1/tf_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>black</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.199997</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>396.899994</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2.0</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.900002</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.799999</td>\n",
       "      <td>396.899994</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3.0</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.099998</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.799999</td>\n",
       "      <td>392.829987</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.700001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4.0</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.799999</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.700001</td>\n",
       "      <td>394.630005</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.400002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5.0</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.200001</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.700001</td>\n",
       "      <td>396.899994</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.200001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        crim    zn  indus  chas    nox     rm        age     dis  rad    tax  \\\n",
       "1.0  0.00632  18.0   2.31   0.0  0.538  6.575  65.199997  4.0900  1.0  296.0   \n",
       "2.0  0.02731   0.0   7.07   0.0  0.469  6.421  78.900002  4.9671  2.0  242.0   \n",
       "3.0  0.02729   0.0   7.07   0.0  0.469  7.185  61.099998  4.9671  2.0  242.0   \n",
       "4.0  0.03237   0.0   2.18   0.0  0.458  6.998  45.799999  6.0622  3.0  222.0   \n",
       "5.0  0.06905   0.0   2.18   0.0  0.458  7.147  54.200001  6.0622  3.0  222.0   \n",
       "\n",
       "       ptratio       black  lstat       medv  \n",
       "1.0  15.300000  396.899994   4.98  24.000000  \n",
       "2.0  17.799999  396.899994   9.14  21.600000  \n",
       "3.0  17.799999  392.829987   4.03  34.700001  \n",
       "4.0  18.700001  394.630005   2.94  33.400002  \n",
       "5.0  18.700001  396.899994   5.33  36.200001  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Boston = pd.read_table('Boston.txt', sep=',', dtype='float32')\n",
    "Boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Float64Index: 506 entries, 1.0 to 506.0\n",
      "Data columns (total 14 columns):\n",
      "crim       506 non-null float32\n",
      "zn         506 non-null float32\n",
      "indus      506 non-null float32\n",
      "chas       506 non-null float32\n",
      "nox        506 non-null float32\n",
      "rm         506 non-null float32\n",
      "age        506 non-null float32\n",
      "dis        506 non-null float32\n",
      "rad        506 non-null float32\n",
      "tax        506 non-null float32\n",
      "ptratio    506 non-null float32\n",
      "black      506 non-null float32\n",
      "lstat      506 non-null float32\n",
      "medv       506 non-null float32\n",
      "dtypes: float32(14)\n",
      "memory usage: 31.6 KB\n"
     ]
    }
   ],
   "source": [
    "Boston.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>black</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>3.613523</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136797</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554696</td>\n",
       "      <td>6.284636</td>\n",
       "      <td>68.574921</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237152</td>\n",
       "      <td>18.455584</td>\n",
       "      <td>356.674561</td>\n",
       "      <td>12.653064</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322390</td>\n",
       "      <td>6.860355</td>\n",
       "      <td>0.253993</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148869</td>\n",
       "      <td>2.105711</td>\n",
       "      <td>8.707269</td>\n",
       "      <td>168.537170</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294838</td>\n",
       "      <td>7.141063</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377487</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440002</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.074999</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200001</td>\n",
       "      <td>396.225006</td>\n",
       "      <td>16.954999</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>88.976196</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.899994</td>\n",
       "      <td>37.970001</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             crim          zn       indus        chas         nox          rm  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613523   11.363636   11.136797    0.069170    0.554696    6.284636   \n",
