07_MLP_Loan_Defaulter_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as pyplot\n",
    "import ppscore as pps\n",
    "import optuna\n",
    "from bayes_opt import BayesianOptimization\n",
    "from skopt import BayesSearchCV\n",
    "from skopt import gp_minimize\n",
    "from skopt.space import Real, Integer, Categorical\n",
    "import hyperopt\n",
    "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
    "from scipy.stats import shapiro, normaltest\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.constraints import max_norm, unit_norm, min_max_norm\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Input, Dense, Activation, Dropout, BatchNormalization, GaussianNoise\n",
    "from tensorflow.keras.layers import ReLU, PReLU, LeakyReLU, ELU\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "from tensorflow.keras.optimizers import SGD, Adam, Adagrad, RMSprop, Adamax, Nadam\n",
    "from tensorflow.keras.callbacks import LearningRateScheduler\n",
    "from tensorflow.keras.regularizers import l1, l2, L1L2\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold, StratifiedKFold, RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import classification_report,confusion_matrix, plot_confusion_matrix\n",
    "from sklearn.metrics import f1_score, accuracy_score, roc_auc_score\n",
    "from imblearn.combine import SMOTEENN\n",
    "from imblearn.combine import SMOTETomek\n",
    "from imblearn.under_sampling import TomekLinks\n",
    "from imblearn.under_sampling import EditedNearestNeighbours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load & Prepare Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Data/classification_testData_shape(395219,82).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395219, 83)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Split Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X = df.drop('loan_status',axis=1)\n",
    "y = df['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(395219, 82) (395219,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "395219\n",
      "82\n"
     ]
    }
   ],
   "source": [
    "print(X.shape[0])\n",
    "print(X.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# label encode the target variable to have the classes 0 and 1 FOR BINARY CLASSIFICATION\n",
    "y = LabelEncoder().fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# one hot encode output variable FOR MULTICLASS CLASSIFICATION\n",
    "#y = to_categorical(y)\n",
    "\n",
    "# For Regression\n",
    "# created scaler\n",
    "#scaler = StandardScaler()\n",
    "# fit scaler on training dataset\n",
    "#scaler.fit(trainy)\n",
    "# transform training dataset\n",
    "#trainy = scaler.transform(trainy)\n",
    "# transform test dataset\n",
    "#testy = scaler.transform(testy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# FOR BINARY CLASSIFICATION\n",
    "n_input = X_train.shape[1]\n",
    "n_classes = len(np.unique(y_train))\n",
    "num_nodes = n_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# FOR MULTICLASS CLASSIFICATION\n",
    "#n_input, n_classes = X_train.shape[1], y_train.shape[1]\n",
    "#num_nodes = n_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'num_nodes' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-06ad3ebc8bfd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnum_nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'num_nodes' is not defined"
     ]
    }
   ],
   "source": [
    "num_nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(num_nodes+n_classes)//2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(num_nodes+n_classes) * (2/3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Normality Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def is_normal(data):\n",
    "    alpha = 0.05\n",
    "    stat, p = normaltest(data)\n",
    "    if p > alpha:\n",
    "        normalTest = True\n",
    "    else:\n",
    "        normalTest = False\n",
    "    \n",
    "    stat, p = shapiro(data)\n",
    "    if p > alpha:\n",
    "        shapiroTest = True\n",
    "    else:\n",
    "        shapiroTest = False\n",
    "        \n",
    "    return normalTest and shapiroTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Z:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\morestats.py:1681: UserWarning: p-value may not be accurate for N > 5000.\n",
      "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Like columns: []\n",
      "Non-Gaussian Like columns: ['Unnamed: 0', 'loan_amnt', 'term', 'int_rate', 'installment', 'annual_inc', 'dti', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc', 'mort_acc', 'pub_rec_bankruptcies', 'A2', 'A3', 'A4', 'A5', 'B1', 'B2', 'B3', 'B4', 'B5', 'C1', 'C2', 'C3', 'C4', 'C5', 'D1', 'D2', 'D3', 'D4', 'D5', 'E1', 'E2', 'E3', 'E4', 'E5', 'F1', 'F2', 'F3', 'F4', 'F5', 'G1', 'G2', 'G3', 'G4', 'G5', 'OTHER', 'OWN', 'RENT', 'verification_status_Source Verified', 'verification_status_Verified', 'application_type_INDIVIDUAL', 'application_type_JOINT', 'initial_list_status_w', 'purpose_credit_card', 'purpose_debt_consolidation', 'purpose_educational', 'purpose_home_improvement', 'purpose_house', 'purpose_major_purchase', 'purpose_medical', 'purpose_moving', 'purpose_other', 'purpose_renewable_energy', 'purpose_small_business', 'purpose_vacation', 'purpose_wedding', 'issue_month', 'issue_year', 'cr_month', 'cr_year', '05113', '11650', '22690', '29597', '30723', '48052', '70466', '86630', '93700']\n"
     ]
    }
   ],
   "source": [
    "Gaussian_Like = []\n",
    "Non_Gaussian = []\n",
    "\n",
    "for i, name in enumerate (X.columns):\n",
    "    if is_normal(X[name]):\n",
    "        Gaussian_Like.append(name)\n",
    "    else:\n",
    "        Non_Gaussian.append(name)\n",
    "        \n",
    "print (f\"Gaussian Like columns: {Gaussian_Like}\")\n",
    "print (f\"Non-Gaussian Like columns: {Non_Gaussian}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def create_baseline_model():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    #Input Layer\n",
    "    model.add(Input(shape=(n_input,)))\n",
    "    #Hidden Layers\n",
    "    model.add(Dense(units=32, activation='relu', kernel_initializer='he_uniform'))\n",
    "    model.add(Dense(units=32, activation='relu', kernel_initializer='he_uniform'))\n",
    "    # Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))   \n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "estimators = []\n",
    "estimators.append(('norm', MinMaxScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_baseline_model, epochs=300, batch_size=32, verbose=0)))\n",
    "pipeline = Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline: 88.48% (0.15%)\n",
      "Execution time :  7:13:16.131950\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "results = cross_val_score(pipeline, X, y, cv=kfold)\n",
    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tunning with HyperOpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = dict()\n",
    "params['layers'] = hp.choice('layers', [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
    "params['units'] = hp.choice('units', [16, 32, 64, 128])\n",
    "params['batch_size'] = hp.choice('batch_size', [16, 32, 64, 128, 256, 512])\n",
    "params['optimizer'] = hp.choice('optimizer', [SGD(), RMSprop(), Adagrad(), Adam(), Adamax(), Nadam()])\n",
    "params['activation']= hp.choice('activation', [ReLU(), PReLU(), LeakyReLU(), ELU()])\n",
    "params['learning_rate'] = hp.uniform('learning_rate',1e-7, 1.0)\n",
    "params['init_mode']= hp.choice('init_mode', \n",
    "['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform'])\n",
    "params['dropout_rate']= hp.choice('dropout_rate', [0.0, 0.1, 0.2, 0.3, 0.4, 0.5])\n",
    "params['weight_constraint'] = hp.choice('weight_constraint',\n",
    "                            [unit_norm(), max_norm(1), max_norm(2), max_norm(3), max_norm(4), max_norm(5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm = MinMaxScaler()\n",
    "norm.fit(X_train)\n",
    "X_train = norm.transform(X_train)\n",
    "X_test = norm.transform(X_test)\n",
    "X_valid = norm.transform(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_score = 0\n",
    "best_param = []\n",
    "testing = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hyperparameters(params):  \n",
    "    global best_score\n",
    "    global testing\n",
    "    \n",
    "    #print (f'Testing - {testing}: {params}')\n",
    "    #create model\n",
    "    model = Sequential()\n",
    "    #Input Layer\n",
    "    model.add(Input(shape=(n_input,)))\n",
    "    \n",
    "    #Hidden Layers  \n",
    "    for i in range(params['layers']):\n",
    "        model.add(Dense(units=params['units'], kernel_initializer=params['init_mode'], \n",
    "                        activation=params['activation'], \n",
    "                        kernel_constraint=params['weight_constraint']))\n",
    "        model.add(Dropout(params['dropout_rate']))\n",
    "        #model.add(BatchNormalization())\n",
    "    \n",
    "    #Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    #Compile model\n",
    "    optimizer = params['optimizer']\n",
    "    optimizer.learning_rate=params['learning_rate'];\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "    #model.summary()  \n",
    "    \n",
    "    model.fit(x=X_train, \n",
    "              y=y_train, \n",
    "              epochs=1000,\n",
    "              batch_size=params['batch_size'],\n",
    "              validation_data=(X_valid, y_valid), \n",
    "              #validation_split=0.2, \n",
    "              verbose=0,\n",
    "              callbacks=[early_stop]\n",
    "             )\n",
    "\n",
    "    pred_auc =model.predict(X_test, batch_size = params['batch_size'], verbose = 0)\n",
    "    acc = roc_auc_score(y_test, pred_auc) #accuracy_score roc_auc_score\n",
    "    testing = testing + 1\n",
    "    if best_score==0:\n",
    "        best_score=acc\n",
    "    if acc > best_score:\n",
    "        best_score=acc\n",
    "        best_param=params\n",
    "        print(f'BEST AUC:{best_score:.4f}')\n",
    "        print (f'BEST PARAMS: {testing} -> {params}')\n",
    "        \n",
    "    #sys.stdout.flush() \n",
    "    return {'loss': acc, 'status': STATUS_OK, 'model': model}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trials = Trials()\n",
    "best = fmin(fn=hyperparameters,\n",
    "            space=params,\n",
    "            algo=tpe.suggest,\n",
    "            max_evals=100,\n",
    "            trials=trials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print (f\"Best Score: {best_score}\")\n",
    "print (f\"Best Params: {best_param}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = trials.best_trial['result']['loss']\n",
    "print ('loss :', loss)\n",
    "model = trials.best_trial['result']['model']\n",
    "print (model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 1-2. Tune Batch Size and Number of Epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def create_model():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    # Input + Hidden Layers\n",
    "    model.add(Dense(num_nodes, input_dim=n_input, activation='relu'))\n",
    "    # Hidden Layers\n",
    "    model.add(Dense((num_nodes+1)//2, activation='relu'))\n",
    "    # Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))   \n",
    "    # Compile model\n",
    "    #sgd = SGD(lr=0.01, momentum=0.8)\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "estimators = []\n",
    "estimators.append(('norm', MinMaxScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_model, verbose=0)))\n",
    "pipeline = Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.884988 using {'mlp__batch_size': 512, 'mlp__epochs': 50}\n",
      "0.876638 (0.003409) with: {'mlp__batch_size': 16, 'mlp__epochs': 50}\n",
      "0.864177 (0.004338) with: {'mlp__batch_size': 16, 'mlp__epochs': 100}\n",
      "0.860116 (0.003607) with: {'mlp__batch_size': 16, 'mlp__epochs': 150}\n",
      "0.858155 (0.002768) with: {'mlp__batch_size': 16, 'mlp__epochs': 200}\n",
      "0.860837 (0.001818) with: {'mlp__batch_size': 16, 'mlp__epochs': 200}\n",
      "0.855157 (0.001381) with: {'mlp__batch_size': 16, 'mlp__epochs': 250}\n",
      "0.854638 (0.002760) with: {'mlp__batch_size': 16, 'mlp__epochs': 300}\n",
      "0.869490 (0.002094) with: {'mlp__batch_size': 32, 'mlp__epochs': 50}\n",
      "0.857446 (0.006471) with: {'mlp__batch_size': 32, 'mlp__epochs': 100}\n",
      "0.853892 (0.004657) with: {'mlp__batch_size': 32, 'mlp__epochs': 150}\n",
      "0.851564 (0.004904) with: {'mlp__batch_size': 32, 'mlp__epochs': 200}\n",
      "0.852917 (0.001733) with: {'mlp__batch_size': 32, 'mlp__epochs': 200}\n",
      "0.847073 (0.003577) with: {'mlp__batch_size': 32, 'mlp__epochs': 250}\n",
      "0.845694 (0.002205) with: {'mlp__batch_size': 32, 'mlp__epochs': 300}\n",
      "0.872754 (0.003941) with: {'mlp__batch_size': 64, 'mlp__epochs': 50}\n",
      "0.864822 (0.005129) with: {'mlp__batch_size': 64, 'mlp__epochs': 100}\n",
      "0.856093 (0.001774) with: {'mlp__batch_size': 64, 'mlp__epochs': 150}\n",
      "0.850083 (0.007917) with: {'mlp__batch_size': 64, 'mlp__epochs': 200}\n",
      "0.851197 (0.004243) with: {'mlp__batch_size': 64, 'mlp__epochs': 200}\n",
      "0.851703 (0.006456) with: {'mlp__batch_size': 64, 'mlp__epochs': 250}\n",
      "0.848312 (0.006480) with: {'mlp__batch_size': 64, 'mlp__epochs': 300}\n",
      "0.877220 (0.005229) with: {'mlp__batch_size': 128, 'mlp__epochs': 50}\n",
      "0.866277 (0.008670) with: {'mlp__batch_size': 128, 'mlp__epochs': 100}\n",
      "0.862595 (0.006978) with: {'mlp__batch_size': 128, 'mlp__epochs': 150}\n",
      "0.859850 (0.004417) with: {'mlp__batch_size': 128, 'mlp__epochs': 200}\n",
      "0.858383 (0.008539) with: {'mlp__batch_size': 128, 'mlp__epochs': 200}\n",
      "0.853525 (0.006307) with: {'mlp__batch_size': 128, 'mlp__epochs': 250}\n",
      "0.849970 (0.005437) with: {'mlp__batch_size': 128, 'mlp__epochs': 300}\n",
      "0.881180 (0.003939) with: {'mlp__batch_size': 256, 'mlp__epochs': 50}\n",
      "0.878017 (0.004669) with: {'mlp__batch_size': 256, 'mlp__epochs': 100}\n",
      "0.875930 (0.004178) with: {'mlp__batch_size': 256, 'mlp__epochs': 150}\n",
      "0.867061 (0.009842) with: {'mlp__batch_size': 256, 'mlp__epochs': 200}\n",
      "0.862684 (0.016167) with: {'mlp__batch_size': 256, 'mlp__epochs': 200}\n",
      "0.860521 (0.010464) with: {'mlp__batch_size': 256, 'mlp__epochs': 250}\n",
      "0.865733 (0.004133) with: {'mlp__batch_size': 256, 'mlp__epochs': 300}\n",
      "0.884988 (0.003328) with: {'mlp__batch_size': 512, 'mlp__epochs': 50}\n",
      "0.875727 (0.002975) with: {'mlp__batch_size': 512, 'mlp__epochs': 100}\n",
      "0.871603 (0.008046) with: {'mlp__batch_size': 512, 'mlp__epochs': 150}\n",
      "0.878321 (0.003924) with: {'mlp__batch_size': 512, 'mlp__epochs': 200}\n",
      "0.872969 (0.005016) with: {'mlp__batch_size': 512, 'mlp__epochs': 200}\n",
      "0.866695 (0.002704) with: {'mlp__batch_size': 512, 'mlp__epochs': 250}\n",
      "0.865720 (0.007080) with: {'mlp__batch_size': 512, 'mlp__epochs': 300}\n",
      "Execution time :  5:54:42.997249\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "# create model\n",
    "#model = KerasClassifier(build_fn=create_model, verbose=0)\n",
    "\n",
    "# define the grid search parameters\n",
    "param_grid = {\n",
    "    # Key = step name from pipeline + __ + hyperparameter, value = tuple of possible values\n",
    "    'mlp__batch_size': (16, 32, 64, 128, 256, 512),\n",
    "    'mlp__epochs': (100, 150, 200, 250, 300, 350)\n",
    "}\n",
    "#batch_size = [16, 32, 64, 128, 256, 512]\n",
    "#epochs = [50, 100, 150, 200, 200, 250, 300]\n",
    "#param_grid = dict(batch_size=batch_size, epochs=epochs)\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "grid = GridSearchCV(estimator=pipeline, param_grid=param_grid, n_jobs=-1, cv=kfold)\n",
    "grid_result = grid.fit(X_test, y_test)\n",
    "\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
    "    \n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 3. Tune the Training Optimization Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Function to create model, required for KerasClassifier\n",
    "def create_model(optimizer='adam'):\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    # Input + Hidden Layers\n",
    "    model.add(Dense(num_nodes, input_dim=n_input, activation='relu'))\n",
    "    # Hidden Layers\n",
    "    model.add(Dense((num_nodes+1)//2, activation='relu'))\n",
    "    # Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "estimators = []\n",
    "estimators.append(('norm', MinMaxScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)))\n",
    "pipeline = Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.889479 using {'mlp__optimizer': 'Adagrad'}\n",
      "0.884963 (0.002223) with: {'mlp__optimizer': 'SGD'}\n",
      "0.887594 (0.002305) with: {'mlp__optimizer': 'RMSprop'}\n",
      "0.889479 (0.000944) with: {'mlp__optimizer': 'Adagrad'}\n",
      "0.888530 (0.001181) with: {'mlp__optimizer': 'Adadelta'}\n",
      "0.848768 (0.004870) with: {'mlp__optimizer': 'Adam'}\n",
      "0.869161 (0.000882) with: {'mlp__optimizer': 'Adamax'}\n",
      "0.847566 (0.001521) with: {'mlp__optimizer': 'Nadam'}\n",
      "Execution time :  2:34:12.603498\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "# create model\n",
    "# model = KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)\n",
    "# define the grid search parameters\n",
    "param_grid = {\n",
    "    # Key = step name from pipeline + __ + hyperparameter, value = tuple of possible values\n",
    "    'mlp__optimizer': ('SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam')\n",
    "}\n",
    "#optimizer = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']\n",
    "#param_grid = dict(optimizer=optimizer)\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "grid = GridSearchCV(estimator=pipeline, param_grid=param_grid, n_jobs=-1, cv=kfold)\n",
    "grid_result = grid.fit(X_test, y_test)\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
    "\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 4-5. Tune Learning Rate and Momentum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Function to create model, required for KerasClassifier\n",
    "def create_model(learn_rate=0.01):\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    # Input + Hidden Layers\n",
    "    model.add(Dense(num_nodes, input_dim=n_input, activation='relu'))\n",
    "    # Hidden Layers\n",
    "    model.add(Dense((num_nodes+1)//2, activation='relu'))\n",
    "    # Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    #optimizer = SGD(lr=learn_rate, momentum=momentum)\n",
    "    optimizer = Adagrad(learning_rate=learn_rate)\n",
    "    #optimizer.learning_rate=learn_rate;\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "estimators = []\n",
    "estimators.append(('norm', MinMaxScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)))\n",
    "pipeline = Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.889302 using {'mlp__learn_rate': 0.001}\n",
      "0.889302 (0.001483) with: {'mlp__learn_rate': 0.001}\n",
      "0.884406 (0.002373) with: {'mlp__learn_rate': 0.01}\n",
      "0.851349 (0.004219) with: {'mlp__learn_rate': 0.1}\n",
      "0.844896 (0.003451) with: {'mlp__learn_rate': 0.2}\n",
      "0.846503 (0.003708) with: {'mlp__learn_rate': 0.3}\n",
      "Execution time :  1:37:40.910201\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "# create model\n",
    "# model = KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)\n",
    "# define the grid search parameters\n",
    "# define the grid search parameters\n",
    "param_grid = {\n",
    "    # Key = step name from pipeline + __ + hyperparameter, value = tuple of possible values\n",
    "    'mlp__learn_rate': (0.0001, 0.001, 0.01, 0.1, 0.2, 0.3)\n",
    "}\n",
    "#learn_rate = [0.001, 0.01, 0.1, 0.2, 0.3]\n",
    "#momentum = [0.0, 0.2, 0.4, 0.6, 0.8, 0.9]\n",
    "#param_grid = dict(learn_rate=learn_rate, momentum=momentum)\n",
    "#param_grid = dict(learn_rate=learn_rate)\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "grid = GridSearchCV(estimator=pipeline, param_grid=param_grid, n_jobs=-1, cv=kfold)\n",
    "grid_result = grid.fit(X_test, y_test)\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
    "\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 6. Tune Network Kernel/Weight Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Function to create model, required for KerasClassifier\n",
    "def create_model(init_mode='uniform'):\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    # Input + Hidden Layers\n",
    "    model.add(Dense(num_nodes, input_dim=n_input, kernel_initializer=init_mode, activation='relu'))\n",
    "    # Hidden Layers\n",
    "    model.add(Dense((num_nodes+1)//2, kernel_initializer=init_mode, activation='relu'))\n",
    "    # Output Layer\n",
    "    model.add(Dense(1, kernel_initializer=init_mode, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='Adagrad', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "estimators = []\n",
    "estimators.append(('norm', MinMaxScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)))\n",
    "pipeline = Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.889454 using {'mlp__init_mode': 'uniform'}\n",
      "0.889454 (0.002410) with: {'mlp__init_mode': 'uniform'}\n",
      "0.889264 (0.002288) with: {'mlp__init_mode': 'lecun_uniform'}\n",
      "0.889289 (0.002312) with: {'mlp__init_mode': 'normal'}\n",
      "0.804906 (0.000025) with: {'mlp__init_mode': 'zero'}\n",
      "0.889176 (0.002344) with: {'mlp__init_mode': 'glorot_normal'}\n",
      "0.889062 (0.002333) with: {'mlp__init_mode': 'glorot_uniform'}\n",
      "0.888973 (0.002088) with: {'mlp__init_mode': 'he_normal'}\n",
      "0.888708 (0.002065) with: {'mlp__init_mode': 'he_uniform'}\n",
      "Execution time :  2:23:11.523127\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "# create model\n",
    "#model = KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)\n",
    "# define the grid search parameters\n",
    "param_grid = {\n",
    "    # Key = step name from pipeline + __ + hyperparameter, value = tuple of possible values\n",
    "    'mlp__init_mode': ('uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform')\n",
    "}\n",
    "#init_mode = ['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform']\n",
    "#param_grid = dict(init_mode=init_mode)\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "grid = GridSearchCV(estimator=pipeline, param_grid=param_grid, n_jobs=-1, cv=kfold)\n",
    "grid_result = grid.fit(X_test, y_test)\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
    "    \n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 7. Tune the Activation Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Function to create model, required for KerasClassifier\n",
    "def create_model(activation='relu'):\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    # Input + Hidden Layers\n",
    "    model.add(Dense(num_nodes, input_dim=n_input, activation=activation))\n",
    "    # Hidden Layers\n",
    "    model.add(Dense((num_nodes+1)//2, activation=activation))\n",
    "    # Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='Adagrad', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "estimators = []\n",
    "estimators.append(('norm', MinMaxScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)))\n",
    "pipeline = Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.889479 using {'mlp__activation': 'relu'}\n",
      "0.889479 (0.001221) with: {'mlp__activation': 'relu'}\n",
      "nan (nan) with: {'mlp__activation': 'prelu'}\n",
      "nan (nan) with: {'mlp__activation': 'leakyrelu'}\n",
      "0.889252 (0.001135) with: {'mlp__activation': 'elu'}\n",
      "Execution time :  0:42:57.677364\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "# create model\n",
    "# model = KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)\n",
    "# define the grid search parameters\n",
    "# activation = ['softmax', 'softplus', 'softsign', 'relu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear']\n",
    "# define the grid search parameters  ReLU, PReLU, LeakyReLU, ELU\n",
    "param_grid = {\n",
    "    # Key = step name from pipeline + __ + hyperparameter, value = tuple of possible values\n",
    "    'mlp__activation': ('relu', 'prelu', 'leakyrelu', 'elu')\n",
    "}\n",
    "#activation = ['relu', 'LReLU', 'PReLU', 'elu']\n",
    "#param_grid = dict(activation=activation)\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "grid = GridSearchCV(estimator=pipeline, param_grid=param_grid, n_jobs=-1, cv=kfold)\n",
    "grid_result = grid.fit(X_test, y_test)\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
    "    \n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 8-9. Tune Activity Regularization (L1) & Weight Regularization (L2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "A modern recommendation for regularization is to use early stopping with dropout and a weight constraint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Function to create model, required for KerasClassifier\n",
    "def create_model(l1=0.0, l2=0.0):\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    # Input + Hidden Layers\n",
    "    model.add(Dense(num_nodes, input_dim=n_input, activation='relu', \n",
    "                     kernel_regularizer=L1L2(l1=l1, l2=l2)))\n",
    "    model.add(Dropout(0.2))\n",
    "    # Hidden Layers\n",
    "    model.add(Dense((num_nodes+1)//2, activation='relu'))\n",
    "    model.add(Dropout(0.2))\n",
    "    # Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='Adagrad', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "estimators = []\n",
    "estimators.append(('norm', MinMaxScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)))\n",
    "pipeline = Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.889353 using {'mlp__l1': 0.0001, 'mlp__l2': 0.0}\n",
      "0.804906 (0.000025) with: {'mlp__l1': 0.1, 'mlp__l2': 0.1}\n",
      "0.804906 (0.000025) with: {'mlp__l1': 0.1, 'mlp__l2': 0.01}\n",
      "0.804906 (0.000025) with: {'mlp__l1': 0.1, 'mlp__l2': 0.001}\n",
      "0.804906 (0.000025) with: {'mlp__l1': 0.1, 'mlp__l2': 0.0001}\n",
      "0.804906 (0.000025) with: {'mlp__l1': 0.1, 'mlp__l2': 1e-05}\n",
      "0.804906 (0.000025) with: {'mlp__l1': 0.1, 'mlp__l2': 1e-06}\n",
      "0.804906 (0.000025) with: {'mlp__l1': 0.1, 'mlp__l2': 0.0}\n",
      "0.888303 (0.001864) with: {'mlp__l1': 0.01, 'mlp__l2': 0.1}\n",
      "0.888923 (0.001714) with: {'mlp__l1': 0.01, 'mlp__l2': 0.01}\n",
      "0.888733 (0.001696) with: {'mlp__l1': 0.01, 'mlp__l2': 0.001}\n",
      "0.888720 (0.001735) with: {'mlp__l1': 0.01, 'mlp__l2': 0.0001}\n",
      "0.888897 (0.001674) with: {'mlp__l1': 0.01, 'mlp__l2': 1e-05}\n",
      "0.888897 (0.001674) with: {'mlp__l1': 0.01, 'mlp__l2': 1e-06}\n",
      "0.888897 (0.001674) with: {'mlp__l1': 0.01, 'mlp__l2': 0.0}\n",
      "0.888758 (0.001955) with: {'mlp__l1': 0.001, 'mlp__l2': 0.1}\n",
      "0.888644 (0.002058) with: {'mlp__l1': 0.001, 'mlp__l2': 0.01}\n",
      "0.888391 (0.002089) with: {'mlp__l1': 0.001, 'mlp__l2': 0.001}\n",
      "0.888379 (0.001732) with: {'mlp__l1': 0.001, 'mlp__l2': 0.0001}\n",
      "0.888594 (0.002337) with: {'mlp__l1': 0.001, 'mlp__l2': 1e-05}\n",
      "0.888404 (0.001852) with: {'mlp__l1': 0.001, 'mlp__l2': 1e-06}\n",
      "0.888581 (0.001599) with: {'mlp__l1': 0.001, 'mlp__l2': 0.0}\n",
      "0.889011 (0.001974) with: {'mlp__l1': 0.0001, 'mlp__l2': 0.1}\n",
      "0.889062 (0.001970) with: {'mlp__l1': 0.0001, 'mlp__l2': 0.01}\n",
      "0.888783 (0.001427) with: {'mlp__l1': 0.0001, 'mlp__l2': 0.001}\n",
      "0.889024 (0.001402) with: {'mlp__l1': 0.0001, 'mlp__l2': 0.0001}\n",
      "0.888961 (0.001757) with: {'mlp__l1': 0.0001, 'mlp__l2': 1e-05}\n",
      "0.888859 (0.001622) with: {'mlp__l1': 0.0001, 'mlp__l2': 1e-06}\n",
      "0.889353 (0.001438) with: {'mlp__l1': 0.0001, 'mlp__l2': 0.0}\n",
      "0.889011 (0.001970) with: {'mlp__l1': 1e-05, 'mlp__l2': 0.1}\n",
      "0.889252 (0.001900) with: {'mlp__l1': 1e-05, 'mlp__l2': 0.01}\n",
      "0.889163 (0.001747) with: {'mlp__l1': 1e-05, 'mlp__l2': 0.001}\n",
      "0.889024 (0.001475) with: {'mlp__l1': 1e-05, 'mlp__l2': 0.0001}\n",
      "0.889011 (0.001966) with: {'mlp__l1': 1e-05, 'mlp__l2': 1e-05}\n",
      "0.889188 (0.001773) with: {'mlp__l1': 1e-05, 'mlp__l2': 1e-06}\n",
      "0.888910 (0.001803) with: {'mlp__l1': 1e-05, 'mlp__l2': 0.0}\n",
      "0.888999 (0.001835) with: {'mlp__l1': 1e-06, 'mlp__l2': 0.1}\n",
      "0.889252 (0.001957) with: {'mlp__l1': 1e-06, 'mlp__l2': 0.01}\n",
      "0.889327 (0.001690) with: {'mlp__l1': 1e-06, 'mlp__l2': 0.001}\n",
      "0.888961 (0.001388) with: {'mlp__l1': 1e-06, 'mlp__l2': 0.0001}\n",
      "0.888834 (0.001412) with: {'mlp__l1': 1e-06, 'mlp__l2': 1e-05}\n",
      "0.889315 (0.001757) with: {'mlp__l1': 1e-06, 'mlp__l2': 1e-06}\n",
      "0.889087 (0.001442) with: {'mlp__l1': 1e-06, 'mlp__l2': 0.0}\n",
      "0.889125 (0.001847) with: {'mlp__l1': 0.0, 'mlp__l2': 0.1}\n",
      "0.889036 (0.001619) with: {'mlp__l1': 0.0, 'mlp__l2': 0.01}\n",
      "0.889024 (0.001507) with: {'mlp__l1': 0.0, 'mlp__l2': 0.001}\n",
      "0.889024 (0.001561) with: {'mlp__l1': 0.0, 'mlp__l2': 0.0001}\n",
      "0.889315 (0.001242) with: {'mlp__l1': 0.0, 'mlp__l2': 1e-05}\n",
      "0.889062 (0.001485) with: {'mlp__l1': 0.0, 'mlp__l2': 1e-06}\n",
      "0.889239 (0.001444) with: {'mlp__l1': 0.0, 'mlp__l2': 0.0}\n",
      "Execution time :  17:01:17.154350\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "# create model\n",
    "#model = KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)\n",
    "# define the grid search parameters\n",
    "param_grid = {\n",
    "    # Key = step name from pipeline + __ + hyperparameter, value = tuple of possible values\n",
    "    'mlp__l1': (0.100000, 0.010000, 0.001000, 0.000100, 0.000010, 0.000001, 0.0),\n",
    "    'mlp__l2': (0.100000, 0.010000, 0.001000, 0.000100, 0.000010, 0.000001, 0.0)\n",
    "}\n",
    "#l1 = [0.100000, 0.010000, 0.001000, 0.000100, 0.000010, 0.000001, 0.0]\n",
    "#l2 = [0.100000, 0.010000, 0.001000, 0.000100, 0.000010, 0.000001, 0.0]\n",
    "#param_grid = dict(l1=l1, l2=l2)\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "grid = GridSearchCV(estimator=pipeline, param_grid=param_grid, n_jobs=-1, cv=kfold)\n",
    "grid_result = grid.fit(X_test, y_test)\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
    "    \n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 10-11. Tune Dropout Regularization and Weight Constraint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* Early stopping should be used almost universally.\n",
    "* The constraints are specified per-layer,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Function to create model, required for KerasClassifier\n",
    "def create_model(dropout_rate=0.0, weight_constraint=0):\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    # Input + Hidden Layers\n",
    "    model.add(Dense(num_nodes, input_dim=n_input, activation='relu', \n",
    "                    kernel_constraint=max_norm(weight_constraint)))\n",
    "    model.add(Dropout(dropout_rate))\n",
    "    # Hidden Layers\n",
    "    model.add(Dense((num_nodes+1)//2, activation='relu', kernel_constraint=max_norm(weight_constraint)))\n",
    "    model.add(Dropout(dropout_rate))\n",
    "    # Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='Adagrad', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "estimators = []\n",
    "estimators.append(('norm', MinMaxScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)))\n",
    "pipeline = Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.889593 using {'mlp__dropout_rate': 0.0, 'mlp__weight_constraint': 1}\n",
      "0.889593 (0.001632) with: {'mlp__dropout_rate': 0.0, 'mlp__weight_constraint': 1}\n",
      "0.889479 (0.001832) with: {'mlp__dropout_rate': 0.0, 'mlp__weight_constraint': 2}\n",
      "0.889441 (0.001793) with: {'mlp__dropout_rate': 0.0, 'mlp__weight_constraint': 3}\n",
      "0.889492 (0.002170) with: {'mlp__dropout_rate': 0.0, 'mlp__weight_constraint': 4}\n",
      "0.889176 (0.001563) with: {'mlp__dropout_rate': 0.0, 'mlp__weight_constraint': 5}\n",
      "0.889087 (0.002129) with: {'mlp__dropout_rate': 0.1, 'mlp__weight_constraint': 1}\n",
      "0.888910 (0.001595) with: {'mlp__dropout_rate': 0.1, 'mlp__weight_constraint': 2}\n",
      "0.888986 (0.002102) with: {'mlp__dropout_rate': 0.1, 'mlp__weight_constraint': 3}\n",
      "0.889036 (0.001986) with: {'mlp__dropout_rate': 0.1, 'mlp__weight_constraint': 4}\n",
      "0.889201 (0.001874) with: {'mlp__dropout_rate': 0.1, 'mlp__weight_constraint': 5}\n",
      "0.889290 (0.001738) with: {'mlp__dropout_rate': 0.2, 'mlp__weight_constraint': 1}\n",
      "0.889074 (0.001925) with: {'mlp__dropout_rate': 0.2, 'mlp__weight_constraint': 2}\n",
      "0.889138 (0.002075) with: {'mlp__dropout_rate': 0.2, 'mlp__weight_constraint': 3}\n",
      "0.889176 (0.001858) with: {'mlp__dropout_rate': 0.2, 'mlp__weight_constraint': 4}\n",
      "0.888809 (0.001957) with: {'mlp__dropout_rate': 0.2, 'mlp__weight_constraint': 5}\n",
      "0.889226 (0.001712) with: {'mlp__dropout_rate': 0.3, 'mlp__weight_constraint': 1}\n",
      "0.889049 (0.002120) with: {'mlp__dropout_rate': 0.3, 'mlp__weight_constraint': 2}\n",
      "0.888973 (0.002123) with: {'mlp__dropout_rate': 0.3, 'mlp__weight_constraint': 3}\n",
      "0.888872 (0.002132) with: {'mlp__dropout_rate': 0.3, 'mlp__weight_constraint': 4}\n",
      "0.888999 (0.001933) with: {'mlp__dropout_rate': 0.3, 'mlp__weight_constraint': 5}\n",
      "0.889138 (0.002092) with: {'mlp__dropout_rate': 0.4, 'mlp__weight_constraint': 1}\n",
      "0.889150 (0.002126) with: {'mlp__dropout_rate': 0.4, 'mlp__weight_constraint': 2}\n",
      "0.888935 (0.001853) with: {'mlp__dropout_rate': 0.4, 'mlp__weight_constraint': 3}\n",
      "0.889074 (0.002234) with: {'mlp__dropout_rate': 0.4, 'mlp__weight_constraint': 4}\n",
      "0.889150 (0.002064) with: {'mlp__dropout_rate': 0.4, 'mlp__weight_constraint': 5}\n",
      "0.889087 (0.002057) with: {'mlp__dropout_rate': 0.5, 'mlp__weight_constraint': 1}\n",
      "0.888910 (0.002088) with: {'mlp__dropout_rate': 0.5, 'mlp__weight_constraint': 2}\n",
      "0.889036 (0.001954) with: {'mlp__dropout_rate': 0.5, 'mlp__weight_constraint': 3}\n",
      "0.888948 (0.002027) with: {'mlp__dropout_rate': 0.5, 'mlp__weight_constraint': 4}\n",
      "0.888935 (0.002055) with: {'mlp__dropout_rate': 0.5, 'mlp__weight_constraint': 5}\n",
      "0.888986 (0.002121) with: {'mlp__dropout_rate': 0.6, 'mlp__weight_constraint': 1}\n",
      "0.888999 (0.002092) with: {'mlp__dropout_rate': 0.6, 'mlp__weight_constraint': 2}\n",
      "0.888897 (0.002185) with: {'mlp__dropout_rate': 0.6, 'mlp__weight_constraint': 3}\n",
      "0.888961 (0.001964) with: {'mlp__dropout_rate': 0.6, 'mlp__weight_constraint': 4}\n",
      "0.889024 (0.002177) with: {'mlp__dropout_rate': 0.6, 'mlp__weight_constraint': 5}\n",
      "0.888859 (0.002057) with: {'mlp__dropout_rate': 0.7, 'mlp__weight_constraint': 1}\n",
      "0.888910 (0.002004) with: {'mlp__dropout_rate': 0.7, 'mlp__weight_constraint': 2}\n",
      "0.888872 (0.002077) with: {'mlp__dropout_rate': 0.7, 'mlp__weight_constraint': 3}\n",
      "0.888910 (0.002046) with: {'mlp__dropout_rate': 0.7, 'mlp__weight_constraint': 4}\n",
      "0.888961 (0.002133) with: {'mlp__dropout_rate': 0.7, 'mlp__weight_constraint': 5}\n",
      "0.872033 (0.026101) with: {'mlp__dropout_rate': 0.8, 'mlp__weight_constraint': 1}\n",
      "0.882230 (0.009016) with: {'mlp__dropout_rate': 0.8, 'mlp__weight_constraint': 2}\n",
      "0.872033 (0.029482) with: {'mlp__dropout_rate': 0.8, 'mlp__weight_constraint': 3}\n",
      "0.875614 (0.023046) with: {'mlp__dropout_rate': 0.8, 'mlp__weight_constraint': 4}\n",
      "0.861293 (0.029198) with: {'mlp__dropout_rate': 0.8, 'mlp__weight_constraint': 5}\n",
      "0.804906 (0.000025) with: {'mlp__dropout_rate': 0.9, 'mlp__weight_constraint': 1}\n",
      "0.804906 (0.000025) with: {'mlp__dropout_rate': 0.9, 'mlp__weight_constraint': 2}\n",
      "0.804906 (0.000025) with: {'mlp__dropout_rate': 0.9, 'mlp__weight_constraint': 3}\n",
      "0.804906 (0.000025) with: {'mlp__dropout_rate': 0.9, 'mlp__weight_constraint': 4}\n",
