### PyTorch - How to concatenate tensors along a given dimension?

In PyTorch, to concatenate tensors along a given dimension, we use torch.cat() method. This method accepts the sequence of tensors and dimension (along that the concatenation is to be done) as input parameters. This method concatenates the sequence of tensors along the given dimension. All tensors must have the same shape (except in the concatenating dimension) i.e., the sizes of tensors must match except in the concatenating dimension.

 Fig: Concatenate two tensors along different dimensions

#### Prerequisites

• Python 3
• PyTorch
• Jupyter Notebook

### Syntax

`torch.cat((tensor1, tensor2, ...), dim)`

### Parameters:

tensor1, tensor2, ...: These are the input tensors. Note that the sizes of tensors must be the same except the concatenating dimension. For better understanding go thorough the examples below.

dim: The tensors - tensor1, tensor2,..., will be concatenated along this dimension.

Note: If dimension of the tensors is n then dim must be in range [-n, n-1]. Such as if tensors are two-dimensional then dim can take value in range [-2, 1].

#### Examples of Concatenating Tensors

`torch.cat((t1, t2), 0) # concatenate along first dimtorch.cat((t1, t2), 1) # concatenate along second dim`
Now look at some complete examples to understand deeply.

### Example 1: Concatenating Two Tensors of Same Sizes

In this program example, we concatenate two 2-dimensional tensors of same size along dimension 0 and 1.
`#import required moduleimport torch# create two tensorst1 = torch.randn(2,3)t2 = torch.randn(2,3)# dispaly the tensorsprint(t1)print("------------------------------------")print(t2)print("------------------------------------")# concatenate the above tensorst = torch.cat((t1,t2), 0)print(t)print(t.shape)print("------------------------------------")t = torch.cat((t1,t2), 1)print(t)print(t.shape)`

#### Output

```tensor([[-0.8608,  0.3543,  0.7615],
[-1.5630, -0.7468, -1.7119]])
------------------------------------
tensor([[ 0.1708, -1.4171, -0.3423],
[-0.2184,  2.3268,  1.4466]])
------------------------------------
tensor([[-0.8608,  0.3543,  0.7615],
[-1.5630, -0.7468, -1.7119],
[ 0.1708, -1.4171, -0.3423],
[-0.2184,  2.3268,  1.4466]])
torch.Size([4, 3])
------------------------------------
tensor([[-0.8608,  0.3543,  0.7615,  0.1708, -1.4171, -0.3423],
[-1.5630, -0.7468, -1.7119, -0.2184,  2.3268,  1.4466]])
torch.Size([2, 6])```

Notice from the output, we are able to concatenate the tensors along both dimensions as the size of the tensors are same.

### Example 2: Concatenating Two Tensors of Different Sizes

In this program example, we concatenate two 2-dimensional tensors of different sizes along dimension 0 and 1. But you notice we can't concatenate along dimension 1 as the dimension along 0 is different, i.e., first tensor has 2 and second has 1.
`#import required moduleimport torch# create two 2D tensorst1 = torch.randn(2,3)t2 = torch.randn(1,3)# dispaly the tensorsprint(t1)print("------------------------------------")print(t2)print("------------------------------------")# concatenate the above tensorst = torch.cat((t1,t2), 0)print(t)print(t.shape)print("------------------------------------")t = torch.cat((t1,t2), 1)print(t)print(t.shape)`

#### Output

 Fig -Concatenate two tensors of different size.

Notice that get a RuntimeError when concatenating the tensors along dim 1. It's so because the dimension of tensors along dim 0 are not the same.

### Example 3: concatenating two 3-D tensors with same sizes

In the following program, we concatenate two tensors (3-dimensional) along dims 0, 1 and 2. Note here we are able to concatenate along these dims as the sizes of tensors are the same.
`#import required moduleimport torch# create two 3D tensors t1 = torch.randn(2,3,4)t2 = torch.randn(2,3,4)# dispaly the tensorsprint(t1)print("-------------------------------------------")print(t2)print("-------------------------------------------")# concatenate the above tensorst = torch.cat((t1,t2), 0)print(t)print(t.shape)print("-------------------------------------------")t = torch.cat((t1,t2), 1)print(t)print(t.shape)print("-------------------------------------------")t = torch.cat((t1,t2), 2)print(t)print(t.shape)`

#### Output

```tensor([[[-0.4088, -0.3405, -0.5122, -1.1153],
[-0.5247, -0.2283,  0.0185, -1.2393],
[ 0.5817, -0.8054,  1.0051,  0.1855]],

[[ 0.4781,  0.6894,  1.5388, -0.1348],
[ 1.7484, -1.8953, -0.4417,  0.9228],
[ 2.7996, -0.3553, -1.4830, -0.8816]]])
-------------------------------------------
tensor([[[-0.1227,  0.3787,  0.4890, -0.3944],
[ 0.1819,  0.2270, -0.1462, -0.2637],
[-1.3739,  0.4905, -0.5923,  0.6304]],

