deep_learning_25_back_propagation_adam_momentum_sgd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: not using Google CoLab\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    %tensorflow_version 2.x\n",
    "    COLAB = True\n",
    "    print(\"Note: using Google CoLab\")\n",
    "except:\n",
    "    print(\"Note: not using Google CoLab\")\n",
    "    COLAB = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from scipy.stats import zscore\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression chart.\n",
    "def chart_regression(pred, y, sort=True):\n",
    "    t = pd.DataFrame({'pred': pred, 'y': y.flatten()})\n",
    "    if sort:\n",
    "        t.sort_values(by=['y'], inplace=True)\n",
    "    plt.plot(t['y'].tolist(), label='expected')\n",
    "    plt.plot(t['pred'].tolist(), label='prediction')\n",
    "    plt.ylabel('output')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data set\n",
    "df = pd.read_csv(\n",
    "    \"https://data.heatonresearch.com/data/t81-558/jh-simple-dataset.csv\",\n",
    "    na_values=['NA','?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate dummies for job\n",
    "df = pd.concat([df,pd.get_dummies(df['job'],prefix=\"job\",dtype=int)],axis=1)\n",
    "df.drop('job', axis=1, inplace=True)\n",
    "# Generate dummies for area\n",
    "df = pd.concat([df,pd.get_dummies(df['area'],prefix=\"area\",dtype=int)],axis=1)\n",
    "df.drop('area', axis=1, inplace=True)\n",
    "\n",
    "# Generate dummies for product\n",
    "df = pd.concat([df,pd.get_dummies(df['product'],prefix=\"product\",dtype=int)],axis=1)\n",
    "df.drop('product', axis=1, inplace=True)\n",
    "\n",
    "# Missing values for income\n",
    "med = df['income'].median()\n",
    "df['income'] = df['income'].fillna(med)\n",
    "\n",
    "# Standardize ranges\n",
    "df['income'] = zscore(df['income'])\n",
    "df['aspect'] = zscore(df['aspect'])\n",
    "df['save_rate'] = zscore(df['save_rate'])\n",
    "df['subscriptions'] = zscore(df['subscriptions'])\n",
    "\n",
    "# Convert to numpy - Classification\n",
    "x_columns = df.columns.drop('age').drop('id')\n",
    "x = df[x_columns].values\n",
    "y = df['age'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>id</th>\n",
       "      <th>income</th>\n",
       "      <th>aspect</th>\n",
       "      <th>subscriptions</th>\n",
       "      <th>dist_healthy</th>\n",
       "      <th>save_rate</th>\n",
       "      <th>dist_unhealthy</th>\n",
       "      <th>age</th>\n",
       "      <th>pop_dense</th>\n",
       "      <th>retail_dense</th>\n",
       "      <th>...</th>\n",
       "      <th>area_b</th>\n",
       "      <th>area_c</th>\n",
       "      <th>area_d</th>\n",
       "      <th>product_a</th>\n",
       "      <th>product_b</th>\n",
       "      <th>product_c</th>\n",
       "      <th>product_d</th>\n",
       "      <th>product_e</th>\n",
       "      <th>product_f</th>\n",
       "      <th>product_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.607550</td>\n",
       "      <td>-0.664918</td>\n",
       "      <td>-0.208449</td>\n",
       "      <td>9.017895</td>\n",
       "      <td>-0.215764</td>\n",
       "      <td>11.738935</td>\n",
       "      <td>49</td>\n",
       "      <td>0.885827</td>\n",
       "      <td>0.492126</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.338053</td>\n",
       "      <td>-0.207748</td>\n",
       "      <td>0.839031</td>\n",
       "      <td>7.766643</td>\n",
       "      <td>0.196869</td>\n",
       "      <td>6.805396</td>\n",
       "      <td>51</td>\n",
       "      <td>0.874016</td>\n",
       "      <td>0.342520</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.184205</td>\n",
       "      <td>1.127906</td>\n",
       "      <td>-0.208449</td>\n",
       "      <td>3.632069</td>\n",
       "      <td>-0.714362</td>\n",
       "      <td>13.671772</td>\n",
       "      <td>44</td>\n",
       "      <td>0.944882</td>\n",
       "      <td>0.724409</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.526467</td>\n",
       "      <td>-0.440815</td>\n",
       "      <td>-0.208449</td>\n",
       "      <td>5.372942</td>\n",
       "      <td>-0.542432</td>\n",
       "      <td>4.333286</td>\n",
       "      <td>50</td>\n",
       "      <td>0.889764</td>\n",
       "      <td>0.444882</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-2.851675</td>\n",
       "      <td>1.638861</td>\n",
       "      <td>1.886511</td>\n",
       "      <td>3.822477</td>\n",
       "      <td>-0.473660</td>\n",
       "      <td>5.967121</td>\n",
       "      <td>38</td>\n",
       "      <td>0.744094</td>\n",
       "      <td>0.661417</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1996</td>\n",
       "      <td>-0.593504</td>\n",
       "      <td>1.414758</td>\n",
       "      <td>-0.208449</td>\n",
       "      <td>5.454545</td>\n",
       "      <td>-0.232957</td>\n",
       "      <td>14.013489</td>\n",
       "      <td>41</td>\n",
       "      <td>0.881890</td>\n",
       "      <td>0.744094</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1997</td>\n",
       "      <td>-3.028085</td>\n",
       "      <td>1.011372</td>\n",
       "      <td>0.839031</td>\n",
       "      <td>3.632069</td>\n",