      "0.804906 (0.000025) with: {'mlp__dropout_rate': 0.9, 'mlp__weight_constraint': 5}\n",
      "Execution time :  17:09:07.442736\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "# create model\n",
    "# model = KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)\n",
    "# define the grid search parameters\n",
    "# define the grid search parameters\n",
    "param_grid = {\n",
    "    # Key = step name from pipeline + __ + hyperparameter, value = tuple of possible values\n",
    "    'mlp__dropout_rate': (0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9),\n",
    "    'mlp__weight_constraint': (1, 2, 3, 4, 5)\n",
    "}\n",
    "#weight_constraint = [1, 2, 3, 4, 5]\n",
    "#dropout_rate = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
    "#param_grid = dict(dropout_rate=dropout_rate, weight_constraint=weight_constraint)\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "grid = GridSearchCV(estimator=pipeline, param_grid=param_grid, n_jobs=-1, cv=kfold)\n",
    "grid_result = grid.fit(X_test, y_test)\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
    "    \n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 12. Tune with Batch Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Function to create model, required for KerasClassifier\n",
    "def create_model():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    # Input + Hidden Layers\n",
    "    model.add(Dense(num_nodes, input_dim=n_input, activation='relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    # Hidden Layers\n",
    "    model.add(Dense((num_nodes+1)//2, activation='relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    # Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='Adagrad', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "estimators = []\n",
    "estimators.append(('norm', MinMaxScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)))\n",
    "pipeline = Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline: 88.75% (0.20%)\n",
      "Execution time :  0:45:37.850725\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "results = cross_val_score(pipeline, X_test, y_test, cv=kfold)\n",
    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 13. Tune the Number of Neurons in the Hidden Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Function to create model, required for KerasClassifier\n",
    "def create_model(neurons=num_nodes):\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    # Input + Hidden Layers\n",
    "    model.add(Dense(neurons, input_dim=n_input, activation='relu', \n",
    "                    kernel_constraint=max_norm(1)))\n",
    "    model.add(Dropout(0.0))\n",
    "    # Hidden Layers\n",
    "    model.add(Dense((neurons+1)//2, activation='relu'))\n",
    "    model.add(Dropout(0.0))\n",
    "    # Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='Adagrad', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "estimators = []\n",
    "estimators.append(('norm', MinMaxScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)))\n",
    "pipeline = Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.889631 using {'mlp__neurons': 128}\n",
      "0.889252 (0.002310) with: {'mlp__neurons': 512}\n",
      "0.888935 (0.002385) with: {'mlp__neurons': 256}\n",
      "0.889631 (0.002567) with: {'mlp__neurons': 128}\n",
      "0.889429 (0.002557) with: {'mlp__neurons': 64}\n",
      "0.889062 (0.002145) with: {'mlp__neurons': 32}\n",
      "0.889037 (0.002306) with: {'mlp__neurons': 16}\n",
      "Execution time :  3:16:54.855942\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "# create model\n",
    "#model = KerasClassifier(build_fn=create_model, epochs=300, batch_size=32, verbose=0)\n",
    "# define the grid search parameters\n",
    "param_grid = {\n",
    "    # Key = step name from pipeline + __ + hyperparameter, value = tuple of possible values\n",
    "    'mlp__neurons': (512, 256, 128, 64, 32, 16)\n",
    "}\n",
    "#neurons = [512, 256, 128, 64, 32, 16]\n",
    "#param_grid = dict(neurons=neurons)\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "grid = GridSearchCV(estimator=pipeline, param_grid=param_grid, n_jobs=-1, cv=kfold)\n",
    "grid_result = grid.fit(X_test, y_test)\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
    "    \n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* A modern recommendation for regularization is to use early stopping with dropout and a weight constraint instead of l1 & l2.\n",
    "* The constraints (weight_constraint) are specified per-layer\n",
    "* Early stopping should be used almost universally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks_list = []\n",
    "early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=25)\n",
    "callbacks_list.append(early_stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = dict()\n",
    "#params['epochs'] = [1000]\n",
    "#params['validation_data']=[(X_test, y_test)],\n",
    "#params['callbacks']= early_stop,\n",
    "params['batch_size'] = hp.choice('batch_size', [16, 32, 64, 128, 256, 512])\n",
    "#params['neurons']= [16, 32, 64, 128, 256, 512]\n",
    "params['optimizer'] = hp.choice('optimizer', [SGD(), RMSprop(), Adagrad(), Adam(), Adamax(), Nadam()])\n",
    "params['activation']= hp.choice('activation', [ReLU(), PReLU(), LeakyReLU(), ELU()])\n",
    "params['learning_rate'] = hp.uniform('learning_rate',1e-7, 1.0)\n",
    "params['init_mode']= hp.choice('init_mode', \n",
    "['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform'])\n",
    "params['dropout_rate']= hp.choice('dropout_rate', [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])\n",
    "params['weight_constraint'] = hp.choice('weight_constraint',\n",
    "                            [unit_norm(), max_norm(1), max_norm(2), max_norm(3), max_norm(4), max_norm(5)])\n",
    "\n",
    "#params['l1'] = hp.uniform('l1', 0, 0.1)\n",
    "#params['l2'] = hp.uniform('l2', 0, 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HyperOpt Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hyperopt functions for optimization:\n",
    "\n",
    "    hp.choice(label, options) — Returns one of the options, which should be a list or tuple.\n",
    "    hp.randint(label, upper) — Returns a random integer between the range [0, upper).\n",
    "    hp.uniform(label, low, high) — Returns a value uniformly between low and high.\n",
    "    hp.quniform(label, low, high, q) — Returns a value round(uniform(low, high) / q) * q, i.e it rounds the decimal values and returns an integer\n",
    "    hp.normal(label, mean, std) — Returns a real value that’s normally-distributed with mean and standard deviation sigma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_score = 0\n",
    "best_param = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hyperparameters(params):  \n",
    "    global best_score\n",
    "    \n",
    "    #print ('Params testing: ', params)\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    # Input Layer\n",
    "    model.add(Input(shape=(n_input,)))\n",
    "    #Hidden Layers  \n",
    "    model.add(Dense(num_nodes, kernel_initializer=params['init_mode'], activation=params['activation'], \n",
    "                    kernel_constraint=params['weight_constraint']))\n",
    "    model.add(Dropout(params['dropout_rate']))\n",
    "    #model.add(BatchNormalization())\n",
    "    # Hidden Layers\n",
    "    model.add(Dense(num_nodes, kernel_initializer=params['init_mode'], activation=params['activation'],\n",
    "                   kernel_constraint=params['weight_constraint']))\n",
    "    model.add(Dropout(params['dropout_rate']))\n",
    "    #model.add(BatchNormalization())\n",
    "    # Output Layer\n",
    "    model.add(Dense(1, kernel_initializer=params['init_mode'], activation='sigmoid'))\n",
    "    # Compile model\n",
    "    optimizer = params['optimizer']\n",
    "    optimizer.learning_rate=params['learning_rate'];\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "    model.summary()  \n",
    "    \n",
    "    model.fit(x=X_train, \n",
    "              y=y_train, \n",
    "              epochs=1000,\n",
    "              batch_size=params['batch_size'],\n",
    "              validation_data=(X_valid, y_valid), \n",
    "              verbose=0,\n",
    "              callbacks=callbacks_list\n",
    "             )\n",
    "\n",
    "    #model.fit(X_train,y_train,epochs=params['epochs'],validation_data=(X_test,y_test), batch_size = 32,verbose=0)\n",
    "\n",
    "    pred_auc =model.predict(X_test, batch_size = params['batch_size'], verbose = 0)\n",
    "    acc = roc_auc_score(y_test, pred_auc)\n",
    "    if best_score==0:\n",
    "        best_score=acc\n",
    "    if acc > best_score:\n",
    "        best_score=acc\n",
    "        best_param=params\n",
    "        print(f'AUC:{best_score:.4f}')\n",
    "        print(\"PARAMS: \", paramas)\n",
    "    sys.stdout.flush() \n",
    "    return {'loss': acc, 'status': STATUS_OK, 'model': model}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Started at: 2020-11-29 01:59:49.616154\n",
      "Model: \"sequential_7\"                                                                                                               \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_19 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_12 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_20 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_13 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_21 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,695                                                                                                                \n",
      "Trainable params: 13,695                                                                                                            \n",
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00114: early stopping                                                                                                         \n",
      "Model: \"sequential_8\"                                                                                                               \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_22 (Dense)             (None, 82)                6888                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_14 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_23 (Dense)             (None, 82)                6888                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_15 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_24 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,777                                                                                                                \n",
      "Trainable params: 13,777                                                                                                            \n",
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00059: early stopping                                                                                                         \n",
      "Model: \"sequential_9\"                                                                                                               \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_25 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_16 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_26 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_17 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_27 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,695                                                                                                                \n",
      "Trainable params: 13,695                                                                                                            \n",
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00069: early stopping                                                                                                         \n",
      "Model: \"sequential_10\"                                                                                                              \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_28 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_18 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_29 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_19 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_30 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,695                                                                                                                \n",
      "Trainable params: 13,695                                                                                                            \n",
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00040: early stopping                                                                                                         \n",
      "Model: \"sequential_11\"                                                                                                              \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_31 (Dense)             (None, 82)                6888                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_20 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_32 (Dense)             (None, 82)                6888                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_21 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_33 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,777                                                                                                                \n",
      "Trainable params: 13,777                                                                                                            \n",
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00035: early stopping                                                                                                         \n",
      "Model: \"sequential_12\"                                                                                                              \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_34 (Dense)             (None, 82)                6888                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_22 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_35 (Dense)             (None, 82)                6888                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_23 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_36 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,777                                                                                                                \n",
      "Trainable params: 13,777                                                                                                            \n",
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00028: early stopping                                                                                                         \n",
      "Model: \"sequential_13\"                                                                                                              \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_37 (Dense)             (None, 82)                6888                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_24 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dense_38 (Dense)             (None, 82)                6888                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_25 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_39 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,777                                                                                                                \n",
      "Trainable params: 13,777                                                                                                            \n",
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00028: early stopping                                                                                                         \n",
      "Model: \"sequential_14\"                                                                                                              \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_40 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_26 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_41 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_27 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_42 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,695                                                                                                                \n",
      "Trainable params: 13,695                                                                                                            \n",
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00046: early stopping                                                                                                         \n",
      "Model: \"sequential_15\"                                                                                                              \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_43 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_28 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_44 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_29 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_45 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,695                                                                                                                \n",
      "Trainable params: 13,695                                                                                                            \n",
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00031: early stopping                                                                                                         \n",
      "Model: \"sequential_16\"                                                                                                              \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_46 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_30 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_47 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_31 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_48 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,695                                                                                                                \n",
      "Trainable params: 13,695                                                                                                            \n",
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00027: early stopping                                                                                                         \n",
      "Model: \"sequential_17\"                                                                                                              \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_49 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_32 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_50 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_33 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_51 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,695                                                                                                                \n",
      "Trainable params: 13,695                                                                                                            \n",
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00071: early stopping                                                                                                         \n",
      "Model: \"sequential_18\"                                                                                                              \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_52 (Dense)             (None, 82)                6888                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_34 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_53 (Dense)             (None, 82)                6888                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_35 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_54 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,777                                                                                                                \n",
      "Trainable params: 13,777                                                                                                            \n",
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00166: early stopping                                                                                                         \n",
      "Model: \"sequential_19\"                                                                                                              \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_55 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_36 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_56 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_37 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_57 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,695                                                                                                                \n",
      "Trainable params: 13,695                                                                                                            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00047: early stopping                                                                                                         \n",
      "Model: \"sequential_20\"                                                                                                              \n",
      "_________________________________________________________________                                                                   \n",
      "Layer (type)                 Output Shape              Param #                                                                      \n",
      "=================================================================                                                                   \n",
      "dense_58 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_38 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_59 (Dense)             (None, 82)                6806                                                                         \n",
      "_________________________________________________________________                                                                   \n",
      "dropout_39 (Dropout)         (None, 82)                0                                                                            \n",
      "_________________________________________________________________                                                                   \n",
      "dense_60 (Dense)             (None, 1)                 83                                                                           \n",
      "=================================================================                                                                   \n",
      "Total params: 13,695                                                                                                                \n",
      "Trainable params: 13,695                                                                                                            \n",
      "Non-trainable params: 0                                                                                                             \n",
      "_________________________________________________________________                                                                   \n",
      "Epoch 00148: early stopping                                                                                                         \n",
      "AUC:0.6479                                                                                                                          \n",
      " 13%|████████▉                                                            | 13/100 [2:03:18<14:33:09, 602.18s/trial, best loss: 0.5]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "job exception: name 'paramas' is not defined\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 13%|████████▉                                                            | 13/100 [2:03:18<13:45:10, 569.09s/trial, best loss: 0.5]\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'paramas' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-ea1c3163ec56>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34mf\"Job Started at: {init_time}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mtrials\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m best = fmin(fn=hyperparameters,\n\u001b[0m\u001b[0;32m      5\u001b[0m             \u001b[0mspace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m             \u001b[0malgo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtpe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msuggest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\hyperopt\\fmin.py\u001b[0m in \u001b[0;36mfmin\u001b[1;34m(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[0;32m    505\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    506\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mallow_trials_fmin\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"fmin\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 507\u001b[1;33m         return trials.fmin(\n\u001b[0m\u001b[0;32m    508\u001b[0m             \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    509\u001b[0m             \u001b[0mspace\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\hyperopt\\base.py\u001b[0m in \u001b[0;36mfmin\u001b[1;34m(self, fn, space, algo, max_evals, timeout, loss_threshold, max_queue_len, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[0;32m    680\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mfmin\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfmin\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    681\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 682\u001b[1;33m         return fmin(\n\u001b[0m\u001b[0;32m    683\u001b[0m             \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    684\u001b[0m             \u001b[0mspace\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\hyperopt\\fmin.py\u001b[0m in \u001b[0;36mfmin\u001b[1;34m(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[0;32m    551\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    552\u001b[0m     \u001b[1;31m# next line is where the fmin is actually executed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 553\u001b[1;33m     \u001b[0mrval\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexhaust\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    554\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    555\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mreturn_argmin\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\hyperopt\\fmin.py\u001b[0m in \u001b[0;36mexhaust\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    354\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mexhaust\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    355\u001b[0m         \u001b[0mn_done\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 356\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_evals\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mn_done\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblock_until_done\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masynchronous\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    357\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrefresh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    358\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\hyperopt\\fmin.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, N, block_until_done)\u001b[0m\n\u001b[0;32m    290\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    291\u001b[0m                     \u001b[1;31m# -- loop over trials and do the jobs directly\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 292\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mserial_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    293\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    294\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrefresh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\hyperopt\\fmin.py\u001b[0m in \u001b[0;36mserial_evaluate\u001b[1;34m(self, N)\u001b[0m\n\u001b[0;32m    168\u001b[0m                 \u001b[0mctrl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCtrl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcurrent_trial\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrial\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    169\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 170\u001b[1;33m                     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdomain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mspec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mctrl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    171\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    172\u001b[0m                     \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"job exception: %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mZ:\\ProgramData\\Anaconda3\\lib\\site-packages\\hyperopt\\base.py\u001b[0m in \u001b[0;36mevaluate\u001b[1;34m(self, config, ctrl, attach_attachments)\u001b[0m\n\u001b[0;32m    905\u001b[0m                 \u001b[0mprint_node_on_error\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrec_eval_print_node_on_error\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    906\u001b[0m             )\n\u001b[1;32m--> 907\u001b[1;33m             \u001b[0mrval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpyll_rval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    908\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    909\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrval\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-25-8417d1566e3a>\u001b[0m in \u001b[0;36mhyperparameters\u001b[1;34m(params)\u001b[0m\n\u001b[0;32m     44\u001b[0m         \u001b[0mbest_param\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'AUC:{best_score:.4f}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"PARAMS: \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparamas\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     47\u001b[0m     \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'loss'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0macc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'status'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mSTATUS_OK\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'model'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'paramas' is not defined"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "print (f\"Job Started at: {init_time}\")\n",
    "trials = Trials()\n",
    "best = fmin(fn=hyperparameters,\n",
    "            space=params,\n",
    "            algo=tpe.suggest,\n",
    "            max_evals=100,\n",
    "            trials=trials)\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print (f\"Best Score: {best_score}\")\n",
    "print (f\"Best Params: {best_param}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = trials.best_trial['result']['loss']\n",
    "print ('loss :', loss)\n",
    "model = trials.best_trial['result']['model']\n",
    "print (model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### skopt Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = dict()\n",
    "params['epochs'] = [1000]\n",
    "params['validation_data']=[(X_test, y_test)],\n",
    "params['callbacks']= early_stop,\n",
    "params['batch_size'] = [16, 32, 64, 128, 256, 512]\n",
    "#params['neurons']= [16, 32, 64, 128, 256, 512]\n",
    "params['optimizer'] = [SGD(), RMSprop(), Adagrad(), Adam(), Adamax(), Nadam()]\n",
    "params['activation']= [ReLU(), PReLU(), LeakyReLU(), ELU()]\n",
    "params['learning_rate'] = (1e-7, 1.0)\n",
    "params['init_mode']= \n",
    "['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform']\n",
    "params['l1'] = (0, 0.1)\n",
    "params['l2'] = (0, 0.1)\n",
    "#params['dropout_rate']= [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],\n",
    "#params['weight_constraint'] = [1, 2, 3, 4, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Function to create model, required for KerasClassifier\n",
    "#dropout_rate=0.0, weight_constraint=0\n",
    "#kernel_constraint=max_norm(weight_constraint)\n",
    "#learn_rate=0.01\n",
    "def create_model(neurons=num_nodes, activation=ReLU(), optimizer=Adam(), learning_rate=0.01, init_mode='uniform', \n",
    "                 l1=0.0, l2=0.0):\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    # Input Layer\n",
    "    model.add(Input(shape=(n_input,)))\n",
    "    #Hidden Layers  \n",
    "    model.add(Dense(neurons, kernel_initializer=init_mode, activation=activation, \n",
    "                    kernel_regularizer=L1L2(l1=l1, l2=l2)))\n",
    "    #model.add(Dropout(0.0))\n",
    "    model.add(BatchNormalization())\n",
    "    # Hidden Layers\n",
    "    model.add(Dense((neurons+1)//2, kernel_initializer=init_mode, activation=activation))\n",
    "    #model.add(Dropout(0.0))\n",
    "    model.add(BatchNormalization())\n",
    "    # Output Layer\n",
    "    model.add(Dense(1, kernel_initializer=init_mode, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    optimizer.learning_rate=learning_rate;\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "mlp_model = KerasClassifier(build_fn=create_model, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "search = BayesSearchCV(estimator=mlp_model, search_spaces=params, n_jobs=-1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# callback handler\n",
    "def on_step(optim_result):\n",
    "    score = search.best_score_\n",
    "    print (f\"{datetime.now()}: Best Score- {score:.4f}\")\n",
    "    print (f\"Best Params- {search.best_params_}\")\n",
    "    if score >= 0.98:\n",
    "        print('Interrupting!')\n",
    "        return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Started at: 2020-11-27 19:06:43.432621\n",
      "2020-11-28 00:40:11.624274: Best Score- 0.7786\n",
      "2020-11-28 03:12:48.363644: Best Score- 0.8042\n",
