nn_project.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](https://zewailcity.edu.eg/main/images/logo3.png)\n",
    "#### Ezzat Eisawy 201901195\n",
    "##### NN implementation projet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np;\n",
    "import pandas as pd\n",
    "from numpy import exp\n",
    "from numpy import array;\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "class nn_classifier :\n",
    "    def sig_activation(self,x) : return 1/(1+exp(-x));\n",
    "\n",
    "    def relu_activation(self ,x) : return np.maximum(0,x);\n",
    "        \n",
    "    def __init__(self , hidden_layers = (5,5) , epochs = 20, learning_rate = 0.1 , activation_type = 'sigmoid') -> None:\n",
    "        self.hidden_layers = hidden_layers;\n",
    "        self.learning_rate = learning_rate ;\n",
    "        self.activation_type = activation_type\n",
    "        if activation_type == 'sigmoid' :\n",
    "            self.activation = np.vectorize(self.sig_activation)  ;\n",
    "        if activation_type == 'relu' :\n",
    "             self.activation = np.vectorize(self.relu_activation)  ;\n",
    "        self.epochs = epochs;\n",
    "\n",
    "    def _init_layers(self, n_input,hidden_layers ,n_output ) :\n",
    "        ''' Initialize all the weights with random nambers (-0.5 --> 0.5)\n",
    "        '''\n",
    "        total = len(hidden_layers) ;\n",
    "        weights_list = np.empty(shape=(total+1) , dtype= object) ; \n",
    "        weights_list[0] = np.random.uniform(-0.5 , high = 0.5 , size  = (hidden_layers[0] , n_input))  # Inner layer\n",
    "\n",
    "        for i in range(1,total) :   # Hidden layers\n",
    "            weights_list[i] = np.random.uniform(low=- 0.5 , high = 0.5 , size  = (hidden_layers[i] , hidden_layers[i-1])) \n",
    "\n",
    "        weights_list[total] = np.random.uniform(low=-0.5 , high = 0.5, size  = ( n_output, hidden_layers[total-1]));  # output layers\n",
    "        self.weights_list = weights_list;\n",
    "        \n",
    "\n",
    "\n",
    "    def _forward(self,x ) :\n",
    "        ''' Forward propagation , returns the result value on each neuron on each layer \n",
    "        '''\n",
    "        res = np.empty(shape= len(self.weights_list) , dtype= object );\n",
    "        res[0] = self.activation(self.weights_list[0] @ np.array(x)) ;\n",
    "\n",
    "\n",
    "        for i in range(1,len(self.weights_list)) :\n",
    "            res[i] = self.activation (self.weights_list[i] @ res[i-1]) ;\n",
    "\n",
    "        return res;\n",
    "\n",
    "    def _backward(self,expected , outputs ,x) :\n",
    "        ''' Backward propagation , Recalculates the weights \n",
    "        '''\n",
    "        \n",
    "\n",
    "        # Output layer\n",
    "        deltas = np.empty(shape= len(self.weights_list) , dtype= object );\n",
    "        last = len(self.weights_list) -1;\n",
    "\n",
    "        diff = outputs[last] * (1 - outputs[last]) if self.activation_type =='sigmoid' else (outputs[last]>0 ).astype(np.int32)\n",
    "        deltas[last]  = diff* (expected - outputs[last]) ; \n",
    "\n",
    "        self.weights_list[last] += self.learning_rate *np.outer(deltas[last] ,outputs[last-1] )    ; \n",
    "\n",
    "        \n",
    "        for i in range(last-1, 0 , -1) :   # Inner layers\n",
    "            diff = outputs[i] * (1 - outputs[i]) if self.activation_type =='sigmoid' else  (outputs[i]>0 ).astype(np.int32)\n",
    "            deltas[i] = diff * np.sum(deltas[i+1] * self.weights_list[i+1].T , axis=1)\n",
    "            self.weights_list[i] += self.learning_rate * np.outer( deltas[i] , outputs[i-1])  ; \n",
    "        \n",
    "\n",
    "        #Input layer\n",
    "\n",
    "        diff = outputs[0] * (1 - outputs[0]) if self.activation_type =='sigmoid' else (outputs[0]>0 ).astype(np.int32)\n",
    "        \n",
    "        deltas [0] = diff * np.sum(deltas[1] * self.weights_list[1].T , axis=1)\n",
    "        self.weights_list[0] +=self.learning_rate * np.outer( deltas[0] ,x )  ;\n",
    "        \n",
    "\n",
    "    def fit(self,X , Y  , verbose = 1) :\n",
    "        # Shuffle the X \n",
    "        shuffler = np.random.permutation(len(X))\n",
    "        x = X[shuffler]\n",
    "        y = Y[shuffler]\n",
    "        # Extract the classes of the output\n",
    "        classes = np.array(list(set(y)) );\n",
    "        self.classes = classes\n",
    "        print(classes)\n",
    "\n",
    "        # Initialize weights\n",
    "        self._init_layers(len(x[0]) , self.hidden_layers , len(classes ));\n",
    "\n",
    "        # Epochs loop\n",
    "        best_acc = 0\n",
    "        for i in range (self.epochs) :\n",
    "            mse_arr = np.zeros(len(x))\n",
    "            itr = tqdm(range(len(x))) if verbose else range(len(x))\n",
    "            for j in itr  :\n",
    "                x_ = x[j]\n",
    "                mapped = np.zeros(len(classes)) \n",
    "                mapped[classes == y[j]] = 1\n",
    "                outputs = self._forward(x_);\n",
    "                self._backward(mapped ,outputs ,x_)   # Update weights\n",
    "                mse_arr[j] = np.mean(mapped - outputs[-1])**2\n",
    "            acc = accuracy_score(y,self.predict(x))\n",
    "            if acc > best_acc :\n",
    "                best_acc = best_acc;\n",
    "                self.best_weights = self.weights_list.copy()\n",
    "\n",
    "            \n",
    "            print(f'Epoch {i+1} #####' ,f'Mean square error = {( np.mean(mse_arr)) : .5f}' ,\n",
    "            f', Accuracy = { acc :.3f}' )\n",
    "        return self;\n",
    "\n",
    "    def apply_best_accuracy(self) : \n",
    "        ''' Apply the weights with the best accuracy \n",
    "        '''\n",
    "        self.weights_list = self.best_weights;\n",
    "        return self\n",
    "\n",
    "    def _predict(self,x) :    \n",
    "        res = self._forward(x)[-1];\n",
    "        idx =  np.argmax(res);\n",
    "\n",
    "        return self.classes[idx];\n",
    "\n",
    "    def predict(self , x) :    # Predict X \n",
