diff --git a/Experiment_Script_Adult.ipynb b/Experiment_Script_Adult.ipynb
index 31336b8..a98cf49 100644
--- a/Experiment_Script_Adult.ipynb
+++ b/Experiment_Script_Adult.ipynb
@@ -27,9 +27,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  1%|█▋                                                                                                                               | 4/300 [3:00:00<219:14:43, 2666.50s/it]"
+     ]
+    }
+   ],
    "source": [
     "synthesizer =  CTABGAN(raw_csv_path = real_path,\n",
     "                 test_ratio = 0.20,\n",
@@ -38,17 +53,22 @@
     "                 mixed_columns= {'capital-loss':[0.0],'capital-gain':[0.0]},\n",
     "                 integer_columns = ['age', 'fnlwgt','capital-gain', 'capital-loss','hours-per-week'],\n",
     "                 problem_type= {\"Classification\": 'income'},\n",
-    "                 epochs = 300) \n",
+    "                 epochs = 300,\n",
+    "                 batch_size = 500,\n",
+    "                 class_dim = (256, 256, 256, 256),\n",
+    "                 random_dim = 100,\n",
+    "                 num_channels = 64,\n",
+    "                 l2scale = 1e-5) \n",
     "\n",
     "for i in range(num_exp):\n",
     "    synthesizer.fit()\n",
-    "    syn = synthesizer.generate_samples()\n",
+    "    syn = synthesizer.generate_samples(100)\n",
     "    syn.to_csv(fake_file_root+\"/\"+dataset+\"/\"+ dataset+\"_fake_{exp}.csv\".format(exp=i), index= False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,77 +77,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Acc</th>\n",
-       "      <th>AUC</th>\n",
-       "      <th>F1_Score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>lr</th>\n",
-       "      <td>8.383663</td>\n",
-       "      <td>0.076099</td>\n",
-       "      <td>-0.016126</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>11.372710</td>\n",
-       "      <td>0.116821</td>\n",
-       "      <td>0.126888</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rf</th>\n",
-       "      <td>11.587675</td>\n",
-       "      <td>0.119969</td>\n",
-       "      <td>0.120840</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mlp</th>\n",
-       "      <td>11.700276</td>\n",
-       "      <td>0.095017</td>\n",
-       "      <td>0.088192</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           Acc       AUC  F1_Score\n",
-       "lr    8.383663  0.076099 -0.016126\n",
-       "dt   11.372710  0.116821  0.126888\n",
-       "rf   11.587675  0.119969  0.120840\n",
-       "mlp  11.700276  0.095017  0.088192"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "classifiers_list = [\"lr\",\"dt\",\"rf\",\"mlp\"]\n",
     "result_mat = get_utility_metrics(real_path,fake_paths,\"MinMax\",classifiers_list, test_ratio = 0.20)\n",
@@ -139,59 +91,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Average WD (Continuous Columns</th>\n",
-       "      <th>Average JSD (Categorical Columns)</th>\n",
-       "      <th>Correlation Distance</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.018747</td>\n",
-       "      <td>0.085125</td>\n",
-       "      <td>1.847952</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Average WD (Continuous Columns  Average JSD (Categorical Columns)  \\\n",
-       "0                        0.018747                           0.085125   \n",
-       "\n",
-       "   Correlation Distance  \n",
-       "0              1.847952  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "adult_categorical = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'gender', 'native-country', 'income']\n",
     "stat_res_avg = []\n",
@@ -206,68 +108,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>DCR between Real and Fake (5th perc)</th>\n",
-       "      <th>DCR within Real(5th perc)</th>\n",
-       "      <th>DCR within Fake (5th perc)</th>\n",
-       "      <th>NNDR between Real and Fake (5th perc)</th>\n",
-       "      <th>NNDR within Real (5th perc)</th>\n",
-       "      <th>NNDR within Fake (5th perc)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.569546</td>\n",
-       "      <td>0.216545</td>\n",
-       "      <td>0.451031</td>\n",
-       "      <td>0.634042</td>\n",
-       "      <td>0.442052</td>\n",
-       "      <td>0.567227</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   DCR between Real and Fake (5th perc)  DCR within Real(5th perc)  \\\n",
-       "0                              0.569546                   0.216545   \n",
-       "\n",
-       "   DCR within Fake (5th perc)  NNDR between Real and Fake (5th perc)  \\\n",
-       "0                    0.451031                               0.634042   \n",
-       "\n",
-       "   NNDR within Real (5th perc)  NNDR within Fake (5th perc)  \n",
-       "0                     0.442052                     0.567227  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "priv_res_avg = []\n",
     "for fake_path in fake_paths:\n",
@@ -281,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/model/ctabgan.py b/model/ctabgan.py
index fde96e7..df7fc93 100644
--- a/model/ctabgan.py
+++ b/model/ctabgan.py
@@ -21,11 +21,17 @@ def __init__(self,
                  mixed_columns= {'capital-loss':[0.0],'capital-gain':[0.0]},
                  integer_columns = ['age', 'fnlwgt','capital-gain', 'capital-loss','hours-per-week'],
                  problem_type= {"Classification": 'income'},
-                 epochs = 1):
+                 epochs = 1,
+                 batch_size=500,
+                 class_dim=(256, 256, 256, 256),
+                 random_dim=100,
+                 num_channels=64,
+                 l2scale=1e-5):
 
         self.__name__ = 'CTABGAN'
               
-        self.synthesizer = CTABGANSynthesizer(epochs = epochs)
+        self.synthesizer = CTABGANSynthesizer(epochs = epochs, batch_size = batch_size, class_dim = class_dim, random_dim = random_dim,
+                                              num_channels = num_channels, l2scale = l2scale)
         self.raw_df = pd.read_csv(raw_csv_path)
         self.test_ratio = test_ratio
         self.categorical_columns = categorical_columns
@@ -33,20 +39,20 @@ def __init__(self,
         self.mixed_columns = mixed_columns
         self.integer_columns = integer_columns
         self.problem_type = problem_type
-        
+
     def fit(self):
-        
+
         start_time = time.time()
         self.data_prep = DataPrep(self.raw_df,self.categorical_columns,self.log_columns,self.mixed_columns,self.integer_columns,self.problem_type,self.test_ratio)
-        self.synthesizer.fit(train_data=self.data_prep.df, categorical = self.data_prep.column_types["categorical"], 
+        self.synthesizer.fit(train_data=self.data_prep.df, categorical = self.data_prep.column_types["categorical"],
         mixed = self.data_prep.column_types["mixed"],type=self.problem_type)
         end_time = time.time()
         print('Finished training in',end_time-start_time," seconds.")
 
 
-    def generate_samples(self):
-        
-        sample = self.synthesizer.sample(len(self.raw_df)) 
+    def generate_samples(self, num_samples):
+
+        sample = self.synthesizer.sample(num_samples)
         sample_df = self.data_prep.inverse_prep(sample)
         
         return sample_df