6/tf_6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.utils import shuffle\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>104</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>70</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "45    46            4.8           3.0            1.4           0.3   \n",
       "62    63            6.0           2.2            4.0           1.0   \n",
       "103  104            6.3           2.9            5.6           1.8   \n",
       "20    21            5.4           3.4            1.7           0.2   \n",
       "69    70            5.6           2.5            3.9           1.1   \n",
       "\n",
       "             Species  \n",
       "45       Iris-setosa  \n",
       "62   Iris-versicolor  \n",
       "103   Iris-virginica  \n",
       "20       Iris-setosa  \n",
       "69   Iris-versicolor  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_table('iris.csv', sep=',')\n",
    "data = shuffle(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 150 entries, 45 to 53\n",
      "Data columns (total 6 columns):\n",
      "Id               150 non-null int64\n",
      "SepalLengthCm    150 non-null float64\n",
      "SepalWidthCm     150 non-null float64\n",
      "PetalLengthCm    150 non-null float64\n",
      "PetalWidthCm     150 non-null float64\n",
      "Species          150 non-null object\n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 8.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>104</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>70</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
       "45    46            4.8           3.0            1.4           0.3        0\n",
       "62    63            6.0           2.2            4.0           1.0        1\n",
       "103  104            6.3           2.9            5.6           1.8        2\n",
       "20    21            5.4           3.4            1.7           0.2        0\n",
       "69    70            5.6           2.5            3.9           1.1        1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:, ['Species']] = data.loc[:, ['Species']].applymap(lambda x: 0 if (x=='Iris-setosa') else x)\n",
    "data.loc[:, ['Species']] = data.loc[:, ['Species']].applymap(lambda x: 1 if (x=='Iris-versicolor') else x)\n",
    "data.loc[:, ['Species']] = data.loc[:, ['Species']].applymap(lambda x: 2 if (x=='Iris-virginica') else x)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data.iloc[:,1:5].values[:120,:]\n",
    "Y_train_labels = data.iloc[:,5].values[:120]\n",
    "Y_train = np.reshape(np.eye(3)[Y_train_labels],(-1,3))\n",
    "\n",
    "X_test = data.iloc[:,1:5].values[120:,:]\n",
    "Y_test_labels = data.iloc[:,5].values[120:]\n",
    "Y_test = np.reshape(np.eye(3)[Y_test_labels],(-1,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
    "        v = tf.transpose(z)\n",
    "        u = tf.math.exp(v)\n",
    "        return u/tf.math.reduce_sum(u, axis=1, keepdims=True)\n",
    "    \n",
    "class Layer3():\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",
    "\n",
    "    def __call__(self, inputs):\n",
    "        return tf.reshape(tf.math.argmax(inputs, axis=1), shape=(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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.reduce_mean(y_true*tf.math.log(y_pred), axis=1), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
    "        maximum = tf.math.reduce_max(y_pred, axis=1, keepdims=True)\n",
    "        y_pred_ = tf.sign(1+tf.sign(y_pred-maximum))\n",
    "        return tf.math.reduce_mean(tf.math.reduce_mean(1-tf.square(tf.sign(y_true-y_pred_)), axis=1), axis=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Optimizer():\n",
    "\n",
    "    def __init__(self, model, learning_rate, beta_1, beta_2, epsilon):\n",
    "        self.model = model\n",
    "        self.learning_rate = learning_rate\n",
    "        self.beta_1 = beta_1\n",
    "        self.beta_2 = beta_2\n",
    "        self.epsilon= epsilon\n",
    "        self.stop_training = False\n",
    "        self.build()\n",
    "        \n",
    "    def add_weight(self, shape):\n",
    "        weight_init = tf.zeros(shape=shape, dtype=\"float32\")\n",
    "        return  tf.Variable(initial_value=weight_init, trainable=False)\n",
    "    \n",
    "    def build(self):\n",
    "        self.weights = []\n",
    "        for weight in self.model.weights:\n",
    "            m = self.add_weight(shape=weight.shape)\n",
    "            v = self.add_weight(shape=weight.shape)\n",
    "            self.weights.append([m,v])\n",
    "            \n",
    "    def apply(self, grads, weights):\n",
    "        for i in range(len(weights)):\n",
    "            w = weights[i]\n",
    "            grad_w = grads[i]\n",
    "            m = self.weights[i][0]\n",
    "            v = self.weights[i][1]\n",
    "            self.weights[i][0].assign(self.beta_1*m + (1-self.beta_1)*grad_w)  \n",
    "            self.weights[i][1].assign(self.beta_2*v + (1-self.beta_2)*grad_w*grad_w)\n",
    "            m_ = (1/(1-self.beta_1))*self.weights[i][0]\n",
    "            v_ = (1/(1-self.beta_2))*self.weights[i][1]\n",
    "            weights[i].assign(w - self.learning_rate*m_/(tf.math.sqrt(v_)+self.epsilon))\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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LDA():\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.output_dim)\n",
    "        self.h2 = Layer2(model=self, input_dim=self.input_dim, output_dim=self.output_dim)\n",
    "        self.h3 = Layer3(model=self, input_dim=self.input_dim, output_dim=self.output_dim)\n",
    "        self.layers = [self.h1, self.h2, self.h3]\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, beta_1, beta_2, epsilon, verbose):\n",
