diff --git a/cornac/models/ngcf/recom_ngcf.py b/cornac/models/ngcf/recom_ngcf.py
index 7284b8af7..daa7b3109 100644
--- a/cornac/models/ngcf/recom_ngcf.py
+++ b/cornac/models/ngcf/recom_ngcf.py
@@ -42,13 +42,10 @@ class NGCF(Recommender):
         Maximum number of iterations or the number of epochs.
 
     learning_rate: float, default: 0.001
-        The learning rate that determines the step size at each iteration
+        The learning rate that determines the step size at each iteration.
 
-    train_batch_size: int, default: 1024
-        Mini-batch size used for train set
-
-    test_batch_size: int, default: 100
-        Mini-batch size used for test set
+    batch_size: int, default: 1024
+        Mini-batch size used for training.
 
     early_stopping: {min_delta: float, patience: int}, optional, default: None
         If `None`, no early stopping. Meaning of the arguments:
@@ -62,7 +59,7 @@ class NGCF(Recommender):
                         training should be stopped.
 
     lambda_reg: float, default: 1e-4
-        Weight decay for the L2 normalization
+        Weight decay for the L2 normalization.
 
     trainable: boolean, optional, default: True
         When False, the model is not trained and Cornac assumes that the model
@@ -87,8 +84,7 @@ def __init__(
         dropout_rates=[0.1, 0.1, 0.1],
         num_epochs=1000,
         learning_rate=0.001,
-        train_batch_size=1024,
-        test_batch_size=100,
+        batch_size=1024,
         early_stopping=None,
         lambda_reg=1e-4,
         trainable=True,
@@ -101,8 +97,7 @@ def __init__(
         self.dropout_rates = dropout_rates
         self.num_epochs = num_epochs
         self.learning_rate = learning_rate
-        self.train_batch_size = train_batch_size
-        self.test_batch_size = test_batch_size
+        self.batch_size = batch_size
         self.early_stopping = early_stopping
         self.lambda_reg = lambda_reg
         self.seed = seed
@@ -163,11 +158,11 @@ def fit(self, train_set, val_set=None):
             accum_loss = 0.0
             for batch_u, batch_i, batch_j in tqdm(
                 train_set.uij_iter(
-                    batch_size=self.train_batch_size,
+                    batch_size=self.batch_size,
                     shuffle=True,
                 ),
                 desc="Epoch",
-                total=train_set.num_batches(self.train_batch_size),
+                total=train_set.num_batches(self.batch_size),
                 leave=False,
                 position=1,
                 disable=not self.verbose,
@@ -221,6 +216,7 @@ def monitor_value(self):
             metrics=[Recall(k=20)],
             train_set=self.train_set,
             test_set=self.val_set,
+            verbose=True
         )[0][0]
 
         return recall_20  # Section 4.2.3 in the paper
diff --git a/examples/ngcf_example.py b/examples/ngcf_example.py
index 89abba836..b901317d5 100644
--- a/examples/ngcf_example.py
+++ b/examples/ngcf_example.py
@@ -41,7 +41,7 @@
     layer_sizes=[64, 64, 64],
     dropout_rates=[0.1, 0.1, 0.1],
     early_stopping={"min_delta": 1e-4, "patience": 50},
-    train_batch_size=1024,
+    batch_size=1024,
     learning_rate=0.001,
     lambda_reg=1e-5,
     verbose=True,