To compute the error function of a tensor, we use the torch.special.erf() method. It returns a new tensor with computed error function. It accepts torch tensor of any dimension. It is also known as Gauss error functionStepsWe could use the following steps to compute the error function of a tensor ... Read More

torch.linalg.eig() computes the Eigen value decomposition of a square matrix or a batch of square matrices. It accepts matrix and batch of matrices of float, double, cfloat and cdouble data types. It returns a named tuple (eigenvalues, eigenvectors). The eigenvalues and eigenvectors are always complex valued. The eigenvectors are given ... Read More

The RandomInvert() transform inverts the colors of an image randomly with a given probability. The torchvision.transforms module provides many important transforms that can be used to perform different types of manipulations on the image data.RandomInvert() accepts both PIL and tensor images or batch of tensor images. A tensor image is ... Read More

RandomRotation() rotates an image by a random angle. The chosen random angle is from a given range of angles in degree. RandomRotation() is one of the many important transforms provided by the torchvision.transforms module. RandomRotation() transform accepts both PIL and tensor images.A tensor image is a Torch tensor with shape ... Read More

To pad an image on all sides, we can apply Pad() transform provided by the torchvision.transforms module. This module contains many important transformations that can be used to perform different types of manipulations on the image data.Pad() transformation accepts both PIL and tensor images or a batch of tensor images. ... Read More

To compute the element-wise entropy of an input tensor, we use torch.special.entr() method. It returns a new tensor with entropy computed element-wise.If the element of tensor is negative, the entropy is negative infinity.If the element of the tensor is a zero, the entropy is zero.The entropy for a positive number element ... Read More

The RandomErasing() transform randomly selects a rectangular region in an input image and erases its pixels. The torchvision.transforms module provides many important transforms that can be used to perform different types of manipulations on the image data. RandomErasing() transformation accepts only tensor images of any size. A tensor image is ... Read More

The Normalize() transform normalizes an image with mean and standard deviation. The torchvision.transforms module provides many important transforms that can be used to perform different types of manipulations on the image data.Normalize() accepts only tensor images of any size. A tensor image is a torch tensor. A tensor image may ... Read More

The torchvision.transforms module provides many important transformations that can be used to perform different types of manipulations on the image data. GaussianBlur() transformation is used to blur an image with randomly chosen Gaussian blur.The GaussianBlur() transformation accepts both PIL and tensor images or a batch of tensor images. A tensor ... Read More

The Resize() transform resizes the input image to a given size. It's one of the transforms provided by the torchvision.transforms module. Resize() accepts both PIL and tensor images. A tensor image is a torch tensor with shape [C, H, W], where C is the number of channels, H is the ... Read More