PyTorch Random Number Generation Quiz

randn_like = Returns a tensor with the same size as input that is filled with random numbers from a normal distribution with mean 0 and variance 1. randperm = Returns a random permutation of integers from 0 to n - 1. torch.Tensor.bernoulli_() = In-place version of torch.bernoulli() torch.Tensor.cauchy_() = Numbers drawn from the Cauchy distribution

torch.Tensor.exponential_() = Numbers drawn from the exponential distribution torch.Tensor.geometric_() = Elements drawn from the geometric distribution torch.Tensor.log_normal_() = Samples from the log-normal distribution torch.Tensor.normal_() = In-place version of torch.normal()

torch.Tensor.random_() = Numbers sampled from the discrete uniform distribution torch.Tensor.uniform_() = Numbers sampled from the continuous uniform distribution quasirandom.SobolEngine = An engine for generating (scrambled) Sobol sequences torch.bernoulli() = An out-of-place version of torch.Tensor.bernoulli_()

<p>torch.cauchy() = An out-of-place version of torch.Tensor.cauchy_() torch.exponential() = An out-of-place version of torch.Tensor.exponential_() torch.geometric() = An out-of-place version of torch.Tensor.geometric_() torch.log_normal() = An out-of-place version of torch.Tensor.log_normal_()</p> Signup and view all the answers

<p>torch.normal() = An out-of-place version of torch.Tensor.normal_() torch.random() = An out-of-place version of torch.Tensor.random_() torch.uniform() = An out-of-place version of torch.Tensor.uniform_() torch.quasirandom() = An out-of-place version of quasirandom.SobolEngine</p> Signup and view all the answers

<p>randn_like = Returns a tensor with the same size as input that is filled with random numbers from a normal distribution with mean 0 and variance 1. randperm = Returns a random permutation of integers from 0 to n - 1. torch.Tensor.bernoulli_() = In-place version of torch.bernoulli() torch.Tensor.cauchy_() = Numbers drawn from the Cauchy distribution</p> Signup and view all the answers

<p>torch.Tensor.exponential_() = Numbers drawn from the exponential distribution torch.Tensor.geometric_() = Elements drawn from the geometric distribution torch.Tensor.log_normal_() = Samples from the log-normal distribution torch.Tensor.normal_() = In-place version of torch.normal()</p> Signup and view all the answers

<p>torch.Tensor.random_() = Numbers sampled from the discrete uniform distribution torch.Tensor.uniform_() = Numbers sampled from the continuous uniform distribution quasirandom.SobolEngine = An engine for generating (scrambled) Sobol sequences torch.bernoulli() = An out-of-place version of torch.Tensor.bernoulli_()</p> Signup and view all the answers

<p>torch.cauchy() = An out-of-place version of torch.Tensor.cauchy_() torch.exponential() = An out-of-place version of torch.Tensor.exponential_() torch.geometric() = An out-of-place version of torch.Tensor.geometric_() torch.log_normal() = An out-of-place version of torch.Tensor.log_normal_()</p> Signup and view all the answers

<p>torch.normal() = An out-of-place version of torch.Tensor.normal_() torch.random() = An out-of-place version of torch.Tensor.random_() torch.uniform() = An out-of-place version of torch.Tensor.uniform_() torch.quasirandom() = An out-of-place version of quasirandom.SobolEngine</p> Signup and view all the answers

<p>manual_seed = Sets the seed for generating random numbers get_rng_state = Returns the random number generator state as a torch.ByteTensor set_rng_state = Sets the random number generator state bernoulli = Draws binary random numbers (0 or 1) from a Bernoulli distribution</p> Signup and view all the answers

<p>multinomial = Returns a tensor where each row contains num_samples indices sampled from the multinomial probability distribution located in the corresponding row of tensor input normal = Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given poisson = Returns a tensor of the same size as input with each element sampled from a Poisson distribution with rate parameter given by the corresponding element in input rand = Returns a tensor filled with random numbers from a uniform distribution on the interval $[ 0 , 1 )$</p> Signup and view all the answers

<p>rand_like = Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval $[ 0 , 1 )$ randint = Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive) randint_like = Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly between low (inclusive) and high (exclusive) randn = Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution)</p> Signup and view all the answers

<p>manual_seed = Sets the seed for generating random numbers torch.default_generator = Returns the default CPU torch.Generator bernoulli = Draws binary random numbers (0 or 1) from a Bernoulli distribution poisson = Returns a tensor of the same size as input with each element sampled from a Poisson distribution with rate parameter given by the corresponding element in input</p> Signup and view all the answers

