.cat()

The .cat() function in PyTorch concatenates two or more tensors along a specified dimension. The tensors must have the same shape in all dimensions except for the dimension along which they are concatenated.

Syntax

torch.cat(tensors, dim=0, out=None)

Parameters

• tensors: A sequence (like a list or tuple) of tensors to be concatenated. All tensors must have the same shape in all dimensions except for the specified dimension.
• dim: An integer specifying the dimension along which the tensors will be concatenated. The default value is 0, which means concatenation will occur along the first dimension.
• out: A pre-allocated tensor with the correct shape to store the result of the concatenation. If not provided, a new tensor will be allocated.

Example 1

The example below showcases concatenating tensors along the first dimension using the .cat() function:

import torch

# Create two tensors of shape (2, 3)
tensor1 = torch.tensor([[1, 2, 3], [4, 5, 6]])
tensor2 = torch.tensor([[7, 8, 9], [10, 11, 12]])

# Concatenate the tensors along the first dimension
result = torch.cat((tensor1, tensor2), dim=0)

print("tensor1:")
print(tensor1)

print("\ntensor2:")
print(tensor2)

print("\nresult:")
print(result)

The output shows the two tensors and the concatenated tensor along the first dimension:

tensor1:
tensor([[1, 2, 3],
        [4, 5, 6]])

tensor2:
tensor([[ 7,  8,  9],
        [10, 11, 12]])

result:
tensor([[ 1,  2,  3],
        [ 4,  5,  6],
        [ 7,  8,  9],
        [10, 11, 12]])

Example 2

The example below showcases concatenating tensors along the second dimension using the .cat() function:

import torch

# Create two tensors of shape (2, 3)
tensor1 = torch.tensor([[1, 2, 3], [4, 5, 6]])
tensor2 = torch.tensor([[7, 8, 9], [10, 11, 12]])

# Concatenate the tensors along the second dimension
result = torch.cat((tensor1, tensor2), dim=1)

print("tensor1:")
print(tensor1)

print("\ntensor2:")
print(tensor2)

print("\nresult:")
print(result)

The output shows the two tensors and the concatenated tensor along the second dimension:

tensor1:
tensor([[1, 2, 3],
        [4, 5, 6]])

tensor2:
tensor([[ 7,  8,  9],
        [10, 11, 12]])

result:
tensor([[ 1,  2,  3,  7,  8,  9],
        [ 4,  5,  6, 10, 11, 12]])

Example 3

The example below showcases concatenating tensors along the third dimension using the .cat() function:

import torch

# Create two tensors of shape (2, 2, 3)
tensor1 = torch.tensor([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
tensor2 = torch.tensor([[[13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24]]])

# Concatenate the tensors along the third dimension
result = torch.cat((tensor1, tensor2), dim=2)

print("tensor1:")
print(tensor1)

print("\ntensor2:")
print(tensor2)

print("\nresult:")
print(result)

The output shows the two tensors and the concatenated tensor along the third dimension:

tensor1:
tensor([[[ 1,  2,  3],
         [ 4,  5,  6]],

        [[ 7,  8,  9],
         [10, 11, 12]]])

tensor2:
tensor([[[13, 14, 15],
         [16, 17, 18]],

        [[19, 20, 21],
         [22, 23, 24]]])

result:
tensor([[[ 1,  2,  3, 13, 14, 15],
         [ 4,  5,  6, 16, 17, 18]],

        [[ 7,  8,  9, 19, 20, 21],
         [10, 11, 12, 22, 23, 24]]])