Shapes and Dimensions in PyTorch
Similarly to NumPy arrays, a tensor's shape determines its dimensions. You can inspect a tensor's shape using the .shape attribute:
123import 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.
1234567import 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.
12345import 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.
12345import torch tensor = torch.arange(12) # 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.
1234567import 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
The following two methods allow you to add or remove dimensions:
unsqueeze(dim)adds a new dimension at the specified position;squeeze(dim)removes dimensions of size 1.
12345678import 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}")
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Completion rate improved to 5Shapes and Dimensions in PyTorch
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Similarly to NumPy arrays, a tensor's shape determines its dimensions. You can inspect a tensor's shape using the .shape attribute:
123import 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.
1234567import 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.
12345import 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.
12345import torch tensor = torch.arange(12) # 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.
1234567import 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
The following two methods allow you to add or remove dimensions:
unsqueeze(dim)adds a new dimension at the specified position;squeeze(dim)removes dimensions of size 1.
12345678import 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}")
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