       "std      8.601545   23.322390    6.860355    0.253993    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976196  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              age         dis         rad         tax     ptratio       black  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574921    3.795043    9.549407  408.237152   18.455584  356.674561   \n",
       "std     28.148869    2.105711    8.707269  168.537170    2.164946   91.294838   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377487   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440002   \n",
       "75%     94.074999    5.188425   24.000000  666.000000   20.200001  396.225006   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.899994   \n",
       "\n",
       "            lstat        medv  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653064   22.532806  \n",
       "std      7.141063    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200001  \n",
       "75%     16.954999   25.000000  \n",
       "max     37.970001   50.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Boston.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = Boston.drop('medv', axis=1).values[:404,:]\n",
    "Y_train = Boston.loc[:,['medv']].values[:404,:]\n",
    "\n",
    "X_test = Boston.drop('medv', axis=1).values[404:,:]\n",
    "Y_test = Boston.loc[:,['medv']].values[404:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Layer1():\n",
    "    \n",
    "    def __init__(self, model, input_dim, output_dim):\n",
    "        self.model = model\n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        self.build()\n",
    "        \n",
    "    def build(self):\n",
    "        self.weights = []\n",
    "        self.mu = tf.zeros(shape=(1,self.output_dim), dtype=\"float32\")\n",
    "        self.sigma = tf.ones(shape=(1,self.output_dim), dtype=\"float32\")\n",
    "\n",
    "    def __call__(self, inputs):\n",
    "        return (inputs-self.mu)/self.sigma\n",
    "    \n",
    "class Layer2():\n",
    "    \n",
    "    def __init__(self, model, input_dim, output_dim):\n",
    "        self.model = model\n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        self.build()\n",
    "        \n",
    "    def add_weight(self, shape):\n",
    "        weight_init = tf.random.normal(shape=shape, mean=0.0, stddev=0.05, dtype=\"float32\")\n",
    "        return tf.Variable(initial_value=weight_init, trainable=True)\n",
    "        \n",
    "    def build(self):\n",
    "        self.w = self.add_weight(shape=(self.output_dim, self.input_dim))\n",
    "        self.b = self.add_weight(shape=(self.output_dim, 1))\n",
    "        self.weights = [self.w, self.b]\n",
    "\n",
    "    def __call__(self, inputs):\n",
    "        z = tf.matmul(self.w, tf.transpose(inputs)) + self.b\n",
    "        return tf.transpose(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LossFunction():\n",
    "    \n",
    "    def __init__(self, model):\n",
    "        self.model = model\n",
    "\n",
    "    def __call__(self, y_true, y_pred):\n",
    "        return tf.math.reduce_mean(tf.math.square(y_true-y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MetricFunction():\n",
    "                    \n",
    "    def __init__(self, model):\n",
    "        self.model = model\n",
    "        \n",
    "    def __call__(self, y_true, y_pred):\n",
    "        return tf.math.reduce_mean(tf.math.abs(y_true-y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Optimizer():\n",
    "    \n",
    "    def __init__(self, model, learning_rate):\n",
    "        self.model = model\n",
    "        self.learning_rate = learning_rate\n",
    "        self.stop_training = False\n",
    "        \n",
    "    def apply(self, grads, weights):\n",
    "        for i in range(len(weights)):\n",
    "            w = weights[i]\n",
    "            grad_w = grads[i]\n",
    "            weights[i].assign(w - self.learning_rate*grad_w)\n",
    "            \n",
    "    def train_step(self, X, Y):\n",
    "        with tf.GradientTape() as tape:\n",
    "            H = self.model(X)\n",
    "            loss = self.model.loss(Y, H)\n",
    "        grads = tape.gradient(loss, self.model.weights)\n",
    "        self.apply(grads, self.model.weights)    \n",
    "        H = self.model(X)\n",
    "        loss = self.model.loss(Y, H)\n",
    "        metric = self.model.metric(Y, H)\n",
    "        logs = {'loss': loss,\n",
    "                'metric': metric}\n",
    "        return logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Callback1():\n",
    "\n",
    "    def __init__(self, model, verbose):\n",
    "        self.model = model\n",
    "        self.verbose = verbose\n",
    "    \n",
    "    def on_epoch_begin(self, epoch, logs=None):\n",
    "        self.start_time = tf.timestamp()\n",
    "        \n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        if self.verbose:\n",
    "            now = tf.timestamp()\n",
    "            time = now - self.start_time\n",