    "        self.epochs = epochs\n",
    "        self.learning_rate = learning_rate\n",
    "        self.beta_1 = beta_1 \n",
    "        self.beta_2 = beta_2 \n",
    "        self.epsilon = epsilon\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, beta_1=self.beta_1, beta_2=self.beta_2, epsilon=self.epsilon)\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=10000, learning_rate=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-07, 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, beta_1, beta_2, epsilon, 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.h3(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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 120 samples\n",
      "Epochs 1/10000 - Loss: 0.2749832272529602 - Metric: 0.855555534362793\n",
      "----- 0.051s -----\n",
      "Epochs 2/10000 - Loss: 0.20961284637451172 - Metric: 0.9222222566604614\n",
      "----- 0.006s -----\n",
      "Epochs 3/10000 - Loss: 0.16437633335590363 - Metric: 0.8999999761581421\n",
      "----- 0.004s -----\n",
      "Epochs 4/10000 - Loss: 0.13604636490345 - Metric: 0.9055555462837219\n",
      "----- 0.004s -----\n",
      "Epochs 5/10000 - Loss: 0.11728604882955551 - Metric: 0.9277777075767517\n",
      "----- 0.004s -----\n",
      "Epochs 6/10000 - Loss: 0.10393581539392471 - Metric: 0.9333333373069763\n",
      "----- 0.004s -----\n",
      "Epochs 7/10000 - Loss: 0.09408546984195709 - Metric: 0.9333333373069763\n",
      "----- 0.004s -----\n",
      "Epochs 8/10000 - Loss: 0.08635871857404709 - Metric: 0.9388889074325562\n",
      "----- 0.004s -----\n",
      "Epochs 9/10000 - Loss: 0.07944746315479279 - Metric: 0.944444477558136\n",
      "----- 0.005s -----\n",
      "Epochs 10/10000 - Loss: 0.07262767851352692 - Metric: 0.949999988079071\n",
      "----- 0.004s -----\n",
      "Epochs 11/10000 - Loss: 0.06601487845182419 - Metric: 0.9611111283302307\n",
      "----- 0.005s -----\n",
      "Epochs 12/10000 - Loss: 0.06003884598612785 - Metric: 0.9611111283302307\n",
      "----- 0.004s -----\n",
      "Epochs 13/10000 - Loss: 0.054914187639951706 - Metric: 0.9666666388511658\n",
      "----- 0.005s -----\n",
      "Epochs 14/10000 - Loss: 0.05057560279965401 - Metric: 0.9666666388511658\n",
      "----- 0.004s -----\n",
      "Epochs 15/10000 - Loss: 0.04688609391450882 - Metric: 0.9777777194976807\n",
      "----- 0.005s -----\n",
      "Epochs 16/10000 - Loss: 0.04378144443035126 - Metric: 0.9777777194976807\n",
      "----- 0.004s -----\n",
      "Epochs 17/10000 - Loss: 0.04125306382775307 - Metric: 0.9777777194976807\n",
      "----- 0.004s -----\n",
      "Epochs 18/10000 - Loss: 0.03926587849855423 - Metric: 0.9777777194976807\n",
      "----- 0.004s -----\n",
      "Epochs 19/10000 - Loss: 0.037710439413785934 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 20/10000 - Loss: 0.036426711827516556 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 21/10000 - Loss: 0.03527520224452019 - Metric: 0.9777777194976807\n",
      "----- 0.005s -----\n",
      "Epochs 22/10000 - Loss: 0.03419211134314537 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 23/10000 - Loss: 0.03318558260798454 - Metric: 0.9777777194976807\n",
      "----- 0.005s -----\n",
      "Epochs 24/10000 - Loss: 0.03228963911533356 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 25/10000 - Loss: 0.03152124583721161 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 26/10000 - Loss: 0.030866501852869987 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 27/10000 - Loss: 0.03029218129813671 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 28/10000 - Loss: 0.029765097424387932 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 29/10000 - Loss: 0.02926524356007576 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 30/10000 - Loss: 0.028788050636649132 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 31/10000 - Loss: 0.028338540345430374 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 32/10000 - Loss: 0.02792339399456978 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 33/10000 - Loss: 0.0275458674877882 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 34/10000 - Loss: 0.027205098420381546 - Metric: 0.9833333492279053\n",
      "----- 0.006s -----\n",
      "Epochs 35/10000 - Loss: 0.026898404583334923 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 36/10000 - Loss: 0.026623567566275597 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 37/10000 - Loss: 0.02637932263314724 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 38/10000 - Loss: 0.026164067909121513 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 39/10000 - Loss: 0.025974102318286896 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 40/10000 - Loss: 0.025803066790103912 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 41/10000 - Loss: 0.025642985478043556 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 42/10000 - Loss: 0.025486525148153305 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 43/10000 - Loss: 0.025329025462269783 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 44/10000 - Loss: 0.02516942098736763 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 45/10000 - Loss: 0.02500966191291809 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 46/10000 - Loss: 0.024853207170963287 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 47/10000 - Loss: 0.024703267961740494 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 48/10000 - Loss: 0.024561632424592972 - Metric: 0.9833333492279053\n",
      "----- 0.025s -----\n",
      "Epochs 49/10000 - Loss: 0.024428389966487885 - Metric: 0.9833333492279053\n",
      "----- 0.009s -----\n",
