Torch provides Matlab-like functions for manipulating Tensor objects. Functions fall into several types of categories:
- constructors like zeros, ones
- extractors like diag and triu,
- Element-wise mathematical operations like abs and pow,
- BLAS operations,
- column or row-wise operations like sum and max,
- matrix-wide operations like trace and norm.
- Convolution and cross-correlation operations like conv2.
- Basic linear algebra operations like eigen value/vector calculation.
- Logical Operations on Tensors.
By default, all operations allocate a new tensor to return the result. However, all functions also support passing the target tensor(s) as the first argument(s), in which case the target tensor(s) will be resized accordingly and filled with result. This property is especially useful when one wants have tight control over when memory is allocated.
The torch package adopts the same concept, so that calling a function directly on the tensor itself using an object-oriented syntax is equivalent to passing the tensor as the optional resulting tensor. The following two calls are equivalent.
torch.log(x,x)
x:log()Similarly, torch.conv2 function can be used in the following manner.
x = torch.rand(100,100)
k = torch.rand(10,10)
res1 = torch.conv2(x,k)
res2 = torch.Tensor()
torch.conv2(res2,x,k)
=res2:dist(res1)
0
The advantage of second case is, same res2 tensor can be used successively in a loop without any new allocation.
-- no new memory allocations...
for i=1,100 do
torch.conv2(res2,x,k)
end
=res2:dist(res1)
0If dimension is not specified it is the last dimension.
The other dimensions of x_1 and x_2 have to be equal.
Examples:
> print(torch.cat(torch.ones(3),torch.zeros(2)))
1
1
1
0
0
[torch.Tensor of dimension 5]
> print(torch.cat(torch.ones(3,2),torch.zeros(2,2),1))
1 1
1 1
1 1
0 0
0 0
[torch.DoubleTensor of dimension 5x2]
> print(torch.cat(torch.ones(2,2),torch.zeros(2,2),1))
1 1
1 1
0 0
0 0
[torch.DoubleTensor of dimension 4x2]
> print(torch.cat(torch.ones(2,2),torch.zeros(2,2),2))
1 1 0 0
1 1 0 0
[torch.DoubleTensor of dimension 2x4]
> print(torch.cat(torch.cat(torch.ones(2,2),torch.zeros(2,2),1),torch.rand(3,2),1))
1.0000 1.0000
1.0000 1.0000
0.0000 0.0000
0.0000 0.0000
0.3227 0.0493
0.9161 0.1086
0.2206 0.7449
[torch.DoubleTensor of dimension 7x2]
y=torch.diag(x) when x is of dimension 1 returns a diagonal matrix with diagonal elements constructed from x.
y=torch.diag(x) when x is of dimension 2 returns a tensor of dimension 1
with elements constructed from the diagonal of x.
y=torch.diag(x,k) returns the k-th diagonal of x,
where k = 0 is the main diagonal, k > 0 is above the main diagonal and k < 0
is below the main diagonal.
y=torch.eye(n) returns the n x n identity matrix.
y=torch.eye(n,m) returns an n x m identity matrix with ones on the diagonal and zeros elsewhere.
y=torch.histc(x) returns the histogram of the elements in x. By default the
elements are sorted into 100 equally spaced bins between the minimum and maximum
values of x.
y=torch.histc(x,n) same as above with n bins.
y=torch.histc(x,n,min,max) same as above with n bins and [min, max] as elements
range.
y=torch.linspace(x1,x2) returns a one-dimensional tensor of size 100 equally spaced points between x1 and x2.
y=torch.linspace(x1,x2,n) returns a one-dimensional tensor of n equally spaced points between x1 and x2.
y=torch.logspace(x1,x2) returns a one-dimensional tensor of 50 logarithmically eqally spaced points between 10^x1 and 10^x2.
y=torch.logspace(x1,x2,n) returns a one-dimensional tensor of n logarithmically equally spaced points between 10^x1 and 10^x2.
y=torch.multinomial(p,n) returns a tensor y where each row contains
n indices sampled from the
multinomial probability distribution
located in the corresponding row of tensor p.
The rows of p do not need to sum to one (in which case we use the values as weights),
but must be non-negative and have a non-zero sum.
Indices are ordered from left to right according to
when each was sampled (first samples are placed in first column).
If p is a vector, y is a vector size n.
If p is a m-rows matrix, y is an m x n matrix.
If replacement is true, samples are drawn with replacement.
If not, they are drawn without replacement, which means that when a sample index is drawn for a row, it
cannot be drawn again for that row. This implies the constraint that n must be lower than p length (or number of columns of p if it is a matrix).
The default value for replacement is false.
p = torch.Tensor{1, 1, 0.5, 0}
a = torch.multinomial(p, 10000, true)
> a
...
[torch.LongTensor of dimension 10000]
> for i=1,4 do print(a:eq(i):sum()) end
3967
4016
2017
0y=torch.ones(n) returns a one-dimensional tensor of size n filled with ones.
y=torch.ones(m,n) returns a m x n tensor filled with ones.
For more than 4 dimensions, you can use a storage as argument:
y=torch.ones(torch.LongStorage{m,n,k,l,o}).
y=torch.rand(n) returns a one-dimensional tensor of size n filled with random numbers from a uniform distribution on the interval (0,1).
y=torch.rand(m,n) returns a m x n tensor of random numbers from a uniform distribution on the interval (0,1).
For more than 4 dimensions, you can use a storage as argument:
y=torch.rand(torch.LongStorage{m,n,k,l,o})
y=torch.randn(n) returns a one-dimensional tensor of size n filled with random numbers from a normal distribution with mean zero and variance one.
y=torch.randn(m,n) returns a m x n tensor of random numbers from a normal distribution with mean zero and variance one.
For more than 4 dimensions, you can use a storage as argument:
y=torch.rand(torch.LongStorage{m,n,k,l,o})
y=torch.range(x,y) returns a tensor of size floor((y - x) / step) + 1 with values
from x to y with step step (default to 1).
> print(torch.range(2,5))
2
3
4
5
[torch.Tensor of dimension 4]
> print(torch.range(2,5,1.2))
2.0000
3.2000
4.4000
[torch.DoubleTensor of dimension 3]y=torch.randperm(n) returns a random permutation of integers from 1 to n.
y=torch.reshape(x,m,n) returns a new m x n tensor y whose elements
are taken rowwise from x, which must have m*n elements. The elements are copied into the new tensor.