    "            tf.print('Epochs {}/{} - Loss: {} - Metric: {}'.format(epoch+1, self.model.epochs, logs['loss'], logs['metric']))\n",
    "            tf.print('----- {}s -----'.format(tf.round(1000*time)/1000))\n",
    "            \n",
    "class Callback2():\n",
    "\n",
    "    def __init__(self, model):\n",
    "        self.model = model\n",
    "        self.history = {'loss':[],\n",
    "                        'metric':[]}\n",
    "        \n",
    "    def on_epoch_end(self, epoch, logs):\n",
    "        self.history['loss'].append(logs['loss'].numpy())\n",
    "        self.history['metric'].append(logs['metric'].numpy())\n",
    "        \n",
    "    def on_train_end(self):\n",
    "        self.model.history = self.history\n",
    "\n",
    "class Callback3():\n",
    "        \n",
    "    def __init__(self, model, patience, error, reduce_factor, min_learning_rate):\n",
    "        self.model = model\n",
    "        self.patience = patience\n",
    "        self.error = error\n",
    "        self.reduce_factor = reduce_factor\n",
    "        self.min_learning_rate = min_learning_rate\n",
    "                        \n",
    "    def on_epoch_end(self, epoch, logs):\n",
    "        if epoch==0:\n",
    "            self.loss = logs['loss']\n",
    "            self.non_decreasing_epochs = 0\n",
    "        else:\n",
    "            if ((self.loss-logs['loss'])>self.error):\n",
    "                self.loss = logs['loss']\n",
    "                self.non_decreasing_epochs = 0\n",
    "            else:\n",
    "                self.non_decreasing_epochs = self.non_decreasing_epochs+1\n",
    "        if (self.non_decreasing_epochs == self.patience):\n",
    "            if (self.model.optimizer.learning_rate>self.min_learning_rate):\n",
    "                self.model.optimizer.learning_rate = self.reduce_factor*self.model.optimizer.learning_rate\n",
    "                self.non_decreasing_epochs = 0\n",
    "        \n",
    "class Callback4():\n",
    "        \n",
    "    def __init__(self, model, patience, error):\n",
    "        self.model = model\n",
    "        self.patience = patience\n",
    "        self.error = error\n",
    "        \n",
    "    def on_epoch_end(self, epoch, logs):\n",
    "        if epoch==0:\n",
    "            self.loss = logs['loss']\n",
    "            self.non_decreasing_epochs = 0\n",
    "        else:\n",
    "            if ((self.loss-logs['loss'])>self.error):\n",
    "                self.loss = logs['loss']\n",
    "                self.non_decreasing_epochs = 0\n",
    "            else:\n",
    "                self.non_decreasing_epochs = self.non_decreasing_epochs+1\n",
    "        if (self.non_decreasing_epochs == self.patience):\n",
    "            self.model.optimizer.stop_training = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegression():\n",
    "    \n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        self.build()\n",
    "    \n",
    "    def build(self):\n",
    "        self.h1 = Layer1(model=self, input_dim=self.input_dim, output_dim=self.input_dim)\n",
    "        self.h2 = Layer2(model=self, input_dim=self.input_dim, output_dim=self.output_dim)\n",
    "        self.layers = [self.h1, self.h2]\n",
    "        self.weights = []\n",
    "        for layer in self.layers:\n",
    "            for weight in layer.weights:\n",
    "                self.weights.append(weight)\n",
    "        \n",
    "    def __call__(self, inputs):\n",
    "        a1 = self.h1(inputs)\n",
    "        y = self.h2(a1) \n",
    "        return y\n",
    "        \n",
    "    def train_setup(self, epochs, learning_rate, verbose):\n",
    "        self.epochs = epochs\n",
    "        self.learning_rate = learning_rate\n",
    "        self.verbose = verbose\n",
    "        self.loss = LossFunction(model=self)\n",
    "        self.metric = MetricFunction(model=self)\n",
    "        self.optimizer = Optimizer(model=self, learning_rate=self.learning_rate) \n",
    "        self.callbacks = [Callback1(model=self, verbose=self.verbose),\n",
    "                          Callback2(model=self),\n",
    "                          Callback3(model=self, patience=1000, error=0.001, reduce_factor=0.1, min_learning_rate=0.001),\n",
    "                          Callback4(model=self, patience=2000, error=0.001)]\n",
    "        \n",
    "    def fit(self, X, Y, epochs=1000, learning_rate=0.01, verbose=True):\n",
    "        self.layers[0].mu = tf.math.reduce_mean(tf.constant(X, dtype=\"float32\"), axis=0)\n",
    "        self.layers[0].sigma = tf.math.reduce_std(tf.constant(X, dtype=\"float32\"), axis=0)\n",
    "        self.train_setup(epochs, learning_rate, verbose)\n",
    "        if verbose:\n",
    "            print('Train on {} samples'.format(X.shape[0]))\n",
    "        for epoch in range(self.epochs):\n",
    "            self.callbacks[0].on_epoch_begin(epoch)\n",