      "Epochs 50/10000 - Loss: 0.02430243045091629 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 51/10000 - Loss: 0.024182241410017014 - Metric: 0.9833333492279053\n",
      "----- 0.007s -----\n",
      "Epochs 52/10000 - Loss: 0.024066615849733353 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 53/10000 - Loss: 0.023954929783940315 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 54/10000 - Loss: 0.023847078904509544 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 55/10000 - Loss: 0.023743145167827606 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 56/10000 - Loss: 0.023643119260668755 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 57/10000 - Loss: 0.023546773940324783 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 58/10000 - Loss: 0.023453716188669205 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 59/10000 - Loss: 0.023363571614027023 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 60/10000 - Loss: 0.023276107385754585 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 61/10000 - Loss: 0.023191280663013458 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 62/10000 - Loss: 0.02310911752283573 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 63/10000 - Loss: 0.02302963100373745 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 64/10000 - Loss: 0.022952675819396973 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 65/10000 - Loss: 0.022877927869558334 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 66/10000 - Loss: 0.022804955020546913 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 67/10000 - Loss: 0.02273334003984928 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 68/10000 - Loss: 0.022662797942757607 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 69/10000 - Loss: 0.022593211382627487 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 70/10000 - Loss: 0.022524623200297356 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 71/10000 - Loss: 0.022457187995314598 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 72/10000 - Loss: 0.022391032427549362 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 73/10000 - Loss: 0.02232624776661396 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 74/10000 - Loss: 0.022262828424572945 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 75/10000 - Loss: 0.022200724110007286 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 76/10000 - Loss: 0.022139839828014374 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 77/10000 - Loss: 0.02208009548485279 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 78/10000 - Loss: 0.02202143892645836 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 79/10000 - Loss: 0.0219638179987669 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 80/10000 - Loss: 0.021907199174165726 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 81/10000 - Loss: 0.02185152843594551 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 82/10000 - Loss: 0.021796738728880882 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 83/10000 - Loss: 0.021742764860391617 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 84/10000 - Loss: 0.021689530462026596 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 85/10000 - Loss: 0.021636998280882835 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 86/10000 - Loss: 0.021585136651992798 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 87/10000 - Loss: 0.021533949300646782 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 88/10000 - Loss: 0.021483445540070534 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 89/10000 - Loss: 0.021433621644973755 - Metric: 0.9833333492279053\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.004s -----\n",
      "Epochs 90/10000 - Loss: 0.021384481340646744 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 91/10000 - Loss: 0.02133600227534771 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 92/10000 - Loss: 0.021288160234689713 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 93/10000 - Loss: 0.021240929141640663 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 94/10000 - Loss: 0.021194282919168472 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 95/10000 - Loss: 0.021148204803466797 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 96/10000 - Loss: 0.02110268361866474 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 97/10000 - Loss: 0.021057704463601112 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 98/10000 - Loss: 0.02101326733827591 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 99/10000 - Loss: 0.020969361066818237 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 100/10000 - Loss: 0.020925968885421753 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 101/10000 - Loss: 0.02088308334350586 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 102/10000 - Loss: 0.020840691402554512 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 103/10000 - Loss: 0.020798778161406517 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 104/10000 - Loss: 0.020757339894771576 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 105/10000 - Loss: 0.02071637474000454 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 106/10000 - Loss: 0.020675865933299065 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 107/10000 - Loss: 0.020635811612010002 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 108/10000 - Loss: 0.020596208050847054 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 109/10000 - Loss: 0.020557036623358727 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 110/10000 - Loss: 0.020518284291028976 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 111/10000 - Loss: 0.020479945465922356 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 112/10000 - Loss: 0.020442010834813118 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 113/10000 - Loss: 0.020404471084475517 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 114/10000 - Loss: 0.020367318764328957 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 115/10000 - Loss: 0.020330555737018585 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 116/10000 - Loss: 0.02029416337609291 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 117/10000 - Loss: 0.020258145406842232 - Metric: 0.9833333492279053\n",