      "2020-11-28 11:02:11.661192: Best Score- 0.8042\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Dimensions must be equal, but are 64 and 32 for '{{node dense_10/p_re_lu_4/mul}} = Mul[T=DT_FLOAT](dense_10/p_re_lu_4/Neg, dense_10/p_re_lu_4/Relu_1)' with input shapes: [64], [?,32].",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31m_RemoteTraceback\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 1812, in _create_c_op\n    c_op = pywrap_tf_session.TF_FinishOperation(op_desc)\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Dimensions must be equal, but are 64 and 32 for '{{node dense_10/p_re_lu_4/mul/mul}} = Mul[T=DT_FLOAT](dense_10/p_re_lu_4/Neg, dense_10/p_re_lu_4/Relu_1)' with input shapes: [64], [?,32].\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py\", line 1136, in binary_op_wrapper\n    out = r_op(x)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py\", line 1156, in r_binary_op_wrapper\n    return func(x, y, name=name)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py\", line 1457, in _mul_dispatch\n    return multiply(x, y, name=name)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py\", line 201, in wrapper\n    return target(*args, **kwargs)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py\", line 509, in multiply\n    return gen_math_ops.mul(x, y, name)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/gen_math_ops.py\", line 6175, in mul\n    _, _, _op, _outputs = _op_def_library._apply_op_helper(\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py\", line 742, in _apply_op_helper\n    op = g._create_op_internal(op_type_name, inputs, dtypes=None,\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py\", line 591, in _create_op_internal\n    return super(FuncGraph, self)._create_op_internal(  # pylint: disable=protected-access\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 3477, in _create_op_internal\n    ret = Operation(\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 1974, in __init__\n    self._c_op = _create_c_op(self._graph, node_def, inputs,\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 1815, in _create_c_op\n    raise ValueError(str(e))\nValueError: Dimensions must be equal, but are 64 and 32 for '{{node dense_10/p_re_lu_4/mul/mul}} = Mul[T=DT_FLOAT](dense_10/p_re_lu_4/Neg, dense_10/p_re_lu_4/Relu_1)' with input shapes: [64], [?,32].\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py\", line 431, in _process_worker\n    r = call_item()\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py\", line 285, in __call__\n    return self.fn(*self.args, **self.kwargs)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py\", line 595, in __call__\n    return self.func(*args, **kwargs)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\", line 252, in __call__\n    return [func(*args, **kwargs)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\", line 252, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/wrappers/scikit_learn.py\", line 223, in fit\n    return super(KerasClassifier, self).fit(x, y, **kwargs)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/wrappers/scikit_learn.py\", line 157, in fit\n    self.model = self.build_fn(**self.filter_sk_params(self.build_fn))\n  File \"<ipython-input-52-f9d230b647aa>\", line 17, in create_model\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/training/tracking/base.py\", line 457, in _method_wrapper\n    result = method(self, *args, **kwargs)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py\", line 221, in add\n    output_tensor = layer(self.outputs[0])\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 925, in __call__\n    return self._functional_construction_call(inputs, args, kwargs,\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 1117, in _functional_construction_call\n    outputs = call_fn(cast_inputs, *args, **kwargs)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/layers/core.py\", line 1193, in call\n    return core_ops.dense(\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/layers/ops/core.py\", line 67, in dense\n    outputs = activation(outputs)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 925, in __call__\n    return self._functional_construction_call(inputs, args, kwargs,\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 1117, in _functional_construction_call\n    outputs = call_fn(cast_inputs, *args, **kwargs)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/layers/advanced_activations.py\", line 161, in call\n    neg = -self.alpha * K.relu(-inputs)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py\", line 1141, in binary_op_wrapper\n    raise e\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py\", line 1125, in binary_op_wrapper\n    return func(x, y, name=name)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py\", line 1457, in _mul_dispatch\n    return multiply(x, y, name=name)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py\", line 201, in wrapper\n    return target(*args, **kwargs)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py\", line 509, in multiply\n    return gen_math_ops.mul(x, y, name)\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/gen_math_ops.py\", line 6175, in mul\n    _, _, _op, _outputs = _op_def_library._apply_op_helper(\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py\", line 742, in _apply_op_helper\n    op = g._create_op_internal(op_type_name, inputs, dtypes=None,\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py\", line 591, in _create_op_internal\n    return super(FuncGraph, self)._create_op_internal(  # pylint: disable=protected-access\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 3477, in _create_op_internal\n    ret = Operation(\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 1974, in __init__\n    self._c_op = _create_c_op(self._graph, node_def, inputs,\n  File \"/home/mhossain/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 1815, in _create_c_op\n    raise ValueError(str(e))\nValueError: Dimensions must be equal, but are 64 and 32 for '{{node dense_10/p_re_lu_4/mul}} = Mul[T=DT_FLOAT](dense_10/p_re_lu_4/Neg, dense_10/p_re_lu_4/Relu_1)' with input shapes: [64], [?,32].\n\"\"\"",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-59-a73a7bcb3ec8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34mf\"Job Started at: {init_time}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# perform the search\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0msearch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mon_step\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#callback=on_step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mfin_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Execution time : \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfin_time\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0minit_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/skopt/searchcv.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, callback)\u001b[0m\n\u001b[1;32m    690\u001b[0m                 \u001b[0mn_points_adjusted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_points\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 692\u001b[0;31m                 optim_result = self._step(\n\u001b[0m\u001b[1;32m    693\u001b[0m                     \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msearch_space\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m                     \u001b[0mgroups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_points\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_points_adjusted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/skopt/searchcv.py\u001b[0m in \u001b[0;36m_step\u001b[0;34m(self, X, y, search_space, optimizer, groups, n_points)\u001b[0m\n\u001b[1;32m    577\u001b[0m         \u001b[0mrefit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrefit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrefit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 579\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    580\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrefit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrefit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/skopt/searchcv.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, groups, parameter_iterable)\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m         \u001b[0mcv_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 410\u001b[0;31m         out = Parallel(\n\u001b[0m\u001b[1;32m    411\u001b[0m             \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    412\u001b[0m             \u001b[0mpre_dispatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1042\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1043\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1044\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    919\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    920\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 921\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    922\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    437\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    440\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    386\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Dimensions must be equal, but are 64 and 32 for '{{node dense_10/p_re_lu_4/mul}} = Mul[T=DT_FLOAT](dense_10/p_re_lu_4/Neg, dense_10/p_re_lu_4/Relu_1)' with input shapes: [64], [?,32]."
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "print (f\"Job Started at: {init_time}\")\n",
    "# perform the search\n",
    "search.fit(X_train, y_train, callback=on_step) \n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# report the best result\n",
    "print(search.best_score_)\n",
    "print(search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keras Tuner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'kerastuner'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-51aa5bbcdf6b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkerastuner\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHyperParameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkerastuner\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHyperModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkerastuner\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBayesianOptimization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'kerastuner'"
     ]
    }
   ],
   "source": [
    "from kerastuner import HyperParameters\n",
    "from kerastuner import HyperModel\n",
    "from kerastuner import BayesianOptimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_mode = ['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', \\\n",
    "             'glorot_uniform', 'he_normal', 'he_uniform']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(hp):\n",
    "    model = Sequential()\n",
    "    #model.add(Input(shape=(n_input,)))\n",
    "    model.add(Dense(units=hp.Int('units',\n",
    "                                 min_value=80,\n",
    "                                 max_value=256,\n",
    "                                 step=16),\n",
    "                    input_dim=n_input,\n",
    "                    kernel_initializer=hp.Choice('init_mode', init_mode),\n",
    "                    activation='relu'\n",
    "                    #activation=hp.Choice('activation', ['relu', 'tanh'])\n",
    "                    )\n",
    "             )\n",
    "    for i in range(hp.Int('num_layers', 2, 10)):\n",
    "        model.add(Dense(units=hp.Int('units_' + str(i),\n",
    "                                     min_value=80,\n",
    "                                     max_value=256,\n",
    "                                     step=16),\n",
    "                        kernel_initializer=hp.Choice('init_mode_' + str(i), init_mode),\n",
    "                        activation='relu'\n",
    "                        #activation=hp.Choice('activation', ['relu', 'tanh'])\n",
    "                       )\n",
    "                 )\n",
    "        model.add(Dropout(hp.Float('dropout_' + str(i),\n",
    "                                   min_value=0.0,\n",
    "                                   max_value=1.0,\n",
    "                                   step=0.1\n",
    "                                  )\n",
    "                         )\n",
    "                 )\n",
    "        #model.add(BatchNormalization())\n",
    "    #[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
    "                \n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    \n",
    "    model.compile(\n",
    "        optimizer=Adam(\n",
    "            hp.Choice('learning_rate',\n",
    "                      values=[1e-2, 1e-3, 1e-4])),\n",
    "        loss='binary_crossentropy',\n",
    "        metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'BayesianOptimization' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-521e8ca9ef57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m tuner_bo = BayesianOptimization(\n\u001b[0m\u001b[1;32m      2\u001b[0m             \u001b[0mbuild_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m             \u001b[0mobjective\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m             \u001b[0mmax_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m             \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'BayesianOptimization' is not defined"
     ]
    }
   ],
   "source": [
    "tuner_bo = BayesianOptimization(\n",
    "            build_model,\n",
    "            objective='accuracy',\n",
    "            max_trials=10,\n",
    "            seed=42,\n",
    "            executions_per_trial=2\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    }
   ],
   "source": [
    "tuner_bo.search(X_train, y_train, validation_data=(X_valid, y_valid), \n",
    "                epochs=10000, batch_size=32, verbose=1,\n",
    "                callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = tuner_bo.get_best_models(num_models=1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3706/3706 [==============================] - 4s 1ms/step - loss: 0.4945 - accuracy: 0.8042\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.4944514036178589, 0.8042440414428711]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "6052/6052 [==============================] - 15s 2ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 2/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 3/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 4/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 5/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 6/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 7/1000\n",
      "6052/6052 [==============================] - 16s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 8/1000\n",
      "6052/6052 [==============================] - 16s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 9/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 10/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 11/1000\n",
      "6052/6052 [==============================] - 16s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 12/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 13/1000\n",
      "6052/6052 [==============================] - 19s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 14/1000\n",
      "6052/6052 [==============================] - 18s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 15/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 16/1000\n",
      "6052/6052 [==============================] - 16s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 17/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 18/1000\n",
      "6052/6052 [==============================] - 16s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 19/1000\n",
      "6052/6052 [==============================] - 16s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 20/1000\n",
      "6052/6052 [==============================] - 16s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 21/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 22/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 23/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 24/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 25/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 26/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 27/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 28/1000\n",
      "6052/6052 [==============================] - 18s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 29/1000\n",
      "6052/6052 [==============================] - 18s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 30/1000\n",
      "6052/6052 [==============================] - 19s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 31/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 32/1000\n",
      "6052/6052 [==============================] - 19s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 33/1000\n",
      "6052/6052 [==============================] - 20s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 34/1000\n",
      "6052/6052 [==============================] - 21s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 35/1000\n",
      "6052/6052 [==============================] - 19s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 36/1000\n",
      "6052/6052 [==============================] - 18s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 37/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 38/1000\n",
      "6052/6052 [==============================] - 19s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 39/1000\n",
      "6052/6052 [==============================] - 18s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 40/1000\n",
      "6052/6052 [==============================] - 17s 3ms/step - loss: 0.4947 - accuracy: 0.8041 - val_loss: 0.4966 - val_accuracy: 0.8027\n",
      "Epoch 00040: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fa551010700>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.fit(x=X_train, \n",
    "               y=y_train, \n",
    "               epochs=1000,\n",
    "               batch_size=32,\n",
    "               validation_data=(X_valid, y_valid), verbose=1,\n",
    "               callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-33-10e79d90968e>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
      "Instructions for updating:\n",
      "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
     ]
    }
   ],
   "source": [
    "predictions = best_model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      1.00      0.89     95356\n",
      "           1       0.00      0.00      0.00     23210\n",
      "\n",
      "    accuracy                           0.80    118566\n",
      "   macro avg       0.40      0.50      0.45    118566\n",
      "weighted avg       0.65      0.80      0.72    118566\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mhossain/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multilayer Perceotron (MLP) Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Common Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def evaluate_model(X_train, y_train, X_valid, y_valid, model, optimizer):\n",
    "    # evaluate the model\n",
    "    _, train_acc = model.evaluate(X_train, y_train, verbose=0)\n",
    "    _, test_acc = model.evaluate(X_valid, y_valid, verbose=0)\n",
    "    \n",
    "    print(f\"Optimizer: {optimizer} Training Loss: {train_acc:.3f} Validation Accuracy:{test_acc:.3f}\")\n",
    "    \n",
    "# evaluate the Regression model\n",
    "#train_mse = model.evaluate(trainX, trainy, verbose=0)\n",
    "#test_mse = model.evaluate(testX, testy, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def plot_loss(history, optimizer):\n",
    "    # plot loss during training\n",
    "    pyplot.figure(figsize=(15,10))\n",
    "    pyplot.subplot(211) \n",
    "    pyplot.title(f\"Binary Cross-Entropy Loss of optimizer {optimizer}\" , pad=-20)\n",
    "    pyplot.plot(history.history['loss'], label='train')\n",
    "    pyplot.plot(history.history['val_loss'], label='test')\n",
    "    pyplot.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def plot_accuracy(history, optimizer):\n",
    "    # plot accuracy during training\n",
    "    pyplot.figure(figsize=(15,10))\n",
    "    pyplot.subplot(212)\n",
    "    pyplot.title(f\"Classification Accuracy of optimizer {optimizer}\" , pad=-20)\n",
    "    pyplot.plot(history.history['accuracy'], label='train')\n",
    "    pyplot.plot(history.history['val_accuracy'], label='test')\n",
    "    pyplot.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "callbacks_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# checkpoint\n",
    "#filepath=\"weights-improvement-{epoch:02d}-{val_accuracy:.2f}.hdf5\"\n",
    "filepath=\"weights.best.hdf5\" # Checkpoint Best Neural Network Model Only\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')\n",
    "callbacks_list.append(checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=25)\n",
    "callbacks_list.append(early_stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# create learning curves for different optimizers\n",
    "optimizers = ['sgd', 'rmsprop', 'adagrad', 'adam']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Experiment with Dropout + kernel_constraint(max_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def create_experiment_model(hidden_layer=3, optimizer='adam'):\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    # Input Layer\n",
    "    model.add(Dropout(0.2, input_shape=(n_input,)))\n",
    "    \n",
    "    # grid search values\n",
    "    #values = [1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6]\n",
    "    #model.add(Dense(500, input_dim=2, activation='relu', kernel_initializer='he_uniform', kernel_regularizer=l2(0.001)))\n",
    "    \n",
    "    # Hidden Layers\n",
    "    if hidden_layer==1:\n",
    "        model.add(Dense(82, activation='relu', kernel_constraint=max_norm(3)))\n",
    "        model.add(Dropout(0.2))\n",
    "        \n",
    "    if hidden_layer==2:\n",
    "        model.add(Dense(82, activation='relu', kernel_constraint=max_norm(3)))\n",
    "        model.add(Dropout(0.2))\n",
    "        \n",
    "        model.add(Dense(41, activation='relu', kernel_constraint=max_norm(3)))\n",
    "        model.add(Dropout(0.2))\n",
    "    \n",
    "    if hidden_layer==3:\n",
    "        model.add(Dense(82, activation='relu', kernel_constraint=max_norm(3)))\n",
    "        model.add(Dropout(0.2))\n",
    "        \n",
    "        model.add(Dense(41, activation='relu', kernel_constraint=max_norm(3)))\n",
    "        model.add(Dropout(0.2))\n",
    "        \n",
    "        model.add(Dense(21, activation='relu', kernel_constraint=max_norm(3)))\n",
    "        model.add(Dropout(0.2))\n",
    "    \n",
    "    # Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))   \n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Experiment with adam + 3 hidden layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 49.7820 - accuracy: 0.7846\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.80109, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 49.7361 - accuracy: 0.7846 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 2/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.5144 - accuracy: 0.8041\n",
      "Epoch 00002: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.5144 - accuracy: 0.8041 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 3/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.4967 - accuracy: 0.8042\n",
      "Epoch 00003: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.4967 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 4/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.4968 - accuracy: 0.8042\n",
      "Epoch 00004: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.4968 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 5/600\n",
      "7878/7905 [============================>.] - ETA: 0s - loss: 0.4955 - accuracy: 0.8042\n",
      "Epoch 00005: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 12s 1ms/step - loss: 0.4956 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 6/600\n",
      "7891/7905 [============================>.] - ETA: 0s - loss: 0.5167 - accuracy: 0.8042\n",
      "Epoch 00006: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.5166 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 7/600\n",
      "7872/7905 [============================>.] - ETA: 0s - loss: 0.5054 - accuracy: 0.8042\n",
      "Epoch 00007: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.5054 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 8/600\n",
      "7901/7905 [============================>.] - ETA: 0s - loss: 0.4999 - accuracy: 0.8042\n",
      "Epoch 00008: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4999 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 9/600\n",
      "7879/7905 [============================>.] - ETA: 0s - loss: 0.5064 - accuracy: 0.8041\n",
      "Epoch 00009: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.5063 - accuracy: 0.8042 - val_loss: 0.4991 - val_accuracy: 0.8011\n",
      "Epoch 10/600\n",
      "7877/7905 [============================>.] - ETA: 0s - loss: 0.4953 - accuracy: 0.8042\n",
      "Epoch 00010: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.4953 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 11/600\n",
      "7901/7905 [============================>.] - ETA: 0s - loss: 0.4945 - accuracy: 0.8042\n",
      "Epoch 00011: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.4946 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 12/600\n",
      "7866/7905 [============================>.] - ETA: 0s - loss: 0.4945 - accuracy: 0.8042\n",
      "Epoch 00012: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 13/600\n",
      "7871/7905 [============================>.] - ETA: 0s - loss: 0.4955 - accuracy: 0.8042\n",
      "Epoch 00013: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4954 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 14/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.4952 - accuracy: 0.8042\n",
      "Epoch 00014: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4952 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 15/600\n",
      "7893/7905 [============================>.] - ETA: 0s - loss: 0.4947 - accuracy: 0.8042\n",
      "Epoch 00015: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4947 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 16/600\n",
      "7863/7905 [============================>.] - ETA: 0s - loss: 0.4958 - accuracy: 0.8042\n",
      "Epoch 00016: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4959 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 17/600\n",
      "7869/7905 [============================>.] - ETA: 0s - loss: 0.5004 - accuracy: 0.8042\n",
      "Epoch 00017: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.5004 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 18/600\n",
      "7867/7905 [============================>.] - ETA: 0s - loss: 0.4947 - accuracy: 0.8042\n",
      "Epoch 00018: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4947 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 19/600\n",
      "7893/7905 [============================>.] - ETA: 0s - loss: 0.4947 - accuracy: 0.8042\n",
      "Epoch 00019: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4947 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 20/600\n",
      "7873/7905 [============================>.] - ETA: 0s - loss: 0.4946 - accuracy: 0.8042\n",
      "Epoch 00020: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.4946 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 21/600\n",
      "7904/7905 [============================>.] - ETA: 0s - loss: 0.4951 - accuracy: 0.8042\n",
      "Epoch 00021: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4951 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 22/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.4956 - accuracy: 0.8042\n",
      "Epoch 00022: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4956 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 23/600\n",
      "7884/7905 [============================>.] - ETA: 0s - loss: 0.4949 - accuracy: 0.8042\n",
      "Epoch 00023: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4950 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 24/600\n",
      "7888/7905 [============================>.] - ETA: 0s - loss: 0.4947 - accuracy: 0.8042\n",
      "Epoch 00024: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4947 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 25/600\n",
      "7866/7905 [============================>.] - ETA: 0s - loss: 0.4945 - accuracy: 0.8042\n",
      "Epoch 00025: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 26/600\n",
      "7871/7905 [============================>.] - ETA: 0s - loss: 0.4945 - accuracy: 0.8042\n",
      "Epoch 00026: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 27/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.4974 - accuracy: 0.8042\n",
      "Epoch 00027: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4974 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 28/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.4946 - accuracy: 0.8042\n",
      "Epoch 00028: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4946 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 29/600\n",
      "7890/7905 [============================>.] - ETA: 0s - loss: 0.4946 - accuracy: 0.8042\n",
      "Epoch 00029: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 30/600\n",
      "7888/7905 [============================>.] - ETA: 0s - loss: 0.4946 - accuracy: 0.8042\n",
      "Epoch 00030: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 31/600\n",
      "7863/7905 [============================>.] - ETA: 0s - loss: 0.4947 - accuracy: 0.8042\n",
      "Epoch 00031: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4947 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 32/600\n",
      "7883/7905 [============================>.] - ETA: 0s - loss: 0.4947 - accuracy: 0.8041\n",
      "Epoch 00032: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4946 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 33/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.4946 - accuracy: 0.8042\n",
      "Epoch 00033: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4946 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 34/600\n",
      "7887/7905 [============================>.] - ETA: 0s - loss: 0.4947 - accuracy: 0.8041\n",
      "Epoch 00034: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4946 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 35/600\n",
      "7863/7905 [============================>.] - ETA: 0s - loss: 0.4954 - accuracy: 0.8042\n",
      "Epoch 00035: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4954 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 36/600\n",
      "7890/7905 [============================>.] - ETA: 0s - loss: 0.5001 - accuracy: 0.8042\n",
      "Epoch 00036: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.5001 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 37/600\n",
      "7872/7905 [============================>.] - ETA: 0s - loss: 0.4947 - accuracy: 0.8042\n",
      "Epoch 00037: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.4947 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 38/600\n",
      "7882/7905 [============================>.] - ETA: 0s - loss: 0.4946 - accuracy: 0.8042\n",
      "Epoch 00038: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 39/600\n",
      "7874/7905 [============================>.] - ETA: 0s - loss: 0.4948 - accuracy: 0.8042\n",
      "Epoch 00039: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4948 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 40/600\n",
      "7869/7905 [============================>.] - ETA: 0s - loss: 0.4951 - accuracy: 0.8042\n",
      "Epoch 00040: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4951 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 41/600\n",
      "7885/7905 [============================>.] - ETA: 0s - loss: 0.4945 - accuracy: 0.8043 ETA: 0s - loss: 0.4948 - accuracy: 0.\n",
      "Epoch 00041: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.4946 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 42/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.5016 - accuracy: 0.8042\n",
      "Epoch 00042: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.5015 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 43/600\n",
      "7876/7905 [============================>.] - ETA: 0s - loss: 0.4945 - accuracy: 0.8042\n",
      "Epoch 00043: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 44/600\n",
      "7875/7905 [============================>.] - ETA: 0s - loss: 0.4945 - accuracy: 0.8042\n",
      "Epoch 00044: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 45/600\n",
      "7900/7905 [============================>.] - ETA: 0s - loss: 0.4946 - accuracy: 0.8042\n",
      "Epoch 00045: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.4946 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 46/600\n",
      "7881/7905 [============================>.] - ETA: 0s - loss: 0.4946 - accuracy: 0.8042\n",
      "Epoch 00046: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 47/600\n",
      "7883/7905 [============================>.] - ETA: 0s - loss: 0.4946 - accuracy: 0.8042 ETA: 0s\n",
      "Epoch 00047: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 48/600\n",
      "7883/7905 [============================>.] - ETA: 0s - loss: 0.4946 - accuracy: 0.8042\n",
      "Epoch 00048: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 49/600\n",
      "7902/7905 [============================>.] - ETA: 0s - loss: 0.4945 - accuracy: 0.8042\n",
      "Epoch 00049: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 50/600\n",
      "7901/7905 [============================>.] - ETA: 0s - loss: 0.4946 - accuracy: 0.8042\n",
      "Epoch 00050: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4947 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 51/600\n",
      "7881/7905 [============================>.] - ETA: 0s - loss: 0.4945 - accuracy: 0.8042\n",
      "Epoch 00051: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 52/600\n",
      "7905/7905 [==============================] - ETA: 0s - loss: 0.4962 - accuracy: 0.8042\n",
      "Epoch 00052: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4962 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 53/600\n",
      "7874/7905 [============================>.] - ETA: 0s - loss: 0.4946 - accuracy: 0.8042\n",
      "Epoch 00053: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.4945 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 54/600\n",
      "7882/7905 [============================>.] - ETA: 0s - loss: 0.4947 - accuracy: 0.8042\n",
      "Epoch 00054: val_accuracy did not improve from 0.80109\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.4947 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 00054: early stopping\n",
      "Execution time :  0:09:15.354543\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "model = create_experiment_model()\n",
    "history = model.fit(x=X_train, \n",
    "                    y=y_train, \n",
    "                    epochs=600,\n",
    "                    batch_size=32,\n",
    "                    validation_data=(X_valid, y_valid), verbose=1,\n",
    "                    callbacks=callbacks_list\n",
    "                   )\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer: adam Train: 0.804 Test:0.801\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(X_train, y_train, X_valid, y_valid, model, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_accuracy(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Experiment with Stabilize Learning with Data Scaling (Normalization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "norm_scaler = MinMaxScaler()\n",
    "norm_scaler.fit(X_train)\n",
    "\n",
    "X_train_norm = norm_scaler.transform(X_train)\n",
    "X_test_norm = norm_scaler.transform(X_test)\n",
    "X_valid_norm = norm_scaler.transform(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "7878/7905 [============================>.] - ETA: 0s - loss: 0.3310 - accuracy: 0.8693\n",
      "Epoch 00001: val_accuracy improved from 0.80109 to 0.88693, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 8s 955us/step - loss: 0.3309 - accuracy: 0.8693 - val_loss: 0.2777 - val_accuracy: 0.8869\n",
      "Epoch 2/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.3208 - accuracy: 0.8716\n",
      "Epoch 00002: val_accuracy did not improve from 0.88693\n",
      "7905/7905 [==============================] - 7s 908us/step - loss: 0.3208 - accuracy: 0.8716 - val_loss: 0.2769 - val_accuracy: 0.8869\n",
      "Epoch 3/600\n",
      "7877/7905 [============================>.] - ETA: 0s - loss: 0.3198 - accuracy: 0.8717\n",
      "Epoch 00003: val_accuracy did not improve from 0.88693\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3199 - accuracy: 0.8717 - val_loss: 0.2795 - val_accuracy: 0.8869\n",
      "Epoch 4/600\n",
      "7888/7905 [============================>.] - ETA: 0s - loss: 0.3200 - accuracy: 0.8712\n",
      "Epoch 00004: val_accuracy did not improve from 0.88693\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3200 - accuracy: 0.8712 - val_loss: 0.2780 - val_accuracy: 0.8869\n",
      "Epoch 5/600\n",
      "7901/7905 [============================>.] - ETA: 0s - loss: 0.3206 - accuracy: 0.8713\n",
      "Epoch 00005: val_accuracy did not improve from 0.88693\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3206 - accuracy: 0.8713 - val_loss: 0.2787 - val_accuracy: 0.8869\n",
      "Epoch 6/600\n",
      "7871/7905 [============================>.] - ETA: 0s - loss: 0.3204 - accuracy: 0.8709\n",
      "Epoch 00006: val_accuracy did not improve from 0.88693\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3204 - accuracy: 0.8709 - val_loss: 0.2790 - val_accuracy: 0.8869\n",
      "Epoch 7/600\n",
      "7864/7905 [============================>.] - ETA: 0s - loss: 0.3200 - accuracy: 0.8712\n",