<p>rand_like = Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval $[ 0 , 1 )$ randint_like = Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly between low (inclusive) and high (exclusive) normal = Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given rand = Returns a tensor filled with random numbers from a uniform distribution on the interval $[ 0 , 1 )$</p> Signup and view all the answers

<p>manual_seed = Sets the seed for generating random numbers get_rng_state = Returns the random number generator state as a torch.ByteTensor set_rng_state = Sets the random number generator state multinomial = Returns a tensor where each row contains num_samples indices sampled from the multinomial probability distribution located in the corresponding row of tensor input</p> Signup and view all the answers

<p>randn = Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution) randint = Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive) poisson = Returns a tensor of the same size as input with each element sampled from a Poisson distribution with rate parameter given by the corresponding element in input bernoulli = Draws binary random numbers (0 or 1) from a Bernoulli distribution</p> Signup and view all the answers

<p>torch.default_generator = Returns the default CPU torch.Generator normal = Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given randint_like = Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly between low (inclusive) and high (exclusive) rand_like = Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval $[ 0 , 1 )$</p> Signup and view all the answers

<p>randn = Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution) randint = Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive) multinomial = Returns a tensor where each row contains num_samples indices sampled from the multinomial probability distribution located in the corresponding row of tensor input get_rng_state = Returns the random number generator state as a torch.ByteTensor</p> Signup and view all the answers

<p>set_rng_state = Sets the random number generator state bernoulli = Draws binary random numbers (0 or 1) from a Bernoulli distribution poisson = Returns a tensor of the same size as input with each element sampled from a Poisson distribution with rate parameter given by the corresponding element in input torch.default_generator = Returns the default CPU torch.Generator</p> Signup and view all the answers

<p>randn_like = Returns a tensor with the same size as input that is filled with random numbers from a normal distribution with mean 0 and variance 1 randperm = Returns a random permutation of integers from 0 to n - 1 torch.Tensor.bernoulli_() = In-place version of torch.bernoulli() torch.Tensor.cauchy_() = Numbers drawn from the Cauchy distribution</p> Signup and view all the answers

<p>torch.Tensor.exponential_() = Numbers drawn from the exponential distribution torch.Tensor.geometric_() = Elements drawn from the geometric distribution torch.Tensor.log_normal_() = Samples from the log-normal distribution torch.Tensor.normal_() = In-place version of torch.normal()</p> Signup and view all the answers

<p>torch.Tensor.random_() = Numbers sampled from the discrete uniform distribution torch.Tensor.uniform_() = Numbers sampled from the continuous uniform distribution quasirandom.SobolEngine = An engine for generating (scrambled) Sobol sequences torch.Tensor.bernoulli_() = In-place version of torch.bernoulli()</p> Signup and view all the answers

<p>torch.Tensor.cauchy_() = Numbers drawn from the Cauchy distribution torch.Tensor.exponential_() = Numbers drawn from the exponential distribution torch.Tensor.geometric_() = Elements drawn from the geometric distribution torch.Tensor.log_normal_() = Samples from the log-normal distribution</p> Signup and view all the answers

<p>torch.Tensor.normal_() = In-place version of torch.normal() torch.Tensor.random_() = Numbers sampled from the discrete uniform distribution torch.Tensor.uniform_() = Numbers sampled from the continuous uniform distribution quasirandom.SobolEngine = An engine for generating (scrambled) Sobol sequences</p> Signup and view all the answers

<p>randn_like = Returns a tensor with the same size as input that is filled with random numbers from a normal distribution with mean 0 and variance 1 randperm = Returns a random permutation of integers from 0 to n - 1 torch.Tensor.bernoulli_() = In-place version of torch.bernoulli() torch.Tensor.cauchy_() = Numbers drawn from the Cauchy distribution</p> Signup and view all the answers

<p>torch.Tensor.exponential_() = Numbers drawn from the exponential distribution torch.Tensor.geometric_() = Elements drawn from the geometric distribution torch.Tensor.log_normal_() = Samples from the log-normal distribution torch.Tensor.normal_() = In-place version of torch.normal()</p> Signup and view all the answers

<p>torch.Tensor.random_() = Numbers sampled from the discrete uniform distribution torch.Tensor.uniform_() = Numbers sampled from the continuous uniform distribution quasirandom.SobolEngine = An engine for generating (scrambled) Sobol sequences torch.Tensor.bernoulli_() = In-place version of torch.bernoulli()</p> Signup and view all the answers

<p>torch.Tensor.cauchy_() = Numbers drawn from the Cauchy distribution torch.Tensor.exponential_() = Numbers drawn from the exponential distribution torch.Tensor.geometric_() = Elements drawn from the geometric distribution torch.Tensor.log_normal_() = Samples from the log-normal distribution</p> Signup and view all the answers

