diff --git a/ctgan/synthesizer.py b/ctgan/synthesizer.py
index 93a94520..a57cc2bf 100644
--- a/ctgan/synthesizer.py
+++ b/ctgan/synthesizer.py
@@ -35,7 +35,7 @@ class CTGANSynthesizer(object):
     """
 
     def __init__(self, embedding_dim=128, gen_dim=(256, 256), dis_dim=(256, 256),
-                 l2scale=1e-6, batch_size=500):
+                 l2scale=1e-6, batch_size=500, log_frequency=True):
 
         self.embedding_dim = embedding_dim
         self.gen_dim = gen_dim
@@ -43,6 +43,7 @@ def __init__(self, embedding_dim=128, gen_dim=(256, 256), dis_dim=(256, 256),
 
         self.l2scale = l2scale
         self.batch_size = batch_size
+        self.log_frequency = log_frequency
         self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
         self.trained_epoches = 0
 
@@ -63,6 +64,9 @@ def _gumbel_softmax(logits, tau=1, hard=False, eps=1e-10, dim=-1):
                 but will be differentiated as if it is the soft sample in autograd
             dim (int):
                 a dimension along which softmax will be computed. Default: -1.
+            log_frequency (boolean):
+                Whether to use log frequency of categorical levels in conditional
+                sampling. Defaults to ``True``.
 
         Returns:
             Sampled tensor of same shape as logits from the Gumbel-Softmax distribution.
@@ -130,7 +134,7 @@ def _cond_loss(self, data, c, m):
 
         return (loss * m).sum() / data.size()[0]
 
-    def fit(self, train_data, discrete_columns=tuple(), epochs=300, log_frequency=True):
+    def fit(self, train_data, discrete_columns=tuple(), epochs=300):
         """Fit the CTGAN Synthesizer models to the training data.
 
         Args:
@@ -144,9 +148,6 @@ def fit(self, train_data, discrete_columns=tuple(), epochs=300, log_frequency=Tr
                 a ``pandas.DataFrame``, this list should contain the column names.
             epochs (int):
                 Number of training epochs. Defaults to 300.
-            log_frequency (boolean):
-                Whether to use log frequency of categorical levels in conditional
-                sampling. Defaults to ``True``.
         """
 
         if not hasattr(self, "transformer"):
@@ -162,7 +163,7 @@ def fit(self, train_data, discrete_columns=tuple(), epochs=300, log_frequency=Tr
             self.cond_generator = ConditionalGenerator(
                 train_data,
                 self.transformer.output_info,
-                log_frequency
+                self.log_frequency
             )
 
         if not hasattr(self, "generator"):
diff --git a/tests/integration/test_ctgan.py b/tests/integration/test_ctgan.py
index 263f5eb9..07308c6f 100644
--- a/tests/integration/test_ctgan.py
+++ b/tests/integration/test_ctgan.py
@@ -66,8 +66,8 @@ def test_log_frequency():
     counts = sampled['discrete'].value_counts()
     assert counts['a'] < 6500
 
-    ctgan = CTGANSynthesizer()
-    ctgan.fit(data, discrete_columns, epochs=100, log_frequency=False)
+    ctgan = CTGANSynthesizer(log_frequency=False)
+    ctgan.fit(data, discrete_columns, epochs=100)
 
     sampled = ctgan.sample(10000)
     counts = sampled['discrete'].value_counts()