# Creating tensors

    library(torch)

In this article we describe various ways of creating `torch` tensors in R.

## From R objects

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 `torch_tensor`.

You can see a few examples below:

    torch_tensor(c(1,2,3))
    #> torch_tensor
    #>  1
    #>  2
    #>  3
    #> [ CPUFloatType{3} ]

    # conform to row-major indexing used in torch
    torch_tensor(matrix(1:10, ncol = 5, nrow = 2, byrow = TRUE))
    #> torch_tensor
    #>   1   2   3   4   5
    #>   6   7   8   9  10
    #> [ CPULongType{2,5} ]
    torch_tensor(array(runif(12), dim = c(2, 2, 3)))
    #> torch_tensor
    #> (1,.,.) =
    #>   0.8004  0.7253  0.9551
    #>   0.5068  0.3267  0.8677
    #>
    #> (2,.,.) =
    #>   0.8755  0.1164  0.0132
    #>   0.6977  0.9201  0.9417
    #> [ CPUFloatType{2,2,3} ]

By default, we will create tensors in the `cpu` device, converting their R datatype to the corresponding torch `dtype`.

> Note currently, only numeric and boolean types are supported.

You can always modify `dtype` and `device` when converting an R object to a torch tensor. For example:

    torch_tensor(1, dtype = torch_long())
    #> torch_tensor
    #>  1
    #> [ CPULongType{1} ]
    torch_tensor(1, device = "cpu", dtype = torch_float64())
    #> torch_tensor
    #>  1
    #> [ CPUDoubleType{1} ]

Other options available when creating a tensor are:

- `requires_grad`: boolean indicating if you want `autograd` to 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.

## Using creation functions

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 use the `...` 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
    #> torch_tensor
    #> -0.1352 -0.3267 -0.4535
    #> -1.6530 -0.1420  0.5372
    #>  0.4575 -0.9364  0.1152
    #> -1.5834 -1.5565  0.8754
    #> -0.0757 -0.0130  0.5759
    #> [ CPUFloatType{5,3} ]

Another example is `torch_ones`, which creates a tensor filled with ones.

    x <- torch_ones(2, 4, dtype = torch_int64(), device = "cpu")
    x
    #> torch_tensor
    #>  1  1  1  1
    #>  1  1  1  1
    #> [ CPULongType{2,4} ]

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.

## Conversion

Once a tensor exists you can convert between `dtype`s and move to a different device with `to` method. For example:

    x <- torch_tensor(1)
    y <- x$to(dtype = torch_int32())
    x
    #> torch_tensor
    #>  1
    #> [ CPUFloatType{1} ]
    y
    #> torch_tensor
    #>  1
    #> [ CPUIntType{1} ]

You can also copy a tensor to the GPU using:

    x <- torch_tensor(1)
    y <- x$cuda())