For more than 4 dimensions, you can use a storage:
y=torch.reshape(x,torch.LongStorage{m,n,k,l,o})
y=torch.tril(x) returns the lower triangular part of x, the other elements of y are set to 0.
torch.tril(x,k) returns the elements on and below the k-th diagonal of x as non-zero.
k = 0 is the main diagonal, k > 0 is above the main diagonal and k < 0
is below the main diagonal.
y=torch.triu(x) returns the upper triangular part of x,
the other elements of y are set to 0.
torch.triu(x,k) returns the elements on and above the k-th diagonal of x as non-zero.
k = 0 is the main diagonal, k > 0 is above the main diagonal and k < 0
is below the main diagonal.
y=torch.zeros(n) returns a one-dimensional tensor of size n filled with zeros.
y=torch.zeros(m,n) returns a m x n tensor filled with zeros.
For more than 4 dimensions, you can use a storage:
y=torch.zeros(torch.LongStorage{m,n,k,l,o})
y=torch.abs(x) returns a new tensor with the absolute values of the elements of x.
x:abs() replaces all elements in-place with the absolute values of the elements of x.
y=torch.acos(x) returns a new tensor with the arcosine of the elements of x.
x:acos() replaces all elements in-place with the arcosine of the elements of x.
y=torch.asin(x) returns a new tensor with the arcsine of the elements of x.
x:asin() replaces all elements in-place with the arcsine of the elements of x.
y=torch.atan(x) returns a new tensor with the arctangent of the elements of x.
x:atan() replaces all elements in-place with the arctangent of the elements of x.
y=torch.ceil(x) returns a new tensor with the values of the elements of x rounded up to the nearest integers.
x:ceil() replaces all elements in-place with the values of the elements of x rounded up to the nearest integers.
y=torch.cos(x) returns a new tensor with the cosine of the elements of x.
x:cos() replaces all elements in-place with the cosine of the elements of x.
y=torch.cosh(x) returns a new tensor with the hyberbolic cosine of the elements of x.
x:cosh() replaces all elements in-place with the hyberbolic cosine of the elements of x.
y=torch.exp(x) returns, for each element in x, e (the base of natural logarithms) raised to the power of the element in x.
x:exp() returns, for each element in x, e (the base of natural logarithms) raised to the power of the element in x.
y=torch.floor(x) returns a new tensor with the values of the elements of x rounded down to the nearest integers.
x:floor() replaces all elements in-place with the values of the elements of x rounded down to the nearest integers.
y=torch.log(x) returns a new tensor with the natural logarithm of the elements of x.
x:log() replaces all elements in-place with the natural logarithm of the elements of x.
y=torch.log1p(x) returns a new tensor with the natural logarithm of the elements of x+1.
x:log1p() replaces all elements in-place with the natural logarithm of the elements of x+1.
This function is more accurate than log() for small values of x.
Let x be a tensor and n a number.
y=torch.pow(x,n) returns a new tensor with the elements of x to the power of n.
y=torch.pow(n,x) returns, a new tensor with n to the power of the elements of x.
x:pow(n) replaces all elements in-place with the elements of x to the power of n.
y=torch.round(x) returns a new tensor with the values of the elements of x rounded to the nearest integers.
x:round() replaces all elements in-place with the values of the elements of x rounded to the nearest integers.
y=torch.sin(x) returns a new tensor with the sine of the elements of x.
x:sin() replaces all elements in-place with the sine of the elements of x.
y=torch.sinh(x) returns a new tensor with the hyperbolic sine of the elements of x.
x:sinh() replaces all elements in-place with the hyperbolic sine of the elements of x.
y=torch.sqrt(x) returns a new tensor with the square root of the elements of x.
x:sqrt() replaces all elements in-place with the square root of the elements of x.
y=torch.tan(x) returns a new tensor with the tangent of the elements of x.
x:tan() replaces all elements in-place with the tangent of the elements of x.
y=torch.tanh(x) returns a new tensor with the hyperbolic tangent of the elements of x.
x:tanh() replaces all elements in-place with the hyperbolic tangent of the elements of x.
In this section, we explain basic mathematical operations for Tensors.
### [res] torch.add([res,] tensor, value) ###Add the given value to all elements in the tensor.
y=torch.add(x,value) returns a new tensor.
x:add(value) add value to all elements in place.
Add tensor1 to tensor2 and put result into res. The number
of elements must match, but sizes do not matter.
> x = torch.Tensor(2,2):fill(2)
> y = torch.Tensor(4):fill(3)
> x:add(y)
> = x
5 5
5 5
[torch.Tensor of dimension 2x2]y=torch.add(a,b) returns a new tensor.
torch.add(y,a,b) puts a+b in y.
a:add(b) accumulates all elements of b into a.
y:add(a,b) puts a+b in y.
Multiply elements of tensor2 by the scalar value and add it to
tensor1. The number of elements must match, but sizes do not
matter.
> x = torch.Tensor(2,2):fill(2)
> y = torch.Tensor(4):fill(3)
> x:add(2, y)
> = x
8 8
8 8
[torch.Tensor of dimension 2x2]x:add(value,y) multiply-accumulates values of y into x.
z:add(x,value,y) puts the result of x + value*y in z.
torch.add(x,value,y) returns a new tensor x + value*y.
torch.add(z,x,value,y) puts the result of x + value*y in z.
Multiply all elements in the tensor by the given value.
z=torch.mul(x,2) will return a new tensor with the result of x*2.
torch.mul(z,x,2) will put the result of x*2 in z.
x:mul(2) will multiply all elements of x with 2 in-place.
z:mul(x,2) will put the result of x*2 in z.
Clamp all elements in the tensor into the range [min_value, max_value]. ie:
y_i = x_i, if x_i >= min_value or x_i <= max_value
= min_value, if x_i < min_value
= max_value, if x_i > max_value
z=torch.clamp(x,0,1) will return a new tensor with the result of x bounded between 0 and 1.
torch.clamp(z,x,0,1) will put the result in z.
x:clamp(0,1) will perform the clamp operation in place (putting the result in x).
z:clamp(x,0,1) will put the result in z.
Element-wise multiplication of tensor1 by tensor2. The number
of elements must match, but sizes do not matter.
> x = torch.Tensor(2,2):fill(2)
> y = torch.Tensor(4):fill(3)
> x:cmul(y)
> = x
6 6
6 6
[torch.Tensor of dimension 2x2]z=torch.cmul(x,y) returns a new tensor.
torch.cmul(z,x,y) puts the result in z.
y:cmul(x) multiplies all elements of y with corresponding elements of x.
z:cmul(x,y) puts the result in z.
Element-wise power operation, taking the elements of tensor1 to the powers
given by elements of tensor2. The number of elements must match,
but sizes do not matter.
