What are tensor shapes and why are they important?

What are tensor shapes and why are they important?

Tensor shapes refer to the dimensions of tensors, which are multi-dimensional arrays. They are crucial in machine learning as they define the structure of data.

Can you give me an example of a tensor shape?

Sure! A 2D tensor, or matrix, might have a shape of (3, 4), meaning it has 3 rows and 4 columns.

How do tensor shapes affect neural networks?

Tensor shapes determine how data flows through a neural network. They influence the architecture and the operations that can be performed.

What happens if the tensor shapes don't match?

If tensor shapes don't match during operations like addition or multiplication, it will result in an error, as the operations require compatible dimensions.

Are there tools to visualize tensor shapes?

Yes, there are several tools like TensorBoard that can help visualize tensor shapes and their flow through neural networks.