检查参数与层的形状
准确查看模型中包含的内容
检查参数与层的形状 是 CoddyKit 上的免费 Deep Learning Academy 课时。 这是第 4 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Deep Learning Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Deep Learning Academy 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
Look Inside Your Model
Once a model exists, you can peek at every weight it holds. Inspecting the parameters builds trust before you train.
Loop Over parameters()
Call model.parameters() to iterate over every learnable tensor. The optimizer uses this exact same list.
for p in model.parameters():
print(p.shape)Names With named_parameters()
Use named_parameters() to see each tensor paired with its name, so you know which layer it belongs to.
for name, p in model.named_parameters():
print(name, p.shape)Weight and Bias Shapes
A linear layer holds a weight matrix and a bias vector. Their shapes follow directly from the in and out features.
fc = nn.Linear(4, 3)
print(fc.weight.shape) # torch.Size([3, 4])Count the Parameters
Sum the elements of every tensor to get a total parameter count, a quick measure of model size.
total = sum(p.numel() for p in model.parameters())Print the Whole Model
Just printing the model shows its layers and their sizes in order. It is the fastest overview you can get.
print(model)Check Output Shape Live
Pass a sample batch through and print the result's shape to confirm dimensions line up end to end.
out = model(torch.randn(1, 4))
print(out.shape)See What Trains
The requires_grad flag tells you which tensors get updated. Frozen layers show it as False.
for p in model.parameters():
print(p.requires_grad)Group With state_dict
The state_dict maps every parameter name to its tensor. It is what you save and load to checkpoint a model.
model.state_dict().keys()Debug Shape Mismatches
Most beginner errors are shape mismatches between layers. Printing shapes pinpoints exactly where the chain breaks.
Know Your Model Cold
Inspecting parameters and shapes turns a black box into something you fully understand. Confidence comes from looking. 🔍
Quick Check
Recall the call that lists each weight tensor with its layer name.
Recap
Use parameters, named_parameters, and printing the model to inspect weights and shapes. This habit makes debugging fast and painless. 🎯
常见问题解答
「检查参数与层的形状」课时是免费的吗？
是的 — 「检查参数与层的形状」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Deep Learning Academy 课程的其余内容，请升级到 CoddyKit PRO。 Deep Learning Academy 课程共包含 4 节课。
「检查参数与层的形状」这节课中我会学到什么？
准确查看模型中包含的内容 你通过在浏览器中直接运行的动手代码来练习 Deep Learning Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Deep Learning Academy 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Deep Learning Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 4 节课，共 4 节。
「检查参数与层的形状」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Deep Learning Academy 课中编写并运行代码吗？
能。每节 Deep Learning Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。