PyTorch is an open-source framework available with a Python programming language. Tensor is a multidimensional array that is used to store the data. To use a tensor, we have to import the torch module. To create a tensor, the method used is tensor().
Syntax:
Where the data is a multi-dimensional array.
Mean()
Mean() in PyTorch is used to return the average value of elements present in the input tensor object.
Syntax:
Where:
1. The tensor is the input tensor.
2. Dim is to reduce the dimension. Dim=0 specifies the column comparison which gets the average along a column and dim=1 specifies the row comparison which gets the average along the row.
Example 1:
In this example, we will create a tensor with 2 dimensions that has 3 rows and 5 columns and apply the mean() function on rows and columns.
import torch
#create a tensor with 2 dimensions (3 * 5)
#with random elements using randn() function
data = torch.randn(3,5)
#display
print(data)
print()
#get average along columns with mean()
print("Mean across columns:")
print(torch.mean(data, dim=0))
print()
#get average along rows with mean()
print("Mean across rows:")
print(torch.mean(data, dim=1))
Output:
[-0.9090, -0.6124, 0.4644, 0.3485, 0.6863],
[-1.7201, 0.4546, -0.3618, 0.4858, -1.0712]])
Mean across columns:
tensor([-0.3602, 0.4291, 0.2326, 0.2298, -0.5886])
Mean across rows:
tensor([ 0.4126, -0.0044, -0.4426])
We can see that the mean values are returned across the columns and rows.
Example 2:
Create a Tensor with 5 * 5 matrix and return the average across the rows and columns.
import torch
#create a tensor with 2 dimensions (5 * 5)
#with random elements using randn() function
data = torch.randn(5,5)
#display
print(data)
print()
#get average along columns with mean()
print("Mean across columns:")
print(torch.mean(data, dim=0))
print()
#get average along rows with mean()
print("Mean across rows:")
print(torch.mean(data, dim=1))
Output:
[-1.4042, -0.9700, 0.4683, 1.5860, -0.4229],
[-0.5011, 1.7210, -0.0949, -0.8114, -0.7528],
[ 0.1496, 0.4154, -0.5784, 0.2983, -0.2608],
[ 1.4232, 0.8856, -0.7154, -0.2667, 0.6884]])
Mean across columns:
tensor([-0.4464, 0.4546, -0.1845, 0.5436, 0.0189])
Mean across rows:
tensor([ 0.2148, -0.1486, -0.0878, 0.0048, 0.4030])
We can see that the mean values across the rows and columns were returned.
Without the Dim Parameter
If we don’t specify the dim parameter, it returns the whole value’s average.
Example 1:
Create a 2D tensor with 5*5 matrix and return the average value.
import torch
#create a tensor with 2 dimensions (5 * 5)
#with random elements using randn() function
data = torch.randn(5,5)
#display
print(data)
print()
#get average with mean()
print("Mean :")
print(torch.mean(data))
Output:
[-0.5721, -1.0704, -0.7148, -1.4605, 0.1514],
[-1.5455, 1.5261, 1.3712, -1.3692, -1.0385],
[ 1.0159, 0.0484, -0.4317, -1.3518, 0.9220],
[-1.5225, 0.5126, -0.2473, 0.8433, 1.0807]])
Mean :
tensor(-0.2308)
Example 2:
Create a 1D tensor with 5 values and return the average value.
import torch
#create a tensor with 5 numeric values
data = torch.tensor([10.6,20.7,30.6,40.4,50.0])
#display
print(data)
print()
#get average with mean()
print("Mean :")
print(torch.mean(data))
Output:
Mean :
tensor(30.4600)
Work with CPU
If you want to run an argmax() function on the CPU, we have to create a tensor with a cpu() function. This will run on a CPU machine.
When we create a tensor, this time, we can use the cpu() function.
Syntax:
Example 1:
In this example, we will create a tensor with 2 dimensions that has 3 rows and 5 columns with the cpu() function and apply the mean() function on rows and columns.
import torch
#create a tensor with 2 dimensions (3 * 5)
#with random elements using randn() function
data = torch.randn(3,5).cpu()
#display
print(data)
print()
#get average along columns with mean()
print("Mean across columns:")
print(torch.mean(data, dim=0))
print()
#get average along rows with mean()
print("Mean across rows:")
print(torch.mean(data, dim=1))
Output:
[-0.9090, -0.6124, 0.4644, 0.3485, 0.6863],
[-1.7201, 0.4546, -0.3618, 0.4858, -1.0712]])
Mean across columns:
tensor([-0.3602, 0.4291, 0.2326, 0.2298, -0.5886])
Mean across rows:
tensor([ 0.4126, -0.0044, -0.4426])
We can see that the mean values are returned across the columns and rows.
Example 2:
Create a Tensor with 5 * 5 matrix with the cpu() function and return the average across the rows and columns.
import torch
#create a tensor with 2 dimensions (5 * 5)
#with random elements using randn() function
data = torch.randn(5,5).cpu()
#display
print(data)
print()
#get average along columns with mean()
print("Mean across columns:")
print(torch.mean(data, dim=0))
print()
#get average along rows with mean()
print("Mean across rows:")
print(torch.mean(data, dim=1))
Output:
[-1.4042, -0.9700, 0.4683, 1.5860, -0.4229],
[-0.5011, 1.7210, -0.0949, -0.8114, -0.7528],
[ 0.1496, 0.4154, -0.5784, 0.2983, -0.2608],
[ 1.4232, 0.8856, -0.7154, -0.2667, 0.6884]])
Mean across columns:
tensor([-0.4464, 0.4546, -0.1845, 0.5436, 0.0189])
Mean across rows:
tensor([ 0.2148, -0.1486, -0.0878, 0.0048, 0.4030])
We can see that the mean values across the rows and columns were returned.
Conclusion
In this PyTorch lesson, we learned about the mean() function and how to apply it on a tensor to return the average values across the columns and rows.
We also created a tensor with the cpu() function and returned the average values. If the dim is not specified in two or multi-dimensional tensor, it returns whole values on average.
