Found 135 Articles for 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 More

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 More

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 More

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 More

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 ... Read More

With given absolute values and angles, we can construct a complex number in PyTorch using torch.polar() method. The absolute value and angles must be float or double. Both the absolute value and the angle must be of the same type.If abs is a float, then angle must also be float.If the inputs are torch.float32, then the constructed complex tensor must be torch.complex64.If the inputs are torch.float64, then the complex tensor must be torch.complex128.Syntaxtorch.polar(abs, angle)Parametersabs – The absolute length of the complex tensor.angle – The angle of the complex tensor.StepsWe could use the following steps to construct a complex tensor with ... Read More

The hessian() function computes the Hessian of a given function. The hessian() function can be accessed from the torch.autograd.functional module. The function whose Hessian is being computed takes a tensor as the input and returns a tuple of tensors or a tensor. The hessian() function returns a tensor with the Hessian values computed for a function with the given input.Syntaxtorch.autograd.functional.hessian(func, input)Parametersfunc − It's a Python function for which the Hessian is computed.input − It’s input to the function, func.StepsWe could use the following steps to compute the Hessian of a given function −Import the required library. In all the following ... Read More

The jacobian() function computes the Jacobian of a given function. The jacobian() function can be accessed from the torch.autograd.functional module. The function whose Jacobian is being computed takes a tensor as the input and returns a tuple of tensors or a tensor. The jacobian() function returns a tensor with Jacobian values computed for a function with the given input.Syntaxtorch.autograd.functional.jacobian(func, input)Parametersfunc − It's a Python function for which the Jacobian is computed.input − It’s the input to the function,  func.StepsWe could use the following steps to compute the Jacobian of a given function −Import the required library. In all the following ... Read More

To adjust the sharpness of an image, we apply adjust_sharpness(). It's one of the functional transforms provided by the torchvision.transforms module. adjust_sharpness() transformation accepts both PIL and tensor images.A tensor image is a PyTorch tensor with shape [C, H, W], where C is number of channels, H is image height, and W is image width. This transform also accepts a batch of tensor images. If the image is neither a PIL image nor tensor image, then we first convert it to a tensor image and then apply the adjust_sharpness(). The sharpness should be any non-negative number.Syntaxtorchvision.transforms.functional.adjust_sharpness(img, sharpness_factor)Parametersimg – Image of ... Read More

With given real and imaginary parts, we can construct a complex number in PyTorch using torch.complex() method. The real and imaginary parts must be float or double. Both the real and imaginary parts must be of the same type. If the real part is float, then the imaginary must also be float.If the inputs are torch.float32, then the constructed complex tensor must be torch.complex64.If the inputs are torch.float64, then the complex tensor must be torch.complex128.Syntaxtorch.complex(real, imag)Parametersreal and imag − Real and imaginary parts of the complex tensor. Both must be of the same dtype, float or double only.StepsWe could use the ... Read More