diff --git a/chapter_convolutional-neural-networks/cnn-scratch.md b/chapter_convolutional-neural-networks/cnn-scratch.md
index 9fe551db22a6018a53d26d5c24277bf58349de6a..013db39b7ee3689638245407423e306306f38b14 100644
--- a/chapter_convolutional-neural-networks/cnn-scratch.md
+++ b/chapter_convolutional-neural-networks/cnn-scratch.md
@@ -58,7 +58,7 @@ out = nd.Convolution(data, w, b, kernel=w.shape[2:], num_filter=w.shape[0])
 print('input:', data, '\n\nweight:', w, '\n\nbias:', b, '\n\noutput:', out)
 ```
 
-当输入需要多通道时，每个输出通道有对应权重，然后每个通道上做卷积。
+当输出需要多通道时，每个输出通道有对应权重，然后每个通道上做卷积。
 
 $$conv(data, w, b)[:,i,:,:] = conv(data, w[i,:,:,:], b[i])$$
 
diff --git a/utils.py b/utils.py
index db2566abd050aa96a081e3cfe17963c1dab65a9c..ee277d85259706a21eddf63fd6442ef9f94d1aee 100644
--- a/utils.py
+++ b/utils.py
@@ -38,7 +38,7 @@ class DataLoader(object):
     def __len__(self):
         return len(self.dataset)//self.batch_size
 
-def load_data_fashion_mnist(batch_size, resize=None):
+def load_data_fashion_mnist(batch_size, resize=None, root="~/.mxnet/datasets/fashion-mnist"):
     """download the fashion mnist dataest and then load into memory"""
     def transform_mnist(data, label):
         # transform a batch of examples
@@ -50,8 +50,8 @@ def load_data_fashion_mnist(batch_size, resize=None):
             data = new_data
         # change data from batch x height x weight x channel to batch x channel x height x weight
         return nd.transpose(data.astype('float32'), (0,3,1,2))/255, label.astype('float32')
-    mnist_train = gluon.data.vision.FashionMNIST(train=True, transform=transform_mnist)
-    mnist_test = gluon.data.vision.FashionMNIST(train=False, transform=transform_mnist)
+    mnist_train = gluon.data.vision.FashionMNIST(root=root, train=True, transform=transform_mnist)
+    mnist_test = gluon.data.vision.FashionMNIST(root=root, train=False, transform=transform_mnist)
     train_data = DataLoader(mnist_train, batch_size, shuffle=True)
     test_data = DataLoader(mnist_test, batch_size, shuffle=False)
     return (train_data, test_data)