Found 135 Articles for PyTorch

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 More

We use the torch.log2() method to compute logarithm to the base 2 of the elements of a tensor. It returns a new tensor with the logarithm values of the elements of the original input tensor. It takes a tensor as the input parameter and outputs a tensor.Syntaxtorch.log2(input)where input is a PyTorch tensor.It returns a new tensor with logarithm base 2 values.StepsImport the torch library. Make sure you have it already installed.import torch Create a tensor and print it.tensor1 = torch.rand(5, 3) print("Tensor:", tensor1)Compute torch.log2(input) and optionally assign this value to a new variable. Here, input is the created tensor.logb2 = ... Read More

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 More

To compute the gradients, a tensor must have its parameter requires_grad = true. The gradients are same as the partial derivatives.For example, in the function y = 2*x + 1, x is a tensor with requires_grad = True. We can compute the gradients using y.backward() function and the gradient can be accessed using x.grad.Here, the value of x.gad is same as the partial derivative of y with respect to x. If the tensor x is without requires_grad, then the gradient is None. We can define a function of multiple variables. Here the variables are the PyTorch tensors.StepsWe can use the ... Read More

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 ... Read More

torch.narrow() method is used to perform narrow operation on a PyTorch tensor. It returns a new tensor that is a narrowed version of the original input tensor.For example, a tensor of [4, 3] can be narrowed to a tensor of size [2, 3] or [4, 2]. We can narrow down a tensor along a single dimension at a time. Here, we cannot narrow down both dimensions to a size of [2, 2]. We can also use Tensor.narrow() to narrow down a tensor.Syntaxtorch.narrow(input, dim, start, length) Tensor.narrow(dim, start, length)Parametersinput – It's the PyTorch tensor to narrow.dim – It's the dimension along ... Read More

torch.permute() method is used to perform a permute operation on a PyTorch tensor. It returns a view of the input tensor with its dimension permuted. It doesn't make a copy of the original tensor.For example, a tensor with dimension [2, 3] can be permuted to [3, 2]. We can also permute a tensor with new dimension using Tensor.permute().Syntaxtorch.permute(input, dims)Parametersinput – PyTorch tensor.dims – Tuple of desired dimensions.StepsImport the torch library. Make sure you have it already installed.import torch Create a PyTorch tensor and print the tensor and the size of the tensor.t = torch.tensor([[1, 2], [3, 4], [5, 6]]) print("Tensor:", ... Read More

Tensor.expand() attribute is used to perform expand operation. It expands the Tensor to new dimensions along the singleton dimension.Expanding a tensor only creates a new view of the original tensor; it doesn't make a copy of the original tensor.If you set a particular dimension as -1, the tensor will not be expanded along this dimension.For example, if we have a tensor of size (3, 1), we can expand this tensor along the dimension of size 1.StepsTo expand a tensor, one could follow the steps given below −Import the torch library. Make sure you have already installed it.import torchDefine a tensor ... Read More

To create a tensor with gradients, we use an extra parameter "requires_grad = True" while creating a tensor.requires_grad is a flag that controls whether a tensor requires a gradient or not.Only floating point and complex dtype tensors can require gradients.If requires_grad is false, then the tensor is same as the tensor without the requires_grad parameter.Syntaxtorch.tensor(value, requires_grad = True)Parametersvalue – tensor data, user-defined or randomly generated.requires_grad – a flag, if True, the tensor is included in the gradient computation.OutputIt returns a tensor with requires_grad as True.StepsImport the required library. The required library is torch.Define a tensor with requires_grad = TrueDisplay the ... Read More

Element-wise remainder when a tensor is divided by other tensor is computed using the torch.remainder() method. We can also apply torch.fmod() to find the remainder.The difference between these two methods is that in torch.remainder(), when the sign of result is different than the sign of divisor, then the divisor is added to the result; whereas in torch.fmod(), it is not added.Syntaxtorch.remainder(input, other) torch.fmod(input, other)ParametersInput – It is a PyTorch tensor or scalar, the dividend.Other – It is also a PyTorch tensor or scalar, the divisor.OutputIt returns a tensor of element-wise remainder values.StepsImport the torch library.Define tensors, the dividend and the ... Read More