    "            logs = self.optimizer.train_step(tf.constant(X, dtype=\"float32\"), tf.constant(Y, dtype=\"float32\"))\n",
    "            for callback in self.callbacks:\n",
    "                callback.on_epoch_end(epoch, logs)\n",
    "            if self.optimizer.stop_training:\n",
    "                break\n",
    "        self.callbacks[1].on_train_end()\n",
    "            \n",
    "    def predict(self, inputs):\n",
    "        return self(tf.constant(inputs, dtype=\"float32\")).numpy()\n",
    "    \n",
    "    def evaluate(self, X, Y):\n",
    "        loss = self.loss(tf.constant(Y, dtype=\"float32\"), self(tf.constant(X, dtype=\"float32\")))\n",
    "        loss_numpy = loss.numpy()\n",
    "        metric = self.metric(tf.constant(Y, dtype=\"float32\"), self(tf.constant(X, dtype=\"float32\")))\n",
    "        metric_numpy = metric.numpy()\n",
    "        tf.print('Loss: {} - Metric: {}'.format(loss_numpy, metric_numpy))\n",
    "        return [loss_numpy, metric_numpy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 404 samples\n",
      "Epochs 1/1000 - Loss: 640.2774047851562 - Metric: 23.653839111328125\n",
      "----- 0.015s -----\n",
      "Epochs 2/1000 - Loss: 612.3370361328125 - Metric: 23.180757522583008\n",
      "----- 0.003s -----\n",
      "Epochs 3/1000 - Loss: 586.1819458007812 - Metric: 22.717144012451172\n",
      "----- 0.002s -----\n",
      "Epochs 4/1000 - Loss: 561.603515625 - Metric: 22.26280403137207\n",
      "----- 0.004s -----\n",
      "Epochs 5/1000 - Loss: 538.4297485351562 - Metric: 21.81754493713379\n",
      "----- 0.003s -----\n",
      "Epochs 6/1000 - Loss: 516.5184326171875 - Metric: 21.38119125366211\n",
      "----- 0.003s -----\n",
      "Epochs 7/1000 - Loss: 495.7506103515625 - Metric: 20.953569412231445\n",
      "----- 0.003s -----\n",
      "Epochs 8/1000 - Loss: 476.026611328125 - Metric: 20.534496307373047\n",
      "----- 0.004s -----\n",
      "Epochs 9/1000 - Loss: 457.2615966796875 - Metric: 20.123804092407227\n",
      "----- 0.004s -----\n",
      "Epochs 10/1000 - Loss: 439.3831481933594 - Metric: 19.721328735351562\n",
      "----- 0.004s -----\n",
      "Epochs 11/1000 - Loss: 422.32879638671875 - Metric: 19.326902389526367\n",
      "----- 0.004s -----\n",
      "Epochs 12/1000 - Loss: 406.0440368652344 - Metric: 18.94036293029785\n",
      "----- 0.005s -----\n",
      "Epochs 13/1000 - Loss: 390.4810485839844 - Metric: 18.561555862426758\n",
      "----- 0.003s -----\n",
      "Epochs 14/1000 - Loss: 375.59722900390625 - Metric: 18.190326690673828\n",
      "----- 0.004s -----\n",
      "Epochs 15/1000 - Loss: 361.3545837402344 - Metric: 17.82651710510254\n",
      "----- 0.005s -----\n",
      "Epochs 16/1000 - Loss: 347.7189025878906 - Metric: 17.469985961914062\n",
      "----- 0.003s -----\n",
      "Epochs 17/1000 - Loss: 334.65887451171875 - Metric: 17.120586395263672\n",
      "----- 0.006s -----\n",
      "Epochs 18/1000 - Loss: 322.1458740234375 - Metric: 16.778175354003906\n",
      "----- 0.003s -----\n",
      "Epochs 19/1000 - Loss: 310.1536865234375 - Metric: 16.442611694335938\n",
      "----- 0.002s -----\n",
      "Epochs 20/1000 - Loss: 298.6577453613281 - Metric: 16.113758087158203\n",
      "----- 0.003s -----\n",
      "Epochs 21/1000 - Loss: 287.63543701171875 - Metric: 15.791485786437988\n",
      "----- 0.003s -----\n",
      "Epochs 22/1000 - Loss: 277.0654296875 - Metric: 15.475654602050781\n",
      "----- 0.004s -----\n",
      "Epochs 23/1000 - Loss: 266.9277038574219 - Metric: 15.166141510009766\n",
      "----- 0.004s -----\n",
      "Epochs 24/1000 - Loss: 257.2034606933594 - Metric: 14.862818717956543\n",
      "----- 0.003s -----\n",
      "Epochs 25/1000 - Loss: 247.8748779296875 - Metric: 14.565561294555664\n",
      "----- 0.006s -----\n",
      "Epochs 26/1000 - Loss: 238.92503356933594 - Metric: 14.274250030517578\n",
      "----- 0.003s -----\n",
      "Epochs 27/1000 - Loss: 230.3379364013672 - Metric: 13.988764762878418\n",
      "----- 0.004s -----\n",
      "Epochs 28/1000 - Loss: 222.098388671875 - Metric: 13.708988189697266\n",
      "----- 0.003s -----\n",
      "Epochs 29/1000 - Loss: 214.19186401367188 - Metric: 13.434809684753418\n",
      "----- 0.004s -----\n",
      "Epochs 30/1000 - Loss: 206.60452270507812 - Metric: 13.166114807128906\n",
      "----- 0.003s -----\n",
      "Epochs 31/1000 - Loss: 199.32318115234375 - Metric: 12.902791023254395\n",
      "----- 0.005s -----\n",
      "Epochs 32/1000 - Loss: 192.33523559570312 - Metric: 12.644734382629395\n",
      "----- 0.002s -----\n",
      "Epochs 33/1000 - Loss: 185.628662109375 - Metric: 12.391840934753418\n",
      "----- 0.004s -----\n",
      "Epochs 34/1000 - Loss: 179.19189453125 - Metric: 12.144003868103027\n",
      "----- 0.003s -----\n",
      "Epochs 35/1000 - Loss: 173.01390075683594 - Metric: 11.901124000549316\n",
      "----- 0.005s -----\n",
