Inspect Parameters and Layer Shapes
See exactly what your model holds.
Inspect Parameters and Layer Shapes is a free Deep Learning Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Deep Learning Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
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. 🎯
Frequently asked questions
Is the “Inspect Parameters and Layer Shapes” lesson free?
Yes — the full text of “Inspect Parameters and Layer Shapes” is free to read here on the web, and the Deep Learning Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Deep Learning Academy course, upgrade to CoddyKit PRO.
What will I learn in “Inspect Parameters and Layer Shapes”?
See exactly what your model holds. You practise Deep Learning Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Deep Learning Academy?
No prior experience is required. Deep Learning Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Inspect Parameters and Layer Shapes” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Deep Learning Academy lesson?
Yes. Every Deep Learning Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Subclass nn.Module: __init__ and forward
- Stacking Linear Layers
- nn.Sequential for Quick Models
- Inspect Parameters and Layer Shapes