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Technical articles with clear explanations and examples
Smart ways to use Canva for Social Media
Starting out new on Canva and completely overwhelmed? When first starting out, Canva can be a tricky place to find your way through. The wide assortment of features which makes designing very easy may also sometimes lead to confusion if you are not well-versed with the platform. This may keep users from discovering the multiple smart and underrated uses of these features.Smart and unique ways to use Canva for Social MediaIn this article, we will see how you can use Canva and its features to promote a brand in smart and unique ways on Social Media platforms.Branding ImagesIf the images ...
Read MoreHow to check if an object is a PyTorch Tensor?
To check if an object is a tensor or not, we can use the torch.is_tensor() method. It returns True if the input is a tensor; False otherwise.Syntaxtorch.is_tensor(input)Parametersinput – The object to be checked, if it is a tensor or not .OutputIt returns True if the input is a tensor; else False.StepsImport the required library. The required library is torch.Define a tensor or other object.Check if the created object is a tensor or not using torch.is_tensor(input).Display the result.Example 1# import the required library import torch # create an object x x = torch.rand(4) print(x) # check if the above ...
Read MoreWhat does "with torch no_grad" do in PyTorch?
The use of "with torch.no_grad()" is like a loop where every tensor inside the loop will have requires_grad set to False. It means any tensor with gradient currently attached with the current computational graph is now detached from the current graph. We no longer be able to compute the gradients with respect to this tensor.A tensor is detached from the current graph until it is within the loop. As soon as it is out of the loop, it is again attached to the current graph if the tensor was defined with gradient.Let's take a couple of examples for a better ...
Read MoreWhat does backward() do in PyTorch?
The backward() method is used to compute the gradient during the backward pass in a neural network.The gradients are computed when this method is executed.These gradients are stored in the respective variables.The gradients are computed with respect to these variables, and the gradients are accessed using .grad.If we do not call the backward() method for computing the gradient, the gradients are not computed.And, if we access the gradients using .grad, the result is None.Let's have a couple of examples to demonstrate how it works.Example 1In this example, we attempt to access the gradients without calling the backward() method. We notice ...
Read MorePyTorch – How to check if a tensor is contiguous or not?
A contiguous tensor is a tensor whose elements are stored in a contiguous order without leaving any empty space between them. A tensor created originally is always a contiguous tensor. A tensor can be viewed with different dimensions in contiguous manner.A transpose of a tensor creates a view of the original tensor which follows non-contiguous order. The transpose of a tensor is non-contiguous.SyntaxTensor.is_contiguous()It returns True if the Tensor is contiguous; False otherwise.Let's take a couple of example to demonstrate how to use this function to check if a tensor is contiguous or non-contiguous.Example 1# import torch library import torch ...
Read MoreHow to find the transpose of a tensor in PyTorch?
To transpose a tensor, we need two dimensions to be transposed. If a tensor is 0-D or 1-D tensor, the transpose of the tensor is same as is. For a 2-D tensor, the transpose is computed using the two dimensions 0 and 1 as transpose(input, 0, 1).SyntaxTo find the transpose of a scalar, a vector or a matrix, we can apply the first syntax defined below.And for any dimensional tensor, we can apply the second syntax.For
Read MoreHow to get the rank of a matrix in PyTorch?
The rank of a matrix can be obtained using torch.linalg.matrix_rank(). It takes a matrix or a batch of matrices as the input and returns a tensor with rank value(s) of the matrices. torch.linalg module provides us many linear algebra operations.Syntaxtorch.linalg.matrix_rank(input)where input is the 2D tensor/matrix or batch of matrices.StepsWe could use the following steps to get the rank of a matrix or batch of matrices −Import the torch library. Make sure you have it already installed.import torch Create a 2D tensor/matrix or a batch of matrices and print it.t = torch.tensor([[1., 2., 3.], [4., 5., 6.]]) print("Tensor:", t)Compute the rank ...
Read MorePyTorch – How to get the exponents of tensor elements?
To find the exponential of the elements of an input tensor, we can apply Tensor.exp() or torch.exp(input). Here, input is the input tensor for which the exponentials are computed. Both the methods return a new tensor with the exponential values of the elements of the input tensor.SyntaxTensor.exp()ortorch.exp(input) StepsWe could use the following steps to compute the exponentials of the elements of an input tensor −Import the torch library. Make sure you have it already installed.import torchCreate a tensor and print it.t1 = torch.rand(4, 3) print("Tensor:", t1)Compute the exponential of the elements of the tensor. For this, use torch.exp(input) and optionally ...
Read MoreWhat does Tensor.detach() do in PyTorch?
Tensor.detach() is used to detach a tensor from the current computational graph. It returns a new tensor that doesn't require a gradient.When we don't need a tensor to be traced for the gradient computation, we detach the tensor from the current computational graph.We also need to detach a tensor when we need to move the tensor from GPU to CPU.SyntaxTensor.detach()It returns a new tensor without requires_grad = True. The gradient with respect to this tensor will no longer be computed.StepsImport the torch library. Make sure you have it already installed.import torch Create a PyTorch tensor with requires_grad = True and ...
Read MorePyTorch – How to compute element-wise logical XOR of tensors?
torch.logical_xor() computes the element-wise logical XOR of the given two input tensors. In a tensor, the elements with zero values are treated as False and non-zero elements are treated as True. It takes two tensors as input parameters and returns a tensor with values after computing the logical XOR.Syntaxtorch.logical_xor(tensor1, tensor2)where tensor1 and tensor2 are the two input tensors.StepsTo compute element-wise logical XOR of given input tensors, one could follow the steps given below −Import the torch library. Make sure you have it already installed.Create two tensors, tensor1 and tensor2, and print the tensors.Compute torch.logical_xor(tesnor1, tesnor2) and assign the value to ...
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