       "      <td>-0.473660</td>\n",
       "      <td>8.380497</td>\n",
       "      <td>38</td>\n",
       "      <td>0.944882</td>\n",
       "      <td>0.877953</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1998</td>\n",
       "      <td>-2.826971</td>\n",
       "      <td>1.513363</td>\n",
       "      <td>1.886511</td>\n",
       "      <td>7.168218</td>\n",
       "      <td>0.884591</td>\n",
       "      <td>4.626950</td>\n",
       "      <td>36</td>\n",
       "      <td>0.759843</td>\n",
       "      <td>0.744094</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1999</td>\n",
       "      <td>1.093101</td>\n",
       "      <td>-1.274478</td>\n",
       "      <td>-1.255928</td>\n",
       "      <td>8.936292</td>\n",
       "      <td>-0.370502</td>\n",
       "      <td>3.281439</td>\n",
       "      <td>46</td>\n",
       "      <td>0.909449</td>\n",
       "      <td>0.598425</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>2000</td>\n",
       "      <td>0.447425</td>\n",
       "      <td>-0.351174</td>\n",
       "      <td>-1.255928</td>\n",
       "      <td>4.312097</td>\n",
       "      <td>-0.679976</td>\n",
       "      <td>9.405648</td>\n",
       "      <td>48</td>\n",
       "      <td>0.925197</td>\n",
       "      <td>0.539370</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id    income    aspect  subscriptions  dist_healthy  save_rate  \\\n",
       "0        1 -0.607550 -0.664918      -0.208449      9.017895  -0.215764   \n",
       "1        2  0.338053 -0.207748       0.839031      7.766643   0.196869   \n",
       "2        3 -0.184205  1.127906      -0.208449      3.632069  -0.714362   \n",
       "3        4 -0.526467 -0.440815      -0.208449      5.372942  -0.542432   \n",
       "4        5 -2.851675  1.638861       1.886511      3.822477  -0.473660   \n",
       "...    ...       ...       ...            ...           ...        ...   \n",
       "1995  1996 -0.593504  1.414758      -0.208449      5.454545  -0.232957   \n",
       "1996  1997 -3.028085  1.011372       0.839031      3.632069  -0.473660   \n",
       "1997  1998 -2.826971  1.513363       1.886511      7.168218   0.884591   \n",
       "1998  1999  1.093101 -1.274478      -1.255928      8.936292  -0.370502   \n",
       "1999  2000  0.447425 -0.351174      -1.255928      4.312097  -0.679976   \n",
       "\n",
       "      dist_unhealthy  age  pop_dense  retail_dense  ...  area_b  area_c  \\\n",
       "0          11.738935   49   0.885827      0.492126  ...       0       1   \n",
       "1           6.805396   51   0.874016      0.342520  ...       0       1   \n",
       "2          13.671772   44   0.944882      0.724409  ...       0       1   \n",
       "3           4.333286   50   0.889764      0.444882  ...       0       1   \n",
       "4           5.967121   38   0.744094      0.661417  ...       0       0   \n",
       "...              ...  ...        ...           ...  ...     ...     ...   \n",
       "1995       14.013489   41   0.881890      0.744094  ...       0       1   \n",
       "1996        8.380497   38   0.944882      0.877953  ...       0       0   \n",
       "1997        4.626950   36   0.759843      0.744094  ...       0       0   \n",
       "1998        3.281439   46   0.909449      0.598425  ...       0       1   \n",
       "1999        9.405648   48   0.925197      0.539370  ...       0       1   \n",
       "\n",
       "      area_d  product_a  product_b  product_c  product_d  product_e  \\\n",
       "0          0          0          1          0          0          0   \n",
       "1          0          0          0          1          0          0   \n",
       "2          0          0          1          0          0          0   \n",
       "3          0          0          1          0          0          0   \n",
       "4          1          1          0          0          0          0   \n",
       "...      ...        ...        ...        ...        ...        ...   \n",
       "1995       0          0          1          0          0          0   \n",
       "1996       1          1          0          0          0          0   \n",
       "1997       1          0          0          0          0          0   \n",
       "1998       0          0          0          1          0          0   \n",
       "1999       0          0          0          1          0          0   \n",
       "\n",
       "      product_f  product_g  \n",
       "0             0          0  \n",
       "1             0          0  \n",
       "2             0          0  \n",
       "3             0          0  \n",
       "4             0          0  \n",
       "...         ...        ...  \n",
       "1995          0          0  \n",
       "1996          0          0  \n",
       "1997          1          0  \n",
       "1998          0          0  \n",
       "1999          0          0  \n",
       "\n",
       "[2000 rows x 55 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create train/test\n",
    "x_train, x_test, y_train, y_test = train_test_split(    \n",
    "    x, y, test_size=0.25, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 1856.2451 - val_loss: 1411.2688\n",
      "Epoch 2/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1195.0109 - val_loss: 458.9049\n",
