docs(zh-cn): Reviewed No. 29 - Write your training loop in PyTorch

subtitles/zh-CN/29_write-your-training-loop-in-pytorch.srt

@@ -15,8 +15,8 @@
 
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-- 使用 PyTorch 编写你自己的训练循环。
-- Write your own training loop with PyTorch.
+使用 PyTorch 编写你自己的训练循环。
+Write your own training loop with PyTorch.
 
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@@ -25,7 +25,7 @@ In this video, we'll look at
 
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-我们如何进行与培训师视频中相同的微调，
+我们如何进行与训练器视频中相同的微调，
 how we can do the same fine-tuning as in the Trainer video,
 
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@@ -35,12 +35,12 @@ but without relying on that class.
 
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-这样，你就可以轻松自定义每个步骤
+这样，你就可以根据你的需要轻松自定义
 This way, you'll be able to easily customize each step
 
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-到你需要的训练循环。
+训练循环的每个步骤。
 to the training loop to your needs.
 
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@@ -50,12 +50,12 @@ This is also very useful
 
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-手动调试某些东西
+对于手动调试
 to manually debug something
 
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-Trainer API 出了问题。
+Trainer API 出现的问题。
 that went wrong with the Trainer API.
 
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@@ -85,8 +85,8 @@ That number is not useful in its own,
 
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-用于计算
-that is used to compute
+它是用于计算
+but is used to compute
 
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@@ -95,12 +95,12 @@ the ingredients of our model weights,
 
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-那是损失的导数
+即，损失关于
 that is the derivative of the loss
 
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-关于每个模型的重量。
+每个模型权重的导数。
 with respect to each model weight.
 
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@@ -125,7 +125,7 @@ We then repeat the process
 
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-与一批新的训练数据。
+使用一批新的训练数据。
 with a new batch of training data.
 
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@@ -165,7 +165,7 @@ Check out the videos link below
 
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-如果你还没有看到它们。
+如果你还没有看过它们。
 if you haven't seen them already.
 
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@@ -180,8 +180,8 @@ which will be responsible to convert
 
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-我们数据集的元素到补丁中。
-the elements of our dataset into patches.
+我们数据集的元素到批次数据中。
+the elements of our dataset into batches.
 
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@@ -190,17 +190,17 @@ We use our DataColletorForPadding as a collate function,
 
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-并洗牌训练集
+并打乱训练集的次序
 and shuffle the training set
 
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-确保我们不会检查样品
+确保我们不会每个纪元
 to make sure we don't go over the samples
 
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-在一个时代 * 以相同的顺序。
+以相同的顺序遍历样本。
 in the same order at a epoch*.
 
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@@ -216,16 +216,16 @@ we try to grab a batch of data, and inspect it.
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 就像我们的数据集元素一样，它是一个字典，
-Like our data set elements, it's a dictionary,
+Like our dataset elements, it's a dictionary,
 
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-但这些时候值不是一个整数列表
-but these times the values are not a single list of integers
+但这里的值不是一个整数列表
+but this times the values are not a single list of integers
 
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-但是按序列长度形状批量大小的张量。
+而是形状为批量大小乘以序列长度的张量。
 but a tensor of shape batch size by sequence length.
 
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@@ -240,7 +240,7 @@ For that, we'll need to actually create a model.
 
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-如 Model API 视频中所示，
+如模型 API 视频中所示，
 As seen in the Model API video,
 
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@@ -250,12 +250,12 @@ we use the from_pretrained method,
 
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-并将标签数量调整为类别数量
+并将标签数量调整为这个数据集
 and adjust the number of labels to the number of classes
 
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-我们有这个数据集，这里有两个。
+拥有的类别数量，这里是 2。
 we have on this data set, here two.
 
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@@ -275,7 +275,7 @@ and check there is no error.
 
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-如果提供标签，
+如果提供了标签，
 If the labels are provided,
 
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@@ -290,12 +290,12 @@ always returns a loss directly.
 
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-我们将能够做 loss.backward ()
+我们将能够做损失的反向传播
 We will be able to do loss.backward ()
 
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-计算所有梯度，
+以计算所有梯度，
 to compute all the gradients,
 
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@@ -325,17 +325,17 @@ Using the previous loss,
 
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-并使用 loss.backward () 计算梯度，
+并使用 loss.backward() 计算梯度，
 and computing the gradients with loss.backward (),
 
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-我们检查我们是否可以执行优化器步骤
+我们检查我们是否可以无误
 we check that we can do the optimizer step
 
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-没有任何错误。
+执行优化器步骤。
 without any error.
 
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@@ -360,7 +360,7 @@ We could already write our training loop,
 
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-但我们还要添加两件事
+但我们还要再做两件事
 but we will add two more things
 
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@@ -390,7 +390,7 @@ is just a convenience function
 
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-轻松构建这样的调度程序。
+轻松构建这样的调度器。
 to easily build such a scheduler.
 
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@@ -400,7 +400,7 @@ You can again use
 
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-取而代之的是任何 PyTorch 学习率调度程序。
+取而代之的是任何 PyTorch 学习率调度器。
 any PyTorch learning rate scheduler instead.
 
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@@ -426,7 +426,7 @@ The first step is to get one,
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 例如通过使用协作笔记本。
-for instance by using a collab notebook.
+for instance by using a colab notebook.
 
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@@ -435,12 +435,12 @@ Then you need to actually send your model,
 
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-并使用火炬设备对其进行训练数据。
+并使用 torch 设备对其进行训练数据。
 and training data on it by using a torch device.
 
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-仔细检查以下行
+仔细检查以下代码
 Double-check the following lines
 
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@@ -560,12 +560,12 @@ then go through all the data in the evaluation data loader.
 
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-正如我们在培训师视频中看到的那样，
+正如我们在训练器视频中看到的那样，
 As we have seen in the Trainer video,
 
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-模型输出 logits，
+模型输出逻辑斯蒂，
 the model outputs logits,
 
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@@ -610,7 +610,7 @@ Congratulations, you have now fine-tuned a model
 
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-靠你自己。
+全靠你自己。
 all by yourself.
 
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