      "----- 0.005s -----\n",
      "Epochs 118/10000 - Loss: 0.020222492516040802 - Metric: 0.9833333492279053\n",
      "----- 0.004s -----\n",
      "Epochs 119/10000 - Loss: 0.020187197253108025 - Metric: 0.9833333492279053\n",
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     ]
    },
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     "name": "stdout",
     "output_type": "stream",
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     ]
    },
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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",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.004s -----\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.004s -----\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.006s -----\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "----- 0.008s -----\n",
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      "----- 0.021s -----\n",
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      "----- 0.027s -----\n",
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      "----- 0.006s -----\n",
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      "Epochs 1143/10000 - Loss: 0.014141623862087727 - Metric: 0.9888889193534851\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.005s -----\n",
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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": [
      "----- 0.006s -----\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "----- 0.006s -----\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.005s -----\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.006s -----\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.004s -----\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.005s -----\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
      "Epochs 1926/10000 - Loss: 0.013781546615064144 - Metric: 0.9888889193534851\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.004s -----\n",
      "Epochs 1927/10000 - Loss: 0.01378152146935463 - Metric: 0.9888889193534851\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.006s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.006s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "Epochs 2006/10000 - Loss: 0.013779313303530216 - Metric: 0.9888889193534851\n",
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      "Epochs 2011/10000 - Loss: 0.013779175467789173 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2012/10000 - Loss: 0.013779147528111935 - Metric: 0.9888889193534851\n",
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      "Epochs 2013/10000 - Loss: 0.013779119588434696 - Metric: 0.9888889193534851\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.005s -----\n",
      "Epochs 2014/10000 - Loss: 0.013779091648757458 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.005s -----\n",
      "Epochs 2021/10000 - Loss: 0.013778891414403915 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2022/10000 - Loss: 0.013778865337371826 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2023/10000 - Loss: 0.013778838329017162 - Metric: 0.9888889193534851\n",
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      "Epochs 2054/10000 - Loss: 0.013777952641248703 - Metric: 0.9888889193534851\n",
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      "Epochs 2064/10000 - Loss: 0.013777666725218296 - Metric: 0.9888889193534851\n",
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      "Epochs 2100/10000 - Loss: 0.013776629231870174 - Metric: 0.9888889193534851\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.004s -----\n",
      "Epochs 2101/10000 - Loss: 0.013776597566902637 - Metric: 0.9888889193534851\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "----- 0.006s -----\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "----- 0.005s -----\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
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      "----- 0.004s -----\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.007s -----\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "----- 0.007s -----\n",
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      "Epochs 2630/10000 - Loss: 0.0137602174654603 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
      "Epochs 2632/10000 - Loss: 0.013760153204202652 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2633/10000 - Loss: 0.013760119676589966 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2634/10000 - Loss: 0.013760088942945004 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.01s -----\n",
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      "----- 0.008s -----\n",
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      "----- 0.008s -----\n",
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      "----- 0.008s -----\n",
      "Epochs 2641/10000 - Loss: 0.013759857043623924 - Metric: 0.9888889193534851\n",
      "----- 0.007s -----\n",
      "Epochs 2642/10000 - Loss: 0.013759822584688663 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2643/10000 - Loss: 0.013759788125753403 - Metric: 0.9888889193534851\n",
      "----- 0.008s -----\n",
      "Epochs 2644/10000 - Loss: 0.013759753666818142 - Metric: 0.9888889193534851\n",
      "----- 0.007s -----\n",
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      "----- 0.004s -----\n",
