by Michael Stolzer | Nov 22, 2023 | How-To/Tips
Cosine similarity is utilized to find the similarity of two tensors/vectors. PyTorch provides a “torch.nn” module that has a “CosineSimilarity()” function to calculate the cosine similarity between two particular tensors. This function returns the computed cosine...
by Michael Stolzer | Nov 22, 2023 | How-To/Tips
The norm of a matrix or vector is a computation of its length/magnitude. In PyTorch, a vector is simply a 1D tensor that has only one dimension whereas a matrix is a 2D tensor that has two dimensions i.e. rows and columns. Sometimes, users may want to calculate the...
by Michael Stolzer | Nov 22, 2023 | How-To/Tips
Sine is a basic trigonometric function. While working with tensors in PyTorch, users may want to calculate the sine of each element in the specific tensor. PyTorch provides a “torch.sin()” method to perform this operation. This method accepts a specific input tensor...
by Michael Stolzer | Nov 22, 2023 | How-To/Tips
The normal distribution is often utilized in statistics and probability where data is symmetrically distributed around the mean. It shows that data near the mean more frequently occurred than data far from the mean. Sometimes, users may be required to generate random...
by Michael Stolzer | Nov 22, 2023 | How-To/Tips
In PyTorch, the “torchvision.transforms” module has a collection of classes and functions to apply different transformations on desired images including rotating, cropping, resizing, and many more. It provides the “RandomResizedCrop()” method to crop a random area of...
by Michael Stolzer | Nov 22, 2023 | How-To/Tips
Binary cross entropy is a logarithmic loss function utilized in machine learning. It computes the difference between the estimated probabilities of a model and actual labels of data. Sometimes, users may need to find/compute the binary cross entropy between two (input...
