knitr::opts_chunk$set( collapse = TRUE, comment = "#>", eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1"), purl = FALSE )
In this article we describe various ways of creating
tensors in R.
You can create tensors from R objects using the
torch_tensor function. The
torch_tensor function takes an R vector, matrix or array and creates an equivalent
You can see a few examples below:
torch_tensor(c(1,2,3)) # conform to row-major indexing used in torch torch_tensor(matrix(1:10, ncol = 5, nrow = 2, byrow = TRUE)) torch_tensor(array(runif(12), dim = c(2, 2, 3)))
By default, we will create tensors in the
cpu device, converting their R datatype to the corresponding torch
Note currently, only numeric and boolean types are supported.
You can always modify
device when converting an R object to
a torch tensor. For example:
torch_tensor(1, dtype = torch_long()) torch_tensor(1, device = "cpu", dtype = torch_float64())
Other options available when creating a tensor are:
requires_grad: boolean indicating if you want
autogradto record operations on them for automatic differentiation.
pin_memory: – If set, the tensor returned would be allocated in pinned memory. Works only for CPU tensors.
These options are available for all functions that can be used to create new tensors, including the factory functions listed in the next section.
You can also use the
torch_* functions listed below to create torch tensors
using some algorithm.
For example, the
torch_randn function will create tensors using the normal
distribution with mean 0 and standard deviation 1. You can
... argument to pass the size of the dimensions. For example, the code below will
create a normally distributed tensor with shape 5x3.
x <- torch_randn(5, 3) x
Another example is
torch_ones, which creates a tensor filled with
x <- torch_ones(2, 4, dtype = torch_int64(), device = "cpu") x
Here is the full list of functions that can be used to bulk-create tensors in torch:
torch_arange: Returns a tensor with a sequence of integers,
torch_empty: Returns a tensor with uninitialized values,
torch_eye: Returns an identity matrix,
torch_full: Returns a tensor filled with a single value,
torch_linspace: Returns a tensor with values linearly spaced in some interval,
torch_logspace: Returns a tensor with values logarithmically spaced in some interval,
torch_ones: Returns a tensor filled with all ones,
torch_rand: Returns a tensor filled with values drawn from a uniform distribution on [0, 1).
torch_randint: Returns a tensor with integers randomly drawn from an interval,
torch_randn: Returns a tensor filled with values drawn from a unit normal distribution,
torch_randperm: Returns a tensor filled with a random permutation of integers in some interval,
torch_zeros: Returns a tensor filled with all zeros.
Once a tensor exists you can convert between
dtypes and move to a different
to method. For example:
x <- torch_tensor(1) y <- x$to(dtype = torch_int32()) x y
You can also copy a tensor to the GPU using:
x <- torch_tensor(1) y <- x$cuda())
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