How to Apply Matmul to Every Row in a PyTorch Tensor

Learn how to efficiently use PyTorch's matmul function to apply operations to each row of a tensor, avoiding common errors along the way.
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Understanding Row-wise Matrix Multiplication in PyTorch

The Problem

Suppose you have a tensor x that looks like this:

[[See Video to Reveal this Text or Code Snippet]]

And another 2D tensor i:

[[See Video to Reveal this Text or Code Snippet]]

Your goal is to apply the matrix multiplication to each row of tensor x using i. If you try to do this using a simple lambda function, you may come across the error message: “only one element tensors can be converted to Python scalars.” This can be frustrating, especially if you're unsure how to resolve it.

The Solution

Step 1: Import PyTorch

Make sure to start by importing the PyTorch library:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Define Your Tensors

Next, define your tensors as follows:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Apply the Matrix Multiplication

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Stack the Results

[[See Video to Reveal this Text or Code Snippet]]

Full Code Example

Putting it all together, your complete code should look like this:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Now you can leverage the powerful capabilities of PyTorch for more complex tensor manipulations in your machine learning projects!

If you have any questions or run into issues, feel free to reach out or leave a comment!