      "Epoch 3/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 367.6988 - val_loss: 251.9515\n",
      "Epoch 4/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 236.1468 - val_loss: 206.2058\n",
      "Epoch 5/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 201.0395 - val_loss: 169.8992\n",
      "Epoch 6/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 157.8730 - val_loss: 139.5538\n",
      "Epoch 7/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 131.2739 - val_loss: 115.2930\n",
      "Epoch 8/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 107.5265 - val_loss: 95.4971\n",
      "Epoch 9/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 86.1215 - val_loss: 80.5102\n",
      "Epoch 10/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 75.4332 - val_loss: 68.9533\n",
      "Epoch 11/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 59.4470 - val_loss: 60.0607\n",
      "Epoch 12/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 51.9425 - val_loss: 52.3884\n",
      "Epoch 13/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 46.8885 - val_loss: 45.9872\n",
      "Epoch 14/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 43.1178 - val_loss: 40.7965\n",
      "Epoch 15/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 34.7295 - val_loss: 36.2327\n",
      "Epoch 16/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 31.6365 - val_loss: 32.9431\n",
      "Epoch 17/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 27.3292 - val_loss: 29.0064\n",
      "Epoch 18/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 23.6813 - val_loss: 26.2764\n",
      "Epoch 19/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 23.1601 - val_loss: 23.7374\n",
      "Epoch 20/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 20.0599 - val_loss: 21.6715\n",
      "Epoch 21/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 18.2263 - val_loss: 19.8453\n",
      "Epoch 22/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 15.5166 - val_loss: 18.3712\n",
      "Epoch 23/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 15.3661 - val_loss: 17.1004\n",
      "Epoch 24/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 14.8820 - val_loss: 16.1917\n",
      "Epoch 25/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 14.1377 - val_loss: 15.2994\n",
      "Epoch 26/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 12.3657 - val_loss: 14.6133\n",
      "Epoch 27/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 13.4145 - val_loss: 14.0660\n",
      "Epoch 28/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 12.6364 - val_loss: 13.3698\n",
      "Epoch 29/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 11.3319 - val_loss: 13.0321\n",
      "Epoch 30/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 11.3156 - val_loss: 12.5712\n",
      "Epoch 31/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 11.4575 - val_loss: 12.0053\n",
      "Epoch 32/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 10.7803 - val_loss: 11.6451\n",
      "Epoch 33/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 10.2847 - val_loss: 11.4272\n",
      "Epoch 34/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 10.2061 - val_loss: 10.9450\n",
      "Epoch 35/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 10.3722 - val_loss: 10.6373\n",
      "Epoch 36/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.3448 - val_loss: 10.3049\n",
      "Epoch 37/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.1101 - val_loss: 10.0259\n",
      "Epoch 38/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.4420 - val_loss: 9.6909\n",
      "Epoch 39/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.3596 - val_loss: 9.3743\n",
      "Epoch 40/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.4312 - val_loss: 9.0641\n",
      "Epoch 41/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.5683 - val_loss: 8.7855\n",
      "Epoch 42/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.8637 - val_loss: 8.5247\n",
      "Epoch 43/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 7.6836 - val_loss: 8.2645\n",
      "Epoch 44/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 7.1764 - val_loss: 8.0009\n",
      "Epoch 45/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 7.9760 - val_loss: 7.8840\n",
      "Epoch 46/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 6.7711 - val_loss: 7.5210\n",
      "Epoch 47/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 6.7095 - val_loss: 7.1925\n",
      "Epoch 48/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 6.3821 - val_loss: 6.9938\n",
      "Epoch 49/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 6.4881 - val_loss: 6.7211\n",
      "Epoch 50/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.9993 - val_loss: 6.5694\n",
      "Epoch 51/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.5994 - val_loss: 6.2694\n",
      "Epoch 52/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.5678 - val_loss: 6.1065\n",
      "Epoch 53/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.1018 - val_loss: 5.7138\n",
      "Epoch 54/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.3601 - val_loss: 5.4793\n",
      "Epoch 55/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.8024 - val_loss: 5.3552\n",
