In PyTorch, the squeeze() function is used to remove single-dimensional entries from the shape of a tensor. This can be helpful when working with models that require a specific input shape.
To use the squeeze() function, simply pass in the tensor you want to squeeze as an argument. For example:
import torch
# Create a tensor with a single dimension
t = torch.tensor([[1], [2], [3]])
# Print the original shape
print(t.shape) # Output: torch.Size([3, 1])
# Use the squeeze() function to remove the single dimension
squeezed_t = torch.squeeze(t)
# Print the new shape
print(squeezed_t.shape) # Output: torch.Size([3])In this example, the original tensor had a shape of [3, 1], but after using squeeze(), the single dimension was removed, resulting in a new shape of [3].
Overall, the squeeze() function is a useful tool for manipulating tensor shapes in PyTorch and can be particularly handy when working with deep learning models.