> x = torch.Tensor(2,2):fill(2)
> y = torch.Tensor(4):fill(3)
> x:cpow(y)
> = x
8 8
8 8
[torch.Tensor of dimension 2x2]z=torch.cpow(x,y) returns a new tensor.
torch.cpow(z,x,y) puts the result in z.
y:cpow(x) takes all elements of y to the powers given by the
corresponding elements of x.
z:cpow(x,y) puts the result in z.
Performs the element-wise multiplication of tensor1 by tensor2,
multiply the result by the scalar value (1 if not present) and add it
to x. The number of elements must match, but sizes do not matter.
> x = torch.Tensor(2,2):fill(2)
> y = torch.Tensor(4):fill(3)
> z = torch.Tensor(2,2):fill(5)
> x:addcmul(2, y, z)
> = x
32 32
32 32
[torch.Tensor of dimension 2x2]z:addcmul(value,x,y) accumulates the result in z.
torch.addcmul(z,value,x,y) returns a new tensor with the result.
torch.addcmul(z,z,value,x,y) puts the result in z.
Divide all elements in the tensor by the given value.
z=torch.div(x,2) will return a new tensor with the result of x/2.
torch.div(z,x,2) will put the result of x/2 in z.
x:div(2) will divide all elements of x with 2 in-place.
z:div(x,2) with put the result of x/2 in z.
Performs the element-wise division of tensor1 by tensor2. The
number of elements must match, but sizes do not matter.
> x = torch.Tensor(2,2):fill(1)
> y = torch.Tensor(4)
> for i=1,4 do y[i] = i end
> x:cdiv(y)
> = x
1.0000 0.3333
0.5000 0.2500
[torch.Tensor of dimension 2x2]z=torch.cdiv(x,y) returns a new tensor.
torch.cdiv(z,x,y) puts the result in z.
y:cdiv(x) divides all elements of y with corresponding elements of x.
z:cdiv(x,y) puts the result in z.
Performs the element-wise division of tensor1 by tensor1,
multiply the result by the scalar value and add it to x.
The number of elements must match, but sizes do not matter.
> x = torch.Tensor(2,2):fill(1)
> y = torch.Tensor(4)
> z = torch.Tensor(2,2):fill(5)
> for i=1,4 do y[i] = i end
> x:addcdiv(2, y, z)
> = x
1.4000 2.2000
1.8000 2.6000
[torch.Tensor of dimension 2x2]z:addcdiv(value,x,y) accumulates the result in z.
torch.addcdiv(z,value,x,y) returns a new tensor with the result.
torch.addcdiv(z,z,value,x,y) puts the result in z.
Performs the dot product between tensor1 and tensor2. The number of
elements must match: both tensors are seen as a 1D vector.
> x = torch.Tensor(2,2):fill(2)
> y = torch.Tensor(4):fill(3)
> = x:dot(y)
24torch.dot(x,y) returns dot product of x and y.
x:dot(y) returns dot product of x and y.
Performs a matrix-vector multiplication between mat (2D tensor)
and vec2 (1D tensor) and add it to vec1.
Optional values v1 and v2 are scalars that multiply
vec1 and vec2 respectively.
Optional value beta is a scalar that scales the result tensor, before accumulating the result into the tensor. Defaults to 1.0.
In other words,
res = beta * res + v1 * vec1 + v2 * mat * vec2
Sizes must respect the matrix-multiplication operation: if mat is
a n x m matrix, vec2 must be vector of size m and vec1 must
be a vector of size n.
> x = torch.Tensor(3):fill(0)
> M = torch.Tensor(3,2):fill(3)
> y = torch.Tensor(2):fill(2)
> x:addmv(M, y)
> = x
12
12
12
[torch.Tensor of dimension 3]torch.addmv(x,y,z) returns a new tensor with the result.
torch.addmv(r,x,y,z) puts the result in r.
x:addmv(y,z) accumulates y*z into x.
r:addmv(x,y,z) puts the result of x+y*z into r.
Performs the outer-product between vec1 (1D tensor) and vec2 (1D tensor).
Optional values v1 and v2 are scalars that multiply
M and vec1 [out] vec2 respectively.
In other words,
res_ij = v1 * mat_ij + v2 * vec1_i * vec2_j
If vec1 is a vector of size n and vec2 is a vector of size m,
then mat must be a matrix of size n x m.
> x = torch.Tensor(3)
> y = torch.Tensor(2)
> for i=1,3 do x[i] = i end
> for i=1,2 do y[i] = i end
> M = torch.Tensor(3, 2):zero()
> M:addr(x, y)
> = M
1 2
2 4
3 6
[torch.Tensor of dimension 3x2]torch.addr(M,x,y) returns the result in a new tensor.
torch.addr(r,M,x,y) puts the result in r.
M:addr(x,y) puts the result in M.
r:addr(M,x,y) puts the result in r.
Performs a matrix-matrix multiplication between mat1 (2D tensor)
and mat2 (2D tensor).
Optional values v1 and v2 are scalars that multiply
M and mat1 * mat2 respectively.
Optional value beta is a scalar that scales the result tensor, before accumulating the result into the tensor. Defaults to 1.0.
In other words,
res = res * beta + v1 * M + v2 * mat1*mat2
If mat1 is a n x m matrix, mat2 a m x p matrix,
M must be a n x p matrix.
torch.addmm(M,mat1,mat2) returns the result in a new tensor.
torch.addmm(r,M,mat1,mat2) puts the result in r.
M:addmm(mat1,mat2) puts the result in M.
r:addmm(M,mat1,mat2) puts the result in r.
Batch matrix matrix product of matrices stored in batch1 and batch2,
with a reduced add step (all matrix multiplications get accumulated in a
single place).
batch1 and batch2 must be 3D tensors each containing the same number
of matrices. If batch1 is a b x n x m tensor, batch2 a b x m x p
tensor, res will be a n x p tensor.
In other words,
res = v1 * M + v2 * sum(batch1_i * batch2_i, i=1,b)
torch.addbmm(M,x,y) puts the result in a new tensor.
M:addbmm(x,y) puts the result in M, resizing M if necessary.
M:addbmm(beta,M2,alpha,x,y) puts the result in M, resizing M if necessary.
Batch matrix matrix product of matrices stored in batch1 and batch2,
with batch add.
batch1 and batch2 must be 3D tensors each containing the same number
of matrices. If batch1 is a b x n x m tensor, batch2 a b x m x p
tensor, res will be a b x n x p tensor.
In other words,
res_i = v1 * M_i + v2 * batch1_i * batch2_i
torch.baddbmm(M,x,y) puts the result in a new tensor.
