diff --git a/dezero/functions.py b/dezero/functions.py
index 9b1f7a6..b34e9ba 100644
--- a/dezero/functions.py
+++ b/dezero/functions.py
@@ -595,7 +595,7 @@ def backward(self, gy):
         return gx, ggamma, gbeta
 
 
-def batch_nrom(x, gamma, beta, mean, var, decay=0.9, eps=2e-5):
+def batch_norm(x, gamma, beta, mean, var, decay=0.9, eps=2e-5):
     return BatchNorm(mean, var, decay, eps)(x, gamma, beta)
 
 
diff --git a/dezero/layers.py b/dezero/layers.py
index 564279f..75d65a1 100644
--- a/dezero/layers.py
+++ b/dezero/layers.py
@@ -327,5 +327,5 @@ def _init_params(self, x):
     def __call__(self, x):
         if self.avg_mean.data is None:
             self._init_params(x)
-        return F.batch_nrom(x, self.gamma, self.beta, self.avg_mean.data,
+        return F.batch_norm(x, self.gamma, self.beta, self.avg_mean.data,
                             self.avg_var.data)
\ No newline at end of file
diff --git a/tests/gpu/gpu_test_batchnorm.py b/tests/gpu/gpu_test_batchnorm.py
index 67d5280..7d3ea82 100644
--- a/tests/gpu/gpu_test_batchnorm.py
+++ b/tests/gpu/gpu_test_batchnorm.py
@@ -26,7 +26,7 @@ def test_type1(self):
         N, C = 8, 3
         x, gamma, beta, mean, var = get_params(N, C)
         with dezero.test_mode():
-            y = F.batch_nrom(x, gamma, beta, mean, var)
+            y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(y.data.dtype == np.float32)
 
     def test_forward1(self):
@@ -34,7 +34,7 @@ def test_forward1(self):
         x, gamma, beta, mean, var = get_params(N, C)
         cy = CF.fixed_batch_normalization(x, gamma, beta, mean, var)
         with dezero.test_mode():
-            y = F.batch_nrom(x, gamma, beta, mean, var)
+            y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_forward2(self):
@@ -42,7 +42,7 @@ def test_forward2(self):
         x, gamma, beta, mean, var = get_params(N, C)
         cy = CF.fixed_batch_normalization(x, gamma, beta, mean, var)
         with dezero.test_mode():
-            y = F.batch_nrom(x, gamma, beta, mean, var)
+            y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_forward3(self):
@@ -50,7 +50,7 @@ def test_forward3(self):
         x, gamma, beta, mean, var = get_params(N, C)
         cy = CF.fixed_batch_normalization(x, gamma, beta, mean, var)
         with dezero.test_mode():
-            y = F.batch_nrom(x, gamma, beta, mean, var)
+            y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_forward4(self):
@@ -58,7 +58,7 @@ def test_forward4(self):
         x, gamma, beta, mean, var = get_params(N, C, H, W)
         cy = CF.fixed_batch_normalization(x, gamma, beta, mean, var)
         with dezero.test_mode():
-            y = F.batch_nrom(x, gamma, beta, mean, var)
+            y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
 
@@ -69,71 +69,71 @@ class TestBatchNorm(unittest.TestCase):
     def test_type1(self):
         N, C = 8, 3
         x, gamma, beta, mean, var = get_params(N, C)
-        y = F.batch_nrom(x, gamma, beta, mean, var)
+        y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(y.data.dtype == np.float32)
 
     def test_forward1(self):
         N, C = 8, 1
         x, gamma, beta, mean, var = get_params(N, C)
         cy = CF.batch_normalization(x, gamma, beta, running_mean=mean, running_var=var)
-        y = F.batch_nrom(x, gamma, beta, mean, var)
+        y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_forward2(self):
         N, C = 1, 10
         x, gamma, beta, mean, var = get_params(N, C)
         cy = CF.batch_normalization(x, gamma, beta)
-        y = F.batch_nrom(x, gamma, beta, mean, var)
+        y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_forward3(self):
         N, C = 20, 10
         x, gamma, beta, mean, var = get_params(N, C)
         cy = CF.batch_normalization(x, gamma, beta)
-        y = F.batch_nrom(x, gamma, beta, mean, var)
+        y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_forward4(self):
         N, C, H, W = 20, 10, 5, 5
         x, gamma, beta, mean, var = get_params(N, C, H, W)
         cy = CF.batch_normalization(x, gamma, beta)
-        y = F.batch_nrom(x, gamma, beta, mean, var)
+        y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_backward1(self):
         N, C = 8, 3
         x, gamma, beta, mean, var = get_params(N, C, dtype=np.float64)
-        f = lambda x: F.batch_nrom(x, gamma, beta, mean, var)
+        f = lambda x: F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(gradient_check(f, x))
 
     def test_backward2(self):
         N, C = 8, 3
         x, gamma, beta, mean, var = get_params(N, C, dtype=np.float64)
-        f = lambda gamma: F.batch_nrom(x, gamma, beta, mean, var)
+        f = lambda gamma: F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(gradient_check(f, gamma))
 
     def test_backward3(self):
         N, C = 8, 3
         x, gamma, beta, mean, var = get_params(N, C, dtype=np.float64)
-        f = lambda beta: F.batch_nrom(x, gamma, beta, mean, var)
+        f = lambda beta: F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(gradient_check(f, beta))
 
     def test_backward4(self):
         params = 10, 20, 5, 5
         x, gamma, beta, mean, var = get_params(*params, dtype=np.float64)
-        f = lambda x: F.batch_nrom(x, gamma, beta, mean, var)
+        f = lambda x: F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(gradient_check(f, x))
 
