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The goal of rTorch is providing an R wrapper to PyTorch. rTorch provides
all the functionality of PyTorch plus all the features that R provides.
We have borrowed some ideas and code used in R tensorflow to implement
rTorch.
Besides the module torch, which directly provides
PyTorch methods, classes and functions, the package also
provides the modules numpy as a method called
np, and torchvision, as well. The dollar sign
$ after the module will provide you access to all their
sub-objects. Example:
tv <- rTorch::torchvision
tv
#> Module(torchvision)
np <- rTorch::np
np
#> Module(numpy)
torch_module <- rTorch::torch
torch_module
#> Module(torch)To lighten up the time in building this rTorch package,
we moved the examples that use tensor operations and neural
networks to separate repositories. There are two sets of
examples:
rTorch is available via CRAN and from GitHub.
Install from CRAN using install.packages("rTorch") from
the R console, or from RStudio using Tools,
Install Packages from the menu.
For the latest version install from GitHub. Install
rTorch with:
devtools::install_github("f0nzie/rTorch")
Installing from GitHub gives you the flexibility of experimenting
with the latest development version of rTorch. For
instance, to install rTorch from the develop
branch:
devtools::install_github("f0nzie/rTorch", ref="develop")
or clone with Git with:
git clone https://github.com/f0nzie/rTorch.gitThere are five major type of Tensors in PyTorch: * Byte * Float * Double * Long * Bool
library(rTorch)
byte_tensor <- torch$ByteTensor(3L, 3L)
float_tensor <- torch$FloatTensor(3L, 3L)
double_tensor <- torch$DoubleTensor(3L, 3L)
long_tensor <- torch$LongTensor(3L, 3L)
bool_tensor <- torch$BoolTensor(5L, 5L)
byte_tensor
#> tensor([[58, 0, 0],
#> [ 0, 0, 0],
#> [ 0, 0, 0]], dtype=torch.uint8)
float_tensor
#> tensor([[0., 0., 0.],
#> [0., 0., 0.],
#> [0., 0., 0.]])
double_tensor
#> tensor([[6.9259e-310, 6.9259e-310, 1.2095e-312],
#> [1.2697e-321, 6.9259e-310, 4.6888e-310],
#> [ 0.0000e+00, 0.0000e+00, 0.0000e+00]], dtype=torch.float64)
long_tensor
#> tensor([[140182629985840, 140182629985840, 0],
#> [ 94902887985680, 0, 0],
#> [ 0, 0, 0]])
bool_tensor
#> tensor([[False, False, False, False, False],
#> [False, False, False, False, False],
#> [False, False, False, False, False],
#> [False, False, False, False, False],
#> [False, False, False, False, False]])A 4D tensor like in MNIST hand-written digits
recognition dataset:
mnist_4d <- torch$FloatTensor(60000L, 3L, 28L, 28L)
# size
mnist_4d$size()
#> torch.Size([60000, 3, 28, 28])
# length
length(mnist_4d)
#> [1] 141120000
# shape, like in numpy
mnist_4d$shape
#> torch.Size([60000, 3, 28, 28])
# number of elements
mnist_4d$numel()
#> [1] 141120000A 3D tensor:
ft3d <- torch$FloatTensor(4L, 3L, 2L)
ft3d
#> tensor([[[0., 0.],
#> [0., 0.],
#> [0., 0.]],
#>
#> [[0., 0.],
#> [0., 0.],
#> [0., 0.]],
#>
#> [[0., 0.],
#> [0., 0.],
#> [0., 0.]],
#>
#> [[0., 0.],
#> [0., 0.],
#> [0., 0.]]])# get first element in a tensor
ft3d[1, 1, 1]
#> tensor(0.)# create a tensor with a value
torch$full(list(2L, 3L), 3.141592)
#> tensor([[3.1416, 3.1416, 3.1416],
#> [3.1416, 3.1416, 3.1416]])# 3x5 matrix uniformly distributed between 0 and 1
mat0 <- torch$FloatTensor(3L, 5L)$uniform_(0L, 1L)
# fill a 3x5 matrix with 0.1
mat1 <- torch$FloatTensor(3L, 5L)$uniform_(0.1, 0.1)
# a vector with all ones
mat2 <- torch$FloatTensor(5L)$uniform_(1, 1)# add two tensors
mat0 + mat1
#> tensor([[1.0472, 0.3601, 0.8105, 0.2573, 0.9311],
#> [0.8394, 0.1631, 0.5836, 0.2908, 0.1806],
#> [1.0795, 0.7679, 0.7024, 0.1542, 1.0788]])# add three tensors
mat0 + mat1 + mat2
#> tensor([[2.0472, 1.3601, 1.8105, 1.2573, 1.9311],
#> [1.8394, 1.1631, 1.5836, 1.2908, 1.1806],
#> [2.0795, 1.7679, 1.7024, 1.1542, 2.0788]])# PyTorch add two tensors using add() function
x = torch$rand(5L, 4L)
y = torch$rand(5L, 4L)
