Combining Tensors in PyTorch: torch.stack() and torch.cat()

When working with deep learning models in PyTorch, you often need to combine multiple tensors into a single tensor. This can be done using two main functions: `torch.stack()` and `torch.cat()`. While both achieve concatenation, they differ in how they handle the dimensions of the tensors being combined.

torch.cat()

`torch.cat()` is used when you want to combine tensors along an existing dimension. For example, if you have two tensors with different shapes initially (e.g., temperature and humidity), you can use `unsqueeze(1)` to add a new dimension of size 1 to each tensor. This code combines temperature and humidity features into a single tensor.

`combined_features = torch.cat((temp, humidity), dim=1)`

The `torch.cat()` function concatenates tensors along the specified dimension (in this case, the 1st dimension or columns). Tensors must have the same shape except for the dimension being concatenated. This function is useful when you want to combine features or channels that represent different aspects of the data.

torch.stack()

`torch.stack()` is used when you want to create a new dimension to group tensors together, such as creating mini-batches. In this case, the code creates three mini-batches, each containing two data points.

`mini_batches = torch.stack(torch.split(data, batch_size), dim=0)`

The `torch.stack()` function stacks tensors along a new dimension at the specified index (in this case, the 0th dimension). This function is useful when you want to create data structures like mini-batches, where each element represents a different sample.

Choosing between torch.stack() and torch.cat()

The choice between `torch.stack()` and `torch.cat()` depends on the desired outcome:

* Use `torch.cat()` when you want to combine tensors along an existing dimension, such as concatenating features or channels.

* Use `torch.stack()` when you want to create a new dimension to group tensors together, such as creating mini-batches.

In summary, understanding the differences between `torch.stack()` and `torch.cat()` allows you to effectively combine tensors in your deep learning projects using PyTorch. By choosing the right function for the task at hand, you can achieve the desired outcome and create efficient data structures for your models.

Torch stack