<p>torch.Tensor.normal_() = In-place version of torch.normal() torch.Tensor.random_() = Numbers sampled from the discrete uniform distribution torch.Tensor.uniform_() = Numbers sampled from the continuous uniform distribution quasirandom.SobolEngine = An engine for generating (scrambled) Sobol sequences</p> Signup and view all the answers

<p>manual_seed = Sets the seed for generating random numbers get_rng_state = Returns the random number generator state as a torch.ByteTensor bernoulli = Draws binary random numbers (0 or 1) from a Bernoulli distribution randint = Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive)</p> Signup and view all the answers

<p>rand_like = Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval $[0,1)$ poisson = Returns a tensor of the same size as input with each element sampled from a Poisson distribution with rate parameter given by the corresponding element in input normal = Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given multinomial = Returns a tensor where each row contains num_samples indices sampled from the multinomial probability distribution located in the corresponding row of tensor input</p> Signup and view all the answers

<p>randn = Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution) randint_like = Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly between low (inclusive) and high (exclusive) torch.default_generator = Returns the default CPU torch.Generator set_rng_state = Sets the random number generator state</p> Signup and view all the answers

<p>rand = Returns a tensor filled with random numbers from a uniform distribution on the interval $[0,1)$ initial_seed = Returns the initial seed for generating random numbers as a Python long random sampling seed = Sets the seed for generating random numbers to a non-deterministic random number bernoulli = Draws binary random numbers (0 or 1) from a Bernoulli distribution</p> Signup and view all the answers

<p>manual_seed = Sets the seed for generating random numbers normal = Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given randint_like = Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly between low (inclusive) and high (exclusive) rand_like = Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval $[0,1)$</p> Signup and view all the answers

<p>poisson = Returns a tensor of the same size as input with each element sampled from a Poisson distribution with rate parameter given by the corresponding element in input randn = Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution) multinomial = Returns a tensor where each row contains num_samples indices sampled from the multinomial probability distribution located in the corresponding row of tensor input torch.default_generator = Returns the default CPU torch.Generator</p> Signup and view all the answers

<p>set_rng_state = Sets the random number generator state initial_seed = Returns the initial seed for generating random numbers as a Python long rand = Returns a tensor filled with random numbers from a uniform distribution on the interval $[0,1)$ get_rng_state = Returns the random number generator state as a torch.ByteTensor</p> Signup and view all the answers

<p>bernoulli = Draws binary random numbers (0 or 1) from a Bernoulli distribution randint = Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive) random sampling seed = Sets the seed for generating random numbers to a non-deterministic random number manual_seed = Sets the seed for generating random numbers</p> Signup and view all the answers

<p>randint_like = Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly between low (inclusive) and high (exclusive) normal = Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given poisson = Returns a tensor of the same size as input with each element sampled from a Poisson distribution with rate parameter given by the corresponding element in input multinomial = Returns a tensor where each row contains num_samples indices sampled from the multinomial probability distribution located in the corresponding row of tensor input</p> Signup and view all the answers

<p>rand_like = Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval $[0,1)$ torch.default_generator = Returns the default CPU torch.Generator set_rng_state = Sets the random number generator state randn = Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution)</p> Signup and view all the answers

<p>torch.Tensor.bernoulli_() = In-place version of torch.bernoulli() torch.Tensor.cauchy_() = Numbers drawn from the Cauchy distribution torch.Tensor.exponential_() = Numbers drawn from the exponential distribution torch.Tensor.geometric_() = Elements drawn from the geometric distribution</p> Signup and view all the answers

<p>torch.Tensor.log_normal_() = Samples from the log-normal distribution torch.Tensor.normal_() = In-place version of torch.normal() torch.Tensor.random_() = Numbers sampled from the discrete uniform distribution torch.Tensor.uniform_() = Numbers sampled from the continuous uniform distribution</p> Signup and view all the answers

<p>randn_like = Returns a tensor with the same size as input that is filled with random numbers from a normal distribution with mean 0 and variance 1. randperm = Returns a random permutation of integers from 0 to n - 1. torch.Tensor.bernoulli_() = In-place version of torch.bernoulli() torch.Tensor.cauchy_() = Numbers drawn from the Cauchy distribution</p> Signup and view all the answers

<p>torch.Tensor.exponential_() = Numbers drawn from the exponential distribution torch.Tensor.geometric_() = Elements drawn from the geometric distribution torch.Tensor.log_normal_() = Samples from the log-normal distribution torch.Tensor.normal_() = In-place version of torch.normal()</p> Signup and view all the answers

