diff --git a/pyod/models/mo_gaal.py b/pyod/models/mo_gaal.py
index a80bfe926..ae3c01365 100644
--- a/pyod/models/mo_gaal.py
+++ b/pyod/models/mo_gaal.py
@@ -70,6 +70,11 @@ class MO_GAAL(BaseDetector):
     momentum : float, optional (default=0.9)
         The momentum parameter for SGD.
 
+    verbose : int, optional (default=1)
+        Verbosity mode.
+        - 0 = silent
+        - 1 = show print
+
     Attributes
     ----------
     decision_scores_ : numpy array of shape (n_samples,)
@@ -89,13 +94,14 @@ class MO_GAAL(BaseDetector):
         ``threshold_`` on ``decision_scores_``.
     """
 
-    def __init__(self, k=10, stop_epochs=20, lr_d=0.01, lr_g=0.0001, momentum=0.9, contamination=0.1):
+    def __init__(self, k=10, stop_epochs=20, lr_d=0.01, lr_g=0.0001, momentum=0.9, contamination=0.1, verbose:int = 1):
         super(MO_GAAL, self).__init__(contamination=contamination)
         self.k = k
         self.stop_epochs = stop_epochs
         self.lr_d = lr_d
         self.lr_g = lr_g
         self.momentum = momentum
+        self.verbose = verbose
 
     def fit(self, X, y=None):
         """Fit detector. y is ignored in unsupervised methods.
@@ -143,13 +149,15 @@ def fit(self, X, y=None):
 
         # Start iteration
         for epoch in range(epochs):
-            print('Epoch {} of {}'.format(epoch + 1, epochs))
+            if self.verbose > 0:
+                print('Epoch {} of {}'.format(epoch + 1, epochs))
             batch_size = min(500, data_size)
             num_batches = int(data_size / batch_size)
 
             for index in range(num_batches):
-                print('\nTesting for epoch {} index {}:'.format(epoch + 1,
-                                                                index + 1))
+                if self.verbose > 0:
+                    print('\nTesting for epoch {} index {}:'.format(epoch + 1,
+                                                                    index + 1))
 
                 # Generate noise
                 noise_size = batch_size
@@ -197,7 +205,7 @@ def fit(self, X, y=None):
                     discriminator_loss)
 
                 # Get the target value of sub-generator
-                pred_scores = self.discriminator.predict(X).ravel()
+                pred_scores = self.discriminator.predict(X, verbose=self.verbose).ravel()
 
                 for i in range(self.k):
                     names['T' + str(i)] = np.percentile(pred_scores,
@@ -218,8 +226,10 @@ def fit(self, X, y=None):
                 else:
                     for i in range(self.k):
                         names['sub_generator' + str(i) + '_loss'] = names[
-                            'combine_model' + str(i)].evaluate(noise, names[
-                            'trick' + str(i)])
+                            'combine_model' + str(i)].evaluate(noise,
+                                                               names['trick' + str(i)],
+                                                               verbose=self.verbose
+                                                               )
                         self.train_history[
                             'sub_generator{}_loss'.format(i)].append(
                             names['sub_generator' + str(i) + '_loss'])
@@ -236,7 +246,7 @@ def fit(self, X, y=None):
                     stop = 1
 
         # Detection result
-        self.decision_scores_ = self.discriminator.predict(X).ravel()
+        self.decision_scores_ = self.discriminator.predict(X, verbose=self.verbose).ravel()
         self._process_decision_scores()
         return self
 
@@ -260,5 +270,5 @@ def decision_function(self, X):
         """
         check_is_fitted(self, ['discriminator'])
         X = check_array(X)
-        pred_scores = self.discriminator.predict(X).ravel()
+        pred_scores = self.discriminator.predict(X, verbose=self.verbose).ravel()
         return pred_scores