M:baddbmm(x,y) puts the result in M, resizing M if necessary.
M:baddbmm(beta,M2,alpha,x,y) puts the result in M, resizing M if necessary.
Matrix vector product of mat and vec. Sizes must respect
the matrix-multiplication operation: if mat is a n x m matrix,
vec must be vector of size m and res must be a vector of size n.
torch.mv(x,y) puts the result in a new tensor.
torch.mv(M,x,y) puts the result in M.
M:mv(x,y) puts the result in M.
Matrix matrix product of mat1 and mat2. If mat1 is a
n x m matrix, mat2 a m x p matrix, res must be a
n x p matrix.
torch.mm(x,y) puts the result in a new tensor.
torch.mm(M,x,y) puts the result in M.
M:mm(x,y) puts the result in M.
Batch matrix matrix product of matrices stored in batch1 and batch2.
batch1 and batch2 must be 3D tensors each containing the same number
of matrices. If batch1 is a b x n x m tensor, batch2 a b x m x p
tensor, res will be a b x n x p tensor.
torch.bmm(x,y) puts the result in a new tensor.
torch.bmm(M,x,y) puts the result in M, resizing M if necessary.
M:bmm(x,y) puts the result in M, resizing M if necessary.
Outer product of vec1 and vec2. If vec1 is a vector of
size n and vec2 is a vector of size m, then res must
be a matrix of size n x m.
torch.ger(x,y) puts the result in a new tensor.
torch.ger(M,x,y) puts the result in M.
M:ger(x,y) puts the result in M.
It is possible to use basic mathematic operators like +, -, / and *
with tensors. These operators are provided as a convenience. While they
might be handy, they create and return a new tensor containing the
results. They are thus not as fast as the operations available in the
previous section.
Another important point to note is that these operators are only overloaded when the first operand is a tensor. For example, this will NOT work:
> x = 5 + torch.rand(3)You can add a tensor to another one with the + operator. Subtraction is done with -.
The number of elements in the tensors must match, but the sizes do not matter. The size
of the returned tensor will be the size of the first tensor.
> x = torch.Tensor(2,2):fill(2)
> y = torch.Tensor(4):fill(3)
> = x+y
5 5
5 5
[torch.Tensor of dimension 2x2]
> = y-x
1
1
1
1
[torch.Tensor of dimension 4]A scalar might also be added or subtracted to a tensor. The scalar needs to be on the right of the operator.
> x = torch.Tensor(2,2):fill(2)
> = x+3
5 5
5 5
[torch.Tensor of dimension 2x2]
A tensor can be negated with the - operator placed in front:
> x = torch.Tensor(2,2):fill(2)
> = -x
-2 -2
-2 -2
[torch.Tensor of dimension 2x2]Multiplication between two tensors is supported with the * operators. The result of the multiplication
depends on the sizes of the tensors.
- 1D and 1D: Returns the dot product between the two tensors (scalar).
- 2D and 1D: Returns the matrix-vector operation between the two tensors (1D tensor).
- 2D and 2D: Returns the matrix-matrix operation between the two tensors (2D tensor).
- 4D and 2D: Returns a tensor product (2D tensor).
Sizes must be relevant for the corresponding operation.
A tensor might also be multiplied by a scalar. The scalar might be on the right or left of the operator.
Examples:
> M = torch.Tensor(2,2):fill(2)
> N = torch.Tensor(2,4):fill(3)
> x = torch.Tensor(2):fill(4)
> y = torch.Tensor(2):fill(5)
> = x*y -- dot product
40
> = M*x --- matrix-vector
16
16
[torch.Tensor of dimension 2]
> = M*N -- matrix-matrix
12 12 12 12
12 12 12 12
[torch.Tensor of dimension 2x4]Only the division of a tensor by a scalar is supported with the operator /.
Example:
> x = torch.Tensor(2,2):fill(2)
> = x/3
0.6667 0.6667
0.6667 0.6667
[torch.Tensor of dimension 2x2]y=torch.cross(a,b) returns the cross product of a and b along the first dimension of length 3.
y=torch.cross(a,b,n) returns the cross product of vectors in dimension n of a and b.
a and b must have the same size, and both a:size(n) and b:size(n) must be 3.
y=torch.cumprod(x) returns the cumulative product of the elements
of x, performing the operation over the last dimension.
y=torch.cumprod(x,n) returns the cumulative product of the
elements of x, performing the operation over dimension n.
y=torch.cumsum(x) returns the cumulative sum of the elements
of x, performing the operation over the first dimension.
y=torch.cumsum(x,n) returns the cumulative sum of the elements
of x, performing the operation over dimension n.
y=torch.max(x) returns the single largest element of x.
y,i=torch.max(x,1) returns the largest element in each column
(across rows) of x, and a tensor i of their corresponding indices in
x.
y,i=torch.max(x,2) performs the max operation across rows.
y,i=torch.max(x,n) performs the max operation over the dimension n.
y=torch.mean(x) returns the mean of all elements of x.
y=torch.mean(x,1) returns a tensor y of the mean of the elements in
each column of x.
y=torch.mean(x,2) performs the mean operation for each row.
y=torch.mean(x,n) performs the mean operation over the dimension n.
y=torch.min(x) returns the single smallest element of x.
y,i=torch.min(x,1) returns the smallest element in each column
(across rows) of x, and a tensor i of their corresponding indices in
x.
y,i=torch.min(x,2) performs the min operation across rows.
y,i=torch.min(x,n) performs the min operation over the dimension n.
Compute the maximum of each pair of values in tensor1 and tensor2.
c=torch.cmax(a, b) returns a new tensor containing the element-wise maximum of a and b.
a:cmax(b) stores the element-wise maximum of a and b in a.
c:max(a, b) stores the element-wise maximum of a and b in c.
> a = torch.Tensor{1, 2, 3}
> b = torch.Tensor{3, 2, 1}
> = torch.cmax(a, b)
3
2
3
[torch.DoubleTensor of size 3]Compute the maximum between each value in tensor and value.
c=torch.cmax(a, v) returns a new tensor containing the maxima of each element
in a and v.
a:cmax(v) stores the maxima of each element in a and v in a.
c:max(a, v) stores the maxima of each element in a and v in c.
> a = torch.Tensor{1, 2, 3}
> = torch.cmax(a, 2)
2
2
3
[torch.DoubleTensor of size 3]Compute the minimum of each pair of values in tensor1 and tensor2.
c=torch.cmin(a, b) returns a new tensor containing the element-wise minimum of a and b.
a:cmin(b) stores the element-wise minimum of a and b in a.
c:min(a, b) stores the element-wise minimum of a and b in c.