      "Epochs 36/1000 - Loss: 167.08412170410156 - Metric: 11.663100242614746\n",
      "----- 0.003s -----\n",
      "Epochs 37/1000 - Loss: 161.3925323486328 - Metric: 11.432151794433594\n",
      "----- 0.004s -----\n",
      "Epochs 38/1000 - Loss: 155.9293670654297 - Metric: 11.205820083618164\n",
      "----- 0.003s -----\n",
      "Epochs 39/1000 - Loss: 150.6853790283203 - Metric: 10.984012603759766\n",
      "----- 0.004s -----\n",
      "Epochs 40/1000 - Loss: 145.65162658691406 - Metric: 10.766633987426758\n",
      "----- 0.003s -----\n",
      "Epochs 41/1000 - Loss: 140.81967163085938 - Metric: 10.55359935760498\n",
      "----- 0.003s -----\n",
      "Epochs 42/1000 - Loss: 136.18125915527344 - Metric: 10.34482192993164\n",
      "----- 0.003s -----\n",
      "Epochs 43/1000 - Loss: 131.72862243652344 - Metric: 10.1405611038208\n",
      "----- 0.003s -----\n",
      "Epochs 44/1000 - Loss: 127.45423889160156 - Metric: 9.941341400146484\n",
      "----- 0.003s -----\n",
      "Epochs 45/1000 - Loss: 123.35086822509766 - Metric: 9.746879577636719\n",
      "----- 0.003s -----\n",
      "Epochs 46/1000 - Loss: 119.41162872314453 - Metric: 9.556303024291992\n",
      "----- 0.004s -----\n",
      "Epochs 47/1000 - Loss: 115.62992095947266 - Metric: 9.369534492492676\n",
      "----- 0.005s -----\n",
      "Epochs 48/1000 - Loss: 111.99933624267578 - Metric: 9.186494827270508\n",
      "----- 0.004s -----\n",
      "Epochs 49/1000 - Loss: 108.5138168334961 - Metric: 9.007113456726074\n",
      "----- 0.005s -----\n",
      "Epochs 50/1000 - Loss: 105.16752624511719 - Metric: 8.831314086914062\n",
      "----- 0.003s -----\n",
      "Epochs 51/1000 - Loss: 101.95482635498047 - Metric: 8.659879684448242\n",
      "----- 0.005s -----\n",
      "Epochs 52/1000 - Loss: 98.87033081054688 - Metric: 8.4922456741333\n",
      "----- 0.003s -----\n",
      "Epochs 53/1000 - Loss: 95.9089126586914 - Metric: 8.327960014343262\n",
      "----- 0.005s -----\n",
      "Epochs 54/1000 - Loss: 93.0655746459961 - Metric: 8.167783737182617\n",
      "----- 0.004s -----\n",
      "Epochs 55/1000 - Loss: 90.33560180664062 - Metric: 8.011602401733398\n",
      "----- 0.005s -----\n",
      "Epochs 56/1000 - Loss: 87.71444702148438 - Metric: 7.8588385581970215\n",
      "----- 0.003s -----\n",
      "Epochs 57/1000 - Loss: 85.19772338867188 - Metric: 7.709114074707031\n",
      "----- 0.004s -----\n",
      "Epochs 58/1000 - Loss: 82.78121185302734 - Metric: 7.562371730804443\n",
      "----- 0.003s -----\n",
      "Epochs 59/1000 - Loss: 80.46090698242188 - Metric: 7.419059753417969\n",
      "----- 0.003s -----\n",
      "Epochs 60/1000 - Loss: 78.2329330444336 - Metric: 7.2797088623046875\n",
      "----- 0.003s -----\n",
      "Epochs 61/1000 - Loss: 76.0936050415039 - Metric: 7.143191337585449\n",
      "----- 0.004s -----\n",
      "Epochs 62/1000 - Loss: 74.0393295288086 - Metric: 7.010407447814941\n",
      "----- 0.004s -----\n",
      "Epochs 63/1000 - Loss: 72.06673431396484 - Metric: 6.880881309509277\n",
      "----- 0.003s -----\n",
      "Epochs 64/1000 - Loss: 70.17253112792969 - Metric: 6.755368232727051\n",
      "----- 0.003s -----\n",
      "Epochs 65/1000 - Loss: 68.35357666015625 - Metric: 6.633387088775635\n",
      "----- 0.003s -----\n",
      "Epochs 66/1000 - Loss: 66.60686492919922 - Metric: 6.515166759490967\n",
      "----- 0.004s -----\n",
      "Epochs 67/1000 - Loss: 64.9294662475586 - Metric: 6.400773048400879\n",
      "----- 0.003s -----\n",
      "Epochs 68/1000 - Loss: 63.318626403808594 - Metric: 6.289355754852295\n",
      "----- 0.004s -----\n",
      "Epochs 69/1000 - Loss: 61.771690368652344 - Metric: 6.181547164916992\n",
      "----- 0.004s -----\n",
      "Epochs 70/1000 - Loss: 60.286075592041016 - Metric: 6.076582431793213\n",
      "----- 0.005s -----\n",
      "Epochs 71/1000 - Loss: 58.859352111816406 - Metric: 5.974461555480957\n",
      "----- 0.003s -----\n",
      "Epochs 72/1000 - Loss: 57.48915100097656 - Metric: 5.874977111816406\n",
      "----- 0.003s -----\n",
      "Epochs 73/1000 - Loss: 56.173213958740234 - Metric: 5.778430938720703\n",
      "----- 0.002s -----\n",
      "Epochs 74/1000 - Loss: 54.90937423706055 - Metric: 5.684242248535156\n",
      "----- 0.003s -----\n",
      "Epochs 75/1000 - Loss: 53.69554138183594 - Metric: 5.592507362365723\n",
      "----- 0.004s -----\n",
      "Epochs 76/1000 - Loss: 52.52972412109375 - Metric: 5.5037617683410645\n",
      "----- 0.003s -----\n",
      "Epochs 77/1000 - Loss: 51.409976959228516 - Metric: 5.416802883148193\n",
      "----- 0.003s -----\n",
      "Epochs 78/1000 - Loss: 50.334503173828125 - Metric: 5.331569671630859\n",
      "----- 0.004s -----\n",
      "Epochs 79/1000 - Loss: 49.3015022277832 - Metric: 5.248204231262207\n",
      "----- 0.003s -----\n",
      "Epochs 80/1000 - Loss: 48.309288024902344 - Metric: 5.167388439178467\n",
      "----- 0.003s -----\n",