print(x$add(y))
#> tensor([[1.3989, 1.0835, 0.5394, 1.1483],
#> [1.3036, 0.3943, 1.2853, 1.2172],
#> [1.0630, 1.6868, 1.1131, 1.5882],
#> [0.5632, 0.6763, 1.1735, 1.5093],
#> [1.5244, 0.9094, 1.4205, 0.5644]])
print(x + y)
#> tensor([[1.3989, 1.0835, 0.5394, 1.1483],
#> [1.3036, 0.3943, 1.2853, 1.2172],
#> [1.0630, 1.6868, 1.1131, 1.5882],
#> [0.5632, 0.6763, 1.1735, 1.5093],
#> [1.5244, 0.9094, 1.4205, 0.5644]])# add an element of a tensor to another tensor
mat1[1, 1] + mat2
#> tensor([1.1000, 1.1000, 1.1000, 1.1000, 1.1000])mat1
#> tensor([[0.1000, 0.1000, 0.1000, 0.1000, 0.1000],
#> [0.1000, 0.1000, 0.1000, 0.1000, 0.1000],
#> [0.1000, 0.1000, 0.1000, 0.1000, 0.1000]])# extract part of the tensor
indices <- torch$tensor(c(0L, 3L))
torch$index_select(mat1, 1L, indices) # rows = 0; columns = 1
#> tensor([[0.1000, 0.1000],
#> [0.1000, 0.1000],
#> [0.1000, 0.1000]])# add a scalar to a tensor
mat0 + 0.1
#> tensor([[1.0472, 0.3601, 0.8105, 0.2573, 0.9311],
#> [0.8394, 0.1631, 0.5836, 0.2908, 0.1806],
#> [1.0795, 0.7679, 0.7024, 0.1542, 1.0788]])# Multiply tensor by scalar
tensor = torch$ones(4L, dtype=torch$float64)
scalar = np$float64(4.321)
message("a numpy scalar: ", scalar)
#> a numpy scalar: 4.321
message("a PyTorch scalar: ", torch$scalar_tensor(scalar))
#> a PyTorch scalar: tensor(4.3210)
message("\nResult")
#>
#> Result
(prod = torch$mul(tensor, torch$scalar_tensor(scalar)))
#> tensor([4.3210, 4.3210, 4.3210, 4.3210], dtype=torch.float64)# short version using generics
(prod = tensor * scalar)
#> tensor([4.3210, 4.3210, 4.3210, 4.3210], dtype=torch.float64)t1 = torch$tensor(c(1, 2))
t2 = torch$tensor(c(3, 2))
t1
#> tensor([1., 2.])
t2
#> tensor([3., 2.])t1 * t2
#> tensor([3., 4.])t1 = torch$tensor(list(
c(1, 2, 3),
c(1, 2, 3)
))
t2 = torch$tensor(list(
c(1, 2),
c(1, 2),
c(1, 2)
))
t1
#> tensor([[1., 2., 3.],
#> [1., 2., 3.]])
t2
#> tensor([[1., 2.],
#> [1., 2.],
#> [1., 2.]])torch$mm(t1, t2)
#> tensor([[ 6., 12.],
#> [ 6., 12.]])t1 = torch$tensor(c(1, 2))
t2 = torch$tensor(c(3, 2))
t1
#> tensor([1., 2.])
t2
#> tensor([3., 2.])# dot product of two vectors
torch$dot(t1, t2)
#> tensor(7.)# Dot product of 1D tensors is a scalar
p <- torch$Tensor(list(4L, 2L))
q <- torch$Tensor(list(3L, 1L))
(r = torch$dot(p, q)) # 14
#> tensor(14.)
(r <- p %.*% q)
#> tensor(14.)# torch$dot product will work for vectors not matrices
t1 = torch$tensor(list(
c(1, 2, 3),
c(1, 2, 3)
))
t2 = torch$tensor(list(
c(1, 2),
c(1, 2),
c(1, 2)
))
t1$shape
#> torch.Size([2, 3])
t2$shape
#> torch.Size([3, 2])# RuntimeError: 1D tensors expected, got 2D, 2D tensors
torch$dot(t1, t2)The number of columns of the first matrix must be equal to the number of rows of the second matrix.
# for the dot product of nD tensors we use torch$mm()
t1 = torch$tensor(list(
c(1, 2, 3),
c(1, 2, 3)
))
t2 = torch$tensor(list(
c(1, 2),
c(1, 2),
c(1, 2)
))
torch$mm(t1, t2)
#> tensor([[ 6., 12.],
#> [ 6., 12.]])torch$mm(t2, t1)
#> tensor([[3., 6., 9.],
#> [3., 6., 9.],
#> [3., 6., 9.]])# for the dot product of 2D tensors we use torch$mm()
t1 = torch$arange(1, 11)$view(c(2L,5L))
t2 = torch$arange(11, 21)$view(c(5L,2L))
t1
#> tensor([[ 1., 2., 3., 4., 5.],
#> [ 6., 7., 8., 9., 10.]])
t2
#> tensor([[11., 12.],
#> [13., 14.],
#> [15., 16.],
#> [17., 18.],
#> [19., 20.]])# result
torch$mm(t1, t2)
#> tensor([[245., 260.],
#> [620., 660.]])# 1D tensor
t1 = torch$tensor(c(1, 2))
t2 = torch$tensor(c(3, 2))
torch$matmul(t1, t2)
#> tensor(7.)# 2D tensor
t1 = torch$tensor(list(
c(1, 2, 3),
c(1, 2, 3)
))
t2 = torch$tensor(list(
c(1, 2),
c(1, 2),
c(1, 2)
))
torch$matmul(t1, t2)
#> tensor([[ 6., 12.],
#> [ 6., 12.]])# for the dot product of 3D tensors we use torch$matmul()
t1 = torch$arange(1, 13)$view(c(2L, 2L, 3L)) # number of columns = 2
t2 = torch$arange(0, 18)$view(c(2L, 3L, 3L)) # number of rows = 2
t1
#> tensor([[[ 1., 2., 3.],
#> [ 4., 5., 6.]],
#>
#> [[ 7., 8., 9.],
#> [10., 11., 12.]]])