[[ 0.6627, -0.4194,  0.0393, -2.6827],
[ 0.1396, -0.0193,  2.2819,  2.9780],
[ 0.3608, -0.1011,  0.7639,  0.8245]]])
-------------------------------------------
tensor([[[-0.4088, -0.3405, -0.5122, -1.1153],
[-0.5247, -0.2283,  0.0185, -1.2393],
[ 0.5817, -0.8054,  1.0051,  0.1855]],

[[ 0.4781,  0.6894,  1.5388, -0.1348],
[ 1.7484, -1.8953, -0.4417,  0.9228],
[ 2.7996, -0.3553, -1.4830, -0.8816]],

[[-0.1227,  0.3787,  0.4890, -0.3944],
[ 0.1819,  0.2270, -0.1462, -0.2637],
[-1.3739,  0.4905, -0.5923,  0.6304]],

[[ 0.6627, -0.4194,  0.0393, -2.6827],
[ 0.1396, -0.0193,  2.2819,  2.9780],
[ 0.3608, -0.1011,  0.7639,  0.8245]]])
torch.Size([4, 3, 4])
-------------------------------------------
tensor([[[-0.4088, -0.3405, -0.5122, -1.1153],
[-0.5247, -0.2283,  0.0185, -1.2393],
[ 0.5817, -0.8054,  1.0051,  0.1855],
[-0.1227,  0.3787,  0.4890, -0.3944],
[ 0.1819,  0.2270, -0.1462, -0.2637],
[-1.3739,  0.4905, -0.5923,  0.6304]],

[[ 0.4781,  0.6894,  1.5388, -0.1348],
[ 1.7484, -1.8953, -0.4417,  0.9228],
[ 2.7996, -0.3553, -1.4830, -0.8816],
[ 0.6627, -0.4194,  0.0393, -2.6827],
[ 0.1396, -0.0193,  2.2819,  2.9780],
[ 0.3608, -0.1011,  0.7639,  0.8245]]])
torch.Size([2, 6, 4])
-------------------------------------------
tensor([[[-0.4088, -0.3405, -0.5122, -1.1153, -0.1227,  0.3787,  0.4890,
-0.3944],
[-0.5247, -0.2283,  0.0185, -1.2393,  0.1819,  0.2270, -0.1462,
-0.2637],
[ 0.5817, -0.8054,  1.0051,  0.1855, -1.3739,  0.4905, -0.5923,
0.6304]],

[[ 0.4781,  0.6894,  1.5388, -0.1348,  0.6627, -0.4194,  0.0393,
-2.6827],
[ 1.7484, -1.8953, -0.4417,  0.9228,  0.1396, -0.0193,  2.2819,
2.9780],
[ 2.7996, -0.3553, -1.4830, -0.8816,  0.3608, -0.1011,  0.7639,
0.8245]]])
torch.Size([2, 3, 8])```

Notice the size of the final tensor after concatenation. The particular dimension values are added along which the tensors are concatenated other dim values remain the same.

### Example 4: concatenating two 3-D tensors with different sizes

In this example program, we concatenate two 3-D tensors of different size. The first dimension values of the tensors are different and other dimensions are the same. So we can concatenate along the first dimension.
`#import required moduleimport torch# create two 3D tensors t1 = torch.randn(10,32,32)t2 = torch.randn(4,32,32)# dispaly the tensors# print(t1)print(t1.shape)print("-------------------------------------------")# print(t2)print(t2.shape)print("-------------------------------------------")# concatenate the above tensorst = torch.cat((t1,t2), 0)print(t)print(t.shape)`

#### Output

```torch.Size([10, 32, 32])
-------------------------------------------
torch.Size([4, 32, 32])
-------------------------------------------
torch.Size([14, 32, 32])```

Notice the size of the final tensor after concatenation. Same as mentioned above the dimension values (along which the tensor are concatenated) - 10 and 4 are added and other dim values (i.e., 32) are same.

### Example 5: Concatenating three tensors

In the below program, we concatenate three 3-D tensors along the first dimension.
`#import required moduleimport torch# create two 3D tensors t1 = torch.randn(10,32,32)t2 = torch.randn(4,32,32)t3 = torch.randn(7,32,32)# dispaly the tensorsprint(t1.shape)print("-------------------------------------------")print(t2.shape)print("-------------------------------------------")print(t3.shape)print("-------------------------------------------")# concatenate the above tensorst = torch.cat((t1,t2, t3), 0)# print(t)print(t.shape)`

#### Output

```torch.Size([10, 32, 32])
-------------------------------------------
torch.Size([4, 32, 32])
-------------------------------------------
torch.Size([7, 32, 32])
-------------------------------------------
torch.Size([21, 32, 32])```

Notice the concatenation of three or more tensors are same as concatenating two tensors.
In this post we have discussed different examples to concatenate the tensors along a given dimension. To concatenate along a particular dimension we used torch.cat() method. The tensors to be concatenated should have the same size (except the dimension along which the concatenation is done).