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Articles on Trending Technologies
Technical articles with clear explanations and examples
How to estimate the gradient of a function in one or more dimensions in PyTorch?
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 MoreHow to compute the inverse hyperbolic sine in PyTorch?
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 Moretorch.rsqrt() Method in Python PyTorch
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 MoreHow to compute the element-wise angle of the given input tensor in PyTorch?
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 MoreHow to compute bitwise AND, OR and NOT of given input tensors in PyTorch?
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 MoreHow to compute the inverse cosine and inverse hyperbolic cosine in PyTorch?
The torch.acos() method computes the inverse cosine of each element of an input tensor. It supports both real and complex-valued inputs. It supports any dimension of the input tensor. The elements of the input tensor must be in the range [-1, 1], as the inverse cosine function has its domain as [-1, 1].The torch.acosh() method computes the inverse hyperbolic cosine of each element of the input tensor. It also supports both real and complex-valued inputs of any dimension. The elements of the input tensor must be any number greater or equal to 1, as the inverse cosine function has its ...
Read MoreHow to create a tensor whose elements are sampled from a Poisson distribution in PyTorch?
To create a tensor whose elements are sampled from a Poisson distribution, we apply the torch.poisson() method. This method takes a tensor whose elements are rate parameters as input tensor. It returns a tensor whose elements are sampled from a Poisson distribution with the rate parameter.Syntaxtorch.poisson(rates)where the parameter rates is a torch tensor of rate parameters. Rate parameters are used to sample elements from a Poisson distribution.StepsWe could use the following steps to create a tensor whose elements are sampled from a Poisson distribution −Import the required library. In all the following examples, the required Python library is torch. Make ...
Read MoreHow to compute the Heaviside step function for each element in input in PyTorch?
To compute the Heaviside step function for each element in the input tensor, we use the torch.heaviside() method. It accepts two parameters − input and values. It returns a new tensor with a computed heaviside step function.The value of heaviside function is the same as values if input=0. The value of heaviside is zero if input is less than zero. The value of heaviside is 1 if input is greater than zero. It accepts torch tensors of any dimension. It is also called the unit step function.Syntaxtorch.heaviside(input, values)StepsWe could use the following steps to compute the Heaviside step function −Import ...
Read MoreHow to draw binary random numbers (0 or 1) from a Bernoulli distribution in PyTorch?
To draw binary random numbers (0 or 1) from a Bernoulli distribution, we apply the torch.bernoulli() method. The input to this method is a torch tensor containing the probabilities of drawing 1. These probabilities are used to draw binary random numbers (0 or 1).As the input tensor contains the probabilities, all the elements should be in the range [0, 1]. It returns a tensor whose elements (0 or 1) are randomly selected from a Bernoulli distribution with the input probabilities.Syntaxtorch.bernoulli(input)where, the parameter input is a torch tensor containing the probabilities of drawing 1. These probabilities are used to draw the ...
Read Moretorch.normal() Method in Python PyTorch
To create a tensor of random numbers drawn from separate normal distributions whose mean and std are given, we apply the torch.normal() method. This method takes two input parameters − mean and std.mean is a tensor with the mean of each output element’s normal distribution, andstd is a tensor with the standard deviation of each output element’s normal distribution.It returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are mean and std.Syntaxtorch.normal(mean, std)StepsWe could use the following steps to create a tensor of random numbers drawn from separate normal distributions −Import the required ...
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