aug epoch 6

chapter_computer-vision/image-augmentation.md

@@ -228,7 +228,7 @@ def train_with_data_aug(train_augs, test_augs, lr=0.001):
     loss = gloss.SoftmaxCrossEntropyLoss()
     train_iter = load_cifar10(True, train_augs, batch_size)
     test_iter = load_cifar10(False, test_augs, batch_size)
-    train(train_iter, test_iter, net, loss, trainer, ctx, num_epochs=8)
+    train(train_iter, test_iter, net, loss, trainer, ctx, num_epochs=6)
 ```
 
 ### 有关图像增广的对比实验

chapter_natural-language-processing/word2vec-gluon.md

@@ -311,7 +311,7 @@ def train(net, lr, num_epochs):
 现在我们可以训练使用负采样的跳字模型了。
 
 ```{.python .input  n=24}
-train(net, 0.005, 3)
+train(net, 0.005, 5)
 ```
 
 ## 应用词嵌入模型