diff --git a/benchmarks/logistic_regression.py b/benchmarks/logistic_regression.py
index b91a91a4f..dc8bc9bb9 100644
--- a/benchmarks/logistic_regression.py
+++ b/benchmarks/logistic_regression.py
@@ -158,14 +158,17 @@ def main():
     intermediate = x @ w + b
     intermediate_q = x_q.affine(w_q, b_q, intermediate.min(), intermediate.max(), output_bits)
 
-    n_y = output_bits
-    q_y = (2 ** output_bits - 1) / (intermediate.max() - intermediate.min())
-    zp_y = int(round(intermediate.min() * q_y))
-    y_parameters = QuantizationParameters(q_y, zp_y, n_y)
+    sigmoid = QuantizedFunction.plain(
+        lambda x: 1 / (1 + np.exp(-x)), intermediate_q.parameters, output_bits
+    )
+
+    y_q = sigmoid.apply(intermediate_q)
+    y_parameters = y_q.parameters
 
     q_x = x_q.parameters.q
     q_w = w_q.parameters.q
     q_b = b_q.parameters.q
+    q_intermediate = intermediate_q.parameters.q
 
     zp_x = x_q.parameters.zp
     zp_w = w_q.parameters.zp
@@ -175,10 +178,10 @@ def main():
     w_q = w_q.values
     b_q = b_q.values
 
-    c1 = q_y / (q_x * q_w)
+    c1 = q_intermediate / (q_x * q_w)
     c2 = w_q + zp_w
     c3 = (q_x * q_w / q_b) * (b_q + zp_b)
-    c4 = intermediate.min() * q_y
+    c4 = intermediate.min() * q_intermediate
 
     def f(x):
         values = ((c1 * (x + c3)) - c4).round().clip(0, 2 ** output_bits - 1).astype(np.uint)
@@ -229,7 +232,10 @@ def main():
         if prediction == y_i:
             correct += 1
 
-    # Measure: Accuracy (%) = (correct / len(y)) * 100
+    accuracy = (correct / len(y)) * 100
+    print(f"Accuracy: {accuracy:.2f}%")
+
+    # Measure: Accuracy (%) = accuracy
 
 
 if __name__ == "__main__":