diff --git a/ch04/gradient_2d.py b/ch04/gradient_2d.py
index cf222ebe..1cf2ef27 100644
--- a/ch04/gradient_2d.py
+++ b/ch04/gradient_2d.py
@@ -59,7 +59,8 @@ def tangent_line(f, x):
     X = X.flatten()
     Y = Y.flatten()
     
-    grad = numerical_gradient(function_2, np.array([X, Y]) )
+    grad = numerical_gradient(function_2, np.array([X, Y]).T)
+    grad = grad.T
     
     plt.figure()
     plt.quiver(X, Y, -grad[0], -grad[1],  angles="xy",color="#666666")#,headwidth=10,scale=40,color="#444444")
diff --git a/dataset/mnist.py b/dataset/mnist.py
index bdea3ea4..a2f7d528 100644
--- a/dataset/mnist.py
+++ b/dataset/mnist.py
@@ -79,7 +79,7 @@ def init_mnist():
         pickle.dump(dataset, f, -1)
     print("Done!")
 
-def _change_ont_hot_label(X):
+def _change_one_hot_label(X):
     T = np.zeros((X.size, 10))
     for idx, row in enumerate(T):
         row[X[idx]] = 1
@@ -114,8 +114,8 @@ def load_mnist(normalize=True, flatten=True, one_hot_label=False):
             dataset[key] /= 255.0
             
     if one_hot_label:
-        dataset['train_label'] = _change_ont_hot_label(dataset['train_label'])
-        dataset['test_label'] = _change_ont_hot_label(dataset['test_label'])    
+        dataset['train_label'] = _change_one_hot_label(dataset['train_label'])
+        dataset['test_label'] = _change_one_hot_label(dataset['test_label'])
     
     if not flatten:
          for key in ('train_img', 'test_img'):