<p>torch.Tensor.random_() = Numbers sampled from the discrete uniform distribution torch.Tensor.uniform_() = Numbers sampled from the continuous uniform distribution quasirandom.SobolEngine = An engine for generating (scrambled) Sobol sequences randn_like = Returns a tensor with the same size as input that is filled with random numbers from a normal distribution with mean 0 and variance 1.</p> Signup and view all the answers

<p>randperm = Returns a random permutation of integers from 0 to n - 1. torch.Tensor.bernoulli_() = In-place version of torch.bernoulli() torch.Tensor.cauchy_() = Numbers drawn from the Cauchy distribution torch.Tensor.exponential_() = Numbers drawn from the exponential distribution</p> Signup and view all the answers

<p>torch.Tensor.geometric_() = Elements drawn from the geometric distribution torch.Tensor.log_normal_() = Samples from the log-normal distribution torch.Tensor.normal_() = In-place version of torch.normal() torch.Tensor.random_() = Numbers sampled from the discrete uniform distribution</p> Signup and view all the answers

<p>torch.Tensor.uniform_() = Numbers sampled from the continuous uniform distribution quasirandom.SobolEngine = An engine for generating (scrambled) Sobol sequences randn_like = Returns a tensor with the same size as input that is filled with random numbers from a normal distribution with mean 0 and variance 1. randperm = Returns a random permutation of integers from 0 to n - 1.</p> Signup and view all the answers

<p>torch.Tensor.bernoulli_() = In-place version of torch.bernoulli() torch.Tensor.cauchy_() = Numbers drawn from the Cauchy distribution torch.Tensor.exponential_() = Numbers drawn from the exponential distribution torch.Tensor.geometric_() = Elements drawn from the geometric distribution</p> Signup and view all the answers

<p>torch.Tensor.log_normal_() = Samples from the log-normal distribution torch.Tensor.normal_() = In-place version of torch.normal() torch.Tensor.random_() = Numbers sampled from the discrete uniform distribution torch.Tensor.uniform_() = Numbers sampled from the continuous uniform distribution</p> Signup and view all the answers

<p>manual_seed = Sets the seed for generating random numbers get_rng_state = Returns the random number generator state as a torch.ByteTensor bernoulli = Draws binary random numbers (0 or 1) from a Bernoulli distribution normal = Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given</p> Signup and view all the answers

<p>poisson = Returns a tensor of the same size as input with each element sampled from a Poisson distribution rand = Returns a tensor filled with random numbers from a uniform distribution on the interval $[0 , 1)$ randint = Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive) randn = Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1</p> Signup and view all the answers

<p>randint_like = Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly between low (inclusive) and high (exclusive) rand_like = Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval $[0 , 1)$ set_rng_state = Sets the random number generator state initial_seed = Returns the initial seed for generating random numbers as a Python long</p> Signup and view all the answers

<p>torch.default_generator = Returns the default CPU torch.Generator multinomial = Returns a tensor where each row contains num_samples indices sampled from the multinomial probability distribution located in the corresponding row of tensor input manual_seed = Sets the seed for generating random numbers get_rng_state = Returns the random number generator state as a torch.ByteTensor</p> Signup and view all the answers

<p>bernoulli = Draws binary random numbers (0 or 1) from a Bernoulli distribution normal = Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given poisson = Returns a tensor of the same size as input with each element sampled from a Poisson distribution rand = Returns a tensor filled with random numbers from a uniform distribution on the interval $[0 , 1)$</p> Signup and view all the answers

<p>randint = Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive) randn = Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 randint_like = Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly between low (inclusive) and high (exclusive) rand_like = Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval $[0 , 1)$</p> Signup and view all the answers

<p>set_rng_state = Sets the random number generator state initial_seed = Returns the initial seed for generating random numbers as a Python long torch.default_generator = Returns the default CPU torch.Generator multinomial = Returns a tensor where each row contains num_samples indices sampled from the multinomial probability distribution located in the corresponding row of tensor input</p> Signup and view all the answers

<p>manual_seed = Sets the seed for generating random numbers get_rng_state = Returns the random number generator state as a torch.ByteTensor bernoulli = Draws binary random numbers (0 or 1) from a Bernoulli distribution normal = Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given</p> Signup and view all the answers

<p>poisson = Returns a tensor of the same size as input with each element sampled from a Poisson distribution rand = Returns a tensor filled with random numbers from a uniform distribution on the interval $[0 , 1)$ randint = Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive) randn = Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1</p> Signup and view all the answers

<p>randint_like = Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly between low (inclusive) and high (exclusive) rand_like = Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval $[0 , 1)$ set_rng_state = Sets the random number generator state initial_seed = Returns the initial seed for generating random numbers as a Python long</p> Signup and view all the answers