     def test_backward5(self):
         params = 10, 20, 5, 5
         x, gamma, beta, mean, var = get_params(*params, dtype=np.float64)
-        f = lambda gamma: F.batch_nrom(x, gamma, beta, mean, var)
+        f = lambda gamma: F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(gradient_check(f, gamma))
 
     def test_backward6(self):
         params = 10, 20, 5, 5
         x, gamma, beta, mean, var = get_params(*params, dtype=np.float64)
-        f = lambda beta: F.batch_nrom(x, gamma, beta, mean, var)
+        f = lambda beta: F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(gradient_check(f, beta))
 
 
diff --git a/tests/test_batchnorm.py b/tests/test_batchnorm.py
index d881e70..6e67686 100644
--- a/tests/test_batchnorm.py
+++ b/tests/test_batchnorm.py
@@ -26,7 +26,7 @@ def test_type1(self):
         N, C = 8, 3
         x, gamma, beta, mean, var = get_params(N, C)
         with dezero.test_mode():
-            y = F.batch_nrom(x, gamma, beta, mean, var)
+            y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(y.data.dtype == np.float32)
 
     def test_forward1(self):
@@ -34,7 +34,7 @@ def test_forward1(self):
         x, gamma, beta, mean, var = get_params(N, C)
         cy = CF.fixed_batch_normalization(x, gamma, beta, mean, var)
         with dezero.test_mode():
-            y = F.batch_nrom(x, gamma, beta, mean, var)
+            y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_forward2(self):
@@ -42,7 +42,7 @@ def test_forward2(self):
         x, gamma, beta, mean, var = get_params(N, C)
         cy = CF.fixed_batch_normalization(x, gamma, beta, mean, var)
         with dezero.test_mode():
-            y = F.batch_nrom(x, gamma, beta, mean, var)
+            y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_forward3(self):
@@ -50,7 +50,7 @@ def test_forward3(self):
         x, gamma, beta, mean, var = get_params(N, C)
         cy = CF.fixed_batch_normalization(x, gamma, beta, mean, var)
         with dezero.test_mode():
-            y = F.batch_nrom(x, gamma, beta, mean, var)
+            y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_forward4(self):
@@ -58,7 +58,7 @@ def test_forward4(self):
         x, gamma, beta, mean, var = get_params(N, C, H, W)
         cy = CF.fixed_batch_normalization(x, gamma, beta, mean, var)
         with dezero.test_mode():
-            y = F.batch_nrom(x, gamma, beta, mean, var)
+            y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
 
@@ -69,35 +69,35 @@ class TestBatchNorm(unittest.TestCase):
     def test_type1(self):
         N, C = 8, 3
         x, gamma, beta, mean, var = get_params(N, C)
-        y = F.batch_nrom(x, gamma, beta, mean, var)
+        y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(y.data.dtype == np.float32)
 
     def test_forward1(self):
         N, C = 8, 1
         x, gamma, beta, mean, var = get_params(N, C)
         cy = CF.batch_normalization(x, gamma, beta, running_mean=mean, running_var=var)
-        y = F.batch_nrom(x, gamma, beta, mean, var)
+        y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_forward2(self):
         N, C = 1, 10
         x, gamma, beta, mean, var = get_params(N, C)
         cy = CF.batch_normalization(x, gamma, beta)
-        y = F.batch_nrom(x, gamma, beta, mean, var)
+        y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_forward3(self):
         N, C = 20, 10
         x, gamma, beta, mean, var = get_params(N, C)
         cy = CF.batch_normalization(x, gamma, beta)
-        y = F.batch_nrom(x, gamma, beta, mean, var)
+        y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_forward4(self):
         N, C, H, W = 20, 10, 5, 5
         x, gamma, beta, mean, var = get_params(N, C, H, W)
         cy = CF.batch_normalization(x, gamma, beta)
-        y = F.batch_nrom(x, gamma, beta, mean, var)
+        y = F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(array_allclose(y.data, cy.data))
 
     def test_forward5(self):
@@ -130,37 +130,37 @@ def test_forward6(self):
     def test_backward1(self):
         N, C = 8, 3
         x, gamma, beta, mean, var = get_params(N, C, dtype=np.float64)
-        f = lambda x: F.batch_nrom(x, gamma, beta, mean, var)
+        f = lambda x: F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(gradient_check(f, x))
 
     def test_backward2(self):
         N, C = 8, 3
         x, gamma, beta, mean, var = get_params(N, C, dtype=np.float64)
-        f = lambda gamma: F.batch_nrom(x, gamma, beta, mean, var)
+        f = lambda gamma: F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(gradient_check(f, gamma))
 
     def test_backward3(self):
         N, C = 8, 3
         x, gamma, beta, mean, var = get_params(N, C, dtype=np.float64)
-        f = lambda beta: F.batch_nrom(x, gamma, beta, mean, var)
+        f = lambda beta: F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(gradient_check(f, beta))
 
     def test_backward4(self):
         params = 10, 20, 5, 5
         x, gamma, beta, mean, var = get_params(*params, dtype=np.float64)
-        f = lambda x: F.batch_nrom(x, gamma, beta, mean, var)
+        f = lambda x: F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(gradient_check(f, x))
 
     def test_backward5(self):
         params = 10, 20, 5, 5
         x, gamma, beta, mean, var = get_params(*params, dtype=np.float64)
-        f = lambda gamma: F.batch_nrom(x, gamma, beta, mean, var)
+        f = lambda gamma: F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(gradient_check(f, gamma))
 
     def test_backward6(self):
         params = 10, 20, 5, 5
         x, gamma, beta, mean, var = get_params(*params, dtype=np.float64)
-        f = lambda beta: F.batch_nrom(x, gamma, beta, mean, var)
+        f = lambda beta: F.batch_norm(x, gamma, beta, mean, var)
         self.assertTrue(gradient_check(f, beta))