> a = torch.Tensor{1, 2, 3}
> b = torch.Tensor{3, 2, 1}
> = torch.cmin(a, b)
1
2
1
[torch.DoubleTensor of size 3]Compute the minimum between each value in tensor and value.
c=torch.cmin(a, v) returns a new tensor containing the minima of each element
in a and v.
a:cmin(v) stores the minima of each element in a and v in a.
c:min(a, v) stores the minima of each element in a and v in c.
> a = torch.Tensor{1, 2, 3}
> = torch.cmin(a, 2)
1
2
2
[torch.DoubleTensor of size 3]y=torch.median(x) returns the median element of x
(one-before-middle in the case of an even number of elements).
y,i=torch.median(x,1) returns the median element in each column
(across rows) of x, and a tensor i of their corresponding indices in
x.
y,i=torch.median(x,2) performs the median operation across rows.
y,i=torch.median(x,n) performs the median operation over the dimension n.
y=torch.kthvalue(x,k) returns the k-th smallest element of x.
y,i=torch.kthvalue(x,k,1) returns the k-th smallest element in each column
(across rows) of x, and a tensor i of their corresponding indices in
x.
y,i=torch.kthvalue(x,k,2) performs the k-th value operation across rows.
y,i=torch.kthvalue(x,k,n) performs the median operation over the dimension n.
y=torch.prod(x) returns the product of all elements in x.
y=torch.prod(x,n) returns a tensor y whom size in dimension n is 1 and where elements are the product of elements of x with respect to dimension n.
> a = torch.Tensor{{{1,2},{3,4}}, {{5,6},{7,8}}}
> a
(1,.,.) =
1 2
3 4
(2,.,.) =
5 6
7 8
[torch.DoubleTensor of dimension 2x2x2]
> torch.prod(a, 1)
(1,.,.) =
5 12
21 32
[torch.DoubleTensor of dimension 1x2x2]y,i=torch.sort(x) returns a tensor y where all entries
are sorted along the last dimension, in ascending order. It also returns a tensor
i that provides the corresponding indices from x.
y,i=torch.sort(x,d) performs the sort operation along
a specific dimension d.
y,i=torch.sort(x) is therefore equivalent to
y,i=torch.sort(x,x:dim())
y,i=torch.sort(x,d,true) performs the sort operation along
a specific dimension d, in descending order.
y=torch.std(x) returns the standard deviation of the elements of x.
y=torch.std(x,dim) performs the std operation over the dimension dim.
y=torch.std(x,dim,false) performs the std operation normalizing by n-1 (this is the default).
y=torch.std(x,dim,true) performs the std operation normalizing by n instead of n-1.
y=torch.sum(x) returns the sum of the elements of x.
y=torch.sum(x,2) performs the sum operation for each row.
y=torch.sum(x,n) performs the sum operation over the dimension n.
y=torch.var(x) returns the variance of the elements of x.
y=torch.var(x,dim) performs the var operation over the dimension dim.
y=torch.var(x,dim,false) performs the var operation normalizing by n-1 (this is the default).
y=torch.var(x,dim,true) performs the var operation normalizing by n instead of n-1.
Note that many of the operations in dimension-wise operations can also be used as matrix-wide operations, by just omitting the dim parameter.
y=torch.norm(x) returns the 2-norm of the tensor x.
y=torch.norm(x,p) returns the p-norm of the tensor x.
y=torch.norm(x,p,dim) returns the p-norms of the tensor x computed over the dimension dim.
y=torch.renorm(x,p,dim,maxnorm) returns a version of x with p-norms lower than maxnorm over non-dim dimensions.
The dim argument is not to be confused with the argument of the same name in function norm.
In this case, the p-norm is measured for each i-th sub-tensor x:select(dim, i). This function is
equivalent to (but faster than) the following:
function renorm(matrix, value, dim, maxnorm)
local m1 = matrix:transpose(dim, 1):contiguous()
-- collapse non-dim dimensions:
m2 = m1:reshape(m1:size(1), m1:nElement()/m1:size(1))
local norms = m2:norm(value,2)
-- clip
local new_norms = norms:clone()
new_norms[torch.gt(norms, maxnorm)] = maxnorm
new_norms:cdiv(norms:add(1e-7))
-- renormalize
m1:cmul(new_norms:expandAs(m1))
return m1:transpose(dim, 1)
endx:renorm(p,dim,maxnorm) returns the equivalent of x:copy(torch.renorm(x,p,dim,maxnorm)).
Note: this function is particularly useful as a regularizer for constraining the norm of parameter tensors. See Hinton et al. 2012, p. 2.
### torch.dist(x,y) ###y=torch.dist(x,y) returns the 2-norm of x-y.
y=torch.dist(x,y,p) returns the p-norm of x-y.
y=torch.numel(x) returns the count of the number of elements in the matrix x.
y=torch.trace(x) returns the trace (sum of the diagonal elements)
of a matrix x. This is equal to the sum of the eigenvalues of x.
The returned value y is a number, not a tensor.
These function implement convolution or cross-correlation of an input image (or set of input images) with a kernel (or set of kernels). The convolution function in Torch can handle different types of input/kernel dimensions and produces corresponding outputs. The general form of operations always remain the same.
### [res] torch.conv2([res,] x, k, [, 'F' or 'V']) ###This function computes 2 dimensional convolutions between x and k. These operations are similar to BLAS operations when number of dimensions of input and kernel are reduced by 2.
xandkare 2D : convolution of a single image with a single kernel (2D output). This operation is similar to multiplication of two scalars.x(p x m x n) andk(p x ki x kj) are 3D : convolution of each input slice with corresponding kernel (3D output).x(p x m x n) 3D,k(q x p x ki x kj) 4D : convolution of all input slices with the corresponding slice of kernel. Output is 3D (q x m x n). This operation is similar to matrix vector product of matrixkand vectorx.
The last argument controls if the convolution is a full ('F') or valid ('V') convolution. The default is valid convolution.
x = torch.rand(100,100)
k = torch.rand(10,10)
c = torch.conv2(x,k)
> c:size()
91
91
[torch.LongStorage of size 2]
c = torch.conv2(x,k,'F')
> c:size()
109
109
[torch.LongStorage of size 2]
This function operates with same options and input/output
configurations as torch.conv2, but performs
cross-correlation of the input with the kernel k.