t2
#> tensor([[[ 0., 1., 2.],
#> [ 3., 4., 5.],
#> [ 6., 7., 8.]],
#>
#> [[ 9., 10., 11.],
#> [12., 13., 14.],
#> [15., 16., 17.]]])
message("result")
#> result
torch$matmul(t1, t2)
#> tensor([[[ 24., 30., 36.],
#> [ 51., 66., 81.]],
#>
#> [[294., 318., 342.],
#> [402., 435., 468.]]])t1 = torch$arange(1, 13)$view(c(3L, 2L, 2L)) # number of columns = 3
t2 = torch$arange(0, 12)$view(c(3L, 2L, 2L)) # number of rows = 3
t1
#> tensor([[[ 1., 2.],
#> [ 3., 4.]],
#>
#> [[ 5., 6.],
#> [ 7., 8.]],
#>
#> [[ 9., 10.],
#> [11., 12.]]])
t2
#> tensor([[[ 0., 1.],
#> [ 2., 3.]],
#>
#> [[ 4., 5.],
#> [ 6., 7.]],
#>
#> [[ 8., 9.],
#> [10., 11.]]])
message("result")
#> result
torch$matmul(t1, t2)
#> tensor([[[ 4., 7.],
#> [ 8., 15.]],
#>
#> [[ 56., 67.],
#> [ 76., 91.]],
#>
#> [[172., 191.],
#> [208., 231.]]])m1 = torch$ones(3L, 5L)
m2 = torch$ones(3L, 5L)
v1 = torch$ones(3L)
# Cross product
# Size 3x5
(r = torch$cross(m1, m2))
#> tensor([[0., 0., 0., 0., 0.],
#> [0., 0., 0., 0., 0.],
#> [0., 0., 0., 0., 0.]])numpy has been made available as a module inside
rTorch. We could call functions from numpy
refrerring to it as np$any_function. Examples:
# a 2D numpy array
syn0 <- np$random$rand(3L, 5L)
syn0
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.1524218 0.7769113 0.6153864 0.00220404 0.78412198
#> [2,] 0.4959399 0.7230621 0.9840282 0.64843544 0.06556167
#> [3,] 0.5931231 0.6513373 0.4399219 0.57722973 0.94843503# numpy arrays of zeros
syn1 <- np$zeros(c(5L, 10L))
syn1
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,] 0 0 0 0 0 0 0 0 0 0
#> [2,] 0 0 0 0 0 0 0 0 0 0
#> [3,] 0 0 0 0 0 0 0 0 0 0
#> [4,] 0 0 0 0 0 0 0 0 0 0
#> [5,] 0 0 0 0 0 0 0 0 0 0# add a scalar to a numpy array
syn1 = syn1 + 0.1
syn1
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
#> [2,] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
#> [3,] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
#> [4,] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
#> [5,] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1# in numpy a multidimensional array needs to be defined with a tuple
# From R we use a vector to refer to a tuple in Python
l1 <- np$ones(c(5L, 5L))
l1
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 1 1 1 1 1
#> [2,] 1 1 1 1 1
#> [3,] 1 1 1 1 1
#> [4,] 1 1 1 1 1
#> [5,] 1 1 1 1 1# vector-matrix multiplication
np$dot(syn0, syn1)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 0.2331045 0.2331045 0.2331045 0.2331045 0.2331045 0.2331045 0.2331045
#> [2,] 0.2917027 0.2917027 0.2917027 0.2917027 0.2917027 0.2917027 0.2917027
#> [3,] 0.3210047 0.3210047 0.3210047 0.3210047 0.3210047 0.3210047 0.3210047
#> [,8] [,9] [,10]
#> [1,] 0.2331045 0.2331045 0.2331045
#> [2,] 0.2917027 0.2917027 0.2917027
#> [3,] 0.3210047 0.3210047 0.3210047# build a numpy array from three R vectors
X <- np$array(rbind(c(1,2,3), c(4,5,6), c(7,8,9)))
X
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 4 5 6
#> [3,] 7 8 9# transpose the array
np$transpose(X)
#> [,1] [,2] [,3]
#> [1,] 1 4 7
#> [2,] 2 5 8
#> [3,] 3 6 9With newer PyTorch versions we should work with NumPy array copies There have been minor changes in the latest versions of PyTorch that prevents a direct use of a NumPy array. You will get this warning:
sys:1: UserWarning: The given NumPy array is not writeable, and PyTorch does
not support non-writeable tensors. This means you can write to the underlying
(supposedly non-writeable) NumPy array using the tensor. You may want to copy
the array to protect its data or make it writeable before converting it to a
tensor. This type of warning will be suppressed for the rest of this program.For instance, this code will produce the warning:
# as_tensor. Modifying tensor modifies numpy object as well
a = np$array(list(1, 2, 3))
t = torch$as_tensor(a)
print(t)
torch$tensor(list( 1, 2, 3))
t[1L]$fill_(-1)
print(a)while this other one -with some extra code- will not:
a = np$array(list(1, 2, 3))
a_copy = r_to_py(a)$copy() # we make a copy of the numpy array first
t = torch$as_tensor(a_copy)
print(t)
#> tensor([1., 2., 3.], dtype=torch.float64)
torch$tensor(list( 1, 2, 3))
#> tensor([1., 2., 3.])
