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content/pytorch/concepts/tensors/terms/cat/cat.md

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+---
+Title: '.cat()'
+Description: 'Concatenates two or more tensors in the same dimension.'
+Subjects:
+  - 'AI'
+  - 'Data Science'
+Tags:
+  - 'AI'
+  - 'Deep Learning'
+  - 'Functions'
+  - 'Machine Learning'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'py-torch-for-classification'
+---
+
+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
+
+```pseudo
+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:
+
+```py
+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:
+
+```shell
+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:
+
+```py
+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:
+
+```shell
+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:
+
+```py
+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:
+
+```shell
+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]]])
+```