Introduction In this tutorial, we will do an in-depth understanding of how to use torch.sum() function to sum the elements in PyTorch tensor. We will first understand its syntax and then cover its functionalities with various examples and illustrations to make it easy for beginners. What is torch.sum() function in PyTorch The torch sum() function is used to sum up the elements inside the tensor in PyTorch along a given dimension or axis. On the surface, this may look like […]

First Tensor: tensor([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]], [[12, 13, 14], [15, 16, 17], [18, 19, 20], [21, 22, 23]]]) Second Tensor: tensor([[[ 4, 5, 6], [ 7, 8, 9], [10, 11, 12], [13, 14, 15]], [[16, 17, 18], [19, 20, 21], [22, 23, 24], [25, 26, 27]]]) Sum of Tensor1 and Tensor2: tensor([[[ 4, 6, 8], [10, 12, 14], [16, 18, 20], [22, 24, 26]], [[28, 30, 32], [34, […]

First Tensor: tensor([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]], [[12, 13, 14], [15, 16, 17], [18, 19, 20], [21, 22, 23]]]) Second Tensor: tensor([[[ 4, 5, 6], [ 7, 8, 9], [10, 11, 12], [13, 14, 15]], [[16, 17, 18], [19, 20, 21], [22, 23, 24], [25, 26, 27]]]) Tensor1 minus Tensor2: tensor([[[-4, -4, -4], [-4, -4, -4], [-4, -4, -4], [-4, -4, -4]], [[-4, -4, -4], [-4, -4, -4], [-4, […]

Introduction In this article, we will show you how to create random tensors in PyTorch with torch.randint() and torch.randint_like() functions. These functions are very useful to initialize

Introduction In this article, we will see how we can perform element-wise multiplication of tensors in PyTorch by using torch.mul() or torch.multiply() function. We will see various examples to understand better how these functions work. Element Wise Tensor Multiplication with torch.mul() & torch.multiply() torch.mul() function in PyTorch is used to do element-wise multiplication of tensors. It should be noted here that torch.multiply() is just an alias for torch.mul() function and they do the same work. Using either of torch.mul()   […]

Introduction In this tutorial, we will explain how to multiply tensors in PyTorch with torch.matmul() function. We will see its syntax and see various examples to understand its functionality in a better way. Syntax of torch.matmul() function The syntax of torch matmul function is as follows – torch.matmul(tensor1, tensor2, out) tensor1 – The first tensor for multiplication tensor2 – The second tensor for multiplication out – Output tensor, result of multiplication of tensor1 with tensor2 Functionality