From f2190747fec63c596d8929bd1fbc79f59eaefa48 Mon Sep 17 00:00:00 2001 From: chenyyx Date: Tue, 22 May 2018 12:44:32 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E5=85=B6=E4=B8=AD=E7=9A=84?= =?UTF-8?q?=E4=B8=80=E4=BA=9B=E6=B3=A8=E9=87=8A=E9=94=99=E8=AF=AF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- competitions/playground/dogs-vs-cats/kernel.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/competitions/playground/dogs-vs-cats/kernel.md b/competitions/playground/dogs-vs-cats/kernel.md index 6e2b6ce..d0f3a70 100644 --- a/competitions/playground/dogs-vs-cats/kernel.md +++ b/competitions/playground/dogs-vs-cats/kernel.md @@ -89,7 +89,7 @@ imgs = [os.path.join(root, img) for img in os.listdir(root)] if self.test or not train: self.trainsforms = T.Compose([T.Resize(224), T.CenterCrop(224), T.ToTensor(), normalize]) else: - # 如果是测试集的话，使用另外的转换 + # 如果是训练集的话，使用另外的转换 self.transforms = T.Compose([T.Resize(256), T.RandomResizedCrop(224), T.RandomHorizontalFlip(), T.ToTensor(), normalize]) ``` @@ -142,7 +142,7 @@ loader_test = data.DataLoader(test_dataset, batch_size=3, shuffle=True, num_work ## 四、构建 CNN 模型 ```python -# 调用我们现成的 AlexNet() 模型 +# 调用已经写好的 AlexNet() 模型 cnn = AlexNet() # 将模型打印出来观察一下 print(cnn)