Shahid Akhtar Khan has Published 120 Answers

torch.argmax() Method in Python PyTorch

Shahid Akhtar Khan
Published on 27-Jan-2022 07:39:52
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. ... Read More

How to compute elementwise logical AND, OR and NOT of given input tensors in PyTorch?

Shahid Akhtar Khan
Published on 27-Jan-2022 07:33:01
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 ... Read More

How to estimate the gradient of a function in one or more dimensions in PyTorch?

Shahid Akhtar Khan
Published on 27-Jan-2022 07:24:55
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 ... Read More

How to compute the inverse hyperbolic sine in PyTorch?

Shahid Akhtar Khan
Published on 27-Jan-2022 07:17:39
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 ... Read More

torch.rsqrt() Method in Python PyTorch

Shahid Akhtar Khan
Published on 27-Jan-2022 07:12:49
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 ... Read More

How to compute the element-wise angle of the given input tensor in PyTorch?

Shahid Akhtar Khan
Published on 27-Jan-2022 07:08:23
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 ... Read More

How to compute bitwise AND, OR and NOT of given input tensors in PyTorch?

Shahid Akhtar Khan
Published on 27-Jan-2022 07:02:10
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 ... Read More

How to compute the inverse cosine and inverse hyperbolic cosine in PyTorch?

Shahid Akhtar Khan
Published on 27-Jan-2022 06:53:56
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 ... Read More

How to create a tensor whose elements are sampled from a Poisson distribution in PyTorch?

Shahid Akhtar Khan
Published on 27-Jan-2022 06:48:25
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 ... Read More

How to compute the Heaviside step function for each element in input in PyTorch?

Shahid Akhtar Khan
Published on 27-Jan-2022 06:32:30
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 ... Read More
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