t[1L]$fill_(-1)
#> tensor(-1., dtype=torch.float64)
print(a)
#> [1] 1 2 3make_copy()To make easier to copy an object in rTorch we
implemented the function make_copy, which makes a safe copy
regardless if it is a torch, numpy or an R type object.
a = np$array(list(1, 2, 3, 4, 5))
a_copy <- make_copy(a)
t <- torch$as_tensor(a_copy)
t
#> tensor([1., 2., 3., 4., 5.], dtype=torch.float64)# convert a numpy array to a tensor
np_a = np$array(c(c(3, 4), c(3, 6)))
t_a = torch$from_numpy(r_to_py(np_a)$copy())
print(t_a)
#> tensor([3., 4., 3., 6.], dtype=torch.float64)# a random 1D tensor
np_arr <- np$random$rand(5L)
ft1 <- torch$FloatTensor(r_to_py(np_arr)$copy()) # make a copy of numpy array
ft1
#> tensor([0.9408, 0.8752, 0.5924, 0.7329, 0.6719])# tensor as a float of 64-bits
np_copy <- r_to_py(np$random$rand(5L))$copy() # make a copy of numpy array
ft2 <- torch$as_tensor(np_copy, dtype= torch$float64)
ft2
#> tensor([0.0462, 0.5851, 0.4886, 0.0725, 0.8959], dtype=torch.float64)This is a very common operation in machine learning:
# convert tensor to a numpy array
a = torch$rand(5L, 4L)
b = a$numpy()
print(b)
#> [,1] [,2] [,3] [,4]
#> [1,] 0.8167720 0.8075168 0.2668687105 0.7421414
#> [2,] 0.6829966 0.5185235 0.0005332828 0.9414444
#> [3,] 0.2030416 0.7496545 0.0358363986 0.3475423
#> [4,] 0.7263991 0.6163300 0.2169904113 0.9363614
#> [5,] 0.4336911 0.5996053 0.2127178907 0.8461853# convert tensor to float 16-bits
ft2_dbl <- torch$as_tensor(ft2, dtype = torch$float16)
ft2_dbl
#> tensor([0.0462, 0.5850, 0.4885, 0.0724, 0.8960], dtype=torch.float16)Create a tensor of size (5 x 7) with uninitialized memory:
a <- torch$FloatTensor(5L, 7L)
print(a)
#> tensor([[1.4013e-45, 1.4013e-45, 1.4013e-45, 1.4013e-45, 1.4013e-45, 1.4013e-45,
#> 1.4013e-45],
#> [1.4013e-45, 1.4013e-45, 1.4013e-45, 1.4013e-45, 1.4013e-45, 1.4013e-45,
#> 1.4013e-45],
#> [1.4013e-45, 1.4013e-45, 1.4013e-45, 1.4013e-45, 1.4013e-45, 1.4013e-45,
#> 1.4013e-45],
#> [1.4013e-45, 1.4013e-45, 1.4013e-45, 1.4013e-45, 1.4013e-45, 1.4013e-45,
#> 1.4013e-45],
#> [1.4013e-45, 1.4013e-45, 0.0000e+00, 1.4013e-45, 1.4013e-45, 1.4013e-45,
#> 1.4013e-45]])# using arange to create tensor. starts from 0
v = torch$arange(9L)
(v = v$view(3L, 3L))
#> tensor([[0, 1, 2],
#> [3, 4, 5],
#> [6, 7, 8]])Initialize a tensor randomized with a normal distribution with mean=0, var=1:
a <- torch$randn(5L, 7L)
print(a)
#> tensor([[-0.9889, 0.6058, 0.5492, -0.0291, 1.3752, -0.2528, -1.8089],
#> [ 0.6621, -0.4370, 1.5590, 0.8149, 0.4004, -0.5299, 0.9275],
#> [ 0.0880, 0.5931, 0.2601, 1.6336, 1.0154, 1.2189, -1.6278],
#> [ 1.2171, 0.1377, -0.2377, 0.0792, -0.2885, 0.6316, 1.7481],
#> [-0.7538, 0.6162, 1.3023, -1.5574, 0.1196, -1.1652, 1.5082]])
print(a$size())
#> torch.Size([5, 7])library(rTorch)
# 3x5 matrix uniformly distributed between 0 and 1
mat0 <- torch$FloatTensor(3L, 5L)$uniform_(0L, 1L)
# fill a 3x5 matrix with 0.1
mat1 <- torch$FloatTensor(3L, 5L)$uniform_(0.1, 0.1)
# a vector with all ones
mat2 <- torch$FloatTensor(5L)$uniform_(1, 1)
mat0
#> tensor([[0.9587, 0.3368, 0.3534, 0.0709, 0.6827],
#> [0.5521, 0.6874, 0.6756, 0.3705, 0.9120],
#> [0.4415, 0.9895, 0.4699, 0.5890, 0.0901]])
mat1
#> tensor([[0.1000, 0.1000, 0.1000, 0.1000, 0.1000],
#> [0.1000, 0.1000, 0.1000, 0.1000, 0.1000],
#> [0.1000, 0.1000, 0.1000, 0.1000, 0.1000]])Binomial <- torch$distributions$binomial$Binomial
m = Binomial(100, torch$tensor(list(0 , .2, .8, 1)))
(x = m$sample())
#> tensor([ 0., 29., 80., 100.])m = Binomial(torch$tensor(list(list(5.), list(10.))),
torch$tensor(list(0.5, 0.8)))
(x = m$sample())
#> tensor([[0., 4.],
#> [2., 8.]])Exponential <- torch$distributions$exponential$Exponential
m = Exponential(torch$tensor(list(1.0)))
m$sample() # Exponential distributed with rate=1
#> tensor([0.4214])Weibull <- torch$distributions$weibull$Weibull
m = Weibull(torch$tensor(list(1.0)), torch$tensor(list(1.0)))
m$sample() # sample from a Weibull distribution with scale=1, concentration=1
#> tensor([0.0745])Only floating-point types are supported as the default type.