      "Epochs 81/1000 - Loss: 47.35624694824219 - Metric: 5.088871955871582\n",
      "----- 0.004s -----\n",
      "Epochs 82/1000 - Loss: 46.4407844543457 - Metric: 5.013087272644043\n",
      "----- 0.004s -----\n",
      "Epochs 83/1000 - Loss: 45.56142807006836 - Metric: 4.939990997314453\n",
      "----- 0.004s -----\n",
      "Epochs 84/1000 - Loss: 44.716739654541016 - Metric: 4.868803024291992\n",
      "----- 0.003s -----\n",
      "Epochs 85/1000 - Loss: 43.905311584472656 - Metric: 4.800084590911865\n",
      "----- 0.002s -----\n",
      "Epochs 86/1000 - Loss: 43.125831604003906 - Metric: 4.7331862449646\n",
      "----- 0.002s -----\n",
      "Epochs 87/1000 - Loss: 42.37702178955078 - Metric: 4.668699741363525\n",
      "----- 0.003s -----\n",
      "Epochs 88/1000 - Loss: 41.65767288208008 - Metric: 4.606637477874756\n",
      "----- 0.004s -----\n",
      "Epochs 89/1000 - Loss: 40.96660232543945 - Metric: 4.547499179840088\n",
      "----- 0.003s -----\n",
      "Epochs 90/1000 - Loss: 40.30268859863281 - Metric: 4.490393161773682\n",
      "----- 0.004s -----\n",
      "Epochs 91/1000 - Loss: 39.66484451293945 - Metric: 4.434858798980713\n",
      "----- 0.002s -----\n",
      "Epochs 92/1000 - Loss: 39.052032470703125 - Metric: 4.381653785705566\n",
      "----- 0.002s -----\n",
      "Epochs 93/1000 - Loss: 38.46327209472656 - Metric: 4.330721855163574\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.003s -----\n",
      "Epochs 94/1000 - Loss: 37.89759063720703 - Metric: 4.281454563140869\n",
      "----- 0.004s -----\n",
      "Epochs 95/1000 - Loss: 37.354068756103516 - Metric: 4.234046459197998\n",
      "----- 0.004s -----\n",
      "Epochs 96/1000 - Loss: 36.831825256347656 - Metric: 4.188727855682373\n",
      "----- 0.002s -----\n",
      "Epochs 97/1000 - Loss: 36.330020904541016 - Metric: 4.145537853240967\n",
      "----- 0.002s -----\n",
      "Epochs 98/1000 - Loss: 35.84783172607422 - Metric: 4.103662967681885\n",
      "----- 0.003s -----\n",
      "Epochs 99/1000 - Loss: 35.384490966796875 - Metric: 4.06328821182251\n",
      "----- 0.002s -----\n",
      "Epochs 100/1000 - Loss: 34.939247131347656 - Metric: 4.024385452270508\n",
      "----- 0.005s -----\n",
      "Epochs 101/1000 - Loss: 34.511383056640625 - Metric: 3.9866318702697754\n",
      "----- 0.002s -----\n",
      "Epochs 102/1000 - Loss: 34.10020446777344 - Metric: 3.9507534503936768\n",
      "----- 0.005s -----\n",
      "Epochs 103/1000 - Loss: 33.705055236816406 - Metric: 3.9163920879364014\n",
      "----- 0.003s -----\n",
      "Epochs 104/1000 - Loss: 33.325286865234375 - Metric: 3.883119583129883\n",
      "----- 0.003s -----\n",
      "Epochs 105/1000 - Loss: 32.96030044555664 - Metric: 3.8510921001434326\n",
      "----- 0.002s -----\n",
      "Epochs 106/1000 - Loss: 32.6094970703125 - Metric: 3.819687604904175\n",
      "----- 0.002s -----\n",
      "Epochs 107/1000 - Loss: 32.272335052490234 - Metric: 3.7894039154052734\n",
      "----- 0.003s -----\n",
      "Epochs 108/1000 - Loss: 31.948257446289062 - Metric: 3.760469913482666\n",
      "----- 0.003s -----\n",
      "Epochs 109/1000 - Loss: 31.636743545532227 - Metric: 3.732862710952759\n",
      "----- 0.005s -----\n",
      "Epochs 110/1000 - Loss: 31.337310791015625 - Metric: 3.706101417541504\n",
      "----- 0.003s -----\n",
      "Epochs 111/1000 - Loss: 31.049463272094727 - Metric: 3.680227041244507\n",
      "----- 0.003s -----\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epochs 367/1000 - Loss: 23.18633460998535 - Metric: 3.2674357891082764\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "----- 0.004s -----\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.003s -----\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "----- 0.002s -----\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epochs 823/1000 - Loss: 22.837200164794922 - Metric: 3.2892160415649414\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epochs 914/1000 - Loss: 22.819372177124023 - Metric: 3.2928988933563232\n",
      "----- 0.003s -----\n",
      "Epochs 915/1000 - Loss: 22.819210052490234 - Metric: 3.292936086654663\n",
      "----- 0.004s -----\n",
      "Epochs 916/1000 - Loss: 22.819049835205078 - Metric: 3.292973041534424\n",
      "----- 0.003s -----\n",
      "Epochs 917/1000 - Loss: 22.818891525268555 - Metric: 3.2930102348327637\n",
      "----- 0.002s -----\n",
      "Epochs 918/1000 - Loss: 22.818729400634766 - Metric: 3.293046712875366\n",
      "----- 0.004s -----\n",
      "Epochs 919/1000 - Loss: 22.818574905395508 - Metric: 3.293083667755127\n",
      "----- 0.003s -----\n",
      "Epochs 920/1000 - Loss: 22.81841278076172 - Metric: 3.2931201457977295\n",
      "----- 0.005s -----\n",
      "Epochs 921/1000 - Loss: 22.818256378173828 - Metric: 3.293156385421753\n",
      "----- 0.003s -----\n",
      "Epochs 922/1000 - Loss: 22.818098068237305 - Metric: 3.2931926250457764\n",
      "----- 0.005s -----\n",