      "Epoch 00007: val_accuracy did not improve from 0.88693\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3201 - accuracy: 0.8712 - val_loss: 0.2793 - val_accuracy: 0.8869\n",
      "Epoch 8/600\n",
      "7900/7905 [============================>.] - ETA: 0s - loss: 0.3197 - accuracy: 0.8712\n",
      "Epoch 00008: val_accuracy did not improve from 0.88693\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3198 - accuracy: 0.8712 - val_loss: 0.2790 - val_accuracy: 0.8869\n",
      "Epoch 9/600\n",
      "7867/7905 [============================>.] - ETA: 0s - loss: 0.3193 - accuracy: 0.8715\n",
      "Epoch 00009: val_accuracy did not improve from 0.88693\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3193 - accuracy: 0.8715 - val_loss: 0.2782 - val_accuracy: 0.8869\n",
      "Epoch 10/600\n",
      "7871/7905 [============================>.] - ETA: 0s - loss: 0.3194 - accuracy: 0.8714\n",
      "Epoch 00010: val_accuracy improved from 0.88693 to 0.88698, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3195 - accuracy: 0.8713 - val_loss: 0.2795 - val_accuracy: 0.8870\n",
      "Epoch 11/600\n",
      "7861/7905 [============================>.] - ETA: 0s - loss: 0.3194 - accuracy: 0.8716\n",
      "Epoch 00011: val_accuracy did not improve from 0.88698\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3193 - accuracy: 0.8716 - val_loss: 0.2766 - val_accuracy: 0.8869\n",
      "Epoch 12/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.3201 - accuracy: 0.8707\n",
      "Epoch 00012: val_accuracy improved from 0.88698 to 0.88702, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3201 - accuracy: 0.8706 - val_loss: 0.2805 - val_accuracy: 0.8870\n",
      "Epoch 13/600\n",
      "7868/7905 [============================>.] - ETA: 0s - loss: 0.3189 - accuracy: 0.8716\n",
      "Epoch 00013: val_accuracy did not improve from 0.88702\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3189 - accuracy: 0.8716 - val_loss: 0.2775 - val_accuracy: 0.8870\n",
      "Epoch 14/600\n",
      "7888/7905 [============================>.] - ETA: 0s - loss: 0.3197 - accuracy: 0.8710\n",
      "Epoch 00014: val_accuracy did not improve from 0.88702\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3197 - accuracy: 0.8711 - val_loss: 0.2784 - val_accuracy: 0.8869\n",
      "Epoch 15/600\n",
      "7878/7905 [============================>.] - ETA: 0s - loss: 0.3203 - accuracy: 0.8709\n",
      "Epoch 00015: val_accuracy did not improve from 0.88702\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3203 - accuracy: 0.8709 - val_loss: 0.2783 - val_accuracy: 0.8869\n",
      "Epoch 16/600\n",
      "7866/7905 [============================>.] - ETA: 0s - loss: 0.3198 - accuracy: 0.8708 ETA: 0s - loss: 0.3\n",
      "Epoch 00016: val_accuracy did not improve from 0.88702\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3196 - accuracy: 0.8709 - val_loss: 0.2769 - val_accuracy: 0.8869\n",
      "Epoch 17/600\n",
      "7892/7905 [============================>.] - ETA: 0s - loss: 0.3186 - accuracy: 0.8714\n",
      "Epoch 00017: val_accuracy did not improve from 0.88702\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3186 - accuracy: 0.8714 - val_loss: 0.2795 - val_accuracy: 0.8870\n",
      "Epoch 18/600\n",
      "7868/7905 [============================>.] - ETA: 0s - loss: 0.3200 - accuracy: 0.8711\n",
      "Epoch 00018: val_accuracy did not improve from 0.88702\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3200 - accuracy: 0.8711 - val_loss: 0.2786 - val_accuracy: 0.8869\n",
      "Epoch 19/600\n",
      "7867/7905 [============================>.] - ETA: 0s - loss: 0.3188 - accuracy: 0.8713\n",
      "Epoch 00019: val_accuracy did not improve from 0.88702\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3189 - accuracy: 0.8713 - val_loss: 0.2787 - val_accuracy: 0.8869\n",
      "Epoch 20/600\n",
      "7870/7905 [============================>.] - ETA: 0s - loss: 0.3200 - accuracy: 0.8709\n",
      "Epoch 00020: val_accuracy improved from 0.88702 to 0.88709, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3199 - accuracy: 0.8709 - val_loss: 0.2768 - val_accuracy: 0.8871\n",
      "Epoch 21/600\n",
      "7883/7905 [============================>.] - ETA: 0s - loss: 0.3191 - accuracy: 0.8709\n",
      "Epoch 00021: val_accuracy did not improve from 0.88709\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3190 - accuracy: 0.8709 - val_loss: 0.2776 - val_accuracy: 0.8869\n",
      "Epoch 22/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.3191 - accuracy: 0.8715\n",
      "Epoch 00022: val_accuracy did not improve from 0.88709\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3191 - accuracy: 0.8714 - val_loss: 0.2790 - val_accuracy: 0.8869\n",
      "Epoch 23/600\n",
      "7874/7905 [============================>.] - ETA: 0s - loss: 0.3189 - accuracy: 0.8711\n",
      "Epoch 00023: val_accuracy did not improve from 0.88709\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3190 - accuracy: 0.8711 - val_loss: 0.2775 - val_accuracy: 0.8869\n",
      "Epoch 24/600\n",
      "7904/7905 [============================>.] - ETA: 0s - loss: 0.3187 - accuracy: 0.8716\n",
      "Epoch 00024: val_accuracy did not improve from 0.88709\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3187 - accuracy: 0.8716 - val_loss: 0.2750 - val_accuracy: 0.8869\n",
      "Epoch 25/600\n",
      "7876/7905 [============================>.] - ETA: 0s - loss: 0.3189 - accuracy: 0.8713\n",
      "Epoch 00025: val_accuracy did not improve from 0.88709\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3188 - accuracy: 0.8713 - val_loss: 0.2785 - val_accuracy: 0.8870\n",
      "Epoch 26/600\n",
      "7884/7905 [============================>.] - ETA: 0s - loss: 0.3191 - accuracy: 0.8712\n",
      "Epoch 00026: val_accuracy did not improve from 0.88709\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3191 - accuracy: 0.8712 - val_loss: 0.2772 - val_accuracy: 0.8869\n",
      "Epoch 27/600\n",
      "7886/7905 [============================>.] - ETA: 0s - loss: 0.3201 - accuracy: 0.8711\n",
      "Epoch 00027: val_accuracy improved from 0.88709 to 0.88720, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3201 - accuracy: 0.8710 - val_loss: 0.2775 - val_accuracy: 0.8872\n",
      "Epoch 28/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.3186 - accuracy: 0.8715\n",
      "Epoch 00028: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3187 - accuracy: 0.8714 - val_loss: 0.2781 - val_accuracy: 0.8871\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/600\n",
      "7861/7905 [============================>.] - ETA: 0s - loss: 0.3193 - accuracy: 0.8710\n",
      "Epoch 00029: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3193 - accuracy: 0.8710 - val_loss: 0.2787 - val_accuracy: 0.8869\n",
      "Epoch 30/600\n",
      "7871/7905 [============================>.] - ETA: 0s - loss: 0.3189 - accuracy: 0.8715\n",
      "Epoch 00030: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3188 - accuracy: 0.8714 - val_loss: 0.2780 - val_accuracy: 0.8870\n",
      "Epoch 31/600\n",
      "7891/7905 [============================>.] - ETA: 0s - loss: 0.3188 - accuracy: 0.8713\n",
      "Epoch 00031: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3188 - accuracy: 0.8713 - val_loss: 0.2772 - val_accuracy: 0.8869\n",
      "Epoch 32/600\n",
      "7867/7905 [============================>.] - ETA: 0s - loss: 0.3195 - accuracy: 0.8713\n",
      "Epoch 00032: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3196 - accuracy: 0.8712 - val_loss: 0.2791 - val_accuracy: 0.8869\n",
      "Epoch 33/600\n",
      "7887/7905 [============================>.] - ETA: 0s - loss: 0.3196 - accuracy: 0.8709\n",
      "Epoch 00033: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3197 - accuracy: 0.8709 - val_loss: 0.2776 - val_accuracy: 0.8869\n",
      "Epoch 34/600\n",
      "7864/7905 [============================>.] - ETA: 0s - loss: 0.3189 - accuracy: 0.8712\n",
      "Epoch 00034: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3189 - accuracy: 0.8712 - val_loss: 0.2793 - val_accuracy: 0.8869\n",
      "Epoch 35/600\n",
      "7860/7905 [============================>.] - ETA: 0s - loss: 0.3191 - accuracy: 0.8714\n",
      "Epoch 00035: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3190 - accuracy: 0.8714 - val_loss: 0.2770 - val_accuracy: 0.8870\n",
      "Epoch 36/600\n",
      "7881/7905 [============================>.] - ETA: 0s - loss: 0.3186 - accuracy: 0.8715\n",
      "Epoch 00036: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3186 - accuracy: 0.8715 - val_loss: 0.2770 - val_accuracy: 0.8869\n",
      "Epoch 37/600\n",
      "7868/7905 [============================>.] - ETA: 0s - loss: 0.3189 - accuracy: 0.8713\n",
      "Epoch 00037: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3187 - accuracy: 0.8714 - val_loss: 0.2760 - val_accuracy: 0.8870\n",
      "Epoch 38/600\n",
      "7873/7905 [============================>.] - ETA: 0s - loss: 0.3194 - accuracy: 0.8709\n",
      "Epoch 00038: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3194 - accuracy: 0.8708 - val_loss: 0.2765 - val_accuracy: 0.8870\n",
      "Epoch 39/600\n",
      "7889/7905 [============================>.] - ETA: 0s - loss: 0.3190 - accuracy: 0.8714\n",
      "Epoch 00039: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3191 - accuracy: 0.8713 - val_loss: 0.2796 - val_accuracy: 0.8870\n",
      "Epoch 40/600\n",
      "7863/7905 [============================>.] - ETA: 0s - loss: 0.3178 - accuracy: 0.8717\n",
      "Epoch 00040: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3179 - accuracy: 0.8717 - val_loss: 0.2783 - val_accuracy: 0.8869\n",
      "Epoch 41/600\n",
      "7877/7905 [============================>.] - ETA: 0s - loss: 0.3182 - accuracy: 0.8714 ETA: 0s - los\n",
      "Epoch 00041: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3181 - accuracy: 0.8715 - val_loss: 0.2772 - val_accuracy: 0.8869\n",
      "Epoch 42/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.3179 - accuracy: 0.8717\n",
      "Epoch 00042: val_accuracy did not improve from 0.88720\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3179 - accuracy: 0.8717 - val_loss: 0.2766 - val_accuracy: 0.8869\n",
      "Epoch 43/600\n",
      "7867/7905 [============================>.] - ETA: 0s - loss: 0.3177 - accuracy: 0.8718\n",
      "Epoch 00043: val_accuracy improved from 0.88720 to 0.88802, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3178 - accuracy: 0.8717 - val_loss: 0.2790 - val_accuracy: 0.8880\n",
      "Epoch 44/600\n",
      "7895/7905 [============================>.] - ETA: 0s - loss: 0.3182 - accuracy: 0.8716\n",
      "Epoch 00044: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3182 - accuracy: 0.8716 - val_loss: 0.2774 - val_accuracy: 0.8869\n",
      "Epoch 45/600\n",
      "7879/7905 [============================>.] - ETA: 0s - loss: 0.3194 - accuracy: 0.8708\n",
      "Epoch 00045: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3195 - accuracy: 0.8707 - val_loss: 0.2774 - val_accuracy: 0.8869\n",
      "Epoch 46/600\n",
      "7862/7905 [============================>.] - ETA: 0s - loss: 0.3190 - accuracy: 0.8713\n",
      "Epoch 00046: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3189 - accuracy: 0.8713 - val_loss: 0.2788 - val_accuracy: 0.8869\n",
      "Epoch 47/600\n",
      "7889/7905 [============================>.] - ETA: 0s - loss: 0.3192 - accuracy: 0.8714\n",
      "Epoch 00047: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3193 - accuracy: 0.8714 - val_loss: 0.2775 - val_accuracy: 0.8869\n",
      "Epoch 48/600\n",
      "7868/7905 [============================>.] - ETA: 0s - loss: 0.3186 - accuracy: 0.8712\n",
      "Epoch 00048: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3186 - accuracy: 0.8712 - val_loss: 0.2774 - val_accuracy: 0.8870\n",
      "Epoch 49/600\n",
      "7875/7905 [============================>.] - ETA: 0s - loss: 0.3188 - accuracy: 0.8712\n",
      "Epoch 00049: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3189 - accuracy: 0.8712 - val_loss: 0.2780 - val_accuracy: 0.8870\n",
      "Epoch 00049: early stopping\n",
      "Execution time :  0:07:37.137330\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "model = create_experiment_model()\n",
    "history = model.fit(x=X_train_norm, \n",
    "                    y=y_train, \n",
    "                    epochs=600,\n",
    "                    batch_size=32,\n",
    "                    validation_data=(X_valid_norm, y_valid), verbose=1,\n",
    "                    callbacks=callbacks_list\n",
    "                   )\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer: adam Train: 0.888 Test:0.887\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(X_train_norm, y_train, X_valid_norm, y_valid, model, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yCuGLr8CJFx/8vPLHw1/9Hk6+Dv7nLvjN/4LezqGrq4w4ifw5+2JgqpmVAzUEF2K5IW6cLcDFwBtmNg6YDmwgaCU8y8xygM5wnMqY6S4BVrt7dX9BeCGYRnePmNnxwNRwXiIiIuIOLTVQ+17wx8y170H9h5A1CgpKoWACFEwMHueX7i7LGnXgLQYicvhFI/DSt2DRfXD8RfDpn0H26EOfb3o2XPsgjJ0Br3wPGjfA/EeD7wM55uw39Ll7n5ndBrwIpAIPu/sKM7s1HP4AcBfwczNbRhD0bnf3eqDezJ4G3iW4sMt7wIMxs5/P3hdwOR/4npn1ARHgVndvPJSVFBGRONEItG6D5iporg7OIWnbEVztbXQZjCoL7nPGKCgMt9btQbCrDQNe7XvQHp7LnpIGY2dC2XnQ3RqEwa3vQ/uOveeTnhMGwZhQ2H/LnxC0DOSWQGr6kV0/kYPhDh4FSxnZ31FdzfD0X8O6l+Ajt8LH/hlSE2mTSZAZnPcNKJkOv/licIGX+Y/CxNOGbhkyIpgnQR/fiooKr6ys3P+IIkcLd4j0Qm8H9HUFXS76uoLnvV2AB0fo0rIhPWv3fXoOpGYc+A9cXw907oTOxvB+J3Q0DlzW1RTsSGbkhbfc8JYHmXHPdw0P7zPzID0X0jKDeqamj+wf40RFoxDpCW5mwes1lD/aB6O3KwhzzVvCUFcVBLz++5YaiPbtOU16LvS271mWkR+Ev9HHhfcxt8LJwfsyGUWj0NcZbMd9fU73GN4dvN8H+nxkxjxOyxr8M9HeAFvDYFcT3reGZ1RYCpScFJyv038bN3vg16CvB9q2QUvtnrfW2Mdb934PYJBTFATAvLGQF97v8Xwc5I+DzPxD38b9nxuPgKUG3z0pqeHj1GPj+yMRkb59vx8jPbu/b1Mz4h5n7lme6DaNRiHaG74+vcH7u/9x/2vW/7iva3d9Bv2MxH6GOvesfzQSvAei0fA+Enc/QHn/5SXSsoODFblFwX1OMeT232KflwT36dmH9aU6IA3r4bHroXE9XHEPVNy8/2kOxbblwfLad8An7w8u+pKMIr3Q0xZcAbWnPby17n7c3Rq8J/f5/o59n/fs/Z7vf3zrm0fdQTIzW+LuFQMOU+g7TDYshHceGu5aHLo9fnz771MSK3ff/5f2HuV9u8tG+vsy2jfAj1/sD11ncITyoFgYCMMQGBsK07KCYX3dYZhrCsJcT9u+Z5eSFnQjyR4T3o8KXoee9mC6ntgvzUHms8/5p4chMH6HZKCyjIMLtUMpGtnHF3z8jk5MmUf2nk9KWkxY30eA73+9+u/TswE7wM9NTHm0L2y9q967pcdSgq5+hZOCP/MtnLz7vnByUJ6ZF/xINm2BnZv2vDVtDu77umJnGrQQ9YfAgtLgtdujrtHEPvPRyO567vX9kjLA980A5ZDgD3bM8NjXsa873EntDJ4fDpYaFwbDgJiWGXTRbNqye9yiqXsGvPEnB9MMlWg0aDHsD4Jt24NWxbbw1rotaP1t2x4EgHjpubsDYVZh3PbtHjggxO5kDfS52YsN/rqnpCX+Hhls3L1+rwb6zA3yfrb91HOwckuJCU5xYam/bK9wfrBsz4NyqRnB8uNfp6FaXmrG3r9P8d+HqekJbrsBvhssBbpboL0eOuqD93N7Q3Af2ccflGfkBQc2ckuC++Hcad/0ZvDeue5XUD7gxeqHXns9PHEjbFkEJ1x8dIXgA+HR4LMxULA70O/vlLS4fZN9HERJy9iz7NqHgs/TUUShbzis+h0s/MFw1+LQDLqztp8fwf5Ak8gP3q4v87Q9v8hH8hFeSxkkmIU/egOWZe9uCdj1w9+559HRvY6oxt93BPPIHr1nmMsZPUDZmOAHMNFtHQ2/ZHcFwrj7/i/fSE/cTt8+drL3FaqGk9nAX/ppA4TT1Iy9fxQ8uo/XqjPuyPdAZR2AH+DnJuZ5SlqwE144CQqnhKFuUhDqCkoPfecmGg3C5B6BcPPux621HNJO+l6Bd3+hMa4c9v2DPeCBh/gf9Mx9fyYH2lmNDfRpmeHBkra9D5h0D/BZiT2Y0t0WvPajy4IuV6VzYcKpQZA6GkSjQQ+A1m1B62HbjphAGN53NQ+8PXdt5/20QlnKngcvBnzdB3g/DMm40eDElL3eo/s6yBn/fk4ZYL6D1SnuYId7sK3i32+D/n70D88Ktl+kd/CQPVgIj0b38xkZoHyPcTP3/ZlJSR2e96x78BmLDYG7QmF9eKuDjoZDOAA7BPLGwsd/CGPKj+xy+3qCcwg3vXFklzukwgPgsQfPYnsfDdYzKTNv9/s0JT34TCcJhT458txHdmgTGQ4j/XMz3PUf7uWLiIgMo8FCX/JEWzm6aMdL5MCN9M/NcNd/uJcvIiJylFLoExERERERSWIKfSIiIiIiIklMoU9ERERERCSJKfSJiIiIiIgkMYU+ERERERGRJKbQJyIiIiIiksQU+kRERERERJKYQp+IiIiIiEgSU+gTERERERFJYgp9IiIiIiIiSUyhT0REREREJIkp9ImIiIiIiCQxhT4REREREZEkptAnIiIiIiKSxBT6REREREREkphCn4iIiIiISBJT6BMREREREUliCYU+M7vczNaY2Tozu2OA4YVm9ryZvW9mK8zs5phhXw/LlpvZY2aWFZZ/x8xqzGxpeLsiZpo7w2WtMbPLhmJFRUREREREjkX7DX1mlgr8BJgHzASuN7OZcaN9GVjp7qcCFwL3mlmGmU0EvgJUuPtsIBWYHzPdv7v7nPD2Qri8meE4s4DLgfvDOoiIiIiIiMgBSqSl70xgnbtvcPce4HHg6rhxHMg3MwPygEagLxyWBmSbWRqQA9TuZ3lXA4+7e7e7bwTWhXUQERERERGRA5RI6JsIVMU8rw7LYt0HzCAIdMuAr7p71N1rgHuALcBWoNndF8RMd5uZfWBmD5vZ6ANYHmZ2i5lVmlllXV1dAqshIiIiIiJy7Ekk9NkAZR73/DJgKVAKzAHuM7OCMMhdDZSHw3LN7MZwmv8ETgjH3wrcewDLw90fdPcKd68oKSlJYDVERERERESOPYmEvmpgcszzSezdRfNm4BkPrAM2AicBlwAb3b3O3XuBZ4BzANx9u7tH3D0KPMTuLpyJLE9EREREREQSkEjoWwxMNbNyM8sguMjKc3HjbAEuBjCzccB0YENYfpaZ5YTn+10MrArHmxAz/TXA8vDxc8B8M8s0s3JgKvDOwayciIiIiIjIsS5tfyO4e5+Z3Qa8SHD1zYfdfYWZ3RoOfwC4C/i5mS0j6J55u7vXA/Vm9jTwLsGFXd4DHgxn/a9mNoeg6+Ym4Evh/FaY2ZPAynCaL7t7ZKhWWERERERE5Fhi7nudLjfiVFRUeGVl5XBXQ0REREREZFiY2RJ3rxhoWEJ/zi4iIiIiIiIjk0KfiIiIiIhIElPoExERERERSWIKfSIiIiIiIklMoU9ERERERCSJKfSJiIiIiIgkMYU+ERERERGRJKbQJyIiIiIiksQU+kRERERERJKYQp+IiIiIiEgSU+gTERERERFJYgp9IiIiIiIiSUyhT0REREREJIkp9ImIiIiIiCQxhT4REREREZEkptAnIiIiIiKSxBT6REREREREkphCn4iIiIiISBJT6BMREREREUliCn0iIiIiIiJJTKFPREREREQkiSUU+szscjNbY2brzOyOAYYXmtnzZva+ma0ws5tjhn09LFtuZo+ZWVZY/m9mttrMPjCzZ81sVFheZmadZrY0vD0wVCsrIiIiIiJyrNlv6DOzVOAnwDxgJnC9mc2MG+3LwEp3PxW4ELjXzDLMbCLwFaDC3WcDqcD8cJqXgNnufgrwIXBnzPzWu/uc8Hbrwa+eiIiIiIjIsS2Rlr4zgXXuvsHde4DHgavjxnEg38wMyAMagb5wWBqQbWZpQA5QC+DuC9y9f5y3gUmHtCYiIiIiIiKyl0RC30SgKuZ5dVgW6z5gBkGgWwZ81d2j7l4D3ANsAbYCze6+YIBlfAH4Q8zzcjN7z8xeM7PzBqqUmd1iZpVmVllXV5fAaoiIiIiIiBx7Egl9NkCZxz2/DFgKlAJzgPvMrMDMRhO0CpaHw3LN7MY9Zm72DwStgr8Oi7YCU9x9LvAN4FEzK9irAu4PunuFu1eUlJQksBoiIiIiIiLHnkRCXzUwOeb5JMIumjFuBp7xwDpgI3AScAmw0d3r3L0XeAY4p38iM7sJuBL4rLs7gLt3u3tD+HgJsB6YdjArJyIiIiIicqxLJPQtBqaaWbmZZRBciOW5uHG2ABcDmNk4YDqwISw/y8xywvP9LgZWheNdDtwOXOXuHf0zMrOS8OIxmNnxwNRwXiIiIiIiInKA0vY3grv3mdltwIsEV9982N1XmNmt4fAHgLuAn5vZMoLuoLe7ez1Qb2ZPA+8SdOF8D3gwnPV9QCbwUpAHeTu8Uuf5wPfMrA+IALe6e+OQrbGIiIiIiMgxxMJelSNaRUWFV1ZWDnc1REREREREhoWZLXH3ioGGJfTn7CIiIiIiIjIyKfSJiIiIiIgkMYU+ERERERGRJKbQJyIiIiIiksQU+kRERERERJKYQp+IiIiIiEgSU+gTERERERFJYgp9IiIiIiIiSUyhT0REREREJIkp9ImIiIiIiCQxhT4REREREZEkptAnIiIiIiKSxBT6REREREREkphCn4iIiIiISBJT6BMREREREUliCn0iIiIiIiJJTKFPREREREQkiSn0iYiIiIiIJDGFPhERERERkSSm0CciIiIiIpLEFPpERERERESSWEKhz8wuN7M1ZrbOzO4YYHihmT1vZu+b2Qozuzlm2NfDsuVm9piZZYXlY8zsJTNbG96PjpnmznBZa8zssqFYURERERERkWPRfkOfmaUCPwHmATOB681sZtxoXwZWuvupwIXAvWaWYWYTga8AFe4+G0gF5ofT3AG84u5TgVfC54Tzng/MAi4H7g/rICIiIiIiIgcokZa+M4F17r7B3XuAx4Gr48ZxIN/MDMgDGoG+cFgakG1maUAOUBuWXw38Inz8C+CTMeWPu3u3u28E1oV1EBERERERkQOUlsA4E4GqmOfVwEfixrkPeI4g0OUDn3H3KFBjZvcAW4BOYIG7LwinGefuWwHcfauZjY1Z3ttxy5sYXykzuwW4BWDKlCkJrIaIiIiIiCSr3t5eqqur6erqGu6qHFZZWVlMmjSJ9PT0hKdJJPTZAGUe9/wyYCnwF8AJwEtm9gZBd86rgXKgCXjKzG5090cOcXm4+4PAgwAVFRV7DRcRERERkWNHdXU1+fn5lJWVEXRATD7uTkNDA9XV1ZSXlyc8XSLdO6uByTHPJ7G7i2a/m4FnPLAO2AicBFwCbHT3OnfvBZ4Bzgmn2W5mEwDC+x0HsDwREREREZFdurq6KCoqStrAB2BmFBUVHXBrZiKhbzEw1czKzSyD4CIrz8WNswW4OKzIOGA6sCEsP8vMcsLz/S4GVoXTPAfcFD6+CfhtTPl8M8s0s3JgKvDOAa2ViIiIiIgcc5I58PU7mHXcb+hz9z7gNuBFgsD2pLuvMLNbzezWcLS7gHPMbBnBlThvd/d6d/8z8DTwLrAsXN6D4TR3A5ea2Vrg0vA57r4CeBJYCfwR+LK7Rw54zURERERERI6QpqYm7r///gOe7oorrqCpqekw1Gg3cx/5p8NVVFR4ZWXlcFdDRERERESGyapVq5gxY8awLX/Tpk1ceeWVLF++fI/ySCRCaurQ/gPdQOtqZkvcvWKg8RO5kIuIiIiIiIgM4o477mD9+vXMmTOH9PR08vLymDBhAkuXLmXlypV88pOfpKqqiq6uLr761a9yyy23AFBWVkZlZSVtbW3MmzePj370o7z11ltMnDiR3/72t2RnZx9y3RT6REREREQkqXz3+RWsrG0Z0nnOLC3g25+Ytc/hd999N8uXL2fp0qUsXLiQj3/84yxfvnzXVTYffvhhxowZQ2dnJ2eccQaf+tSnKCoq2mMea9eu5bHHHuOhhx7iuuuu4ze/+Q033njjIdddoU9ERERERGSInXnmmXv8rcKPf/xjnn32WQCqqqpYu3btXqGvvLycOXPmAHD66aezadOmIamLQp+IiIiIiCSVwVrkjpTc3NxdjxcuXA3ndAEAACAASURBVMjLL7/MokWLyMnJ4cILLxzwbxcyMzN3PU5NTaWzs3NI6pLIXzaIiIiIiIjIIPLz82ltbR1wWHNzM6NHjyYnJ4fVq1fz9ttvH9G6qaVPRERERETkEBUVFXHuuecye/ZssrOzGTdu3K5hl19+OQ888ACnnHIK06dP56yzzjqiddNfNoiIiIiIyIg33H/ZcCQd6F82qHuniIiIiIhIElPoExERERERSWIKfSIiIiIiIklMoU9ERERERCSJKfSJiIiIiIgkMYU+ERERERGRJKbQJyIiIiIicoiampq4//77D2raH/3oR3R0dAxxjXZT6BMRERERETlER3PoSztscxYRERERETlG3HHHHaxfv545c+Zw6aWXMnbsWJ588km6u7u55ppr+O53v0t7ezvXXXcd1dXVRCIR/umf/ont27dTW1vLRRddRHFxMa+++uqQ102hT0REREREkssf7oBty4Z2nuNPhnl373Pw3XffzfLly1m6dCkLFizg6aef5p133sHdueqqq3j99depq6ujtLSU3//+9wA0NzdTWFjID3/4Q1599VWKi4uHts4hde8UEREREREZQgsWLGDBggXMnTuX0047jdWrV7N27VpOPvlkXn75ZW6//XbeeOMNCgsLj0h91NInIiIiIiLJZZAWuSPB3bnzzjv50pe+tNewJUuW8MILL3DnnXfysY99jG9961uHvT5q6RMRERERETlE+fn5tLa2AnDZZZfx8MMP09bWBkBNTQ07duygtraWnJwcbrzxRr75zW/y7rvv7jXt4aCWPhERERERkUNUVFTEueeey+zZs5k3bx433HADZ599NgB5eXk88sgjrFu3jr/9278lJSWF9PR0/vM//xOAW265hXnz5jFhwoTDciEXc/f9j2R2OfD/A6nAT9397rjhhcAjwBSCIHmPu//MzKYDT8SMejzwLXf/kZk9AUwPy0cBTe4+x8zKgFXAmnDY2+5+62D1q6io8MrKyv2uh4iIiIiIJKdVq1YxY8aM4a7GETHQuprZEnevGGj8/bb0mVkq8BPgUqAaWGxmz7n7ypjRvgysdPdPmFkJsMbMfu3ua4A5MfOpAZ4FcPfPxCzjXqA5Zn7r3X3OftdWREREREREBpXIOX1nAuvcfYO79wCPA1fHjeNAvpkZkAc0An1x41xMEOY2xxaG01wHPHYQ9RcREREREZFBJBL6JgJVMc+rw7JY9wEzgFpgGfBVd4/GjTOfgYPdecB2d18bU1ZuZu+Z2Wtmdt5AlTKzW8ys0swq6+rqElgNERERERGRY08ioc8GKIs/EfAyYClQStCd8z4zK9g1A7MM4CrgqQHmdT17hsGtwBR3nwt8A3g0dl67KuD+oLtXuHtFSUlJAqshIiIiIiLJLJHrlYx0B7OOiYS+amByzPNJBC16sW4GnvHAOmAjcFLM8HnAu+6+PXYiM0sDriXmYi/u3u3uDeHjJcB6YFpiqyMiIiIiIseirKwsGhoakjr4uTsNDQ1kZWUd0HSJ/GXDYmCqmZUTXIhlPnBD3DhbCM7Ze8PMxhFclXNDzPD41rx+lwCr3b26vyC8EEyju0fM7Hhgaty8RERERERE9jBp0iSqq6tJ9lO/srKymDRp0gFNs9/Q5+59ZnYb8CLBXzY87O4rzOzWcPgDwF3Az81sGUF30NvdvR7AzHIIrvy599/RD3ye3/nA98ysD4gAt7p74wGtlYiIiIiIHFPS09MpLy8f7moclRL6n76jnf6nT0REREREjmWD/U9fIuf0iYiIiIiIyAil0CciIiIiIpLEFPpERERERESSmEKfiIiIiIhIElPoExERERERSWIKfSIiIiIiIklMoU9ERERERCSJKfSJiIiIiIgkMYU+ERERERGRJKbQJyIiIiIiksQU+kRERERERJKYQp+IiIiIiEgSU+gTERERERFJYgp9IiIiIiIiSUyhT0REREREJIkp9ImIiIiIiCQxhT4REREREZEkptAnIiIiIiKSxBT6REREREREkphCn4iIiIiISBJT6BMREREREUliCYU+M7vczNaY2Tozu2OA4YVm9ryZvW9mK8zs5rB8upktjbm1mNnXwmHfMbOamGFXxMzvznBZa8zssqFaWRERERERkWNN2v5GMLNU4CfApUA1sNjMnnP3lTGjfRlY6e6fMLMSYI2Z/drd1wBzYuZTAzwbM92/u/s9ccubCcwHZgGlwMtmNs3dIwe9liIiIiIiIseoRFr6zgTWufsGd+8BHgeujhvHgXwzMyAPaAT64sa5GFjv7pv3s7yrgcfdvdvdNwLrwjqIiIiIiIjIAUok9E0EqmKeV4dlse4DZgC1wDLgq+4ejRtnPvBYXNltZvaBmT1sZqMPYHkiIiIiIiKSgERCnw1Q5nHPLwOWEnTHnAPcZ2YFu2ZglgFcBTwVM81/AieE428F7j2A5WFmt5hZpZlV1tXVJbAaIiIiIiIix55EQl81MDnm+SSCFr1YNwPPeGAdsBE4KWb4POBdd9/eX+Du2909ErYIPsTuLpyJLA93f9DdK9y9oqSkJIHVEBEREREROfYkEvoWA1PNrDxssZsPPBc3zhaCc/Yws3HAdGBDzPDrievaaWYTYp5eAywPHz8HzDezTDMrB6YC7yS2OiIiIiIiIhJrv1fvdPc+M7sNeBFIBR529xVmdms4/AHgLuDnZraMoHvm7e5eD2BmOQRX/vxS3Kz/1czmEHTd3NQ/PJz3k8BKgovBfFlX7hQRERERETk45r7X6XIjTkVFhVdWVg53NURERERERIaFmS1x94qBhiX05+wiIiIiIiIyMin0iYiIiIiIJDGFPhERERERkSSm0CciIiIiIpLEFPpERERERESSmEKfiIiIiIhIElPoExERERERSWIKfSIiIiIiIklMoU9ERERERCSJKfSJiIiIiIgkMYU+ERERERGRJKbQJyIiIiIiksQU+kRERERERJKYQp+IiIiIiEgSU+gTERERERFJYgp9IiIiIiIiSUyhT0REREREJIkp9ImIiIiIiCQxhT4REREREZEkptAnIiIiIiKSxBT6REREREREklhCoc/MLjezNWa2zszuGGB4oZk9b2bvm9kKM7s5LJ9uZktjbi1m9rVw2L+Z2Woz+8DMnjWzUWF5mZl1xkzzwFCusIiIiIiIyLFkv6HPzFKBnwDzgJnA9WY2M260LwMr3f1U4ELgXjPLcPc17j7H3ecApwMdwLPhNC8Bs939FOBD4M6Y+a3vn87dbz2E9RMRERERETmmJdLSdyawzt03uHsP8Dhwddw4DuSbmQF5QCPQFzfOxQRhbjOAuy9w9/5x3gYmHeQ6iIiIiIiIyD4kEvomAlUxz6vDslj3ATOAWmAZ8FV3j8aNMx94bB/L+ALwh5jn5Wb2npm9ZmbnJVBHERERERERGUAioc8GKPO455cBS4FSYA5wn5kV7JqBWQZwFfDUXjM3+weCVsFfh0VbgSnuPhf4BvBo7LxiprvFzCrNrLKuri6B1RARERERETn2JBL6qoHJMc8nEbToxboZeMYD64CNwEkxw+cB77r79tiJzOwm4Ergs+7uAO7e7e4N4eMlwHpgWnyl3P1Bd69w94qSkpIEVkNEREREROTYk0joWwxMNbPysMVuPvBc3DhbCM7Zw8zGAdOBDTHDryeua6eZXQ7cDlzl7h0x5SXhxWMws+OBqXHzEhERERERkQSl7W8Ed+8zs9uAF4FU4GF3X2Fmt4bDHwDuAn5uZssIuoPe7u71AGaWA1wKfClu1vcBmcBLwfVfeDu8Uuf5wPfMrA+IALe6e+Ohr6qIiIiIiMixx8JelSNaRUWFV1ZWDnc1REREREREhoWZLXH3ioGGJfTn7CIiIiIiIjIyKfSJiIiIiIgkMYU+ERERERGRJLbfC7nI0aWrN8LOjh4a23vY2d5LY0cPO9vD5x2x970U5WZw3RmTuWzWODLTUoe76iIiIiLDrqapkz8u38aba+sYV5DFzNICZk4oYMaEAnIzj9yusbvTF3X6Ik5vNEokvO+LOJGo0xuJ7hreF43SG5b3RaL0Rp0Ug5yMNHIzU8nNSCMnI5XczDQy01IIL5IosotC31Fq1dYWHnp9A/Xte4a6jp7IgOObQWF2OmNyMhidm8HEUVms3tbKVx57jzG5GXz69Elcf+YUyopzj/CayJESjTob6ttZUdvMzAkFTB2Xf8Tr0NzRy+OLt3DyxELOPqFIPzoyIlQ1dvBkZRXdfVHOn1rCGeWjdaBMJMlsaejgD8u38sLybbxf1QTA8cW5vFfVxOOLq4BgX6qsKHdXCJxZWsCs0gLG5mcd9HLbuvvYVN/O5oYONjW0s7mhnU0NHWxuaGd7S/eQrFu81BQjJyOVvMzdQXD3890h8fTjRnP57PH6rT5G6OqdR6HeSJTLf/Q6O1q6OWFsHmNyMxidk8GY3HRG52bsCna7yzMozE4nNWXPD2006ryxrp5H/7yZl1ftIBJ1zj2xiBvOPI5LZ44jI029e4dae3cff1pXT/XOTsqKcygrymXymBzSU4d2W7s7NU2dfFDdzPvVTXxQ1czymmZau/sASE817pg3gy+cW3bEvsyXbG7kK48tpaapE4CpY/P4/DllXDt34hE9ciqSiGjUeXNdPb9ctJlXVm/HgLSUFHoiUbLTUzn7hCIunF7CBdNKOK5IB8skubg7De09VO/spKOnj7H5WYwvzCIvyb6r19e18cfl23hh2VZW1LYAcPLEQuadPJ55sydQXpyLu7O1uYuVtS2s3NrCitpmVm5toaqxc9d8ivMydwXAmROC+7KiXFLC/a7mzt7dYa5+d6jb1NBBfduewa4kP5OyohyOK8qltDCLjLQU0lJTSEux4Nb/eNd9WJ6SEj5O2VUWdejo6aOjJ0J7d19w64nQ0dNHe3dQ1tETob2nj47uCG3dfcGwnghtXX109ka4ZMY4vn/NbMYWHHywlaPHYFfvVOg7Cj385ka+97uV/NdNFVw8Y9yQzHN7SxdPLq7i8cVV1DR1UpyXwV+ePpkbzpzClKKcIVnGscjdWV/XzsI1O3h1zQ7e2dhIb2TPz1RqijFlTA5lRTmUF+dRXpJLeVEu5SW5TCjI2vWjMZi61m6W1TTxflUzH1Q38UF1Mw3tPUAQ8GZMKOCUSYWcMmkU08fl8x//s5aXV+3goukl3PPpUynKyzws6w/BzvMDr6/n3gUfMnFUNvdedyqb6tv5xaJNLK9pIT8zjb+smMTnzjqO40vyDls9ZGTri0Rp6epjTG7GYV1OS1cvT1dW88jbm9lQ305xXgbzz5jCDR+ZwqicdBatb+C1D+tYuKaOLY0dAJQV5XDBtBIumF7C2ccXk52RPK2A7s7ymhbWbG/lzLIxw/J70NkToaWrl+K8zL0OXsrBiUadurZuqnd2UL2zk+qdndQ0hfc7O6hp6qSrN7rXdHmZaYwryGRcQRbjC7IYVxjeFwShcHxBFsV5GaQN8YHMoeLurN3RxgvLtvKHZdtYs70VgLlTRnHF7AlcPns8k8ck9h5v7uxl1daWmDDYwtrtrfRFg9/4nIxUpozJYUdrN43h73G/8QVZHFcUHPg9LjwAfFwY9I6WYB2JOj/700b+7cU1ZKal8O1PzOLa0yaq1W+EU+gbQRraurnwnoXMnTKaX9x8xpB/+CJR5/W1dTz65y28smo7UYfzphZzw5lTuGTmuCFvkUpGnT0RFm2o59XVdby6ZgfVO4OjgdPH5XPh9BIunD6WE8fmsaWxg4317Wysb2NTfQcb6tvZVN9OZ+/uLrqZaSmUFeVSXpxLWXEuxxcHYbC3L8r71UHAe7+qidrmLgBSDKaOzefkSYWcGoa8kybk79UVzd35xVub+P4LqxmVk86PPjOHc04sHvJtUdfazTeeXMoba+u58pQJfP/akynISt9Vh/eqmvjFW5t4YdlWeiPO+dNKuOns47hw+tik27lbX9fGb5ZU09zZy6zSQmaVFjB9fD5Z6ckTEA6HdTvaeGpJFc++W8OO1m7Ki3M554Qizj2xmLOOLxqyELh6Wwu/XLSZ/36vho6eCKdNGcXnzy5j3snj99mVc1N9O699WMdrH9bx1vp6unqjZKSl8JHyMUEInFbCiWPzRtxOUldvhEXrG3hp1XZeWbV9jy5mU8fmcfGMcVwyYyxzp4w+LJ/T/h3z19YE2/adjY30RKKkpRjjC7MoHZXNxFHZlI4KHu9+nn3U7DAPt86eCNtautja3Mm25q4wzHVS3dRBzc5Oapu66InsGerG5GYwcVQ2k0Zn774fnUNuZio7WrrZ1tLFtuYudrQG99tbutne0rUr5PRLsaDla3xhEAZPnljI/DMnH1IXyEPh7qzc2sIflm3jheVb2VDXjhmccdwY5p08nstnj2dCYfaQLKu7L8K6HW2sqA3C4OaGdsYXZu9quSsvzmXKmJwRdWBoQ10bf/f0B1Ru3slfnDSW719zMuMLD/9r2drVS31bD+3dfbSFrZTBfWSPsvaePtriy7r76OqNMqUoJzwXM58ZEwqYNk6/uQp9I8g/PLuMxxdX8eLXzuPEsYf3nKxtzV08sbiKxxdvYWtzFyX5mVxXMYn5Z0zZ55GwaNTZ2dFDfVsP9W3d1Ld1U9favcfz+rZuGtt6OL4kj4tnjOWSGeMSPrJ2tNpY39+aV8fbGxro6YuSk5HKuScW7wp6E0ft/0fF3dne0s2G+jY2hiFwY307G+rbqWrs2KuVcMqYHE6ZVMipk0ZxyqRCZk8sPKCukitrW7jtsXfZWN/O/3PBCXz90mlDFuzfXFvP155YSmtXL9+5ahbzz5i8z53fHa1dPP5OFb/+82a2t3QzZUwOnzvrOK6rmExhTvqQ1Gc4tHf38fsPtvJkZRWVm3fuOo+itSvoZpuaYpxYkses0gJmTQyC4MzSgl3BeCh09kTY2tzJ1uYu6lq7OXFssLyjOYi0dPXyu/e38tSSKt7b0kRqinHR9BLmThnNks07+fOGBtp7IpjBjPEFnHtiEeecWMyZZWMO6P3fG4ny4opt/HLRZt7Z2EhmWgpXzynl82eXMXti4QHVuas3wuJNjbuCytodbQBMHJXN+dNKuGBaMWXFuWSkppCZnkpmWgoZaSnBferwX1ShrrWbV1fv4OVV23ljbT2dvRFyM1I5f1oJl8wYx4wJBSza0MArq7bzzsZG+qLO6Jx0Lpo+lotnjOP8acXkH8L7tqWrl7fW1Qchek3drgNZ08blccG0EqaMyWFrcxe1TUFgqWnqZFtLF5G4wFGQlbZHCCwNw2FRbiY5MRezyMtMIycz9ajY9gfC3Wnp7Nsj0PWHsa3NXWxvCe6bO3v3mrYkPzMm0OUwcXQQ7CaNymbi6GxyMg48MEejQTfQ7WEdtrV07Xq8vbWbbc2dfLi9jfRU48pTSrn53DJOmTRqKDbFfn24vZVn36vhhWVb2dzQQYrBWccXMe/kCVw2a9ywhdCRKBINDhT/64urSU9N4Z+unMmnT590WD47K2tb+OmbG3j+/dq99nniZacH5yPmZfbfB7fczDTSU1PYWN/G6m2tu653kWJQXpzLjPCiPP0X5xlXkDmivgcOhULfCLGytoUr/+MNbjqnjG9/YtYRW24k6ixcs4NH/7yFV9fswIHzp5YwfXw+9a3d1LXtDnWN7T17/QhD0MWwKDeT4vwMivMyGZ2TwQfVTayvaweCH/a/OOnwHj0eSl29Ef68sZFXV+9g4ZodbGoIunmdUJLLhdPHctH0sUN+sYe+SJSapk421LeTasbJEwsZPQStHB09fXz3uZU8UVnF3Cmj+PH8uYcUwvsiUf795Q+5f+F6TizJ474bTmP6+MQOUOzaCX9rM+9saiQrPYVr5k7k82eXMWNCwUHX6Uhyd5Zs3smTlVX87oOtdPREOL4kl89UTOaa0yZSkpdJ9c5OVtQ2s6K2heU1wf2O1t2tKVPG5ARBsLQgaBWcOPCFAvoD3bbmLmqbu9jW3BneBzvH21q6aOoYeOfv/KklXDi9hPOnlhwVwToadd5a38BTS6r44/JtdPdFmTYuj0+fPpmr55busf69kSgfVDfz1rp6/rS+nnc3N+1qCZo7ZRRnn1DMuScUMXfK6AHPTd7R0sVj71Tx6DvBQYbJY7L53FnH8enTJw/JZwqCq/+9/mEdC9fs4E/rGmgLz6fdl/4AmJmWGt6n7FmWnsK4giymjMlhypgcJo/JZvKYHEryDm5npb817aWVQWvee1VNuENpYRaXzBzHxTPGcdbxYwb8Dmvp6uX1D+t4ZVXQbb2po5f0VOMj5UX8xUnBgbz9dQONRoPWl/6Qt2TLTiJRJz8zjXNPLOaC6SWcP61k0INlkaizozV4r9c09QfCIBTWNnVS29w54Ps/VlqKkZuZRm5GKjnhzmJuRio5GcGOZE74fNq4fM45sTihg3dDpaqxgz+tq2fxpp3UNnXuCnSxvUEguLhIcV4m48MulhPCFrYJhbu7XJaOyh62Fo4NdW38ctFmnqqsoj1sRb/53HIunz1+yHsP1bV289z7tTz7XjXLa1pITTHOOaGIj588gUtnjjuspzIcCzbVt/N3v/mAdzY2csG0En5w7cmUDsFnIhp1Xvuwjp++uYE/rWsgOz2VT1dMYs7kUbvCXGy4Cz6naQntK0ajzpbGDlZtbQm6425tZfW2ll29sABG56Rz0viCMAwGrYInjs0jxSy4Gmo0Gt4HV0ftvwXPo3uU9z/uizgfKR+T0Ck6R5JC3wjg7lz/0Nus2dbKwm9eNGw7aTVNnTyxuIonF1fR2NFDSV4mxXlBkCvO2x3q+m8l4fPC7PQBd0w21bfz8qrtvLJqB4s3BUePx+RmcOH04OjyeVMP7eixe3Dewpptraze2srqba2s2d7Cxrp2Iu4YRn+1DDAzLHzSX1uzYJz+4RBcbaunL0pWegrnnBC25k0bO6LPf3z+/Vr+/pllAPzgUydz5SmlBzyPmqZOvvrYe1Ru3slnKibznatmHXQ3lpW1Lfxy0Sb+e2kNXb1Rziwbw03nlPGxWUdnN+MdrV08824NT1ZWsaGundyMVK48pZTrzpjEaVNG73fHvK61e1cQXFkbXCyg/2ACBEFtVmkBKWaDBroxuRlMCHf8JhRm79oJnFCYTVFeBsuqm1n4YR1vrK2jqaOXFIO5U0Zz4bSgRXpWacER/ZHa3NDO00uq+c2SamqbuyjISuOqOaV8+vTJnDKpMKFA09kToXJzI39a18Ci9fUsq2km6sFR4DPKx3Bu2B20szfCLxdt5g/LttIXdS6YVsJN5xzHBdMOb3fi3kiUpVVN1Ld2090XpacvSndfhO6+aMwtEpZH6e6N0hOJ0t0bCe+j/N/27jy66vLO4/j7mw0SSFiyAUnYQmQRkSUqAipKrVCtCxYHe2yt005nsR3b6WY7nfa0U6d2ZurpnGN7Oj3W6tiKo6N0UduKFC2aIvtSCIEAIQmErGTf733mj98v8RajBsjNTW4+r3Nyfr/fc29unuR8k/y+93me79PWFegdSQmVGB9LzsREPxFMImeClxROTfXOQ3//ugJBdpyoY1NhJa8WVvYWoliQPY4PzM1k1dwM5k0+v1Hg7kCQ3aX1bD7s/R0v9kc4+5oGWtfSydaj3kjoH4/U9BawuHRKCtf58bdo6vgB/f1u6ejmdH0b9W1dXpGKjp6iFV6xit5CFv40sZYOr8hFc8fbxS6a2rvo6PamQk5LTWJZbirLcr3pxenJA5dE1LV0UnCshjeLa3izuLZ3zWja2ASmpY7xfpd71s6FJHcZyaOHReG1xvYunttZzpMFJZTWtTIpZTQfu3oad1859aKmabd3Bdh0qJKNe07x+pFqAkHHZVnjuGNRFrcunEKaEr0BFQw6ntp2kod/e5i4GOPrt8zlrvx3n8nzXtq7Amzcc4qfvnGC4qpmMlNG8YllM/jolVPDfp/b2N7F4Yqm3mSw8EwTRWca+1zLeqGKvrN6yFV6VtI3DPz2QAV//4vdfOf2+dyzdFqku0NPXAzkcHhDW8+7x5VsKaqmoe3td4/7Mw20rTPAkcomis40UXim0Uv0zjT9xQLq9ORRzJmUTG76WOJjzf9ewPlHAIcjNOydc+94PCkhjmW5qSydmRpV88PL6lr57IY97C2rZ/0VOXzjw/P6PeXnlYNn+NL/7ScQdDx0x3xuW5g1IH2qb+3kuZ3l/M+2Esrq2kgdk8CMtDHezU5IQYGe88yUwasw1xUIsuVwFc/uLGNLkXezccX0CazLz+HmyyZfdFXSxvYuCk97BQIO+sUCYgwmj0vsfRd/yvjRTErxprBlpozudzwGgo69ZfW8XlTFa0eq2V/eAHgjBtdeksbK2Rlcm5fG+KSBL5zS0tHNywcqeG5XOdtP1GEG1+Sls25JNjfOy7zo36mG1i62naj1RwJrexMR8Kb/rcvP4Z6l05gxDLeoae8KUH62jbK6Vkr9j7KQY8s52/akJ49i6sQkxifGs72kjqb2bkbFxbBiVhqr/EQvcwCr8p2sbeHVwqp3TAPNmpDIwdONOAfjk+K5Ns9b83jNJWlDfppdMOg4UtVEQXEtBcdqeet4bW8l5NmZyVydm8qy3FSumpnKuMT+36i2dnazo+QsbxbX8MbRGg5VeNUjk0fFsTQ3leW5qazISyM3ffitC30vgaBjy+Eqnigo4Y3iGhLiYrh94RTuWz6j3zM6gkHHjpI6Nu45xUv7K2jq6GbyuNHcviiLtYuyIrIl0UhTWtvKl5/fx7bjdVyTl8bDdy7o90h4TXMHT/3pJD/fdpLalk7mTU7hb66dwc2XTYnoGxiBoKOktoXCikZKalowM2L9qqk9x5je65je9tiYc58XQ0wMXDUjdcjNXFPSN8S1dwX4wCOvM3ZUHC9+dsWQrYo1kLoDQXadPMvmw97NQ+g00FX+CGBjWxeFFV6SV1TZREltS29ilhgfyyWZY5k9KZk5I0j6bgAADT9JREFUk1KYMymZ2ZOSNbWjH7oCQR7ZdIQfv36MmWljePSji9/zH3FHd4DvvnyYJwpKmJ+VwqN3Lw7Lfo8904xfOlBBRX07lU3tVDV29DltbkxCbG9S2FNZLiNlNJkp3gh0UkIsSQmxJCbEkRQfS2JC7HltVltc1cRzO8t5fvcpapo7SE8exZ2Ls1mXn03uMK1AWtPc4U9HrOaPIaOAC3PGs3J2BitnpzN/yrh3HQV07u2pLd3+5sC9mwoHvKkxpxvaeGG3t8amtTPAjLQxfGRJNmsXZw1YIYW+VDW2U3CslkDQseaySRe0dmk4cM5R19LZmwyWn22jtNY7r27uYMnUCayam8GKvLRB+RmETgM9Vd/GstxUrrsknQXZ44fcjdD56A4EOXi6kYJjtRQcq2FHSR3tXUFiDOb7e5Auy03jiukT/uLn3B3wCnB5I3k17C49S1fAkRAbw+Jp41kxK41ls9JYkDVuRPyfB2/d3c/eLGHjnnLau4IsnTmRTyybwY3zMvuMkRM1LWzcXc4Le05RfraNpIRY1syfzNrFWSydOfRusKNdMOj4xfZSvvtyITFmfO1Dc7n7yncf9Tta2cRP3zjBC3tO0dkdZNWcDD55zQyunql9eweLkr4h7tE/HOU/XznC05+6KiwVFoeDvqaBwtsbpfYkdXP8JC9nYpL++F+kN47W8Pln99LQ1sXXb57Lx5ZOe8cf5RM1LXzm6d0cPN3Ifcun8+CaOYM+laG5o5tKv3hAlV9NrrKxw08KewoLdNDZ/d5TNmLMe7MgMcH7SIqP887jexJE7/xYdTO7S+uJizFumJPBXfk5rJydHlU3aYGgY195Pa8VVfN6URX7TzXgHIxLjCcxPtZP7IJ0B0KP/ftf0TPtdV1+Nkumvf+0V5GhrqM7wL6yBgqO1VBwrJY9fjIXH2sszBnP4qkTOFbdzLbjdTR3dGPmTWldnpvG8llpXDF94rCq5hgO9a2dPLOjjKf+dJJT9W1kT0jk3qunc1d+Dg7Hb/ZX8MLucvaU1hNjsHxWGmsXZ3HTpdH7Js5wUlbXylee30/BsVqWz0rl4bULemdmOed4s7iWx944zmtF1YyKi+HOJdn89fIZzMoYnm+SDmdK+oawMw3t3PD917g2L50ff2xJpLszJDS0dbHrZB1pY0eRl5E84v9ZhlNtcwdffG4fW4qquXFeJv9+54LeQhe/3HOKf954gPi4GP7jI5dz47yB2TMyHJxzNLR1UdnoVY9t6wzQ2hWgzd+0tq0r4LX5H22d3qa03rl3bPevxyfFs3ZxFncsyh7Q9TxDWW1zB1uP1vDWiTq6A0HiYmOIj/WmscTHxnhHf7Ngr82b+hLfs1Gwv4Hw2FFxgzbKJBIprZ3d7Dp51h8JrOVAeT05E5NYPiuNFbPSuHpm6oAVDIo23YEgmw5V8rOCErafqPPfZArSFXDMzkxm7eIsbluYNShbBsj5cc6xYXsZ//ZyIUHn+OqaOSQmxPHY1uMcPtNE2tgEPn71dO5ZOi3s+63Ku1PSN4T90//u5cUDFbz6+euGdZEQGb6CQcfjb57ge787TOqYUTx852W8tN9bj3XF9An81/pFA1K9S0QkGnUFgkOy+NRQ9+dTDWzYXkpifCx3LM467yJDEhmn6tt48Pn9bD1aA3jLcj61Yia3LpwSVTUQhislfUPU7tKzrP1RAfdfn8uXbpoT6e7ICHegvIHPbthNSW0rZvCZ62fxwKq8qJrWKCIiIhfHOcerhVWMjveKRilZHzreK+nTHJwICQYd3/rNITKSR/EPK2dFujsiXJY9jhf/8Rr++/VjvYUKREREREKZ2ZBe8iF9U9IXIRv3nGJfWT2P3HX5RZd9FxkoY0fF8YUPzo50N0RERERkAGneVgQ0d3Tzvd8dZmHOeG4foL3ORERERERE+qKkLwJ+tKWYqqYOvvnhee+6J5aIiIiIiMhA6FfSZ2arzazIzIrN7ME+Hh9nZr8xs31mdtDM7vPbZ5vZ3pCPRjP7nP/YRDPbZGZH/eOEkNf7qv+1iszspoH6ZoeC0tpWHtt6grWLs1g0dcL7f4KIiIiIiMhFeN+kz8xigR8Ca4B5wN1mNu+cp90PHHLOXQ6sBL5vZgnOuSLn3ELn3EJgCdAKbPQ/50Fgs3MuD9jsX+O/9nrgUmA18CO/D1HhoZcPERdrfGW1qnWKiIiIiEj49Wek70qg2Dl33DnXCTwD3HbOcxyQbF7N1rFAHdB9znNWAceccyf969uAJ/3zJ4HbQ9qfcc51OOdOAMV+H4a9guIafn+wkvuvn0VmijYeFRERERGR8OtP0pcFlIVcl/ttoR4F5gKngQPAA8654DnPWQ9sCLnOdM5VAPjHjPP4esNOdyDIt188RPaERD65YkakuyMiIiIiIiNEf5K+viqNnLuj+03AXmAKsBB41MxSel/ALAG4FXhugL4eZvZpM9tpZjurq6v78bKRtWFHGYfPNPH1m+cyOj5qZquKiIiIiMgQ15+krxzICbnOxhvRC3Uf8ILzFAMngNBFa2uA3c65ypC2SjObDOAfq87j6+Gc+4lzLt85l5+ent6PbyNy6ls7eeSVIq6emcpNl06KdHdERERERGQE6U/StwPIM7MZ/ojdeuDX5zynFG/NHmaWCcwGjoc8fjd/ObUT/zXu9c/vBX4V0r7ezEaZ2QwgD9jev29naPrBq0dpaOviGx+eh7fsUUREREREZHDEvd8TnHPdZvYZ4PdALPC4c+6gmf2d//iPgX8FnjCzA3jTM7/inKsBMLMk4Ebgb8956YeBZ83sk3hJ4zr/9Q6a2bPAIbxiMPc75wIX/61GxtHKJp7adpKPXjWVuZNT3v8TREREREREBpA5947lcsNOfn6+27lzZ6S78Q7OOT7++Hb2ldXz2peuZ+KYhEh3SUREREREopCZ7XLO5ff1WL82Z5cLs7mwiq1Ha/j8jZco4RMRERERkYhQ0hcmHd0BvvPSIWZljOWepdMi3R0RERERERmhlPSFydNvlVJS28q/3DKP+Fj9mEVEREREJDLet5CLXJi/uiKHcYnxXHfJ0N5OQkREREREopuGoMIkKSGOtYuzI90NEREREREZ4ZT0iYiIiIiIRDElfSIiIiIiIlFMSZ+IiIiIiEgUU9InIiIiIiISxZT0iYiIiIiIRDElfSIiIiIiIlFMSZ+IiIiIiEgUU9InIiIiIiISxZT0iYiIiIiIRDFzzkW6DxfNzKqBk5HuRx/SgJpId0JGJMWeRILiTiJFsSeRoLiTSHm32JvmnEvv6xOiIukbqsxsp3MuP9L9kJFHsSeRoLiTSFHsSSQo7iRSLiT2NL1TREREREQkiinpExERERERiWJK+sLrJ5HugIxYij2JBMWdRIpiTyJBcSeRct6xpzV9IiIiIiIiUUwjfSIiIiIiIlFMSV+YmNlqMysys2IzezDS/ZHoZWaPm1mVmf05pG2imW0ys6P+cUIk+yjRx8xyzGyLmRWa2UEze8BvV+xJ2JjZaDPbbmb7/Lj7lt+uuJNBYWaxZrbHzF70rxV7ElZmVmJmB8xsr5nt9NvOO+6U9IWBmcUCPwTWAPOAu81sXmR7JVHsCWD1OW0PApudc3nAZv9aZCB1A19wzs0FlgL3+3/nFHsSTh3ADc65y4GFwGozW4riTgbPA0BhyLViTwbD9c65hSHbNJx33CnpC48rgWLn3HHnXCfwDHBbhPskUco590eg7pzm24An/fMngdsHtVMS9ZxzFc653f55E95NUBaKPQkj52n2L+P9D4fiTgaBmWUDNwOPhTQr9iQSzjvulPSFRxZQFnJd7reJDJZM51wFeDfnQEaE+yNRzMymA4uAt1DsSZj50+v2AlXAJuec4k4Gyw+ALwPBkDbFnoSbA14xs11m9mm/7bzjLi6MHRzJrI82lUkVkahjZmOB54HPOecazfr68ycycJxzAWChmY0HNprZ/Ej3SaKfmd0CVDnndpnZykj3R0aU5c6502aWAWwys8MX8iIa6QuPciAn5DobOB2hvsjIVGlmkwH8Y1WE+yNRyMzi8RK+XzjnXvCbFXsyKJxz9cBreGuaFXcSbsuBW82sBG/Zzg1m9nMUexJmzrnT/rEK2Ii3jOy8405JX3jsAPLMbIaZJQDrgV9HuE8ysvwauNc/vxf4VQT7IlHIvCG9nwKFzrlHQh5S7EnYmFm6P8KHmSUCHwAOo7iTMHPOfdU5l+2cm453X/cH59w9KPYkjMxsjJkl95wDHwT+zAXEnTZnDxMz+xDe3O9Y4HHn3EMR7pJEKTPbAKwE0oBK4JvAL4FngalAKbDOOXdusReRC2ZmK4CtwAHeXt/yNbx1fYo9CQszW4BXtCAW743rZ51z3zazVBR3Mkj86Z1fdM7dotiTcDKzmXije+Aty3vaOffQhcSdkj4REREREZEopumdIiIiIiIiUUxJn4iIiIiISBRT0iciIiIiIhLFlPSJiIiIiIhEMSV9IiIiIiIiUUxJn4iIiIiISBRT0iciIiIiIhLFlPSJiIiIiIhEsf8HjfmEzLZukSoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_accuracy(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Experiment with Stabilize Learning with Data Scaling (Standardization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "standard_scaler = StandardScaler()\n",
    "standard_scaler.fit(X_train)\n",
    "\n",
    "X_train_std = standard_scaler.transform(X_train)\n",
    "X_test_std = standard_scaler.transform(X_test)\n",
    "X_valid_std = standard_scaler.transform(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "7881/7905 [============================>.] - ETA: 0s - loss: 0.3329 - accuracy: 0.8692\n",
      "Epoch 00001: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 8s 1ms/step - loss: 0.3328 - accuracy: 0.8692 - val_loss: 0.2802 - val_accuracy: 0.8869\n",
      "Epoch 2/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.3231 - accuracy: 0.8710\n",
      "Epoch 00002: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3231 - accuracy: 0.8710 - val_loss: 0.2793 - val_accuracy: 0.8869\n",
      "Epoch 3/600\n",
      "7867/7905 [============================>.] - ETA: 0s - loss: 0.3217 - accuracy: 0.8711\n",
      "Epoch 00003: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3215 - accuracy: 0.8712 - val_loss: 0.2786 - val_accuracy: 0.8869\n",
      "Epoch 4/600\n",
      "7867/7905 [============================>.] - ETA: 0s - loss: 0.3212 - accuracy: 0.8712\n",
      "Epoch 00004: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3213 - accuracy: 0.8712 - val_loss: 0.2794 - val_accuracy: 0.8869\n",
      "Epoch 5/600\n",
      "7874/7905 [============================>.] - ETA: 0s - loss: 0.3216 - accuracy: 0.8709\n",
      "Epoch 00005: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3216 - accuracy: 0.8709 - val_loss: 0.2789 - val_accuracy: 0.8869\n",
      "Epoch 6/600\n",
      "7870/7905 [============================>.] - ETA: 0s - loss: 0.3203 - accuracy: 0.8712\n",
      "Epoch 00006: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3203 - accuracy: 0.8712 - val_loss: 0.2786 - val_accuracy: 0.8869\n",
      "Epoch 7/600\n",