# Default data type
torch$tensor(list(1.2, 3))$dtype # default for floating point is torch.float32
#> torch.float32# change default data type to float64
torch$set_default_dtype(torch$float64)
torch$tensor(list(1.2, 3))$dtype # a new floating point tensor
#> torch.float64torch$set_default_dtype(torch$double)
torch$tensor(list(1.2, 3))$dtype
#> torch.float64x = torch$randn(2L, 3L) # Size 2x3
y = x$view(6L) # Resize x to size 6
z = x$view(-1L, 2L) # Size 3x2
print(y)
#> tensor([ 0.8073, -0.7656, 1.0641, 0.3801, 0.5983, 0.7950])
print(z)
#> tensor([[ 0.8073, -0.7656],
#> [ 1.0641, 0.3801],
#> [ 0.5983, 0.7950]])# 0 1 2
# 3 4 5
# 6 7 8
v = torch$arange(9L)
(v = v$view(3L, 3L))
#> tensor([[0, 1, 2],
#> [3, 4, 5],
#> [6, 7, 8]])# concatenate tensors
x = torch$randn(2L, 3L)
print(x)
#> tensor([[-0.6563, 1.5943, -0.0617],
#> [ 0.5502, 1.6150, -2.0000]])
# concatenate tensors by dim=0"
torch$cat(list(x, x, x), 0L)
#> tensor([[-0.6563, 1.5943, -0.0617],
#> [ 0.5502, 1.6150, -2.0000],
#> [-0.6563, 1.5943, -0.0617],
#> [ 0.5502, 1.6150, -2.0000],
#> [-0.6563, 1.5943, -0.0617],
#> [ 0.5502, 1.6150, -2.0000]])
# concatenate tensors by dim=1
torch$cat(list(x, x, x), 1L)
#> tensor([[-0.6563, 1.5943, -0.0617, -0.6563, 1.5943, -0.0617, -0.6563, 1.5943,
#> -0.0617],
#> [ 0.5502, 1.6150, -2.0000, 0.5502, 1.6150, -2.0000, 0.5502, 1.6150,
#> -2.0000]])# ----- Reshape tensors -----
img <- torch$ones(3L, 28L, 28L)
print(img$size())
#> torch.Size([3, 28, 28])
img_chunks <- torch$chunk(img, chunks = 3L, dim = 0L)
print(length(img_chunks))
#> [1] 3
# 1st chunk member
img_chunk_1 <- img_chunks[[1]]
print(img_chunk_1$size())
#> torch.Size([1, 28, 28])
print(img_chunk_1$sum())
#> tensor(784.)
# 2nd chunk member
img_chunk_1 <- img_chunks[[2]]
print(img_chunk_1$size())
#> torch.Size([1, 28, 28])
print(img_chunk_1$sum())
#> tensor(784.)
# index_select. get layer 1
indices = torch$tensor(c(0L))
img2 <- torch$index_select(img, dim = 0L, index = indices)
print(img2$size())
#> torch.Size([1, 28, 28])
print(img2$sum())
#> tensor(784.)
# index_select. get layer 2
indices = torch$tensor(c(1L))
img2 <- torch$index_select(img, dim = 0L, index = indices)
print(img2$size())
#> torch.Size([1, 28, 28])
print(img2$sum())
#> tensor(784.)
# index_select. get layer 3
indices = torch$tensor(c(2L))
img2 <- torch$index_select(img, dim = 0L, index = indices)
print(img2$size())
#> torch.Size([1, 28, 28])
print(img2$sum())
#> tensor(784.)# identity matrix
eye = torch$eye(3L) # Create an identity 3x3 tensor
print(eye)
#> tensor([[1., 0., 0.],
#> [0., 1., 0.],
#> [0., 0., 1.]])(v = torch$ones(10L)) # A tensor of size 10 containing all ones
#> tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
(v = torch$ones(2L, 1L, 2L, 1L)) # Size 2x1x2x1
#> tensor([[[[1.],
#> [1.]]],
#>
#>
#> [[[1.],
#> [1.]]]])v = torch$ones_like(eye) # A tensor with same shape as eye. Fill it with 1.
v
#> tensor([[1., 1., 1.],
#> [1., 1., 1.],
#> [1., 1., 1.]])(z = torch$zeros(10L)) # A tensor of size 10 containing all zeros
#> tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])# a tensor filled with ones
(v = torch$ones(3L, 3L))
#> tensor([[1., 1., 1.],
#> [1., 1., 1.],
#> [1., 1., 1.]])# change two rows in the tensor
# we are using 1-based index
v[2L, ]$fill_(2L) # fill row 1 with 2s
#> tensor([2., 2., 2.])
