diff --git a/examples/cgan_example.py b/examples/cgan_example.py
index 3eccd04d..9bcb95db 100644
--- a/examples/cgan_example.py
+++ b/examples/cgan_example.py
@@ -2,6 +2,7 @@
 from ydata_synthetic.preprocessing.credit_fraud import transformations
 
 import pandas as pd
+import numpy as np
 from sklearn import cluster
 
 #Read the original data and have it preprocessed
@@ -45,6 +46,8 @@
 log_step = 100
 epochs = 200+1
 learning_rate = 5e-4
+beta_1 = 0.5
+beta_2 = 0.9
 models_dir = './cache'
 
 train_sample = fraud_w_classes.copy().reset_index(drop=True)
@@ -54,7 +57,7 @@
 train_sample[ data_cols ] = train_sample[ data_cols ] / 10 # scale to random noise size, one less thing to learn
 train_no_label = train_sample[ data_cols ]
 
-gan_args = [batch_size, learning_rate, noise_dim, train_sample.shape[1], 2, (0, 1), dim]
+gan_args = [batch_size, learning_rate, beta_1, beta_2, noise_dim, train_sample.shape[1], 2, (0, 1), dim]
 train_args = ['', label_cols[0], epochs, log_step, '']
 
 #Init the Conditional GAN providing the index of the label column as one of the arguments
diff --git a/examples/gan_example.ipynb b/examples/gan_example.ipynb
index 659b061b..4c2de1d0 100644
--- a/examples/gan_example.ipynb
+++ b/examples/gan_example.ipynb
@@ -17,36 +17,39 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "outputs": [],
-   "source": [
-    "# Note: You can select between running the Notebook on \"CPU\" or \"GPU\"\n",
-    "# Click \"Runtime > Change Runtime time\" and set \"GPU\""
-   ],
    "metadata": {
-    "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   }
+   },
+   "outputs": [],
+   "source": [
+    "# Note: You can select between running the Notebook on \"CPU\" or \"GPU\"\n",
+    "# Click \"Runtime > Change Runtime time\" and set \"GPU\""
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "outputs": [],
-   "source": [
-    "#Install ydata-synthetic lib\n",
-    "#! pip install pip install git+https://github.com/ydataai/ydata-synthetic.git"
-   ],
    "metadata": {
-    "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   }
+   },
+   "outputs": [],
+   "source": [
+    "#Install ydata-synthetic lib\n",
+    "#! pip install pip install git+https://github.com/ydataai/ydata-synthetic.git"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import importlib\n",
@@ -61,28 +64,21 @@
     "from ydata_synthetic.preprocessing.credit_fraud import *\n",
     "\n",
     "model = VanilllaGAN"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "outputs": [],
-   "source": [
-    "#Read the original data and have it preprocessed\n",
-    "data = pd.read_csv('data/creditcard.csv', index_col=[0])"
-   ],
    "metadata": {
-    "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   }
+   },
+   "outputs": [],
+   "source": [
+    "#Read the original data and have it preprocessed\n",
+    "data = pd.read_csv('data/creditcard.csv', index_col=[0])"
+   ]
   },
   {
    "cell_type": "code",
@@ -179,6 +175,8 @@
     "log_step = 100\n",
     "epochs = 200+1\n",
     "learning_rate = 5e-4\n",
+    "beta_1 = 0.5\n",
+    "beta_2 = 0.9\n",
     "models_dir = './cache'\n",
     "\n",
     "train_sample = fraud_w_classes.copy().reset_index(drop=True)\n",
@@ -188,7 +186,7 @@
     "train_sample[ data_cols ] = train_sample[ data_cols ] / 10 # scale to random noise size, one less thing to learn\n",
     "train_no_label = train_sample[ data_cols ]\n",
     "\n",
-    "gan_args = [batch_size, learning_rate, noise_dim, train_sample.shape[1], dim]\n",
+    "gan_args = [batch_size, learning_rate, beta_1, beta_2, noise_dim, train_sample.shape[1], dim]\n",
     "train_args = ['', epochs, log_step]"
    ]
   },
@@ -508,30 +506,29 @@
   {
    "cell_type": "code",
    "execution_count": 10,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "ename": "AssertionError",
      "evalue": "Please provide a valid path. Path must be a directory.",
      "output_type": "error",
      "traceback": [
-      "\u001B[0;31m\u001B[0m",
-      "\u001B[0;31mAssertionError\u001B[0mTraceback (most recent call last)",
-      "\u001B[0;32m<ipython-input-10-b5e799d6bacd>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m()\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;31m#You can easily save the trained generator and loaded it aftwerwards\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0msynthesizer\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msave\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'./saved/gan'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m'generator_fraud'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m",
-      "\u001B[0;32m/home/fabiana/PycharmProjects/YData/gan-playground/models/gan/model.pyc\u001B[0m in \u001B[0;36msave\u001B[0;34m(self, path, name)\u001B[0m\n\u001B[1;32m    107\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0msave\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mpath\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mname\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    108\u001B[0m         \u001B[0;32massert\u001B[0m \u001B[0mos\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpath\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0misdir\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mpath\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m==\u001B[0m \u001B[0mTrue\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;31m \u001B[0m\u001B[0;31m\\\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 109\u001B[0;31m             \u001B[0;34m\"Please provide a valid path. Path must be a directory.\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    110\u001B[0m         \u001B[0mmodel_path\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mos\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpath\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mjoin\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mpath\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mname\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    111\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mgenerator\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msave_weights\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel_path\u001B[0m\u001B[0;34m)\u001B[0m  \u001B[0;31m# Load the generator\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
-      "\u001B[0;31mAssertionError\u001B[0m: Please provide a valid path. Path must be a directory."
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-b5e799d6bacd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#You can easily save the trained generator and loaded it aftwerwards\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msynthesizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./saved/gan'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'generator_fraud'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/home/fabiana/PycharmProjects/YData/gan-playground/models/gan/model.pyc\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, path, name)\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m             \u001b[0;34m\"Please provide a valid path. Path must be a directory.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m         \u001b[0mmodel_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Load the generator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: Please provide a valid path. Path must be a directory."
      ]
     }
    ],
    "source": [
     "#You can easily save the trained generator and loaded it aftwerwards\n",
     "synthesizer.save('./saved/gan', 'generator_fraud')"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
@@ -554,7 +551,9 @@
     {
      "data": {
       "image/png": 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\n",
-      "text/plain": "<Figure size 1008x648 with 6 Axes>"
+      "text/plain": [
+       "<Figure size 1008x648 with 6 Axes>"
+      ]
      },
      "metadata": {
       "needs_background": "light"
@@ -647,23 +646,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "name": "tensorflow15",
+   "display_name": "Python 3",
    "language": "python",
-   "display_name": "Tensorflow1"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.17"
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
\ No newline at end of file
+}
diff --git a/src/ydata_synthetic/synthesizers/gan.py b/src/ydata_synthetic/synthesizers/gan.py
index e30eac2c..63bfd58e 100644
--- a/src/ydata_synthetic/synthesizers/gan.py
+++ b/src/ydata_synthetic/synthesizers/gan.py
@@ -7,7 +7,7 @@ def __init__(
             model_parameters
     ):
         self._model_parameters = model_parameters
-        [self.batch_size, self.lr, self.noise_dim,
+        [self.batch_size, self.lr, self.beta_1, self.beta_2, self.noise_dim,
          self.data_dim, self.layers_dim] = model_parameters
         self.define_gan()
 
