandDims_12:0' shape=(None, 1) dtype=float32>, 'discounted': <tf.Tensor 'ExpandDims_4:0' shape=(None, 1) dtype=int64>, 'width_d': <tf.Tensor 'ExpandDims_13:0' shape=(None, 1) dtype=int64>, 'height_d': <tf.Tensor 'ExpandDims_6:0' shape=(None, 1) dtype=int64>, 'depth_d': <tf.Tensor 'ExpandDims_2:0' shape=(None, 1) dtype=int64>, 'discount_amount': <tf.Tensor 'ExpandDims_3:0' shape=(None, 1) dtype=float32>, 'size': <tf.Tensor 'ExpandDims_11:0' shape=(None, 1) dtype=float32>}\n",
      "Consider rewriting this model with the Functional API.\n",
      "369/369 [==============================] - 1s 1ms/step - loss: 0.9891 - accuracy: 0.6881\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9891471266746521, 0.6880651116371155]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The following variables are the hyperparameters.\n",
    "learning_rate = 0.001\n",
    "epochs = 200\n",
    "batch_size = 3\n",
    "\n",
    "\n",
    "label_name = \"category\"\n",
    "# Establish the model's topography.\n",
    "my_model = create_model(learning_rate, my_feature_layer)\n",
    "\n",
    "# Train the model on the normalized training set.\n",
    "epochs, hist = train_model(my_model, df_train, dummy_y_train, \n",
    "                           epochs, batch_size)\n",
    "\n",
    "# Plot a graph of the metric vs. epochs.\n",
    "list_of_metrics_to_plot = ['accuracy']\n",
    "plot_curve(epochs, hist, list_of_metrics_to_plot)\n",
    "\n",
    "# Evaluate against the test set.\n",
    "print(\"\\n Evaluate the new model against the test set:\")\n",
    "features = {name:np.array(value) for name, value in df_test.items()}\n",
    "\n",
    "my_model.evaluate(x=features, y=dummy_y_test, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "urban-nylon",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}