    "        if hasattr(x, \"__len__\") : #return (np.vectorize(self._predict))(x) ; \n",
    "            if isinstance(self.classes[0] , str) :\n",
    "                res = np.empty(len(x) ,dtype= object) \n",
    "            else :\n",
    "                res = np.zeros(len(x) , dtype = np.int32 ) \n",
    "            for i in range(len(x)) :\n",
    "                res[i] = self._predict(x[i])\n",
    "            return res;\n",
    "            \n",
    "        return self._predict(x);\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing with iris dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3               4\n",
       "0    5.1  3.5  1.4  0.2     Iris-setosa\n",
       "1    4.9  3.0  1.4  0.2     Iris-setosa\n",
       "2    4.7  3.2  1.3  0.2     Iris-setosa\n",
       "3    4.6  3.1  1.5  0.2     Iris-setosa\n",
       "4    5.0  3.6  1.4  0.2     Iris-setosa\n",
       "..   ...  ...  ...  ...             ...\n",
       "145  6.7  3.0  5.2  2.3  Iris-virginica\n",
       "146  6.3  2.5  5.0  1.9  Iris-virginica\n",
       "147  6.5  3.0  5.2  2.0  Iris-virginica\n",
       "148  6.2  3.4  5.4  2.3  Iris-virginica\n",
       "149  5.9  3.0  5.1  1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/bezdekIris.data'\n",
    "data = pd.read_csv(data_path , header= None)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_data = data.iloc[:,0:4].copy()\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "input_data = StandardScaler().fit_transform(input_data)\n",
    "output_data = data.iloc[:,4].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "input_train ,input_test , output_train , output_test = train_test_split(input_data , output_data , test_size=1/5, shuffle = 1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Iris-versicolor' 'Iris-setosa' 'Iris-virginica']\n"
     ]
    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 ##### Mean square error =  0.00307 , Accuracy = 0.358\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2 ##### Mean square error =  0.00103 , Accuracy = 0.583\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3 ##### Mean square error =  0.00097 , Accuracy = 0.692\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4 ##### Mean square error =  0.00101 , Accuracy = 0.708\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 5 ##### Mean square error =  0.00107 , Accuracy = 0.717\n"
     ]
    },
    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6 ##### Mean square error =  0.00114 , Accuracy = 0.717\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 7 ##### Mean square error =  0.00121 , Accuracy = 0.717\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 8 ##### Mean square error =  0.00127 , Accuracy = 0.717\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 9 ##### Mean square error =  0.00132 , Accuracy = 0.725\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 10 ##### Mean square error =  0.00136 , Accuracy = 0.725\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11 ##### Mean square error =  0.00138 , Accuracy = 0.733\n"
     ]
    },
    {
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       "version_minor": 0
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12 ##### Mean square error =  0.00140 , Accuracy = 0.733\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 13 ##### Mean square error =  0.00141 , Accuracy = 0.733\n"
     ]
    },
    {
     "data": {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 14 ##### Mean square error =  0.00141 , Accuracy = 0.767\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "815f3e6032e847688a22d6f2675d980b",
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 15 ##### Mean square error =  0.00141 , Accuracy = 0.775\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad756a3c73a0458d8a0a28d018f5567a",
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 16 ##### Mean square error =  0.00140 , Accuracy = 0.775\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7caeadd21e7e4a0c895570d37dc0d60c",
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     "text": [
      "Epoch 17 ##### Mean square error =  0.00140 , Accuracy = 0.783\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80d15aa6284e4667947c3666e38a7695",
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    },
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     "output_type": "stream",
     "text": [
      "Epoch 18 ##### Mean square error =  0.00139 , Accuracy = 0.800\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf063d637811467fbc594f8a40a741a2",
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     "output_type": "stream",
     "text": [
      "Epoch 19 ##### Mean square error =  0.00139 , Accuracy = 0.800\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ad2ff58686344f78c2afe27b692b8dd",
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     "text": [
      "Epoch 20 ##### Mean square error =  0.00139 , Accuracy = 0.808\n"
     ]
    },
    {
     "data": {
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       "model_id": "e15c0e3664c44d59a23fb702a6ba0298",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 21 ##### Mean square error =  0.00139 , Accuracy = 0.808\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "90e3b80652004c4c92d89dcd4b7df38c",
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    {
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     "text": [
      "Epoch 22 ##### Mean square error =  0.00139 , Accuracy = 0.817\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b269939b58534ddca7ec356a75878e49",
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 23 ##### Mean square error =  0.00139 , Accuracy = 0.817\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b4b1572e61942c5bdee617f3b70e012",
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     "output_type": "stream",
     "text": [
      "Epoch 24 ##### Mean square error =  0.00140 , Accuracy = 0.842\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b87fbb3f9b44c3a8f3d319b81a37f36",