      "7901/7905 [============================>.] - ETA: 0s - loss: 0.3208 - accuracy: 0.8712\n",
      "Epoch 00007: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3208 - accuracy: 0.8712 - val_loss: 0.2796 - val_accuracy: 0.8869\n",
      "Epoch 8/600\n",
      "7891/7905 [============================>.] - ETA: 0s - loss: 0.3201 - accuracy: 0.8712 ETA: 0s - loss: 0.3200 - accura\n",
      "Epoch 00008: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3201 - accuracy: 0.8712 - val_loss: 0.2789 - val_accuracy: 0.8869\n",
      "Epoch 9/600\n",
      "7863/7905 [============================>.] - ETA: 0s - loss: 0.3210 - accuracy: 0.8711\n",
      "Epoch 00009: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3209 - accuracy: 0.8711 - val_loss: 0.2772 - val_accuracy: 0.8870\n",
      "Epoch 10/600\n",
      "7872/7905 [============================>.] - ETA: 0s - loss: 0.3203 - accuracy: 0.8712\n",
      "Epoch 00010: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3202 - accuracy: 0.8713 - val_loss: 0.2780 - val_accuracy: 0.8868\n",
      "Epoch 11/600\n",
      "7892/7905 [============================>.] - ETA: 0s - loss: 0.3202 - accuracy: 0.8711\n",
      "Epoch 00011: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3203 - accuracy: 0.8711 - val_loss: 0.2799 - val_accuracy: 0.8869\n",
      "Epoch 12/600\n",
      "7886/7905 [============================>.] - ETA: 0s - loss: 0.3201 - accuracy: 0.8711\n",
      "Epoch 00012: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3201 - accuracy: 0.8712 - val_loss: 0.2784 - val_accuracy: 0.8869\n",
      "Epoch 13/600\n",
      "7882/7905 [============================>.] - ETA: 0s - loss: 0.3205 - accuracy: 0.8712\n",
      "Epoch 00013: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3205 - accuracy: 0.8712 - val_loss: 0.2802 - val_accuracy: 0.8869\n",
      "Epoch 14/600\n",
      "7890/7905 [============================>.] - ETA: 0s - loss: 0.3203 - accuracy: 0.8712\n",
      "Epoch 00014: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3203 - accuracy: 0.8713 - val_loss: 0.2798 - val_accuracy: 0.8869\n",
      "Epoch 15/600\n",
      "7904/7905 [============================>.] - ETA: 0s - loss: 0.3198 - accuracy: 0.8713\n",
      "Epoch 00015: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3198 - accuracy: 0.8713 - val_loss: 0.2778 - val_accuracy: 0.8869\n",
      "Epoch 16/600\n",
      "7884/7905 [============================>.] - ETA: 0s - loss: 0.3211 - accuracy: 0.8709\n",
      "Epoch 00016: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3210 - accuracy: 0.8710 - val_loss: 0.2760 - val_accuracy: 0.8869\n",
      "Epoch 17/600\n",
      "7863/7905 [============================>.] - ETA: 0s - loss: 0.3196 - accuracy: 0.8715\n",
      "Epoch 00017: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3198 - accuracy: 0.8713 - val_loss: 0.2815 - val_accuracy: 0.8869\n",
      "Epoch 18/600\n",
      "7868/7905 [============================>.] - ETA: 0s - loss: 0.3198 - accuracy: 0.8714\n",
      "Epoch 00018: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3197 - accuracy: 0.8715 - val_loss: 0.2769 - val_accuracy: 0.8869\n",
      "Epoch 19/600\n",
      "7887/7905 [============================>.] - ETA: 0s - loss: 0.3192 - accuracy: 0.8717\n",
      "Epoch 00019: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 9s 1ms/step - loss: 0.3192 - accuracy: 0.8717 - val_loss: 0.2775 - val_accuracy: 0.8870\n",
      "Epoch 20/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.3209 - accuracy: 0.8707\n",
      "Epoch 00020: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3209 - accuracy: 0.8707 - val_loss: 0.2771 - val_accuracy: 0.8869\n",
      "Epoch 21/600\n",
      "7869/7905 [============================>.] - ETA: 0s - loss: 0.3193 - accuracy: 0.8715\n",
      "Epoch 00021: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3194 - accuracy: 0.8714 - val_loss: 0.2787 - val_accuracy: 0.8869\n",
      "Epoch 22/600\n",
      "7874/7905 [============================>.] - ETA: 0s - loss: 0.3197 - accuracy: 0.8713\n",
      "Epoch 00022: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3197 - accuracy: 0.8712 - val_loss: 0.2774 - val_accuracy: 0.8870\n",
      "Epoch 23/600\n",
      "7861/7905 [============================>.] - ETA: 0s - loss: 0.3199 - accuracy: 0.8713\n",
      "Epoch 00023: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3199 - accuracy: 0.8712 - val_loss: 0.2780 - val_accuracy: 0.8870\n",
      "Epoch 24/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.3202 - accuracy: 0.8713\n",
      "Epoch 00024: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3202 - accuracy: 0.8713 - val_loss: 0.2786 - val_accuracy: 0.8869\n",
      "Epoch 25/600\n",
      "7877/7905 [============================>.] - ETA: 0s - loss: 0.3206 - accuracy: 0.8709\n",
      "Epoch 00025: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3206 - accuracy: 0.8709 - val_loss: 0.2778 - val_accuracy: 0.8870\n",
      "Epoch 26/600\n",
      "7888/7905 [============================>.] - ETA: 0s - loss: 0.3199 - accuracy: 0.8711\n",
      "Epoch 00026: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3198 - accuracy: 0.8711 - val_loss: 0.2776 - val_accuracy: 0.8870\n",
      "Epoch 27/600\n",
      "7887/7905 [============================>.] - ETA: 0s - loss: 0.3202 - accuracy: 0.8710 E\n",
      "Epoch 00027: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.3202 - accuracy: 0.8711 - val_loss: 0.2804 - val_accuracy: 0.8869\n",
      "Epoch 28/600\n",
      "7883/7905 [============================>.] - ETA: 0s - loss: 0.3203 - accuracy: 0.8711\n",
      "Epoch 00028: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3203 - accuracy: 0.8711 - val_loss: 0.2763 - val_accuracy: 0.8869\n",
      "Epoch 29/600\n",
      "7895/7905 [============================>.] - ETA: 0s - loss: 0.3209 - accuracy: 0.8705\n",
      "Epoch 00029: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.3208 - accuracy: 0.8705 - val_loss: 0.2783 - val_accuracy: 0.8869\n",
      "Epoch 30/600\n",
      "7868/7905 [============================>.] - ETA: 0s - loss: 0.3194 - accuracy: 0.8713\n",
      "Epoch 00030: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3194 - accuracy: 0.8713 - val_loss: 0.2766 - val_accuracy: 0.8869\n",
      "Epoch 31/600\n",
      "7866/7905 [============================>.] - ETA: 0s - loss: 0.3195 - accuracy: 0.8711\n",
      "Epoch 00031: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3195 - accuracy: 0.8711 - val_loss: 0.2775 - val_accuracy: 0.8870\n",
      "Epoch 32/600\n",
      "7889/7905 [============================>.] - ETA: 0s - loss: 0.3199 - accuracy: 0.8710\n",
      "Epoch 00032: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3199 - accuracy: 0.8711 - val_loss: 0.2802 - val_accuracy: 0.8869\n",
      "Epoch 33/600\n",
      "7893/7905 [============================>.] - ETA: 0s - loss: 0.3203 - accuracy: 0.8709\n",
      "Epoch 00033: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3203 - accuracy: 0.8708 - val_loss: 0.2772 - val_accuracy: 0.8871\n",
      "Epoch 34/600\n",
      "7874/7905 [============================>.] - ETA: 0s - loss: 0.3201 - accuracy: 0.8707\n",
      "Epoch 00034: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3201 - accuracy: 0.8707 - val_loss: 0.2763 - val_accuracy: 0.8869\n",
      "Epoch 35/600\n",
      "7880/7905 [============================>.] - ETA: 0s - loss: 0.3193 - accuracy: 0.8711\n",
      "Epoch 00035: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.3192 - accuracy: 0.8712 - val_loss: 0.2772 - val_accuracy: 0.8869\n",
      "Epoch 36/600\n",
      "7863/7905 [============================>.] - ETA: 0s - loss: 0.3199 - accuracy: 0.8709\n",
      "Epoch 00036: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 11s 1ms/step - loss: 0.3199 - accuracy: 0.8709 - val_loss: 0.2791 - val_accuracy: 0.8869\n",
      "Epoch 37/600\n",
      "7878/7905 [============================>.] - ETA: 0s - loss: 0.3205 - accuracy: 0.8710\n",
      "Epoch 00037: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3205 - accuracy: 0.8710 - val_loss: 0.2792 - val_accuracy: 0.8869\n",
      "Epoch 38/600\n",
      "7867/7905 [============================>.] - ETA: 0s - loss: 0.3202 - accuracy: 0.8708\n",
      "Epoch 00038: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3202 - accuracy: 0.8708 - val_loss: 0.2781 - val_accuracy: 0.8869\n",
      "Epoch 39/600\n",
      "7893/7905 [============================>.] - ETA: 0s - loss: 0.3188 - accuracy: 0.8715\n",
      "Epoch 00039: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3188 - accuracy: 0.8715 - val_loss: 0.2781 - val_accuracy: 0.8869\n",
      "Epoch 40/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.3196 - accuracy: 0.8710\n",
      "Epoch 00040: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3196 - accuracy: 0.8710 - val_loss: 0.2774 - val_accuracy: 0.8869\n",
      "Epoch 41/600\n",
      "7873/7905 [============================>.] - ETA: 0s - loss: 0.3194 - accuracy: 0.8714\n",
      "Epoch 00041: val_accuracy did not improve from 0.88802\n",
      "7905/7905 [==============================] - 10s 1ms/step - loss: 0.3194 - accuracy: 0.8714 - val_loss: 0.2779 - val_accuracy: 0.8869\n",
      "Epoch 00041: early stopping\n",
      "Execution time :  0:06:42.831133\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "model = create_experiment_model()\n",
    "history = model.fit(x=X_train_norm, \n",
    "                    y=y_train, \n",
    "                    epochs=600,\n",
    "                    batch_size=32, \n",
    "                    validation_data=(X_valid_norm, y_valid), verbose=1,\n",
    "                    callbacks=callbacks_list\n",
    "                   )\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer: adam Train: 0.888 Test:0.887\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(X_train_norm, y_train, X_valid_norm, y_valid, model, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_accuracy(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Experiment with Accelerate Learning with Batch Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def create_batch_normalization_model(optimizer):\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "   \n",
    "    # Hidden Layers\n",
    "    #model.add(Dense(82, activation='relu', kernel_initializer='he_uniform', kernel_constraint=max_norm(3)))\n",
    "    #model.add(Dense(500, input_dim=2, activation='relu', kernel_regularizer=l2(0.001)))\n",
    "    #values of c range from 3 to 4.\n",
    "    model.add(Dense(82, input_dim=n_input, activation='relu', kernel_constraint=max_norm(3)))\n",
    "    model.add(BatchNormalization())\n",
    "        \n",
    "    model.add(Dense(82, activation='relu', kernel_constraint=max_norm(3)))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    model.add(Dense(41, activation='relu', kernel_constraint=max_norm(3)))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    model.add(Dense(21, activation='relu', kernel_constraint=max_norm(3)))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    # Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))   \n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2798 - accuracy: 0.8832\n",
      "Epoch 00001: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 38s 5ms/step - loss: 0.2798 - accuracy: 0.8833 - val_loss: 0.2606 - val_accuracy: 0.8871\n",
      "Epoch 2/600\n",
      "7893/7905 [============================>.] - ETA: 0s - loss: 0.2647 - accuracy: 0.8876\n",
      "Epoch 00002: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2646 - accuracy: 0.8876 - val_loss: 0.2600 - val_accuracy: 0.8870\n",
      "Epoch 3/600\n",
      "7887/7905 [============================>.] - ETA: 0s - loss: 0.2627 - accuracy: 0.8881\n",
      "Epoch 00003: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2627 - accuracy: 0.8881 - val_loss: 0.2606 - val_accuracy: 0.8871\n",
      "Epoch 4/600\n",
      "7901/7905 [============================>.] - ETA: 0s - loss: 0.2614 - accuracy: 0.8882\n",
      "Epoch 00004: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 20s 2ms/step - loss: 0.2614 - accuracy: 0.8881 - val_loss: 0.2606 - val_accuracy: 0.8869\n",
      "Epoch 5/600\n",
      "7904/7905 [============================>.] - ETA: 0s - loss: 0.2609 - accuracy: 0.8883\n",
      "Epoch 00005: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.2609 - accuracy: 0.8883 - val_loss: 0.2590 - val_accuracy: 0.8870\n",
      "Epoch 6/600\n",
      "7893/7905 [============================>.] - ETA: 0s - loss: 0.2603 - accuracy: 0.8880\n",
      "Epoch 00006: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.2603 - accuracy: 0.8880 - val_loss: 0.2608 - val_accuracy: 0.8876\n",
      "Epoch 7/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.2598 - accuracy: 0.8882\n",
      "Epoch 00007: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.2598 - accuracy: 0.8882 - val_loss: 0.2572 - val_accuracy: 0.8884\n",
      "Epoch 8/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.2593 - accuracy: 0.8886\n",
      "Epoch 00008: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2593 - accuracy: 0.8886 - val_loss: 0.2577 - val_accuracy: 0.8883\n",
      "Epoch 9/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.2590 - accuracy: 0.8884\n",
      "Epoch 00009: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 21s 3ms/step - loss: 0.2590 - accuracy: 0.8884 - val_loss: 0.2581 - val_accuracy: 0.8878\n",
      "Epoch 10/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.2589 - accuracy: 0.8884\n",
      "Epoch 00010: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2589 - accuracy: 0.8884 - val_loss: 0.2587 - val_accuracy: 0.8871\n",
      "Epoch 11/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2589 - accuracy: 0.8886\n",
      "Epoch 00011: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 20s 3ms/step - loss: 0.2589 - accuracy: 0.8886 - val_loss: 0.2572 - val_accuracy: 0.8878\n",
      "Epoch 12/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2588 - accuracy: 0.8886\n",
      "Epoch 00012: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 29s 4ms/step - loss: 0.2588 - accuracy: 0.8886 - val_loss: 0.2566 - val_accuracy: 0.8890\n",
      "Epoch 13/600\n",
      "7885/7905 [============================>.] - ETA: 0s - loss: 0.2587 - accuracy: 0.8884\n",
      "Epoch 00013: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 25s 3ms/step - loss: 0.2587 - accuracy: 0.8884 - val_loss: 0.2576 - val_accuracy: 0.8877\n",
      "Epoch 14/600\n",
      "7885/7905 [============================>.] - ETA: 0s - loss: 0.2583 - accuracy: 0.8882\n",
      "Epoch 00014: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.2583 - accuracy: 0.8882 - val_loss: 0.2574 - val_accuracy: 0.8883\n",
      "Epoch 15/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.2585 - accuracy: 0.8887\n",
      "Epoch 00015: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.2586 - accuracy: 0.8887 - val_loss: 0.2578 - val_accuracy: 0.8876\n",
      "Epoch 16/600\n",
      "7895/7905 [============================>.] - ETA: 0s - loss: 0.2586 - accuracy: 0.8884\n",
      "Epoch 00016: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.2586 - accuracy: 0.8884 - val_loss: 0.2573 - val_accuracy: 0.8872\n",
      "Epoch 17/600\n",
      "7891/7905 [============================>.] - ETA: 0s - loss: 0.2584 - accuracy: 0.8885\n",
      "Epoch 00017: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.2584 - accuracy: 0.8885 - val_loss: 0.2575 - val_accuracy: 0.8879\n",
      "Epoch 18/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.2585 - accuracy: 0.8882\n",
      "Epoch 00018: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 37s 5ms/step - loss: 0.2585 - accuracy: 0.8882 - val_loss: 0.2571 - val_accuracy: 0.8883\n",
      "Epoch 19/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.2584 - accuracy: 0.8885\n",
      "Epoch 00019: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2584 - accuracy: 0.8885 - val_loss: 0.2575 - val_accuracy: 0.8890\n",
      "Epoch 20/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.2584 - accuracy: 0.8885\n",
      "Epoch 00020: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2584 - accuracy: 0.8885 - val_loss: 0.2580 - val_accuracy: 0.8885\n",
      "Epoch 21/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.2582 - accuracy: 0.8886\n",
      "Epoch 00021: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2582 - accuracy: 0.8886 - val_loss: 0.2598 - val_accuracy: 0.8884\n",
      "Epoch 22/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2585 - accuracy: 0.8885\n",
      "Epoch 00022: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2584 - accuracy: 0.8885 - val_loss: 0.2614 - val_accuracy: 0.8872\n",
      "Epoch 23/600\n",
      "7901/7905 [============================>.] - ETA: 0s - loss: 0.2585 - accuracy: 0.8887\n",
      "Epoch 00023: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2585 - accuracy: 0.8887 - val_loss: 0.2578 - val_accuracy: 0.8883\n",
      "Epoch 24/600\n",
      "7893/7905 [============================>.] - ETA: 0s - loss: 0.2582 - accuracy: 0.8885\n",
      "Epoch 00024: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2582 - accuracy: 0.8885 - val_loss: 0.2576 - val_accuracy: 0.8886\n",
      "Epoch 25/600\n",
      "7902/7905 [============================>.] - ETA: 0s - loss: 0.2584 - accuracy: 0.8885\n",
      "Epoch 00025: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2584 - accuracy: 0.8885 - val_loss: 0.2579 - val_accuracy: 0.8876\n",
      "Epoch 26/600\n",
      "7895/7905 [============================>.] - ETA: 0s - loss: 0.2585 - accuracy: 0.8887\n",
      "Epoch 00026: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2585 - accuracy: 0.8886 - val_loss: 0.2572 - val_accuracy: 0.8886\n",
      "Epoch 27/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.2584 - accuracy: 0.8884\n",
      "Epoch 00027: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2584 - accuracy: 0.8884 - val_loss: 0.2573 - val_accuracy: 0.8888\n",
      "Epoch 28/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.2582 - accuracy: 0.8886\n",
      "Epoch 00028: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2583 - accuracy: 0.8886 - val_loss: 0.2598 - val_accuracy: 0.8882\n",
      "Epoch 29/600\n",
      "7904/7905 [============================>.] - ETA: 0s - loss: 0.2584 - accuracy: 0.8888\n",
      "Epoch 00029: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2584 - accuracy: 0.8888 - val_loss: 0.2576 - val_accuracy: 0.8883\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.2580 - accuracy: 0.8886\n",
      "Epoch 00030: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2580 - accuracy: 0.8886 - val_loss: 0.2568 - val_accuracy: 0.8890\n",
      "Epoch 31/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.2581 - accuracy: 0.8889\n",
      "Epoch 00031: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2580 - accuracy: 0.8889 - val_loss: 0.2590 - val_accuracy: 0.8872\n",
      "Epoch 32/600\n",
      "7902/7905 [============================>.] - ETA: 0s - loss: 0.2580 - accuracy: 0.8888\n",
      "Epoch 00032: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2580 - accuracy: 0.8888 - val_loss: 0.2568 - val_accuracy: 0.8879\n",
      "Epoch 33/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.2580 - accuracy: 0.8890\n",
      "Epoch 00033: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2580 - accuracy: 0.8890 - val_loss: 0.2580 - val_accuracy: 0.8886\n",
      "Epoch 34/600\n",
      "7901/7905 [============================>.] - ETA: 0s - loss: 0.2580 - accuracy: 0.8887\n",
      "Epoch 00034: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2579 - accuracy: 0.8887 - val_loss: 0.2576 - val_accuracy: 0.8882\n",
      "Epoch 35/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.2580 - accuracy: 0.8889\n",
      "Epoch 00035: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2580 - accuracy: 0.8889 - val_loss: 0.2571 - val_accuracy: 0.8879\n",
      "Epoch 36/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.2583 - accuracy: 0.8886\n",
      "Epoch 00036: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2583 - accuracy: 0.8886 - val_loss: 0.2576 - val_accuracy: 0.8884\n",
      "Epoch 37/600\n",
      "7905/7905 [==============================] - ETA: 0s - loss: 0.2582 - accuracy: 0.8888\n",
      "Epoch 00037: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2582 - accuracy: 0.8888 - val_loss: 0.2563 - val_accuracy: 0.8881\n",
      "Epoch 38/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.2580 - accuracy: 0.8888\n",
      "Epoch 00038: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2580 - accuracy: 0.8888 - val_loss: 0.2575 - val_accuracy: 0.8884\n",
      "Epoch 39/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.2581 - accuracy: 0.8887\n",
      "Epoch 00039: val_accuracy did not improve from 0.88927\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2581 - accuracy: 0.8887 - val_loss: 0.2564 - val_accuracy: 0.8880\n",
      "Epoch 40/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.2582 - accuracy: 0.8885\n",
      "Epoch 00040: val_accuracy improved from 0.88927 to 0.88929, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2582 - accuracy: 0.8885 - val_loss: 0.2579 - val_accuracy: 0.8893\n",
      "Epoch 41/600\n",
      "7900/7905 [============================>.] - ETA: 0s - loss: 0.2582 - accuracy: 0.8888\n",
      "Epoch 00041: val_accuracy did not improve from 0.88929\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2582 - accuracy: 0.8888 - val_loss: 0.2574 - val_accuracy: 0.8886\n",
      "Epoch 42/600\n",
      "7895/7905 [============================>.] - ETA: 0s - loss: 0.2579 - accuracy: 0.8885\n",
      "Epoch 00042: val_accuracy did not improve from 0.88929\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2580 - accuracy: 0.8885 - val_loss: 0.2567 - val_accuracy: 0.8888\n",
      "Epoch 43/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.2582 - accuracy: 0.8888\n",
      "Epoch 00043: val_accuracy did not improve from 0.88929\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2582 - accuracy: 0.8888 - val_loss: 0.2595 - val_accuracy: 0.8881\n",
      "Epoch 44/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.2583 - accuracy: 0.8887\n",
      "Epoch 00044: val_accuracy improved from 0.88929 to 0.88935, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2583 - accuracy: 0.8887 - val_loss: 0.2568 - val_accuracy: 0.8893\n",
      "Epoch 45/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.2584 - accuracy: 0.8887\n",
      "Epoch 00045: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2584 - accuracy: 0.8887 - val_loss: 0.2569 - val_accuracy: 0.8885\n",
      "Epoch 46/600\n",
      "7900/7905 [============================>.] - ETA: 0s - loss: 0.2580 - accuracy: 0.8888\n",
      "Epoch 00046: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2580 - accuracy: 0.8888 - val_loss: 0.2578 - val_accuracy: 0.8878\n",
      "Epoch 47/600\n",
      "7905/7905 [==============================] - ETA: 0s - loss: 0.2582 - accuracy: 0.8885\n",
      "Epoch 00047: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2582 - accuracy: 0.8885 - val_loss: 0.2568 - val_accuracy: 0.8887\n",
      "Epoch 48/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.2578 - accuracy: 0.8890\n",
      "Epoch 00048: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2579 - accuracy: 0.8890 - val_loss: 0.2596 - val_accuracy: 0.8880\n",
      "Epoch 49/600\n",
      "7893/7905 [============================>.] - ETA: 0s - loss: 0.2581 - accuracy: 0.8886\n",
      "Epoch 00049: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 36s 5ms/step - loss: 0.2581 - accuracy: 0.8885 - val_loss: 0.2569 - val_accuracy: 0.8883\n",
      "Epoch 50/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.2581 - accuracy: 0.8886\n",
      "Epoch 00050: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2581 - accuracy: 0.8886 - val_loss: 0.2580 - val_accuracy: 0.8883\n",
      "Epoch 51/600\n",
      "7893/7905 [============================>.] - ETA: 0s - loss: 0.2580 - accuracy: 0.8888\n",
      "Epoch 00051: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 36s 5ms/step - loss: 0.2580 - accuracy: 0.8888 - val_loss: 0.2577 - val_accuracy: 0.8881\n",
      "Epoch 52/600\n",
      "7901/7905 [============================>.] - ETA: 0s - loss: 0.2580 - accuracy: 0.8887\n",
      "Epoch 00052: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 36s 5ms/step - loss: 0.2580 - accuracy: 0.8887 - val_loss: 0.2582 - val_accuracy: 0.8875\n",
      "Epoch 53/600\n",
      "7900/7905 [============================>.] - ETA: 0s - loss: 0.2579 - accuracy: 0.8886\n",
      "Epoch 00053: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2580 - accuracy: 0.8886 - val_loss: 0.2573 - val_accuracy: 0.8883\n",
      "Epoch 54/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.2579 - accuracy: 0.8890\n",
      "Epoch 00054: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2579 - accuracy: 0.8890 - val_loss: 0.2592 - val_accuracy: 0.8874\n",
      "Epoch 55/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.2580 - accuracy: 0.8887\n",
      "Epoch 00055: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2580 - accuracy: 0.8887 - val_loss: 0.2568 - val_accuracy: 0.8885\n",
      "Epoch 56/600\n",
      "7890/7905 [============================>.] - ETA: 0s - loss: 0.2580 - accuracy: 0.8888\n",
      "Epoch 00056: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2579 - accuracy: 0.8888 - val_loss: 0.2567 - val_accuracy: 0.8887\n",
      "Epoch 57/600\n",
      "7902/7905 [============================>.] - ETA: 0s - loss: 0.2579 - accuracy: 0.8888\n",
      "Epoch 00057: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2580 - accuracy: 0.8888 - val_loss: 0.2589 - val_accuracy: 0.8869\n",
      "Epoch 58/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2581 - accuracy: 0.8888\n",
      "Epoch 00058: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2581 - accuracy: 0.8888 - val_loss: 0.2569 - val_accuracy: 0.8881\n",
      "Epoch 59/600\n",
      "7902/7905 [============================>.] - ETA: 0s - loss: 0.2581 - accuracy: 0.8885\n",
      "Epoch 00059: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2581 - accuracy: 0.8885 - val_loss: 0.2599 - val_accuracy: 0.8883\n",
      "Epoch 60/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2579 - accuracy: 0.8890\n",
      "Epoch 00060: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2579 - accuracy: 0.8890 - val_loss: 0.2586 - val_accuracy: 0.8883\n",
      "Epoch 61/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.2582 - accuracy: 0.8889\n",
      "Epoch 00061: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2583 - accuracy: 0.8888 - val_loss: 0.2571 - val_accuracy: 0.8887\n",
      "Epoch 62/600\n",
      "7900/7905 [============================>.] - ETA: 0s - loss: 0.2578 - accuracy: 0.8889\n",
      "Epoch 00062: val_accuracy did not improve from 0.88935\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2578 - accuracy: 0.8889 - val_loss: 0.2569 - val_accuracy: 0.8889\n",
      "Epoch 00062: early stopping\n",
      "Execution time :  0:31:55.065557\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "optimizer = Adam()\n",
    "model = create_batch_normalization_model(optimizer)\n",
    "history = model.fit(x=X_train_norm, \n",
    "                    y=y_train, \n",
    "                    epochs=600,\n",
    "                    batch_size=32,\n",
    "                    validation_data=(X_valid_norm, y_valid), verbose=1,\n",
    "                    callbacks=callbacks_list\n",
    "                   )\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer: adam Training Loss: 0.890 Validation Accuracy:0.889\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(X_train_norm, y_train, X_valid_norm, y_valid, model, optimizers[3])\n",
    "#Training Loss: 0.890 Validation Accuracy:0.889"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_accuracy(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6200721519537532\n",
      "0.8894160214564041\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_test, predictions))\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.99      0.94     63623\n",
      "           1       0.94      0.46      0.62     15421\n",
      "\n",
      "    accuracy                           0.89     79044\n",
      "   macro avg       0.91      0.73      0.78     79044\n",
      "weighted avg       0.89      0.89      0.87     79044\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[63170   453]\n",
      " [ 8288  7133]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#plot_confusion_matrix(model, X_test_norm, y_test, values_format='d', display_labels=[\"Paid\",\"Not Paid\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Optimizing Adam with LearningRateScheduler (step_decay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Default parameers: \n",
    "    learning_rate=0.001,\n",
    "    beta_1=0.9,\n",
    "    beta_2=0.999,\n",
    "    epsilon=1e-07,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def step_decay(epoch):\n",
    "    initial_lrate = 0.1\n",
    "    drop = 0.5\n",
    "    epochs_drop = 10.0\n",
    "    lrate = initial_lrate * math.pow(drop, math.floor((1+epoch)/epochs_drop))\n",
    "    \n",
    "    return lrate\n",
    "\n",
    "lrate = LearningRateScheduler(step_decay)\n",
    "callbacks_list.append(lrate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "7874/7905 [============================>.] - ETA: 0s - loss: 0.3519 - accuracy: 0.8631\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.84646, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 13s 2ms/step - loss: 0.3518 - accuracy: 0.8631 - val_loss: 0.4450 - val_accuracy: 0.8465\n",
      "Epoch 2/600\n",
      "7885/7905 [============================>.] - ETA: 0s - loss: 0.3483 - accuracy: 0.8649\n",
      "Epoch 00002: val_accuracy improved from 0.84646 to 0.87690, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.3483 - accuracy: 0.8649 - val_loss: 0.3350 - val_accuracy: 0.8769\n",
      "Epoch 3/600\n",
      "7905/7905 [==============================] - ETA: 0s - loss: 0.3465 - accuracy: 0.8657\n",
      "Epoch 00003: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.3465 - accuracy: 0.8657 - val_loss: 35.7169 - val_accuracy: 0.1989\n",
      "Epoch 4/600\n",
      "7880/7905 [============================>.] - ETA: 0s - loss: 0.3462 - accuracy: 0.8670\n",
      "Epoch 00004: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.3463 - accuracy: 0.8669 - val_loss: 0.9590 - val_accuracy: 0.8118\n",
      "Epoch 5/600\n",
      "7891/7905 [============================>.] - ETA: 0s - loss: 0.4307 - accuracy: 0.8319\n",
      "Epoch 00005: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.4308 - accuracy: 0.8319 - val_loss: 0.4991 - val_accuracy: 0.8011\n",
      "Epoch 6/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.4990 - accuracy: 0.8041\n",
      "Epoch 00006: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.4991 - accuracy: 0.8041 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 7/600\n",
      "7889/7905 [============================>.] - ETA: 0s - loss: 0.4993 - accuracy: 0.8042\n",
      "Epoch 00007: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.4993 - accuracy: 0.8042 - val_loss: 0.4991 - val_accuracy: 0.8011\n",
      "Epoch 8/600\n",
      "7892/7905 [============================>.] - ETA: 0s - loss: 0.4989 - accuracy: 0.8042\n",
      "Epoch 00008: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.4990 - accuracy: 0.8042 - val_loss: 0.4999 - val_accuracy: 0.8011\n",
      "Epoch 9/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.4991 - accuracy: 0.8042\n",
      "Epoch 00009: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.4990 - accuracy: 0.8042 - val_loss: 0.5021 - val_accuracy: 0.8011\n",
      "Epoch 10/600\n",
      "7892/7905 [============================>.] - ETA: 0s - loss: 0.4963 - accuracy: 0.8042\n",
      "Epoch 00010: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.4963 - accuracy: 0.8042 - val_loss: 0.4995 - val_accuracy: 0.8011\n",
      "Epoch 11/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.4964 - accuracy: 0.8042\n",
      "Epoch 00011: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.4965 - accuracy: 0.8042 - val_loss: 0.4997 - val_accuracy: 0.8011\n",
      "Epoch 12/600\n",
      "7900/7905 [============================>.] - ETA: 0s - loss: 0.4965 - accuracy: 0.8042\n",
      "Epoch 00012: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.4965 - accuracy: 0.8042 - val_loss: 0.4991 - val_accuracy: 0.8011\n",
      "Epoch 13/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.4966 - accuracy: 0.8042\n",
      "Epoch 00013: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.4966 - accuracy: 0.8042 - val_loss: 0.4991 - val_accuracy: 0.8011\n",
      "Epoch 14/600\n",
      "7890/7905 [============================>.] - ETA: 0s - loss: 0.4967 - accuracy: 0.8042\n",
      "Epoch 00014: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 21s 3ms/step - loss: 0.4967 - accuracy: 0.8042 - val_loss: 0.4996 - val_accuracy: 0.8011\n",
      "Epoch 15/600\n",
      "7886/7905 [============================>.] - ETA: 0s - loss: 0.4964 - accuracy: 0.8042\n",
      "Epoch 00015: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 22s 3ms/step - loss: 0.4964 - accuracy: 0.8042 - val_loss: 0.4992 - val_accuracy: 0.8011\n",
      "Epoch 16/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.4965 - accuracy: 0.8042\n",
      "Epoch 00016: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 21s 3ms/step - loss: 0.4965 - accuracy: 0.8042 - val_loss: 0.4993 - val_accuracy: 0.8011\n",
      "Epoch 17/600\n",
      "7882/7905 [============================>.] - ETA: 0s - loss: 0.4966 - accuracy: 0.8042\n",
      "Epoch 00017: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.4966 - accuracy: 0.8042 - val_loss: 0.4994 - val_accuracy: 0.8011\n",
      "Epoch 18/600\n",
      "7887/7905 [============================>.] - ETA: 0s - loss: 0.4964 - accuracy: 0.8043\n",
      "Epoch 00018: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.4965 - accuracy: 0.8042 - val_loss: 0.5010 - val_accuracy: 0.8011\n",
      "Epoch 19/600\n",
      "7902/7905 [============================>.] - ETA: 0s - loss: 0.4965 - accuracy: 0.8042\n",
      "Epoch 00019: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.4965 - accuracy: 0.8042 - val_loss: 0.5063 - val_accuracy: 0.8011\n",
      "Epoch 20/600\n",
      "7902/7905 [============================>.] - ETA: 0s - loss: 0.4954 - accuracy: 0.8042\n",
      "Epoch 00020: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.4954 - accuracy: 0.8042 - val_loss: 0.4993 - val_accuracy: 0.8011\n",
      "Epoch 21/600\n",
      "7882/7905 [============================>.] - ETA: 0s - loss: 0.4953 - accuracy: 0.8043\n",
      "Epoch 00021: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.4955 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 22/600\n",
      "7880/7905 [============================>.] - ETA: 0s - loss: 0.4955 - accuracy: 0.8041\n",
      "Epoch 00022: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.4954 - accuracy: 0.8042 - val_loss: 0.5021 - val_accuracy: 0.8011\n",
      "Epoch 23/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.4953 - accuracy: 0.8042\n",
      "Epoch 00023: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.4953 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 24/600\n",
      "7905/7905 [==============================] - ETA: 0s - loss: 0.4953 - accuracy: 0.8042\n",
      "Epoch 00024: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.4953 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 25/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.4954 - accuracy: 0.8042\n",
      "Epoch 00025: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.4954 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 26/600\n",
      "7884/7905 [============================>.] - ETA: 0s - loss: 0.4953 - accuracy: 0.8042\n",
      "Epoch 00026: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.4953 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 27/600\n",
      "7895/7905 [============================>.] - ETA: 0s - loss: 0.4954 - accuracy: 0.8042\n",
      "Epoch 00027: val_accuracy did not improve from 0.87690\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.4953 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 00027: early stopping\n",
