[Term Entry] PyTorch Tensor Operations: .neg()

content/pytorch/concepts/tensor-operations/terms/neg/neg.md

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+---
+Title: '.neg()'
+Description: 'Returns a new tensor with the negative of each element in the input tensor.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Deep Learning'
+  - 'Methods'
+  - 'PyTorch'
+  - 'Tensor'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/data-science'
+---
+
+The **`.neg()`** method in PyTorch returns a new [tensor](https://www.codecademy.com/resources/docs/pytorch/tensors) with the negative of each element in the input tensor. This operation multiplies each element by -1, effectively flipping the sign of all values. The method is commonly used in mathematical operations, gradient computations, and transformations in neural networks.
+
+## Syntax
+
+```pseudo
+torch.neg(input, *, out=None) → Tensor
+```
+
+**Parameters:**
+
+- `input` (Tensor): The input tensor.
+- `out` (Tensor, optional): The output tensor to store the result. Must have the same shape as `input`.
+
+**Return value:**
+
+Returns a new tensor where each element is the negative of the corresponding element in `input`.
+
+## Example
+
+The following example demonstrates how to use the `.neg()` method to negate tensor elements:
+
+```py
+import torch
+
+# Create a tensor with positive and negative values
+tensor = torch.tensor([1.5, -2.3, 0.0, 4.8, -1.2])
+
+# Compute the negative using the method form
+neg_tensor = tensor.neg()
+
+# Alternative: use the functional form
+neg_functional = torch.neg(tensor)
+
+# Alternative: use the operator form
+neg_operator = -tensor
+
+print("Original Tensor:")
+print(tensor)
+
+print("\nNegated Tensor (using .neg()):")
+print(neg_tensor)
+
+print("\nNegated Tensor (using torch.neg()):")
+print(neg_functional)
+
+print("\nNegated Tensor (using - operator):")
+print(neg_operator)
+```
+
+This example results in the following output:
+
+```shell
+Original Tensor:
+tensor([ 1.5000, -2.3000,  0.0000,  4.8000, -1.2000])
+
+Negated Tensor (using .neg()):
+tensor([-1.5000,  2.3000, -0.0000, -4.8000,  1.2000])
+
+Negated Tensor (using torch.neg()):
+tensor([-1.5000,  2.3000, -0.0000, -4.8000,  1.2000])
+
+Negated Tensor (using - operator):
+tensor([-1.5000,  2.3000, -0.0000, -4.8000,  1.2000])
+```
+
+In this example:
+
+- Positive values become negative: `1.5` → `-1.5`, `4.8` → `-4.8`
+- Negative values become positive: `-2.3` → `2.3`, `-1.2` → `1.2`
+- Zero remains zero: `0.0` → `-0.0` (negative zero in floating-point)
+- All three forms (`.neg()`, `torch.neg()`, and `-`) produce identical results