v[3L, ]$fill_(3L) # fill row 2 with 3s
#> tensor([3., 3., 3.])print(v)
#> tensor([[1., 1., 1.],
#> [2., 2., 2.],
#> [3., 3., 3.]])# Initialize Tensor with a range of values
(v = torch$arange(10L)) # similar to range(5) but creating a Tensor
#> tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])(v = torch$arange(0L, 10L, step = 1L)) # Size 5. Similar to range(0, 5, 1)
#> tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])u <- torch$arange(0, 10, step = 0.5)
u
#> tensor([0.0000, 0.5000, 1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000, 4.0000,
#> 4.5000, 5.0000, 5.5000, 6.0000, 6.5000, 7.0000, 7.5000, 8.0000, 8.5000,
#> 9.0000, 9.5000])# range of values with increments including the end value
start <- 0
end <- 10
step <- 0.25
w <- torch$arange(start, end+step, step)
w
#> tensor([ 0.0000, 0.2500, 0.5000, 0.7500, 1.0000, 1.2500, 1.5000, 1.7500,
#> 2.0000, 2.2500, 2.5000, 2.7500, 3.0000, 3.2500, 3.5000, 3.7500,
#> 4.0000, 4.2500, 4.5000, 4.7500, 5.0000, 5.2500, 5.5000, 5.7500,
#> 6.0000, 6.2500, 6.5000, 6.7500, 7.0000, 7.2500, 7.5000, 7.7500,
#> 8.0000, 8.2500, 8.5000, 8.7500, 9.0000, 9.2500, 9.5000, 9.7500,
#> 10.0000])# Initialize a linear or log scale Tensor
# Create a Tensor with 10 linear points for (1, 10) inclusively
(v = torch$linspace(1L, 10L, steps = 10L))
#> tensor([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
# Size 5: 1.0e-10 1.0e-05 1.0e+00, 1.0e+05, 1.0e+10
(v = torch$logspace(start=-10L, end = 10L, steps = 5L))
#> tensor([1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10])a = torch$rand(5L, 4L)
print(class(a))
#> [1] "torch.Tensor" "torch._C._TensorBase" "python.builtin.object"# converting the tensor to a numpy array, R automatically converts it
b = a$numpy()
print(class(b))
#> [1] "matrix"a$fill_(3.5)
#> tensor([[3.5000, 3.5000, 3.5000, 3.5000],
#> [3.5000, 3.5000, 3.5000, 3.5000],
#> [3.5000, 3.5000, 3.5000, 3.5000],
#> [3.5000, 3.5000, 3.5000, 3.5000],
#> [3.5000, 3.5000, 3.5000, 3.5000]])
# a has now been filled with the value 3.5
# add a scalar to a tensor.
# notice that was auto-converted from an array to a tensor
b <- a$add(4.0)
# a is still filled with 3.5
# new tensor b is returned with values 3.5 + 4.0 = 7.5
print(a)
#> tensor([[3.5000, 3.5000, 3.5000, 3.5000],
#> [3.5000, 3.5000, 3.5000, 3.5000],
#> [3.5000, 3.5000, 3.5000, 3.5000],
#> [3.5000, 3.5000, 3.5000, 3.5000],
#> [3.5000, 3.5000, 3.5000, 3.5000]])
print(b)
#> tensor([[7.5000, 7.5000, 7.5000, 7.5000],
#> [7.5000, 7.5000, 7.5000, 7.5000],
#> [7.5000, 7.5000, 7.5000, 7.5000],
#> [7.5000, 7.5000, 7.5000, 7.5000],
#> [7.5000, 7.5000, 7.5000, 7.5000]])# this will throw an error because we don't still have a function for assignment
a[1, 1] <- 7.7
print(a)
# Error in a[1, 1] <- 7.7 : object of type 'environment' is not subsettable# This would be the right wayy to assign a value to a tensor element
a[1, 1]$fill_(7.7)
#> tensor(7.7000)# we can see that the first element has been changed
a
#> tensor([[7.7000, 3.5000, 3.5000, 3.5000],
#> [3.5000, 3.5000, 3.5000, 3.5000],
#> [3.5000, 3.5000, 3.5000, 3.5000],
#> [3.5000, 3.5000, 3.5000, 3.5000],
#> [3.5000, 3.5000, 3.5000, 3.5000]])Some operations like
narrowdo not have in-place versions, and hence,.narrow_does not exist. Similarly, some operations likefill_do not have an out-of-place version, so.filldoes not exist.
# a[[0L, 3L]]
a[1, 4]
#> tensor(3.5000)# replace an element at position 0, 0
(new_tensor = torch$Tensor(list(list(1, 2), list(3, 4))))
#> tensor([[1., 2.],
#> [3., 4.]])# first row, firt column
print(new_tensor[1L, 1L])
#> tensor(1.)# change row 1, col 1 with value of 5
new_tensor[1L, 1L]$fill_(5)
#> tensor(5.)# which is the same as doing this
new_tensor[1, 1]$fill_(5)
#> tensor(5.)Notice that the element was changed in-place because of
fill_.
print(new_tensor) # tensor([[ 5., 2.],[ 3., 4.]])
#> tensor([[5., 2.],
#> [3., 4.]])# access an element at position (1, 0), 0-based index
print(new_tensor[2L, 1L]) # tensor([ 3.])
#> tensor(3.)# convert it to a scalar value
print(new_tensor[2L, 1L]$item()) # 3.