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 25 ##### Mean square error =  0.00140 , Accuracy = 0.850\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56d2ba2aa725403e96e1feb92745e30e",
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 26 ##### Mean square error =  0.00141 , Accuracy = 0.858\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bad62ea988414deeb1dc49bdfd20ebb4",
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     "text": [
      "Epoch 27 ##### Mean square error =  0.00142 , Accuracy = 0.858\n"
     ]
    },
    {
     "data": {
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       "model_id": "dcda66b974b74992a50bd2616d8443a6",
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     "output_type": "stream",
     "text": [
      "Epoch 28 ##### Mean square error =  0.00143 , Accuracy = 0.858\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "69342fef6d79461aa63d6f130bf0df4a",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 29 ##### Mean square error =  0.00144 , Accuracy = 0.858\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9b1407c3e2b4bde97c6105ecad42913",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 30 ##### Mean square error =  0.00145 , Accuracy = 0.858\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "acb084ee0ca54c6796c2c88ca0ddf032",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 31 ##### Mean square error =  0.00146 , Accuracy = 0.867\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93b84327dd1d472099da87a827702749",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 32 ##### Mean square error =  0.00148 , Accuracy = 0.875\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a15f01a6c12411ea5da8372512d307d",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 33 ##### Mean square error =  0.00149 , Accuracy = 0.875\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ae48a735df943e494c9d6f8ed20c08f",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 34 ##### Mean square error =  0.00150 , Accuracy = 0.875\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "623e27ffb00c4c56b96dea86b7c5342b",
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 35 ##### Mean square error =  0.00151 , Accuracy = 0.875\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e86c2a45f9914962958756464a8ea2ce",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 36 ##### Mean square error =  0.00152 , Accuracy = 0.875\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c8a974131b24d45b8c5fb8cc981f84c",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 37 ##### Mean square error =  0.00154 , Accuracy = 0.875\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1cb6fbfb6f2c4e8f96d5ca6878e3aeb2",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 38 ##### Mean square error =  0.00155 , Accuracy = 0.875\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f704f9df339f405c81c79b9747d3bf35",
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     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 39 ##### Mean square error =  0.00156 , Accuracy = 0.875\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5343a02c4cb945e6aaf44f2dd81a997c",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 40 ##### Mean square error =  0.00157 , Accuracy = 0.875\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94a7b92eb4a94420bd23942f42ae210c",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 41 ##### Mean square error =  0.00158 , Accuracy = 0.875\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a722f481b6904259b7ce9f5a22ffd6eb",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 42 ##### Mean square error =  0.00159 , Accuracy = 0.875\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ecf8411690fd4044971324bf9a2cbfb3",
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     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 43 ##### Mean square error =  0.00160 , Accuracy = 0.892\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e2131a63cd143d9b986b404f4f89394",
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     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 44 ##### Mean square error =  0.00161 , Accuracy = 0.892\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e123b8fa0f14538ae7b5b4284e9c885",
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     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 45 ##### Mean square error =  0.00163 , Accuracy = 0.892\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0d98015d01b452eb2c56bcfc26407d1",
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 46 ##### Mean square error =  0.00164 , Accuracy = 0.892\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d52e27ab68f649f68d6149b9c3a0e858",
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 47 ##### Mean square error =  0.00164 , Accuracy = 0.892\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "48c66ccd27494a93ac40bf9844cfba51",
       "version_major": 2,
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 48 ##### Mean square error =  0.00165 , Accuracy = 0.892\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "085728ee37344274afbb88481a8d89dc",
       "version_major": 2,
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49 ##### Mean square error =  0.00166 , Accuracy = 0.892\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43503deb244340c6a6371ebf163550fe",
       "version_major": 2,
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 50 ##### Mean square error =  0.00167 , Accuracy = 0.892\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<__main__.nn_classifier at 0x2c542945b90>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = nn_classifier(hidden_layers=[100 ,50] ,learning_rate=1e-2 , epochs=50);\n",
    "model.fit(array(input_train),array(output_train))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Predicting test samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.633\n"
     ]
    }
   ],
   "source": [
    "print(f'Accuracy = {accuracy_score(output_test , model.predict(np.array(input_test))) : 0.3f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing the model with digits dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "digits = datasets.load_digits() \n",
