Chapter 4 Converting tensors

Last update: Sun Oct 25 13:00:09 2020 -0500 (f5e8a1973)

library(rTorch)

4.1 Tensor to numpy array

This is a frequent operation. I have found that this is necessary when:

• a numpy function is not implemented in PyTorch
• We need to convert a tensor to R
• Perform a boolean operation that is not directly available in PyTorch

x <- torch$arange(1, 10)
y <- x^2

If we attempt to plot these two tensors we get an error:

plot(x, y)

#> Error in as.double(x): cannot coerce type 'environment' to vector of type 'double'

They need to be converted to numpy, and then to R (which happens in the background):

plot(x$numpy(), y$numpy())

4.2 numpy array to tensor

• Explain how transform a tensor back and forth to numpy.
• Why is this important?
• In what cases in this necessary?

p <- np$arange(1, 10)
class(p)

#> [1] "array"

(pt <- torch$as_tensor(p))

#> tensor([1., 2., 3., 4., 5., 6., 7., 8., 9.], dtype=torch.float64)

class(pt)

#> [1] "torch.Tensor"          "torch._C._TensorBase"  "python.builtin.object"

4.2.1 numpy array to R

This is mainly required for these reasons:

1. Create a data structure in R
2. Plot using r-base or ggplot2
3. Perform an analysis on parts of a tensor
4. Use R statistical functions that are not available in PyTorch

4.3 R objects to numpy objects

Given the R matrix \(m\):

m <- matrix(seq(1,10), nrow = 2)
m

#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    3    5    7    9
#> [2,]    2    4    6    8   10

We explicitly convert it to a numpy object with the function r_to_py():

(mp <- r_to_py(m))

#> [[ 1  3  5  7  9]
#>  [ 2  4  6  8 10]]

class(mp)

#> [1] "numpy.ndarray"         "python.builtin.object"

class(mp)

#> [1] "numpy.ndarray"         "python.builtin.object"