      "Epochs 923/1000 - Loss: 22.81794548034668 - Metric: 3.293229103088379\n",
      "----- 0.003s -----\n",
      "Epochs 924/1000 - Loss: 22.81778907775879 - Metric: 3.2932655811309814\n",
      "----- 0.004s -----\n",
      "Epochs 925/1000 - Loss: 22.81763458251953 - Metric: 3.2933011054992676\n",
      "----- 0.005s -----\n",
      "Epochs 926/1000 - Loss: 22.817480087280273 - Metric: 3.29333758354187\n",
      "----- 0.003s -----\n",
      "Epochs 927/1000 - Loss: 22.817325592041016 - Metric: 3.2933733463287354\n",
      "----- 0.003s -----\n",
      "Epochs 928/1000 - Loss: 22.81717300415039 - Metric: 3.2934093475341797\n",
      "----- 0.002s -----\n",
      "Epochs 929/1000 - Loss: 22.81702423095703 - Metric: 3.2934446334838867\n",
      "----- 0.003s -----\n",
      "Epochs 930/1000 - Loss: 22.816869735717773 - Metric: 3.2934799194335938\n",
      "----- 0.005s -----\n",
      "Epochs 931/1000 - Loss: 22.81671714782715 - Metric: 3.29351544380188\n",
      "----- 0.003s -----\n",
      "Epochs 932/1000 - Loss: 22.81656837463379 - Metric: 3.293550729751587\n",
      "----- 0.003s -----\n",
      "Epochs 933/1000 - Loss: 22.816415786743164 - Metric: 3.293586015701294\n",
      "----- 0.002s -----\n",
      "Epochs 934/1000 - Loss: 22.816268920898438 - Metric: 3.293621063232422\n",
      "----- 0.003s -----\n",
      "Epochs 935/1000 - Loss: 22.816118240356445 - Metric: 3.29365611076355\n",
      "----- 0.002s -----\n",
      "Epochs 936/1000 - Loss: 22.81597328186035 - Metric: 3.2936909198760986\n",
      "----- 0.004s -----\n",
      "Epochs 937/1000 - Loss: 22.815826416015625 - Metric: 3.2937257289886475\n",
      "----- 0.002s -----\n",
      "Epochs 938/1000 - Loss: 22.815675735473633 - Metric: 3.293760299682617\n",
      "----- 0.002s -----\n",
      "Epochs 939/1000 - Loss: 22.815532684326172 - Metric: 3.293795347213745\n",
      "----- 0.003s -----\n",
      "Epochs 940/1000 - Loss: 22.815387725830078 - Metric: 3.2938296794891357\n",
      "----- 0.003s -----\n",
      "Epochs 941/1000 - Loss: 22.815242767333984 - Metric: 3.2938640117645264\n",
      "----- 0.004s -----\n",
      "Epochs 942/1000 - Loss: 22.815095901489258 - Metric: 3.293898105621338\n",
      "----- 0.003s -----\n",
      "Epochs 943/1000 - Loss: 22.814952850341797 - Metric: 3.2939324378967285\n",
      "----- 0.006s -----\n",
      "Epochs 944/1000 - Loss: 22.81481170654297 - Metric: 3.29396653175354\n",
      "----- 0.003s -----\n",
      "Epochs 945/1000 - Loss: 22.814666748046875 - Metric: 3.2940006256103516\n",
      "----- 0.005s -----\n",
      "Epochs 946/1000 - Loss: 22.814523696899414 - Metric: 3.294034481048584\n",
      "----- 0.003s -----\n",
      "Epochs 947/1000 - Loss: 22.81438636779785 - Metric: 3.2940680980682373\n",
      "----- 0.005s -----\n",
      "Epochs 948/1000 - Loss: 22.814239501953125 - Metric: 3.294102191925049\n",
      "----- 0.004s -----\n",
      "Epochs 949/1000 - Loss: 22.81410026550293 - Metric: 3.294135093688965\n",
      "----- 0.003s -----\n",
      "Epochs 950/1000 - Loss: 22.813962936401367 - Metric: 3.2941689491271973\n",
      "----- 0.004s -----\n",
      "Epochs 951/1000 - Loss: 22.81382179260254 - Metric: 3.2942018508911133\n",
      "----- 0.003s -----\n",
      "Epochs 952/1000 - Loss: 22.813682556152344 - Metric: 3.2942354679107666\n",
      "----- 0.002s -----\n",
      "Epochs 953/1000 - Loss: 22.81354331970215 - Metric: 3.294268846511841\n",
      "----- 0.002s -----\n",
      "Epochs 954/1000 - Loss: 22.813405990600586 - Metric: 3.2943015098571777\n",
      "----- 0.003s -----\n",
      "Epochs 955/1000 - Loss: 22.813268661499023 - Metric: 3.2943344116210938\n",
      "----- 0.005s -----\n",
      "Epochs 956/1000 - Loss: 22.813133239746094 - Metric: 3.2943673133850098\n",
      "----- 0.002s -----\n",
      "Epochs 957/1000 - Loss: 22.812997817993164 - Metric: 3.294400453567505\n",
      "----- 0.003s -----\n",
      "Epochs 958/1000 - Loss: 22.812862396240234 - Metric: 3.2944328784942627\n",
      "----- 0.002s -----\n",
      "Epochs 959/1000 - Loss: 22.812726974487305 - Metric: 3.2944653034210205\n",
      "----- 0.002s -----\n",
      "Epochs 960/1000 - Loss: 22.812597274780273 - Metric: 3.2944979667663574\n",
      "----- 0.004s -----\n",
      "Epochs 961/1000 - Loss: 22.812461853027344 - Metric: 3.294529914855957\n",
      "----- 0.003s -----\n",
      "Epochs 962/1000 - Loss: 22.812328338623047 - Metric: 3.294562578201294\n",
      "----- 0.004s -----\n",
      "Epochs 963/1000 - Loss: 22.81219482421875 - Metric: 3.2945950031280518\n",
      "----- 0.003s -----\n",
      "Epochs 964/1000 - Loss: 22.81205940246582 - Metric: 3.2946271896362305\n",
      "----- 0.003s -----\n",
      "Epochs 965/1000 - Loss: 22.81192970275879 - Metric: 3.294658899307251\n",
      "----- 0.003s -----\n",
      "Epochs 966/1000 - Loss: 22.81180191040039 - Metric: 3.2946910858154297\n",
      "----- 0.002s -----\n",