diff --git a/src/ydata_synthetic/synthesizers/regular/cgan/model.py b/src/ydata_synthetic/synthesizers/regular/cgan/model.py
index e8a55c60..87325f43 100644
--- a/src/ydata_synthetic/synthesizers/regular/cgan/model.py
+++ b/src/ydata_synthetic/synthesizers/regular/cgan/model.py
@@ -13,7 +13,7 @@
 class CGAN():
 
     def __init__(self, model_parameters):
-        [self.batch_size, lr, self.noise_dim,
+        [self.batch_size, lr,self.beta_1, self.beta_2, self.noise_dim,
          self.data_dim, num_classes, self.classes, layers_dim] = model_parameters
 
         self.generator = Generator(self.batch_size, num_classes). \
@@ -22,7 +22,7 @@ def __init__(self, model_parameters):
         self.discriminator = Discriminator(self.batch_size, num_classes). \
             build_model(input_shape=(self.data_dim,), dim=layers_dim)
 
-        optimizer = Adam(lr, beta_1=0.5)
+        optimizer = Adam(lr, beta_1=self.beta_1, beta_2=self.beta_2)
 
         # Build and compile the discriminator
         self.discriminator.compile(loss='binary_crossentropy',
diff --git a/src/ydata_synthetic/synthesizers/regular/vanillagan/model.py b/src/ydata_synthetic/synthesizers/regular/vanillagan/model.py
index 9ccede1c..744dfaed 100644
--- a/src/ydata_synthetic/synthesizers/regular/vanillagan/model.py
+++ b/src/ydata_synthetic/synthesizers/regular/vanillagan/model.py
@@ -21,7 +21,7 @@ def define_gan(self):
         self.discriminator = Discriminator(self.batch_size).\
             build_model(input_shape=(self.data_dim,), dim=self.layers_dim)
 
-        optimizer = Adam(self.lr, 0.5)
+        optimizer = Adam(self.lr, beta_1=self.beta_1, beta_2=self.beta_2)
 
         # Build and compile the discriminator
         self.discriminator.compile(loss='binary_crossentropy',
diff --git a/src/ydata_synthetic/synthesizers/regular/wgan/model.py b/src/ydata_synthetic/synthesizers/regular/wgan/model.py
index 2de91337..a13fad7b 100644
--- a/src/ydata_synthetic/synthesizers/regular/wgan/model.py
+++ b/src/ydata_synthetic/synthesizers/regular/wgan/model.py
@@ -41,8 +41,8 @@ def define_gan(self):
         self.critic = Critic(self.batch_size). \
             build_model(input_shape=(self.data_dim,), dim=self.layers_dim)
 
-        optimizer = Adam(self.lr, beta_1=0.5, beta_2=0.9)
-        self.critic_optimizer = Adam(self.lr, beta_1=0.5, beta_2=0.9)
+        optimizer = Adam(self.lr, beta_1=self.beta_1, beta_2=self.beta_2)
+        self.critic_optimizer = Adam(self.lr, beta_1=self.beta_1, beta_2=self.beta_2)
 
         # Build and compile the critic
         self.critic.compile(loss=self.wasserstein_loss,
@@ -111,7 +111,7 @@ def train(self, data, train_arguments):
             # Plot the progress
             print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100 * d_loss[1], g_loss))
 
-            # If at save interval => save generated events
+            #If at save interval => save generated events
             if epoch % sample_interval == 0:
                 # Test here data generation step
                 # save model checkpoints