      "Epochs 2646/10000 - Loss: 0.013759694062173367 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2647/10000 - Loss: 0.013759657740592957 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.006s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.006s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.006s -----\n",
      "Epochs 2654/10000 - Loss: 0.013759428635239601 - Metric: 0.9888889193534851\n",
      "----- 0.007s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.006s -----\n",
      "Epochs 2658/10000 - Loss: 0.013759293593466282 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
      "Epochs 2660/10000 - Loss: 0.013759229332208633 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2661/10000 - Loss: 0.013759193941950798 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2662/10000 - Loss: 0.013759157620370388 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2663/10000 - Loss: 0.013759126886725426 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2664/10000 - Loss: 0.013759094290435314 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
      "Epochs 2667/10000 - Loss: 0.013758998364210129 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2668/10000 - Loss: 0.013758962042629719 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.006s -----\n",
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      "----- 0.006s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.007s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
      "Epochs 2676/10000 - Loss: 0.013758697547018528 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2677/10000 - Loss: 0.013758662156760693 - Metric: 0.9888889193534851\n",
      "----- 0.007s -----\n",
      "Epochs 2678/10000 - Loss: 0.013758632354438305 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.007s -----\n",
      "Epochs 2681/10000 - Loss: 0.013758528046309948 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.005s -----\n",
      "Epochs 2684/10000 - Loss: 0.013758431188762188 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2685/10000 - Loss: 0.013758399523794651 - Metric: 0.9888889193534851\n",
      "----- 0.007s -----\n",
      "Epochs 2686/10000 - Loss: 0.01375836506485939 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2687/10000 - Loss: 0.013758325949311256 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2688/10000 - Loss: 0.013758295215666294 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2689/10000 - Loss: 0.013758263550698757 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2690/10000 - Loss: 0.01375823188573122 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2691/10000 - Loss: 0.013758198358118534 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2692/10000 - Loss: 0.013758163899183273 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2693/10000 - Loss: 0.013758128508925438 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2694/10000 - Loss: 0.013758097775280476 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2695/10000 - Loss: 0.013758061453700066 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2696/10000 - Loss: 0.013758030720055103 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2697/10000 - Loss: 0.013757993467152119 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2698/10000 - Loss: 0.013757960870862007 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2699/10000 - Loss: 0.01375793106853962 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2700/10000 - Loss: 0.013757895678281784 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2701/10000 - Loss: 0.013757863081991673 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2702/10000 - Loss: 0.013757830485701561 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2703/10000 - Loss: 0.01375779788941145 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2704/10000 - Loss: 0.013757764361798763 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2705/10000 - Loss: 0.013757731765508652 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2706/10000 - Loss: 0.013757695443928242 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2707/10000 - Loss: 0.013757665641605854 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2708/10000 - Loss: 0.013757631182670593 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2709/10000 - Loss: 0.013757598586380482 - Metric: 0.9888889193534851\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.004s -----\n",
      "Epochs 2710/10000 - Loss: 0.01375756599009037 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2711/10000 - Loss: 0.013757530599832535 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
      "Epochs 2714/10000 - Loss: 0.013757430948317051 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
      "Epochs 2717/10000 - Loss: 0.013757328502833843 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
      "Epochs 2719/10000 - Loss: 0.013757262378931046 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2720/10000 - Loss: 0.01375722698867321 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.006s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.005s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.006s -----\n",
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      "----- 0.004s -----\n",
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      "----- 0.007s -----\n",
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      "----- 0.008s -----\n",
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      "----- 0.004s -----\n",