    "x_data = digits.images.reshape(len(digits.images),8*8)\n",
    "y_data = digits.target.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train , x_test ,y_train,y_test = train_test_split(x_data,y_data,test_size = 1/4 , shuffle = 1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Testing a model with two hidden layers ( 200 , 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2db157b66a6e4eaebd405ec02f16a06d",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 ##### Mean square error =  0.01051 , Accuracy = 0.566\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0604be7c5cd94312a524ca3baef2709b",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2 ##### Mean square error =  0.00118 , Accuracy = 0.695\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f01945b8b69643b0a222ad0047e01fa2",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3 ##### Mean square error =  0.00178 , Accuracy = 0.845\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f8a9bb632cb4ea496e4063a9f9bffc7",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4 ##### Mean square error =  0.00182 , Accuracy = 0.879\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a395c4463bb848da8ae652e101d1fd1f",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5 ##### Mean square error =  0.00165 , Accuracy = 0.904\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dac4d38657764ab5b836c5efeb9ee6e2",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6 ##### Mean square error =  0.00147 , Accuracy = 0.932\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "216332c6ff9342929517157bf4af3039",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7 ##### Mean square error =  0.00128 , Accuracy = 0.946\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "217d63a69c8c4fc191ac13ee0f4a5401",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8 ##### Mean square error =  0.00112 , Accuracy = 0.960\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5064c81c36044137baafae0f2334267e",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9 ##### Mean square error =  0.00099 , Accuracy = 0.964\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27409202443740208f6e322bcf0ec141",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10 ##### Mean square error =  0.00088 , Accuracy = 0.967\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa231b1747854cc5aee36299cc44b19d",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11 ##### Mean square error =  0.00078 , Accuracy = 0.971\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6428656bceca4cac848d2e4f2364e9f1",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12 ##### Mean square error =  0.00071 , Accuracy = 0.976\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4fca1cf7cd934a78b38660641e61da1d",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13 ##### Mean square error =  0.00064 , Accuracy = 0.979\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ffc6b9a2c07a4f61a46b8663e464a6c7",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14 ##### Mean square error =  0.00058 , Accuracy = 0.981\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4747823c251c4828860c7f54adf204a6",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15 ##### Mean square error =  0.00053 , Accuracy = 0.986\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "66b341f66f64412fb8e40c969adc667b",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16 ##### Mean square error =  0.00049 , Accuracy = 0.987\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c061b9a05744162a82b21d45d08aa85",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17 ##### Mean square error =  0.00046 , Accuracy = 0.988\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8545a6cd00cb48cdbcd4c50b4ad1b89d",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18 ##### Mean square error =  0.00042 , Accuracy = 0.988\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54d29c86b92c454b8da300e84c3c6454",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19 ##### Mean square error =  0.00040 , Accuracy = 0.991\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "105825dc4e494c6ea510458c31247ae6",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20 ##### Mean square error =  0.00037 , Accuracy = 0.991\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<__main__.nn_classifier at 0x2c542a22350>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = nn_classifier(hidden_layers= [200 , 100] , epochs= 20 ,learning_rate= 1e-2)\n",
    "model.fit(x_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy =  0.960\n"
     ]
    }
   ],
   "source": [
    "print(f'Test Accuracy = {accuracy_score(y_test , model.predict(np.array(x_test))) : 0.3f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Testing with one hidden layer (100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e31d0ffc633f43d2bcfbcaf46214d1c0",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 ##### Mean square error =  0.00640 , Accuracy = 0.668\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a7222dda5f64b4b85d5c53686e87d2b",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2 ##### Mean square error =  0.00171 , Accuracy = 0.795\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3589636477f54efda8e04313d6ed1b8d",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3 ##### Mean square error =  0.00161 , Accuracy = 0.866\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7cc26114db2047a9ae6b74680f06d45c",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4 ##### Mean square error =  0.00139 , Accuracy = 0.902\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e73222fcb234dbca34236eb465022e1",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5 ##### Mean square error =  0.00121 , Accuracy = 0.929\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18dbb53f91224c4dab77c10080d85ba7",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6 ##### Mean square error =  0.00106 , Accuracy = 0.935\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1be7bcb6a073448d8be5be7c3fea89cf",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7 ##### Mean square error =  0.00094 , Accuracy = 0.938\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "252c892978324a2b88bf5eab01c7b893",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8 ##### Mean square error =  0.00085 , Accuracy = 0.944\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e73a1dab57847e49e3a0be48cebe997",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9 ##### Mean square error =  0.00077 , Accuracy = 0.950\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d3e43617fe694fae814603c6a59810e9",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10 ##### Mean square error =  0.00070 , Accuracy = 0.953\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fbd45cb41e2143cf9a22f4899b4641b0",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11 ##### Mean square error =  0.00064 , Accuracy = 0.957\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "236ccea86f1f4b25bd46d9f1b328f0b7",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12 ##### Mean square error =  0.00059 , Accuracy = 0.963\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7999d09e1a164fcfb878d1d800ab861b",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13 ##### Mean square error =  0.00055 , Accuracy = 0.967\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82d50b28f00646c9bc51953cb85889a7",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14 ##### Mean square error =  0.00052 , Accuracy = 0.970\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d93a6a7063448de9fdd9f718a4c23f9",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15 ##### Mean square error =  0.00049 , Accuracy = 0.971\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1c0069ecc70435ebdaa9681fd7d7b73",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16 ##### Mean square error =  0.00046 , Accuracy = 0.973\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38aedd4833f04407ac64630a367555f1",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17 ##### Mean square error =  0.00043 , Accuracy = 0.975\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c344a944f4a4461496bc8f1f0e7a54e1",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18 ##### Mean square error =  0.00041 , Accuracy = 0.976\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "036cdc279e34493095e0e27c254e3ee6",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19 ##### Mean square error =  0.00039 , Accuracy = 0.978\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99f1ab56f9dc4f6390ffed9d1d99cac3",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20 ##### Mean square error =  0.00037 , Accuracy = 0.979\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<__main__.nn_classifier at 0x2c542b66790>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = nn_classifier(hidden_layers= [100] , epochs= 20 ,learning_rate= 1e-2)\n",
    "model.fit(x_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.956\n"
     ]
    }
   ],
   "source": [
    "print(f'Accuracy = {accuracy_score(y_test , model.predict(np.array(x_test))) : 0.3f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Model with three hidden layers (50,40,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a766cb1eaca046cbb998e207938b83e8",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 ##### Mean square error =  0.01713 , Accuracy = 0.125\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14d452ee03c441b28625161a8bfa79c2",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2 ##### Mean square error =  0.00015 , Accuracy = 0.159\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f53b948d38444828ed3bb7b6eda09b8",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3 ##### Mean square error =  0.00002 , Accuracy = 0.188\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cce77cf0f9974497804e6cd6be6e42e3",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4 ##### Mean square error =  0.00001 , Accuracy = 0.214\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "245fa99548a34a529560fe1c8ef3065c",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5 ##### Mean square error =  0.00001 , Accuracy = 0.236\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e56890a8043341b7ac5d8359c50826f0",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6 ##### Mean square error =  0.00002 , Accuracy = 0.258\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9cdd0345e02406e9474df6bd26fdf7d",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7 ##### Mean square error =  0.00002 , Accuracy = 0.272\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d428f4c2b7724fd08dc23274ddaeb955",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8 ##### Mean square error =  0.00003 , Accuracy = 0.290\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7347565e1db044faa7cbbb356023a97b",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9 ##### Mean square error =  0.00004 , Accuracy = 0.289\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00478e5be9a64fa5bdb905524524cc17",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10 ##### Mean square error =  0.00005 , Accuracy = 0.311\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9433eddb702345e9beb1f462d20e75ee",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11 ##### Mean square error =  0.00007 , Accuracy = 0.316\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b71c2d2d723471684c532cef792f83e",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12 ##### Mean square error =  0.00009 , Accuracy = 0.321\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "121fa4a6b9c64c3a8e58b6b901d17921",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13 ##### Mean square error =  0.00012 , Accuracy = 0.335\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8c5f74f3ee94a99a38d8249b338994d",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14 ##### Mean square error =  0.00016 , Accuracy = 0.347\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb432e0c95b84dc4993f7f761715e58c",
       "version_major": 2,