      "Epoch 56/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.7092 - val_loss: 5.0790\n",
      "Epoch 57/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.5392 - val_loss: 4.7732\n",
      "Epoch 58/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.2549 - val_loss: 4.4100\n",
      "Epoch 59/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.8113 - val_loss: 4.4327\n",
      "Epoch 60/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.6722 - val_loss: 4.2017\n",
      "Epoch 61/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.3691 - val_loss: 3.8922\n",
      "Epoch 62/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.3253 - val_loss: 3.5730\n",
      "Epoch 63/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.2552 - val_loss: 3.5060\n",
      "Epoch 64/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 2.7705 - val_loss: 3.1620\n",
      "Epoch 65/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.8716 - val_loss: 3.0433\n",
      "Epoch 66/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.6382 - val_loss: 2.9579\n",
      "Epoch 67/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.6773 - val_loss: 2.6883\n",
      "Epoch 68/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.3387 - val_loss: 2.7143\n",
      "Epoch 69/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.5017 - val_loss: 2.4250\n",
      "Epoch 70/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 2.2280 - val_loss: 2.3192\n",
      "Epoch 71/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 2.1409 - val_loss: 2.1651\n",
      "Epoch 72/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1.9977 - val_loss: 2.1728\n",
      "Epoch 73/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1.7751 - val_loss: 1.9536\n",
      "Epoch 74/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1.6139 - val_loss: 2.0869\n",
      "Epoch 75/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1.7303 - val_loss: 1.7597\n",
      "Epoch 76/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1.4562 - val_loss: 1.7429\n",
      "Epoch 77/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1.3816 - val_loss: 1.6972\n",
      "Epoch 78/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1.3109 - val_loss: 1.5519\n",
      "Epoch 79/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1.3486 - val_loss: 1.5952\n",
      "Epoch 80/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1.2922 - val_loss: 1.4105\n",
      "Epoch 81/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1.2456 - val_loss: 1.3661\n",
      "Epoch 82/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.1567 - val_loss: 1.6657\n",
      "Epoch 83/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.0781 - val_loss: 1.2659\n",
      "Epoch 84/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.9430 - val_loss: 1.1962\n",
      "Epoch 85/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.0612 - val_loss: 1.1434\n",
      "Epoch 86/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.8815 - val_loss: 1.1081\n",
      "Epoch 87/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.8926 - val_loss: 1.1084\n",
      "Epoch 88/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.8249 - val_loss: 1.0591\n",
      "Epoch 89/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.8913 - val_loss: 1.0451\n",
      "Epoch 90/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.7713 - val_loss: 0.9917\n",
      "Epoch 91/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.9542 - val_loss: 0.9603\n",
      "Epoch 92/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.9474 - val_loss: 1.0904\n",
      "Epoch 93/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.8396 - val_loss: 0.9788\n",
      "Epoch 94/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.8277 - val_loss: 0.9235\n",
      "Epoch 95/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6350 - val_loss: 0.9383\n",
      "Epoch 96/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.7614 - val_loss: 0.9373\n",
      "Epoch 97/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.7657 - val_loss: 0.9099\n",
      "Epoch 98/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6295 - val_loss: 0.8493\n",
      "Epoch 99/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.5946 - val_loss: 0.8752\n",
      "Epoch 100/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.5865 - val_loss: 0.8186\n",
      "Epoch 101/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.6320 - val_loss: 0.8294\n",
      "Epoch 102/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.5744 - val_loss: 0.8453\n",
      "Epoch 103/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.6757 - val_loss: 0.9314\n",
      "Epoch 104/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.5974 - val_loss: 0.7752\n",
      "Epoch 105/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.5726 - val_loss: 0.8014\n",
      "Epoch 106/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.5923 - val_loss: 0.7880\n",
      "Epoch 107/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.5215 - val_loss: 0.7531\n",
      "Epoch 108/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.8149 - val_loss: 1.1603\n",
      "Epoch 109/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.7754 - val_loss: 0.8093\n",
