Found 135 Articles for PyTorch

Pytorch Lightning which is a very powerful framework simplifies the process of training the neural networks. As we know neural networks have become a fundamental tool for solving problems related to machine larning, howevere training neural networks have become a necessary yet challenging task which requires careful management of models, data and training loops that is why we use PyTorch Lightning In this article, we will explore what is PyTorch Lightning, how to train neural networks using PyTorch Lightning, its benefits, and various techniques to enhance the training process. What is PyTorch Lightning? PyTorch Lightning is a user-friendly Python ... Read More

When it comes to deep learning frameworks, PyTorch and TensorFlow are two popular choices. Both have gained significant traction in the field and are widely used by researchers, developers, and data scientists. In this article, we will compare PyTorch and TensorFlow to help you understand their similarities, differences, and use cases. PyTorch: A Deep Dive PyTorch is an open-source machine learning library that provides a dynamic computational graph and intuitive interface for building and training neural networks. It offers flexibility and customization, allowing users to easily define and modify models. PyTorch's strong support for GPU acceleration enables efficient training ... Read More

What is Pytorch? Pytorch is a Python - based framework in machine learning primarily used for scientific computations and to build deep learning models. A Tensor is a popular concept in Machine learning and Data science. Tensors are a generalized term for matrices and vectors. In simple words, Tensors can be anything - a n-dimensional array, a vector, or a matrix. In this article, we will be looking at the multiple methods used to create Tensors in Pytorch. Method of Creating Tensors Tensors can be created in multiple methods. Some of the important methods are listed below. ... Read More

To find the indices of the maximum value of the elements in an input tensor, we can apply the torch.argmax() function. It returns the indices only, not the element value. If the input tensor has multiple maximal values, then the function will return the index of the first maximal element. We can apply the torch.argmax() function to compute the indices of the maximum values of a tensor across a dimension..Syntaxtorch.argmax(input)StepsWe could use the following steps to find the indices of the maximum values of all elements in the input tensor −Import the required library. In all the following examples, the ... Read More

To compute elementwise logical AND of given input tensors we apply torch.logical_and(). It takes two input tensors and computes the logical AND element wise. The zeros in the tensors are treated as False and non-zeros as True. The input tensors may be of any dimension.The torch.logical_or() function computes elementwise logical OR of the given input tensors. It also takes two input tensors and outputs a tensor with True or False. As same in logical AND zeros are treated as False and non-zeros are treated as True.The input tensors may be of any dimension.To compute the elementwise NOT of a given ... Read More

To estimate the gradient of a function, we can apply the torch.gradient() function. This function estimates the gradient using the second-order accurate central differences method. We can estimate the gradient in one or more dimensions. The function of which the gradient is to be estimated may be defined on a real or complex domain. In the process of estimating the gradients, the gradient is estimated by estimating each partial derivative of the function independently.Syntaxtorch.gradient(values)where the parameter values is the tensor that represents the values of the function.StepsWe could use the following steps to estimate the gradient of a function −Import ... Read More

The torch.asinh() method computes the inverse hyperbolic sine of each element of the input tensor. It supports both real and complex-valued inputs. It supports any dimension of the input tensor.Syntaxtorch.asinh(input)where input is the input tensor.OutputIt returns a tensor inverse hyperbolic sine of each element.StepsTo compute the inverse hyperbolic sine of each element in the input tensor, you could follow the steps given below −Import the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.import torchCreate a torch tensor and print it.input = torch.randn(3, 4) print("Input Tensor:", input)Compute the inverse ... Read More

The torch.rsqrt() method computes the reciprocal of square-root of each element of the input tensor. It supports both real and complex-valued inputs. If an element in the input tensor is zero, then the corresponding element in the output tensor is NaN.Syntaxtorch.rsqrt(input)Parametersinput – Input tensorOutputIt returns a tensor with reciprocal of square-root.StepsImport the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.import torchCreate a torch tensor and print it.input = torch.randn(3, 4) print("Input Tensor:", input)Compute the reciprocal of the square-root of each element in the input tensor using torch.rsqrt(input). Here ... Read More

To compute the elementwise angle of the given input tensor, we apply torch.angle(). It takes an input tensor and returns a tensor with angle in radian computed element wise. To convert the angles into the degree we multiply the angle in radian by 180/np.pi. It supports both real and complex-valued tensors.Syntaxtorch.angle(input)StepsTo compute the elementwise angle, you could follow the steps given below −Import the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.import torchDefine torch tensors and print them.input = torch.tensor([1 + 1j, -1 -4j, 3-2j])Compute torch.angle(input). It is ... Read More

To compute bitwise AND of given input tensors we apply torch.bitwise_and(). The input tensors must be of integral or Boolean types. For bool tensors, it computes the logical AND.To compute bitwise NOT of a given input tensor we apply torch.bitwise_not() method. The input tensors must be of integral or Boolean types. For bool tensors, it computes the logical OR.To compute the bitwise NOT of a given input tensor we apply torch.bitwise_not() method. The input tensor must be of integral or Boolean types. For bool tensors, it computes the logical NOT.Syntaxtorch.bitwise_and(input1, input2) torch.bitwise_or(input1, input2) torch.bitwise_not(input)StepsImport the required library. In all the ... Read More