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15 ##### Mean square error =  0.00020 , Accuracy = 0.371\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "296fcf25c2a04e2987de1a02fa616be2",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16 ##### Mean square error =  0.00026 , Accuracy = 0.402\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4dd8b2a7676a45258409a2accb5136e0",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17 ##### Mean square error =  0.00033 , Accuracy = 0.419\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "781ae3b221034e2eafc1abe1965a108b",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18 ##### Mean square error =  0.00041 , Accuracy = 0.441\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36079854a64b4652a6a2ebfe0d90b45d",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19 ##### Mean square error =  0.00049 , Accuracy = 0.457\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9858dba56bf4e9aae844e8d80302cef",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20 ##### Mean square error =  0.00059 , Accuracy = 0.477\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b8bf60dafa704469882e17f0fe40675f",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21 ##### Mean square error =  0.00068 , Accuracy = 0.486\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e539d411d794409abd85655b4c6f145",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22 ##### Mean square error =  0.00078 , Accuracy = 0.489\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2af18c85046e42d79e54d38dd79be780",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23 ##### Mean square error =  0.00089 , Accuracy = 0.503\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9fbc6e49ad9a43d780ed8f06653471e0",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24 ##### Mean square error =  0.00099 , Accuracy = 0.512\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "57ba3c179ac94403be4597a6049296d3",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25 ##### Mean square error =  0.00110 , Accuracy = 0.526\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ee067252f06443baa535f3fc336e7c9",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26 ##### Mean square error =  0.00121 , Accuracy = 0.548\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef96c93b2ecc4b5d9415d9907ac634ad",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27 ##### Mean square error =  0.00131 , Accuracy = 0.569\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7cf22c73044a4fbf8f26bf8608d0de19",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
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    },
    {
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     "text": [
      "Epoch 28 ##### Mean square error =  0.00141 , Accuracy = 0.593\n"
     ]
    },
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     "text": [
      "Epoch 29 ##### Mean square error =  0.00150 , Accuracy = 0.625\n"
     ]
    },
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     "text": [
      "Epoch 30 ##### Mean square error =  0.00157 , Accuracy = 0.657\n"
     ]
    },
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     "text": [
      "Epoch 31 ##### Mean square error =  0.00163 , Accuracy = 0.687\n"
     ]
    },
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     "text": [
      "Epoch 32 ##### Mean square error =  0.00168 , Accuracy = 0.714\n"
     ]
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     "text": [
      "Epoch 33 ##### Mean square error =  0.00171 , Accuracy = 0.744\n"
     ]
    },
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     "text": [
      "Epoch 34 ##### Mean square error =  0.00175 , Accuracy = 0.762\n"
     ]
    },
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     "text": [
      "Epoch 35 ##### Mean square error =  0.00177 , Accuracy = 0.789\n"
     ]
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     "text": [
      "Epoch 36 ##### Mean square error =  0.00178 , Accuracy = 0.810\n"
     ]
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     "text": [
      "Epoch 37 ##### Mean square error =  0.00177 , Accuracy = 0.820\n"
     ]
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     "text": [
      "Epoch 38 ##### Mean square error =  0.00175 , Accuracy = 0.834\n"
     ]
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     "text": [
      "Epoch 39 ##### Mean square error =  0.00171 , Accuracy = 0.839\n"
     ]
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     "text": [
      "Epoch 40 ##### Mean square error =  0.00166 , Accuracy = 0.846\n"
     ]
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     "text": [
      "Epoch 41 ##### Mean square error =  0.00161 , Accuracy = 0.853\n"
     ]
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     "text": [
      "Epoch 42 ##### Mean square error =  0.00155 , Accuracy = 0.858\n"
     ]
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     "text": [
      "Epoch 43 ##### Mean square error =  0.00148 , Accuracy = 0.863\n"
     ]
    },
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     "text": [
      "Epoch 44 ##### Mean square error =  0.00142 , Accuracy = 0.869\n"
     ]
    },
    {
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     "text": [
      "Epoch 45 ##### Mean square error =  0.00137 , Accuracy = 0.872\n"
     ]
    },
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     "text": [
      "Epoch 46 ##### Mean square error =  0.00131 , Accuracy = 0.875\n"
     ]
    },
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     "text": [
      "Epoch 47 ##### Mean square error =  0.00126 , Accuracy = 0.885\n"
     ]
    },
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     "data": {
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       "model_id": "71f5d88213294fafb820a3df76d1e0a9",
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     "text": [
      "Epoch 48 ##### Mean square error =  0.00121 , Accuracy = 0.889\n"
     ]
    },
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     "data": {
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       "model_id": "44656f2c5b8046aba7d17e080bff9cc4",
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     "text": [