#> [1] 3# which is the same as
print(new_tensor[2, 1])
#> tensor(3.)# and the scalar
print(new_tensor[2, 1]$item())
#> [1] 3# Select indices
x = torch$randn(3L, 4L)
print(x)
#> tensor([[-2.4324, 0.5563, 1.3308, -0.6363],
#> [ 1.1925, 2.4744, -0.0463, -2.2281],
#> [-1.5476, 1.1377, 0.3645, -0.8908]])# extract first and third row
# Select indices, dim=0
indices = torch$tensor(list(0L, 2L))
torch$index_select(x, 0L, indices)
#> tensor([[-2.4324, 0.5563, 1.3308, -0.6363],
#> [-1.5476, 1.1377, 0.3645, -0.8908]])# extract first and third column
# Select indices, dim=1
torch$index_select(x, 1L, indices)
#> tensor([[-2.4324, 1.3308],
#> [ 1.1925, -0.0463],
#> [-1.5476, 0.3645]])# Take by indices
src = torch$tensor(list(list(4, 3, 5),
list(6, 7, 8)) )
print(src)
#> tensor([[4., 3., 5.],
#> [6., 7., 8.]])
print( torch$take(src, torch$tensor(list(0L, 2L, 5L))) )
#> tensor([4., 5., 8.])# two dimensions: 3x3
x <- torch$arange(9L)
x <- x$view(c(3L, 3L))
t <- torch$transpose(x, 0L, 1L)
x # "Original tensor"
#> tensor([[0, 1, 2],
#> [3, 4, 5],
#> [6, 7, 8]])
t # "Transposed"
#> tensor([[0, 3, 6],
#> [1, 4, 7],
#> [2, 5, 8]])# three dimensions: 1x2x3
x <- torch$ones(c(1L, 2L, 3L))
t <- torch$transpose(x, 1L, 0L)
print(x) # original tensor
#> tensor([[[1., 1., 1.],
#> [1., 1., 1.]]])
print(t) # transposed
#> tensor([[[1., 1., 1.]],
#>
#> [[1., 1., 1.]]])
print(x$shape) # original tensor
#> torch.Size([1, 2, 3])
print(t$shape) # transposed
#> torch.Size([2, 1, 3])x <- torch$tensor(list(list(list(1,2)), list(list(3,4)), list(list(5,6))))
xs <- torch$as_tensor(x$shape)
xp <- x$permute(c(1L, 2L, 0L))
xps <- torch$as_tensor(xp$shape)
print(x) # original tensor
#> tensor([[[1., 2.]],
#>
#> [[3., 4.]],
#>
#> [[5., 6.]]])
print(xp) # permuted tensor
#> tensor([[[1., 3., 5.],
#> [2., 4., 6.]]])
print(xs) # shape original tensor
#> tensor([3, 1, 2])
print(xps) # shape permuted tensor
#> tensor([1, 2, 3])torch$manual_seed(1234)
#> <torch._C.Generator>
x <- torch$randn(10L, 480L, 640L, 3L)
x[1:3, 1:2, 1:3, 1:2]
#> tensor([[[[-0.0883, 0.3420],
#> [ 1.0051, -0.1117],
#> [-0.0982, -0.3511]],
#>
#> [[-0.1465, 0.3960],
#> [-1.6878, 0.5720],
#> [ 0.9426, 2.1187]]],
#>
#>
#> [[[ 0.8107, 0.9289],
#> [ 0.4210, -1.5109],
#> [-1.8483, -0.4636]],
#>
#> [[-1.8324, -1.9304],
#> [-2.7020, 0.3491],
#> [ 0.9180, -1.9872]]],
#>
#>
#> [[[ 1.6555, -0.3531],
#> [ 0.4763, 0.8037],
#> [-0.2171, -0.0839]],
#>
#> [[-0.0886, -1.3389],
#> [ 0.7163, -0.9050],
#> [-0.8144, -1.4922]]]])xs <- torch$as_tensor(x$size()) # torch$tensor(c(10L, 480L, 640L, 3L))
xp <- x$permute(0L, 3L, 1L, 2L) # specify dimensions order
xps <- torch$as_tensor(xp$size()) # torch$tensor(c(10L, 3L, 480L, 640L))
print(xs) # original tensor size
#> tensor([ 10, 480, 640, 3])
print(xps) # permuted tensor size
#> tensor([ 10, 3, 480, 640])xp[1:3, 1:2, 1:3, 1:2]
#> tensor([[[[-0.0883, 1.0051],
#> [-0.1465, -1.6878],
#> [-0.6429, 0.5577]],
#>
#> [[ 0.3420, -0.1117],
#> [ 0.3960, 0.5720],
#> [ 0.3014, 0.7813]]],
#>
#>
#> [[[ 0.8107, 0.4210],
#> [-1.8324, -2.7020],
#> [ 1.1724, 0.4434]],
#>
#> [[ 0.9289, -1.5109],
#> [-1.9304, 0.3491],
#> [ 0.9901, -1.3630]]],
#>
#>
#> [[[ 1.6555, 0.4763],
#> [-0.0886, 0.7163],
#> [-0.7774, -0.6281]],
#>
#> [[-0.3531, 0.8037],
#> [-1.3389, -0.9050],
#> [-0.7920, 1.3634]]]])(m0 = torch$zeros(3L, 5L))
#> tensor([[0., 0., 0., 0., 0.],
#> [0., 0., 0., 0., 0.],
#> [0., 0., 0., 0., 0.]])(m1 = torch$ones(3L, 5L))
#> tensor([[1., 1., 1., 1., 1.],
#> [1., 1., 1., 1., 1.],
#> [1., 1., 1., 1., 1.]])(m2 = torch$eye(3L, 5L))
#> tensor([[1., 0., 0., 0., 0.],
#> [0., 1., 0., 0., 0.],
#> [0., 0., 1., 0., 0.]])# is m1 equal to m0
print(m1 == m0)
#> tensor([[False, False, False, False, False],
#> [False, False, False, False, False],
#> [False, False, False, False, False]])print(as_boolean(m1 == m0))
#> tensor([[False, False, False, False, False],
#> [False, False, False, False, False],
#> [False, False, False, False, False]])# is it not equal
print(m1 != m1)