      "Execution time :  0:07:51.153694\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "opt = Adam(learning_rate=0.1)\n",
    "model = create_batch_normalization_model(opt)\n",
    "history = model.fit(x=X_train_norm, \n",
    "                    y=y_train, \n",
    "                    epochs=600,\n",
    "                    batch_size=32,\n",
    "                    validation_data=(X_valid_norm, y_valid), verbose=1,\n",
    "                    callbacks=callbacks_list\n",
    "                   )\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer: adam Training Loss: 0.804 Validation Accuracy:0.801\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(X_train_norm, y_train, X_valid_norm, y_valid, model, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "plot_loss(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "plot_accuracy(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.804906128232377\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_test, predictions))\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Optimizing Adam with LearningRateScheduler (Exponential Decay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def exp_decay(epoch):\n",
    "    initial_lrate = 0.1\n",
    "    k = 0.1\n",
    "    lrate = initial_lrate * math.exp(-k*epoch)\n",
    "    return lrate\n",
    "\n",
    "lrate = LearningRateScheduler(exp_decay)\n",
    "callbacks_list.append(lrate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "7880/7905 [============================>.] - ETA: 0s - loss: 0.3491 - accuracy: 0.8644\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.84112, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 13s 2ms/step - loss: 0.3491 - accuracy: 0.8644 - val_loss: 0.4580 - val_accuracy: 0.8411\n",
      "Epoch 2/600\n",
      "7890/7905 [============================>.] - ETA: 0s - loss: 0.3461 - accuracy: 0.8660\n",
      "Epoch 00002: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 13s 2ms/step - loss: 0.3461 - accuracy: 0.8660 - val_loss: 8.0028 - val_accuracy: 0.8011\n",
      "Epoch 3/600\n",
      "7880/7905 [============================>.] - ETA: 0s - loss: 0.4504 - accuracy: 0.8233\n",
      "Epoch 00003: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 15s 2ms/step - loss: 0.4505 - accuracy: 0.8233 - val_loss: 0.4996 - val_accuracy: 0.8011\n",
      "Epoch 4/600\n",
      "7884/7905 [============================>.] - ETA: 0s - loss: 0.4975 - accuracy: 0.8043\n",
      "Epoch 00004: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 15s 2ms/step - loss: 0.4976 - accuracy: 0.8042 - val_loss: 0.5105 - val_accuracy: 0.8011\n",
      "Epoch 5/600\n",
      "7901/7905 [============================>.] - ETA: 0s - loss: 0.4975 - accuracy: 0.8042\n",
      "Epoch 00005: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 15s 2ms/step - loss: 0.4975 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 6/600\n",
      "7905/7905 [==============================] - ETA: 0s - loss: 0.4972 - accuracy: 0.8042\n",
      "Epoch 00006: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 15s 2ms/step - loss: 0.4972 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 7/600\n",
      "7888/7905 [============================>.] - ETA: 0s - loss: 0.4971 - accuracy: 0.8042\n",
      "Epoch 00007: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 15s 2ms/step - loss: 0.4971 - accuracy: 0.8042 - val_loss: 0.4991 - val_accuracy: 0.8011\n",
      "Epoch 8/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.4966 - accuracy: 0.8042\n",
      "Epoch 00008: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 15s 2ms/step - loss: 0.4966 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 9/600\n",
      "7902/7905 [============================>.] - ETA: 0s - loss: 0.4962 - accuracy: 0.8042\n",
      "Epoch 00009: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 15s 2ms/step - loss: 0.4962 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 10/600\n",
      "7892/7905 [============================>.] - ETA: 0s - loss: 0.4961 - accuracy: 0.8042\n",
      "Epoch 00010: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 15s 2ms/step - loss: 0.4961 - accuracy: 0.8042 - val_loss: 0.5007 - val_accuracy: 0.8011\n",
      "Epoch 11/600\n",
      "7889/7905 [============================>.] - ETA: 0s - loss: 0.4961 - accuracy: 0.8042\n",
      "Epoch 00011: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 15s 2ms/step - loss: 0.4961 - accuracy: 0.8042 - val_loss: 0.4993 - val_accuracy: 0.8011\n",
      "Epoch 12/600\n",
      "7878/7905 [============================>.] - ETA: 0s - loss: 0.4960 - accuracy: 0.8042\n",
      "Epoch 00012: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 15s 2ms/step - loss: 0.4960 - accuracy: 0.8042 - val_loss: 0.4999 - val_accuracy: 0.8011\n",
      "Epoch 13/600\n",
      "7887/7905 [============================>.] - ETA: 0s - loss: 0.4955 - accuracy: 0.8042\n",
      "Epoch 00013: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 15s 2ms/step - loss: 0.4955 - accuracy: 0.8042 - val_loss: 0.5118 - val_accuracy: 0.8011\n",
      "Epoch 14/600\n",
      "7879/7905 [============================>.] - ETA: 0s - loss: 0.4954 - accuracy: 0.8042\n",
      "Epoch 00014: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 15s 2ms/step - loss: 0.4955 - accuracy: 0.8042 - val_loss: 0.4991 - val_accuracy: 0.8011\n",
      "Epoch 15/600\n",
      "7887/7905 [============================>.] - ETA: 0s - loss: 0.4952 - accuracy: 0.8042\n",
      "Epoch 00015: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.4953 - accuracy: 0.8042 - val_loss: 0.5000 - val_accuracy: 0.8011\n",
      "Epoch 16/600\n",
      "7889/7905 [============================>.] - ETA: 0s - loss: 0.4950 - accuracy: 0.8042\n",
      "Epoch 00016: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.4950 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 17/600\n",
      "7905/7905 [==============================] - ETA: 0s - loss: 0.4950 - accuracy: 0.8042\n",
      "Epoch 00017: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.4950 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 18/600\n",
      "7883/7905 [============================>.] - ETA: 0s - loss: 0.4951 - accuracy: 0.8042\n",
      "Epoch 00018: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.4951 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 19/600\n",
      "7893/7905 [============================>.] - ETA: 0s - loss: 0.4950 - accuracy: 0.8043\n",
      "Epoch 00019: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.4951 - accuracy: 0.8042 - val_loss: 0.4996 - val_accuracy: 0.8011\n",
      "Epoch 20/600\n",
      "7891/7905 [============================>.] - ETA: 0s - loss: 0.4949 - accuracy: 0.8042\n",
      "Epoch 00020: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.4949 - accuracy: 0.8042 - val_loss: 0.4989 - val_accuracy: 0.8011\n",
      "Epoch 21/600\n",
      "7892/7905 [============================>.] - ETA: 0s - loss: 0.4949 - accuracy: 0.8042\n",
      "Epoch 00021: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.4950 - accuracy: 0.8042 - val_loss: 0.4996 - val_accuracy: 0.8011\n",
      "Epoch 22/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.4949 - accuracy: 0.8042\n",
      "Epoch 00022: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.4949 - accuracy: 0.8042 - val_loss: 0.4994 - val_accuracy: 0.8011\n",
      "Epoch 23/600\n",
      "7888/7905 [============================>.] - ETA: 0s - loss: 0.4949 - accuracy: 0.8042\n",
      "Epoch 00023: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.4948 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 24/600\n",
      "7885/7905 [============================>.] - ETA: 0s - loss: 0.4948 - accuracy: 0.8042\n",
      "Epoch 00024: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.4948 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 25/600\n",
      "7884/7905 [============================>.] - ETA: 0s - loss: 0.4947 - accuracy: 0.8042\n",
      "Epoch 00025: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.4948 - accuracy: 0.8042 - val_loss: 0.4990 - val_accuracy: 0.8011\n",
      "Epoch 26/600\n",
      "7886/7905 [============================>.] - ETA: 0s - loss: 0.4949 - accuracy: 0.8042\n",
      "Epoch 00026: val_accuracy did not improve from 0.84112\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.4948 - accuracy: 0.8042 - val_loss: 0.4994 - val_accuracy: 0.8011\n",
      "Epoch 00026: early stopping\n",
      "Execution time :  0:06:50.115232\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "opt = Adam(learning_rate=0.1)\n",
    "model = create_batch_normalization_model(opt)\n",
    "history = model.fit(x=X_train_norm, \n",
    "                    y=y_train, \n",
    "                    epochs=600,\n",
    "                    batch_size=32,\n",
    "                    validation_data=(X_valid_norm, y_valid), verbose=1,\n",
    "                    callbacks=callbacks_list\n",
    "                   )\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer: adam Training Loss: 0.804 Validation Accuracy:0.801\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(X_train_norm, y_train, X_valid_norm, y_valid, model, optimizers[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Resampling with Batch Normalization & kernel_constraint (max_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time :  2:19:44.264295\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "#resample = SMOTETomek(tomek=TomekLinks(sampling_strategy='majority')) \n",
    "#Training Loss: 0.859 Validation Accuracy:0.841 \n",
    "resample = SMOTEENN(enn=EditedNearestNeighbours(sampling_strategy='majority'))\n",
    "\n",
    "# fit and apply the transform\n",
    "X_sample, y_sample = resample.fit_resample(X_train_norm, y_train)\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "10763/10784 [============================>.] - ETA: 0s - loss: 0.2622 - accuracy: 0.8738\n",
      "Epoch 00001: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 16s 1ms/step - loss: 0.2622 - accuracy: 0.8739 - val_loss: 0.5362 - val_accuracy: 0.7302\n",
      "Epoch 2/600\n",
      "10766/10784 [============================>.] - ETA: 0s - loss: 0.2383 - accuracy: 0.8864\n",
      "Epoch 00002: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 19s 2ms/step - loss: 0.2383 - accuracy: 0.8864 - val_loss: 0.5587 - val_accuracy: 0.7209\n",
      "Epoch 3/600\n",
      "10764/10784 [============================>.] - ETA: 0s - loss: 0.2313 - accuracy: 0.8907\n",
      "Epoch 00003: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 19s 2ms/step - loss: 0.2313 - accuracy: 0.8907 - val_loss: 0.5768 - val_accuracy: 0.7030\n",
      "Epoch 4/600\n",
      "10773/10784 [============================>.] - ETA: 0s - loss: 0.2276 - accuracy: 0.8931\n",
      "Epoch 00004: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 18s 2ms/step - loss: 0.2276 - accuracy: 0.8931 - val_loss: 0.6053 - val_accuracy: 0.7092\n",
      "Epoch 5/600\n",
      "10775/10784 [============================>.] - ETA: 0s - loss: 0.2245 - accuracy: 0.8952\n",
      "Epoch 00005: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 19s 2ms/step - loss: 0.2245 - accuracy: 0.8952 - val_loss: 0.5380 - val_accuracy: 0.7067\n",
      "Epoch 6/600\n",
      "10775/10784 [============================>.] - ETA: 0s - loss: 0.2222 - accuracy: 0.8967\n",
      "Epoch 00006: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 19s 2ms/step - loss: 0.2222 - accuracy: 0.8966 - val_loss: 0.5358 - val_accuracy: 0.7363\n",
      "Epoch 7/600\n",
      "10757/10784 [============================>.] - ETA: 0s - loss: 0.2203 - accuracy: 0.8970\n",
      "Epoch 00007: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 18s 2ms/step - loss: 0.2203 - accuracy: 0.8970 - val_loss: 0.5302 - val_accuracy: 0.7365\n",
      "Epoch 8/600\n",
      "10766/10784 [============================>.] - ETA: 0s - loss: 0.2194 - accuracy: 0.8982\n",
      "Epoch 00008: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 19s 2ms/step - loss: 0.2194 - accuracy: 0.8982 - val_loss: 0.5792 - val_accuracy: 0.7139\n",
      "Epoch 9/600\n",
      "10767/10784 [============================>.] - ETA: 0s - loss: 0.2182 - accuracy: 0.8992\n",
      "Epoch 00009: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 19s 2ms/step - loss: 0.2183 - accuracy: 0.8992 - val_loss: 0.5800 - val_accuracy: 0.7210\n",
      "Epoch 10/600\n",
      "10762/10784 [============================>.] - ETA: 0s - loss: 0.2161 - accuracy: 0.9004\n",
      "Epoch 00010: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 19s 2ms/step - loss: 0.2162 - accuracy: 0.9004 - val_loss: 0.5403 - val_accuracy: 0.7379\n",
      "Epoch 11/600\n",
      "10784/10784 [==============================] - ETA: 0s - loss: 0.2150 - accuracy: 0.9006\n",
      "Epoch 00011: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 19s 2ms/step - loss: 0.2150 - accuracy: 0.9006 - val_loss: 0.5150 - val_accuracy: 0.7575\n",
      "Epoch 12/600\n",
      "10780/10784 [============================>.] - ETA: 0s - loss: 0.2143 - accuracy: 0.9013\n",
      "Epoch 00012: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2143 - accuracy: 0.9013 - val_loss: 0.5668 - val_accuracy: 0.7368\n",
      "Epoch 13/600\n",
      "10777/10784 [============================>.] - ETA: 0s - loss: 0.2141 - accuracy: 0.9015\n",
      "Epoch 00013: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2141 - accuracy: 0.9015 - val_loss: 0.5365 - val_accuracy: 0.7170\n",
      "Epoch 14/600\n",
      "10762/10784 [============================>.] - ETA: 0s - loss: 0.2128 - accuracy: 0.9020\n",
      "Epoch 00014: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2127 - accuracy: 0.9020 - val_loss: 0.5789 - val_accuracy: 0.7245\n",
      "Epoch 15/600\n",
      "10766/10784 [============================>.] - ETA: 0s - loss: 0.2111 - accuracy: 0.9034\n",
      "Epoch 00015: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2112 - accuracy: 0.9034 - val_loss: 0.5074 - val_accuracy: 0.7530\n",
      "Epoch 16/600\n",
      "10757/10784 [============================>.] - ETA: 0s - loss: 0.2110 - accuracy: 0.9037\n",
      "Epoch 00016: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2110 - accuracy: 0.9037 - val_loss: 0.5570 - val_accuracy: 0.7355\n",
      "Epoch 17/600\n",
      "10784/10784 [==============================] - ETA: 0s - loss: 0.2106 - accuracy: 0.9036\n",
      "Epoch 00017: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2106 - accuracy: 0.9036 - val_loss: 0.5628 - val_accuracy: 0.7264\n",
      "Epoch 18/600\n",
      "10767/10784 [============================>.] - ETA: 0s - loss: 0.2091 - accuracy: 0.9048\n",
      "Epoch 00018: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2091 - accuracy: 0.9048 - val_loss: 0.5648 - val_accuracy: 0.7239\n",
      "Epoch 19/600\n",
      "10767/10784 [============================>.] - ETA: 0s - loss: 0.2086 - accuracy: 0.9051\n",
      "Epoch 00019: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2085 - accuracy: 0.9051 - val_loss: 0.5911 - val_accuracy: 0.7317\n",
      "Epoch 20/600\n",
      "10773/10784 [============================>.] - ETA: 0s - loss: 0.2082 - accuracy: 0.9051\n",
      "Epoch 00020: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2082 - accuracy: 0.9051 - val_loss: 0.5877 - val_accuracy: 0.7162\n",
      "Epoch 21/600\n",
      "10778/10784 [============================>.] - ETA: 0s - loss: 0.2076 - accuracy: 0.9054\n",
      "Epoch 00021: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.2076 - accuracy: 0.9054 - val_loss: 0.5363 - val_accuracy: 0.7399\n",
      "Epoch 22/600\n",
      "10766/10784 [============================>.] - ETA: 0s - loss: 0.2066 - accuracy: 0.9059\n",
      "Epoch 00022: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 22s 2ms/step - loss: 0.2066 - accuracy: 0.9059 - val_loss: 0.5742 - val_accuracy: 0.7317\n",
      "Epoch 23/600\n",
      "10771/10784 [============================>.] - ETA: 0s - loss: 0.2060 - accuracy: 0.9061\n",
      "Epoch 00023: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2060 - accuracy: 0.9061 - val_loss: 0.5490 - val_accuracy: 0.7335\n",
      "Epoch 24/600\n",
      "10782/10784 [============================>.] - ETA: 0s - loss: 0.2053 - accuracy: 0.9070\n",
      "Epoch 00024: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.2053 - accuracy: 0.9071 - val_loss: 0.5286 - val_accuracy: 0.7382\n",
      "Epoch 25/600\n",
      "10771/10784 [============================>.] - ETA: 0s - loss: 0.2044 - accuracy: 0.9072\n",
      "Epoch 00025: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2044 - accuracy: 0.9072 - val_loss: 0.5750 - val_accuracy: 0.7332\n",
      "Epoch 26/600\n",
      "10774/10784 [============================>.] - ETA: 0s - loss: 0.2046 - accuracy: 0.9075\n",
      "Epoch 00026: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2046 - accuracy: 0.9075 - val_loss: 0.5635 - val_accuracy: 0.7195\n",
      "Epoch 27/600\n",
      "10756/10784 [============================>.] - ETA: 0s - loss: 0.2042 - accuracy: 0.9075\n",
      "Epoch 00027: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2042 - accuracy: 0.9075 - val_loss: 0.5135 - val_accuracy: 0.7476\n",
      "Epoch 28/600\n",
      "10780/10784 [============================>.] - ETA: 0s - loss: 0.2034 - accuracy: 0.9079\n",
      "Epoch 00028: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.2033 - accuracy: 0.9080 - val_loss: 0.5302 - val_accuracy: 0.7353\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/600\n",
      "10779/10784 [============================>.] - ETA: 0s - loss: 0.2031 - accuracy: 0.9081\n",
      "Epoch 00029: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2031 - accuracy: 0.9081 - val_loss: 0.4915 - val_accuracy: 0.7602\n",
      "Epoch 30/600\n",
      "10774/10784 [============================>.] - ETA: 0s - loss: 0.2021 - accuracy: 0.9090\n",
      "Epoch 00030: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2021 - accuracy: 0.9090 - val_loss: 0.5224 - val_accuracy: 0.7451\n",
      "Epoch 31/600\n",
      "10757/10784 [============================>.] - ETA: 0s - loss: 0.2018 - accuracy: 0.9082\n",
      "Epoch 00031: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2018 - accuracy: 0.9082 - val_loss: 0.5334 - val_accuracy: 0.7392\n",
      "Epoch 32/600\n",
      "10763/10784 [============================>.] - ETA: 0s - loss: 0.2011 - accuracy: 0.9091\n",
      "Epoch 00032: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 23s 2ms/step - loss: 0.2011 - accuracy: 0.9090 - val_loss: 0.5110 - val_accuracy: 0.7623\n",
      "Epoch 33/600\n",
      "10781/10784 [============================>.] - ETA: 0s - loss: 0.2019 - accuracy: 0.9090\n",
      "Epoch 00033: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2019 - accuracy: 0.9090 - val_loss: 0.4678 - val_accuracy: 0.7642\n",
      "Epoch 34/600\n",
      "10764/10784 [============================>.] - ETA: 0s - loss: 0.2008 - accuracy: 0.9095\n",
      "Epoch 00034: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.2008 - accuracy: 0.9095 - val_loss: 0.5285 - val_accuracy: 0.7427\n",
      "Epoch 35/600\n",
      "10778/10784 [============================>.] - ETA: 0s - loss: 0.2007 - accuracy: 0.9099\n",
      "Epoch 00035: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.2006 - accuracy: 0.9098 - val_loss: 0.5595 - val_accuracy: 0.7347\n",
      "Epoch 36/600\n",
      "10772/10784 [============================>.] - ETA: 0s - loss: 0.1998 - accuracy: 0.9101\n",
      "Epoch 00036: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1998 - accuracy: 0.9101 - val_loss: 0.5574 - val_accuracy: 0.7296\n",
      "Epoch 37/600\n",
      "10784/10784 [==============================] - ETA: 0s - loss: 0.1997 - accuracy: 0.9095\n",
      "Epoch 00037: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1997 - accuracy: 0.9095 - val_loss: 0.5488 - val_accuracy: 0.7438\n",
      "Epoch 38/600\n",
      "10780/10784 [============================>.] - ETA: 0s - loss: 0.1999 - accuracy: 0.9100\n",
      "Epoch 00038: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1998 - accuracy: 0.9100 - val_loss: 0.5698 - val_accuracy: 0.7289\n",
      "Epoch 39/600\n",
      "10781/10784 [============================>.] - ETA: 0s - loss: 0.1988 - accuracy: 0.9107\n",
      "Epoch 00039: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1988 - accuracy: 0.9107 - val_loss: 0.5135 - val_accuracy: 0.7606\n",
      "Epoch 40/600\n",
      "10774/10784 [============================>.] - ETA: 0s - loss: 0.1984 - accuracy: 0.9111\n",
      "Epoch 00040: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1985 - accuracy: 0.9111 - val_loss: 0.5224 - val_accuracy: 0.7468\n",
      "Epoch 41/600\n",
      "10778/10784 [============================>.] - ETA: 0s - loss: 0.1989 - accuracy: 0.9105\n",
      "Epoch 00041: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1989 - accuracy: 0.9105 - val_loss: 0.5310 - val_accuracy: 0.7539\n",
      "Epoch 42/600\n",
      "10782/10784 [============================>.] - ETA: 0s - loss: 0.1979 - accuracy: 0.9112\n",
      "Epoch 00042: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1979 - accuracy: 0.9112 - val_loss: 0.6076 - val_accuracy: 0.7320\n",
      "Epoch 43/600\n",
      "10781/10784 [============================>.] - ETA: 0s - loss: 0.1976 - accuracy: 0.9110\n",
      "Epoch 00043: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1975 - accuracy: 0.9110 - val_loss: 0.5251 - val_accuracy: 0.7538\n",
      "Epoch 44/600\n",
      "10764/10784 [============================>.] - ETA: 0s - loss: 0.1980 - accuracy: 0.9112\n",
      "Epoch 00044: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1979 - accuracy: 0.9113 - val_loss: 0.6112 - val_accuracy: 0.7212\n",
      "Epoch 45/600\n",
      "10778/10784 [============================>.] - ETA: 0s - loss: 0.1967 - accuracy: 0.9123\n",
      "Epoch 00045: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1967 - accuracy: 0.9123 - val_loss: 0.5463 - val_accuracy: 0.7451\n",
      "Epoch 46/600\n",
      "10781/10784 [============================>.] - ETA: 0s - loss: 0.1970 - accuracy: 0.9120\n",
      "Epoch 00046: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1970 - accuracy: 0.9120 - val_loss: 0.5574 - val_accuracy: 0.7302\n",
      "Epoch 47/600\n",
      "10775/10784 [============================>.] - ETA: 0s - loss: 0.1967 - accuracy: 0.9118\n",
      "Epoch 00047: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1967 - accuracy: 0.9118 - val_loss: 0.5506 - val_accuracy: 0.7465\n",
      "Epoch 48/600\n",
      "10774/10784 [============================>.] - ETA: 0s - loss: 0.1966 - accuracy: 0.9123\n",
      "Epoch 00048: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1966 - accuracy: 0.9123 - val_loss: 0.5443 - val_accuracy: 0.7449\n",
      "Epoch 49/600\n",
      "10782/10784 [============================>.] - ETA: 0s - loss: 0.1973 - accuracy: 0.9112\n",
      "Epoch 00049: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1973 - accuracy: 0.9112 - val_loss: 0.5250 - val_accuracy: 0.7502\n",
      "Epoch 50/600\n",
      "10782/10784 [============================>.] - ETA: 0s - loss: 0.1973 - accuracy: 0.9119\n",
      "Epoch 00050: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1973 - accuracy: 0.9119 - val_loss: 0.5192 - val_accuracy: 0.7406\n",
      "Epoch 51/600\n",
      "10780/10784 [============================>.] - ETA: 0s - loss: 0.1966 - accuracy: 0.9119\n",
      "Epoch 00051: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.1966 - accuracy: 0.9119 - val_loss: 0.5492 - val_accuracy: 0.7414\n",
      "Epoch 52/600\n",
      "10773/10784 [============================>.] - ETA: 0s - loss: 0.1963 - accuracy: 0.9118\n",
      "Epoch 00052: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1963 - accuracy: 0.9118 - val_loss: 0.5129 - val_accuracy: 0.7571\n",
      "Epoch 53/600\n",
      "10759/10784 [============================>.] - ETA: 0s - loss: 0.1965 - accuracy: 0.9118\n",
      "Epoch 00053: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1965 - accuracy: 0.9118 - val_loss: 0.5976 - val_accuracy: 0.7214\n",
      "Epoch 54/600\n",
      "10764/10784 [============================>.] - ETA: 0s - loss: 0.1961 - accuracy: 0.9119\n",
      "Epoch 00054: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.1961 - accuracy: 0.9119 - val_loss: 0.5458 - val_accuracy: 0.7367\n",
      "Epoch 55/600\n",
      "10781/10784 [============================>.] - ETA: 0s - loss: 0.1958 - accuracy: 0.9125\n",
      "Epoch 00055: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1958 - accuracy: 0.9125 - val_loss: 0.5759 - val_accuracy: 0.7287\n",
      "Epoch 56/600\n",
      "10775/10784 [============================>.] - ETA: 0s - loss: 0.1956 - accuracy: 0.9124\n",
      "Epoch 00056: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 21s 2ms/step - loss: 0.1955 - accuracy: 0.9124 - val_loss: 0.5695 - val_accuracy: 0.7392\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57/600\n",
      "10764/10784 [============================>.] - ETA: 0s - loss: 0.1955 - accuracy: 0.9124\n",
      "Epoch 00057: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 20s 2ms/step - loss: 0.1955 - accuracy: 0.9124 - val_loss: 0.5280 - val_accuracy: 0.7493\n",
      "Epoch 58/600\n",
      "10764/10784 [============================>.] - ETA: 0s - loss: 0.1954 - accuracy: 0.9125\n",
      "Epoch 00058: val_accuracy did not improve from 0.88957\n",
      "10784/10784 [==============================] - 22s 2ms/step - loss: 0.1954 - accuracy: 0.9124 - val_loss: 0.5748 - val_accuracy: 0.7332\n",
      "Epoch 00058: early stopping\n",
      "Execution time :  0:19:35.773169\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "optimizer = Adam()\n",
    "model = create_batch_normalization_model(optimizer)\n",
    "history = model.fit(x=X_sample, \n",
    "                    y=y_sample, \n",
    "                    epochs=600,\n",
    "                    batch_size=32,\n",
    "                    validation_data=(X_valid_norm, y_valid), verbose=1,\n",
    "                    callbacks=callbacks_list\n",
    "                   )\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer: adam Training Loss: 0.918 Validation Accuracy:0.733\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(X_sample, y_sample, X_valid_norm, y_valid, model, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+v2VF+QzpU1p3Pvm57h4FMMnPdTL4qDsfBR1+uT4QgVVHu5KjRolEdD66/WojSIlQP4KXOlLF6qN9ycDFUs5Td11y5G/V0cDUkb78WIxuRXmUFeVTWpgM1jxQa0uHhbSNAjgREZE0hBBYUR2vSw/rSJU1HnyEQMqBWH2aEKSkEDXYXlWbYEV1raeNRX8ra2rrz1fXp5mtiFLLKmvi1MQD1bXJ0/oRmera+oPuhtcnA7P2Ko166pMHvmV1p37AXJAXY2V1nOWp7a5LjVv1uabDDMoK6w/Ck6fr9iiuGzkrys+jMD9GUX6MwrwYRQXJ07zVLlv0nq2sSXl9Vzu/alpfeWUNJQV59CsrZGi/bpQV5dO92APahkFC8rRbUT4FMaMmEaiN3pN4FDzXRtf5qY+2eUCUIBFYJV3R0xctJdXRLxempDUW5MXanFJoZhTmW4f/n7RVaWE+pX3yGdyntMXbxhOB5dW1lBbkkZ/XNZ+PrH0UwImIyBqvsbkPNfEES1bUsHhFNYtX1LA4mSK2IvpbXrPa5WSwUlKQR4+SfHoUF9CjpIAexfnRaQE9SjxlqH5bAbEYlK+sYenKGspX1vpppV/266LTSt9WXdv+oCgdpXWjQR60pM5dSo6UFXWL1c1XSh7sF+XHyI/5bVLnNDU8nwwK8lMChura+lGn5SkjZMurV01Nm71kJRVVNSyvilNTm0gZUfHRrO7F+azTo4jSwmi0pWDV7SWFeasEQd2L8ykrKqCsOJ/SgjxiGRr1kzVbXszoUVyQ7WaIrEIBnIjIWiwZ2MQbzElIPe+pTKumyFXXJurT5mobm0vjowF1j7Pa46acT9kago8gVdbUFyVYWROnMrVAQTRitDLlfHVtYtXnkUw9SkkzSpcZ9C4tpHdpAb1LCxncp5Rt1+9F726F9CotoKY2QXmlB2LllR6ILaioZtqC5ZRHQVi8hZy7mEGPkoK6QK9nSQHr9ixeJfDrXpyPQd28jtQ5Hs3NDSnKj9UHMgX1aWSlKYFNaWEexfkKYkREcpECOBGRHJZIBJaurGFhNFK0sKJ6lepwi5dXr7JtyYpqauKhLjDryvO2Y+apTsUFeZQUxnyOS1Qook+3Qkp6rTq/pW7CesNqZnUT1lfdnh8zepUk5/AUREFbIT1KCto1JyuZapkcZStf6QFdz5L60bluhfkKnkREpE0UwImItEEi4XN/qlNGoKqjogep81x8Xk793JfUuUd122viVNfGSSSoG+1KRKep5xOBuuvi0eMvWVHdZIGF0sK8uuICfboVskn/MnqVFlKYHyMvpRJXXkpgk1rC2bfVX18YzQVKzo9JnUuTn7ycH5UHj0p9p8Yo9UW0o8urbKtXlJ9HcaHvLxcnxJuZF+coymcgJdlujoiIrGEUwIlIl1Vdm2BBRRXzl0V/FVUsrKiqqxK3SqW4lAAqWTkuNahKNCgGAVHQ0Og6MvXr6iTLaacWbkimD7ZVySppbZ7qVpTn6x8VxGJ1a+LkmRGL1a9/5Oepu64wP0afbj5q1DeqEpcasLV3TSMRERHpehTAicgqaqOAJRms1J1PqUaXDJqSVelq4gkCoW5x0bzYqouNJtdkyasb4fHtNfHA/IrK+gAtCtKS5xevqGm0jXkx88pw0YhQYcr5ZPW40sJ8eqVsj1n9XCJobB2Z1RdABer2XVfgIaWQwyrX5ydHoGKadyQiIiIdRgGcyFpkZXWcueWVfLN0JXOXVvLN0sq60+R1C5dXZ6VtRfkxBvQoYkD3YjbqV8ZOG/alf/ci/ysrYkAPP9+3W1GXLU0tIiIi0tHSCuDM7CDg70AecFsI4doG23sDdwAbA5XAD0MIk9K5r4i0LJEIVNbWz5mqTKnGl1xraGXKukPJ65esqGHu0pUeqJVXsqSREa1epQWs26OY9XoWs836vVinRxElBXl1I0/JkuHNjT4V5PmomhfFCMQT1M3dSlYEjCeS2+orA+bHrC5IKyvKz8n5TiIiIiKdqcUAzszygBuB/YFZwGgzezqE8EnKzX4BjA8hHGlmm0e33zfN+4rklGTxitpEoCpabHdZZf1aRsn1jVIv+/aaaN2jOLUJL7necOHV2kQgnrot4dvaUimwIM/oXlzAej2LWb93CSOH9ma9niV1wdp6vUrqFq0VERERkdyQzgjcjsAXIYRpAGb2IHA4kBqEbQn8ESCE8JmZDTWzdYCN0rivSIdLJAKLV1Tz7bIq/yuv5NtontW3y3wO1orquK9jlfA5Xb6mlQdRNbUJaqJgqzW1K2JGtIBsAWXRgrLdi/MpzIuRF/NqfXkxIz9m5OcZeTEfzWq4LS9mFEfzqoqjUurJQhjFhfXnSwrqLxfkKc1QREREZE2TTgA3CJiZcnkWsFOD20wAjgLeNrMdgQ2A9dO8LwBmdjZwNsCQIUPSabus5WriCRavqGbx8hoWLq9i8fIaFq2oZmFFMkirYv6y+kCtsaqB3Yvy6d+jiAHdi1i3RzH5KeXQPajygKogWTo9Vl9CvSDPKMrPqw/MotOylNOSgjylBYqIiIhIxqQTwDV29NnwSPha4O9mNh6YCHwE1KZ5X78yhFuAWwBGjhzZhZeWlY4STwQWLa9epRLhwooqFq2oZlGDxYkXLa+mvLK2yX317VZI/+5FDOhRzCYDukfFMbxARup5pQ+KiIiISC5JJ4CbBQxOubw+MCf1BiGEcuB0APPhhq+iv9KW7itrnngi1K3DVVWboKomQWVtvD44a1AqPnV9r8bSEwvzY/TtVr/G1fq9S+vWvupTVkiflLWvencroHdpodIHRURERGSNlE4ANxoYZmYbArOBE4BRqTcws17AihBCNXAm8GYIodzMWryvdE2JRGB+RRVzlngFw+Tp3KU+X6yyNk5VTUqQVpugqsbPp7PAcUGe0b/Mqw8O7FXMtoN71l3u372IftH5vmVFdCtUGqKIiIiICKQRwIUQas3sfOAlfCmAO0IIk83snGj7TcAWwN1mFscLlJzR3H075qlIa1TXJvhqwXKmL1zuAdrSlXyzxNcCm7OkknnllasFYsUFMQb2LInW4iqkKD+PooL6hZOL8mPR5eh8foyigjxfXLkgRp/SwroArWdJgYIyEREREZFWstCW+uQdbOTIkWHMmDHZbsYaobo2wdcLlzNl3jKmzKvgi2/99OsFy1cJ0ArzYqzb08vLD+xVUldmfmDPYtbr6Zd7lSroEhERERHpDGY2NoQwsuH1aS3kLV1fTTzB9IXLmTKvginzljE1Ov0qJVCLGWzQtxvDBpRx0PB1GbZOGRv268Z6PUvo262QWEzBmYiIiIhIV6YALgetqK7l02+W8cmcpUyeU86kOUuZMreC6ngCADPYoE8pw9bpzv5brsOm63Rn2DplbNy/jOICVV0UEREREclVCuC6uKUrapj8zVImzy5n8pylTJpTzrT5FXXVGnuXFrDVoJ6cvttQNl+3O8MGdGeTAQrURERERETWRArgupAQApNml/PGlG+ZNNtH1mYtXlm3fb2exQwf2INDt16P4QN7sNWgnqzXs1jz0kRERERE1hIK4LqAKfOW8cyEOTwzYQ5fL1wBwNC+pWw7uBejdhrCVgN7MnxgD/qWFWW5pSIiIiIikk0K4LLkqwXLeXbCHJ75eA5T5lUQM9hl476cs+fGHDh8XXp3K8x2E0VEREREpItRANeJZi1ewXMff8MzH89h0uxyAHYY2purDx/OwVutR//uGmETEREREZGmKYDrYN+WV/LcxG94ZsIcxs1YAsC26/fkykO34JCt12Ngr5Ist1BERERERHKFArgOUhNPcPmjH/PE+NmEAJuv253LDtyM720zkCF9S7PdPBERERERyUEK4DpATTzBBfd/xIuT53Lmbhtywo6D2WRA92w3S0REREREcpwCuAyrjSe46MHxvDh5Lr8+bEt+uNuG2W6SiIiIiIisIWLZbsCapDae4KKHxvPcxG+48tAtFLyJiIiIiEhGKYDLkHgicMkjE3j242/4xSGbc+buG2W7SSIiIiIisoZRAJcB8UTg0kcm8NT4OVx+0OacvcfG2W6SiIiIiIisgRTAtVM8Ebjs0Qk88dFsLjtwM87dS8GbiIiIiIh0DAVw7ZBIBC5/7GMeHzebi/fflPP23iTbTRIRERERkTWYArg2SiQCP398Io+OncWF+w7jJ/sOy3aTRERERERkDacArg0SicAvn5zEQ2NmcsE+m3DRfgreRERERESk4ymAa6UQAr9+ehIPfDiDH++1MRfvvylmlu1miYiIiIjIWkABXCuEEPjN05O59/0Z/GjPjbjswM0UvImIiIiISKdRAJemEAJXPfMJd783nbP32IgrDtpcwZuIiIiIiHQqBXBpCCFwzbOfcte7X3PGbhvy84MVvImIiIiISOdTAJeGyXPKuevdrzh916FceegWCt5ERERERCQr8rPdgFyw1aCePHnermw9qKeCNxERERERyRoFcGnaZv1e2W6CiIiIiIis5ZRCKSIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5Iq0AzswOMrPPzewLM7uike09zewZM5tgZpPN7PSUbV+b2UQzG29mYzLZeBERERERkbVJfks3MLM84EZgf2AWMNrMng4hfJJys/OAT0II3zOz/sDnZnZfCKE62r53CGFBphsvIiIiIiKyNklnBG5H4IsQwrQoIHsQOLzBbQLQ3cwMKAMWAbUZbamIiIiIiMhaLp0AbhAwM+XyrOi6VDcAWwBzgInAhSGERLQtAC+b2VgzO7ud7RUREREREVlrpRPAWSPXhQaXDwTGAwOBEcANZtYj2rZrCGF74GDgPDPbo9EHMTvbzMaY2Zj58+en13oREREREZG1SDoB3CxgcMrl9fGRtlSnA48H9wXwFbA5QAhhTnT6LfAEnpK5mhDCLSGEkSGEkf3792/dsxAREREREVkLpBPAjQaGmdmGZlYInAA83eA2M4B9AcxsHWAzYJqZdTOz7tH13YADgEmZaryIiIiIiMjapMUqlCGEWjM7H3gJyAPuCCFMNrNzou03AdcAd5nZRDzl8vIQwgIz2wh4wmubkA/cH0J4sYOei4iIiIiIyBrNQmg4nS37Ro4cGcaM0ZJxIiIiIiKydjKzsSGEkQ2vT2shbxEREREREck+BXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOQIBXAiIiIiIiI5QgGciIiIiIhIjlAAJyIiIiIikiMUwImIiIiIiOSItAI4MzvIzD43sy/M7IpGtvc0s2fMbIKZTTaz09O9r4iIiIiIiKSnxQDOzPKAG4GDgS2BE81sywY3Ow/4JISwLbAX8BczK0zzviIiIiIiIpKGdEbgdgS+CCFMCyFUAw8Chze4TQC6m5kBZcAioDbN+4qIiIiIiEga0gngBgEzUy7Piq5LdQOwBTAHmAhcGEJIpHlfEXppmroAACAASURBVBERERERSUM6AZw1cl1ocPlAYDwwEBgB3GBmPdK8rz+I2dlmNsbMxsyfPz+NZomIiIiIiKxd0gngZgGDUy6vj4+0pTodeDy4L4CvgM3TvC8AIYRbQggjQwgj+/fvn277RURERERE1hrpBHCjgWFmtqGZFQInAE83uM0MYF8AM1sH2AyYluZ9RUREREREJA35Ld0ghFBrZucDLwF5wB0hhMlmdk60/SbgGuAuM5uIp01eHkJYANDYfTvmqYiIiIiIiKzZLIRGp6Rl1ciRI8OYMWOy3QwREREREZGsMLOxIYSRDa9PayFvERERERERyT4FcCIiIiIiIjlCAZyIiIiIiEiOUAAnIiIiIiKSIxTAiYiIiIiI5AgFcCIiIiIiIjlCAZyIiIiIiEiOUAAnIiIiIiKSIxTAiYiIiIiI5AgFcCIiIiIiIjlCAZyIiIiIiEiOUAAnIiIiIiKSIxTAiYiIiIiI5AgFcCIiIiIiIjlCAZyIiIiIiEiOUAAnIiIiIiKSIxTAiYiIiIiI5AgFcCIiIiIiIjlCAZyIiIiIiEiOUAAnIiIiIiKSIxTAiYiIiIiI5AgFcCIiIiIiIjlCAZyIiIiIiEiOUAAnIiIiIiKSIxTAiYiIiIiI5AgFcCIiIiIiIjlCAZyIiIiIiEiOUAAnIiIiIiKSIxTAiYiIiIiI5AgFcCIiIiIiIjlCAZyIiIiIiEiOUAAnIiIiIiKSI/Kz3QARERFpg8pyWDgVFkR/C6fCommw849hxKhst05ERDqIAjgREZGuKhGHJTNg4RewYEoUqEXnK+bV387yoM+GEK+GF66AYQdAt37Za7eIiHQYBXAiIiJdzYKp8PhZMO8TiFfVX1/SG/oOg032h36bQL9N/XLvoZBfCPM/h3/tAv/9HXzvb1lrvoiIdBwFcCIiIl3NS7+AhdNgpx9Bv2EepPXbFLr1bf5+/TeDHc+GD26CkT+E9bbpnPaKiEinURETERFZ+1RVwMePQLw22y1Z3VdvwdSXYY9L4IBrYPtTYYNdWg7ekva63EfqXvw5hNCxbV2ThAD3HQdPneepqyIiXZQCOBERWfv87/fw+Jnw8i+z3ZJVhQCv/gZ6DPKRtLYo6Q37/gqmvw2fPJXZ9q3J5oyDqS/BR/fCi1co+BWRLksBnIiIrF2WzYUxd0C3/p5qOObObLeo3qdPw+yxsPcvoKCk7fvZ/jRYZyt4+VdQszJz7VuTjbsb8ks89fTDW+Ctv2S7RSIijVIAJ9JWM973FCwRyS3v/B3iNXD6i7DJfvD8pfD129lulbfptauh/xaw7Ynt21csDw66FpbOgHdvyEz71mRVFTDxUdjqKDjkL7D1cfDfa2DcPdlumUjLqiqy3QLpZArgRNpi+QJ44ESvEjd7bLZbIyLpSo6+bXuiV3E85g7osxE8dAos+iq7bfvoHl8iYL/feADWXhvuDlseDm9fD0tnt39/a7LJT0B1hc83jMXg8Bth433gmQvh8xez3TqRpr37T/i/jWDupGy3RDqRAjiRtnjpF1C1DEr7wnOXaMK7SK54+28+0rXHJX65uCec+CCEBDxwgi+OnQ3Vy+H1a2HILrDpQZnb7/7X+PfTq7/N3D7XROP+A/02g8E7+eX8Qjjublh3a3jkBzDzw6w2T6RRX/4PXvm1LzUy4YFst0Y6kQI4kdb64jX4+CHY7adw8J9gzkf+4y8iXduyuTD2Thhxoo+6JfXd2A/WF0yFx87MTofM+//yhbn3uwrMMrff3hvArj+BiQ/DjA8yt981ybxPYNZoH31Lfe2LusNJj0KP9eD+43yNPZGuYvF0ePSH3vGw4Z4w6XFIJLLdKukkCuBEWqN6OTz7U1+TafdLYKujYeju8OpVsHxhtlsnIs1Jjr7tfunq2zbaEw75P69C2NmjVcsXwtt/h80PgyE7ZX7/u/0Uug+EF36mA7zGfHQPxAoan3dY1h9Ofty333MUlM9p/+OFAJ8+A3cfDt983P79tWTuRP9M671fc9SshIdO9s6mE+7zzodlc2DGu9lumXQSBXAirfH6tbBkOnzvb1BQ7L21h1zncyde+222W9d+S2fB/cfDt59luyUimbXK6NuGjd9mhzP9791/wPj7O69tb/0ZapbDvr/umP0XdoP9r4JvxsOETnxeuaCm0lPPtjis6XX2+mwIJz8KlUvh3qNh5eK2P97cifCf7/nB97TXPQW/I5crSMThyXPh7b/6shKS+0LwjuS5H8NRt3gGwWYHQ0GpF+KRtUJaAZyZHWRmn5vZF2Z2RSPbLzOz8dHfJDOLm1mfaNvXZjYx2jYm009ApNN8MwHeu9F7uobuVn/9gC1g53O9BPXM0dlrXya8+0+Y8iI8doYf2IisKZobfUt10LWejvTMhZ2Tcrj4a/jwVtjuZOi/Wcc9ztbHwvo7erZAtub5dUWfPesB2fanNX+79bb1kY4FU+GBUa1fmqFiPjz9E7hpd5g3GQ79Cxz2V5j1IUx6rO3tb8n4+zxotBh8dF/HPY50ng9v9U6HvX4Om0XzZQu7weaHwidPQm11dtsnnaLFAM7M8oAbgYOBLYETzWzL1NuEEK4LIYwIIYwAfg68EUJYlHKTvaPtIzPYdpHOE6/1H9/SvrD/1atv3/NyT1F6PocLmlQu9QVsBwyHeZO8nLnImqD8G6882dzoW1JeARx7F/RcHx46CZbM6Ni2/ff3EMv3g7GOZAYHXwvLv/URP3Hj/gO9hnjQ3pKN9oSjboYZ76U/V7K2ypet+Md2Hkzt/GP4yTgf6d3+NC+S8upvO2atvqpl8No1Hrhvf6ov6l65NPOPI51n+rvw0s9h04Nhj5+tum2rY7wzYtr/stM26VTpjMDtCHwRQpgWQqgGHgQOb+b2JwIqhSNrlg9v9vSjg/8EJb1X317UHQ78vY/Sje1CiwK3xkf3eSroETfCDmfB+zfCl//NdqtE2u+dv0GItzz6llTaB058yHuyHxjVcWssfTPBi4vsfC70GNgxj5Fq0HdgxEnw3r9g4Zcd/3hd3aJp8NWbsF20dEA6tjraR2k/e7b59MfkPLcbd/QqgUN3hR9/AAf9of43JJYHB/4Bls707I5Me+t6D9gPutafY+1KL3Qh6av4Fl7/Ezx4ko9kZlP5HHj4NOi1gXckNPzMbrwPFPdSGuVaIp1vrEHAzJTLs6LrVmNmpcBBQGo+QABeNrOxZnZ2Uw9iZmeb2RgzGzN//vw0miXSSRZPh//+DoYdCMOPbPp2w4/0XtzXrvZ0mVySiMMHN3kJ84HbwQHXeGWrJ85VcRbJbeXfwJg7vUBFS6Nvqfpv6mvEfTsZnvhRxxSAePUqP5jf9cLM77sp+/4a8ovg5Ss77zG7qnH3eGrhdie17n47nwO7XuSddW/8afXtqfPc8ku8CMqoh3zdwYY23AM2O9TnqC2b17bn0ZjFX3tQuM0JsP53YND2vkD8eKVRpmXWWHj8bLh+S3j9Dx7o37afZ6lkQ20VPHyqF1I74T5f/qSh/EIYfgR89hxUr+j8NmZbCPD+vz1bKl6T7dZ0uHQCuMbqGTc14/Z7wDsN0id3DSFsj6dgnmdmezR2xxDCLSGEkSGEkf3790+jWSKdIAR47mLAfM5Cc+W9zeCQP/sXZ66tufT5C16cZedz/XJBCRx9G6xYCM/8pGMn2Yt0pLrRt0taf99h+8EBv/fRlv/9PrPtmvY6fPmajwqW9MrsvpvTfV3Y4zL4/HlfEmVtFa/1YGbYAW0b/dzvt7DtKHj9j56eC43Pczvnbdhk3+b3dcA1foD+32ta346mvPIbD06ThXHMPFCdNVrLITSltho+fhhu3Rdu2wc+ex52OAPOHwsXjIXBO8JT58FT53dMymtzXrjc37sj/uXz7puy1TFeEGnKC53Xtq6gegU8fha8eIWnRb92VbZb1OHSCeBmAYNTLq8PNFVH9wQapE+GEOZEp98CT+ApmSK5YdJj8MWrsO+voNfglm/ff1PY5TwYf29urbn0/r+h5xDvCU5abxvY7zd+8Dru7uy1TdYeleVw027w1l8ys7/yOW0bfUu187mw3Sk+b+zjRzLTrkTCD7B7Dva5UJ1t53Oh94bw4s/Xip7qRk19ydfda6l4SVPM4Pv/8ADwuUu8KmBj89zy8lveV9+NYcezfXQnE8sKTH/Xi1nsdhH0TEmY2uZ4n2+ZrVGkrmrZPK8w/betPAioXAIHXwcXf+LTJvptAmUD4JQnvcPlo3vg9v09BbczjLvbR3t3vchH2JqzwXd9Pv7alEa5ZCbccaA/531/DSPP8IJsnzyd7ZZ1qHQCuNHAMDPb0MwK8SBttVfFzHoCewJPpVzXzcy6J88DBwCTMtFwkQ63YpH3eg3c3n9c07XHZdBjkBc0idd2XPsy5ZuPvbz0jmetfrCx83meFvriFbDgi+y0T9YeH93j6WevXe3FPdo78vt2NPq2R5pz3xpjBodeD0O+673vs8a2r03gB9ffjIe9f+nLkXS2/CKfe7Xgcxh9e+c/