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      "Epochs 2785/10000 - Loss: 0.01375503372400999 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2786/10000 - Loss: 0.01375499926507473 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2787/10000 - Loss: 0.01375496294349432 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2788/10000 - Loss: 0.013754932209849358 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2789/10000 - Loss: 0.013754898682236671 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2790/10000 - Loss: 0.013754861429333687 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2791/10000 - Loss: 0.013754827901721 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2792/10000 - Loss: 0.013754795305430889 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2793/10000 - Loss: 0.013754756189882755 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2794/10000 - Loss: 0.013754726387560368 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2795/10000 - Loss: 0.013754690997302532 - Metric: 0.9888889193534851\n",
      "----- 0.007s -----\n",
      "Epochs 2796/10000 - Loss: 0.013754654675722122 - Metric: 0.9888889193534851\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 0.004s -----\n",
      "Epochs 2797/10000 - Loss: 0.013754624873399734 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2798/10000 - Loss: 0.013754590414464474 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2799/10000 - Loss: 0.013754556886851788 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2800/10000 - Loss: 0.013754522427916527 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2801/10000 - Loss: 0.013754485175013542 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2802/10000 - Loss: 0.013754456304013729 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2803/10000 - Loss: 0.013754415325820446 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2804/10000 - Loss: 0.013754382729530334 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2805/10000 - Loss: 0.013754350133240223 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2806/10000 - Loss: 0.013754317536950111 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2807/10000 - Loss: 0.013754280284047127 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2808/10000 - Loss: 0.013754249550402164 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2809/10000 - Loss: 0.013754216954112053 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2810/10000 - Loss: 0.013754177838563919 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2811/10000 - Loss: 0.013754146173596382 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2812/10000 - Loss: 0.013754108920693398 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2813/10000 - Loss: 0.013754078187048435 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2814/10000 - Loss: 0.013754043728113174 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2815/10000 - Loss: 0.013754009269177914 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2816/10000 - Loss: 0.013753973878920078 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2817/10000 - Loss: 0.013753939419984818 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2818/10000 - Loss: 0.013753903098404408 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2819/10000 - Loss: 0.01375387143343687 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2820/10000 - Loss: 0.013753836043179035 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2821/10000 - Loss: 0.0137538006529212 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2822/10000 - Loss: 0.013753769919276237 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2823/10000 - Loss: 0.013753737322986126 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2824/10000 - Loss: 0.013753700070083141 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2825/10000 - Loss: 0.01375366747379303 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2826/10000 - Loss: 0.013753633014857769 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2827/10000 - Loss: 0.013753596693277359 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2828/10000 - Loss: 0.013753565959632397 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2829/10000 - Loss: 0.013753529638051987 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2830/10000 - Loss: 0.013753492385149002 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2831/10000 - Loss: 0.013753458857536316 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2832/10000 - Loss: 0.013753427192568779 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2833/10000 - Loss: 0.013753392733633518 - Metric: 0.9888889193534851\n",
      "----- 0.004s -----\n",
      "Epochs 2834/10000 - Loss: 0.013753357343375683 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2835/10000 - Loss: 0.01375332660973072 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2836/10000 - Loss: 0.013753288425505161 - Metric: 0.9888889193534851\n",
      "----- 0.007s -----\n",
      "Epochs 2837/10000 - Loss: 0.01375325582921505 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2838/10000 - Loss: 0.013753220438957214 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n",
      "Epochs 2839/10000 - Loss: 0.013753186911344528 - Metric: 0.9888889193534851\n",
      "----- 0.006s -----\n",
      "Epochs 2840/10000 - Loss: 0.013753151521086693 - Metric: 0.9888889193534851\n",
      "----- 0.005s -----\n"
     ]
    }
   ],
   "source": [
    "lda = LDA(input_dim=X_train.shape[1], output_dim=Y_train.shape[1])\n",
    "\n",
    "lda.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.01929466985166073 - Metric: 0.9777777791023254\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.01929467, 0.9777778]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.evaluate(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc944ba6ba8>]"
      ]
     },
     "execution_count": 15,
     "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 = lda.history['loss']\n",
    "metric = lda.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
}