diff --git a/chapter_convolutional-neural-networks/conv-layer.md b/chapter_convolutional-neural-networks/conv-layer.md
index 4a0e3203432bf70702d4865673f109985d6b0a2c..28318a0783b5e6339e7a631dfaa9bd98ccf3544d 100644
--- a/chapter_convolutional-neural-networks/conv-layer.md
+++ b/chapter_convolutional-neural-networks/conv-layer.md
@@ -207,7 +207,7 @@ for i in range(10):
     # 迭代卷积核
     conv2d.weight.data()[:] -= lr * conv2d.weight.grad()
     if (i + 1) % 2 == 0:
-        print(f'batch {i+1}, loss {float(l.sum()):.3f}')
+        print(f'epoch {i+1}, loss {float(l.sum()):.3f}')
 ```
 
 ```{.python .input}
@@ -229,7 +229,7 @@ for i in range(10):
     # 迭代卷积核
     conv2d.weight.data[:] -= lr * conv2d.weight.grad
     if (i + 1) % 2 == 0:
-        print(f'batch {i+1}, loss {l.sum():.3f}')
+        print(f'epoch {i+1}, loss {l.sum():.3f}')
 ```
 
 ```{.python .input}
@@ -255,7 +255,7 @@ for i in range(10):
         weights[0] = conv2d.weights[0] - update
         conv2d.set_weights(weights)
         if (i + 1) % 2 == 0:
-            print(f'batch {i+1}, loss {tf.reduce_sum(l):.3f}')
+            print(f'epoch {i+1}, loss {tf.reduce_sum(l):.3f}')
 ```
 
 在$10$次迭代之后，误差已经降到足够低。现在我们来看看我们[**所学的卷积核的权重张量**]。