flcw7m4oW9cDsLZKFrwZuL2PwjU2zy1de17m93n5l+373CcS/n3dYxB89yerbisbAJseBBMezP3AvbIcHj0DHjjRf6ff+xd8+qx/f6RbqGXWGHjsLPjrcB9JXW9bOPkxOG807HQ2FPdY9faxPO/MHfWwBw037+Xpih1p1ljvINho7/SWGYnlwVZHwdRX2rfcRa74+h24ZS9PGR71kGdaHPRHn+/75I/X6OOWFgO4EEItcD7wEvAp8HAIYbKZnWNm56Tc9Ejg5RDC8pTr1gHeNrMJwIfAcyGEFzPXfJEO9PKv/Avw+//wL8V0FZX5wdHcifWpNV3ZBzdBQTfY/pTVt8VicMS/Ia/QeyZz/Udfuq54LbwfzcPc7hR48/88paytB7Plc2DsXTBiFPQe2r625RfC8fdA93XgwVHtKwASr/EAdcBw2Oa49rWrPTY72A8KX//D2jfPtXwOTH3ZUwrTGSFrTmE3OPUpOPuNpue5paOkt1ci/epNT2lvqwkPeHGc/X4LhaWrbx9xkhc2mfpK2x+jOUtmwtLZHbPvpBC8M2XyE37g/tG9XpnxoZN8BP/aIXDtBnDzHj4P8aVfwge3wJSX4NtPozTJfeC2ff213uFMuGAcnPQIbLJfywVtNj0QfvSmj+o/OMqL1HREZ23FfHj4FE97PuaO9I9Dtj4GEjVr9ghUCL6cwt3f96JTZ/3X3xfwDqpj/+MdLA+f4vMG10BpfXOFEJ4Hnm9w3U0NLt8F3NXgumnAtu1qoUg2THvD0yB3vcjLPLfWlof7wdF/f+cpD2UDMt/GTKj4FiY+4mlETfUY9xzkQezDp3qayb6/6tw2ytrhs2dg6QzvPd3sED9YeesvkKiF/a5qfv5pY95ux9y3xnTrByc+CLcfCDfs4Ouq7fZTGLB56/Yz9i5Y/BWMeqR1HUOZZuav9b939fl9h12fvbZ0to/ug5DwtfcyoagMBo5o/35Gng6jb/UCM5vs5x0HrVFV4Z0Dg0b6XKjGDNsfug3wVM/ND2l/m1PVVsGdB0PNCj+gbm/HSVPe/zd8+jTsfw3sGs3RXrnYg7klM3w+95IZXoBs/hSY+qpX4EzVd5jPWd/2BK8i3Vq9N4AfvuSB4zt/95GyY+7wTp5MiNfCo6f7PPQzXvYgJV3rjYA+G8OkR+E7bUwR7spqq3xU8qN7fET5qFtWL+rSazAcczvcc5Sv6XnUra3/Deni2tn1JLIGqlkJz17kc0T2Wm3d+vSYwSHXwb928bkuR/47s23MlDF3QLwadjqn+dtteTiMOBnevt4n5G/w3c5pn6w93rvR/+c2O9h7wA/9q8/XeefvXiX1gN+l/wOcydG3VOsMh/M+gPdu8P1//CBsfhjsdrFX+mtJVYVXLdxgNz+QzrYBW/jow+hbYeQPYd2tst2i1YUAr/wKvnwdTn3SA+n2SCTgo7u9+mOfjTLSxIzJK/CiOfcfC6Nvg11+3Lr7v/M3qJgLx9/b9ChSXgFse7wHQRXfZrZzccydviRCfgncf4IHHg3TENtrxvv+edj8MPjuBX6dmQc4pX282mZDIcDy+R7QLZnun6Ghe6S/dERTCop9MfbBO8EzF8HNu3sQN3S39u0XfFTv67fgyFs8tbM1zLyD6Y0/eRXeHuu1vz1dxbK58NApMOtDn4+49y+bfh833se3/+93/h7teFbntrWDtfPTK7IGevM6n5x82F+9GmNb9RvmvYMT7ofp72WufZlSW+UHCcMOTC/t5+Brff2Zx3+kxWAls2Z+6BXWdv5x/ahULOY95Dud4wHTi1ekn0759l8zO/qWqucgH7m6aBLsebkfZN22j5eN//J/zbfxvRv9QHL/NowodpS9rvDe66fPh6Wzst2a1b37T/+bN9HT4Wqr2re/r1730Zm2Fi/paMP29wPPN671edjpWjLDX6etjoHBOzR/2xEn+8j2xw+1r62pqpd7oZ+hu3sq6cKp8NgZ6S12nq6K+fDI6V785/Ab0/8fMvNAdfAOnl640V7tD95SbXuCjzgWdYf/fD8a/W/HPMaPH/F1WHc6x4Ptttj6GCB4mumaYtYYuHlPr/B67H88G6il93H3S3ye64s/9/uvQRTAiaSaN9l7/LcdBRvv3f797X6J/9g8f2nXK2gy6TE/mEwuHdCSou6+tED5bHiuHUUhRBp670YPIkaMWvV6M1+EeOfzfK7m85e2vB5bR42+NdStL+z9C/jpZE/lmj8F7jnC59Z8+szq7ayYD+/+A7b4Pqw/suPa1VqlfeCwv3lp+Rt39k6djljzri0mPeajLVseAUffDjPeg2cvbt/B8bi7PV1888My185MMvNRuKplnrKeruTSNfv9tuXbDtgc1t/B545laomYD27235N9fgUb7ekZKFNf9pGkTEjEPSBcuQiOu7tzl95Ixzpbwtmvwxbfg1d/43PjVi5p/j6JuN9myQyYO8mrh058FJ6+ADbY1bMO2qrfMB+5m5ihyrnZ9tG9np6bXwRnvtJyNc6kWAyOvNlHIR8+FZYv6Nh2diKlUIokJeK+hk9xz/Z9caYq7Oa99Q+d7GlK6QZLHS254GX/Lbw3Ml3rj/RRh9f/4L1a2xzbUS2UtcXi6T6f5bsX+FyihszgwN/7yNy7//D/00Ovb7rn9e2/+vym3Tupk6Gou4+073i2F5B45+/+/95vMy/jvvWxnrb25nWenp1OJbnONvwIn8P1zIU+t2TiYz7vtd+w7LVp+rvwxDkweGc/ACsohgVTPC2s/2b+mrfW8gVeqXDHs7JT/TNd62wJ3/mBB9M7nOnL0zRnxgce7O7xs/SWuwEvZvLsRTB7XHrpv81ZucQ/98MOgCE7+XUjf+idAu/dAP02bf9crNevha/egO/f4EvcdEVF3b0q6Qc3+TzGW/b039fKcqgqrz+tWubnq5c1vp8eg3w/eQXta89Wx3gHyMIvfamKXBSv8SI0H97sFbGPvat18wHBb3/cPXD7Ad4JcPLj2Z1/nCEagRNJGn07zB7jPf7d+mZuv5sf5hPS//cHz9/uCqa/C3M/hp3PaX0q1+6XeD75cxf7wbfUq1rm1dGmvJTtluSOD272BYd3/FHTtzGD/a/2uWZj7/TF5RsbJaobfTvJiwx0poJiL0Jx/hgfLcorgCfP9bXB3rzO55tuf2p2g6Lm9B7q61wd/i/4drIXN3nzz9mpPDt/ipeH77UBnPhAfbC15xU+H/eVX8PnbShoPeFBr863/amZbW9H2PuX3gH48pXN3y65bED39WDXC9Pf/1ZH+Vy18RlYE+69G33ttH0atPWA3/tv33MXw1dvtX3/U1/xqrQjTm68WnJXYuYdtae/4NWbP3vOq4KuWOhTMvoN8wJn25/in+cD/+BB6XF3+//fWf/1ebaZmJu41VF+Oumx9u8rG5YvgHuO9OBtl/M98Gpt8JY0cAQc+meY9rovGbEGsJCp4fMMGjlyZBgzZs3KVZUubuksuHEnGLyjf0lken7Kwi/hXzvD8CO9YlK2PXiSB3EXf9K2eX6Lv4Z/7+YVOn/w7BrRm5UR//ujz10BXzT3oGvb/oOzNqgsh+u3hM0O8vTcloTgP75v/MnTnA+/YdXP3vOXeaB0wbjOD+AaCsFTyN66Hma+DwWl8JOPvCR4V7dsHrxwGXzyFKyzNRz+Txi4Xec8dsW3Xt69ZiWc8crqC7BXr/BUqoVf+PZ1tkxvvyHAjTtCcS9PwcoF7/zdg9VTnvB5cY2Z8CA88SM44iYYcWLr9v/42V5G/5LPG19yIB3LF8Dft/XiVsfdvfr2yqVw2/6+dMGZr7V+JGjJDF8OoMcgOPPV9s1LXxvdeYintp73YdeZd5uOeZ/A/cdDxTzPBtj2hMzs96nzPB1z1MP1yw50cWY2NoSwWt69RuBEQvA5XcnUrI74kuu7sfeOfvyQLzyZTYu+8l7Bkae3/cew91DvzZrxrqesiR/IvHcDbHao96xOeswPGCc/me2W+Wd8xgc+t+K6TeDeY/wzkO15mR/d42lEO6dZbc/M553t9QsvDvTEOfXPYens7I2+NcbMDxDOeMnLjZ/yRG4Eb+Cl0I+7G46/zw/+bt3H18WsXtGxj1u9HO4/zucLjnpo9eANPNA48QEoLIMHjk9/TsvMDzwFMxdG35J2OsdHIV+6svFiINXL4dWrPLjepg3FLrY72VP6Pnu27W18+6++bMDev2x8e3FPGPUgYPDACS3PC0tVWwUPn+bP/bi7Fby1xVZH++d+7sRstyR9U1/xdMd4NfzwhcwFb+CFsdbd2te1Xfx15vabBQrgJPMSCU+7qVnpZbMrl3pPe1c15g6Y8gLs88vGDxgyZbeLoecQn2OSzQWxP7zVRy12OLN9+9nmeBh+lI+IzB6bmbblsuSBzH6/gb1/7gv79hgEj5zmZY+Xzev8Ni2d7Wup3TAS7jjA5zYN2QXmTfJJ9n/b2lN7s1F9sG7h7u82Xvq7OXtd7sUSJj4MT5zt+6qb+9YBlSfba8jO/pdrtjjM07m2O8XnH/77u77QdEeI18KjP/R0s2PugEHNzMvqMRBOvN9H69KtTDnubijs7lkQuSK/yFOHv53s7W/onX/Asjk+0t+Wqoob7OYB4kdtTKMs/8bn6W1zvM9LbEqfjXxpg0Vf+dpm6XYcvfRLmDMOjvhX7s7hyrYtj/DlWCY9mpn9rVjkcxs7ygc3eydOn6GeTtrc90BbFJT4fDjw3+WayszuvxMphVJa56u3PF2jZqWX6U4kotO4lyUOTZUMNjj4/2Cnszu1uS2aO8l7l4fuBic9mtnSwo357Dk/cN7vKi9w0NmqlnnK2qYHppey1pKViz2VMr8IfvRm40Uo1gZLZ8E/tvfSzUf8q/76eC28909PrSwogYP/5Ac7HZnKUlPpPerj74dp//OgZoPdvCrjlof7exSvhSkv+nyyL17z9gw7wAsnDDugc1JiJz8Bj/zAR3m2aGNFwLf/5hXfhh3oz3XEKPje3zPaTIl89aYXOVk0zUex9r8mc5UAQ/COrTG3ew95uus1TXrMg74RJ3s6bVP/V5VL4c+beUn2XPt8hOBpcAunempwcl21pbPgnyM9/fjYu9q+/9f/5EWpLvy49SPXz14M4/7j8z7T6fwcd48vV7Hj2V6lsjkfPwKPn+lznw78fevaJau671hPSbxoYvuOcSrL4bb9fERvx7O9jH9bFkFvTLzW53KOvhU2O8QX3u7I44nPX/QR/O1O8e+OLqypFEpVoZTWeft67+0cfqT36sTyvABBLM8vW150Pi/lfL5X/nrtap9U296FWDOlqsJ7A0t6eZWzjg7eADY/1FPsXr/WK791ZJnzxoy/31NmdspQNcyS3nDkTb4G1kMn+5duWf/M7DuXvPEnD5QaLvyelw+7/dTf86fP986PSY/5GoM918/c44fg1eTG3+v7r1zqy1fsfqnPi2m4YHFevgdNWxzmhWjG3e3pjFNe9FHD7U/1H7aegzLXxoZSF+5uq90u8u+Xl3/pp11x9G1NseEecO67PuL+7g0w5WVPo978sPZ3SLzzdw/edr2wdYvtbnW0jwa0VJly4qNQuzK30ieTklVYb93bR9P3v8qvf/Uq/87Z76r27X/Eif6eTnhg9e+v5iz+2oO37U9LP3Nl+1Ngwee+Xl2/TZt+r7/9zDsLhuyS3rII0rytj4WpZ3ka8Qa7tG0fiYTPmVz4hf/ffXgLfP68/5YN27997atc6uv7ffmaVyPe76qO70Tc7CD/fXzrz177IAe/GzQCJ+lb8AXc8B3Y+0rY87LW3Xf+FC/iscMZLfe8dZYnzvUfrVOf8nVrOktdwZSd4OTHOm9icSIB/9weuvXP/CT+cXf7PMKSXl6kZaO9Mrv/rmzBFz7XbYcz4ZD/a/p2ibinr752lXduHHCNj3i15/1fNtfnVY6/H+Z/5lXltvy+j0QN3aN1nRLxGi9oMPZO+PK/3jGz6UHexk32y+wP6swP4fb9o1H5ZqpPpuvjh739253U/n1Jy+Z8BE9d4ItrD9jSS8Zvc5zPd2qtiY96ae/hR3n1ztZ2pCUS8OgP4JOn4cQH/cCsoZv38Nud81ZuFXJI9cQ53jlz/mifI3j7ft5hkYllKe4+HBZOgwsnpP/6P3EuTH4cfjLe19hKVyLuWShTX/Hfv4brrVZVeFbMykXwo7dat29pXFWFz30eMQoOu75t+3jtau9ASI6Qz/wQnjrfA/Ktj2t79e7FX3uxkoVfeA2C9i430RqJuFe5nPG+HxOtt23nPXYrNDUCpwBO0vfCFZ7vfvEnbStxm0y3+PEH0G+TzLevNcY/AE+e42ua7f2Lzn/892+CFy/3A5atj+mcx/z8BZ9Efsyd9eWFM2nuJE9nWjAFdr8Y9vp5+9exyQWPnO7LBlw4Pr3/i0VfeRn8r970UY3v/aP5HuwQvDz+oi/9R25hyumiL70XfvBO/uM8nuLi5gAAHSFJREFU/Mi2HUQ31sZxd/vcmOXf+mjeXld40YNMePg0+PJ//l2ytqbd5rp4jXeAjb4dvhnvVTa3PsaDuXQrVn79ji9+PmikF3lp69pszVWmnDPe1+M6+Lqul8LfGuVz4J/f8dGO8jlenfGCsZlJYUsG0ac+lV7n2/zPvUN25x+3Lb2xapkXqSif7ZUpk0trhODtmPyEl9TvzI7VNd0jp/s6epd83vrf5WSq8vaneQpyshOktsqr7L71F0/tPehP/h2QbifJjA88mE/U+Ly0bLzfyxd4B08sH370hmcVdTEK4KR9qirg+i3aN3eq4ltfE2njvX1Cc7YsmAo37+nrgpz6tKeTdbZE3HPJl8708r6dUWr+P9+r72XtqOdcvRxeuNzT8dbf0T8rXaEiYEf5ZoJ/+e9+qc8HSFcI3pnx0pU+b3TfX3uay6JpUXCW/JvmQVpNSvW//GLos7FP6h+wpf9gdtTaYrXVnibz3o0w68O2lSpvaPF0+McIT5XZ/+rMtFOya/Y4LwY16TH/rA7czgO5rY72tcwaM/9zH4UtW8erdLb3O7B8DtyyN+QXwln/q0/Vf/ZiGH8fXPJZlzw4a5XXr61fw+rwGzPXoVKzEv6ymc8lPfrWlm//8Gnwxas+b66ta6Yunu4jbcU9PIgr7eMZCs9f6gWK9ri0bfuVxiXn35/0aOtSHueMhzsOqj9eyi9c/TbzPvEKx7PH+BzqQ69veUH5jx/xkv49B8GoR7LbqT9ztHckHXtX+9NBO4ACOGmfMXfAsz+FH74MQ3Zq+37euA7+9zv/wc5GVbaalR44lc+Bc9/xambZ8s3HcMtenvb1/X927GPNm+wV5DqreMqkx+CZiwDzNVyGH9Hxj9mUmaN9cvTAEZ7+kckUqnuPgVmjPShuS0GHpbP8/2rqy6teb3k+P7LvJh6o9d04Or8JdB/YOfM1U9VWwX3H+IjJiQ/Cpge0fV8v/sIXZr3w446dYyedb+UST2cdcwfM/xSKengJ8O+cvuqo2LJ5/j1cW+mpS5maCzx7rBf8GLidjyYl4h6YbHZw11h/s72qV3i6dmlfD1Iz+T1QF+h+3vx3WbLTao+feeXm9pjxvncsDt7JFwG/6zBf7+7EBzv/O25NV1sFfx4Gmx4MR92c3n0qvvVOEYCzX29+fnsi7vPiXrva0+/3/Y1PK2j4Pqau5bnBbnD8PV1jrdQVi7pGOxqhAK49QvAfpG2OXzvTfULwg/9YvlcabM8BcPUKn4fVc31Pdens+QjPXuyT5Uc90r6D0Ex5+Uqf0H36C7DBdzvucZ4634Oqn07uvC+pRV95OszssX4Ad9AfO3cdn8ql/mMy+nYfBaiu8Dz9nTNUwGX6u562td9vvVBJW4UAnz7jKVH9hvnoWu8Nul76aWU5/Ocwn8962jMweIe27SNZBfWY2zPfRukaQvCD8zF3wCdP+npOQ3bxUblN9vPe7gVT4QfPtX4JiZakVqYcuis8eS784Hk/vyZYsQjyCjN/LDJ7nBdKOeyv/j415b7jvBjGRR9nJl07uRB5LN87p370Rpc9kM55T53v6amXTm154fbaKvjP9z1gP+Ol9OeHLZ4Oz17k86gH7+Sd08klJmpW+qjbpMf8//OwvzY+oier0ELe7fHNBC9xfOvePs9nbTP9Hfj2E5+42t6Aq7DUF/ycNdp/2DvTJ0958PbdC7pG8AY+T6znEK+4lc5aRm2xfIH3im97Quf+MPbZ0Edad73QC2Pcsjd8+2nHP24I/iN1ww5+ALnTOT7XavPDfF2haW9k5jFeuxrK1oUd21mEw8wLj3z3fA9s+m3S9YI38FSnkx71xajvP7ZtawElF+7e5bzMt0+6DjOvdnf0rXDxZ77kwLK5vnjudZv4osLH3Jn54A08bXPPy70i6wtX+Kh1R3aOdbbSPh3TkTxwO0/Jbm5NuJkfwtToOz0TwRv479Iel3lq+HH/UfDWkbY+1jsyp77U/O1C8FTWme/7sjitKe7RewM4+XFPt18wBW7azZeqWDrLR1snPeadnoffoOCtnRTApWPgCDjtae/Rv21fGHOnf8DXFh/eCsW9YKsMFdsYMQoGDPcyyLXVmdlnSxZ/7VXTBn0H9slA1a5MKezmVaEWTPE1rTrCmDshXuWBTGfLK/B5Tic/DisWeMromDs67v9n8XRfBPSRH3igceZrcPC1frBx5E0+wvXID/x27TH1FZjxnldjbaknc01SNsCLTcQKvHpXaxYAb8/C3ZK7uvX18v4XjPPCFFsdDUf8u/FqkZmy5xW+gHHVUi8PnquVJzuTmc+pmz226Y62166GbgMyUzk21T5Xws+m6Xuhow3dzTsdJ7awqPeHt3oRq90vbVvBMzOfK33eaO84/f/27jy8qurq4/h3J0YiEKYACgZNFKphDDIURRQoIGBAEEUZrHUCSm1rW6hQS621Dq+2ilYQURGtVkUUpQoaBwa1IgRBjYIySEsMRQiDgIzJfv9YSQkYMpCbnHtzf5/nyZPc4dzsSzbJWWevvdbCO2FyG1sAGfp3y1jR/8kKUwBXVikXwJj37UreqzdZati+b4MeVeX7NsfSu865KnQnqjGxdlK//StbEatshw5YSg3AZTPC76pPi95WQvvdv1haUSgdOmCNMZv3OpzGEITmP7IeUqefZ3u+Xrja9suESt5B6yU15Ye2T+uiu+D6d448IaiRAFf+o6CM9QhL5z0e+fl2IlM/GdpHXu+YCmuQYuW/930LTw+xlK6yWP1P2Pkfrb5Fq5gYK2A15FFbdans7zXoYauY16kcfeWiXdsrLJWxuFW49Qthw7vWuuBYhWkq4oQaoX9NOVJMrFUqXpNx7L+/6xfZnvGz+lu2VEXUbgSXP2F7Gpv3hmvnW7aJhIT2wJVXfj68fz+8c4ctFV/2hK3QVVcL7oRF98AvVpS9WWdZeG/7IDZ9bH1kjqcARFkV7jMb+hS0vKTyvk9F7NoMUzrBKW1tf1Gork59MsvSlka+aEFc0PLz4V8Pwju3236HgQ/A6V0r9sd74zK7qLI5yxpm97+n5CbZa96EZy63K4tDHi//v3Vhye3B06HdFcc/7kj31WIL4JqkWcGI0i7wPNYL9myxlZjKbtIqIsfnuRG2f/E3qw+ncntv/393/Rd+8ZGCrUiWvRwe6wkDH7IL80Vt+8q2CtU+2WoUxNcJZoxFHDx4kOzsbPbt2xf0UCpdfHw8SUlJxMUduYXiWHvgAqifHuFiYuwK1Gnn2arO472hzx2h2R8Wbg4dsPS7Fn1CG7yB/Vv1vt2qWb13X+WVE//yDQveOl0fvsEbQMLJViHy1ZusElgoykN7D0umQsOz4MwfVfz1QiEmxqpgJp8Ps6+xNLyYOGicahdCmrSDJu2tYl1pBU/27bQ03MwZVk30imcgNb30MbTobWX7377NAubyVOXMOwgL7jhcvj+apVxgbSJmXW1pqVc+c+y9exuX2r7XfvcoeBMJZ+2vgtWv2t/Owt+nX8y3EvEDHlTwFulOPQfqp0DW7CMDuP27rM2A9zDs2bAI3gCys7NJSEggOTkZV93OsYvw3pObm0t2djYpKWU731YK5fE6/VwY8x6c0QPmj4dZV4U2JSwcrJprTXw7V1IKSpO2lkqzZJpV4Au1nV/DnDFwchsLssPdOVdDsy62Yrhna8Vfb+OHkLMCuowJv4sLSR0tpfLymVa8o2YirHrVigU91hPuPBWmngcvj4UPH7GGnwf22LFFi5Qsf8L29v3sw7IFb4XO/5Wlkrz1R1jzVtmPW/mM9WrrOUmBCNhFkYv/apvi5/7i2HsbP5gCNepC2oiqHZ+IlE/zXrYCs/IZu52fbxetGpxh+9clsjlnFx+/WmyZP2A/4zljrDDV5TPtZx0m9u3bR2JiYrUO3gCccyQmJpZrpVErcBVRK9Fye5dMsRPBR7rBZTMhqUPQIwuNpY/alZrKXL3p+Xs7GX/nz6Ht05N3yFIHD+23HOy4+NC9dmWJibE9G9POhzd+d/z/Hvn51iT67dssMGpbyftNjleNBAuiWg22295bUYxNKy21Nmel5eoXnki4GEhsYVcGs5fZat2w545v47tz1gh36xp48VrrqZR4ZsnHHNxr1bSSOltfKTGdrrPUyIV32Z6Ho1fTt//bLgad9/PobMMiEkliT7ALq/96yE7wN7xr6elDHg/P6rhSfq0vg8X32rlXlzGw6G5bde17t+1TDTPVPXgrVN73qRW4ioqJsROTa14HD8zoY7/4wnBvYbls+sRKyBbXiDGU6iZZX65PnrcT9lBZfI+1P0i/zyoPRorGZ1tK3yfPWx+V8spZAY/3slTMxi2tz1KkVEl0Duo1g9QBFtiPnG39an692gK1C2+2K4P7dxdfpKS8TqxlaX8uxvZ97N9V8vOXPQ67ciz9Mkr+oJTZhTdb76j3H7Dff0V9+AjgoPOoQIYmIuWUNhJ8nl08W3iXVY1udRzVCCU8NT7bMpM+fcHaKy36P/uZB1GpOszt2LGDqVOnlvu4/v37s2NH5WblKYALlWadYMxi+EFfyLgFnr2y7NXZwtGyR+GEk6B9FaQ8nf8rWyl6c1JoAt/1i6zwStqIyq92Vhm6jbNmzq/+2lZ9ymLvdks/nN4Ddmy0Ahs/ec32lkUy56BOE1vx6j4Bhj8HP1sC5461K8UVVT/ZUka2fmEpJPn5xT9v37fw7l8tZTqlW8W/b3XjHPT/i6VUZtwCHz9v9+/71spRtxpccmEZEQkfjX5gmQYL74LctdDzlsq9kCtVr80Q29f40mj7WaffpwuTxThWAJeXl1ficfPmzaNevUoszocCuNA6qT5c8bRt1F/3jqXCZYdpNc2S7N0On7wAbS+391TZ4uvaFfyvFluFwIrIXWepkw1bQP97QzO+qhYXD+n3W5uFRfeU/Nz8fFjxDPytY0HT6tFw4zKrjqhfxmVzRnfo82dLIXn3r8U/54MpsHebrb5J8WJi4dJHIbkbvDLW9haqcbdIZGo/EvIOWO/Us/oHPRoJtdZD7HPNBnbequI0xZowYQLr1q0jLS2NTp060aNHD4YPH06bNm0AGDRoEB06dKBVq1ZMn35420tycjJbt25lw4YNpKamcsMNN9CqVSv69OnD3r1lvDBfCrURqCw5K6w624HdMHaJNcCNFP96yK6ij37XCo1UhUMHYGoXy7Ef8375V1f27bSc7iXTrHrhNfPhlNaVM9aqMuen8OksGL0YTm71/cf/+ym8Ns5SXZM6WzGJqvp5VTfew5zR1nZh2LNH7nHbsxUeaAdn9oQr/h7cGCPFvm9hZn+7mFIjwVaTr50f9KhEpDz274JZP4buE6FZ56BHI5Xh87l2blHa/u8ArVq1itRUyyS67Z+f8XlOaPsvt2xah1sHFHN+VWDDhg2kp6eTlZXFwoULufjii8nKyvpfpcht27bRoEED9u7dS6dOnVi0aBGJiYkkJyeTmZnJ7t27ad68OZmZmaSlpTF06FAGDhzIyJHFVxov+n4LHauNgFbgKkvT9jD8eduvU1J1tnCTn2/pk826VG0wcMKJ0OuPsGU1rCymieix5OfB8ifhbx0s8Gx3BdyYGfnBG9iqUI068M+bjkzt27cT5k+ARy6E3DXWz+XaNxS8VYRzVkCmSVt4aRRs+fLwY+/dDwe/s315Urr4OjDiRbtotXuzpbuKSGSpkQBXzVHwVp21HBjWwVs46ty58xFl/h988EHatWtHly5d2LhxI2vWrPneMSkpKaSlWb/oDh06sGHDhpCMRVUoK1PjVOh1q1UU/Ogp6HB10CMq3dq3YPsGK5Ne1VIHWOC44E6rklRaxboN78PrN9tKVLMuMOIFC5yri1qJcNGd8PIYWD4DOl5nm44zfg+7v4GO19jPqWaDoEdaPcSdZL3kpne3fjg3vG1XoZc+Cu2GQaOzgh5h5Eg4GX4816qIKv1KREQqqKSVsqpSq1at/329cOFC3nrrLT744ANq1qxJ9+7di20DUKPG4fTU2NjYkKVQagWusv3wp7Yn5PWJ1j8q3C171HrApA6s+u/tHPS53a7af/DQsZ+3/d+W2jGzP3y3HS6bAde+Xr2Ct0LtroSUC61h9cx0299Xp6kFF+n3K3gLtXrNYOhTtv/wpVG2iR9vBVSkfOqfbj0k1S9PREQiUEJCArt2FV+heufOndSvX5+aNWuyevVqlixZUqVjUwBX2WJiYNDDEHNCQZW7kivXBGrbeisi0uEnltIYhGadrZLd+w8ebjJZaP9uePt2a+D8ZQZ0/50V7Gg9pPoW7HDOArW8A9aLJ/1+uP5t21gulSO5q/XD+fJ1WPG0lcevd1rQoxIREZEqlJiYSNeuXWndujXjx48/4rG+ffty6NAh2rZty6RJk+jSpUuVjk1FTKrKx8/DnFFWxa7bb4IeTfHeuAU+nAY3ZVnp9qDkroMpP7QWBgMesP1fn86yZum7NkGbobZfru6pwY2xquWus4qgWnGrGt7Da7+2HjmRVoRIRESkGiiuqEd1Vp4iJtoDV1XaDoUv5sGCu6B5L2jSLugRHenAd1by++z0YIM3sE21na6DpdPhtHNtD9LXmdD0HEtvi8ZN1dpoXLUKVz4vusvaOoiIiIiECaVQVpXCE8Kaiba35uD3NzoGKmu2VTfsPCrokZgLfgsn1rbS7juzYdA0Sx2MxuBNgqPgTURERMKMAriqVLMBXDLFSuW/c3vQoznMe1vtatwSTj8v6NGYWokweJpVWfz5ckgbZvsJRURERESimM6Iq1qLXtDpequyuH5R0KMxGz+0UvydbwivYiBnXwwXjCu9nYCIiIiISJRQABeE3n+CxObw8ljYuyPo0dgesxp1rDiIiIiIiIiELQVwQTixFgyebhUV598c7Fh2bbZKe2kjtNIlIiIiIhLmFMAFJakDXDAePnkOPnv5+F8nPx8+nwtPXQKzrob3JsP6hbB3e9mO/+hJyD9oaZ0iIiIiIsKOHTuYOnXqcR07efJkvvvuuxCP6DC1EQjSBeNgzRvw6k1wWhdIOKXsx+bnw6q5sPhea/Bc2Gj48yLBYP0UaJoGTdtDkzRrXXBSvcOP5x2EzBlwZk9o2Dw070lEREREJMIVBnBjx44t97GTJ09m5MiR1KxZsxJGpgAuWLFxcOmjMK0bvHIjjHih9CIi+fmw6hVYdA988zkktrB0zNZDIPYE2JMLm1baR84KyF4On805fHyDMyyYa9oeDu23NM70+yv3fYqIiIiIRJAJEyawbt060tLS6N27N40bN2bWrFns37+fwYMHc9ttt7Fnzx6GDh1KdnY2eXl5TJo0ic2bN5OTk0OPHj1o2LAhCxYsCPnYFMAFrWEL6HM7zBtnq2Gdriv+efl5trq26F7Ysgoa/gAufQxaXwoxsYefVysRmv/IPgrtyYVNKyCnMKhbBp+9ZI/VOw1a9Km89yciIiIiUhHzJ1jF9FA6pQ30u/uYD999991kZWWxcuVKMjIymD17NkuXLsV7z8CBA1m8eDFbtmyhadOmvPbaawDs3LmTunXrct9997FgwQIaNmwY2jEXUAAXDjpdD1/Mg4zfQ8qFR6Yz5ufZCtqie2DrF9DobBjyOLQafGTgVpJaidC8l30U2rPVArr6p5f9dUREREREokxGRgYZGRm0b98egN27d7NmzRq6devGuHHjuPnmm0lPT6dbt25VMh4FcOHAOWvwPfVcmDMKrs2w+7JetD1uW7+ERqlw2RPQclBoGlrXamg96UREREREwlkJK2VVwXvPxIkTGT169PceW758OfPmzWPixIn06dOHP/zhD5U+HgVw4aJOU9uLNvsaeOkG+O8nkLsWGreEy5+E1IGhCdxERERERKRECQkJ7Nq1C4CLLrqISZMmMWLECGrXrs3XX39NXFwchw4dokGDBowcOZLatWszc+bMI44NNIXSOdcXeACIBR7z3t991OPjgRFFXjMVaOS931basVJE60stlfLTF+Dk1jD0KTh7gAI3EREREZEqlJiYSNeuXWndujX9+vVj+PDhnHvuuQDUrl2bp59+mrVr1zJ+/HhiYmKIi4vj4YcfBmDUqFH069ePJk2aVEoRE+e9L/kJzsUCXwK9gWxgGTDMe//5MZ4/APiV975neY8t1LFjR5+ZmVne91I9HNxnFSSTOitwExEREZGotGrVKlJTU4MeRpUp7v0655Z77zse/dyyRAidgbXe+/Xe+wPAc8AlJTx/GPDscR4rcfHWE07Bm4iIiIiIHKUsUcKpwMYit7ML7vse51xNoC/wYnmPFRERERERkZKVJYArrrP0sfIuBwDve++3lfdY59wo51ymcy5zy5YtZRiWiIiIiIhIdClLAJcNNCtyOwnIOcZzr+Rw+mS5jvXeT/fed/Ted2zUqFEZhiUiIiIiItVVabU6qovyvs+yBHDLgBbOuRTn3IlYkDb36Cc55+oCFwKvlPdYERERERGRQvHx8eTm5lb7IM57T25uLvHx8WU+ptQ2At77Q865G4E3sFYAM7z3nznnxhQ8Pq3gqYOBDO/9ntKOLfPoREREREQk6iQlJZGdnU00bK2Kj48nKSmpzM8vtY1AEKK6jYCIiIiIiES9irQREBERERERkTCgAE5ERERERCRCKIATERERERGJEGG5B845twX4d9DjKEZDYGvQg5CwojkhxdG8kKNpTkhxNC/kaJoTUtTp3vvv9VcLywAuXDnnMovbSCjRS3NCiqN5IUfTnJDiaF7I0TQnpCyUQikiIiIiIhIhFMCJiIiIiIhECAVw5TM96AFI2NGckOJoXsjRNCekOJoXcjTNCSmV9sCJiIiIiIhECK3AiYiIiIiIRAgFcGXgnOvrnPvCObfWOTch6PFIMJxzM5xz3zjnsorc18A596Zzbk3B5/pBjlGqlnOumXNugXNulXPuM+fcLwvu17yIUs65eOfcUufcxwVz4raC+zUnBOdcrHNuhXPu1YLbmhdRzjm3wTn3qXNupXMus+A+zQspkQK4UjjnYoEpQD+gJTDMOdcy2FFJQGYCfY+6bwLwtve+BfB2wW2JHoeA33jvU4EuwM8Kfj9oXkSv/UBP7307IA3o65zrguaEmF8Cq4rc1rwQgB7e+7Qi7QM0L6RECuBK1xlY671f770/ADwHXBLwmCQA3vvFwLaj7r4EeLLg6yeBQVU6KAmU936T9/6jgq93YSdmp6J5EbW82V1wM67gw6M5EfWcc0nAxcBjRe7WvJDiaF5IiRTAle5UYGOR29kF94kAnOy93wR2Mg80Dng8EhDnXDLQHvgQzYuoVpAmtxL4BnjTe685IQCTgd8C+UXu07wQD2Q455Y750YV3Kd5ISU6IegBRABXzH0q3Ski/+Ocqw28CNzkvf/WueJ+bUi08N7nAWnOuXrAHOdc66DHJMFyzqUD33jvlzvnugc9HgkrXb33Oc65xsCbzrnVQQ9Iwp9W4EqXDTQrcjsJyAloLBJ+NjvnmgAUfP4m4PFIFXPOxWHB2zPe+5cK7ta8ELz3O4CF2N5ZzYno1hUY6JzbgG3F6OmcexrNi6jnvc8p+PwNMAfbuqN5ISVSAFe6ZUAL51yKc+5E4EpgbsBjkvAxF7i64OurgVcCHItUMWdLbY8Dq7z39xV5SPMiSjnnGhWsvOGcOwnoBaxGcyKqee8neu+TvPfJ2HnEO977kWheRDXnXC3nXELh10AfIAvNCymFGnmXgXOuP5a7HgvM8N7fEfCQJADOuWeB7kBDYDNwK/AyMAs4DfgPcLn3/uhCJ1JNOefOB94FPuXwvpbfYfvgNC+ikHOuLVZ0IBa7SDrLe/8n51wimhMCFKRQjvPep2teRDfn3BnYqhvYtqZ/eO/v0LyQ0iiAExERERERiRBKoRQREREREYkQCuBEREREREQihAI4ERERERGRCKEATkREREREJEIogBMREREREYkQCuBEREREREQihAI4ERERERGRCKEATkREREREJEL8P215N/8W2TGkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_accuracy(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.69      0.80     63623\n",
      "           1       0.41      0.87      0.55     15421\n",
      "\n",
      "    accuracy                           0.73     79044\n",
      "   macro avg       0.68      0.78      0.68     79044\n",
      "weighted avg       0.85      0.73      0.75     79044\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[44084 19539]\n",
      " [ 2040 13381]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Experiment with Activity Regularization (L1) & Weight Regularization (L2) \n",
    "[0.100000, 0.010000, 0.001000, 0.000100, 0.000010, 0.000001]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def create_model_with_L1L2():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "   \n",
    "    # Hidden Layers\n",
    "    #model.add(Dense(32, activation='linear', activity_regularizer=l1(0.001)))\n",
    "    #model.add(Activation('relu'))\n",
    "    #model.add(Dense(82, input_dim=n_input, activation='linear', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(GaussianNoise(0.01, input_shape=(2,)))\n",
    "    #model.add(Dense(500, activation='relu'))\n",
    "    \n",
    "    model.add(Dense(82, input_dim=n_input, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "        \n",
    "    model.add(Dense(82, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    model.add(Dense(41, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    model.add(Dense(41, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    model.add(Dense(21, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    model.add(Dense(21, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    # Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))   \n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2888 - accuracy: 0.8801\n",
      "Epoch 00001: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 14s 2ms/step - loss: 0.2888 - accuracy: 0.8801 - val_loss: 0.2622 - val_accuracy: 0.8869\n",
      "Epoch 2/600\n",
      "7901/7905 [============================>.] - ETA: 0s - loss: 0.2653 - accuracy: 0.8878\n",
      "Epoch 00002: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.2653 - accuracy: 0.8878 - val_loss: 0.2610 - val_accuracy: 0.8870\n",
      "Epoch 3/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.2632 - accuracy: 0.8878\n",
      "Epoch 00003: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.2632 - accuracy: 0.8878 - val_loss: 0.2594 - val_accuracy: 0.8870\n",
      "Epoch 4/600\n",
      "7905/7905 [==============================] - ETA: 0s - loss: 0.2621 - accuracy: 0.8879\n",
      "Epoch 00004: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 16s 2ms/step - loss: 0.2621 - accuracy: 0.8879 - val_loss: 0.2599 - val_accuracy: 0.8870\n",
      "Epoch 5/600\n",
      "7884/7905 [============================>.] - ETA: 0s - loss: 0.2609 - accuracy: 0.8878\n",
      "Epoch 00005: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.2609 - accuracy: 0.8878 - val_loss: 0.2593 - val_accuracy: 0.8876\n",
      "Epoch 6/600\n",
      "7900/7905 [============================>.] - ETA: 0s - loss: 0.2599 - accuracy: 0.8882\n",
      "Epoch 00006: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 17s 2ms/step - loss: 0.2599 - accuracy: 0.8882 - val_loss: 0.2579 - val_accuracy: 0.8875\n",
      "Epoch 7/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.2592 - accuracy: 0.8878\n",
      "Epoch 00007: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.2591 - accuracy: 0.8879 - val_loss: 0.2579 - val_accuracy: 0.8875\n",
      "Epoch 8/600\n",
      "7882/7905 [============================>.] - ETA: 0s - loss: 0.2582 - accuracy: 0.8885\n",
      "Epoch 00008: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.2581 - accuracy: 0.8886 - val_loss: 0.2565 - val_accuracy: 0.8890\n",
      "Epoch 9/600\n",
      "7887/7905 [============================>.] - ETA: 0s - loss: 0.2580 - accuracy: 0.8885\n",
      "Epoch 00009: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.2580 - accuracy: 0.8885 - val_loss: 0.2561 - val_accuracy: 0.8888\n",
      "Epoch 10/600\n",
      "7883/7905 [============================>.] - ETA: 0s - loss: 0.2575 - accuracy: 0.8890\n",
      "Epoch 00010: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2575 - accuracy: 0.8890 - val_loss: 0.2573 - val_accuracy: 0.8885\n",
      "Epoch 11/600\n",
      "7904/7905 [============================>.] - ETA: 0s - loss: 0.2571 - accuracy: 0.8887\n",
      "Epoch 00011: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 18s 2ms/step - loss: 0.2571 - accuracy: 0.8887 - val_loss: 0.2564 - val_accuracy: 0.8892\n",
      "Epoch 12/600\n",
      "7888/7905 [============================>.] - ETA: 0s - loss: 0.2566 - accuracy: 0.8890\n",
      "Epoch 00012: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 20s 2ms/step - loss: 0.2566 - accuracy: 0.8890 - val_loss: 0.2558 - val_accuracy: 0.8893\n",
      "Epoch 13/600\n",
      "7895/7905 [============================>.] - ETA: 0s - loss: 0.2566 - accuracy: 0.8890\n",
      "Epoch 00013: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2566 - accuracy: 0.8890 - val_loss: 0.2565 - val_accuracy: 0.8890\n",
      "Epoch 14/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2564 - accuracy: 0.8890\n",
      "Epoch 00014: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2563 - accuracy: 0.8890 - val_loss: 0.2567 - val_accuracy: 0.8890\n",
      "Epoch 15/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.2558 - accuracy: 0.8894\n",
      "Epoch 00015: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2558 - accuracy: 0.8894 - val_loss: 0.2561 - val_accuracy: 0.8891\n",
      "Epoch 16/600\n",
      "7886/7905 [============================>.] - ETA: 0s - loss: 0.2553 - accuracy: 0.8893\n",
      "Epoch 00016: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2552 - accuracy: 0.8893 - val_loss: 0.2566 - val_accuracy: 0.8885\n",
      "Epoch 17/600\n",
      "7885/7905 [============================>.] - ETA: 0s - loss: 0.2551 - accuracy: 0.8895\n",
      "Epoch 00017: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2551 - accuracy: 0.8896 - val_loss: 0.2570 - val_accuracy: 0.8893\n",
      "Epoch 18/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2549 - accuracy: 0.8894\n",
      "Epoch 00018: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2549 - accuracy: 0.8895 - val_loss: 0.2556 - val_accuracy: 0.8893\n",
      "Epoch 19/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.2547 - accuracy: 0.8894\n",
      "Epoch 00019: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2546 - accuracy: 0.8894 - val_loss: 0.2576 - val_accuracy: 0.8874\n",
      "Epoch 20/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.2540 - accuracy: 0.8897\n",
      "Epoch 00020: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2541 - accuracy: 0.8897 - val_loss: 0.2565 - val_accuracy: 0.8891\n",
      "Epoch 21/600\n",
      "7895/7905 [============================>.] - ETA: 0s - loss: 0.2539 - accuracy: 0.8898\n",
      "Epoch 00021: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2538 - accuracy: 0.8898 - val_loss: 0.2558 - val_accuracy: 0.8890\n",
      "Epoch 22/600\n",
      "7888/7905 [============================>.] - ETA: 0s - loss: 0.2540 - accuracy: 0.8897\n",
      "Epoch 00022: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2540 - accuracy: 0.8898 - val_loss: 0.2559 - val_accuracy: 0.8887\n",
      "Epoch 23/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.2536 - accuracy: 0.8897\n",
      "Epoch 00023: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2536 - accuracy: 0.8896 - val_loss: 0.2557 - val_accuracy: 0.8886\n",
      "Epoch 24/600\n",
      "7884/7905 [============================>.] - ETA: 0s - loss: 0.2534 - accuracy: 0.8899\n",
      "Epoch 00024: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2534 - accuracy: 0.8899 - val_loss: 0.2564 - val_accuracy: 0.8886\n",
      "Epoch 25/600\n",
      "7895/7905 [============================>.] - ETA: 0s - loss: 0.2531 - accuracy: 0.8902\n",
      "Epoch 00025: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2531 - accuracy: 0.8902 - val_loss: 0.2571 - val_accuracy: 0.8875\n",
      "Epoch 26/600\n",
      "7901/7905 [============================>.] - ETA: 0s - loss: 0.2528 - accuracy: 0.8900\n",
      "Epoch 00026: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2528 - accuracy: 0.8900 - val_loss: 0.2553 - val_accuracy: 0.8889\n",
      "Epoch 27/600\n",
      "7900/7905 [============================>.] - ETA: 0s - loss: 0.2526 - accuracy: 0.8904\n",
      "Epoch 00027: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2526 - accuracy: 0.8905 - val_loss: 0.2560 - val_accuracy: 0.8893\n",
      "Epoch 28/600\n",
      "7900/7905 [============================>.] - ETA: 0s - loss: 0.2522 - accuracy: 0.8898\n",
      "Epoch 00028: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 23s 3ms/step - loss: 0.2522 - accuracy: 0.8898 - val_loss: 0.2560 - val_accuracy: 0.8888\n",
      "Epoch 29/600\n",
      "7884/7905 [============================>.] - ETA: 0s - loss: 0.2524 - accuracy: 0.8903\n",
      "Epoch 00029: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 20s 3ms/step - loss: 0.2525 - accuracy: 0.8903 - val_loss: 0.2559 - val_accuracy: 0.8882\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/600\n",
      "7902/7905 [============================>.] - ETA: 0s - loss: 0.2519 - accuracy: 0.8906\n",
      "Epoch 00030: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2520 - accuracy: 0.8906 - val_loss: 0.2563 - val_accuracy: 0.8894\n",
      "Epoch 31/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2518 - accuracy: 0.8905\n",
      "Epoch 00031: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2518 - accuracy: 0.8905 - val_loss: 0.2558 - val_accuracy: 0.8888\n",
      "Epoch 32/600\n",
      "7885/7905 [============================>.] - ETA: 0s - loss: 0.2518 - accuracy: 0.8905\n",
      "Epoch 00032: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2518 - accuracy: 0.8905 - val_loss: 0.2563 - val_accuracy: 0.8889\n",
      "Epoch 33/600\n",
      "7892/7905 [============================>.] - ETA: 0s - loss: 0.2513 - accuracy: 0.8908\n",
      "Epoch 00033: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2512 - accuracy: 0.8908 - val_loss: 0.2576 - val_accuracy: 0.8890\n",
      "Epoch 34/600\n",
      "7885/7905 [============================>.] - ETA: 0s - loss: 0.2513 - accuracy: 0.8908\n",
      "Epoch 00034: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2513 - accuracy: 0.8908 - val_loss: 0.2569 - val_accuracy: 0.8886\n",
      "Epoch 35/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.2511 - accuracy: 0.8910\n",
      "Epoch 00035: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2510 - accuracy: 0.8910 - val_loss: 0.2584 - val_accuracy: 0.8887\n",
      "Epoch 36/600\n",
      "7892/7905 [============================>.] - ETA: 0s - loss: 0.2512 - accuracy: 0.8912\n",
      "Epoch 00036: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2512 - accuracy: 0.8911 - val_loss: 0.2569 - val_accuracy: 0.8885\n",
      "Epoch 37/600\n",
      "7904/7905 [============================>.] - ETA: 0s - loss: 0.2508 - accuracy: 0.8906\n",
      "Epoch 00037: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 20s 2ms/step - loss: 0.2508 - accuracy: 0.8906 - val_loss: 0.2564 - val_accuracy: 0.8890\n",
      "Epoch 38/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2508 - accuracy: 0.8909\n",
      "Epoch 00038: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 20s 2ms/step - loss: 0.2508 - accuracy: 0.8909 - val_loss: 0.2568 - val_accuracy: 0.8891\n",
      "Epoch 39/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.2506 - accuracy: 0.8912\n",
      "Epoch 00039: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2505 - accuracy: 0.8912 - val_loss: 0.2566 - val_accuracy: 0.8884\n",
      "Epoch 40/600\n",
      "7904/7905 [============================>.] - ETA: 0s - loss: 0.2501 - accuracy: 0.8913\n",
      "Epoch 00040: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2501 - accuracy: 0.8913 - val_loss: 0.2571 - val_accuracy: 0.8886\n",
      "Epoch 41/600\n",
      "7892/7905 [============================>.] - ETA: 0s - loss: 0.2501 - accuracy: 0.8913\n",
      "Epoch 00041: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2501 - accuracy: 0.8913 - val_loss: 0.2584 - val_accuracy: 0.8881\n",
      "Epoch 42/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.2502 - accuracy: 0.8914\n",
      "Epoch 00042: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2502 - accuracy: 0.8914 - val_loss: 0.2575 - val_accuracy: 0.8885\n",
      "Epoch 43/600\n",
      "7901/7905 [============================>.] - ETA: 0s - loss: 0.2500 - accuracy: 0.8912\n",
      "Epoch 00043: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2500 - accuracy: 0.8912 - val_loss: 0.2569 - val_accuracy: 0.8881\n",
      "Epoch 44/600\n",
      "7887/7905 [============================>.] - ETA: 0s - loss: 0.2498 - accuracy: 0.8911\n",
      "Epoch 00044: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2497 - accuracy: 0.8912 - val_loss: 0.2575 - val_accuracy: 0.8885\n",
      "Epoch 45/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.2497 - accuracy: 0.8914\n",
      "Epoch 00045: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2497 - accuracy: 0.8914 - val_loss: 0.2572 - val_accuracy: 0.8884\n",
      "Epoch 46/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.2492 - accuracy: 0.8916\n",
      "Epoch 00046: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2492 - accuracy: 0.8915 - val_loss: 0.2577 - val_accuracy: 0.8863\n",
      "Epoch 47/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.2494 - accuracy: 0.8916\n",
      "Epoch 00047: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2494 - accuracy: 0.8916 - val_loss: 0.2570 - val_accuracy: 0.8882\n",
      "Epoch 48/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.2491 - accuracy: 0.8915\n",
      "Epoch 00048: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 20s 2ms/step - loss: 0.2491 - accuracy: 0.8915 - val_loss: 0.2574 - val_accuracy: 0.8879\n",
      "Epoch 49/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.2489 - accuracy: 0.8915\n",
      "Epoch 00049: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2490 - accuracy: 0.8915 - val_loss: 0.2577 - val_accuracy: 0.8884\n",
      "Epoch 50/600\n",
      "7893/7905 [============================>.] - ETA: 0s - loss: 0.2486 - accuracy: 0.8918\n",
      "Epoch 00050: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 19s 2ms/step - loss: 0.2486 - accuracy: 0.8918 - val_loss: 0.2597 - val_accuracy: 0.8877\n",
      "Epoch 51/600\n",
      "7891/7905 [============================>.] - ETA: 0s - loss: 0.2484 - accuracy: 0.8917\n",
      "Epoch 00051: val_accuracy did not improve from 0.88992\n",
      "7905/7905 [==============================] - 20s 2ms/step - loss: 0.2484 - accuracy: 0.8917 - val_loss: 0.2582 - val_accuracy: 0.8877\n",
      "Epoch 00051: early stopping\n",
      "Execution time :  0:15:58.213170\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "model = create_model_with_L1L2()\n",
    "history = model.fit(x=X_train_norm, \n",
    "                    y=y_train, \n",
    "                    epochs=600,\n",
    "                    batch_size=32,\n",
    "                    validation_data=(X_valid_norm, y_valid), verbose=1,\n",
    "                    callbacks=callbacks_list\n",
    "                   )\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer: adam Training Loss: 0.894 Validation Accuracy:0.888\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(X_train_norm, y_train, X_valid_norm, y_valid, model, optimizers[3])\n",
    "#Training Loss: 0.892 Validation Accuracy:0.889"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_accuracy(history, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.99      0.93     63623\n",
      "           1       0.90      0.48      0.63     15421\n",
      "\n",
      "    accuracy                           0.89     79044\n",
      "   macro avg       0.90      0.73      0.78     79044\n",
      "weighted avg       0.89      0.89      0.87     79044\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[62832   791]\n",
      " [ 7993  7428]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))\n",
    "#[[63021   602]\n",
    "# [ 8146  7275]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Experiment with Noise Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def create_model_with_noise():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "   \n",
    "    # Hidden Layers\n",
    "    #model.add(Dense(32, activation='linear', activity_regularizer=l1(0.001)))\n",
    "    #model.add(Activation('relu'))\n",
    "    #model.add(Dense(82, input_dim=n_input, activation='linear', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(GaussianNoise(0.01, input_shape=(2,)))\n",
    "    #model.add(Dense(500, activation='relu'))\n",
    "    \n",
    "    model.add(GaussianNoise(0.01, input_shape=(n_input,)))\n",
    "    #model.add(Dense(164, input_dim=n_input, activation='relu', kernel_regularizer=L1L2(l1=0.000001, l2=0.0)))\n",
    "    model.add(Dense(164, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "        \n",
    "    model.add(Dense(164, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    model.add(Dense(82, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    model.add(Dense(82, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    model.add(Dense(41, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    model.add(Dense(41, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    model.add(Dense(21, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    model.add(Dense(21, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))\n",
    "    #model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    # Output Layer\n",
    "    model.add(Dense(1, activation='sigmoid'))   \n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.2877 - accuracy: 0.8805\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.88247, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 26s 3ms/step - loss: 0.2877 - accuracy: 0.8804 - val_loss: 0.2654 - val_accuracy: 0.8825\n",
      "Epoch 2/600\n",
      "7902/7905 [============================>.] - ETA: 0s - loss: 0.2663 - accuracy: 0.8875\n",
      "Epoch 00002: val_accuracy improved from 0.88247 to 0.88702, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 30s 4ms/step - loss: 0.2663 - accuracy: 0.8875 - val_loss: 0.2605 - val_accuracy: 0.8870\n",
      "Epoch 3/600\n",
      "7904/7905 [============================>.] - ETA: 0s - loss: 0.2643 - accuracy: 0.8876\n",
      "Epoch 00003: val_accuracy did not improve from 0.88702\n",
      "7905/7905 [==============================] - 32s 4ms/step - loss: 0.2643 - accuracy: 0.8876 - val_loss: 0.2614 - val_accuracy: 0.8869\n",
      "Epoch 4/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.2629 - accuracy: 0.8876\n",
      "Epoch 00004: val_accuracy did not improve from 0.88702\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2629 - accuracy: 0.8876 - val_loss: 0.2604 - val_accuracy: 0.8869\n",
      "Epoch 5/600\n",
      "7904/7905 [============================>.] - ETA: 0s - loss: 0.2618 - accuracy: 0.8879\n",
      "Epoch 00005: val_accuracy improved from 0.88702 to 0.88791, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2618 - accuracy: 0.8879 - val_loss: 0.2584 - val_accuracy: 0.8879\n",
      "Epoch 6/600\n",
      "7905/7905 [==============================] - ETA: 0s - loss: 0.2610 - accuracy: 0.8878\n",
      "Epoch 00006: val_accuracy improved from 0.88791 to 0.88827, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 36s 5ms/step - loss: 0.2610 - accuracy: 0.8878 - val_loss: 0.2587 - val_accuracy: 0.8883\n",
      "Epoch 7/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.2604 - accuracy: 0.8879\n",
      "Epoch 00007: val_accuracy did not improve from 0.88827\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2604 - accuracy: 0.8879 - val_loss: 0.2584 - val_accuracy: 0.8878\n",
