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# How to compute the mean and standard deviation of a tensor in PyTorch?

A PyTorch tensor is like a numpy array. The only difference is that a tensor utilizes the GPUs to accelerate numeric computations. The **mean** of a tensor is computed using the **torch.mean()** method. It returns the mean value of all the elements in the input tensor. We can also compute the mean row-wise and column-wise, providing suitable axis or dim.

The standard deviation of a tensor is computed using **torch.std()**. It returns the standard deviation of all the elements in the tensor. Like **mean**, we can also compute the **standard deviation**, row or column-wise.

## Steps

Import the required library. In all the following Python examples, the required Python library is

**torch**. Make sure you have already installed it.Define a PyTorch tensor and print it.

Compute the mean using

**torch.mean(input, axis)**. Here, the input is the tensor for which the mean should be computed and axis (or**dim**) is the list of dimensions. Assign the computed mean to a new variable.Compute the standard deviation using

**torch.std(input, axis)**. Here, input is the**tensor**and**axis**(or**dim**) is the list of dimensions. Assign the computed standard deviation to a new variable.Print the above-computed mean and standard deviation .

## Example 1

The following Python program shows how to compute the mean and standard deviation of a 1D tensor.

# Python program to compute mean and standard # deviation of a 1D tensor # import the library import torch # Create a tensor T = torch.Tensor([2.453, 4.432, 0.754, -6.554]) print("T:", T) # Compute the mean and standard deviation mean = torch.mean(T) std = torch.std(T) # Print the computed mean and standard deviation print("Mean:", mean) print("Standard deviation:", std)

## Output

T: tensor([ 2.4530, 4.4320, 0.7540, -6.5540]) Mean: tensor(0.2713) Standard deviation: tensor(4.7920)

## Example 2

The following Python program shows how to compute the mean and standard deviation of a 2D tensor in both dimensions, i.e., row-wise as well as column-wise

# import necessary library import torch # create a 3x4 2D tensor T = torch.Tensor([[2,4,7,-6], [7,33,-62,23], [2,-6,-77,54]]) print("T:\n", T) # compute the mean and standard deviation mean = torch.mean(T) std = torch.std(T) print("Mean:", mean) print("Standard deviation:", std) # Compute column-wise mean and std mean = torch.mean(T, axis = 0) std = torch.std(T, axis = 0) print("Column-wise Mean:\n", mean) print("Column-wise Standard deviation:\n", std) # Compute row-wise mean and std mean = torch.mean(T, axis = 1) std = torch.std(T, axis = 1) print("Row-wise Mean:\n", mean) print("Row-wise Standard deviation:\n", std)

## Output

T: tensor([[ 2., 4., 7., -6.], [ 7., 33., -62., 23.], [ 2., -6., -77., 54.]]) Mean: tensor(-1.5833) Standard deviation: tensor(36.2703) Column-wise Mean: tensor([ 3.6667, 10.3333, -44.0000, 23.6667]) Column-wise Standard deviation: tensor([ 2.8868, 20.2567, 44.7996, 30.0056]) Row-wise Mean: tensor([ 1.7500, 0.2500, -6.7500]) Row-wise Standard deviation: tensor([ 5.5603, 42.8593, 53.8602])

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