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.]]])# 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$tensor(list(
c(1, 2, 3),
c(1, 2, 3)
))
t2 = torch$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.]])# 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])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.]])# 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)$view(c(5L,2L))
t1
#> tensor([[ 1., 2., 3., 4., 5.],
#> [ 6., 7., 8., 9., 10.]])
t2
#> tensor([[11., 12.],
#> [13., 14.],
#> [15., 16.],
#> [17., 18.],
#> [19., 20.]])# 1D tensor
t1 = torch$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$array(rbind(c(1,2,3), c(4,5,6), c(7,8,9)))
X
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 4 5 6
#> [3,] 7 8 9With newer PyTorch versions we should work with NumPy array copies There have been minor changes in the latest versions of PyTorch that prevents a direct use of a NumPy array. You will get this warning:
sys:1: UserWarning: The given NumPy array is not writeable, and PyTorch does
not support non-writeable tensors. This means you can write to the underlying
(supposedly non-writeable) NumPy array using the tensor. You may want to copy
the array to protect its data or make it writeable before converting it to a
tensor. This type of warning will be suppressed for the rest of this program.For instance, this code will produce the warning:
# as_tensor. Modifying tensor modifies numpy object as well
a = np$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.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, ]$fill_(3L) # fill row 2 with 3s
#> tensor([3., 3., 3.])# Initialize Tensor with a range of values
(v = torch$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.
# replace an element at position 0, 0
(new_tensor = torch$Tensor(list(list(1, 2), list(3, 4))))
#> tensor([[1., 2.],
#> [3., 4.]])Notice that the element was changed in-place because of
fill_.
# access an element at position (1, 0), 0-based index
print(new_tensor[2L, 1L]) # tensor([ 3.])
#> tensor(3.)# Select indices
x = torch$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)