This function computes 3 dimensional convolutions between x and k. These operations are similar to BLAS operations when number of dimensions of input and kernel are reduced by 3.
xandkare 3D : convolution of a single image with a single kernel (3D output). This operation is similar to multiplication of two scalars.x(p x m x n x o) andk(p x ki x kj x kk) are 4D : convolution of each input slice with corresponding kernel (4D output).x(p x m x n x o) 4D,k(q x p x ki x kj x kk) 5D : convolution of all input slices with the corresponding slice of kernel. Output is 4Dq x m x n x o. This operation is similar to matrix vector product of matrixkand vectorx.
The last argument controls if the convolution is a full ('F') or valid ('V') convolution. The default is valid convolution.
x = torch.rand(100,100,100)
k = torch.rand(10,10,10)
c = torch.conv3(x,k)
> c:size()
91
91
91
[torch.LongStorage of size 3]
c = torch.conv3(x,k,'F')
> c:size()
109
109
109
[torch.LongStorage of size 3]
This function operates with same options and input/output
configurations as torch.conv3, but performs
cross-correlation of the input with the kernel k.
Functions in this section are implemented with an interface to LAPACK libraries. If LAPACK libraries are not found during compilation step, then these functions will not be available.
### [x,lu] torch.gesv([resb, resa,] b, a) ###X,LU=torch.gesv(B,A) returns the solution of AX=B and LU contains
L and U factors for LU factorization of A.
A has to be a square and non-singular matrix (2D tensor).
A and LU are m x m, X is m x k and B is m x k.
If resb and resa are given, then they will be used for
temporary storage and returning the result.
resawill containLandUfactors forLUfactorization ofA.resbwill contain the solutionX.
Note: Irrespective of the original strides, the returned matrices resb and
resa will be transposed, i.e. with strides 1,m instead of m,1.
a = torch.Tensor({{6.80, -2.11, 5.66, 5.97, 8.23},
{-6.05, -3.30, 5.36, -4.44, 1.08},
{-0.45, 2.58, -2.70, 0.27, 9.04},
{8.32, 2.71, 4.35, -7.17, 2.14},
{-9.67, -5.14, -7.26, 6.08, -6.87}}):t()
b = torch.Tensor({{4.02, 6.19, -8.22, -7.57, -3.03},
{-1.56, 4.00, -8.67, 1.75, 2.86},
{9.81, -4.09, -4.57, -8.61, 8.99}}):t()
> b
4.0200 -1.5600 9.8100
6.1900 4.0000 -4.0900
-8.2200 -8.6700 -4.5700
-7.5700 1.7500 -8.6100
-3.0300 2.8600 8.9900
[torch.DoubleTensor of dimension 5x3]
> a
6.8000 -6.0500 -0.4500 8.3200 -9.6700
-2.1100 -3.3000 2.5800 2.7100 -5.1400
5.6600 5.3600 -2.7000 4.3500 -7.2600
5.9700 -4.4400 0.2700 -7.1700 6.0800
8.2300 1.0800 9.0400 2.1400 -6.8700
[torch.DoubleTensor of dimension 5x5]
x = torch.gesv(b,a)
> x
-0.8007 -0.3896 0.9555
-0.6952 -0.5544 0.2207
0.5939 0.8422 1.9006
1.3217 -0.1038 5.3577
0.5658 0.1057 4.0406
[torch.DoubleTensor of dimension 5x3]
> b:dist(a*x)
1.1682163181673e-14
Solution of least squares and least norm problems for a full rank m x n matrix A.
- If
n <= m, then solve||AX-B||_F. - If
n > m, then solvemin ||X||_Fs.t.AX=B.
On return, first n rows of x matrix contains the solution
and the rest contains residual information. Square root of sum squares
of elements of each column of x starting at row n + 1 is
the residual for corresponding column.
Note: Irrespective of the original strides, the returned matrices resb and
resa will be transposed, i.e. with strides 1,m instead of m,1.
a = torch.Tensor({{ 1.44, -9.96, -7.55, 8.34, 7.08, -5.45},
{-7.84, -0.28, 3.24, 8.09, 2.52, -5.70},
{-4.39, -3.24, 6.27, 5.28, 0.74, -1.19},
{4.53, 3.83, -6.64, 2.06, -2.47, 4.70}}):t()
b = torch.Tensor({{8.58, 8.26, 8.48, -5.28, 5.72, 8.93},
{9.35, -4.43, -0.70, -0.26, -7.36, -2.52}}):t()
> a
1.4400 -7.8400 -4.3900 4.5300
-9.9600 -0.2800 -3.2400 3.8300
-7.5500 3.2400 6.2700 -6.6400
8.3400 8.0900 5.2800 2.0600
7.0800 2.5200 0.7400 -2.4700
-5.4500 -5.7000 -1.1900 4.7000
[torch.DoubleTensor of dimension 6x4]
> b
8.5800 9.3500
8.2600 -4.4300
8.4800 -0.7000
-5.2800 -0.2600
5.7200 -7.3600
8.9300 -2.5200
[torch.DoubleTensor of dimension 6x2]
x = torch.gels(b,a)
> x
-0.4506 0.2497
-0.8492 -0.9020
0.7066 0.6323
0.1289 0.1351
13.1193 -7.4922
-4.8214 -7.1361
[torch.DoubleTensor of dimension 6x2]
> b:dist(a*x:narrow(1,1,4))
17.390200628863
> math.sqrt(x:narrow(1,5,2):pow(2):sumall())
17.390200628863
e,V=torch.symeig(A) returns eigenvalues and eigenvectors of a
symmetric real matrix A.
A and V are m x m matrices and e is a m dimensional vector.
This function calculates all eigenvalues (and vectors) of A such
that A = V' diag(e) V.
Third argument defines computation of eigenvectors or eigenvalues
only. If it is 'N', only eignevalues are computed. If it is 'V', both
eigenvalues and eigenvectors are computed.
Since the input matrix A is supposed to be symmetric, only upper
triangular portion is used by default. If the 4th argument is 'L',
then lower triangular portion is used.