      "Epoch 110/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.6700 - val_loss: 0.8640\n",
      "Epoch 111/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.5062 - val_loss: 0.7329\n",
      "Epoch 112/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.6270 - val_loss: 0.8588\n",
      "Epoch 113/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5685 - val_loss: 0.7896\n",
      "Epoch 114/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.4883 - val_loss: 0.7232\n",
      "Epoch 115/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.6485 - val_loss: 0.7602\n",
      "Epoch 116/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.6492 - val_loss: 0.7460\n",
      "Epoch 117/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5583 - val_loss: 0.7337\n",
      "Epoch 118/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.5741 - val_loss: 0.7348\n",
      "Epoch 119/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.4709 - val_loss: 0.7118\n",
      "Epoch 120/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.5586 - val_loss: 0.7476\n",
      "Epoch 121/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.5760 - val_loss: 0.8188\n",
      "Epoch 122/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.4782 - val_loss: 0.7198\n",
      "Epoch 123/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.5529 - val_loss: 0.7298\n",
      "Epoch 124/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.5190 - val_loss: 0.7101\n",
      "Epoch 125/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.4403 - val_loss: 0.6958\n",
      "Epoch 126/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.5166 - val_loss: 0.6949\n",
      "Epoch 127/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6209 - val_loss: 0.6877\n",
      "Epoch 128/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.4685 - val_loss: 0.8377\n",
      "Epoch 129/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.5120 - val_loss: 0.8368\n",
      "Epoch 130/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.6203 - val_loss: 0.6850\n",
      "Epoch 131/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.5950 - val_loss: 0.6767\n",
      "Epoch 132/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.4245 - val_loss: 0.6959\n",
      "Epoch 133/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.5014 - val_loss: 0.6889\n",
      "Epoch 134/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3730 - val_loss: 0.6772\n",
      "Epoch 135/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.4088 - val_loss: 0.7342\n",
      "Epoch 136/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.4956 - val_loss: 0.6568\n",
      "Epoch 137/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3538 - val_loss: 0.6614\n",
      "Epoch 138/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3430 - val_loss: 0.8225\n",
      "Epoch 139/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.4695 - val_loss: 0.6525\n",
      "Epoch 140/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.5174 - val_loss: 0.7187\n",
      "Epoch 141/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.4158 - val_loss: 0.7607\n",
      "Epoch 142/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.4501 - val_loss: 0.6453\n",
      "Epoch 143/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.5526 - val_loss: 0.6402\n",
      "Epoch 144/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.4113 - val_loss: 0.7482\n",
      "Epoch 145/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.4085 - val_loss: 0.8848\n",
      "Epoch 146/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.4330 - val_loss: 0.7801\n",
      "Epoch 147/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.3801 - val_loss: 0.6326\n",
      "Epoch 148/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.3464 - val_loss: 0.6333\n",
      "Epoch 149/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.5936 - val_loss: 0.7180\n",
      "Epoch 150/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.4414 - val_loss: 0.6403\n",
      "Epoch 151/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.3782 - val_loss: 0.6327\n",
      "Epoch 152/1000\n",
      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.4443 - val_loss: 0.6686\n",
      "Epoch 152: early stopping\n",
      "Restoring model weights from the end of the best epoch: 147.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1ab0a5a4390>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build the neural network\n",
    "model = Sequential()\n",
    "model.add(Dense(25, input_dim=x.shape[1], activation='relu')) # Hidden 1\n",
    "model.add(Dense(10, activation='relu')) # Hidden 2\n",
    "model.add(Dense(1)) # Output\n",
    "model.compile(loss='mean_squared_error', optimizer='adam') # Modify here\n",
    "monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, \n",
    "                        verbose=1, mode='auto', restore_best_weights=True)\n",
    "model.fit(x_train,y_train,validation_data=(x_test,y_test),\n",
    "          callbacks=[monitor],verbose=1,epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the chart\n",
    "pred = model.predict(x_test)\n",
    "chart_regression(pred.flatten(),y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}