Similarly to **NumPy arrays**, a tensor's shape determines its **dimensions**. You can inspect a tensor's shape using the `.shape` attribute:

import torch
tensor = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(f"Tensor shape: {tensor.shape}")

## Reshaping Tensors with view
The `.view()` method creates a new **view** of the tensor with the specified shape **without modifying the original tensor**. The total number of elements must remain the same.

import torch
tensor = torch.arange(12)
# Reshape a tensor to 4x3
reshaped_tensor = tensor.view(4, 3)
print(f"Reshaped tensor: {reshaped_tensor}")
# Original tensor remains unchanged
print(f"Original tensor: {tensor}")

## Reshaping Tensors with reshape

The `.reshape()` method is similar to `.view()` but can handle cases where the tensor is not stored contiguously in memory. It also **doesn't modify the original tensor**.

import torch
tensor = torch.arange(12)
# Reshape a tensor to 6x2
reshaped_tensor = tensor.reshape(6, 2)
print(f"Reshaped tensor: {reshaped_tensor}")

## Using Negative Dimensions
You can use `-1` in the shape to let PyTorch **infer the size** of one dimension based on the total number of elements.

import torch
# Automatically infer the second dimension
inferred_tensor = tensor.view(2, -1)
print("Inferred Tensor:", inferred_tensor)

## Understanding Tensor Views
A view of a tensor **shares the same data** with the original tensor. Changes to the view affect the original tensor and vice versa.

import torch
tensor = torch.arange(12)
view_tensor = tensor.view(2, 6)
view_tensor[0, 0] = 999
# Changes in the view are reflected in the original tensor
print("View Tensor:", view_tensor)
print("Original Tensor:", tensor)

## Changing Dimensions

- **`unsqueeze(dim)`** adds a new dimension at the specified position;
- **`squeeze(dim)`** removes dimensions of size 1.

import torch
tensor = torch.arange(12)
# Add a new dimension
unsqueezed_tensor = tensor.unsqueeze(0)  # Add a batch dimension
print(f"Unsqueezed tensor: {unsqueezed_tensor.shape}")
# Remove a dimension of size 1
squeezed_tensor = unsqueezed_tensor.squeeze(0)
print(f"Squeezed Tensor: {squeezed_tensor.shape}")

Neural Networks with PyTorch

Working with Shapes

Reshaping Tensors with view

Reshaping Tensors with reshape

Using Negative Dimensions

Understanding Tensor Views

Changing Dimensions