Note: Irrespective of the original strides, the returned matrix V will be
transposed, i.e. with strides 1,m instead of m,1.
a = torch.Tensor({{ 1.96, 0.00, 0.00, 0.00, 0.00},
{-6.49, 3.80, 0.00, 0.00, 0.00},
{-0.47, -6.39, 4.17, 0.00, 0.00},
{-7.20, 1.50, -1.51, 5.70, 0.00},
{-0.65, -6.34, 2.67, 1.80, -7.10}}):t()
> a
1.9600 -6.4900 -0.4700 -7.2000 -0.6500
0.0000 3.8000 -6.3900 1.5000 -6.3400
0.0000 0.0000 4.1700 -1.5100 2.6700
0.0000 0.0000 0.0000 5.7000 1.8000
0.0000 0.0000 0.0000 0.0000 -7.1000
[torch.DoubleTensor of dimension 5x5]
e = torch.symeig(a)
> e
-11.0656
-6.2287
0.8640
8.8655
16.0948
[torch.DoubleTensor of dimension 5]
e,v = torch.symeig(a,'V')
> e
-11.0656
-6.2287
0.8640
8.8655
16.0948
[torch.DoubleTensor of dimension 5]
> v
-0.2981 -0.6075 0.4026 -0.3745 0.4896
-0.5078 -0.2880 -0.4066 -0.3572 -0.6053
-0.0816 -0.3843 -0.6600 0.5008 0.3991
-0.0036 -0.4467 0.4553 0.6204 -0.4564
-0.8041 0.4480 0.1725 0.3108 0.1622
[torch.DoubleTensor of dimension 5x5]
> v*torch.diag(e)*v:t()
1.9600 -6.4900 -0.4700 -7.2000 -0.6500
-6.4900 3.8000 -6.3900 1.5000 -6.3400
-0.4700 -6.3900 4.1700 -1.5100 2.6700
-7.2000 1.5000 -1.5100 5.7000 1.8000
-0.6500 -6.3400 2.6700 1.8000 -7.1000
[torch.DoubleTensor of dimension 5x5]
> a:dist(torch.triu(v*torch.diag(e)*v:t()))
1.0219480822443e-14
e,V=torch.eig(A) returns eigenvalues and eigenvectors of a
general real square matrix A.
A and V are m x m matrices and e is a m dimensional vector.
This function calculates all right eigenvalues (and vectors) of A such
that A = V' diag(e) V.
Third argument defines computation of eigenvectors or eigenvalues
only. If it is 'N', only eignevalues are computed. If it is 'V', both
eigenvalues and eigenvectors are computed.
The eigen values returned follow
LAPACK convention
and are returned as complex (real/imaginary) pairs of numbers (2*m dimensional tensor).
Note: Irrespective of the original strides, the returned matrix V will be
transposed, i.e. with strides 1,m instead of m,1.
a = torch.Tensor({{ 1.96, 0.00, 0.00, 0.00, 0.00},
{-6.49, 3.80, 0.00, 0.00, 0.00},
{-0.47, -6.39, 4.17, 0.00, 0.00},
{-7.20, 1.50, -1.51, 5.70, 0.00},
{-0.65, -6.34, 2.67, 1.80, -7.10}}):t()
> a
1.9600 -6.4900 -0.4700 -7.2000 -0.6500
0.0000 3.8000 -6.3900 1.5000 -6.3400
0.0000 0.0000 4.1700 -1.5100 2.6700
0.0000 0.0000 0.0000 5.7000 1.8000
0.0000 0.0000 0.0000 0.0000 -7.1000
[torch.DoubleTensor of dimension 5x5]
b = a + torch.triu(a,1):t()
> b
1.9600 -6.4900 -0.4700 -7.2000 -0.6500
-6.4900 3.8000 -6.3900 1.5000 -6.3400
-0.4700 -6.3900 4.1700 -1.5100 2.6700
-7.2000 1.5000 -1.5100 5.7000 1.8000
-0.6500 -6.3400 2.6700 1.8000 -7.1000
[torch.DoubleTensor of dimension 5x5]
e = torch.eig(b)
> e
16.0948 0.0000
-11.0656 0.0000
-6.2287 0.0000
0.8640 0.0000
8.8655 0.0000
[torch.DoubleTensor of dimension 5x2]
e,v = torch.eig(b,'V')
> e
16.0948 0.0000
-11.0656 0.0000
-6.2287 0.0000
0.8640 0.0000
8.8655 0.0000
[torch.DoubleTensor of dimension 5x2]
> v
-0.4896 0.2981 -0.6075 -0.4026 -0.3745
0.6053 0.5078 -0.2880 0.4066 -0.3572
-0.3991 0.0816 -0.3843 0.6600 0.5008
0.4564 0.0036 -0.4467 -0.4553 0.6204
-0.1622 0.8041 0.4480 -0.1725 0.3108
[torch.DoubleTensor of dimension 5x5]
> v * torch.diag(e:select(2,1))*v:t()
1.9600 -6.4900 -0.4700 -7.2000 -0.6500
-6.4900 3.8000 -6.3900 1.5000 -6.3400
-0.4700 -6.3900 4.1700 -1.5100 2.6700
-7.2000 1.5000 -1.5100 5.7000 1.8000
-0.6500 -6.3400 2.6700 1.8000 -7.1000
[torch.DoubleTensor of dimension 5x5]
> b:dist(v * torch.diag(e:select(2,1)) * v:t())
3.5423944346685e-14
U,S,V=torch.svd(A) returns the singular value decomposition of a real matrix A of size n x m
such that A = USV'*.
U is n x n, S is n x m and V is m x m.
The last argument, if it is string, represents the number of singular
values to be computed. 'S' stands for some and 'A' stands for all.
Note: Irrespective of the original strides, the returned matrix U will be
transposed, i.e. with strides 1,n instead of n,1.
a = torch.Tensor({{8.79, 6.11, -9.15, 9.57, -3.49, 9.84},
{9.93, 6.91, -7.93, 1.64, 4.02, 0.15},
{9.83, 5.04, 4.86, 8.83, 9.80, -8.99},
{5.45, -0.27, 4.85, 0.74, 10.00, -6.02},
{3.16, 7.98, 3.01, 5.80, 4.27, -5.31}}):t()
> a
8.7900 9.9300 9.8300 5.4500 3.1600
6.1100 6.9100 5.0400 -0.2700 7.9800
-9.1500 -7.9300 4.8600 4.8500 3.0100
9.5700 1.6400 8.8300 0.7400 5.8000
-3.4900 4.0200 9.8000 10.0000 4.2700
9.8400 0.1500 -8.9900 -6.0200 -5.3100
u,s,v = torch.svd(a)
> u
-0.5911 0.2632 0.3554 0.3143 0.2299
-0.3976 0.2438 -0.2224 -0.7535 -0.3636
-0.0335 -0.6003 -0.4508 0.2334 -0.3055
-0.4297 0.2362 -0.6859 0.3319 0.1649
-0.4697 -0.3509 0.3874 0.1587 -0.5183
0.2934 0.5763 -0.0209 0.3791 -0.6526
[torch.DoubleTensor of dimension 6x5]
> s
27.4687
22.6432
8.5584
5.9857
2.0149
[torch.DoubleTensor of dimension 5]
> v
-0.2514 0.8148 -0.2606 0.3967 -0.2180
-0.3968 0.3587 0.7008 -0.4507 0.1402
-0.6922 -0.2489 -0.2208 0.2513 0.5891
-0.3662 -0.3686 0.3859 0.4342 -0.6265
-0.4076 -0.0980 -0.4933 -0.6227 -0.4396
[torch.DoubleTensor of dimension 5x5]
> u * torch.diag(s) * v:t()
8.7900 9.9300 9.8300 5.4500 3.1600
6.1100 6.9100 5.0400 -0.2700 7.9800
-9.1500 -7.9300 4.8600 4.8500 3.0100
9.5700 1.6400 8.8300 0.7400 5.8000
-3.4900 4.0200 9.8000 10.0000 4.2700
9.8400 0.1500 -8.9900 -6.0200 -5.3100
[torch.DoubleTensor of dimension 6x5]
> a:dist(u * torch.diag(s) * v:t())
2.8923773593204e-14
Computes the inverse of square matrix x.