#> tensor([[False, False, False, False, False],
#> [False, False, False, False, False],
#> [False, False, False, False, False]])# are both equal
print(m2 == m2)
#> tensor([[True, True, True, True, True],
#> [True, True, True, True, True],
#> [True, True, True, True, True]])print(as_boolean(m2 == m2))
#> tensor([[True, True, True, True, True],
#> [True, True, True, True, True],
#> [True, True, True, True, True]])# some are equal, others don't
m1 != m2
#> tensor([[False, True, True, True, True],
#> [ True, False, True, True, True],
#> [ True, True, False, True, True]])# some are equal, others don't
m0 != m2
#> tensor([[ True, False, False, False, False],
#> [False, True, False, False, False],
#> [False, False, True, False, False]])as_boolean(m0 != m2)
#> tensor([[ True, False, False, False, False],
#> [False, True, False, False, False],
#> [False, False, True, False, False]])# AND
m1 & m1
#> tensor([[1, 1, 1, 1, 1],
#> [1, 1, 1, 1, 1],
#> [1, 1, 1, 1, 1]], dtype=torch.uint8)as_boolean(m1 & m1)
#> tensor([[True, True, True, True, True],
#> [True, True, True, True, True],
#> [True, True, True, True, True]])# OR
m0 | m2
#> tensor([[1, 0, 0, 0, 0],
#> [0, 1, 0, 0, 0],
#> [0, 0, 1, 0, 0]], dtype=torch.uint8)# OR
m1 | m2
#> tensor([[1, 1, 1, 1, 1],
#> [1, 1, 1, 1, 1],
#> [1, 1, 1, 1, 1]], dtype=torch.uint8)as_boolean(m1 | m2)
#> tensor([[True, True, True, True, True],
#> [True, True, True, True, True],
#> [True, True, True, True, True]])# tensor is less than
A <- torch$ones(60000L, 1L, 28L, 28L)
C <- A * 0.5
# is C < A = TRUE
all(torch$lt(C, A))
#> tensor(1, dtype=torch.uint8)
all(C < A)
#> tensor(1, dtype=torch.uint8)
# is A < C = FALSE
all(A < C)
#> tensor(0, dtype=torch.uint8)# tensor is greater than
A <- torch$ones(60000L, 1L, 28L, 28L)
D <- A * 2.0
all(torch$gt(D, A))
#> tensor(1, dtype=torch.uint8)
all(torch$gt(A, D))
#> tensor(0, dtype=torch.uint8)# tensor is less than or equal
A1 <- torch$ones(60000L, 1L, 28L, 28L)
all(torch$le(A1, A1))
#> tensor(1, dtype=torch.uint8)
all(A1 <= A1)
#> tensor(1, dtype=torch.uint8)
# tensor is greater than or equal
A0 <- torch$zeros(60000L, 1L, 28L, 28L)
all(torch$ge(A0, A0))
#> tensor(1, dtype=torch.uint8)
all(A0 >= A0)
#> tensor(1, dtype=torch.uint8)
all(A1 >= A0)
#> tensor(1, dtype=torch.uint8)
all(A1 <= A0)
#> tensor(0, dtype=torch.uint8)# we implement this little function
all_as_boolean <- function(x) {
# convert tensor of 1s and 0s to a unique boolean
as.logical(torch$all(x)$numpy())
}all_as_boolean(torch$gt(D, A))
#> [1] TRUE
all_as_boolean(torch$gt(A, D))
#> [1] FALSE
all_as_boolean(A1 <= A1)
#> [1] TRUE
all_as_boolean(A1 >= A0)
#> [1] TRUE
all_as_boolean(A1 <= A0)
#> [1] FALSE# vector of booleans
all_true <- torch$BoolTensor(list(TRUE, TRUE, TRUE, TRUE))
all_true
#> tensor([True, True, True, True])# logical NOT
# negate vector with "!"
not_all_true <- !all_true
not_all_true
#> tensor([False, False, False, False])# a diagonal matrix
diag <- torch$eye(5L)
diag <- diag$to(dtype=torch$uint8) # convert to unsigned integer
diag
#> tensor([[1, 0, 0, 0, 0],
#> [0, 1, 0, 0, 0],
#> [0, 0, 1, 0, 0],
#> [0, 0, 0, 1, 0],
#> [0, 0, 0, 0, 1]], dtype=torch.uint8)as_boolean(diag)
#> tensor([[ True, False, False, False, False],
#> [False, True, False, False, False],
#> [False, False, True, False, False],
#> [False, False, False, True, False],
#> [False, False, False, False, True]])# logical NOT
not_diag <- !diag
not_diag
#> tensor([[0, 1, 1, 1, 1],
#> [1, 0, 1, 1, 1],
#> [1, 1, 0, 1, 1],
#> [1, 1, 1, 0, 1],
#> [1, 1, 1, 1, 0]], dtype=torch.uint8)# and the negation
!not_diag
#> tensor([[1, 0, 0, 0, 0],
#> [0, 1, 0, 0, 0],
#> [0, 0, 1, 0, 0],
#> [0, 0, 0, 1, 0],
#> [0, 0, 0, 0, 1]], dtype=torch.uint8)as_boolean(!not_diag)
#> tensor([[ True, False, False, False, False],
#> [False, True, False, False, False],
#> [False, False, True, False, False],
#> [False, False, False, True, False],
#> [False, False, False, False, True]])These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.