      "Epoch 8/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.2597 - accuracy: 0.8879\n",
      "Epoch 00008: val_accuracy did not improve from 0.88827\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2597 - accuracy: 0.8879 - val_loss: 0.2575 - val_accuracy: 0.8879\n",
      "Epoch 9/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.2593 - accuracy: 0.8879\n",
      "Epoch 00009: val_accuracy did not improve from 0.88827\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2593 - accuracy: 0.8879 - val_loss: 0.2581 - val_accuracy: 0.8874\n",
      "Epoch 10/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.2587 - accuracy: 0.8879\n",
      "Epoch 00010: val_accuracy did not improve from 0.88827\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2587 - accuracy: 0.8879 - val_loss: 0.2594 - val_accuracy: 0.8878\n",
      "Epoch 11/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.2581 - accuracy: 0.8884\n",
      "Epoch 00011: val_accuracy did not improve from 0.88827\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2582 - accuracy: 0.8884 - val_loss: 0.2586 - val_accuracy: 0.8878\n",
      "Epoch 12/600\n",
      "7902/7905 [============================>.] - ETA: 0s - loss: 0.2581 - accuracy: 0.8881\n",
      "Epoch 00012: val_accuracy did not improve from 0.88827\n",
      "7905/7905 [==============================] - 42s 5ms/step - loss: 0.2582 - accuracy: 0.8881 - val_loss: 0.2591 - val_accuracy: 0.8869\n",
      "Epoch 13/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2576 - accuracy: 0.8887\n",
      "Epoch 00013: val_accuracy did not improve from 0.88827\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2576 - accuracy: 0.8887 - val_loss: 0.2576 - val_accuracy: 0.8879\n",
      "Epoch 14/600\n",
      "7900/7905 [============================>.] - ETA: 0s - loss: 0.2573 - accuracy: 0.8885\n",
      "Epoch 00014: val_accuracy did not improve from 0.88827\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2573 - accuracy: 0.8885 - val_loss: 0.2592 - val_accuracy: 0.8875\n",
      "Epoch 15/600\n",
      "7895/7905 [============================>.] - ETA: 0s - loss: 0.2574 - accuracy: 0.8886\n",
      "Epoch 00015: val_accuracy did not improve from 0.88827\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2573 - accuracy: 0.8886 - val_loss: 0.2584 - val_accuracy: 0.8881\n",
      "Epoch 16/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.2566 - accuracy: 0.8885\n",
      "Epoch 00016: val_accuracy did not improve from 0.88827\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2566 - accuracy: 0.8885 - val_loss: 0.2583 - val_accuracy: 0.8875\n",
      "Epoch 17/600\n",
      "7900/7905 [============================>.] - ETA: 0s - loss: 0.2566 - accuracy: 0.8887\n",
      "Epoch 00017: val_accuracy improved from 0.88827 to 0.88837, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2567 - accuracy: 0.8887 - val_loss: 0.2574 - val_accuracy: 0.8884\n",
      "Epoch 18/600\n",
      "7893/7905 [============================>.] - ETA: 0s - loss: 0.2562 - accuracy: 0.8891\n",
      "Epoch 00018: val_accuracy improved from 0.88837 to 0.88846, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2562 - accuracy: 0.8891 - val_loss: 0.2583 - val_accuracy: 0.8885\n",
      "Epoch 19/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.2563 - accuracy: 0.8890\n",
      "Epoch 00019: val_accuracy did not improve from 0.88846\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2563 - accuracy: 0.8890 - val_loss: 0.2592 - val_accuracy: 0.8876\n",
      "Epoch 20/600\n",
      "7905/7905 [==============================] - ETA: 0s - loss: 0.2562 - accuracy: 0.8892\n",
      "Epoch 00020: val_accuracy did not improve from 0.88846\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2562 - accuracy: 0.8892 - val_loss: 0.2583 - val_accuracy: 0.8884\n",
      "Epoch 21/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2557 - accuracy: 0.8893\n",
      "Epoch 00021: val_accuracy did not improve from 0.88846\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2557 - accuracy: 0.8893 - val_loss: 0.2583 - val_accuracy: 0.8882\n",
      "Epoch 22/600\n",
      "7897/7905 [============================>.] - ETA: 0s - loss: 0.2554 - accuracy: 0.8891\n",
      "Epoch 00022: val_accuracy improved from 0.88846 to 0.88886, saving model to weights.best.hdf5\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2555 - accuracy: 0.8890 - val_loss: 0.2575 - val_accuracy: 0.8889\n",
      "Epoch 23/600\n",
      "7904/7905 [============================>.] - ETA: 0s - loss: 0.2554 - accuracy: 0.8891\n",
      "Epoch 00023: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2554 - accuracy: 0.8891 - val_loss: 0.2588 - val_accuracy: 0.8878\n",
      "Epoch 24/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.2552 - accuracy: 0.8891\n",
      "Epoch 00024: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 37s 5ms/step - loss: 0.2552 - accuracy: 0.8891 - val_loss: 0.2593 - val_accuracy: 0.8878\n",
      "Epoch 25/600\n",
      "7895/7905 [============================>.] - ETA: 0s - loss: 0.2550 - accuracy: 0.8893\n",
      "Epoch 00025: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2550 - accuracy: 0.8893 - val_loss: 0.2600 - val_accuracy: 0.8877\n",
      "Epoch 26/600\n",
      "7891/7905 [============================>.] - ETA: 0s - loss: 0.2543 - accuracy: 0.8899\n",
      "Epoch 00026: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2543 - accuracy: 0.8899 - val_loss: 0.2591 - val_accuracy: 0.8882\n",
      "Epoch 27/600\n",
      "7895/7905 [============================>.] - ETA: 0s - loss: 0.2539 - accuracy: 0.8901\n",
      "Epoch 00027: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2539 - accuracy: 0.8901 - val_loss: 0.2600 - val_accuracy: 0.8874\n",
      "Epoch 28/600\n",
      "7904/7905 [============================>.] - ETA: 0s - loss: 0.2539 - accuracy: 0.8896\n",
      "Epoch 00028: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2539 - accuracy: 0.8896 - val_loss: 0.2586 - val_accuracy: 0.8879\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/600\n",
      "7904/7905 [============================>.] - ETA: 0s - loss: 0.2539 - accuracy: 0.8898\n",
      "Epoch 00029: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2540 - accuracy: 0.8898 - val_loss: 0.2582 - val_accuracy: 0.8882\n",
      "Epoch 30/600\n",
      "7902/7905 [============================>.] - ETA: 0s - loss: 0.2539 - accuracy: 0.8897\n",
      "Epoch 00030: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 33s 4ms/step - loss: 0.2539 - accuracy: 0.8897 - val_loss: 0.2595 - val_accuracy: 0.8876\n",
      "Epoch 31/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.2533 - accuracy: 0.8900\n",
      "Epoch 00031: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2534 - accuracy: 0.8900 - val_loss: 0.2593 - val_accuracy: 0.8878\n",
      "Epoch 32/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2531 - accuracy: 0.8900\n",
      "Epoch 00032: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 36s 5ms/step - loss: 0.2531 - accuracy: 0.8900 - val_loss: 0.2605 - val_accuracy: 0.8879\n",
      "Epoch 33/600\n",
      "7902/7905 [============================>.] - ETA: 0s - loss: 0.2531 - accuracy: 0.8902\n",
      "Epoch 00033: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 37s 5ms/step - loss: 0.2531 - accuracy: 0.8902 - val_loss: 0.2599 - val_accuracy: 0.8875\n",
      "Epoch 34/600\n",
      "7899/7905 [============================>.] - ETA: 0s - loss: 0.2523 - accuracy: 0.8902\n",
      "Epoch 00034: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2523 - accuracy: 0.8901 - val_loss: 0.2608 - val_accuracy: 0.8879\n",
      "Epoch 35/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.2523 - accuracy: 0.8901\n",
      "Epoch 00035: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2523 - accuracy: 0.8901 - val_loss: 0.2610 - val_accuracy: 0.8861\n",
      "Epoch 36/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.2527 - accuracy: 0.8903\n",
      "Epoch 00036: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 35s 4ms/step - loss: 0.2527 - accuracy: 0.8903 - val_loss: 0.2596 - val_accuracy: 0.8879\n",
      "Epoch 37/600\n",
      "7894/7905 [============================>.] - ETA: 0s - loss: 0.2524 - accuracy: 0.8906\n",
      "Epoch 00037: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 31s 4ms/step - loss: 0.2524 - accuracy: 0.8906 - val_loss: 0.2615 - val_accuracy: 0.8875\n",
      "Epoch 38/600\n",
      "7896/7905 [============================>.] - ETA: 0s - loss: 0.2518 - accuracy: 0.8907\n",
      "Epoch 00038: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 34s 4ms/step - loss: 0.2517 - accuracy: 0.8907 - val_loss: 0.2611 - val_accuracy: 0.8877\n",
      "Epoch 39/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.2516 - accuracy: 0.8907\n",
      "Epoch 00039: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 36s 5ms/step - loss: 0.2516 - accuracy: 0.8908 - val_loss: 0.2629 - val_accuracy: 0.8875\n",
      "Epoch 40/600\n",
      "7903/7905 [============================>.] - ETA: 0s - loss: 0.2515 - accuracy: 0.8908\n",
      "Epoch 00040: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 36s 5ms/step - loss: 0.2515 - accuracy: 0.8908 - val_loss: 0.2608 - val_accuracy: 0.8880\n",
      "Epoch 41/600\n",
      "7898/7905 [============================>.] - ETA: 0s - loss: 0.2513 - accuracy: 0.8913\n",
      "Epoch 00041: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 43s 5ms/step - loss: 0.2512 - accuracy: 0.8913 - val_loss: 0.2622 - val_accuracy: 0.8876\n",
      "Epoch 42/600\n",
      "7900/7905 [============================>.] - ETA: 0s - loss: 0.2512 - accuracy: 0.8911\n",
      "Epoch 00042: val_accuracy did not improve from 0.88886\n",
      "7905/7905 [==============================] - 43s 5ms/step - loss: 0.2512 - accuracy: 0.8911 - val_loss: 0.2622 - val_accuracy: 0.8877\n",
      "Epoch 00042: early stopping\n",
      "Execution time :  0:24:09.483480\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "model = create_model_with_noise()\n",
    "history_sampling = model.fit(x=X_train_norm, \n",
    "                    y=y_train, \n",
    "                    epochs=600,\n",
    "                    batch_size=32,\n",
    "                    validation_data=(X_valid_norm, y_valid), verbose=1,\n",
    "                    callbacks=callbacks_list\n",
    "                   )\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer: adam Training Loss: 0.893 Validation Accuracy:0.888\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(X_train_norm, y_train, X_valid_norm, y_valid, model, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AFBcXM3v2bKqrq5k+fTpnnHEGM2bMoLCwkGeeeYasrKzDLpvC3WEqys3indXluPsR0fZXRERERKS7+O5fFrF4U2Vc9zm+YADfufT4fS6/6667WLhwIfPmzeO1117j4osvZuHChbt7tXzwwQcZNGgQdXV1nHTSSVx11VXk5eXtsY8VK1bw6KOPcv/993PNNdfwxz/+kRtuuOGwy96ZDlVkP4pys6je1cTOusZkF0VERERERLrYtGnT9hiu4Oc//zmTJ0/mlFNOYcOGDaxYsWKvbUaPHs2UKVMAOPHEE1m7dm1cyqKau8NUlJsNQMn2OgZmZyS5NCIiIiIiR4791bB1lb59++5+/tprr/Hyyy/z9ttvk52dzTnnnNPhcAZ9+vTZ/Tw1NZW6urq4lEU1d4epKDe0jdV9dyIiIiIivV///v2pqqrqcNnOnTvJzc0lOzubpUuX8s4773Rp2VRzd5hGxNTciYiIiIhI75aXl8fpp5/OhAkTyMrKYujQobuXXXjhhdx7771MmjSJsWPHcsopp3Rp2RTuDtOArDT690lTuBMREREROUI88sgjHc7v06cPL7zwQofLWu+ry8/P32MYha9+9atxK5eaZR4mM6MwN0vNMkVEREREJKkU7uKgKDdbNXciIiIiIpJUCndxUJSbRcn2Otw92UUREREREZEjlMJdHGisOxERERERSTaFuzgoUo+ZIiIiIiKSZAp3caCx7kREREREJNkU7uJAY92JiIiIiBwZduzYwd13331I2/70pz+ltjZxFUIKd3Ggse5ERERERI4M3TncaRDzONBYdyIiIiIiR4Y77riDVatWMWXKFM4//3yGDBnCE088wa5du7jyyiv57ne/S01NDddccw0lJSU0NzfzrW99i9LSUjZt2sS5555Lfn4+r776atzLpnAXJ2GsO4U7EREREZEu88IdsGVBfPc5bCJMv2ufi++66y4WLlzIvHnzeOmll3jqqad49913cXcuu+wy/vGPf1BWVkZBQQHPPfccADt37iQnJ4cf//jHvPrqq+Tn58e3zBE1y4wTjXUnIiIiInJkeemll3jppZf4wAc+wAknnMDSpUtZsWIFEydO5OWXX+b222/njTfeICcnp0vKo5q7OIkd625gdkayiyMiIiIi0vvtp4atK7g73/jGN/jsZz+717I5c+bw/PPP841vfIMPf/jDfPvb3054eVRzFyca605EREREpPfr378/VVVVAFxwwQU8+OCDVFdXA7Bx40a2bt3Kpk2byM7O5oYbbuCrX/0qc+fO3WvbRFDNXZzEjnU3obBrql1FRERERKRr5eXlcfrppzNhwgSmT5/O9ddfz6mnngpAv379ePjhh1m5ciVf+9rXSElJIT09nXvuuQeAW265henTpzN8+PCEdKhiPekesalTp/rs2bOTXYwO7axtZPL3XuLfLx7HzWceleziiIiIiIj0SkuWLGHcuHHJLkaX6OhYzWyOu0/taH01y4wTjXUnIiIiIiLJpHAXJxrrTkREREREkknhLo7CWHequRMRERERka7XqXBnZhea2TIzW2lmd3Sw/GNmNj+aZpjZ5JhlXzazRWa20MweNbPMaP4gM/ubma2IHnPjd1jJobHuREREREQS70g43z6UYzxguDOzVOBXwHRgPHCdmY1vt9oa4Gx3nwT8B3BftG0h8EVgqrtPAFKBa6Nt7gD+7u5jgL9Hr3u02LHuREREREQk/jIzMykvL+/VAc/dKS8vJzMz86C268xQCNOAle6+GsDMHgMuBxbHvPmMmPXfAYravUeWmTUC2cCmaP7lwDnR84eA14DbD6r03UzsWHcayFxEREREJP6KioooKSmhrKws2UVJqMzMTIqKig68YozOhLtCYEPM6xLg5P2sfxPwAoC7bzSzHwLrgTrgJXd/KVpvqLtvjtbbbGZDOtqZmd0C3AIwcuTIThQ3eTTWnYiIiIhIYqWnpzN69OhkF6Nb6sw9d9bBvA7rQM3sXEK4uz16nUuooRsNFAB9zeyGgymgu9/n7lPdfergwYMPZtMuNyKm5k5ERERERKQrdSbclQAjYl4X0da0cjczmwQ8AFzu7uXR7A8Ba9y9zN0bgT8Bp0XLSs1seLTtcGDroR1C96Gx7kREREREJFk6E+5mAWPMbLSZZRA6RHk2dgUzG0kIbje6+/KYReuBU8ws28wMOA9YEi17FvhE9PwTwDOHfhjdg8a6ExERERGRZDngPXfu3mRmtwEvEnq7fNDdF5nZrdHye4FvA3nA3SHD0RQ1pZxpZk8Bc4Em4D2injSBu4AnzOwmQgi8Or6HlhxhrDuFOxERERER6Vqd6VAFd38eeL7dvHtjnt8M3LyPbb8DfKeD+eWEmrxepSg3i3dWh65Zo6ArIiIiIiKScJ0axFw6T2PdiYiIiIhIMijcxVmReswUEREREZEkULiLs9ix7kRERERERLqKwl2caaw7ERERERFJBoW7ONNYdyIiIiIikgwKd3Gmse5ERERERCQZFO4SIIx1p5o7ERERERHpOgp3CVCUm0XJ9jrcPdlFERERERGRI4TCXQJorDsREREREelqCncJoLHuRERERESkqyncJYDGuhMRERERka6mcJcAGutORERERES6msJdAmisOxERERER6WoKdwmgse5ERERERKSrKdwliMa6ExERERGRrqRwlyAa605ERERERLqSwl2CaKw7ERERERHpSgp3CaKx7kREREREpCsp3CWIxroTEREREZGupHCXIBrrTkREREREupLCXYJorDsREREREelKCncJorHuRERERESkKyncJZDGuhMRERERka6icJdARblZbNRYdyIiIiIi0gUU7hKoKDeLql1NVNY1JbsoIiIiIiLSy3Uq3JnZhWa2zMxWmtkdHSz/mJnNj6YZZjY5mj/WzObFTJVm9i/RsjvNbGPMsovie2jJ1zrW3QbddyciIiIiIgmWdqAVzCwV+BVwPlACzDKzZ919ccxqa4Cz3X27mU0H7gNOdvdlwJSY/WwEno7Z7ifu/sP4HEr30zbWXR0TCnOSXBoREREREenNOlNzNw1Y6e6r3b0BeAy4PHYFd5/h7tujl+8ARR3s5zxglbuvO5wC9yRtY92p5k5ERERERBKrM+GuENgQ87okmrcvNwEvdDD/WuDRdvNui5pyPmhmuZ0oS4+ise5ERERERKSrdCbcWQfzOuz+0czOJYS729vNzwAuA56MmX0PcDSh2eZm4Ef72OctZjbbzGaXlZV1orjdR9tYdwp3IiIiIiKSWJ0JdyXAiJjXRcCm9iuZ2STgAeBydy9vt3g6MNfdS1tnuHupuze7ewtwP6H5517c/T53n+ruUwcPHtyJ4nYvYaw7NcsUEREREZHE6ky4mwWMMbPRUQ3ctcCzsSuY2UjgT8CN7r68g31cR7smmWY2PObllcDCgyl4T6Gx7kREREREpCscsLdMd28ys9uAF4FU4EF3X2Rmt0bL7wW+DeQBd5sZQJO7TwUws2xCT5ufbbfrH5jZFEITz7UdLO8VYse6y8lOT3ZxRERERESklzpguANw9+eB59vNuzfm+c3AzfvYtpYQ/NrPv/GgStpDxY51l5Ot4RBERERERCQxOjWIuRy62LHuREREREREEkXhLsE01p2IiIiIiHQFhbsE01h3IiIiIiLSFRTuEkxj3YmIiIiISFdQuOsCGutOREREREQSTeGuC2isOxERERERSTSFuy4QO9adiIiIiIhIIijcdYHYse5EREREREQSQeGuC2isOxERERERSTSFuy6gse5ERERERCTRFO66gMa6ExERERGRRFO46wIa605ERERERBJN4a6LFOVmqVmmiIiIiIgkjMJdFynKzdZYdyIiIiIikjAKd11EY92JiIiIiEgiKdx1kdbhEDTWnYiIiIiIJILCXRcp2j0cgjpVERERERGR+FO46yJtA5mr5k5EREREROJP4a6L5GSl009j3YmIiIiISIIo3HURM4uGQ1C4ExERERGR+FO460Ia605ERERERBJF4a4Laaw7ERERERFJFIW7LqSx7kREREREJFEU7rqQxroTEREREZFEUbjrQhrrTkREREREEkXhrgtprDsREREREUmUToU7M7vQzJaZ2Uozu6OD5R8zs/nRNMPMJkfzx5rZvJip0sz+JVo2yMz+ZmYrosfc+B5a96Ox7kREREREJFEOGO7MLBX4FTAdGA9cZ2bj2622Bjjb3ScB/wHcB+Duy9x9irtPAU4EaoGno23uAP7u7mOAv0evezWNdSciIiIiIonSmZq7acBKd1/t7g3AY8DlsSu4+wx33x69fAco6mA/5wGr3H1d9Ppy4KHo+UPAFQdb+J5IY92JiIiIiEgidCbcFQIbYl6XRPP25SbghQ7mXws8GvN6qLtvBogeh3S0MzO7xcxmm9nssrKyThS3e9NYdyIiIiIikgidCXfWwbwOk4mZnUsId7e3m58BXAY8ebAFdPf73H2qu08dPHjwwW7e7WisOxERERERSYTOhLsSYETM6yJgU/uVzGwS8ABwubuXt1s8HZjr7qUx80rNbHi07XBg68EUvKfSWHciIiIiIpIInQl3s4AxZjY6qoG7Fng2dgUzGwn8CbjR3Zd3sI/r2LNJJtE+PhE9/wTwzMEUvKfSWHciIiIiIpIIaQdawd2bzOw24EUgFXjQ3ReZ2a3R8nuBbwN5wN1mBtDk7lMBzCwbOB/4bLtd3wU8YWY3AeuBq+NzSN2bxroTEREREZFEOGC4A3D354Hn2827N+b5zcDN+9i2lhD82s8vJ/SgeUTRWHciIiIiIpIInRrEXOJHY92JiIiIiEgiKNwlgca6ExERERGReFO4SwKNdSciIiIiIvGmcJcEGutORERERETiTeEuCTTWnYiIiIiIxJvCXRJorDsREREREYk3hbsk0Fh3IiIiIiISbwp3SaCx7kREREREJN4U7pJAY92JiIiIiEi8Kdwlica6ExERERGReFK4SxKNdSciIiIiIvGkcJckGutORERERETiSeEuSTTWnYiIiIiIxJPCXZJorDsREREREYknhbsk0Vh3IiIiIiISTwp3SaKx7kREREREJJ4U7pJEY92JiIiIiEg8Kdwlkca6ExERERGReFG4SyKNdSciIiIiIvGicJdEGutORERERETiReEuiTTWnYiIiIiIxIvCXRJprDsREREREYkXhbsk0lh3IiIiIiISLwp3SaSx7kREREREJF4U7pJIY92JiIiIiEi8dCrcmdmFZrbMzFaa2R0dLP+Ymc2PphlmNjlm2UAze8rMlprZEjM7NZp/p5ltNLN50XRR/A6r59BYdyIiIiIiEg9pB1rBzFKBXwHnAyXALDN71t0Xx6y2Bjjb3beb2XTgPuDkaNnPgP9z94+YWQaQHbPdT9z9h/E4kJ6qKDebmasrcHfMLNnFERERERGRHqozNXfTgJXuvtrdG4DHgMtjV3D3Ge6+PXr5DlAEYGYDgLOAX0frNbj7jngVvjfQWHciIiIiIhIPnQl3hcCGmNcl0bx9uQl4IXp+FFAG/MbM3jOzB8ysb8y6t0VNOR80s9yDKXhv0dpj5voKNc0UEREREZFD15lw11FbQe9wRbNzCeHu9mhWGnACcI+7fwCoAVrv2bsHOBqYAmwGfrSPfd5iZrPNbHZZWVknituzjB+eQ1qK8a9PzGNdeU2yiyMiIiIiIj1UZ8JdCTAi5nURsKn9SmY2CXgAuNzdy2O2LXH3mdHrpwhhD3cvdfdmd28B7ic0/9yLu9/n7lPdfergwYM7c0w9ysi8bH530zTKqndx2S/f4q2V25JdJBERERER6YE6E+5mAWPMbHTUIcq1wLOxK5jZSOBPwI3uvrx1vrtvATaY2dho1nnA4mib4TG7uBJYeMhH0cOddnQ+z37+DIYNyOTjD77Lb99ag3uHlaMiIiIiIiIdOmBvme7eZGa3AS8CqcCD7r7IzG6Nlt8LfBvIA+6Oenxscvep0S6+APwhCoargU9F839gZlMITTzXAp+N21H1QCPzsvnj507jXx6bx51/WczSLVV87/IJZKRpKEIRERERETkw60k1RFOnTvXZs2cnuxgJ1dLi/OTl5fzilZWcVJzLPTecSH6/PskuliSAqicAACAASURBVIiIiIiIdANmNiemIm0PqhbqZlJSjK98eCy/uO4DLNi4k8t+8SaLNu1MdrFERERERKSbU7jrpi6dXMBTt56GA1fdM4Pn5m9OdpFERERERKQbU7jrxiYU5vDsbWdwfEEOn39kLj9+aRktLT2nGa2IiIiIiHQdhbtubnD/PjzymZO5ZmoRP39lJbc+PIfqXU3JLpaIiIiIiHQzCnc9QJ+0VL5/1SS+c+l4Xl5SylV3z2BDRW2yiyUiIiIiIt2Iwl0PYWZ86vTRPPTpaWzeWcdlv3yTt1eVH3hDERERERE5Iijc9TBnjhnMM7edQV6/Ptz465n8/p11yS6SiIiIiIh0Awp3PdDo/L786XOncdaxg/nWnxfyb08voKGpJdnFEhERERGRJFK466EGZKZz/8encuvZR/OHmeu56p4ZvLp0Kz1pUHoREREREYkfhbseLDXFuGP6cfzy+g9QUdPAp347i8t++RYvLdqikCciIiIicoSxnhQCpk6d6rNnz052MbqlxuYWnp67kV++upL1FbUcN6w/X/jgGKZPGEZKiiW7eCIiIiIiEgdmNsfdp3a4TOGud2lqbuHZ9zfxy1dXsrqshjFD+nHbB4/hkkkFpCrkiYiIiIj0aAp3iVS5CdKzICs32SXZQ3OL89yCzfzylRUsL61mdH5fPn/uMVw+pYD0VLXGFRERERHpiRTuEsUd7v8g7KqCjz0Bg45Kdon20tLivLhoCz9/ZSVLNlcyYlAWnzvnGK46oYiMNIU8EREREZGeROEukda+CY/fABhc+wiMOjXZJeqQu/P3JVv5xSsreL9kJwU5mfzzOUdz9dQRZKanJrt4IiIiIiLSCQp3iVa+Cv5wNezcAJf9EiZ/NNkl2id35/XlZfzilZXMWbedoQP68Nmzjua6aSPJylDIExERERHpzhTuukJtBTx+I6x7E86+Hc75Blj37cDE3Xl7VTk/+/sKZq6pIL9fH/75nKP52MkjVZMnIiIiItJNKdx1laYG+OuXYd7DMOEjcPmvID0z2aU6oHfXVPDTl5czY1U5w3My+dJ5Y7jqxCJ1vCIiIiIi0s0o3HUld3jzJ/D370LRtHAfXr/ByS5Vp7y1chv/8+Iy5m3YQXFeNl8+/1gunVSgcfJERERERLoJhbtkWPRnePqz0G8IXP8kDDku2SXqlNaOV3740jKWbqniuGH9+cqHx/KhcUOwbtzMVERERETkSLC/cKd2d4ly/BXwyeehsR5+fT6seiXZJeoUM+ND44fy/BfP5OfXfYD6xmY+87vZXHn3DGas3Jbs4omIiIiIyD4o3CVS0YnwmVcgZwQ8/BGY/WCyS9RpKSnGZZML+Nu/ns1d/zSR0sp6rn9gJtff/w5z129PdvFERERERBKnbgfs2JDsUhw0NcvsCvWV8NSnYeXf4NTb4PzvQUrP6pGyvrGZR2au51evrqS8poEPjRvCVz48lnHDByS7aCIiIiIih88dSmbDnN/Awj/B2Avh6t8mu1R70T133UFzE7z4TXj3f2HsRfBP90Offsku1UGr2dXEb95aw//+YzXVu5q4dFIBXz7/WEbn90120UREREREDl59JSx4Amb/BkoXQkY/mPgROPFTUDAl2aXbi8JddzLzPvi/22Ho8XDd45BTmOwSHZKdtY387z9W8Zu31tLQ3MLVJxbxhfPGUDgwK9lFExERkZ5kzT9g7u/CGMF5Rye7NHIk2Tg31NIt+CM01sCwSTD1UzDxaujTP9ml2yeFu+5m+Uvw1KfCl+a6x7rlFYHO2lpVz92vruKRmetpbGnh1KPyuGJKIRdOHMaAzPRkF09ERES6q+qt8NK/w/zHw+v+w+GTzyngSWLtqoIFT4VQt/l9SM+GCVeFUFdwAvSA3uEPO9yZ2YXAz4BU4AF3v6vd8o8Bt0cvq4F/dvf3o2UDgQeACYADn3b3t81sEPA4UAysBa5x9/321NFrwh3AloXwyEehrgKuegCOuzjZJTosG3fU8cSsDTwzbyNry2vJSEvhg2OHcPmUAs49bgiZ6T3rHkMRERFJkJYWmPtbePlOaKiFM74c7m36w9WQmgGf+CvkH5PsUkpvs/n90OxywZPQUA1DJ8CJn4RJ10BmTrJLd1AOK9yZWSqwHDgfKAFmAde5++KYdU4Dlrj7djObDtzp7idHyx4C3nD3B8wsA8h29x1m9gOgwt3vMrM7gFx3v5396FXhDqCqFB69FjbNhfS+YUy8fkOg7+Do+dC2532HtC3P6L73t7k780t28ud5G/nL+5vZVr2L/n3SuHDCMK74QCGnHJVHqgZFFxEROTJtWQB//TKUzILiM+HiH8PgY8Oy0kXw0KWQkh5q8BTw5HA11MDCP4ZQt2kupGXBhH8K99IVTe0RtXQdOdxwdyohrF0Qvf4GgLv/9z7WzwUWunuhmQ0A3geO8nZvZGbLgHPcfbOZDQdec/ex+ytLrwt3EK5YzX0IdqwPzRNqtobH6q2hVq8j6X2h3+A9w9/g4+C4S7rVPXxNzS28vbqcZ+Zt4v8WbqF6VxND+vfh0skFXD6lgImFORoYXURE5Eiwqwpe/W+YeS9kD4IL/l+4r6n9eUDp4ijgpSngyaGp3gob58DKl2H+E7CrMpwnT/10qKXLyk12CQ/b4Ya7jwAXuvvN0esbgZPd/bZ9rP9V4Dh3v9nMpgD3AYuBycAc4EvuXmNmO9x9YMx22919r0/bzG4BbgEYOXLkievWrTvwEfcWzY1QUxaFvuixujRm3ta2eXVRi9aiaTD+chh/GQwcmdzyx6hvbOaVpVv583sbeW1ZGQ3NLRyV35fLphRw+ZRC9bYpIiLSG7nDkr/AC7dD1eZwX9N5397/CfYeAe+vkD+m68rbk7nD1sVQviqcA+YWQ9bAA27WozXUwKZ5IcxtnBM6SNm5PixL7QPHXxFq6Uae0mNr6TpyuOHuauCCduFumrt/oYN1zwXuBs5w93Izmwq8A5zu7jPN7GdApbt/q7PhLlavrLmLl20rYPEzYdoyP8wrOCEKepfDoNHJLV+MnbWNvLBwM8/M28Q7a8pxh8lFOVw2pZDivGwy0lLISE0Jj2kp9ElLoU9a6l7z01JMNX8iIiLd1fa18PzXYcWLMHQiXPITGHFS57bduiQEPEtVwDuQrUtg0dNh2rZ8z2WZA8M5YG5xNMU8zynqWeMuNzdB2ZI9g9zWxeAtYfnAUVB4Yts0fFK3vpXpcHRJs0wzmwQ8DUx39+XRvGHAO+5eHL0+E7jD3S9Ws8wEqljdFvQ2vRfmDZ8cBb0rulUvVJt31vHX9zfz53kbWbSp8qC2NWN32GsNgAOy0jm+YACTi3KYWDSQccP70yetB/3hEhER6emaGuDtX8LrPwBLgQ/+G0z7LKSmHdx+dge8lNDJSuu9eQJly9oCXdlSwKD4jHCuV3gC7NwYwvX2tbB9TXjcsR5amtr2kZLWVsMXG/oGjYb8sZCWkYQDi7iH8sYGuc3zoLE2LM/K3TPIFZwQblk6QhxuuEsjdKhyHrCR0KHK9e6+KGadkcArwMfdfUa77d8Abnb3ZWZ2J9DX3b9mZv8DlMd0qDLI3b++v7Io3B2C7etgybMh6JXMCvOGTmir0Ru83zzdpTZU1FJR00BDcwsNTWHa1dTS7nXz7uet82PXKavaxYKNO6moaQAgPdUYO6w/k4oGMqkwh4lFORw7tD/pqSlJPloREZFeaO1b8Ny/hsAx7lK48K5QQ3Soti6Fhy5RwAMoWw6L/xwC3dbFgMGo0+D4K2HcZdB/6P63b26Cyg5CX+tUF9NpfXo2jJgGo84IobHwBEjrk6gjC2UrXQjr34Z1M2D9O+H2IwjNK4dPbgtyRSeGMHoEt96Kx1AIFwE/JQyF8KC7/5eZ3Qrg7vea2QPAVUDrDXFNrW8Y3Xf3AJABrAY+FfWqmQc8AYwE1gNXu/s+ehAJFO4O086S0O598TPhlwYPN5iOvyL8Ac4fk9hf3C7i7mzcUceCkp3M37iT+SU7mF+yk6r6cLWqT1oK4wsGMLloIBMLc5g8IofR+f3Ui6eIiMihqtkGf/s2zPtDqA266Idw7AXx2ffWpaEGD0ITzW50YTrhtq1sq6HbuggwGHlqCHTjL4P+w+L3XnU7QsirWAUb3oW1b4bABaGXyREnhR5OR50eepo8nHPGxjoomR3OR9fPgA2zoKEqLBs4MhzjiGlQOBWGjE9uLWI3pEHMZW+Vm9uC3rq3CEMQAhn9oW8eZOdD33zIzgtT3/yYefnROnmQ0a9HXDlpaXHWVdTuDnoLSnaycNNOahuaAeibkcrxhTlMLsrhxFG5nFQ8iLx+PT/oioiIJFRTA7z3O3jlP0OPmKd9Ec76GmRkx/d9ypbBby8Jzz/xFxhyXHz3Hy8tLeG86HDOjcpXRYHuz1C6IMwbeWq4GD/+MhhQEJ+ydkZtRahJW/cWrH0jjNOMQ1omFJ0UavVGnR6ep2fufz8bZkY1c2+H24ZaGgEL4W3UqeEYR57arXp+764U7mT/qkph1d9DVX1tRbj6VrsteiwPj827Ot42tU9bCOw3JFxB6j+8bRowHPoXhHW62U27zS3OqrJq5pe01e4t3lxJQ1O4MXfMkH5MGz2Ik4/K4+TRgxg6YD9/tERERI4kTbtg7u/gzZ9CZUk4wb/4RzBkXOLeszsGvB3rYc0bIfiseSN8FhCakVpqOPfZ/ZgS7nPba17M6+aG0HcCwIiT25pcdpfAU7c9hLO1b8K6N2HzfMDD+WDR1LawN3BkVDP3dpi2RsNjp6SHJp4jTw1NSkdM6xVDE3Q1hTs5PO7QUB2FvYqY4NcuANZshaotYWiG1p6LWllqFPyGtQt+sdMw6DMAUpJ3P1xDUwsLNu5g5poKZq6uYPbaCmqi2r3ivGxOHp0XBb5BFOXG+aqkiIhId9dY1xbqqjaFAHL27XD0B7umJU/ZcvjtxYCHe/C6OuBVborC3D/C447ojqTsvBBsBo8LZWtpBm+OHlvavd7PfPfQbf/4yw/vXsWuUrcjNK1c+0YIfFvm73kOmNE/unfvVBh5Wgh26VnJK28voXAnXau5KYzFV7UphL3K6LFqc5gqo8f6HR1vn5IefvHTMkMVf1pW9BhNu5dlxcyL1uvTP4TGAYWh2UK/YQffO1eMpuYWFm+uZObqCmauqWDW2gp21jUCUDgwi5NHD9pdu1ecl62hGUREOmPnxtCZQ8EHem1X5b1OQy3M+Q289bNwEXfU6SHUjT6r62/PKFseOlnxlqgGL4G1hVWlUXCJauYqVoX5mQNDmCs+E0afGUJdEi9Odxv1O0PY21kSavKGTuh2Lbd6A4U76Z4aaqF6S1vYq9oSBqNsqoPG+j0fm3aFq4VN9TGP9eGxdV5L497vYSkh4A0oiKbCvZ/3H97pG3VbWpxlpVXMXF3Ou2tD7V551DPnkP59mDZ6EFNH5XLU4H4U5/WlYGAmacnombNue/jHo7ApIt3J9nXw5o/hvT+Ev9kpaaEL8+LTQ698I08OF+mk+9hVDbN/DTN+ES7cjj4rhLriM5JbrtaA19IcOlmJV8Cr2dYW5Na+0TZuXJ8BoRlha5gbOlFhTpJG4U6ODC3N4YpR1eZwVbhyY6g1rNwU83xjaGLaXt8hbYGv+AyYeHWnxktxD/fttTbjnLmmnNLKtvsT01ONEbnZjMrLZlReX0bn92VUXjbFeX0pys2Kf/DbOBdevhPWvB7C3bCJofvgYRPDlH8spKbH9z2l9ytbFk5wRp8FmTnJLk1ytDSHDg5W/h0mfqTrmqD1FhWr4Y0fwfuPhYtuJ3wcjj4PSt4NXedvmhvG37JUKJgSaoWKzwjN07rjd665KXwfZj0Q/qYWTIHhU8Lf20FHJ/6kv7EujAFXujA8ZvQN7z18MuSMiM93s74SZt0PM34JdRXhO3/W10Pzuu5i24pwD15LU6jBGzp+3+u6hw5fqreGmsfqLTHPo9tKdm5oC3PpfcOxtoa5YZMPqyWQSDwp3InEqt/ZLvDFPN++FspXhqvJYz4Mk6+DYy/sdM2eu7O1ahdrt9WwrryWNeU1rCuvYe22WtaW1+zunRMgLcUoys3aK/QV5/dlxMEGv/JV8Mp/hJON7Dw48VPhnsgtC6B0UajdBEjNCFc3h02EYZPC49Dju+fJkyRXdRksfCqcjG+eF+alZoQT8uOvhLHTIXNAcsvYFZobYcGTIZiUrwydBjTvCp0BfPDfk1970d1tWwlv/BDmPxFC0ImfhNO/tHdvfw01oSe9tW+FXvlKZoeaPUsJf6dGnRFq90aeCtmDknIou8s59/fw9q9g53rIGxNqGksXtXU8ltEv/H1tDVsFU8J6hxIM3MP/py0LQ5ArXRjeq3xl231N6dnhb3zr66xBbe/dOuWO7nzgrN8JM+8Lg5DX7wj/C8/6eugGvzuKDXjTvx9+RrtDW+y0tW0A7FgpadBvaJj6DwvjqI0+KzQZ1sVQ6aYU7kQORulieP+RcDJSXRp6cZp4dQh6BR845Cui7k5Z9a4Q+rZFoa+8dnf4q97VtHvdjLQUjh3aj3HDBjC+YADjhg9g3LAB5GS3+0dTVQqvfx/mPhROOk/9PJz2hT1Pupubwj0CWxaEG523LAi9W9Vua1snt3jPwDdsYqjFVM3EkaWxDpY9D+8/DitfDjf3D5sEk68N34ll/xcuIFRtCt+3Yz4UBb0Le19TuqZdYbyuN38SesMbOhHO+moYt+u9h+EfPwxX/kefHULeiGnJLnH3snVpCHUL/xi+KyfdFP42dXZMrsY6KJnVFvY2vBuFJwv38BSfHmr3Rp8FWQMTeihAaKr37n1hqtsOI06BM/4FxlwQQlNzYxi0e/P7Ydo0L/ytbaoL26dlwbAJbbV7wyeHcWZjLxw21ELZkijILYqmhXvenz5wVNtFuaETwmPu6BDuti4OF2Jay1C6uO12hT4Dwu9yQcz75x2z571QddvhnXvhnXtg1044djqc/fXQAUZ3t21l6GSlekvbvMyBUWAb2hbedk9D2sJc5kA1r5QeR+FO5FA0N8HqV2HeI7D0uXBiMfi4EPImfTR03BIn7k55TQPrymtYs62W5aVVLNlcyeJNlbvv6YPQicu44f2ZPDiFCyuf5OiVv8VaGrETPxmurPYf2tk3DME1NvBtWRBqAFvHPBwyHs78Sjh5183QvVdLSzh5nv8YLH4WdlWGYD/x6hDq2t/H0tISTroXPQ2L/xyaQaf2gTHnh+/KsRdCn37JOZZ4aKgNF0ve+nkIsYUnht+tYy/Y82JHYx3MfjCEv5oyOOZ8+OC/hQtA3V1jXagRO5wBiPeldBH843/C+Fzp2TDtZjj1C51q5r5fjfWwcU401tabIew11YVal1Gnw9iLwkWG3OK4HMZuFWtCLd17D4f3G3sxnP7F0Fz0QFqaQ61SbODaPL9toObUjBDOBhSGps8Vq9pq3zL6hb/Bwya0Bbkh4w+utrypIYTFTbGBb2FbS4707Lam+6kZMOehULbjLgmhbvjkg/uskq1ue/gf1hrcEvH9FukmFO5EDlfd9nAyO+/RcI+IpYT7DyZfB8ddnLBufd2dsqpdLN5cyZLNVazYuI1j1j/OtfWPM8iq+UvzKfzKriNr2BjGDR/A+OGhlm/ssP7063MITYB2VYerv5veCyeuZUtDc6KzvgoTPtKz7jdoaQlBpSuu6vdEZctCk8sFT4b7TDL6ha63J300NDXsTKBvaQlN6RY9DYufCVfN0zJDM67jrwyBqKf0hLirCmb9OjRFqykLgeGsr8FR5+y/BruhJtTmvPWz8Hdi7MVw7jfDSXlXaWmOxigti5m27ft5Q1X4Gzbo6HCP0pDjo8fxB9d8L9bm+fCPH8CSv4Suz0++BU75PPTNi//xQgguG+fAihdh2QvhbxWEYxl7YQh7BScceo3Mpnkw4+fhu22pMPmjYXDuwWMPr9wtLeH+w9jAV7kp7HfohLYwN7A4MbVJzU3hnrLdYfP9cIGvoSb8/p/1ta797orIIVG4E4mnbSvg/UdD07XKEuiTAxOuhMnXh6ZZiWjK2NIcTsJf+S/YuZ7m0WezevLXeK+xOAp+Yaqsb2vamZWeyqC+GZ2a8vpmMCAznZSUmLK3tMCSZ8NV+NKF4aTvzH+FSdd2+h7EhGtuDE3mtq8JV9gr1rQ9374mXKEuPBFO+kwIG+lH+ED01WWhmdz8x0KAb71IMenacJEi4zDGbmxpDt1fL3o6fG+qS0NTtGMvCJ/9mA8f3v4TpW57uL/onbtD87ejPwhnfjU0+zsY9ZWhOdvbvwwXFY6/Es75xuGHgdj9b54Xfm5bFoTPtzW01ZbvPbYohFDSNx/6Do55jJ43Rs34ti4Ovy+tNfbp2aHMsYFv6PGhNqQjm96D1/8Hlj0X/haeciucfGvX3xdXvgqW/18IeutmhCbFfYe0Bb3RZx/4++ceWmu89TNY/Vpoyjj1U3DyP8e1pUa309ISAr/uvRbpMRTuRBKhpSUMYjrv0XAy21gbroRP+KfQfHPQ6BCIDuckxx1W/C30gLl1Ubhf40N3wtHndrCqs2lnPYs3VbJyazXl1buoqG2goqaB7TUNlNeE57GdusRKTTFys9MZ1DeD3OwMhg7IpGBgFoU5GUyufZsxy+4hq2wB5IwM95p84IauafbSULtnYKtYE658b18DOzaEk7hWaVmhWdag0TDoqHBytuBJKF8ROpr5wI0w9dOQOyrx5e4u6ithxUvhHtL299FN+Ejnm/IejJZmWP92W41eTVkIDWOnw8Rr4Jjzkt9RQc220Nzu3fvDie3Yi0KoKzrx8PZbtz30Ljjz3vA3YeLVodv4vKM7v4/GuhDgNr0XesDdNDdcVGoNYAOKIKcwJqwN3jvA9RvS+XuJGmpCzVdpFPZKF4XHmrK2dbLzQxPdoceHwNdvaKjdX/FiCAWnfB5O/mz3qCmvrQi9mi57Pnznd1WGGuWjzg3fwWMv3PN739wUmhi/9bNQi9VvGJzyzyHYKfCISDekcCeSaLuqwknsvEdh3Zt7LsvMCYEjd3Rb4Gt9HFC475OvDbPg5e+Ee0xyR8N534LxVx52U536xmYqoqDXfiqPgmBFTQNbKuvZvLOOxubWvxHOOSnv8+WMp5nMCipS85kx7AY2HXUNQ/MGUjgwi8LcLIb0zyQ15RBrLxvrQi95698J4aB00Z43yEM4YW0Nb3t8pkeFm+Pb15y6h6vwsx4IJ3sQTu5Oujmc7PXGG+mrSsOxLn0uDIvR3AD9C2DS1aGWbn/dhcdbS3P4Di/8U/gdqasIvfkdf0UIeiNO7tqfQdWWMF7X7AfD9+34K8K9pcMmxvd9asrhrZ+G8NjcAFOuD/cxDRy553rNjW1NoVuD3NYloec/CCGq4IRwL19h9Ng3P75l3ZfqsrbavdbAt3VJW4+DWblw6m0w7Zbu23NqU0P4/i17IUw714f5hVNDrV5G/1Bru2NdaIJ++hdD02TdryUi3ZjCnUhXaqgJA/W2r23aviY0IWxpazpJakbo/Sw29A0ohPmPw9K/hmZFZ38dTvhEUppCtrQ426p3UbKjjk3RtLGilgFbZvDhbb9jYtNCtvpA/rfpYh5pPo86QrAbNiCTwtwsjh7cl6MH99s9FeZm7Rn8aivagtz6t8N9Li2NgIXagYIpe4a3QaPDCeWh2rEB5vwmdBxQuy3UtJ50czjx7g41Dodj28rwnVn6XOjwBA8XFY67JDS5HHFy8jvGaWqAVa/Agidg6fOhg4qckWHcuEnXxG8Q4lgtzaFWasO7obne4mfC7+Cka+CMf4XBx8b/PWNVlYZOV2Y/GJpOnvBxKDophLlNc6MeFaMOLjJz2gW5E8KQAd2p19qWFtixNgwbU3RSz+ol1T0E1GXPh6C3cU6YXzQttEY4dnrvvNgjIr2Owp1Id9HcFO7Tax/6KtaGx9YB1jP6hyvIp3yue/c8uPZNml77Pmlr/0FDn1wWjfo4rw+8nLWVKZRsr2P1thoqdvf26RSnVTC9/xpOS1/BuMaF5NetCUtSM7CCE0IPdKNOC/cuHk6IO5CmXeEk/937Qwc56dmh+dy0z8S/BidRWlpCQGgNdNuWhfnDJ8Nxl4ZAN2Rc9woGsXZVhYC34AlY9WpoLjp0Qvg5TPwI5BQd2n7rdsDG2aHme8PMcAK/qzIsy86DcZfC6f8SLhR0pZ0bw1h5c38XLmCkZ4dm1rE1coOO6r4/r96oaku4X3Ho8ckuiYjIQVG4E+kJ3MN9QDvWhZO8ZA7Ue7DWzww95a18OYSyUz4XasSqNlOz4g3qV71F1uaZZNeXAlBNNrObx/Buy3HMahnLfD+KQTkDohq+vhw9JNT0jc7vy7ABmXt29BJvm+aFJpsLngo1SSNPDWUfd1n36TimVVMDrH0jhLllz4dhCCw1dP5x3CXhvrGBI5JdyoNXXRbuz1vwRFTrSOitcuLVoQe/ff0uuIfBnDfMDDVzG96Nek300FnMkPHhQkHRtPDYHcJT5ebQcUv+scmvSRURkR5J4U5EusbGOaHnvOUv7Dm///AQmkadFmrnhoxnVwusL69lVVk1q8pqWLW1evfz2AHd+6SlMCovm1F5fRmd35dRedkU5/WlOL8vw+MU/BqbW9haupmmOQ+Tt+T39KvdQFXaIP6ePZ25madQlJvFqNw+jMzNYEROH/qlE2qaWppDE7/YR2993jq/OQSKlNQwJpdFjympYbLUdsti50fr7Vgfwtzyl8LgwunZoVOS4y4JvVD2pAsBB1KxGhb8MQS9bcshJT0c48SPhB4Pty6KwtysUOtatz1sl5kTmgmOODk8Fp7Yfe8DExEROQwKdyLStTbPD73P5R8bwtzAUZ2uMXF3tlbtYtXWalZvq2FdeQ1ry2tZu62GdRW1NDS1dfmekZbCyEHZFEeBb1R+393PCwa23d9XWd/Yds/g9jo27qgPz6N5pZX1tER/Co0Wzk6Zz00Z1M9/bQAAFNBJREFUL3M675FCN/kbmZ0Xevo77pIw9lqCxlbsNtzDGFwLngy1qu071skfCyNaw9y0qCZM90uJiEjvp3AnIr1CS4uzpbKetdtC4AvBr4a122pZW17Drpjgl55qDMvJZEdtI1Ux4/+1Lhuek0XBwEwKB2ZTODAa9iE3i4KBWRTkZJGVkRo6jShdRAsplNc1s3FnIxt27mLD9gbW7mhgXUU91Y3QTCpNpJCT3YeivP6MHJxDcX5/iofkUDy4P7mZaaTQElOr1xxTyxfN2/28ec/1vCXUShWeeOQ242tphrVvhprhYRPDZ9GbaitFREQOgsKdiPR6LS2hxm9NTG3fxh11DMpOD4EtCm+FA7MY3K9PXJpzujubd9azvLSKlVurWV5axYqt1awsraYqpmmpGfTLSKN/Zhr9M9Ojx9jn4XFA9Lxfnz2XZ2ek4oTKLHdve45Hj9F8by1XWNYSrW9mFA7MIiNNNVsiIiI9ncKdiEgXcg81jCtKq1m5tZodtQ1U1v//9u4/tq7zruP4+3vO/el7r+914thx4pimS1qabl06UDfUiq0bTC1MdEhM2g/GJCbKEJOGNARlfwzYhDQhfkpsQCnVKsEYk1hZ2diPUgalq8a60Xbr72Zpm6R27Di1ff3r/v7yxzm2rx0ncW0n17M/L/XonOc55x4/t3ra+JPnnOdpMF1pMF2JRhKnq/E+rltaT/DS6EqFvPHgLm481MtNh3u5ur+AdXpyEREREXnVLhTuEpe7MSIi251Z9NjnQDHLT1+1Z02fqdSby8Nf2/F8vYlZdF8jGgk0LN4vleN/CCw+F9c3Ws73T03y0LFxvvmVpwHozae58dBubjzUy42Hetlf2ubv8ImIiOwACnciIltAJhmSSYbsKaQvyf1/6SeideOGJ+f51rFxvnVsnIeOneVLjw0DcGVvbjHo/dSVuyl2JS9JO0REROTS0WOZIiI7lLvz3OgMD8Vh79vHzzJXaxIYvG6wxE3xyN4bhnrIJHfoZC4iIiJbjN65ExGRi6o1Wjx+apKHnh/noWPjPHZykmbLySQDjgx0kwyDxUc+lx79NIKFMkvlc68xMomAfCZBPh1v7cfpBLl4IplcXE4nAr0XKCIisoLCnYiIvGrTlTrfeeEVHjo2zjMj0zTjqTlb7rTiWTtb8dSdrbje2/YLM3YulCv1JjOVBjO1Bmv5oycZ2mLQy6cTdGeT7O3OMFCMtr3xchZ7ixl6c5szA6qIiMhWt+EJVczsFuAvgRC4y90/teL8+4DfjYszwG+4++PxuReBaaAJNBYaYmZ/APwacCb+3Mfc/d/X/rVERORSKmSSvO2aft52Tf+m3rfVcubrTWaqjWirNFY/rjaYjeumqw2m5us8dnKSrz1RodZsLbtnMjT6uzPsK2bZW8wwUMow0J1hoJSNw2CW3bmUAqCIiGxrFw13ZhYCnwZ+FjgFPGJm97n7U22XvQC82d0nzOxW4E7gjW3nb3b38VVu/+fu/ifrb76IiPyoCYJoRC6XTrCe2OjunJ2tcXqqwvDkPKfLFYYnK5yemmd4qnLeAJgKA7qzSTLJgGwyJJuKJrHJxlsmGaxSF5JJLZVz6ZCDvTkGe7oIFRRFRGSLWcvI3Q3AMXc/DmBmnwduAxbDnbs/3Hb9t4HBzWykiIjIAjOjN5+mN5/mtfuLq17TajmvzJ0bAMuVOpVak/l6tFXqTSbn65yeqizVxecbrfM/O5pOBBzszXGoL79sO9ibI53Q5DMiItIZawl3+4GTbeVTLB+VW+mDwFfbyg58w8wc+Ft3v7Pt3IfN7FeA7wIfdfeJtTVbRETk/ILg4gHwYurNFpXFwNdivt5kar7OC+PR4vTHxmZ4/NQkX/nByOI7hIHB0K4uDvXleU1fnkN7loJfIaPlJURE5NJaS7hb7bmTVf8608xuJgp3N7VV3+juw2bWB9xvZs+4+4PAXwOfjO/1SeBPgV9d5Z63A7cDDA0NraG5IiIiG5cMA5JhcE4ou+HgrmXl+VqT43Hg++HYDMfORMf//dwZ6s2lPy77u9Mc6sszWOqilEtSyqYodSXp6UpSzKboaavT0hMiIrIeawl3p4ADbeVBYHjlRWZ2HXAXcKu7n12od/fheD9mZvcSPeb5oLuPtn3274Avr/bD45G+OyGaLXMN7RUREblssqmQa/cVuXbf8hHCRrPFiVfmolG+M0vh75vPjjE5Vz/nncB2mWRAT1eKYjYZB8Ao9JW6UnRnkiRDW7YMRdC25ETQtgxFYEYQgNFWNiObCtiTz9BbSLE7lyaVCC71vyYREbkM1hLuHgEOm9lB4GXg3cB72y8wsyHgi8D73f25tvocELj7dHz8duAT8bkBdx+JL/1F4ImNfhkREZGtIhEGXLknz5V78rx9xTn3aMbQybl6vNWYnK8zMVdbKs/VmZirMzVf49jYDBNx/YXeBVyvYjbJnkKa3nyK3nw6Pk6zp+1YQVBEZOu7aLhz94aZfRj4OtFSCHe7+5Nm9qH4/N8AHwd2A5+JF5xdWPKgH7g3rksAn3P3r8W3/mMzO0r0WOaLwK9v5hcTERHZqsyMrlSCrlSCfaXsmj+3EAqbrWgNQW9bY7B9ncGWR5PKeNu5hesdmKk2GJ+uMj5TY3ymypnpKuMz0fbEy1OMz9SYqTZWbUOpK8mefJqDvTmu3lvgcH+Bq/rzXNmbV/ATEekwLWIuIiIi55ivNaPgN1NlfHphH4XB0XKFH56Z4cWzczTjkcREYFzRm+Oq/jxX9RfiLc8Vu3MkQoU+EZHNsuFFzEVERGRnyaZCDuzq4sCurvNeU200OX5mludGp+NthqeGy3z1idOLM4imwoAr9+Q43F/g6v58PNJXYGiX1goUEdlsCnciIiKyLulEyDUD3Vwz0L2sfr7W5IdnZhYD33Oj0zx6YoJ/e3z5fGxdqZB8OkE+XtR+YV/IJMilw+i47Vw+nSCfWSoDNJpOs+XUWy2aLV9ebjqNVlRutFqL5xpxOQyMPfk0/d0Z+rqjdwuTGmUUkR9hCnciIiKyqbKpkNfuL56zxuBstcHzY1HYe3lintlqg5m2bbba4OXJeWaqdWarTWYqjQvOKrrZzGB3LkVfIQp7/fG+rztDXyEOgYVokhmFQBHZihTuRERE5LLIpRMcPVDi6IHSmj9Ta7TOCYELQdAwwsBIBEYiNBJBEJXDqD65WjmMrg8Do9F0xqYrjJWrjE1H7xKOTVcZi/dPDZcZn6my2gSlu3Mp9hTS7M6nKHWlKGWXL1nR05VcPC5lkxSzyU1797DebFFttKjWm1QbLXblUlobUUQAhTsRERHZwlKJgFQiRU8udUnuv7eYueD5Zss5OxOFv7HpCqPlKmPlKqNxKJyYq/H0SHlxCYsLrVRRyCToiYNfMd53pRLUmy0qcVBb2C8Et/a6hX1zxQ9JhsaRfUWuP1Di+qESbxjqYbAnSzxbuYjsIAp3IiIiIucRBhY9ltmdAYoXvLbVcqarDabmojULJ+ZqTM3XmZiN1jGcnFu+luFLZ2eZrTZIJ0LSiYB0MtpnkgGlrlR8vHAuIJMIl+3TiZBUIuCls3M8emKCf37kJJ99+EUAevMpjh7o4fqhKPC9frBELq1f+0S2O/1XLiIiIrIJgsAoxo9gDu0+/yyjl0qj2eLZ0WkePTEZbScn+I+nR6O2GVzVX+D6oZ54dK/Elb15gnXOWLow2jhfb1KptSh2Rd9bRDpL69yJiIiIbFOTczUeO7kQ9iZ57MQE5Uq0QH0hE70DeWSgm2bLmV8Ia/Um87VmXG5RWTyOzlXqTerNc39/HOzJcmSgmyP7uhf3+0t6PFRks11onTuFOxEREZEdotVyjo/P8uiJCR6NQ9+xsWlSYUA2FZJJhmST4fLjleVUQDYZl1MhmUQYTUAzUuap4SmOj88urnPYnUnEYa/ItfuiwHeoL7+u2Ubdo8dex8rRpDej8TuQo+Xo/cd0IlgWLEtdl+Y9TZFOU7gTERERkctirtbg2dPTPDVS5snhMk8Nl3nmdJlKPVrWIhUGHO7PLxvle01fnvJ8PZqwJp6sZrRcYXRhFtNyFOTm681zfl4+naCvkGau1uR0ubJYv7+U5ZqB7sVQea1GEmWbULgTERERkY5ptpwXxmfj0b3y4ijf+EztvJ/JJAP2xpPZ9C+uNbiw3mCG/ngNwnzbRDFnZ6rLQuWTK0YSi9nkslB57f5uXrNnfSOJIp2icCciIiIiW85YucKTI2VeODNLTy65LLQV0olNGWWbqzV45vR0HPaiYPnMSJlqIx5JTARc3V/gx/cW6O/OUOqK1izsyS2sWRgtW9GdSa57AhqRzaRwJyIiIiISazRbiyOJS4+OTvPK7OqL1kM042gxm6QntxT4Ssv2KXrzKQaKWfYWM+zOpRQG5ZK4ULjTUggiIiIisqMkwoDD/QUO9xe47ej+xfpWy5muNBbXKZxcXLMwWpuw/Xh4ssJTw2Vemastvk/YLhUG9BfTDBSz7Ctm2FvMsq+UYW93hn2lpQCodwBlMynciYiIiIgQr1XYlaTYleQKcmv+XKXeZGKuxvh0jZGpeUamKgxPzXN6qsLIZIXvnZjg9NTIOUtIpBLRe4UDxXgrZSllkxQySbqzCQqZJIVMgu5Mgu5MVJ9JBgqEcl4KdyIiIiIiG5BJhgwUswwUs7xusLjqNa2WMz5b5fRUheHJCqfjEBht8zzy4gSj5REa53suNJYILAp82Sj4FdLJZeV8OkEqDEglAtKJgFQiJJWIyqkwIJ0MSMfnF7b0wjXx+VwqQahHSn8kKdyJiIiIiFxiQWD0FaKZPq8bXP0ad2e21mS6Ume60qA8H+8ry/crz790do7pSp1ypcFsrcFmTKlRSEeBsRhv3dnE4vFSXXLZNcVsNPFMKqHZRztF4U5EREREZAswM/LpaPRtYPUBwItyd+pNp9ZsUWu0bc0mlXrrnPpqfK69PF1pMDVfpzxfZyreXhifXTxe7R3DdulEQDIMSIRGIghIhUYiLieDuD6M6+NyMgxIBLb4uXCDj56aGamELY5iRiOT0QhlMrR4VHN5/cLo5cKoZ08uxf5SdkPtuNwU7kREREREtonFUJMIIH1pfka10YzD37khsDxfZ6baoN50Gq0W9WYrOm62qLeifaPp1FtOvdGi0WoxX4+ubTSderNFo+W0Njj82GqxPMg2WzQv8sjrSu+4boC/eu8bNtSOy03hTkRERERE1iydCOkrhPQVOt2SV6fZ8qURyni0st70ZaOb1bZRzb7uTKeb/Kop3ImIiIiIyLYXBkY2FZJNhUCy0825JPS2o4iIiIiIyDagcCciIiIiIrINKNyJiIiIiIhsAwp3IiIiIiIi28Cawp2Z3WJmz5rZMTO7Y5Xz7zOz78fbw2b2+rZzL5rZD8zsMTP7blv9LjO738yej/c9m/OVREREREREdp6LhjszC4FPA7cCR4D3mNmRFZe9ALzZ3a8DPgncueL8ze5+1N1/sq3uDuABdz8MPBCXRUREREREZB3WMnJ3A3DM3Y+7ew34PHBb+wXu/rC7T8TFbwODa7jvbcA98fE9wDvX1mQRERERERFZaS3hbj9wsq18Kq47nw8CX20rO/ANM/uemd3eVt/v7iMA8b5vbU0WERERERGRldayiLmtUuerXmh2M1G4u6mt+kZ3HzazPuB+M3vG3R9cawPjQHg7wNDQ0Fo/JiIiIiIisqOsJdydAg60lQeB4ZUXmdl1wF3Are5+dqHe3Yfj/ZiZ3Uv0mOeDwKiZDbj7iJkNAGOr/XB3v5P4HT4zO2NmL63pm11evcB4pxsh0kZ9UrYi9UvZatQnZStSv5SL+bHznVhLuHsEOGxmB4GXgXcD722/wMyGgC8C73f359rqc0Dg7tPx8duBT8Sn7wM+AHwq3n/pYg1x9z1raO9lZ2bfXTFZjEhHqU/KVqR+KVuN+qRsReqXshEXDXfu3jCzDwNfB0Lgbnd/0sw+FJ//G+DjwG7gM2YG0Ig7ZT9wb1yXAD7n7l+Lb/0p4Atm9kHgBPCuTf1mIiIiIiIiO4i5r/r6nLwK+hsW2WrUJ2UrUr+UrUZ9UrYi9UvZiDUtYi4XtXJdP5FOU5+UrUj9UrYa9UnZitQvZd00ciciIiIiIrINaORORERERERkG1C42wAzu8XMnjWzY2Z2R6fbIzuTmd1tZmNm9kRb3S4zu9/Mno/3PZ1so+wsZnbAzL5pZk+b2ZNm9pG4Xv1SOsbMMmb2HTN7PO6XfxjXq19KR5lZaGaPmtmX47L6pKybwt06mVkIfBq4FTgCvMfMjnS2VbJDfRa4ZUXdHcAD7n4YeCAui1wuDeCj7n4N8CbgN+P/P6pfSidVgbe6++uBo8AtZvYm1C+l8z4CPN1WVp+UdVO4W78bgGPuftzda8Dngds63CbZgdz9QeCVFdW3AffEx/cA77ysjZIdzd1H3P3/4uNpol9a9qN+KR3kkZm4mIw3R/1SOsjMBoGfB+5qq1aflHVTuFu//cDJtvKpuE5kK+h39xGIftEG+jrcHtmhzOwK4Hrgf1G/lA6LH397DBgD7nd39UvptL8AfgdotdWpT8q6Kdytn61Sp6lHRURiZpYH/gX4LXcvd7o9Iu7edPejwCBwg5m9ttNtkp3LzN4BjLn79zrdFtk+FO7W7xRwoK08CAx3qC0iK42a2QBAvB/rcHtkhzGzJFGw+0d3/2JcrX4pW4K7TwL/RfS+svqldMqNwC+Y2YtEr/e81cz+AfVJ2QCFu/V7BDhsZgfNLAW8G7ivw20SWXAf8IH4+APAlzrYFtlhzMyAvweedvc/azulfikdY2Z7zKwUH2eBnwGeQf1SOsTdf8/dB939CqLfI//T3X8Z9UnZAC1ivgFm9nNEz0qHwN3u/kcdbpLsQGb2T8BbgF5gFPh94F+BLwBDwAngXe6+ctIVkUvCzG4C/gf4AUvvkXyM6L079UvpCDO7jmhyipDoL7e/4O6fMLPdqF9Kh5nZW4Dfdvd3qE/KRijciYiIiIiIbAN6LFNERERERGQbULgTERERERHZBhTuREREREREtgGFOxERERERkW1A4U5ERERERGQbULgTERERERHZBhTuREREREREtgGFOxERERERkW3g/wG0Op+NoMi7jgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history_sampling, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_accuracy(history_sampling, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.99      0.94     63623\n",
      "           1       0.93      0.47      0.62     15421\n",
      "\n",
      "    accuracy                           0.89     79044\n",
      "   macro avg       0.91      0.73      0.78     79044\n",
      "weighted avg       0.89      0.89      0.87     79044\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[63082   541]\n",
      " [ 8226  7195]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Load the Saved Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# load the saved model\n",
    "saved_model = load_model('weights.best.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer: adam Training Loss: 0.891 Validation Accuracy:0.890\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model\n",
    "evaluate_model(X_train_norm, y_train, X_valid_norm, y_valid, saved_model, optimizers[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "predictions = saved_model.predict_classes(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.99      0.94     63623\n",
      "           1       0.94      0.46      0.62     15421\n",
      "\n",
      "    accuracy                           0.89     79044\n",
      "   macro avg       0.91      0.73      0.78     79044\n",
      "weighted avg       0.90      0.89      0.87     79044\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[63187   436]\n",
      " [ 8318  7103]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
 ],
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