      "Epochs 967/1000 - Loss: 22.811668395996094 - Metric: 3.2947230339050293\n",
      "----- 0.003s -----\n",
      "Epochs 968/1000 - Loss: 22.811540603637695 - Metric: 3.2947545051574707\n",
      "----- 0.002s -----\n",
      "Epochs 969/1000 - Loss: 22.81140899658203 - Metric: 3.294786214828491\n",
      "----- 0.005s -----\n",
      "Epochs 970/1000 - Loss: 22.811279296875 - Metric: 3.2948176860809326\n",
      "----- 0.004s -----\n",
      "Epochs 971/1000 - Loss: 22.811153411865234 - Metric: 3.294849157333374\n",
      "----- 0.003s -----\n",
      "Epochs 972/1000 - Loss: 22.811025619506836 - Metric: 3.2948801517486572\n",
      "----- 0.003s -----\n",
      "Epochs 973/1000 - Loss: 22.810897827148438 - Metric: 3.2949118614196777\n",
      "----- 0.003s -----\n",
      "Epochs 974/1000 - Loss: 22.81077003479004 - Metric: 3.2949435710906982\n",
      "----- 0.003s -----\n",
      "Epochs 975/1000 - Loss: 22.810644149780273 - Metric: 3.2949745655059814\n",
      "----- 0.004s -----\n",
      "Epochs 976/1000 - Loss: 22.810516357421875 - Metric: 3.2950050830841064\n",
      "----- 0.004s -----\n",
      "Epochs 977/1000 - Loss: 22.810392379760742 - Metric: 3.2950360774993896\n",
      "----- 0.003s -----\n",
      "Epochs 978/1000 - Loss: 22.810264587402344 - Metric: 3.2950668334960938\n",
      "----- 0.004s -----\n",
      "Epochs 979/1000 - Loss: 22.81014060974121 - Metric: 3.2950973510742188\n",
      "----- 0.003s -----\n",
      "Epochs 980/1000 - Loss: 22.810016632080078 - Metric: 3.295128345489502\n",
      "----- 0.004s -----\n",
      "Epochs 981/1000 - Loss: 22.80989646911621 - Metric: 3.295158863067627\n",
      "----- 0.003s -----\n",
      "Epochs 982/1000 - Loss: 22.809772491455078 - Metric: 3.295189380645752\n",
      "----- 0.005s -----\n",
      "Epochs 983/1000 - Loss: 22.809648513793945 - Metric: 3.2952194213867188\n",
      "----- 0.006s -----\n",
      "Epochs 984/1000 - Loss: 22.809526443481445 - Metric: 3.2952497005462646\n",
      "----- 0.003s -----\n",
      "Epochs 985/1000 - Loss: 22.80940818786621 - Metric: 3.2952799797058105\n",
      "----- 0.004s -----\n",
      "Epochs 986/1000 - Loss: 22.809284210205078 - Metric: 3.2953102588653564\n",
      "----- 0.003s -----\n",
      "Epochs 987/1000 - Loss: 22.809165954589844 - Metric: 3.295340061187744\n",
      "----- 0.004s -----\n",
      "Epochs 988/1000 - Loss: 22.809045791625977 - Metric: 3.29537034034729\n",
      "----- 0.004s -----\n",
      "Epochs 989/1000 - Loss: 22.80892562866211 - Metric: 3.2954001426696777\n",
      "----- 0.005s -----\n",
      "Epochs 990/1000 - Loss: 22.808807373046875 - Metric: 3.2954297065734863\n",
      "----- 0.004s -----\n",
      "Epochs 991/1000 - Loss: 22.808691024780273 - Metric: 3.295459032058716\n",
      "----- 0.004s -----\n",
      "Epochs 992/1000 - Loss: 22.80856704711914 - Metric: 3.2954890727996826\n",
      "----- 0.002s -----\n",
      "Epochs 993/1000 - Loss: 22.80845069885254 - Metric: 3.295518636703491\n",
      "----- 0.003s -----\n",
      "Epochs 994/1000 - Loss: 22.808332443237305 - Metric: 3.2955472469329834\n",
      "----- 0.003s -----\n",
      "Epochs 995/1000 - Loss: 22.808216094970703 - Metric: 3.29557728767395\n",
      "----- 0.005s -----\n",
      "Epochs 996/1000 - Loss: 22.80810546875 - Metric: 3.2956061363220215\n",
      "----- 0.003s -----\n",
      "Epochs 997/1000 - Loss: 22.807987213134766 - Metric: 3.295635223388672\n",
      "----- 0.004s -----\n",
      "Epochs 998/1000 - Loss: 22.807870864868164 - Metric: 3.2956645488739014\n",
      "----- 0.003s -----\n",
      "Epochs 999/1000 - Loss: 22.807758331298828 - Metric: 3.295693874359131\n",
      "----- 0.004s -----\n",
      "Epochs 1000/1000 - Loss: 22.807641983032227 - Metric: 3.295722246170044\n",
      "----- 0.003s -----\n"
     ]
    }
   ],
   "source": [
    "linear_regression = LinearRegression(input_dim=X_train.shape[1], output_dim=Y_train.shape[1])\n",
    "\n",
    "linear_regression.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 31.08544158935547 - Metric: 4.586657524108887\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[31.085442, 4.5866575]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_regression.evaluate(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f988c7682e8>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = linear_regression.history['loss']\n",
    "metric = linear_regression.history['metric']\n",
    "\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "ax1 = fig.add_subplot(1, 2, 1)\n",
    "ax1.set_title('LOSS')\n",
    "ax1.set_xlabel('iterations')\n",
    "ax1.set_ylabel('loss')\n",
    "ax1.plot(loss, marker='.', color='#0F00FF', markersize=1, linestyle='-')\n",
    "ax2 = fig.add_subplot(1, 2, 2)\n",
    "ax2.set_title('METRIC')\n",
    "ax2.set_xlabel('iterations')\n",
    "ax2.set_ylabel('metric')\n",
    "ax2.plot(metric, marker='.', color='#0F00FF', markersize=1, linestyle='-')"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}