      "Epoch 49 ##### Mean square error =  0.00116 , Accuracy = 0.895\n"
     ]
    },
    {
     "data": {
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       "model_id": "e7b0e8baa6df4affb33df23c5e48f23d",
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     "text": [
      "Epoch 50 ##### Mean square error =  0.00112 , Accuracy = 0.899\n"
     ]
    },
    {
     "data": {
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       "model_id": "73393753a3a0405f9f74b02bda61bd2e",
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     "text": [
      "Epoch 51 ##### Mean square error =  0.00108 , Accuracy = 0.901\n"
     ]
    },
    {
     "data": {
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       "model_id": "10d0bffadb77467da2bd7480b01f3f4e",
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     "text": [
      "Epoch 52 ##### Mean square error =  0.00104 , Accuracy = 0.907\n"
     ]
    },
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     "data": {
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       "model_id": "cadacf83a30d4d14831434ccd1dd5fd9",
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     "text": [
      "Epoch 53 ##### Mean square error =  0.00100 , Accuracy = 0.913\n"
     ]
    },
    {
     "data": {
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       "model_id": "5e0f7155c3be4338b009023ebaf62a05",
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     "text": [
      "Epoch 54 ##### Mean square error =  0.00096 , Accuracy = 0.921\n"
     ]
    },
    {
     "data": {
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       "model_id": "1677de3d49d74348bb73d7c5967e2f14",
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     "text": [
      "Epoch 55 ##### Mean square error =  0.00093 , Accuracy = 0.924\n"
     ]
    },
    {
     "data": {
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       "model_id": "7a8ee012d23b4d10adf664af1a73363c",
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     "text": [
      "Epoch 56 ##### Mean square error =  0.00089 , Accuracy = 0.927\n"
     ]
    },
    {
     "data": {
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       "model_id": "30c91ce18c6a49d5b73db9e4315e91f7",
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     "text": [
      "Epoch 57 ##### Mean square error =  0.00086 , Accuracy = 0.933\n"
     ]
    },
    {
     "data": {
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       "model_id": "98c29d11ba5b4fe6b431b2b76c428094",
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     "text": [
      "Epoch 58 ##### Mean square error =  0.00084 , Accuracy = 0.938\n"
     ]
    },
    {
     "data": {
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       "model_id": "ee001f19b20d42be9586c1752af66d7d",
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     "text": [
      "Epoch 59 ##### Mean square error =  0.00081 , Accuracy = 0.939\n"
     ]
    },
    {
     "data": {
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       "model_id": "6214c355768e40b981de0ff6647741e4",
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     "text": [
      "Epoch 60 ##### Mean square error =  0.00078 , Accuracy = 0.941\n"
     ]
    },
    {
     "data": {
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       "model_id": "5aaae6196bf642e5a1c7fc8c13658cd2",
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     "text": [
      "Epoch 61 ##### Mean square error =  0.00076 , Accuracy = 0.948\n"
     ]
    },
    {
     "data": {
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       "model_id": "dc45a887d3504c11b19c576aee9ca31c",
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     "text": [
      "Epoch 62 ##### Mean square error =  0.00073 , Accuracy = 0.950\n"
     ]
    },
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     "data": {
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       "model_id": "89102edc9a21471b804a570ed899b374",
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     "text": [
      "Epoch 63 ##### Mean square error =  0.00071 , Accuracy = 0.952\n"
     ]
    },
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     "data": {
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       "model_id": "fc0b4ac93ecf4fef9ccb01f2e2b3f4ba",
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     "text": [
      "Epoch 64 ##### Mean square error =  0.00069 , Accuracy = 0.959\n"
     ]
    },
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     "text": [
      "Epoch 65 ##### Mean square error =  0.00067 , Accuracy = 0.961\n"
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b523092b5d1349af9c810e4c605a9115",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 66 ##### Mean square error =  0.00065 , Accuracy = 0.966\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4bfbe2c929744b18fb1f147c48c7a20",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 67 ##### Mean square error =  0.00063 , Accuracy = 0.967\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b018fe59189441be80bc715f6ed2b5db",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 68 ##### Mean square error =  0.00061 , Accuracy = 0.968\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4cba5620f754980a493e7f15ba601b8",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 69 ##### Mean square error =  0.00059 , Accuracy = 0.971\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "137d038989a54c4fba8f7aab262b467e",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 70 ##### Mean square error =  0.00057 , Accuracy = 0.974\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "17de48acc2684c14aeb762edde893df8",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 71 ##### Mean square error =  0.00055 , Accuracy = 0.974\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cbe2d113256b403ea701df1a1e19026e",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 72 ##### Mean square error =  0.00053 , Accuracy = 0.977\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4949b262a8824095bace043d61429c00",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 