Reshape, View, Squeeze & Unsqueeze
Change dimensions without copying data.
Reshape, View, Squeeze & Unsqueeze is a free Deep Learning Academy lesson on CoddyKit — lesson 2 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.
Same Data, Different Shape
Often the numbers are right but the layout is wrong. Reshaping rearranges a tensor's dimensions without changing the values inside.
reshape Picks a New Layout
Call reshape with the dimensions you want. The total number of elements must stay the same.
x = torch.arange(6)
y = x.reshape(2, 3)
print(y.shape) # torch.Size([2, 3])Let -1 Infer a Dimension
Pass -1 for one dimension and PyTorch computes it for you from the total count. Handy when you only know the rest.
x = torch.arange(6)
y = x.reshape(-1, 2)
print(y.shape) # torch.Size([3, 2])view Shares the Same Memory
view reshapes without copying, so it is fast. It needs the data to be laid out contiguously in memory.
x = torch.arange(6)
y = x.view(3, 2)
print(y.shape) # torch.Size([3, 2])reshape Is the Safer Default
When in doubt, reach for reshape. It works even on non-contiguous tensors by copying only if it must.
Squeeze Removes Size-1 Dims
squeeze strips out any dimension of length 1. It cleans up shapes like (1, 5) down to a simple (5).
x = torch.zeros(1, 5)
y = x.squeeze()
print(y.shape) # torch.Size([5])Squeeze a Specific Dimension
Give squeeze an index to remove only that dimension. Safer when other size-1 dims should stay put.
x = torch.zeros(1, 5, 1)
y = x.squeeze(0)
print(y.shape) # torch.Size([5, 1])Unsqueeze Adds a Dimension
unsqueeze inserts a new size-1 dimension at the position you choose. It is the exact opposite of squeeze.
x = torch.tensor([1, 2, 3])
y = x.unsqueeze(0)
print(y.shape) # torch.Size([1, 3])Why You Add a Batch Dimension
Models expect a batch dimension up front. unsqueeze(0) turns one sample into a batch of one so the model accepts it.
sample = torch.randn(3)
batch = sample.unsqueeze(0)
print(batch.shape) # torch.Size([1, 3])Flatten Down to One Line
flatten collapses every dimension into a single long vector. It is common right before a final linear layer.
x = torch.zeros(2, 3)
y = x.flatten()
print(y.shape) # torch.Size([6])Element Count Never Changes
Every reshape trick keeps the same total number of values. If the counts don't match, PyTorch raises a shape error.
Quick Check
Let's see if you can predict the shape changes.
Recap: Reshape, View, Squeeze & Unsqueeze
You can now bend tensors into any layout: reshape for flexibility, view for speed, and squeeze or unsqueeze to drop and add dimensions. 🔧
Frequently asked questions
Is the “Reshape, View, Squeeze & Unsqueeze” lesson free?
Yes — the full text of “Reshape, View, Squeeze & Unsqueeze” 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 “Reshape, View, Squeeze & Unsqueeze”?
Change dimensions without copying data. 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 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Reshape, View, Squeeze & Unsqueeze” 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
- Shapes, Dtypes & Indexing
- Reshape, View, Squeeze & Unsqueeze
- Broadcasting Rules That Save You Loops
- Tensors Talk to NumPy