=torch.inverse(x) returns the result as a new matrix.
torch.inverse(y,x) puts the result in y.
Note: Irrespective of the original strides, the returned matrix y will be
transposed, i.e. with strides 1,m instead of m,1.
x = torch.rand(10,10)
y = torch.inverse(x)
z = x * y
> z
1.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000
0.0000 1.0000 -0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000
0.0000 -0.0000 1.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000 0.0000
0.0000 -0.0000 -0.0000 1.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000
0.0000 -0.0000 0.0000 -0.0000 1.0000 0.0000 0.0000 -0.0000 -0.0000 0.0000
0.0000 -0.0000 0.0000 -0.0000 0.0000 1.0000 0.0000 -0.0000 -0.0000 0.0000
0.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 1.0000 -0.0000 0.0000 0.0000
0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000
0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 1.0000 0.0000
0.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 1.0000
[torch.DoubleTensor of dimension 10x10]
> torch.max(torch.abs(z- torch.eye(10))) -- Max nonzero
2.3092638912203e-14
Compute a QR decomposition of the matrix x: matrices q and r such that
x = q * r, with q orthogonal and r upper triangular.
=torch.qr(x) returns the Q and R components as new matrices.
torch.qr(q, r, x) stores them in existing tensors q and r.
Note that precision may be lost if the magnitudes of the elements of x are
large.
Note also that, while it should always give you a valid decomposition, it may not give you the same one across platforms - it will depend on your LAPACK implementation.
Note: Irrespective of the original strides, the returned matrix q will be
transposed, i.e. with strides 1,m instead of m,1.
> a = torch.Tensor{{12, -51, 4}, {6, 167, -68}, {-4, 24, -41}}
> =a
12 -51 4
6 167 -68
-4 24 -41
[torch.DoubleTensor of dimension 3x3]
> q, r = torch.qr(a)
> =q
-0.8571 0.3943 0.3314
-0.4286 -0.9029 -0.0343
0.2857 -0.1714 0.9429
[torch.DoubleTensor of dimension 3x3]
> =r
-14.0000 -21.0000 14.0000
0.0000 -175.0000 70.0000
0.0000 0.0000 -35.0000
[torch.DoubleTensor of dimension 3x3]
> =(q*r):round()
12 -51 4
6 167 -68
-4 24 -41
[torch.DoubleTensor of dimension 3x3]
> =(q:t()*q):round()
1 0 0
0 1 0
0 0 1
[torch.DoubleTensor of dimension 3x3]
This is a low-level function for calling LAPACK directly. You'll generally want
to use torch.qr() instead.
Computes a QR decomposition of a, but without constructing Q and R as explicit
separate matrices. Rather, this directly calls the underlying LAPACK function
?geqrf which produces a sequence of 'elementary reflectors'. See
LAPACK documentation
for further details.
This is a low-level function for calling LAPACK directly. You'll generally want
to use torch.qr() instead.
Constructs a Q matrix from a sequence of elementary reflectors, such as that
given by torch.geqrf. See
LAPACK documentation for
further details.
These functions implement logical comparison operators that take a
tensor as input and another tensor or a number as the comparison
target. They return a ByteTensor in which each element is 0 or 1
indicating if the comparison for the corresponding element was
false or true respectively.
Implements < operator comparing each element in a with b
(if b is a number) or each element in a with corresponding element in b.
Implements <= operator comparing each element in a with b
(if b is a number) or each element in a with corresponding element in b.
Implements > operator comparing each element in a with b
(if b is a number) or each element in a with corresponding element in b.
Implements >= operator comparing each element in a with b
(if b is a number) or each element in a with corresponding element in b.
Implements == operator comparing each element in a with b
(if b is a number) or each element in a with corresponding element in b.
Implements != operator comparing each element in a with b
(if b is a number) or each element in a with corresponding element in b.
Additionally, any and all logically sum a ByteTensor returning true
if any or all elements are logically true respectively. Note that logically true
here is meant in the C sense (zero is false, non-zero is true) such as the output
of the tensor element-wise logical operations.
> a = torch.rand(10)
> b = torch.rand(10)
> a
0.5694
0.5264
0.3041
0.4159
0.1677
0.7964
0.0257
0.2093
0.6564
0.0740
[torch.DoubleTensor of dimension 10]
> b
0.2950
0.4867
0.9133
0.1291
0.1811
0.3921
0.7750
0.3259
0.2263
0.1737
[torch.DoubleTensor of dimension 10]
> torch.lt(a,b)
0
0
1
0
1
0
1
1
0
1
[torch.ByteTensor of dimension 10]
> torch.eq(a,b)
0
0
0
0
0
0
0
0
0
0
[torch.ByteTensor of dimension 10]
> torch.ne(a,b)
1
1
1
1
1
1
1
1
1
1
[torch.ByteTensor of dimension 10]
> torch.gt(a,b)
1
1
0
1
0
1
0
0
1
0
[torch.ByteTensor of dimension 10]
a[torch.gt(a,b)] = 10
> a
10.0000
10.0000
0.3041
10.0000
0.1677
10.0000
0.0257
0.2093
10.0000
0.0740
[torch.DoubleTensor of dimension 10]
a[torch.gt(a,1)] = -1
> a
-1.0000
-1.0000
0.3041
-1.0000
0.1677
-1.0000
0.0257
0.2093
-1.0000
0.0740
[torch.DoubleTensor of dimension 10]
a = torch.ones(3):byte()
> torch.all(a)
true
a[2] = 0
> torch.all(a)
false
> torch.any(a)
true
a:zero()
> torch.any(a)
false