73 ##### Mean square error =  0.00051 , Accuracy = 0.978\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4f28d5501db47b38e1c08870778bac9",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 74 ##### Mean square error =  0.00049 , Accuracy = 0.980\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dbbb9c2961154a56906b2363fa33fd06",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 75 ##### Mean square error =  0.00047 , Accuracy = 0.982\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f226f4fd653499cb41ba82e3cbdf2af",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 76 ##### Mean square error =  0.00045 , Accuracy = 0.983\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a639523832ed42fcb68f842453df7103",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 77 ##### Mean square error =  0.00044 , Accuracy = 0.984\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e9a840ebae140efbbf6f0da675b0bb9",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 78 ##### Mean square error =  0.00042 , Accuracy = 0.986\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "750b50b82a53444f8aa6b6165b0da965",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 79 ##### Mean square error =  0.00041 , Accuracy = 0.987\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "57202d6664734a6fad21e34e08198381",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 80 ##### Mean square error =  0.00039 , Accuracy = 0.987\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98581fcd5b134ffa812e48b6af9fa539",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 81 ##### Mean square error =  0.00038 , Accuracy = 0.987\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "daee32b5a1d8421a8e3ddfb6732902d8",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 82 ##### Mean square error =  0.00037 , Accuracy = 0.987\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e4fa360b1d84325ba361fda9f1c417f",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 83 ##### Mean square error =  0.00036 , Accuracy = 0.987\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f99b9022f03f4cb4a8cb313aa0dd8b24",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 84 ##### Mean square error =  0.00034 , Accuracy = 0.987\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45aeff1edffc4adbbd99de321ab78291",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 85 ##### Mean square error =  0.00033 , Accuracy = 0.987\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "049893a8a312432fa7a9d20f47449745",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 86 ##### Mean square error =  0.00032 , Accuracy = 0.988\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4acce2995fa24cba94bec5345d240b05",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 87 ##### Mean square error =  0.00031 , Accuracy = 0.988\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b47a1fb0054448686c41aba5001d970",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 88 ##### Mean square error =  0.00030 , Accuracy = 0.989\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bcceae27d9d949aabd597848b8a66f17",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 89 ##### Mean square error =  0.00030 , Accuracy = 0.989\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9903af48ce84e698e6a43232452e96b",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 90 ##### Mean square error =  0.00029 , Accuracy = 0.990\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba8a188a217c4a1a83f659f6c7824796",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 91 ##### Mean square error =  0.00028 , Accuracy = 0.990\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f211b56845444f14ac7cbdb9fd8302f6",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 92 ##### Mean square error =  0.00027 , Accuracy = 0.990\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "037247e1302b4d5db535e936af751eb5",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 93 ##### Mean square error =  0.00027 , Accuracy = 0.990\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3daa8bc1d77840038a3b3cc9166196b7",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 94 ##### Mean square error =  0.00026 , Accuracy = 0.990\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1b7363758d8482e87895b8fae12ccc8",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 95 ##### Mean square error =  0.00025 , Accuracy = 0.990\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b2387cdc9e24a9484c7098d798b1f43",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 96 ##### Mean square error =  0.00025 , Accuracy = 0.990\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3f6d3ee44f04866846da57db471bcb0",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97 ##### Mean square error =  0.00024 , Accuracy = 0.990\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f924c61bbf2249fea0fa5f3a87d7a29d",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 98 ##### Mean square error =  0.00023 , Accuracy = 0.990\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "176a58506e064852ab3d4858192c676b",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 99 ##### Mean square error =  0.00023 , Accuracy = 0.990\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2317224fc0cd4086bf349fdcfe3090b4",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 100 ##### Mean square error =  0.00022 , Accuracy = 0.990\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<__main__.nn_classifier at 0x2c542b72910>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = nn_classifier(hidden_layers= [50 , 40 , 20] , epochs= 100 ,learning_rate= 1e-2)\n",
    "model.fit(x_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.949\n"
     ]
    }
   ],
   "source": [
    "print(f'Accuracy = {accuracy_score(y_test , model.predict(np.array(x_test))) : 0.3f}')"
   ]
  }
 ],
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
   "version": "3.11.0"
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  "vscode": {
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 },
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